National Immunization Survey-Child 

A User’s Guide for the 2022

Public-Use Data File

Centers for Disease Control and Prevention

National Center for Immunization

and Respiratory Diseases

Presented by:

NORC at the University of Chicago 

December 2023
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page i 

Acknowledgments 

The development and production of the NIS-Child public-use data files is a team effort that has included 

contributions from many individuals (listed in alphabetical order) in two organizations:

National Center for Immunization and Respiratory Diseases, CDC – Michael Chen, Laurie D. ElamEvans, Holly A. Hill, James A. Singleton, Chalanda S. Smith, Larry Wilkinson, and David Yankey. 

NORC at the University of Chicago – Rachel Francis, Benjamin Skalland, Kirk Wolter, and Maggie 

Yarbrough.
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Table of Contents 

1. Introduction........................................................................................................................................ 9

2. Sample Design .................................................................................................................................. 16

2.1. The NIS-Child RDD Telephone Survey................................................................................... 16

2.2. The NIS-Child Provider Record Check.................................................................................... 18

2.3. Summary of Data Collection .................................................................................................... 19

2.4. Informed Consent, Security, and Confidentiality of Information............................................. 23

3. Content of NIS-Child Questionnaires............................................................................................ 24

3.1. Content of the Household Questionnaire.................................................................................. 24

3.2. Content of the Immunization History Questionnaire (IHQ)..................................................... 26

4. Data Preparation and Processing Procedures............................................................................... 28

4.1. Data Preparation ....................................................................................................................... 28

4.1.1. Editing in the CATI System ........................................................................................ 29

4.1.2. Post-CATI Edits .......................................................................................................... 29

4.1.3. Editing of Provider Data.............................................................................................. 30

4.2. Limitations of Data Editing Procedures.................................................................................... 31

4.3. Variable-Naming Conventions................................................................................................. 32

4.4. Missing Value Codes................................................................................................................ 32

4.5. Imputation for Item Non-Response .......................................................................................... 32

4.6. Vaccine-Specific Recoding of Verbatim Responses................................................................ 34

4.7. Composite Variables................................................................................................................. 34

4.8. Subsets of the NIS-Child Data.................................................................................................. 37

4.9. Confidentiality and Disclosure Avoidance ............................................................................... 37

5. Quality Control and Quality Assurance Procedures.................................................................... 38

6. Sampling Weights............................................................................................................................ 39

6.1. Base Sampling Weight ............................................................................................................. 41

6.2. Adjustments for Non-Resolution of Telephone Numbers, Screener Non-Response, and 

Interview Non-Response .......................................................................................................... 41

6.3. Adjustment for Multiple Cellular Phones and Deriving Annual Weights................................ 42

6.4. Calibration ................................................................................................................................ 42

6.5. Adjustment for Provider Non-Response................................................................................... 44

6.6. Sampling Weights for Territories............................................................................................. 47

7. Contents of the Public-Use Data File ............................................................................................. 49

7.1. Section 1: ID, Weight, and Flag Variables............................................................................... 52

7.2. Section 2: Household-Reported Vaccination and Chickenpox Information............................. 53
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7.3. Section 3: Demographic, Socio-Economic, and Other Household/Child Information............. 53

7.4. Section 4: Geographic Variables.............................................................................................. 55

7.5. Section 5: Number of Providers Identified and Consent Variables.......................................... 55

7.6. Section 6: Number of Responding Providers Variables........................................................... 55

7.7. Section 7: Characteristics of Providers Variables..................................................................... 55

7.8. Section 8: Provider-Reported Up-To-Date Vaccination Variables .......................................... 58

7.8.1. Hib Up-To-Date Variables .......................................................................................... 61

7.8.2. Rotavirus Up-To-Date Variables................................................................................. 63

7.9. Section 9: Provider-Reported Age-At-Vaccination Variables.................................................. 64

7.10. Section 10: Health Insurance Module Variables ...................................................................... 65

8. Analytic and Reporting Guidelines................................................................................................ 67

8.1. Use of NIS-Child Sampling Weights........................................................................................ 68

8.2. Estimation and Analysis........................................................................................................... 69

8.2.1. Estimating Vaccination Coverage Rates ..................................................................... 69

8.2.2. Estimating Standard Errors of Vaccination Coverage Rates....................................... 70

8.3. Combining Multiple Years of NIS-Child Data......................................................................... 71

8.3.1. Estimation of Multi-Year Means................................................................................. 71

8.3.2. Estimation of Multi-Year Contrasts............................................................................. 74

9. Summary Tables.............................................................................................................................. 83

10. Assessment of Total Survey Error.................................................................................................. 85

11. Limitations........................................................................................................................................ 95

12. Citations for NIS-Child Data .......................................................................................................... 96

13. References....................................................................................................................................... 106
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Appendices

Appendix A: Glossary of Abbreviations and Terms................................................................................. 111

Appendix B: Summary Statistics for Sampling Weights by Estimation Area.......................................... 114

Appendix C: Flags for Inconsistent Values in the Breastfeeding Data..................................................... 118

Appendix D: Programs for Estimation: Examples of the Use of SUDAAN, SAS, and R to Estimate 

Vaccination Coverage Rates and Their Standard Errors, and an Example of the Production of a CrossTabulation and Chart................................................................................................................................. 119

Appendix E: Alphabetical Listing of Variables that are in the 2004-2022 Public-Use Data Files........... 152

Appendix F: Summary Tables.................................................................................................................. 188

Appendix G: Trends in NIS-Child Response Rates and Vaccination Coverage Rates, 1995-2022 ......... 197

Appendix H: Vaccine Type Codes............................................................................................................ 205

Appendix I: Key NIS-Child Response Rates by Area .............................................................................. 206
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List of Tables and Figures 

Table 1: Selected Operational Results of Q1/2022-Q4/2022 NIS-Child Data Collection (Excluding 

U.S. Territories) ..................................................................................................................... 22

Table 2: Content of the Household Interview, National Immunization Survey - Child, 2022............. 25

Table 3: Distribution of Age (in Days) at the Birth Dose of Hepatitis B Vaccine, National 

Immunization Survey - Child, 2022....................................................................................... 34

Table 4: Weighted Distribution of Children by Race and Ethnicity and Corresponding Combined 

Vaccine Series* (4:3:1:3*:3:1:4), Pneumococcal, and Varicella Vaccination Coverage 

Estimates, National Immunization Survey - Child, 2022....................................................... 36

Table 5: RDD-Phase and Provider-Phase Weight Variable Names, National Immunization Survey - 

Child, 1995-2022 ................................................................................................................... 40

Table 6: NIS-Child Variables Commonly Used in Analyses or for Published Estimates.................... 50

Table 7: Vaccine Categories and Vaccine Types, National Immunization Survey - Child, 2022 ....... 60

Table 8: Up-To-Date Variables for Hib, National Immunization Survey - Child, 2009-2022 ............ 62

Table 9: Up-To-Date Variables for Vaccine Series Including Hib, National Immunization Survey - 

Child, 2009-2022 ................................................................................................................... 63

Table 10: Summary of Weights and Stratum Variables, National Immunization Survey - Child, 2022 69

Table 11: Cross-Walk between Annual Estimation Area Variables (ITRUEIAP, ESTIAP, ESTIAP06-

ESTIAP22)* and Common Denominator Estimation Area (CDIAP†

), National Immunization 

Survey - Child, 2022 .............................................................................................................. 76

Figure 1: Scatter Plot of Percentage Point Difference between 2021 NIS-Child and Immunization 

Information Systems Annual Report (IISAR) Vaccination Coverage Rates for the Combined 

7-Vaccine Series vs. Immunization Information Systems (IIS) Child Participation Rate 

(CPR) with Regression Line: 56 Estimation Areas................................................................ 88

Table 12: Mean and 95% Credible Interval for the Estimated Total Survey Error (TSE) Distribution 

and Component Error Distributions for National Vaccination Coverage Rate Estimates, 

National Immunization Survey - Child, 2022 ........................................................................ 92

Table 13: Difference Between the Estimates* for the Bridging Birth Cohort† by Age 19 Months, 

National Immunization Survey - Child, 2021 vs. 2022.......................................................... 94

Table B.1: Distribution of Sampling Weights* for Children with Completed Household Interviews, 

National Immunization Survey - Child, 2022 ...................................................................... 114

Table B.2: Distribution of Sampling Weights* for Children with Adequate Provider Data, National 

Immunization Survey - Child, 2022..................................................................................... 116

Table E.1 Alphabetical Listing of Variables that are in the 2004-2022 Public-Use Data Files* ......... 152

Table F.1: Estimated Population Totals and Sample Sizes of Children 19 through 35 Months of Age by 

State and Estimation Area, National Immunization Survey - Child, 2022 .......................... 188

Table F.2: Estimated Population Totals and Sample Sizes for Age Group by Maternal Education, 

National Immunization Survey - Child, 2022 ...................................................................... 190

Table F.3: Estimated Population Totals and Sample Sizes for Age Group by Poverty Status, National 

Immunization Survey - Child, 2022..................................................................................... 191

Table F.4: Estimated Population Totals and Sample Sizes for Race and Ethnicity by Poverty Status, 

National Immunization Survey - Child, 2022 ...................................................................... 192

Table F.5: Estimated Population Totals and Sample Sizes for Age Group by Race and Ethnicity, 

National Immunization Survey - Child, 2022 ...................................................................... 193
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Table F.6: Estimated Population Totals and Sample Sizes for Age Group by Sex, National 

Immunization Survey - Child, 2022..................................................................................... 194

Table F.7: Estimated Vaccination Coverage* with Individual Vaccines and Selected Vaccination Series 

Among Children 19-35 Months of Age by State and Estimation Area, National 

Immunization Survey - Child, 2022† ................................................................................... 195

Table G.1: Key Indicators* from Landline Sample Household and Provider Data Collection by Survey 

Year, National Immunization Survey - Child, 1995-2017†

.................................................. 197

Figure G.1: Trends in Landline Sample Key Indicators from Household and Provider Data Collection by 

Survey Year, National Immunization Survey - Child, 1995-2017*..................................... 198

Table G.2: Key Indicators* from Cellular Phone Sample Household and Provider Data Collection by 

Survey Year, National Immunization Survey - Child, 2011-2022† ..................................... 199

Figure G.2: Trends in Cellular Phone Sample Key Indicators from Household and Provider Data 

Collection by Survey Year, National Immunization Survey - Child, 2011-2022* .............. 200

Table G.3: CASRO Response Rate for the Combined Landline and Cellular Phone Samples by Survey 

Year, National Immunization Survey - Child, 2011-2017* ................................................. 202

Figure G.3: Trend in CASRO Response Rate for the Combined Landline and Cellular Phone Samples by 

Survey Year, National Immunization Survey - Child, 2011-2017*..................................... 202

Table G.4: Vaccine-Specific Coverage Levels Among Children Age 19-35 Months in the United States 

by Survey Year, National Immunization Survey - Child, 1995-2022*................................ 203

Figure G.4: Trends in Vaccine-Specific Coverage Levels among Children 19 through 35 Months 

of Age in the United States by Survey Year, National Immunization Survey - Child, 

1995-2022*†¶ ........................................................................................................................ 204

Table H.1: 2022 NIS-Child Vaccine Type Codes.................................................................................. 205

Table I.1: Key Indicators* for the Cellular Phone Sample by Estimation Area, National Immunization 

Survey - Child, 2022 ............................................................................................................ 206
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Convention for Bolding Text 

The Data User’s Guide uses bold font 

to highlight substantive changes in the 

methodology or study design from the 

previous year’s Guide. 
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1. Introduction 

In 1992, the Childhood Immunization Initiative (CII) (CDC, 1994) was established to 1) improve the 

delivery of vaccines to children; 2) reduce the cost of childhood vaccines; 3) enhance awareness, 

partnerships, and community participation; 4) improve vaccinations and their use; and 5) monitor 

vaccination coverage and occurrences of disease. The Healthy People 2030 objectives set the following

targets for vaccination coverage in young children: maintain the vaccination coverage level of 1 dose of 

the measles, mumps, and rubella (MMR) vaccine in children by age 2 years at 90.8% or higher; increase 

the vaccination coverage level of 4 doses of the diphtheria and tetanus toxoids and acellular pertussis 

(DTaP) vaccine in children by age 2 years to 90.0%; and reduce the proportion of children who get no 

recommended vaccines by age 2 years below 1.3% (https://health.gov/healthypeople/objectives-anddata/browse-objectives/vaccination). To fulfill the CII mandate of monitoring vaccination coverage and 

marking progress toward achieving those objectives, the National Immunization Survey - Child (NISChild) was implemented by the National Center for Immunization and Respiratory Diseases (NCIRD) and 

the National Center for Health Statistics (NCHS) of the Centers for Disease Control and Prevention 

(CDC) in 1994. From 1994 to 2014, the NIS-Child was conducted jointly by NCIRD and NCHS; since 

2015, the NIS-Child has been conducted by NCIRD.

The target population for the NIS-Child is non-institutionalized children aged 19 through 35 months 

living in United States households at the time of the interview. The official coverage estimates reported 

from the NIS-Child are proportions of children up-to-date with respect to the requisite numbers of doses 

of all routinely recommended vaccines for this age group (Wodi et al., 2022). These vaccines and their 

recommended numbers of doses are: 

● diphtheria and tetanus toxoids and acellular pertussis vaccine adsorbed, diphtheria and tetanus 

toxoids and pertussis vaccine, or diphtheria and tetanus toxoids adsorbed (DTaP/DTP/DT) – 4 

doses;
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● inactivated poliovirus vaccine IPV (polio) – 3 doses (for guidance to assess doses documented as 

'OPV', see https://www.cdc.gov/mmwr/volumes/66/wr/mm6606a7.htm?s_cid=mm6606a7_w);

● measles, mumps, and rubella vaccine (MMR) – 1 dose;

● Haemophilus influenzae type b conjugate vaccine (Hib) – 3 or 4 doses depending on product 

type;

● hepatitis B vaccine (Hep B) – 3 doses;

● varicella (chicken pox) vaccine (VAR) – 1 dose;

● pneumococcal conjugate vaccine (PCV) – 4 doses;

● hepatitis A vaccine (Hep A) – 2 doses; 

● influenza vaccine (for the recommended number of doses of influenza vaccine and other 

vaccines, see http://www.cdc.gov/vaccines/hcp/acip-recs/vacc-specific/index.html);

● rotavirus vaccine (RV) – 2 or 3 doses depending on product type.

In addition to these vaccines, interest focuses on the combined vaccine series 4:3:1:3*:3:1:4 (4+ 

DTaP/DTP/DT; 3+ polio; 1+ measles-containing vaccine (MCV); full series Hib, i.e., 3 or 4 doses 

depending on type of vaccine received; 3+ Hep B; 1+ varicella at or after 12 months of age; and 4+ PCV). 

The NIS-Child collects data on each of these vaccines. Varicella vaccine was added in Quarter 3, 1996, 

pneumococcal conjugate vaccine in Quarter 4, 2000, influenza vaccine and hepatitis A vaccine in Quarter 

1, 2003, and rotavirus vaccine in Quarter 3, 2007. The remainder of the vaccines have been included in 

the NIS-Child from its start in 1994. Information about current and past recommendations for each 

vaccine can be found at https://www.cdc.gov/vaccines/hcp/acip-recs/index.html. 

The NIS-Child uses random digit dialing (RDD) telephone survey methodology to identify households 

containing children in the target age range, and interviews are conducted with the adult who is most 
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knowledgeable about the child’s vaccinations. With consent of the child’s parent or guardian, the NISChild also contacts (by mail) the child’s health care provider(s) to request information on vaccinations 

from the child’s medical records. Since 2005, NIS-Child sampling, data collection, and weighting

operations have been conducted by NORC at the University of Chicago.

Samples of cellular telephone numbers are drawn independently for each calendar quarter within selected 

geographical areas, or strata. In 2022, there are 58 geographic strata for which vaccination coverage levels 

can be estimated (see Table F.1), including 5 local areas; the remaining 53 estimation areas are either an 

entire state, the District of Columbia, a U.S. territory (Guam or Puerto Rico), or a “rest of state” area. For 

states with local and “rest of state” estimation areas, estimates for the whole-state area can be produced as 

well. This design makes it possible to produce annual estimates of vaccination coverage levels for each 

state or territory, the District of Columbia, each of the 5 local estimation areas, and each “rest of state” 

area, with a specified degree of precision (a coefficient of variation of approximately 7.5%). Further, by 

using the same data collection methodology and survey instruments in all estimation areas, the NIS-Child

produces comparable vaccination coverage levels across estimation areas and over time. 

When the NIS-Child was established in 1994, 78 areas were chosen for sampling strata, including the 50 

states, 6 urban areas that receive federal Section 317 immunization grants (Bexar County, TX; Chicago, 

IL; District of Columbia; Houston, TX; New York City, NY; Philadelphia County, PA), and 22 other 

urban areas. These areas were called “Immunization Action Plan” (IAP) areas in reference to plans 

developed to improve vaccination coverage following the resurgence of measles during 1989-1991. In 

2005 and 2006, selected non-awardee IAP areas – areas that do not receive separate Section 317 funds – 

were “rotated off” (i.e., the sample design no longer ensured adequate sample size to produce estimates 

for the area) and replaced by new areas “rotated on” (i.e., the sample design did ensure adequate sample 

size to produce estimates for the area). Starting in 2007, the base NIS-Child geographic strata included 56 

areas (5 sub-state awardee urban areas, the District of Columbia, and 50 state or “rest of state” areas). In 

addition, starting in 2007, state immunization programs could choose additional city/county areas of 
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interest to have adequate sample size to produce estimates for the area, using their Section 317 funds. In 

2022, the NIS-Child included Guam and Puerto Rico as additional estimation areas. As noted 

throughout this report, some procedures differed for U.S. territories when compared to the rest of the 

U.S., including the creation of separate survey weight variables for analyses that are to include territories.

The 58 = 56 + 2 areas are called estimation areas. Table 11 in Section 8 shows a cross-walk of estimation 

areas between years. To maintain consistency with past NIS-Child public-use data files (PUFs), variable 

names and descriptions continue to use the term “IAP” to designate areas included as geographic strata, 

which was the term used prior to 2008. The changing geographic strata over time will not cause a 

problem with bias in estimation of state and national coverage levels since the geographic strata are 

nested within state.

Data for Guam are not included in the 2022 public-use data file to protect respondent 

confidentiality, as the sampling fraction was larger in this small-population area. Interested 

researchers can access data for Guam by submitting a proposal and working through the Research Data 

Center. The link and guidelines for developing a proposal are located at the following URL: 

www.cdc.gov/rdc. 

For the 2022 NIS-Child, telephone interviewing began on January 6, 2022 and ended on January 

30, 2023. Provider data collection extended from February 2022 through April 2023. A total 

sample, including the U.S. territory samples, of approximately 26.4 million telephone numbers

yielded household interviews for 35,919 children, 17,674 of whom had adequate provider data (i.e., 

provider-reported vaccination data adequate to determine whether the child was up-to-date with 

respect to the recommended immunization schedule). The 2022 NIS-Child public-use data file, 

which includes data from Puerto Rico but does not include data for Guam, contains data for 35,703

children with completed household interviews (34,675 when Puerto Rico is excluded), and more 

extensive data (e.g. provider-reported vaccinations and facility data) for 17,575 children with 
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adequate provider data, including 257 unvaccinated children (17,232 including 247 unvaccinated 

children, when Puerto Rico is excluded). 

Official NIS-Child vaccination coverage estimates are based on the provider-reported vaccination 

histories for each child. Among children with data received from vaccination providers identified in the 

household interview, it must be determined which children have “adequate provider data,” that is, which 

children have provider data adequate to determine whether the child is up to date with respect to the 

recommended immunization schedule. In 2012, the NIS-Child household questionnaire was modified to 

reduce the length of the household interview, decrease respondent burden, and potentially improve 

response rates, with the result that questions that were previously used to define adequate provider data

were no longer available. With this questionnaire change, it was no longer possible to use the same 

definition of adequate provider data as was used prior to 2012, and so beginning in 2012 all children with 

any provider-reported vaccination data are considered to have adequate provider data. See the user’s 

guide for the 2014 NIS-Child public-use data file (CDC, 2015) for more detail about this change and its 

impact. 

The weights included on this public-use data file afford the data analyst the capability of conducting 

several different types of analyses, depending on interests and aims. One can choose to analyze all 

children with completed household interviews or only the subset of children for whom the providerreported data are adequate. One can also choose to include or exclude children who reside in U.S. 

territories in the analysis. Previous NIS-Child public-use data files have also provided analysts with these 

capabilities. Section 6 of this user’s guide provides information about the creation of the weight variables 

included in the 2022 NIS-Child public-use data file, and Section 8 provides guidance for their use. 

Vaccination coverage estimates are available on the ChildVaxView website, 

https://www.cdc.gov/vaccines/imz-managers/coverage/childvaxview/index.html. An article summarizing 

key findings from the NIS-Child data, as published in the Morbidity and Mortality Weekly Report 
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(MMWR), will also be available on this website. Historically, these estimates have been based on NISChild data collected in a single survey year (e.g., the 2022 NIS-Child) and applied to the population of 

children age 19-35 months; since 2018, the estimates on ChildVaxView and in the MMWR are based on 

data pooled over 2-3 survey years (e.g., the 2020-2022 NIS-Child surveys) and apply to the population of 

children in particular annual birth cohorts (e.g., children born in 2019 or 2020). This cohort-based 

approach to estimating vaccination coverage has been described by Singleton et al. (2019) and Singleton 

(2019), and was first implemented for NIS-Child data published in 2019 

(https://www.cdc.gov/mmwr/volumes/68/wr/pdfs/mm6841e2-H.pdf). The 2022 NIS-Child public-use 

file and this user’s guide focus on a single survey year (2022) and the production of vaccination 

coverage estimates for children age 19-35 months in 2022. Therefore, estimates produced from this 

public-use file will differ from birth cohort-based estimates on ChildVaxView and in the MMWR

(Hill et al., 2023). Researchers interested in analyzing the NIS-Child data by birth year can request 

access to the required data via the Research Data Center (http://www.cdc.gov/rdc). 

The accompanying codebook (NCIRD, 2023) documents the contents of the 2022 NIS-Child public-use 

data file. For reference, Appendix E (Alphabetical Listing of Variables in the NIS-Child Public-Use Data 

Files) provides a full list of variables in the 2022 and previous NIS-Child public-use data files. NIS-Child 

data and documentation for 2015 to the present are available at: https://www.cdc.gov/vaccines/imzmanagers/nis/datasets.html

Additional information on the NIS-Child is available at: http://www.cdc.gov/vaccines/imzmanagers/nis/about.html. 

For additional information on the NIS-Child public-use data file, please contact the NCIRD Information 

Dissemination Staff: 

Information Dissemination Staff, NCIRD 

1600 Clifton Road
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 15

Atlanta, GA 30333

E-mail: cdcinfo@cdc.gov

Internet: http://www.cdc.gov/vaccines/imz-managers/nis/index.html
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2. Sample Design 

The NIS-Child uses two phases of data collection to obtain vaccination information for a large national 

probability sample of young children: an RDD telephone survey designed to identify households with 

children 19 through 35 months of age, followed by the Provider Record Check, a mailed survey to 

children’s vaccination providers. This section summarizes these two phases of data collection. Other 

descriptions of the sample design are given by Ezzati-Rice et al. (1995), Zell et al. (2000), Smith et al. 

(2001a, 2005), and Wolter et al. (2017a). 

2.1. The NIS-Child RDD Telephone Survey 

The NIS-Child Random Digit Dial (RDD) telephone survey phase uses independent, quarterly samples of 

cellular phone numbers. Sampling frames were provided by Marketing Systems Group (MSG). Cellular 

phone numbers were sampled within estimation areas in each quarter of 2022. Table F.1 lists the 

estimation areas for the 2022 NIS-Child by state and shows the estimated number of children living in 

each state and estimation area in 2022. 

Prior to 2011, the NIS-Child used a single-frame landline RDD sample design. In 2011, a cellular phone 

sample was added, and from 2011-2017, the NIS-Child used a dual-frame landline and cellular phone 

RDD sample design. In 2018, the landline sample was dropped, and the NIS-Child now uses a singleframe cellular phone RDD sample design.

The target sample size of completed telephone interviews in each estimation area is designed to achieve 

an approximately equal coefficient of variation of 7.5% for an estimator of vaccination coverage derived 

from provider-reported vaccination histories, given a true coverage parameter of 50%. Cellular phone 

sample sizes were chosen to meet the target coefficient of variation of 7.5%. 

Since 2019, the NIS sample design has included a modification to increase the efficiency of data 

collection. Immunization Information Systems (IIS) are state or local confidential, computerized, 
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population-based data systems that collect and consolidate vaccination doses administered by 

participating vaccination providers to persons residing in a given geopolitical area. In participating 

geographic estimation areas, a two-phase RDD sample of cellular phone numbers is selected, with the 

second-phase sample stratified by the status of the telephone number in the corresponding IIS: 

• Stratum 1: Phone number associated with a 19-35 month old child in the IIS

• Stratum 2: Phone number associated with a 13-17 year old adolescent in the IIS (but not with a 

19-35 month old child in the IIS)

• Stratum 3: Phone number associated with a 6-18 month or 3-12 year old child in the IIS (but not 

with a 19-35 month old child or 13-17 year old adolescent in the IIS)

• Stratum 4: Phone number not associated with a 6 month to 17 year old child in the IIS

In the second phase of sampling, phone numbers falling into Stratum 1 were oversampled. The method 

was designed to maximize the effective sample sizes for the NIS family of surveys, given fixed cost of 

data collection, within each of the participating geographic estimation areas. For the 2022 sample, 30

areas participated in this two-phase sampling process to increase efficiency of sampling.

1

The design and implementation of the NIS-Child cellular phone sample involves three procedures. First, 

statistical models predict the number of sample telephone numbers needed in each estimation area to meet 

the target precision requirements. Second, the sample for an estimation area is divided into random subsamples called replicates. By releasing replicates as needed, it is possible to spread the interviews for each 

1 The participating geographic areas in 2022 were Alaska, Arkansas, Connecticut, Florida, Georgia, Idaho, Iowa, 

Kansas, Kentucky, Louisiana, Maryland, Michigan, Mississippi, Missouri, Nebraska, Nevada, New Mexico, New 

York – City of New York, North Carolina, North Dakota, Ohio, Oklahoma, Rhode Island, South Dakota, Tennessee, 

Utah, Vermont, Washington, Wyoming, and Puerto Rico. Georgia used the integration design only in quarter 4.
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sampling area evenly across the entire calendar quarter. Third, an automated procedure eliminates 

numbers on the NIS do-not-call list from the sample before the interviewers dial them. 

In 2014 and 2015, an automated process was implemented to remove cellular phone numbers flagged as 

having no recent activity and that were therefore very likely to be non-working cellular phones. In 2016, a 

different automated process found to be more efficient in removing non-working cellular phone numbers 

was used. Following a July 2016 Federal Communications Commission (FCC) declaratory ruling (FCC 

16-72, CG Docket No. 02-278) stating that the federal government and contractors working on behalf of 

the federal government are not subject to the restrictions on cellular phone dialing in the Telephone 

Consumer Protection Act of 1991 (TCPA, 47 U.S.C. 227), the NIS transitioned from manual dialing of 

cellular phones to auto-dialing cellular phones in November 2016. After this transition, the automated 

process to remove non-working cellular phone numbers was no longer cost effective, and beginning in 

2017 this process was no longer used in the cellular phone sample.

2.2. The NIS-Child Provider Record Check

At the end of the household interview, consent to contact the child’s vaccination provider(s) is requested 

from the parent/guardian. When oral consent is obtained, each provider is mailed an immunization history 

questionnaire. This mail survey portion of the NIS-Child is the Provider Record Check (PRC). 

The instructions ask vaccination providers to mail or fax the immunization history questionnaire back 

upon completion. Two weeks after the initial mailing, a telephone call is made to providers who have still 

not responded, to remind and encourage them to complete the form and either mail or fax the information 

back. In some instances, provider-reported vaccination histories are completed over the telephone. The 

data from the questionnaires are edited, entered, cleaned, and merged with the household information 

from the RDD survey to produce a child-level record.
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2.3. Summary of Data Collection

Table 1 presents selected operational results of NIS-Child data collection for calendar year 2022. To 

facilitate comparisons with prior years, the numbers in Table 1 and discussed in this section exclude the 

U.S. territory samples. Children aged 19 through 35 months during 2022 data collection were born 

between January 2019 and May 2021. 

The total cellular phone sample consisted of 25,057,932 telephone numbers. Of those, 38,512 were 

eliminated before release to the telephone centers as numbers on the NIS do-not-call list. The 

remaining 25,019,420 numbers were sent to the telephone centers to be dialed, and 1,717,879 active 

personal cellular phone numbers (APCNs) were identified, as shown in Row F. Among the 

identified APCNs, 1,457,051 (84.8%) were successfully screened. Of these, 49,111 (3.4%) were 

deemed eligible for the NIS-Child interview. Among the identified eligible respondents, 33,450

(68.1%) completed the interview.

A standard approach for measuring response rates in telephone surveys has been defined by the Council 

of American Survey Research Organizations (CASRO, 1982). The CASRO response rate is equivalent to 

“RR3” of American Association for Public Opinion Research (AAPOR) Standard Definitions (AAPOR, 

2016). In 2022, the CASRO response rate (Table 1, Row J) was 25.1%. The CASRO response rate 

equals the product of the resolution rate (43.5%, Row E), the screening completion rate (84.8%, 

Row G), and the interview completion rate among eligible households (68.1%, Row I). The

resolution rate is the percentage of the total telephone numbers selected that are classifiable as nonworking, non-residential, or residential. The screening completion rate is the percentage of known 

households that are successfully screened for the presence of age-eligible children. The interview 

completion rate is the percentage of households with one or more age-eligible children who complete the 

household interview.
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 20

Row K of Table 1 shows that household interviews were completed on behalf of 34,675 age-eligible 

children. Rows L through O give results for the Provider Record Check phase. Specifically, Row L 

gives the rate of obtaining oral consent from the household respondent to contact the child’s

vaccination providers – 59.4% in 2022.

The number of immunization history questionnaires mailed to vaccination providers exceeds the number 

of completed interviews for children with consent because some children have more than one vaccination 

provider. Of the questionnaires mailed to vaccination providers of children, 22,923 (91.2%, Row N) 

were returned. Among the children with completed household interviews, 17,232 (49.7%, Row O) 

had adequate vaccination histories based on provider reporting (16,985) including multiple reports 

for a child or were determined to be unvaccinated (247). The other 50.3% of children lacked 

adequate provider data for a variety of reasons, such as the parent did not give consent to contact 

the child’s provider(s), the provider(s) did not have records for the child, or the provider(s) did not 

report the vaccination history. For each estimation area and each state, Table F.1 (see Appendix F) 

shows the number of children with completed household interviews and the number of children with 

adequate provider data. The percentage of children with adequate provider data varied among the 

non-territory estimation areas from 42.5% in Florida to 60.8% in Vermont. Among the U.S. 

territories, the percentages were 33.4% in Puerto Rico, and 45.8% in Guam. 

The phrase “adequate provider data” originally meant that sufficient vaccination history information was 

obtained from the provider(s) to determine whether the child is up-to-date with respect to the 

recommended vaccination schedule. Starting with the 2002 NIS-Child public-use data file, the definition 

of children with adequate provider data was expanded to include unvaccinated children. These are 

children for whom either (1) the respondent reported during the household interview that the child had 

received no vaccinations and has no providers, or (2) the respondent reported during the household 

interview that the child had received no vaccinations but has one or more providers, and those providers 

all reported administering no vaccinations. A report from the National Center for Health Statistics
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 21

(NCHS) on the statistical methodology of the NIS-Child (Smith et al., 2005) includes details of how 

unvaccinated children are included in the estimates of vaccination coverage. This report can be viewed at

http://www.cdc.gov/nchs/data/series/sr_02/sr02_138.pdf. This modification to the NIS-Child produces 

only small changes in vaccination coverage for estimation areas and states, because the number of 

unvaccinated children in the sample is very small (only 257 in 2022, including the U.S. territory 

samples). As described in the introduction, the definition of adequate provider data was modified in 2012 

to include all children with provider-reported vaccination data as well as unvaccinated children. 

Since 2001, the NIS-Child has included an additional Health Insurance Module (HIM) which is 

administered after completion of the main NIS-Child household survey. Among the 34,675 children 

with completed household interviews, 21,285 (61.4%, Row P) went on to complete the HIM, while 

the remainder terminated the interview prior to completing the HIM. Among the 17,232 children 

with adequate provider data, 16,728 (97.1%) completed the HIM.
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 22

Table 1: Selected Operational Results of Q1/2022-Q4/2022 NIS-Child Data Collection 

(Excluding U.S. Territories)

Row Key Indicator 

 

Cellular Phone Sample Formula 

Number Percent 

Household Phase

A Total Selected Telephone Numbers in Released Replicates 25,057,932 -- --

B Phone Numbers Resolved before Computer-Assisted Telephone 

Interviewing 38,512 0.2% B/A

C Total Phone Numbers Released to Telephone Centers 25,019,420 -- A-B 

D Advance Letters Mailed 0 0.0% D/C

E Resolved Phone Numbers1 – Resolution Rate 10,902,190 43.5% E/A

F Households Identified – APCN2 Rate 1,717,879 15.8% F/E

G Households Successfully Screened3 – Screener Completion Rate 1,457,051 84.8% G/F

H Eligible Households – Eligibility Rate4 49,111 3.4% H/G

I Households with Completed Household Interviews – Interview 

Completion Rate 33,450 68.1% I/H

J CASRO5 Response Rate6 -- 25.1% --

K Age-Eligible Children with Completed Household Interviews7 34,675 -- --

Provider Phase

L Children with Consent to Contact Vaccination Providers 20,612 59.4% L/K

M Immunization History Questionnaires Mailed to Providers 25,127 -- --

N Immunization History Questionnaires Returned from Providers 22,923 91.2% N/M

O Children with Adequate Provider Data

17,232 

(includes 247 

unvaccinated 

children)

49.7% O/K

Modules

P Age-Eligible Children with Completed Household Interview and 

Completed Health Insurance Module 21,285 61.4% P/K

1 A phone number is resolved if it was determined to be either a non-working number or a working residential number. This row includes 

phone numbers resolved before computer-assisted telephone interviewing (CATI) (Row B). The numbers resolved before CATI 

interviewing are those on the NIS do-not-call list.

2 Active personal cellular phone number (APCN) rate.

3 The household screener screens for non-minor-only cellular phone households with age-eligible children.

4 Of the screened households, the proportion that were non-minor-only cellular phone households with age-eligible children.

5 CASRO, Council of American Survey Research Organizations.

6 The response rate is the number of households with a completed household interview divided by the estimated number of eligible 

households in the sample. The number of eligible households in the sample was estimated using the CASRO assumptions; these 

assumptions are that the rate of households among the unresolved telephone numbers is the same as the observed rate of households among 

the resolved telephone numbers, and the rate of eligible households among unscreened households is the same as the observed rate of 

eligible households among screened households. Under these assumptions, the CASRO response rate is equal to the product of the 

resolution rate, the screener completion rate, and the interview completion rate. 

7 Rows K-P reflect the removal of children with a completed interview that were later found to be ineligible based on post-survey data 

cleaning operations, the removal of children who were not sampled but reported living in a U.S. territory, and the addition of children 

sampled in a U.S. territory who reported living in the non-territory United States.
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 23

2.4. Informed Consent, Security, and Confidentiality of Information

The introduction to the telephone survey and oral consent assure the respondent of the confidentiality of 

his/her responses and the voluntary nature of the survey. Informed consent is obtained from the person in 

the household most knowledgeable about the eligible child’s vaccination history (generally the parent or 

guardian of the child). Informed consent to contact the child’s vaccination provider(s) is obtained at the 

end of the interview.

Information in the NIS-Child is collected and processed under high security. To ensure privacy of the 

respondents and confidentiality of sensitive information, standards have been established for release of 

data from this survey. All CDC staff and contractor staff involved with the NIS-Child sign confidentiality 

agreements and follow instructions to prevent disclosure.

All information in the NIS-Child is collected under strict confidentiality and can be used only for research 

[Section 308(d) of the Public Health Service Act, 42 U.S. Code 242m(d) and the Privacy Act of 1974 (5 

U.S. Code 552a)]. Prior to public release, the contents of the public-use data file go through extensive 

review by the NCIRD Disclosure Review Board to protect participant privacy as well as data 

confidentiality.
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 24

3. Content of NIS-Child Questionnaires 

This section describes the questionnaires used in the 2022 NIS-Child telephone interview of households 

and Provider Record Check. 

3.1. Content of the Household Questionnaire 

The computer-assisted telephone interview (CATI) questionnaire used in the RDD phase of NIS-Child

data collection consists of two parts: a screener to identify households with children aged 19 through 35 

months and an interview portion. The questionnaire is modeled on the Immunization Supplement to the 

National Health Interview Survey (NHIS) (NCHS, 1999). The NIS-Child CATI questionnaire has been 

translated into Spanish, and LanguageLine Solutions® (formerly part of AT&T) is used for real-time 

translation into many other languages (Wall et al., 1995). Table 2 summarizes the content of each section 

of the NIS-Child household interview. The CATI questionnaire is available at

http://www.cdc.gov/vaccines/imz-managers/nis/datasets.html.

In the screener, the purpose of the survey is explained to the respondent, and the household is screened to 

determine whether it contains any children aged 19 through 35 months (any child who was or would be 

aged 19 through 35 months during the calendar quarter is eligible). If the household has an eligible child, 

the respondent is asked whether he/she is the most knowledgeable person for the child’s vaccination 

history. If the respondent indicates that another person in the household is more knowledgeable, the 

interviewer asks to speak to him/her at that time. If that person is unavailable to be interviewed, the 

interview proceeds to Section MR, the name of the most knowledgeable person is recorded, and a 

“callback” is scheduled for a later date. Prior to screening for age-eligibility, the household is screened to 

ensure that the cellular phone is used by an adult (i.e., to ensure it is not a minor-only cellular phone). If 

the household has more than one age-eligible child, data are collected for each eligible child. 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 25

Table 2: Content of the Household Interview, National Immunization Survey - Child, 2022 

Questionnaire 

Section Content of Section

Section S Screening questions to determine NIS-Child eligibility

Section MR Most-knowledgeable-respondent callback questions

Section B Ever vaccinated and influenza vaccination questions

Section C Demographic and socioeconomic questions

Section D Provider information and request for consent to contact the eligible child’s vaccination 

provider(s)

Section E Health Insurance Module (HIM)

Prior to Q1/2012, the person being interviewed was asked during the screener section whether he/she had 

a written record (shot card) of the child’s vaccination history and whether it was easily accessible. If a 

shot card was available, the respondent was asked to provide information directly from it in Section A

(which asked respondents with shot cards about the shots on the card). However, beginning in Q1/2012, 

Section A and most of Section B were eliminated from the regular questionnaire, and therefore all 

interviews proceeded directly to a reduced form of Section B asking the respondent to recall information 

about the child’s influenza vaccinations. In 2015 and 2016, Section A was reinstated for Guam 

respondents, but was discontinued for all respondents beginning in 2017.

Section C obtains information that includes relationship of respondent to the child, race and Hispanic 

origin of the child, household income, educational attainment of the mother, and other information on the 

socioeconomic characteristics of the household and its eligible children. 

In Section D of the NIS-Child household interview, identifying information (such as name, address, and 

telephone number) for the child’s vaccination provider(s) is requested, as well as the full names of the 

child(ren) and the respondent, so that NIS-Child personnel can contact the provider(s) and identify the 

child(ren) whose immunization information the NIS-Child is requesting. After this information is 

obtained, consent to contact the child’s vaccination provider(s) is requested. When oral consent and 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 26

sufficient identifying information are obtained, the immunization history questionnaire is mailed to the 

child’s vaccination provider(s). 

Beginning in 2006, a Health Insurance Module (HIM) was administered upon completion of Section D to 

collect data regarding the types of medical insurance coverage the child has had since birth. If a 

respondent provided consent to contact medical providers and completed Section D, he/she flowed 

directly into the HIM. If, however, consent or any other critical provider question was refused, the call 

was terminated and the respondent was called back later to attempt to complete the Provider Section and 

obtain consent. Only upon callback on which consent was granted or a second refusal given within 

Section D was the respondent asked the HIM. 

Several changes were made to the NIS-Child questionnaire for 2022: 

• Income questions were updated to refer to the previous year, 2021. That is, the question text at 

CFAMINC, C12_DONT_KNOW, and C12_REFUSED was updated to ask about 2021 income 

rather than 2020 income.

• The Health Insurance Module was updated to reflect current state and territory Medicaid and 

CHIP programs. This was done so that households located in areas where Medicaid and CHIP 

currently have separate program names (including all sampled U.S. territories) are asked about 

coverage separately, while households in areas where Medicaid and CHIP currently share a 

program name are asked about coverage in a single question. 

3.2. Content of the Immunization History Questionnaire (IHQ)

The Immunization History Questionnaire (IHQ) mailed to the vaccination providers is designed to be 

simple and brief, to minimize provider burden and encourage survey participation. The structure and 

content of this form were initially derived from the National Immunization Provider Record Check Study 

(NHIS/NIPRCS), which collected and reconciled vaccination data from the providers of respondents to 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 27

the Immunization Supplement to the NHIS. The IHQ consists of two double-sided pages. Page 1 includes 

space for a label that gives the child’s name, date of birth, and sex. The remainder of page 1 contains 

questions about the facility and vaccination provider. Page 2 gives instructions for filling out the shot 

grid, which appears on page 3. Page 4 thanks the vaccination provider for providing the information, and 

lists websites and telephone numbers that can be used to obtain more information about the NIS-Child

and the NCIRD. The IHQ is available at http://www.cdc.gov/vaccines/imz-managers/nis/datasets.html. 

Since 2015, a Spanish-translated version of the NIS-Child IHQ has been used for Puerto Rico. This 

version differs slightly from the IHQ used for other estimation areas in that Question 5b does not contain 

response options for Indian Health Service or Pharmacy.

Two changes were made to the NIS-Child IHQ for 2022: 

• A new COVID-19 shot category was added to the IHQ shot grid.

• A new vaccine subtype (Conjugate-15) was added to the Pneumococcal section of the IHQ 

shot grid.
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 28

4. Data Preparation and Processing Procedures 

The household and provider data collection in the NIS-Child incorporate extensive data preparation and 

processing procedures. During the household interview, the CATI system supports reconciliation of 

critical errors as interviewers enter the data. After completion of interviewing for a quarter, post-CATI 

editing and data cleaning produce a final interview data file. The editing of the provider data begins with 

a manual review of returned immunization history questionnaires, data entry of the questionnaires, and 

cleaning of the provider data file. After the provider data are merged with the household interview data 

and responses from multiple providers for a child are consolidated into a child-level data record, the 

editing continues. A quality assurance check is performed based on the name, sex, and date-of-birth 

information from all sources to ensure that the provider completed the questionnaire for the correct child 

and to confirm age-eligibility. Editing of the provider-reported vaccination dates then attempts to resolve 

specific types of discrepancies in the provider data. The end product is an analytic file containing 

household and provider data for use in estimating vaccination coverage.

4.1. Data Preparation

The editing and cleaning of NIS-Child data involve several steps. First, the CATI system enables 

interviewers to reconcile potential errors while the respondent is on the telephone. Further cleaning and 

editing take place in a post-CATI clean-up stage, involving an extensive review of data values, cross 

tabulations, and the recoding of verbatim responses for race and ethnicity. The next step involves the 

creation of numerous composite variables. Provider data are cleaned in a separate step. After these steps 

have been completed, imputations are performed for item non-response on selected variables, and weights 

are calculated. The procedures and rules of the NHIS serve as the standard in all stages of data editing and 

cleaning (http://www.cdc.gov/nchs/nhis.htm). 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 29

4.1.1. Editing in the CATI System 

The CATI software checks consistency across data elements and does not allow interviewers to enter 

invalid values. Catching potential errors early increases the efficiency of post-survey data cleaning and 

processing.

To prevent an overly complicated CATI system, out-of-range and inconsistent responses produce a 

warning screen, allowing the interviewer to correct real time errors. This allows the interviewer to 

reconcile errors while respondent is on the telephone. CATI warning screens focus on items critical to the 

survey, such as those that determine a child’s eligibility (e.g., date of birth). 

A CATI system cannot simultaneously incorporate every possible type of error check and maximize 

system performance. To reconcile this trade-off, post-CATI edits are used to resolve problems that do not 

require access to the respondent, as well as unanticipated logic problems that appear in the data.

4.1.2. Post-CATI Edits

The post-CATI editing process produces final, cleaned data files for each quarter. The steps in this 

process, implemented after all data collection activities for a quarter are completed, are described below.

Initial Post-CATI Edits and File Creation

After completion of interviewing each quarter, the raw data are extracted from the CATI data system and 

used to create two files: the sample file and the interview data file. The sample file contains one record for 

each sample telephone number and summary information for telephone numbers and households. The 

interview data file contains one record for each eligible sampled child and all data reported for the child

during the household survey. 

Following creation of these two files, a preliminary analysis of each file identifies out-of-range values and 

extraneous codes. The first check verifies the eligibility status of children. Once the required corrections 

are verified, invalid values are replaced with either an appropriate data value or a missing value code. 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 30

Frequency Review

After the pre-programmed edits are run, frequency distributions of all variables in each file are produced 

and reviewed. Each variable’s range of values is examined for any invalid values or unusual distributions.

If blank values exist for a variable, they are checked to see whether they are allowable and whether they 

occur in excessive numbers. Any problems are investigated and corrected as appropriate. 

File Crosschecks

Crosscheck programs ensure that cases exist across files in a consistent manner. Specifically, checks 

ensure that each case in the interview data file is also present in the sample file and that each case in the 

sample file was released to the telephone center. Checks also ensure that no duplicate households exist in 

the sample file and no duplicate children exist in the interview data file. 

When all checks have been performed, the final quarterly interview data file is created. Programmers and 

statisticians then create composite variables constructed from basic variables for each child. Sampling 

weights (described in Section 6 of this Guide) are added to each record.

4.1.3. Editing of Provider Data 

Six to eight weeks after the close of household data collection for a quarter, the majority of the

immunization history questionnaires have been collected from providers. The data from the hard-copy 

questionnaires are entered and independently re-entered to provide 100% verification. The provider data 

file is cleaned, in a similar fashion to the household data file, for out-of-range values and consistency. A 

computer program back-codes “other shot” verbatim responses into the proper vaccine category (e.g., 

Engerix B counts as Hep B, and Kinrix counts as DTaP and polio). These translations come from a file 

that contains all such verbatim responses ever encountered in the NIS-Child. Also, the provider data file is 

checked for duplicate records, and exact duplicates are removed. If the provider data contain a date of 

birth, sex, or name for the child that differs from the household interview for that child, the questionnaire

is re-examined to see whether it may have been filled out for the incorrect child. Provider data that appear 

to have been filled out for the wrong child are removed from the provider database. When a child has data 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 31

from multiple providers, decision rules are applied to produce the most complete picture of the child’s 

vaccination history. 

Once these data have been cleaned, they are combined with the household data file. Information from up 

to five providers can be added to a child’s record. If more than one provider reported vaccination data for 

the child, the data from the multiple provider reports are combined into a single history for the child, 

called the “synthesized provider-reported vaccination history.” The determination of whether the child is 

up-to-date for recommended vaccines and vaccine series is based on the child’s synthesized providerreported vaccination history.

Many variables in the household data file are checked against or verified with the provider data file. For 

example, a child’s date of birth as recorded by the provider is checked against the date of birth as given 

by the household, to verify that the provider was reporting for that specific child and to form a “best” date 

of birth for the child. All children with at least one provider-reported vaccination are considered to have 

adequate provider data.

4.2. Limitations of Data Editing Procedures

Although data editing procedures were used for the NIS-Child, the data user should be aware that some 

inconsistent data might remain in the public-use data file. The variables that indicate whether a child is 

up-to-date on each vaccine or series (on which the estimates of vaccination coverage are based) are 

derived from provider-reported data, and the NIS-Child does not re-contact households or providers to 

attempt to reconcile potential discrepancies in provider-reported vaccination dates or to resolve date-ofbirth reporting errors. However, beginning with the 1999 NIS-Child, the provider-reported data are 

manually reviewed and edited to correct specific reporting errors. The National Immunization Survey: 

Guide to Quality Control Procedures (CDC, 2002) discusses the change in editing procedures in more 

detail. Some children with adequate provider data may have incomplete vaccination histories. These 

incomplete histories arise from three primary sources: 1) the household does not identify all vaccination 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 32

providers, 2) some but not all providers respond with vaccination data, and 3) all identified providers 

respond with vaccination data but fail to list all the vaccinations in the child’s medical record. Even with 

these limitations, the NIS-Child overall is a rich source of data for assessment of up-to-date status and 

age-appropriate vaccination. Also, the NIS-Child is the only source to provide comparable providerreported vaccination data across states and local areas in the United States. 

4.3. Variable-Naming Conventions

The names of variables follow a systematic pattern as much as possible. The codebook for the public-use 

data file (NCIRD, 2023), available at https://www.cdc.gov/vaccines/imz-managers/nis/datasets.html, 

groups the variables into ten broad categories according to the source of the data (household or provider) 

and the content of the variable. See Section 7 of this report for detailed information on the contents of the 

public-use data file.

4.4. Missing Value Codes

Missing value codes for each variable can be found in the codebook (NCIRD, 2023), available at 

https://www.cdc.gov/vaccines/imz-managers/nis/datasets.html. For household variables, the missing 

value codes usually are 77 for DON’T KNOW and 99 for REFUSED. Some household variables may 

also contain blanks, if the question was not asked. The variables developed from the IHQ generally do not 

have specific missing value codes.

4.5. Imputation for Item Non-Response

The NIS-Child uses imputation primarily to replace missing values in the socioeconomic and 

demographic variables used in weighting. Missing values of these variables are imputed for all children 

with a completed household interview – i.e., all children appearing on the public-use data file. Missing 

values of health insurance variables are also imputed for children with adequate provider data. A 

sequential hot-deck method is used to assign imputed values (Ford, 1983). Class variables are used to 

separate respondents into cells. Donors and recipients must agree on the categories of the class variables, 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 33

which include the estimation area. Within the categories of the class variables, respondents are sorted by 

variables related to the variable to be imputed. The last case with an observed value is used as the donor 

for up to four recipients. The “Label” and “Notes” line for each variable in the codebook (NCIRD, 2023) 

identifies variables that contain imputed values. These variables include the sex, Hispanic origin, race, 

health insurance status, and first-born status of the child; the education level, age group, marital status, 

and mobility status of the mother; and the income-to-poverty ratio of the household. 

The count of vaccinations for a specific vaccine is based on the number of unique vaccination dates

reported by the child’s provider(s). In filling out the immunization history questionnaire a provider may 

not know the date of the first dose of hepatitis B, which is typically given at birth. The provider does, 

however, have the option of checking the “Given at Birth” box for the first dose of hepatitis B. If it was 

checked “yes” and the date of the birth dose of hepatitis B was not reported, a program assigns the date of 

the birth dose for this vaccine. A value is imputed from the distribution of provider-reported dates for the 

birth dose of hepatitis B. The birth dose for this imputation is defined as being given in the first 7 days of 

life - between the date of birth (i.e., 0 days) and the date of birth plus 6 days. This imputation procedure 

was first implemented in 2000. For 2022 (excluding U.S. territories), a total of 101 children had the 

date of the birth dose of hepatitis B assigned using the above procedure (see HEP_FLAG).

Table 3 shows the observed distribution of age in days at the birth dose of hepatitis B for children in 2022 

with a provider-reported birth dose. A similar table is included in the 2000-2021 data user’s guides. For 

1997, 1998, and 1999, Section 5 of the data user’s guide provides information on the distribution of age in 

days for the birth dose of hepatitis B vaccine and gives guidance on imputing age in days at birth dose for 

children with a missing date, but for whom the provider checked the box indicating that a dose was 

administered at birth (see HEP_BRTH).
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 34

Table 3: Distribution of Age (in Days) at the Birth Dose of Hepatitis B Vaccine, National 

Immunization Survey - Child, 2022 

Age in Days at

Birth Dose

Unweighted Percentage

Of Birth Doses*

0 70.2 

1 22.2 

2 3.8 

3 1.4 

4 0.9 

5 0.6 

6+ 1.2 

* Excludes U.S. territories. 

4.6. Vaccine-Specific Recoding of Verbatim Responses

On the IHQ, providers can list vaccinations in the “other” section of the IHQ shot grid. After data 

collection, they are reclassified into the listed categories, if possible, using a vaccination recoding table.

This table is reviewed by NCIRD personnel to ensure the shots are recoded into the appropriate category 

or categories (for combination shots).

4.7. Composite Variables

A number of composite variables (constructed from basic variables) are created and included in the NISChild public-use data file. Composite variables assist users and data analysts by eliminating duplication of 

effort and making NIS-Child data easier to use. 

Since the initial years of NIS-Child data collection, the household composite variables have included upto-date status on individual vaccinations, race of child, household income, and up-to-date status on 

several vaccination series. Many of these household composite variables are included in the NIS-Child

public-use data file. See Section 7 of this report for information on the key variables.

In Quarter 3, 1999, the NIS-Child race questions (see questions C3, C9 and C10 in the household 

questionnaire) were expanded to include Alaska Native, Native Hawaiian, and Pacific Islander, 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 35

implementing the revised Office of Management and Budget (OMB) standards for classification of race 

and ethnicity (https://www.whitehouse.gov/wp-content/uploads/2017/11/Revisions-to-the-Standards-forthe-Classification-of-Federal-Data-on-Race-and-Ethnicity-October30-1997.pdf). The composite race 

variables in the 2002 through present NIS-Child public-use data files, however, contain only three 

categories: white alone; black alone; and all other races alone/multiple races. (The variable RACE_K 

classifies each child into one of these three categories, while the variable RACEETHK includes a separate 

“Hispanic” category.) The “all other races alone” category includes Asian, American Indian or Alaska 

Native, Native Hawaiian or Pacific Islander, and other races. If more than one race was selected during 

administration of the child race questions, the child is classified as multiple races. Because of small 

sample sizes and risk of disclosure within estimation areas, the NIS-Child public-use data files do not 

contain any variables with separate multiple-race categories. Rather, the children with multiple races are 

included in the “all other races” category. Table 4 shows some characteristics of the current race and 

ethnicity categories for the vaccine series and selected individual vaccines.
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 36

Table 4: Weighted Distribution of Children by Race and Ethnicity and Corresponding Combined Vaccine Series* (4:3:1:3*:3:1:4), 

Pneumococcal, and Varicella Vaccination Coverage Estimates, National Immunization Survey - Child, 2022 

Race and Ethnicity 

Classification

Weighted Distribution of 

Children aged 19-35 

Months in U.S.

Estimate (%)

Weighted Percentage 

4:3:1:3*:3:1:4 UTD

Estimate (%)

(Standard Error (%))

Weighted Percentage 4+ 

Pneumococcal

Estimate (%)

(Standard Error (%))

Weighted Percentage 1+ 

Varicella at 12+ Months

Estimate (%)

(Standard Error (%))

Hispanic 28.3 69.8 (1.7) 82.4 (1.4) 93.3 (0.8)

Non-Hispanic white only 43.3 74.9 (0.8) 86.5 (0.7) 92.5 (0.5)

Non-Hispanic black only 13.1 67.2 (1.9) 78.8 (1.8) 91.2 (1.4)

Non-Hispanic American Indian 

or Alaska Native only 1.0 60.7 (4.8) 76.8 (3.8) 85.8 (3.0)

Non-Hispanic Asian only 6.5 77.9 (2.6) 86.2 (2.4) 95.7 (1.3)

Non-Hispanic Native Hawaiian 

or Pacific Islander only 0.3 63.3 (10.7) 80.4 (6.6) 94.3 (3.1)

Non-Hispanic multiple races 7.6 70.2 (2.6) 81.7 (2.5) 91.1 (1.9)

Non-Hispanic white/black 3.5 66.8 (3.9) 78.5 (3.7) 87.8 (3.7)

Non-Hispanic white/ American 

Indian or Alaska Native 0.6 73.6 (5.6) 82.1 (4.6) 97.6 (1.0)

Non-Hispanic white/Asian 2.1 72.7 (5.9) 86.6 (6.2) 95.3 (1.4)

Non-Hispanic other 

combination 1.4 73.1 (4.2) 82.2 (3.4) 90.4 (3.1)

Note: UTD = up-to-date. Weighted by PROVWT_C. Children with an unknown Hispanic origin and/or race were imputed by a hot-deck method. This table excludes U.S. 

territories. 

* 4+ diphtheria and tetanus toxoids and acellular pertussis vaccine adsorbed, diphtheria and tetanus toxoids and pertussis vaccine, or diphtheria and tetanus toxoids vaccine 

adsorbed (DTaP/DTP/DT); 3+ poliovirus vaccine; 1+ measles-containing vaccine (MCV); full series Haemophilus influenzae type b conjugate vaccine (Hib), i.e., 3 or 4 doses 

depending on type of vaccine received; 3+ hepatitis B vaccine (Hep B); 1+ varicella at or after 12 months of age; and 4+ pneumococcal vaccine (PCV). 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 37

4.8. Subsets of the NIS-Child Data

The NIS-Child public-use data file contains data for all eligible children who have a completed household 

interview. An interview is considered complete if the respondent completed Section C of the 

questionnaire. As explained in Section 6 of this guide, each child with a completed household interview is 

assigned a weight (RDDWT_C for the United States, excluding U.S. territories; RDDWT_C_TERR for 

the United States, including U.S. territories) for use in estimation.

The NIS-Child uses the synthesized provider-reported vaccination histories to form the estimates of 

vaccination coverage because the provider data are considered more accurate than household-reported 

data. Thus, the most important subset of the data consists of children with adequate provider data. For 

these children, one or more providers returned an immunization history questionnaire that included 

vaccination data. Unvaccinated children are also considered to have adequate provider data. As discussed 

in Section 7 below, the PDAT variable identifies the children with adequate provider data (PDAT=1).

These children have a separate weight (PROVWT_C for the United States, excluding U.S. territories; 

PROVWT_C_TERR for the United States, including U.S. territories), which should be used to form 

estimates of vaccination coverage (see Section 6).

4.9. Confidentiality and Disclosure Avoidance

To prevent identification of participants in the NIS-Child and the resulting disclosure of information, 

certain items from the questionnaires are not included in the public-use data file. In addition, some of the 

released variables either are top- or bottom-coded, or have their categories collapsed. Variable labels 

indicate which variables have been re-coded in these ways. These decisions are reviewed by the NCHS 

Disclosure Review Board to ensure the public use data files meet acceptable levels of disclosure risk. 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 38

5. Quality Control and Quality Assurance Procedures 

A major contributor to NIS-Child data quality is its sample management system, which in 2022 managed 

over 220 estimation area by quarter samples and used a number of performance measures to track their 

progress toward completion. Khare et al. (2000), Khare et al. (2001), and the National Immunization 

Survey: Guide to Quality Control Procedures (CDC, 2002) describe quality assurance procedures. 

Important aspects of the quality assurance program for the RDD component of the NIS-Child included 

interviewer monitoring; on-line provider look-ups in a database system integrated with the CATI system, 

including names, addresses, and telephone numbers of vaccination providers; and automated range-edits 

and consistency checks. These and other quality assurance procedures contributed to a reduction in total 

data collection cost by minimizing interviewer labor and overall burden to respondents. 

The Provider Record Check component used quality control measures at four junctions: prior to mailing 

packets to providers; during the telephone prompting effort; during the editing of returned questionnaires; 

and during and after their data entry. The final quality assurance activities are implemented during postprocessing of the returned questionnaires or vaccination records. All returned questionnaires were

examined to identify and correct any obvious errors prior to data entry and then key-entered with 100% 

verification. The keying error rate is estimated, by way of a second verification process, to be less than 

1%. 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 39

6. Sampling Weights 

Each of the two phases of data collection results in a separate sampling weight for each child that has data 

at that phase. The RDD-phase sampling weights permit analyses of data for children with completed 

household interviews. Each child with adequate provider data (the subset on which official estimates of 

vaccination coverage are based) has a provider-phase sampling weight. The RDD-phase sampling weight

variable for producing estimates for children with completed household interviews in the United States 

excluding U.S. territories is called RDDWT_C; the RDD-phase weight variable for producing estimates 

for the United States including U.S. territories is called RDDWT_C_TERR. The provider-phase 

sampling weight variable for producing estimates for children with adequate provider data in the United 

States excluding U.S. territories is called PROVWT_C; the provider-phase weight variable for producing 

estimates for the United States including U.S. territories is called PROVWT_C_TERR. See Section 8 of 

this user’s guide for more information about the weights included in the data file and the proper way to 

use them.

As discussed below, revisions in weighting methodology were made on various occasions and the names 

of the weight variables were also changed to keep track of the revisions. Table 5 lists of the RDD-phase 

and provider-phase weight variable names used for each year of the NIS-Child. 

A sampling weight may be interpreted as the approximate number of children in the target population that 

a child in the sample represents. Thus, for example, the sum of the sampling weights of children with 

adequate provider data who are up-to-date (on a particular vaccine or series of vaccines) yields an 

estimate of the total number of children in the target population who are up-to-date. Dividing this sum by 

the total of the sampling weights for all children with adequate provider data gives an estimate of the 

corresponding vaccination coverage rate. 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 40

Table 5: RDD-Phase and Provider-Phase Weight Variable Names, National Immunization 

Survey - Child, 1995-2022

Year(s) RDD-Phase Weight Variable Name(s) Provider-Phase Weight Variable Name(s)

1995-2001 HY_WGT W0

2002 RDD_WT WT

2003-2004 WGT_RDD WGT

2005-2008 RDDWT PROVWT

2009-2010

RDDWT

RDDWTVI

PROVWT

PROVWTVI

2011

RDDWT_LL

RDDWTVI_LL

RDDWT_D

PROVWT_LL

PROVWTVI_LL

PROVWT_D

2012 RDDWT_D

RDDWTVI_D

PROVWT_D

PROVWTVI_D

2013 RDDWT_D

RDDWTVIGU_D

PROVWT_D

PROVWTVIGU_D

2014-2016 RDDWT_D

RDDWT_D_TERR

PROVWT_D

PROVWT_D_TERR

2017 RDDWT_D PROVWT_D

2018 RDDWT_C PROVWT_C

2019-2022 RDDWT_C

RDDWT_C_TERR

PROVWT_C

PROVWT_C_TERR

This section describes how these weights are developed and adjusted so as to achieve an accurate 

representation of the target population. The base weights reflect each telephone number’s probability of 

being selected into the sample; the adjustments take into account non-resolution of residential/nonresidential/non-working status of a telephone number, non-response to the screener and household 

interviews, number of cellular phones used by parents in the household, raking for differential coverage 

rates and non-coverage of households that do not have cellular phones, non-response by providers, and a 

final raking adjustment. 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 41

6.1. Base Sampling Weight

In each quarterly NIS-Child sample, each child with a completed household interview receives a base 

sampling weight. The base sampling weight is equal to the inverse of the probability the phone number 

was sampled from the sampling frame for the quarter and estimation area.

6.2. Adjustments for Non-Resolution of Telephone Numbers, Screener 

Non-Response, and Interview Non-Response 

Non-response occurs in population-based surveys when potential respondents refuse to participate, are not 

available at the time of the interview, or could not be reached during the survey period. Thus, the sum of 

the base sampling weights of children with completed household interviews will underestimate the size of 

the target population in the estimation area, because not all sampled households respond to all stages of 

data collection up to the household interview. As a result, the base sampling weights must be adjusted so 

they accurately reflect the number of children in the target population that each sampled child with a 

completed household interview represents. 

Some sampled households with age-eligible children fail to complete the household interview because of 

unit non-response: for some telephone numbers, it is never determined whether or not the number is a 

working residential number despite multiple call attempts; for some households it is never determined 

whether or not the household contains age-eligible children; and some households with age-eligible 

children do not complete the household interview. To compensate for these three types of unit nonresponse, the sampling weights of children with a completed household interview are adjusted to account 

for the estimated number of age-eligible children in households whose telephone numbers are never 

resolved, the estimated number of age-eligible children in households that fail to complete the screening 

interview, and the number of identified age-eligible children for whom the household interview is not 

completed. Each of these adjustments is carried out within each estimation area by forming weighting 

cells based on the Metropolitan Statistical Area (MSA) status of the wire center associated with the 

cellular phone number (MSA/non-MSA). Each cell in each stage of adjustment must have sufficient 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 42

resolved/responding cases (usually 20, but 15 for interview non-response) at that stage of adjustment. 

The cells with a deficient number of responding cases are collapsed into neighboring cells, i.e., both MSA 

categories are collapsed if either of the cells have a deficient number of responding cases. Once the 

adjustment cells are formed, the weights of the unresolved/non-responding records from the previous 

adjustment step are distributed to the weights of the resolved/responding records within each cell.

6.3. Adjustment for Multiple Cellular Phones and Deriving Annual 

Weights

Once the non-response-adjusted interview weights for households are computed, these weights are 

adjusted for additional cellular phones in the household. Because households with multiple cellular 

phones have a greater chance of being sampled, each child’s household interview weight is adjusted by 

dividing it by the total number of cellular phones used by parents or guardians (up to a maximum of 3).

Up to the previous step, the sampling weights are adjusted separately for each quarter, and the weights in 

each quarter pertain to the target population. However, annual vaccination coverage estimates are 

obtained from data for four consecutive quarters, so the weights in each quarterly file are adjusted when 

the data from the four quarters are combined. The adjustment factor is proportional to the number of 

households with completed household interviews in each quarter and estimation area. 

6.4. Calibration 

Next, survey weights are calibrated to population control totals as described below. The control totals 

used for the NIS-Child are derived from current natality data from NCHS (2019, 2020) (available at 

https://www.cdc.gov/nchs/data_access/vitalstatsonline.htM). Because the Vital Statistics data give the 

counts of all live births in the United States, regardless of whether the household has any cellular phones, 

the control totals include all eligible children. The control total for each raking dimension is derived 

from the NCHS natality files from 2019 and 2020 (children born between July 1, 2019 and 

November 30, 2020 would have been 19 through 35 months on June 30, 2022). Use of the natality 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 43

data to form the required population control totals for the NIS-Child has three limitations: 1) the natality 

file provides a universe of live births and therefore does not reflect infant mortality; 2) the natality file 

does not include children born outside the United States who immigrate to this country before reaching 

ages 19 through 35 months; and 3) the natality file records residence at time of birth, and some children 

may move from one estimation area to another by the time they reach 19 through 35 months of age. 

Adjustments are made to the natality data to account for these three factors. For 2022, the combined 

2019, 2020, and 2021 one-year American Community Survey Public Use Microdata Sample data 

files (https://www.census.gov/programs-surveys/acs/data.html) were used to make the immigration 

and migration adjustments (U.S. Census Bureau, 2020, 2021, 2022). 

Survey weights are adjusted to agree with independent estimates of the population total by telephone 

status. The proportions of 19 through 35 month old children by detailed telephone status (cellular-phoneonly, landline and cellular phone dual user, landline-only, phoneless) within each estimation area were 

derived using a similar small area modeling approach as described in Blumberg et al. (2011). These 

modeled telephone status estimates are applied to the population control total for the estimation area to 

estimate the control totals by detailed telephone status within the estimation area. In each estimation area, 

children in dual landline and cellular phone households are weighted to represent children living in dual 

landline and cellular phone households in the estimation area, and children in cellular-phone-only 

households are weighted to represent children in cellular-phone-only households in the estimation area.

Children in landline-only and phoneless households, which are excluded from the cellular phone sample, 

are accounted for in the raking step described below.

To reduce sampling variability and improve the precision of estimation, extreme weights are trimmed and 

then recalibrated to control totals. RDD sampling weight values exceeding the median weight plus three

times the interquartile range of the weights within an estimation area are truncated and then recalibrated 

to control totals. This is done by up to five iterations. This weight trimming prevents children with 

unusually large weights from having an unusually large impact on immunization coverage estimates.
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 44

The final step in adjusting the RDD sampling weights is a raking adjustment (Deming, 1943) of the 

trimmed, telephone status adjusted weights. The raking procedure uses estimation area-level control totals 

for maternal education categories, maternal race and ethnicity, age group of the child, sex of the child, 

and telephone status. Briefly, raking takes each variable in turn and applies a proportional adjustment to 

the current weights of the children who belong to the same category of the variable. After a number of 

iterations over all the variables, the raked weights have totals that match all the desired control totals. 

Raking makes it possible to incorporate additional variables into the weighting and to use more detailed 

categories for those variables. Wolter et al. (2017a) gives the details of various aspects of the NIS-Child

estimation procedures.

The sampling weights after all the foregoing adjustments constitute the “RDD sampling weights” 

(RDDWT_C for the United States excluding territories; RDDWT_C_TERR for the United States 

including territories).

6.5. Adjustment for Provider Non-Response

Among the 34,675 children with a completed household interview (excluding U.S. territories), 

17,232 (49.7%) had adequate provider data. Starting with the 2002 NIS-Child public-use data file, the 

definition of children with adequate provider data includes unvaccinated children. These are children for 

whom the respondent reported during the household interview that the child had received no vaccinations, 

and for whom no providers were reported, or one or more providers were reported but those providers 

reported administering no vaccinations. Among the 17,232 children with adequate provider data, 247

were unvaccinated children. Failure to obtain adequate provider data for the remaining 17,443 children

(50.3%) was attributable to: 

● parent or guardian not identifying any providers or not giving consent to contact the child’s 

vaccination provider(s) (40.2%); 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 45

● consent to contact vaccination providers obtained but no providers returned the immunization 

history questionnaire (6.4%); and

● one or more providers returned the immunization history questionnaire, but no providers reported 

any vaccination data, despite the parent or guardian indicating that the child has received 

vaccinations (3.7%). 

The 17,443 children for whom a household interview was completed but adequate provider data were not 

obtained are classified as “partial non-responders” because they have only a partial response to the NISChild as a whole.

Empirical results suggest that children with adequate provider data have characteristics believed to be 

associated with a greater likelihood of being up-to-date, compared with children who had missing 

provider data. Specifically, children with adequate provider data are more likely to live in households that 

have higher total family income, have a white mother, and live outside a principal city of a Metropolitan 

Statistical Area. Also, a child with missing provider data is less likely to live in the state where the mother 

lived when the child was born. These factors indicate a potential lack of continuity of health care, and are 

associated with lower vaccination coverage (Coronado et al., 2000). If no adjustment is made to the RDD 

sampling weights to account for these differences, estimated vaccination coverage rates may be biased. 

To reduce potential bias in estimators of vaccination coverage attributable to partial non-response, a 

weighting-class adjustment is used in each estimation area (Brick and Kalton, 1996). This adjustment 

involves three steps. In the first step, sampled children are classified according to the quintile of their 

estimated probabilities of having adequate provider data. In the statistical literature these probabilities are 

called response propensities (Rosenbaum and Rubin, 1983, 1984; Rosenbaum, 1987). Children who have 

similar response propensities will also be similar with respect to variables that are strongly associated 

with the probability of having adequate provider data. In this important respect, children in each class are 

comparable. Because of this comparability, any sub-sample of children in a class may represent all 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 46

children in the class. Therefore, the weighting-class adjustment uses the children with adequate provider 

data to represent all children in the class. Details, including the methodology for forming weighting 

classes based on propensity scores, can be found in the NCHS report on the statistical methodology of the 

NIS-Child (Smith et al., 2005), available at http://www.cdc.gov/nchs/data/series/sr_02/sr02_138.pdf. 

In the second step of this weighting-class adjustment, within each class, an adjustment factor redistributes 

the RDD sample weights of the children with missing provider data to the weights of the children who 

have adequate provider data. These adjusted sampling weights of children with adequate provider data are 

initial non-response-adjusted provider-phase weights. 

Within an estimation area, the sums of non-response adjusted weights of children with adequate provider 

data for the various levels of important socio-demographic variables (such as race and ethnicity) may not 

be equal to corresponding population totals. To reduce bias attributable to these differences, raking was 

used in the third step to adjust the non-response adjusted weights to match estimation area control totals. 

Control totals for these variables were estimated using the weighted totals from the sample of children 

with completed household interviews. Smith et al. (2001b, 2005) describe the development of this 

approach in more detail. Similar to the RDD weighting, the extreme weights exceeding the median weight 

plus three times the interquartile range of the weights within an estimation area are truncated and then 

recalibrated to control totals. These raked weights of children with adequate provider data are called 

“final provider-phase weights” (PROVWT_C for the United States excluding territories and 

PROVWT_C_TERR for the United States including territories). Because of the comparability of 

children within each weighting class, any estimate that uses data only from the children with adequate 

provider data along with their provider-phase sampling weights will have less bias attributable to 

differences between children with adequate provider data and children with missing provider data.

Appendix B summarizes the distribution of the sampling weights (RDDWT_C, PROVWT_C,

RDDWT_C_TERR, and PROVWT_C_TERR) in each estimation area for 2022.
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 47

NIS-Child public-use data files for 1995 to 2001 do not include sampling weights that account for the 

effect of unvaccinated children. An assessment of the effect of accounting for unvaccinated children for 

the period 1995 to 2003 was made. Weights were calculated for each year with and without unvaccinated 

children and the vaccination coverage estimates compared. Details of this assessment and the results are 

available in the user’s guide for the 2004 NIS-Child public-use data file. At the national level, accounting 

for unvaccinated children had very little effect on the estimates of 4:3:1:3 vaccination coverage. Within 

estimation areas also, the two coverage estimates differed little. The largest difference (in either direction) 

was most often around 2 percentage points. Differences of that magnitude are small relative to the 

standard errors of the estimates. Although accounting for unvaccinated children has a small effect on 

estimates of vaccination coverage, data users who use the pre-2002 public-use data files to examine 

estimation area-level trends over time are advised to interpret the results with appropriate caution.

6.6. Sampling Weights for Territories

The NIS-Child weighting process was followed as closely as possible for U.S. territories. Due to 

differences in the availability of external data sources for U.S. territories, slight changes were necessary to 

accurately estimate vaccination coverage for these areas. These differences are stated below.

In step 6.2, each of the non-response adjustments for U.S. territories was done at the estimation area level. 

That is, no weighting cells were formed for U.S. territories. 

Similar to the weights for the United States excluding territories, the final step in adjusting the RDD 

sampling weights for U.S. territories is a raking adjustment. For Guam and Puerto Rico, a different set of 

race and ethnicity categories were used for post-stratification and raking adjustments than were used in 

other areas. The three Guam race and ethnicity categories were: Chamorro/Guamanian, Other 

Asian/Pacific Islander, and All Other. The two Puerto Rico race and ethnicity categories were: White and 

All Other.
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 48

After sampling weights were calculated for all children, they were stored in the variables 

RDDWT_C_TERR and PROVWT_C_TERR. These weight variables permit one to conduct analysis 

of all estimation areas, including U.S. territories. The weight variables RDDWT_C and PROVWT_C

are equal to RDDWT_C_TERR and PROVWT_C_TERR for all children except those in U.S. 

territories, for whom the value of these weight variables is blank or missing. RDDWT_C and 

PROVWT_C permit one to conduct analysis of all estimation areas, excluding U.S. territories.
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 49

7. Contents of the Public-Use Data File

The NIS-Child public-use data file contains a record for each eligible child for whom Section C of the 

household interview was completed, along with household-reported information about the child and the 

child’s mother. For children with IHQs returned by one or more providers, the file also contains provider 

characteristic variables, as well as variables based on the child’s synthesized provider-reported 

vaccination history: the age of the child at each vaccination, the number of each type of vaccination 

received, and indicators of whether the child is up-to-date with respect to various recommended vaccines 

and vaccine series.

The public-use data file consists of ten sections, the contents of which are described below in detail. For 

additional information, users are encouraged to consult the codebook (NCIRD, 2023), available at 

https://www.cdc.gov/vaccines/imz-managers/nis/datasets.html. The codebook is divided into the ten 

sections described below and contains variable names, labels, and response frequencies (for categorical 

variables). For select variables, the codebook also gives additional information about the variable in the 

“Notes” field.

In this section, Table 6 lists key NIS-Child variables commonly used in analyses (these variables have 

been included on all previous NIS-Child public-use data files as well unless otherwise stated). This table 

is followed by a summary of changes from 2021 to 2022 and then a more detailed description of the 2022 

contents. A full list of variables appearing on the 2004-2022 NIS-Child public-use data files appears in 

Appendix E, along with the reason for the addition, subtraction, or modification of the variables in 2005-

2022. Information on changes made between 1995-2004 can be found in the Alphabetical Listing of 

Variables that are Not Available in All Public-Use Data Files, National Immunization Survey, 1995-

2004. http://www.cdc.gov/nchs/data/nis/pufvariables1995to2004.pdf 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 50

Table 6: NIS-Child Variables Commonly Used in Analyses or for Published Estimates

Variable Categories

ID Variables

SEQNUMC – unique child ID variable -

SEQNUMHH – unique household ID variable -

Geographic Variables

ESTIAP22 – estimation area number (introduced in 2022; 

ITRUEIAP used through 2004, ESTIAP in 2005, and ESTIAPyy 

since 2006)

-

STATE – state FIPS code -

CEN_REG – census region

Northeast

Midwest

South 

West

Child Demographic Variables

AGEGRP – age category of child

19-23 months

24-29 months

30-35 months

RACEETHK – race and ethnicity of child

(introduced in 2002; RACEKIDR used in 1995-2001) 

Hispanic

White alone, non-Hispanic

Black alone, non-Hispanic

All other races alone and multiple races, 

non-Hispanic

SEX – sex of child Male

Female

FRSTBRN – firstborn status of the child No

Yes

Mother Demographic Variables

EDUC1 – education of the mother

<12 years

12 years

>12 years, not a college graduate

College graduate

MARITAL2 – marital status of mother

(Living with partner response option added to questionnaire in 

2015)

Currently married

Never married, widowed, divorced, separated, deceased, 

or living with partner

M_AGEGRP2 – age group of mother

(introduced in 2016; M_AGEGRP used through 2015)

<=29 years

30 years or older

Poverty Variables

INCPOV1 – poverty status

(introduced in 2005; INCPOV1R used through 2004)

At or above poverty level, income > $75,000

At or above poverty level, income <= $75,000

Below poverty level

Not determined

INCPORAR – income-to-poverty ratio

(introduced in 2005; INCPORAT used through 2004) -

INCPORAR_I – imputed income-to-poverty ratio

(introduced in 2016) -

WIC Variables

CWIC_01 – child ever participated in WIC program

Yes

No 

Never heard of WIC

Don’t know

Refused 

Missing
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 51

Variable Categories

CWIC_02 – child currently participating in WIC program

Yes

No

Don’t know

Refused 

Missing

Breastfeeding Variables

CBF_01 – child ever fed breast milk

Yes

No

Don’t know

Missing

BF_ENDR06 – length of time in days child was fed breast milk -

BF_EXCLR06 – length of time in days child was exclusively 

fed breast milk or formula (introduced in 2006) -

BF_FORMR20 – age in days when child was first fed formula 

(introduced in 2020; BF_FORMR06 used in 2006 and 2007; 

BF_FORMR08 used 2008-2019)

-

Chicken Pox Variables

HAD_CPOX – did child ever have chicken pox (introduced in 

2005; I_HADCPX used through 2004)

Yes

No

Don’t know

Refused 

Missing

AGECPOXR – age in months when child had chicken pox 

(introduced in 2005; IAGECPXR used through 2004)

0-6 months

7-12 months

13-18 months

19-24 months

25-30 months

31 months or older

Missing

Presence of Provider Data Variables

PDAT – adequate provider data indicator Yes

No

Number of Provider-Reported Doses of Vaccine Variables

P_NUMDTP – total number of DTaP/DTP/DT doses -

P_NUMPOL – total number of polio doses -

P_NUMMMR – total number of MCV doses -

P_NUMHIB – total number of Hib doses -

P_NUMHEP – total number of hepatitis B doses -

P_NUMVRC – total number of varicella doses -

P_NUMPCV – total number of pneumococcal conjugate (PCV) 

doses -

P_NUMFLU – total number of seasonal influenza doses -

P_NUMHEA – total number of hepatitis A doses -

P_NUMROT – total number of rotavirus doses -

Provider Characteristic Variables
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 52

Variable Categories

PROV_FAC – provider facility type

All public facilities

All hospital facilities

All private facilities

All military/other facilities

Mixed types

Unknown 

VFC_ORDER – do child’s providers order vaccines for 

children from state/local health department? (introduced in 

2006)

All providers

Some but not all providers

No providers

Unknown 

REGISTRY – provider(s) reported child’s vaccination(s) to 

state or community immunization registry

All providers

Some but not all providers

No providers

Unknown 

Insurance Status Variables

INS_STAT2_I – child’s current health insurance coverage 

status (introduced in 2017, INS_STAT_I used in 2016)

Private insurance only 

Any Medicaid

Other insurance (CHIP*, IHS*, military, or some other 

form of insurance, alone or in combination with private 

insurance) 

Uninsured 

INS_BREAK_I – child’s insurance history since birth

(introduced in 2016)

Currently insured but uninsured since birth

Currently insured and never uninsured since birth

Currently uninsured but insured since birth

Currently uninsured and never insured since birth

* CHIP = Children’s Health Insurance Program; IHS = Indian Health Service

Before describing the sections of the public-use data file below, we first summarize the differences 

between the 2021 and 2022 NIS-Child public-use data files:

• A new 2022 estimation area variable (ESTIAP22) has been added and the 2021 estimation 

area variable (ESTIAP21) has been dropped. Although data were collected for Guam in 

2022, children in these areas are not included on the public-use data file to protect 

confidentiality.

• A new shot counter variable P_NUMPCC15 has been added to reflect the new 

Pneumococcal vaccine type (Conjugate-15) which was added to the IHQ shot grid in 2022. 

7.1. Section 1: ID, Weight, and Flag Variables

SEQNUMHH and SEQNUMC are the unique household and child identifiers, respectively. PDAT

indicates which children are considered to have adequate provider data. As described in Section 6 of this 

report, RDDWT_C/RDDWT_C_TERR and PROVWT_C/PROVWT_C_TERR are the final 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 53

household- and provider-phase weights, respectively. PROVWT_C/PROVWT_C_TERR should be 

used when analyzing the provider-reported data, i.e., the variables in Sections 7, 8, and 9 of the NIS-Child

public-use data file.

7.2. Section 2: Household-Reported Vaccination and Chickenpox

Information

Section 2 of the public-use data file contains variables derived from the information collected in Section 

B of the household questionnaire. In particular, it contains variables indicating whether the respondent 

reported that the child has had chicken pox disease (HAD_CPOX) and the child’s age in months at 

chicken pox disease (AGECPOXR).

7.3. Section 3: Demographic, Socio-Economic, and Other 

Household/Child Information

Section 3 of the NIS-Child public-use data file consists of information collected during the household 

screening interview and Section C of the household main interview. To protect confidentiality, many of 

these variables have been collapsed, top-coded, or bottom-coded from the original, fully-detailed 

versions; the variable labels (see the public-use date file codebook) indicate which variables have been 

collapsed or recoded. 

AGEGRP is the age of the child in months in three categories (19-23 months, 24-29 months, 30-35 

months), based on the child’s best date of birth and the eligibility date. SEX gives the sex of the child, 

and FRSTBRN indicates whether the child is the first born, with missing values of these variables 

imputed. The language in which the interview was conducted is stored in variable LANGUAGE, and

C5R gives the relationship of the respondent to the child.

The breastfeeding variables include whether the child was ever fed breast milk (CBF_01), length of time 

in days the child was fed breast milk (BF_ENDR06), the age in days when the child was first fed formula 

(BF_FORMR20), and the length of time in days the child was exclusively fed breast milk or formula 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 54

(BF_EXCLR06). Two types of inconsistencies arise in the breastfeeding data: 1) duration of any 

breastfeeding can exceed age of the child, and 2) age when the child was first fed formula can exceed the 

age of the child. BFENDFL06 is set equal to 1 when BF_ENDR06 exceeds the age of the child (with a 

buffer), and BFFORMFL06 is set equal to 1 when BF_FORMR20 exceeds the age of the child (with a 

buffer). Appendix C provides details on how the flags were created. Data users are cautioned to review 

Appendix C before analyzing any of the breastfeeding variables.

The WIC variables include whether the child ever participated in the WIC program (CWIC_01) and 

whether the child is currently participating (CWIC_02). 

C1R and CHILDNM give the number of people and children, respectively, in the household. The child’s

Hispanic origin indicator, race with three categories, and race and ethnicity with four categories are 

presented in variables I_HISP_K, RACE_K, and RACEETHK, respectively; for each of these 

variables, missing values have been imputed. The age, education level, and marital status of the mother of 

the child are stored in variables M_AGEGRP2, EDUC1, and MARITAL2 (married vs. not married), 

with missing values imputed. 

The categorized total combined income for the child’s family is given by INCQ298A. INCPOV1 gives 

the family’s poverty status (at or above poverty, income > $75,000; at or above poverty, income <= 

$75,000; below poverty; unknown), and INCPORAR gives the ratio of the family’s income to the 

poverty level. INCPORAR_I gives the same ratio after missing values of family income have been 

imputed. Household tenure is given by RENT_OWN. 

The number of landline telephone numbers in the household, the number of working cellular phones 

household members have available for personal use, and the number of these cellular phones that are 

usually used by parents or guardians are given by NUM_PHONE, NUM_CELLS_HH, and 

NUM_CELLS_PARENTS, respectively.
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 55

Variable CEN_REG gives the census region of the respondent’s current residence, and MOBIL_I 

indicates whether the mother’s current state of residence is the same as her state of residence at the time 

of the child’s birth.

7.4. Section 4: Geographic Variables

Variables ESTIAP22 and STATE give the 2022 estimation area and state of residence, respectively, for 

each child. EST_GRANT indicates which of the 50 states, District of Columbia, and 5 local areas that 

receive federal Section 317 immunization awards (Bexar County, TX; City of Chicago, IL; City of 

Houston, TX; New York City, NY; Philadelphia County, PA) the child resides in.

7.5. Section 5: Number of Providers Identified and Consent Variables

Variable D7 indicates whether the respondent gave consent to contact the child’s providers. If D7=1, then 

consent was granted; if D7=2 then consent was explicitly denied; and if D7 is missing, consent was not 

granted because the respondent broke off the interview before being explicitly asked for consent. 

Variable D6R gives the number of providers identified by the respondent. Note that sometimes 

respondents report erroneous provider counts and sometimes report the same provider more than one 

time, and D6R does not reflect cleaning or de-duplication of the initially-reported provider count.

7.6. Section 6: Number of Responding Providers Variables

Variable N_PRVR indicates the number of providers returning IHQs with vaccination information for the 

child. That is, N_PRVR is the number of IHQs that were returned for the child that contain information 

on the IHQ shot grid. 

7.7. Section 7: Characteristics of Providers Variables

The variables in this section of the public-use data file summarize the information collected in IHQ 

questions 5b, 6, and 7 across the child’s providers who returned IHQs containing vaccination (i.e., shot 

grid) data.
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 56

PROV_FAC indicates the facility type of the child’s vaccination providers based on responses to IHQ 

question 5b. If all of the child’s providers that returned IHQs containing shot grid data (see Section 6 

variable N_PRVR) reported the facility type to be:

● a public health department-operated clinic, community health center, or rural health clinic, then 

PROV_FAC=1 (all public facilities);

● a hospital-based clinic, then PROV_FAC=2 (all hospital facilities);

● a private practice, then PROV_FAC=3 (all private facilities); 

● a military health care facility, WIC clinic, school-based health center, pharmacy, or other type of 

facility, then PROV_FAC=4 (all military/WIC/school/pharmacy or other facilities).

If the responses of providers that returned IHQs containing shot grid data fell into more than one of the 

above bulleted categories, then PROV_FAC=5 (mixed); otherwise, if at least one of the child’s providers 

returned an IHQ containing shot grid data but the facility type is unknown, then PROV_FAC=6 

(unknown). If none of the child’s providers returned an IHQ containing shot grid data, PROV_FAC is set 

to missing.

The Vaccines For Children (VFC) program is a federally-funded program that provides vaccines at no 

cost to children who might not otherwise be vaccinated because of inability to pay

(http://www.cdc.gov/vaccines/programs/vfc/index.html). CDC buys vaccines at a discount and distributes 

them to awardees—i.e., state health departments and certain local and territorial public health agencies—

which in turn distribute them at no charge to those private physicians’ offices and public health clinics 

registered as VFC providers. VFC_ORDER, based on responses to IHQ question 6, indicates whether the 

child’s vaccination providers order vaccines from a state or local health department to administer to 

children. If all of the child’s providers that returned IHQs containing shot grid data (see Section 6 variable 

N_PRVR) reported that they order vaccines from a state or local health department to administer to 

children, then VFC_ORDER=1 (all providers); if at least one of the child’s providers that returned an 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 57

IHQ containing shot grid data reported that the practice orders vaccines from a state or local health 

department to administer to children and the child’s other providers that returned IHQs containing shot 

grid data reported either that they did not order such vaccines or that they did not know whether or not 

they did, then VFC_ORDER=2 (some but possibly or definitely not all providers); if all of the child’s

providers that returned IHQs containing shot grid data reported that they do not order vaccines from a 

state or local health department to administer to children, then VFC_ORDER=3 (no providers); if none of 

the conditions for VFC_ORDER=1, 2, or 3 were met but at least one of the child’s providers returned an 

IHQ containing shot grid data, VFC_ORDER=4 (unknown). If none of the child’s providers returned an 

IHQ containing shot grid data, VFC_ORDER is set to missing. Note that having a provider that orders 

VFC vaccines does not imply that the child is VFC-entitled; providers enrolled in the VFC program could 

also vaccinate children who are not VFC-entitled. 

REGISTRY is based on responses to IHQ question 7 and indicates whether the child’s vaccination 

providers reported the child’s vaccinations to a local or state immunization registry (also known as an 

Immunization Information System, or IIS). If all of the child’s providers that returned IHQs containing 

shot grid data (see Section 6 variable N_PRVR) indicated that they reported to a registry, then 

REGISTRY=1 (all providers); if at least one of the child’s providers that returned an IHQ containing shot 

grid data indicated that the practice reported to a registry and the child’s other providers that returned 

IHQs containing shot grid data indicated that they did not report to a registry, that they did not know 

whether or not they reported to a registry, or that the question is not applicable, then REGISTRY=2 (some 

but possibly or definitely not all providers); if all of the child’s providers that returned IHQs containing 

shot grid data indicated that they did not report to a registry or that the question is not applicable, then 

REGISTRY=3 (no providers); if none of the conditions for REGISTRY=1, 2, or 3 were met but at least 

one of the child’s providers returned an IHQ containing shot grid data, REGISTRY=4 (unknown). If none 

of the child’s providers returned an IHQ containing shot grid data, REGISTRY is set to missing.
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 58

7.8. Section 8: Provider-Reported Up-To-Date Vaccination Variables

This section contains vaccination count and up-to-date variables based on the child’s synthesized 

provider-reported vaccination history. To facilitate data processing and to accommodate the large and 

continually growing number of vaccination types covered by the NIS-Child, the provider-reported 

vaccination data are organized around the concept of vaccine categories and vaccine types within vaccine 

category. The vaccine categories correspond to the sections of the IHQ shot grid, and the vaccine types 

correspond to the type boxes on the IHQ shot grid. (For each vaccine category, an “unknown” vaccine 

type is created for vaccinations that are reported without a type box being checked. Also, a few vaccine 

types, such as Measles-Mumps, arise through the backcoding of shots initially reported in the “other” 

section of the IHQ shot grid.) Table 7 shows the vaccine categories and types for the 2022 NIS-Child. 

Note that a single vaccination can fall into more than one vaccine category; for example, an MMRVaricella vaccination is part of both the Measles-containing and Varicella-containing vaccine categories.

(The full list of vaccine type codes can also be found in Appendix H.) Provider-reported COVID-19 

vaccination data were collected in 2022, but are not included on the 2022 NIS-Child public-use data 

file to protect respondent confidentiality, as COVID-19 vaccines were only approved for this age 

group for part of the year.

For each vaccine category, Section 8 of the public-use data file contains a variable typically named 

P_NUMYYY – where “YYY” is the vaccine category abbreviation given in Table 7 – that stores the 

number of vaccinations in that vaccine category in the child’s synthesized provider-reported vaccination 

history. For each vaccine type in Table 7, Section 8 also contains a variable that stores the number of 

vaccinations of that vaccine type in the child’s synthesized provider-reported vaccination history. For 

example, P_NUMDHI is the number of DTaP/HepB/IPV shots in the child’s history.

This section of the public-use data file also contains up-to-date indicators for a variety of recommended 

vaccines and vaccine series. These variables’ names typically begin with “P_UTD”. Additional variables 

indicate whether the child is up-to-date for various vaccine series. For example, P_UTD431 indicates 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 59

whether the child has received 4 or more DTaP/DTP/DT shots, 3 or more polio shots, and one or more 

measles-containing shots. The variable labels indicate what is needed to be considered up-to-date for each 

variable, and the “Notes” field in the codebook shows the vaccine type codes (see Table 7) being included 

when determining whether the child is up-to-date.

Note that it is possible that the administration of the NIS-Child interview itself prompts some respondents 

to vaccinate their children following the interview; to ensure that the vaccination coverage estimates are 

not artificially boosted because of this, the synthesized vaccination history count and up-to-date variables 

in this section of the public-use data file count only vaccinations received before the date the household 

interview was completed. Note also that because children are eligible for the NIS-Child if they are 19 to 

35 months old on any day of the survey quarter, some children are less than 19 months old or greater than 

35 months old on the date the household interview is completed. For children with interviews conducted 

before they became 19 months old, the Provider Record Check is not conducted until after the child has 

become 19 months old, and all vaccinations given up to age 19 months are counted, including those given 

after the household interview date. For children with interviews conducted after they became 36 months 

old, only vaccinations given through age 35 months are counted. 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 60

Table 7: Vaccine Categories and Vaccine Types, National Immunization Survey - Child, 2022 

Vaccine Category 

Abbreviation

Vaccination Category 

Description

Vaccine 

Type Code Vaccine Type Description

DTP DTaP/DTP/DTcontaining vaccine

03 DTaP/DTP/DT-containing, unknown type

04 DTaP/DTP/DT

07 DTaP-Hib

08 DTaP-HepB-IPV

D3 DTaP-IPV-Hib

D4 DTaP-IPV-Hib-HepB

POL or POLIO Polio-containing vaccine

08 DTaP-HepB-IPV

20 OPV

21 IPV

22 Polio-containing, unknown type

D3 DTaP-IPV-Hib

D4 DTaP-IPV-Hib-HepB

MCV or MMR Measles-containing 

vaccine

30 MMR

31 Measles only

32 Measles-mumps

33 Measles-rubella

MM Measles-containing, unknown type

VM MMR-Varicella

HIB Hib-containing vaccine

07 DTaP-Hib

43 HepB-Hib

44 Hib-only, unknown type

D3 DTaP-IPV-Hib

D4 DTaP-IPV-Hib-HepB

HG Hib-only (GSK)

HI Hib-containing, unknown type

HM Hib-only (Merck)

HS Hib-only (Sanofi)

HY Hib-MenCY

HEPB or HEP Hepatitis B-containing 

vaccine

08 DTaP-HepB-IPV

43 HepB-Hib

60 HepB-only

D4 DTaP-IPV-Hib-HepB

HB HepB-containing, unknown type

VRC Varicella-containing 

vaccine

VA Varicella-containing, unknown type

VM MMR-Varicella

VO Varicella-only

PCV Pneumococcalcontaining vaccine

70 Conjugate-unknown

71 Polysaccharide

72 Pneumococcal-containing, unknown type

73 Conjugate-7

74 Conjugate-13

75 Conjugate-15

HEPA or HEA Hepatitis A-containing 

vaccine HA Hepatitis A

FLU Seasonal influenza 

vaccine

FL Seasonal influenza, unknown type

FM Seasonal influenza spray

FN Injected seasonal influenza

MP Mumps-only vaccine MP Mumps-only

MPRB or MPR Mumps-Rubella-only 

vaccine MB Mumps-Rubella-only
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 61

Vaccine Category 

Abbreviation

Vaccination Category 

Description

Vaccine 

Type Code Vaccine Type Description

RB Rubella-only vaccine RB Rubella-only

ROT Rotavirus-containing 

vaccine

RG Rotarix® (GSK)

RM RotaTeq® (Merck)

RO Rotavirus, unknown type

7.8.1. Hib Up-To-Date Variables 

A Hib vaccine shortage and interim recommendation to suspend the booster dose for healthy children 

occurred December 2007 to September 2009 (CDC, 2010). Furthermore, the NIS-Child has historically 

considered children to be up-to-date for Hib if the child had 3 or more doses of any Hib-containing 

vaccine, but for some Hib vaccine product types, 4 doses are required. Because the NIS-Child has 

historically not distinguished between product types for Hib vaccine, children who received 3 doses of a 

vaccine product that required 4 doses were misclassified as up-to-date for Hib (CDC, 2010).

Because of the Hib vaccine shortage and because of the dependence of the Hib recommendation on 

product type, in 2009 the IHQ was modified to capture the manufacturer of the Hib vaccinations the child 

has received. Beginning with the 2009 NIS-Child public-use data file, new up-to-date variables were 

added to indicate up-to-date status based on Hib recommendation (i.e., the primary series recommended 

during the shortage vs. the full series) and on the Hib manufacturer.

Table 8 shows the Hib up-to-date variables appearing on the public-use data file beginning in 2009 and

Table 9 shows the up-to-date series variables that include Hib appearing on the public-use-date file 

beginning in 2009: in addition to the existing vaccine series up-to-date variables based on 3+ Hib of any 

type (PUTD4313, PUT43133, PU431331, PU4313313, PU4313314), variables based on the “routine” 

(i.e., full series) Hib recommendations accounting for manufacturer (4+ Hib of any type or 2 Hib of 

Merck types followed by 1 Hib of any type) were added (P_UTD431H_ROUT_S, 

P_UTD431H3_ROUT_S, P_UTD431H31_ROUT_S, P_UTD431H313_ROUT_S, 

P_UTD431H314_ROUT_S). 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 62

Note that for these Hib up-to-date variables that account for the manufacturer, if the manufacturer is 

unknown because the provider failed to check a type box on the IHQ, it has been assumed that the 

manufacturer of the Hib vaccine is not Merck; that is, these variables are based on a “strict” treatment of 

Hib vaccinations of unknown type, erring on the side of classifying the child as not up-to-date.

Table 8: Up-To-Date Variables for Hib, National Immunization Survey - Child, 2009-2022

Name Description Up-To-Date Criteria

P_UTDHIB Historical UTD flag for Hib. 3+ of any type 

(07,43,44,D3,D4,HG,HI,HM,HS,HY) 

P_UTDHIB_SHORT_S

UTD flag for Hib-shortage (i.e., primary 

series) recommendation, accounting for 

manufacturer. Introduced in 2009.

3+ of any type 

(07,43,44,D3,D4,HG,HI,HM,HS,HY) 

OR

2+ Merck types (HM,43)

P_UTDHIB_ROUT_S

UTD flag for routine (i.e., full series) 

Hib recommendation, accounting for 

manufacturer. Introduced in 2009.

4+ of any type 

(07,43,44,D3,D4,HG,HI,HM,HS,HY) 

OR

2 Merck types (HM,43) followed by 1 of 

any type 

(07,43,44,D3,D4,HG,HI,HM,HS,HY) 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 63

Table 9: Up-To-Date Variables for Vaccine Series Including Hib, National Immunization 

Survey - Child, 2009-2022

Name Description

PUTD4313 UTD flag for the 4:3:1:3 series using the 3+ any type UTD definition for HIB

P_UTD431H_ROUT_S UTD flag for the 4:3:1:3 series using the routine (i.e., full series) UTD 

definition for HIB

PUT43133 UTD flag for the 4:3:1:3:3 series using the 3+ any type UTD definition for 

HIB

P_UTD431H3_ROUT_S UTD flag for the 4:3:1:3:3 series using the routine (i.e., full series) UTD 

definition for HIB

PU431331 UTD flag for the 4:3:1:3:3:1 series using the 3+ any type UTD definition for 

HIB

P_UTD431H31_ROUT_S UTD flag for the 4:3:1:3:3:1 series using the routine (i.e., full series) UTD 

definition for HIB

PU4313313 UTD flag for the 4:3:1:3:3:1:3 series using the 3+ any type UTD definition for 

HIB

P_UTD431H313_ROUT_S UTD flag for the 4:3:1:3:3:1:3 series using the routine (i.e., full series) UTD 

definition for HIB

PU4313314 UTD flag for the 4:3:1:3:3:1:4 series using the 3+ any type UTD definition for 

HIB

P_UTD431H314_ROUT_S UTD flag for the 4:3:1:3:3:1:4 series using the routine (i.e., full series) UTD 

definition for HIB

7.8.2. Rotavirus Up-To-Date Variables 

The up-to-date status for rotavirus vaccine depends on the manufacturer of the vaccines received; the 

requirement is two or more doses of Rotarix® (GSK) or three or more doses of rotavirus vaccine of any 

type. Beginning with the 2009 NIS-Child public-use data file, an up-to-date variable for rotavirus vaccine 

(P_UTDROT_S) was added to indicate up-to-date status, accounting for the manufacturer (3+ rotavirus

doses of any type or 2+ Rotarix® doses). 

Note that for this rotavirus up-to-date variable, if the manufacturer is unknown because the provider failed 

to check a type box on the IHQ, it has been assumed that the rotavirus vaccine dose is not Rotarix®; that 

is, this variable is based on a “strict” treatment of rotavirus vaccinations of unknown type, erring on the 

side of classifying the child as not up-to-date.
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 64

7.9. Section 9: Provider-Reported Age-At-Vaccination Variables

This section contains variables storing the child’s age in days and months at each vaccination in the 

synthesized provider-reported vaccination history, along with the vaccine types of those vaccinations.

For each vaccine category, variables named DYYY1 - DYYY9 and YYY_AGE1 - YYY_AGE9 store the 

age in days and months, respectively, of the child when the vaccination was administered for up to nine 

vaccinations in the child’s synthesized provider-reported vaccination history, where “YYY” is the vaccine 

category abbreviation given in Table 7. For vaccine categories that contain multiple vaccine types, 

variables XYYYTY1 - XYYYTY9 give the corresponding vaccine type code (see Table 7). 

Unlike the vaccination count and up-to-date variables in Section 8 of the public-use data file, the 

variables in Section 9 include vaccinations given both before and after the household interview was 

completed. If desired, users can limit the Section 9 variables to only those before the household interview 

date by examining the corresponding Section 8 “P_NUM” variable and limiting the analysis of the 

Section 9 variables to only the first n variables, where n is equal to the number of vaccinations in the 

vaccine category before the household interview date as indicated by the corresponding “P_NUM” 

variable. 

Users of the public-use data file should be aware that the age-at-vaccination variables included in Section 

9 may contain a small number of vaccination ages that are implausible according to the recommended 

immunization schedules (http://www.cdc.gov/vaccines/schedules/hcp/child-adolescent.html). Such ages 

may arise if a medical provider inadvertently records an erroneous vaccination date or if a vaccination 

date is incorrectly transcribed onto an IHQ. The quality control procedures of the NIS-Child address 

implausible ages to every extent possible. Suspicious dates are manually reviewed and corrected if there 

is evidence either from the household interview or from another provider that the date is incorrect. In rare 

cases, however, when there is no further information with which to correct the reported vaccination date, 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 65

the vaccination is treated as having actually occurred and the implausible age at vaccination persists on 

the data file. The data user should consider these issues in deciding how to analyze the NIS-Child data.

7.10. Section 10: Health Insurance Module Variables

The Health Insurance Module (HIM) (Section E) was introduced in 2006 to gather information on the 

health insurance coverage of the child. HIM data were included in the NIS-Child public-use data file for 

the first time in 2007. Prior to 2016, seven variables containing HIM data were included in the NIS-Child

public-use data file:

● INS_1 – “Is child covered by health insurance provided through employer or union?”;

● INS_2 – “Is child covered by any MEDICAID plan?”;

● INS_3 – “Is child covered by S-CHIP?”;

● INS_3A – “Is child covered by any MEDICAID plan or S-CHIP?”;

● INS_4_5 – “Is the child covered by Indian Health Service, Military Health Care, TRICARE, 

CHAMPUS, or CHAMP-VA?”; 

● INS_6 – “Is child covered by any other health insurance or health care plan?”; and

● INS_11 - “Anytime when child was not covered by health insurance?”

In 2016, these variables were replaced by two health insurance variables, INS_STAT_I and 

INS_BREAK_I, which summarize the child’s health insurance status and history across all of the 

insurance questions listed above, while also incorporating the imputation of missing values and recoding 

of verbatim responses. In 2017, INS_STAT_I was replaced with INS_STAT2_I, which provides a 

different categorization of children with both private and non-private, non-Medicaid insurance.

INS_STAT2_I identifies the child’s current health insurance coverage status. If the child has a form of 

private health insurance and is not covered by any other type of health insurance, he/she is classified as 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 66

(1) Private only. If the child is on any form of Medicaid, alone or in addition to other forms of insurance, 

he/she is classified as (2) Any Medicaid. If the child is not covered by Medicaid but is covered by some 

other type of health insurance (including, but not limited to, CHIP, Indian Health Service, Military Health 

Care, TRICARE, CHAMPUS, or CHAMP-VA), either alone or in combination with private insurance, 

he/she is classified as (3) Other. If the child is not covered by any kind of health insurance, he/she is 

classified as (4) Uninsured. 

INS_BREAK_I describes the child’s coverage history since birth and indicates whether there have been 

any breaks in coverage during this period. A child may be (1) currently insured but uninsured at some 

point since birth, (2) currently insured and never uninsured since birth, (3) currently uninsured but insured 

at some point since birth, or (4) currently uninsured and never insured since birth.

Both of these variables are only available for children with adequate provider data.
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 67

8. Analytic and Reporting Guidelines 

Data from the NIS-Child public-use data file can be used to produce national, state, and estimation-area 

estimates of vaccination coverage for children age 19-35 months surveyed in 2022 using the 

PROVWT_C weight (PROVWT_C_TERR if U.S. territories are to be included). As noted in Section 1 

of this user’s guide, since 2018 vaccination coverage estimates appearing on ChildVaxView and in the 

MMWR are based on a birth-cohort estimation approach. That is, the estimates are derived from the 

combination of 2 or 3 years of NIS-Child surveys (e.g., 2020-2022), and the estimates apply to the 

population of children born in particular years (e.g., 2019 or 2020). Therefore, estimates produced using 

the 2022 public-use file, which are based on 2022 NIS-Child data alone and apply to the population of 19-

35 month old children, will differ from those appearing on ChildVaxView and in the MMWR.

Information in the data file can also be used to calculate standard errors of the vaccination coverage 

estimates that reflect the complex sample design of the NIS-Child. The sample is stratified by the 58 

estimation areas. The stratum identifier (STRATUM) and the coded household identifier (SEQNUMHH) 

are key variables for obtaining standard errors for estimation area, state, and national estimates of 

vaccination coverage rates. The estimation area variable ESTIAP22 defines mutually exclusive and 

exhaustive geographic areas, while STRATUM is a combination of the estimation area variable for that 

year and the sampling frame (since 2018, all sample is from the cellular phone sample frame). 

Demographic and socioeconomic variables in the file can be used to obtain national vaccination coverage 

estimates for sub-groups of the population. Data users should, however, be aware that estimates for such 

sub-groups at the state or estimation area level will generally have large standard errors because of small 

sample sizes. The CDC standard for precision of sub-group estimates is that relative standard error (the 

ratio of the standard error to the estimate) should be less than 0.3, and each analytic cell should contain at 

least 30 respondents (Parker, 2017). 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 68

8.1. Use of NIS-Child Sampling Weights

The 2022 NIS-Child public-use data file contains two sets of child-level weights. The RDDWT_C

variable gives the household-phase weight for all children 19 through 35 months in the United States

excluding territories (RDDWT_C_TERR if territories are to be included). These weights should be used 

to form estimates from children with completed household interviews. The weights reflect the stratified 

sample design and also have been adjusted for unit non-response, for the number of cellular phones in the 

household, for post-stratification to population control totals, and for the exclusion of households without 

cellular phones. 

The weight variables that apply to children with adequate provider data are

PROVWT_C/PROVWT_C_TERR. These weights should be used to form estimates of vaccination 

coverage. Each child with adequate provider data (PDAT = 1) has a positive value for 

PROVWT_C/PROVWT_C_TERR. Starting with the 2002 file, the definition of children with adequate 

provider data was expanded to include unvaccinated children (as discussed in Section 2). Table 10

presents a summary of the appropriate weights and stratum variables to use for various types of analyses.

The 2022 NIS-Child public-use data file does not contain any provider-level weights. The NIS-Child does 

not sample providers directly; rather, they are included in the survey through the children they vaccinate. 

A user of the file should not attempt provider-level analyses (e.g., estimate the percentage of providers in 

the United States that are private providers), because the NIS-Child sample was not designed for that 

purpose.
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 69

Table 10: Summary of Weights and Stratum Variables, National Immunization Survey - Child, 

2022 

Weight Variable Population* Sample Frame Strata Stratum 

Variable

RDDWT_C United States excluding 

territories

Single-Frame 

Cellular Phone Estimation Area STRATUM

RDDWT_C_TERR United States including 

territories

Single-Frame 

Cellular Phone Estimation Area STRATUM

PROVWT_C 

United States excluding 

territories, children with 

adequate provider data

Single-Frame 

Cellular Phone Estimation Area STRATUM

PROVWT_C_TERR

United States including 

territories, children with 

adequate provider data

Single-Frame 

Cellular Phone Estimation Area STRATUM

 * Each weight will contain a missing value for all records that are not included in the population covered by the weight.

8.2. Estimation and Analysis

8.2.1. Estimating Vaccination Coverage Rates

Vaccination coverage rates are ratio estimators, as described in the statistical literature on methods for 

complex sample surveys. Because of the adjustment to the sampling weights for provider-phase nonresponse, statistical analyses require only data from children with adequate provider data (PDAT = 1), 

along with their final provider sampling weights (PROVWT_C/PROVWT_C_TERR). To summarize 

the statistical methodology by which vaccination coverage rates and their standard errors are obtained 

from these data, let Yhij be an indicator, for the jth child with adequate provider data in the ith sampled 

household in the hth stratum of the NIS-Child sampling design, equal to 1 if the child is up-to-date 

according to the provider data and 0 otherwise. Also, let Whij denote the value of 

PROVWT_C/PROVWT_C_TERR for this child. Then, letting and , 

the national estimator of the vaccination coverage rate may be expressed as
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 70

where L denotes the number of strata, nh denotes the number of sampled households containing children 

with adequate provider data in the hth stratum, and mhi denotes the number of age-eligible children with 

adequate provider data in the ith household in the hth stratum. 

Letting L instead denote the number of strata in a state, the above formula can also be used to calculate 

vaccination coverage rates for states (regardless of whether the state contains only one or more than one

stratum). 

8.2.2. Estimating Standard Errors of Vaccination Coverage Rates

The Taylor-series method can be used to estimate the sampling variance of vaccination coverage rates for 

the overall United States, the individual states, and the estimation areas. Letting , 

, and yields an estimator of the variance of the estimated vaccination 

coverage rate, , equal to

(Wolter, 2007). The standard error is the square root of the variance. The estimation of standard errors for 

estimates of vaccination coverage rates in the NIS-Child can be implemented in specialized statistical 

software such as SUDAAN (Research Triangle Institute, 2008), SAS (SAS Institute Inc., 2009), R 

(Lumley, 2022), and Stata (Stata Corporation, 2009). Appendix D gives several examples of the use of 

SAS, R, and SUDAAN to estimate vaccination coverage rates and their standard errors for estimation 

areas and states. For all procedures, the option of with-replacement sampling of primary sampling units 

within strata is used, because the sampling fractions for households within an estimation area are all quite 

small. For all estimates, the variable STRATUM is used as the stratum variable and the household 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 71

identifier (SEQNUMHH) is used as the primary sampling unit identifier. The data file should be sorted 

first on STRATUM and then on SEQNUMHH before running the programs for SUDAAN and SAS. 

8.3. Combining Multiple Years of NIS-Child Data

8.3.1. Estimation of Multi-Year Means

With release of the 2022 NIS-Child public-use data file, 27 years of public-use NIS-Child data are now 

available. The precision of estimates of vaccination coverage for sub-domains (e.g., by race and ethnicity 

of child) within estimation areas or states can be improved by combining two or more years of NIS-Child

data. Data users should, however, be aware that estimates from combined years of NIS-Child data 

represent an average over two or more years. Although combining several years of NIS-Child data will 

yield a larger sample size for estimation areas and states, the composition of the population in a 

geographic area may change over time, making interpretation of the results difficult. Furthermore, if 

vaccination administration schedules or vaccination coverage changes over time, the estimate of 

vaccination coverage for the combined time period applies to a hypothetical population that existed at the 

middle of the time period, making interpretation of the results even more difficult. Given the use of 

independent RDD samples in the NIS-Child, it is also possible that a child could appear in more than one 

public-use data file.

To estimate a multi-year mean for a given NIS-Child variable, the weights in each participating file (see 

Table 5) should be divided by the number of years being combined. For example, if data for 2017-2022 

for children in the United States (excluding territories) with adequate provider data are to be combined, 

then the weights that exclude the territories in the six files – called PROVWT_D in 2017 and 

PROVWT_C in 2018-2022 – should be divided by 6 to obtain revised weights, which should be saved as 

a new variable, say NEWWT. It is necessary to use NEWWT in the analysis to obtain correct weighted 

estimates for children aged 19 through 35 months. Furthermore, the child and household ID numbers 

(SEQNUMC and SEQNUMHH) in the files are unique only within a year, not across years. It is 

important for the user to create revised, unique ID numbers when combining data from multiple years. 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 72

The following SAS code can be used:

YRSEQC = 1 * (YEAR || SEQNUMC);

YRSEQHH = 1 * (YEAR || SEQNUMHH); 

YEAR is the 4-digit year variable for the NIS-Child data year (e.g., 2022). 

To produce valid estimates of sampling variability and valid confidence intervals for multi-year coverage 

rates and other multi-year means, it is necessary to use specialized software such as SAS or SUDAAN. 

Beginning in 2005, an important new complication was introduced for variance estimation not 

encountered in previous NIS-Child years, because some traditional estimation areas were removed and 

other new areas were defined and introduced to the survey (see Section 2 above for more information 

about rotating estimation areas). The variance strata for 2004 and all prior years are defined by the 

variable ITRUEIAP, while the variance strata for 2005-2022 are defined by the variables ESTIAP for 

2005, ESTIAP06 for 2006, ESTIAP07 for 2007, ESTIAP08 for 2008, ESTIAP09 for 2009, ESTIAP10

for 2010, STRATUM_D/ESTIAP11 for 2011, and STRATUM for 2012-2022, with STRATUM_D and 

STRATUM being a combination of the estimation area variable for that year and the sampling frame 

(landline or cellular phone). The estimation area variables ITRUEIAP, ESTIAP, and ESTIAP06-

ESTIAP22 define mutually exclusive and exhaustive geographic areas. However, they are not exactly the 

same areas. For example, Dallas County, TX, was a separate estimation area in 2005-2012, 2016-

2017, and 2019 but not in 2013-2015, 2018, and 2020-2022. Other areas, such as New York City, NY

and rest of New York, are estimation areas in all years, including 2005-2022. 

To make inferences concerning multi-year means, the user must take two actions. First, he/she must 

define and save a new stratum variable with a common name for all years included in the analysis.

Second, he/she must define a common set of estimation domains that can be supported by each of the files 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 73

included in the multi-year analysis. To take these actions, the user should follow the following seven-step 

procedure (or its equivalent):

i. Compute and save the new, common variance-stratum variable for each year participating in the 

analysis. The variable should be defined by the equation

STRATUMV = ITRUEIAP, for children in the 2004 or prior years’ public-use data files

= ESTIAP, for children in the 2005 public-use data file

= ESTIAP06, for children in the 2006 public-use data file

= ESTIAP07, for children in the 2007 public-use data file

= ESTIAP08, for children in the 2008 public-use data file

= ESTIAP09, for children in the 2009 public-use data file

= ESTIAP10, for children in the 2010 public-use data file

= STRATUM_D if using PROVWT_D or

ESTIAP11 if using PROVWT_LL, for children in the 2011 public-use data file

= STRATUM, for children in the 2012-2022 public-use data files 

ii. Compute and save the new, common weight variable, NEWWT, as instructed above for each 

year participating in the analysis.

iii. Compute and save the new, unique child and household identification numbers, YRSEQC and 

YRSEQHH, as instructed above for each year participating in the analysis.

iv. Compute and save a variable defining the common estimation domains to be studied for each year 

participating in the analysis. For example, one could use the CDIAP (Common Denominator

Estimation Area) variable set forth in Table 11 or states as geographic domains.

v. Merge the multiple files into one consolidated file in a format compatible with the specialized 

software to be used.

vi. Sort the consolidated file by YEAR, STRATUMV, and YRSEQHH. 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 74

vii. Run the specialized software on the consolidated file, computing estimates, variance estimates, 

and confidence intervals. For SUDAAN users, sampling levels or stages may be specified by the 

statement

NEST YEAR STRATUMV YRSEQHH / PSULEV = 3;

the specification of weights by 

WEIGHT NEWWT;

and the specification of estimation domains, for example, by the two statements

CLASS YEAR CDIAP STATE;

TABLES CDIAP; 

or 

CLASS YEAR CDIAP STATE;

TABLES STATE; 

8.3.2. Estimation of Multi-Year Contrasts 

Considerations similar to those for multi-year means arise in the estimation of contrasts between NISChild years. For example, a typical contrast of interest would be the difference between the immunization 

coverage parameters in 2021 and in 2022.

To make inferences concerning a multi-year contrast, the user will need to work with the original weights 

reported on the files and store them in a common variable. One must not divide the original weights by 

the number of years included in the contrast. For example, one may define the new, common weight 

variable as

NEWWT2 = PROVWT_D/PROVWT_LL , if the child is in the 2011 PUF 

= PROVWT_D , if the child is in the 2012-2017 PUF

= PROVWT_C , if the child is in the 2018-2022 PUF.

The user should follow the seven-step procedure set forth in the section on multi-year means, using 

NEWWT2 in lieu of NEWWT. In SUDAAN, the user should also specify the contrast of interest 

through use of a CONTRAST statement or an appropriate regression model. For example, to compare the 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 75

4:3:1:3:3:1 up-to-date estimate from 2021 to the 2022 estimate, SUDAAN users can use the following 

WEIGHT, VAR, and CONTRAST statements:

WEIGHT NEWWT2;

VAR PU431331;

CONTRAST YEAR = (-1 1);
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 76

Table 11: Cross-Walk between Annual Estimation Area Variables (ITRUEIAP, ESTIAP, ESTIAP06-ESTIAP22)* and Common 

Denominator Estimation Area (CDIAP†), National Immunization Survey - Child, 2022 

CDIAP Area Name ITRUEIAP

(1995-2004) 

ESTIAP

(2005) 

ESTIAP06 

(2006) 

ESTIAP07 

(2007) 

ESTIAP08 

(2008) 

ESTIAP09 

(2009) 

ESTIAP10 

(2010) 

ESTIAP11 

(2011) 

ESTIAP12 

(2012) 

ESTIAP13 

(2013) 

ESTIAP14 

(2014) 

ESTIAP15 

(2015) 

Alabama 

20 AL-Jefferson County 21 21 20 20 20 20 20 20 20 20 20 20 

20 AL-Rest of State 20 20 20 20 20 20 20 20 20 20 20 20 

74 Alaska 74 74 74 74 74 74 74 74 74 74 74 74 

Arizona 

66 AZ-Maricopa County 67 67 67 66 66 66 66 66 66 66 66 66 

66 AZ-Rest of State 66 66 66 66 66 66 66 66 66 66 66 66 

46 Arkansas 46 46 46 46 46 46 46 46 46 46 46 46 

California 

68 CA-Fresno County 68 68 84 68 68 68 68 68 68 68 68 68 

68 CA-Los Angeles County 69 69 69 69 69 69 69 68 68 68 68 68 

68 CA-Northern CA 68 68 85 68 85 68 68 68 68 68 68 68 

68 CA-San Diego County 71 68 71 68 68 68 68 68 68 68 68 68 

68 CA-Santa Clara County 70 68 70 68 70 68 68 68 68 68 68 68 

68 CA-San Bernardino 

County 68 80 68 80 68 68 68 68 68 68 68 68 

68 CA-Alameda County 68 79 68 79 68 68 68 68 68 68 68 68 

68 CA-Rest of State 68 68 68 68 68 68 68 68 68 68 68 68 

Colorado 

60 CO-Denver 60 81 60 60 60 60 60 60 60 60 60 60 

60 CO-Rest of State 60 60 60 60 60 60 60 60 60 60 60 60 

1 Connecticut 1 1 1 1 1 1 1 1 1 1 1 1 

13 Delaware 13 13 13 13 13 13 13 13 13 13 13 13 

12 District of Columbia 12 12 12 12 12 12 12 12 12 12 12 12 

Florida 

22 FL-Miami-Dade County 24 22 24 24 24 22 22 22 22 22 22 22 

22 FL-Duval County 23 23 23 22 22 22 22 22 22 22 22 22 

22 FL-Orange County 22 22 22 22 91 22 22 22 22 22 22 22 

22 FL-Rest of State 22 22 22 22 22 22 22 22 22 22 22 22 

Georgia 

25 GA-Fulton/DeKalb 

Counties 26 26 26 25 25 25 25 25 25 25 25 25 

25 GA-Rest of State 25 25 25 25 25 25 25 25 25 25 25 25 

72 Hawaii 72 72 72 72 72 72 72 72 72 72 72 72 

75 Idaho 75 75 75 75 75 75 75 75 75 75 75 75 

Illinois 

35 IL-City of Chicago 35 35 35 35 35 35 35 35 35 35 35 35 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 77

CDIAP Area Name ITRUEIAP

(1995-2004) 

ESTIAP

(2005) 

ESTIAP06 

(2006) 

ESTIAP07 

(2007) 

ESTIAP08 

(2008) 

ESTIAP09 

(2009) 

ESTIAP10 

(2010) 

ESTIAP11 

(2011) 

ESTIAP12 

(2012) 

ESTIAP13 

(2013) 

ESTIAP14 

(2014) 

ESTIAP15 

(2015) 

34 IL-Madison and St. Clair 

Counties 34 34 34 34 92 34 34 34 34 34 34 34 

34 IL-Rest of State 34 34 34 34 34 34 34 34 34 34 34 34 

Indiana 

36 IN-Lake County 36 36 36 36 36 96 36 36 36 36 36 36 

36 IN-Marion County 37 36 37 37 36 37 36 36 36 36 36 36 

36 IN-Rest of State 36 36 36 36 36 36 36 36 36 36 36 36 

56 Iowa 56 56 56 56 56 56 56 56 56 56 56 56 

Kansas 

57 KS-Eastern KS 57 57 86 57 57 57 57 57 57 57 57 57 

57 KS-Rest of State 57 57 57 57 57 57 57 57 57 57 57 57 

27 Kentucky 27 27 27 27 27 27 27 27 27 27 27 27 

Louisiana 

47 LA-Orleans Parish 48 47 47 47 47 47 47 47 47 47 47 47 

47 LA-Rest of State 47 47 47 47 47 47 47 47 47 47 47 47 

4 Maine 4 4 4 4 4 4 4 4 4 4 4 4 

Maryland 

14 MD-City of Baltimore 15 15 15 14 15 15 14 14 14 14 14 14 

14 MD-Prince George’s 

County 14 14 14 14 14 14 14 103 14 14 14 14 

14 MD-Rest of State 14 14 14 14 14 14 14 14 14 14 14 14 

Massachusetts 

2 MA-City of Boston 3 2 3 2 2 2 2 2 2 2 2 2 

2 MA-Rest of State 2 2 2 2 2 2 2 2 2 2 2 2 

Michigan 

38 MI-City of Detroit 39 39 39 38 38 38 38 38 38 38 38 38 

38 MI-Rest of State 38 38 38 38 38 38 38 38 38 38 38 38 

Minnesota 

40 MN-Twin Cities 40 40 40 40 93 40 40 40 40 40 40 40 

40 MN-Rest of State 40 40 40 40 40 40 40 40 40 40 40 40 

28 Mississippi 28 28 28 28 28 28 28 28 28 28 28 28 

Missouri 

58 MO-St. Louis 

County/City 58 82 58 58 58 58 58 58 58 58 58 58 

58 MO-Rest of State 58 58 58 58 58 58 58 58 58 58 58 58 

61 Montana 61 61 61 61 61 61 61 61 61 61 61 61 

59 Nebraska 59 59 59 59 59 59 59 59 59 59 59 59 

Nevada 

73 NV-Clark County 73 83 73 73 73 73 73 73 73 73 73 73 

73 NV-Rest of State 73 73 73 73 73 73 73 73 73 73 73 73 

5 New Hampshire 5 5 5 5 5 5 5 5 5 5 5 5 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 78

CDIAP Area Name ITRUEIAP

(1995-2004) 

ESTIAP

(2005) 

ESTIAP06 

(2006) 

ESTIAP07 

(2007) 

ESTIAP08 

(2008) 

ESTIAP09 

(2009) 

ESTIAP10 

(2010) 

ESTIAP11 

(2011) 

ESTIAP12 

(2012) 

ESTIAP13 

(2013) 

ESTIAP14 

(2014) 

ESTIAP15 

(2015) 

New Jersey 

8 NJ-City of Newark 9 9 9 8 8 8 8 8 8 8 8 8 

8 NJ-Rest of State 8 8 8 8 8 8 8 8 8 8 8 8 

New Mexico 

49 NM-Southern NM 49 49 88 49 49 49 49 49 49 49 49 49 

49 NM-Rest of State 49 49 49 49 49 49 49 49 49 49 49 49 

New York 

11 NY-City of New York 11 11 11 11 11 11 11 11 11 11 11 11 

10 NY-Rest of State 10 10 10 10 10 10 10 10 10 10 10 10 

29 North Carolina 29 29 29 29 29 29 29 29 29 29 29 29 

62 North Dakota 62 62 62 62 62 62 62 62 62 62 62 62 

Ohio 

41 OH-Cuyahoga County 42 42 42 41 41 41 41 41 41 41 41 41 

41 OH-Franklin County 43 43 41 41 41 41 41 41 41 41 41 41 

41 OH-Rest of State 41 41 41 41 41 41 41 41 41 41 41 41 

50 Oklahoma 50 50 50 50 50 50 50 50 50 50 50 50 

76 Oregon 76 76 76 76 76 76 76 76 76 76 76 76 

Pennsylvania 

16 PA-Allegheny County 16 16 87 16 16 16 16 16 16 16 16 16 

17 PA-Philadelphia County 17 17 17 17 17 17 17 17 17 17 17 17 

16 PA-Rest of State 16 16 16 16 16 16 16 16 16 16 16 16 

6 Rhode Island 6 6 6 6 6 6 6 6 6 6 6 6 

30 South Carolina 30 30 30 30 30 30 30 30 30 30 30 30 

63 South Dakota 63 63 63 63 63 63 63 63 63 63 63 63 

Tennessee 

31 TN-Davidson County 33 33 31 31 31 31 31 31 31 31 31 31 

31 TN-Shelby County 32 32 32 31 31 31 31 31 31 31 31 31 

31 TN-Rest of State 31 31 31 31 31 31 31 31 31 31 31 31 

Texas 

55 TX-Bexar County 55 55 55 55 55 55 55 55 55 55 55 55 

54 TX-City of Houston 54 54 54 54 54 54 54 54 54 54 54 54 

51 TX-Dallas County 52 52 52 52 52 52 52 52 52 51 51 51 

51 TX-El Paso County 53 53 53 53 53 53 53 53 53 53 53 53 

51 TX-Hidalgo County 51 51 51 51 51 51 51 51 51 51 51 107 

51 TX-Travis County 51 51 51 51 51 51 51 51 51 51 51 51 

51 TX-Tarrant County 51 51 51 51 51 51 51 51 51 51 51 109 

51 TX-Rest of State 51 51 51 51 51 51 51 51 51 51 51 51 

64 Utah 64 64 64 64 64 64 64 64 64 64 64 64 

7 Vermont 7 7 7 7 7 7 7 7 7 7 7 7 

18 Virginia 18 18 18 18 18 18 18 18 18 18 18 18 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 79

CDIAP Area Name ITRUEIAP

(1995-2004) 

ESTIAP

(2005) 

ESTIAP06 

(2006) 

ESTIAP07 

(2007) 

ESTIAP08 

(2008) 

ESTIAP09 

(2009) 

ESTIAP10 

(2010) 

ESTIAP11 

(2011) 

ESTIAP12 

(2012) 

ESTIAP13 

(2013) 

ESTIAP14 

(2014) 

ESTIAP15 

(2015) 

Washington§

77 WA-Eastern WA 77 77 771 77 774 774 97 77 77 77 77 77 

77 WA-Western WA 77 77 77 773 774 774 102 77 77 77 77 77 

77 WA-King County 78 78 78 77 77 77 102 77 77 77 77 77 

77 WA-Rest of State 77 77 772 77 77 77 - 77 77 77 77 77 

19 West Virginia 19 19 19 19 19 19 19 19 19 19 19 19 

Wisconsin 

44 WI-Milwaukee County 45 45 45 44 44 44 44 44 44 44 44 44 

44 WI-Rest of State 44 44 44 44 44 44 44 44 44 44 44 44 

65 Wyoming 65 65 65 65 65 65 65 65 65 65 65 65 

- Puerto Rico - - - - - - - - - - 106 106 

* ESTIAP is defined as “estimation Immunization Action Plan (IAP) area of residence”; ESTIAPxx is defined as “estimation Immunization Action Plan (IAP) area of residence” 

for the years 2006-2022; ITRUEIAP is defined as “Immunization Action Plan (IAP) area of current residence”.

† This table can be used to derive a Common Denominator Estimation Area (CDIAP) variable for use in multi-year NIS-Child analyses. This is necessary because certain areas 

may be included as separate estimation areas in one year but subsumed within other estimation areas in another year. The CDIAP variable can be derived for each year by mapping 

the codes in the year-specific estimation area variable column (e.g., ITRUEIAP for the 1995 NIS-Child) to the corresponding codes in the CDIAP column.

§ The estimation area WA-Eastern WA was introduced in 2006, and while this estimation area also existed in 2010, the county definition of the area changed, making cross-year 

comparisons inadvisable. The estimation area WA-Western WA, introduced in 2007, presents the same issue. The counties included in the area changed (e.g., in 2010 it included 

King County). Analysis of Washington state data across years should use the entire state as the “Common Denominator”.
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 80

Table 11 (continued): Cross-Walk between Annual Estimation Area Variables (ITRUEIAP, 

ESTIAP, ESTIAP06-ESTIAP22*) and Common Denominator Estimation 

Area (CDIAP†

), National Immunization Survey - Child, 2022

CDIAP Area Name ESTIAP16 

(2016) 

ESTIAP17 

(2017) 

ESTIAP18 

(2018) 

ESTIAP19 

(2019) 

ESTIAP20 

(2020) 

ESTIAP21 

(2021) 

ESTIAP22 

(2022) 

Alabama 

20 AL-Jefferson County 20 20 20 20 20 20 20 

20 AL-Rest of State 20 20 20 20 20 20 20 

74 Alaska 74 74 74 74 74 74 74 

Arizona 

66 AZ-Maricopa County 66 66 66 66 66 66 66 

66 AZ-Rest of State 66 66 66 66 66 66 66 

46 Arkansas 46 46 46 46 46 46 46 

California 

68 CA-Fresno County 68 68 68 68 68 68 68 

68 CA-Los Angeles County 68 68 68 68 68 68 68 

68 CA-Northern CA 68 68 68 68 68 68 68 

68 CA-San Diego County 68 68 68 68 68 68 68 

68 CA-Santa Clara County 68 68 68 68 68 68 68 

68 CA-San Bernardino 

County 68 68 68 68 68 68 68 

68 CA-Alameda County 68 68 68 68 68 68 68 

68 CA-Rest of State 68 68 68 68 68 68 68 

Colorado 

60 CO-Denver 60 60 60 60 60 60 60 

60 CO-Rest of State 60 60 60 60 60 60 60 

1 Connecticut 1 1 1 1 1 1 1 

13 Delaware 13 13 13 13 13 13 13 

12 District of Columbia 12 12 12 12 12 12 12 

Florida 

22 FL-Miami-Dade County 22 22 22 22 22 22 22 

22 FL-Duval County 22 22 22 22 22 22 22 

22 FL-Orange County 22 22 22 22 22 22 22 

22 FL-Rest of State 22 22 22 22 22 22 22 

Georgia 

25 GA-Fulton/DeKalb 

Counties 25 25 25 25 25 25 25 

25 GA-Rest of State 25 25 25 25 25 25 25 

72 Hawaii 72 72 72 72 72 72 72 

75 Idaho 75 75 75 75 75 75 75 

Illinois 

35 IL-City of Chicago 35 35 35 35 35 35 35 

34 IL-Madison and St. Clair 

Counties 34 34 34 34 34 34 34 

34 IL-Rest of State 34 34 34 34 34 34 34 

Indiana 

36 IN-Lake County 36 36 36 36 36 36 36 

36 IN-Marion County 36 36 36 36 36 36 36 

36 IN-Rest of State 36 36 36 36 36 36 36 

56 Iowa 56 56 56 56 56 56 56 

Kansas 

57 KS-Eastern KS 57 57 57 57 57 57 57 

57 KS-Rest of State 57 57 57 57 57 57 57 

27 Kentucky 27 27 27 27 27 27 27 

Louisiana 

47 LA-Orleans Parish 47 47 47 47 47 47 47 

47 LA-Rest of State 47 47 47 47 47 47 47 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 81

CDIAP Area Name ESTIAP16 

(2016) 

ESTIAP17 

(2017) 

ESTIAP18 

(2018) 

ESTIAP19 

(2019) 

ESTIAP20 

(2020) 

ESTIAP21 

(2021) 

ESTIAP22 

(2022) 

4 Maine 4 4 4 4 4 4 4 

Maryland 

14 MD-City of Baltimore 14 14 14 14 14 14 14 

14 MD-Prince George’s 

County 14 14 14 14 14 14 14 

14 MD-Rest of State 14 14 14 14 14 14 14 

Massachusetts 

2 MA-City of Boston 2 2 2 2 2 2 2 

2 MA-Rest of State 2 2 2 2 2 2 2 

Michigan 

38 MI-City of Detroit 38 38 38 38 38 38 38 

38 MI-Rest of State 38 38 38 38 38 38 38 

Minnesota 

40 MN-Twin Cities 40 40 40 40 40 40 40 

40 MN-Rest of State 40 40 40 40 40 40 40 

28 Mississippi 28 28 28 28 28 28 28 

Missouri 

58 MO-St. Louis 

County/City 58 58 58 58 58 58 58 

58 MO-Rest of State 58 58 58 58 58 58 58 

61 Montana 61 61 61 61 61 61 61 

59 Nebraska 59 59 59 59 59 59 59 

Nevada 

73 NV-Clark County 73 73 73 73 73 73 73 

73 NV-Rest of State 73 73 73 73 73 73 73 

5 New Hampshire 5 5 5 5 5 5 5 

New Jersey 

8 NJ-City of Newark 8 8 8 8 8 8 8 

8 NJ-Rest of State 8 8 8 8 8 8 8 

New Mexico 

49 NM-Southern NM 49 49 49 49 49 49 49 

49 NM-Rest of State 49 49 49 49 49 49 49 

New York 

11 NY-City of New York 11 11 11 11 11 11 11 

10 NY-Rest of State 10 10 10 10 10 10 10 

29 North Carolina 29 29 29 29 29 29 29 

62 North Dakota 62 62 62 62 62 62 62 

Ohio 

41 OH-Cuyahoga County 41 41 41 41 41 41 41 

41 OH-Franklin County 41 41 41 41 41 41 41 

41 OH-Rest of State 41 41 41 41 41 41 41 

50 Oklahoma 50 50 50 50 50 50 50 

76 Oregon 76 76 76 76 76 76 76 

Pennsylvania 

16 PA-Allegheny County 16 16 16 16 16 16 16 

17 PA-Philadelphia County 17 17 17 17 17 17 17 

16 PA-Rest of State 16 16 16 16 16 16 16 

6 Rhode Island 6 6 6 6 6 6 6 

30 South Carolina 30 30 30 30 30 30 30 

63 South Dakota 63 63 63 63 63 63 63 

Tennessee 

31 TN-Davidson County 31 31 31 31 31 31 31 

31 TN-Shelby County 31 31 31 31 31 31 31 

31 TN-Rest of State 31 31 31 31 31 31 31 

Texas 

55 TX-Bexar County 55 55 55 55 55 55 55 

54 TX-City of Houston 54 54 54 54 54 54 54 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 82

CDIAP Area Name ESTIAP16 

(2016) 

ESTIAP17 

(2017) 

ESTIAP18 

(2018) 

ESTIAP19 

(2019) 

ESTIAP20 

(2020) 

ESTIAP21 

(2021) 

ESTIAP22 

(2022) 

51 TX-Dallas County 52 52 51 52 51 51 51 

51 TX-El Paso County 53 53 51 53 51 51 51 

51 TX-Hidalgo County 51 51 107 51 51 51 51 

51 TX-Travis County 51 108 51 51 51 51 51 

51 TX-Tarrant County 51 51 109 51 51 51 51 

51 TX-Rest of State 51 51 51 51 51 51 51 

64 Utah 64 64 64 64 64 64 64 

7 Vermont 7 7 7 7 7 7 7 

18 Virginia 18 18 18 18 18 18 18 

Washington§

77 WA-Eastern WA 77 77 77 77 77 77 77 

77 WA-Western WA 77 77 77 77 77 77 77 

77 WA-King County 77 77 77 77 77 77 77 

77 WA-Rest of State 77 77 77 77 77 77 77 

19 West Virginia 19 19 19 19 19 19 19 

Wisconsin 

44 WI-Milwaukee County 44 44 44 44 44 44 44 

44 WI-Rest of State 44 44 44 44 44 44 44 

65 Wyoming 65 65 65 65 65 65 65 

- Puerto Rico 106 - - 106 106 106 106 

* ESTIAP is defined as “estimation Immunization Action Plan (IAP) area of residence”; ESTIAPxx is defined as “estimation 

Immunization Action Plan (IAP) area of residence” for the years 2006-2022; ITRUEIAP is defined as “Immunization Action 

Plan (IAP) area of current residence”.

† This table can be used to derive a Common Denominator Estimation Area (CDIAP) variable for use in multi-year NIS-Child 

analyses. This is necessary because certain areas may be included as separate estimation areas in one year but subsumed within 

other estimation areas in another year. The CDIAP variable can be derived for each year by mapping the codes in the yearspecific estimation area variable column (e.g., ITRUEIAP for the 1995 NIS-Child) to the corresponding codes in the CDIAP 

column. 

§ The estimation area WA-Eastern WA was introduced in 2006, and while this estimation area also existed in 2010, the county 

definition of the area changed, making cross-year comparisons inadvisable. The estimation area WA-Western WA, introduced in 

2007, presents the same issue. The counties included in the area changed (e.g., in 2010 it included King County). Analysis of 

Washington state data across years should use the entire state as the “Common Denominator”.
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 83

9. Summary Tables 

Appendix F contains seven tables. Appendix Table F.1 lists the 58 estimation areas for the 2022 NISChild by state. At the national level and for each state and estimation area, it provides the estimated 

population total of children aged 19 through 35 months of age in 2022, and (from 2022 NIS-Child data 

collection) the number of children with completed household interviews and number of children with 

adequate provider data.

Appendix Tables F.2 through F.6 summarize pairs of variables: age group of child by maternal education 

(Appendix Table F.2), age group by family poverty status (Appendix Table F.3), race and ethnicity by 

family poverty status (Appendix Table F.4), age group by race and ethnicity (Appendix Table F.5), and 

age group by sex (Appendix Table F.6). Each of these tables gives the unweighted and weighted counts of 

children who have completed household interviews and the unweighted and weighted counts of children 

with adequate provider data.

Appendix Table F.7 presents estimates of vaccination coverage and symmetric 95% confidence intervals 

obtained from SUDAAN. The data user should obtain the same estimates from the 2022 NIS-Child

public-use data file. (As noted in Section 1 of this report, these estimates will differ from those appearing 

on ChildVaxView and in the MMWR, which use multiple years of NIS-Child data and apply to the 

population of children born in particular years rather than using a single year of NIS-Child data and 

applying to the population of children age 19 through 35 months.) 

Appendix G contains four tables and time-series charts. Table G.1 and Figure G.1 show key components 

of the NIS-Child response rates and the CASRO response rates for the landline sample by year of the 

survey. Table G.2 and Figure G.2 show key components of the NIS-Child response rates and the CASRO 

response rates for the cellular phone sample by year of the survey. Table G.3 and Figure G.3 show the 

CASRO response rates for the combined landline and cellular phone samples. Table G.4 and Figure G.4 

show vaccination coverage estimates since 1995.
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 84

Appendix H shows the vaccine type codes used in the 2022 NIS-Child public-use data file. 

Appendix I presents key response rate components and the overall CASRO response rate by estimation 

area in the 2022 NIS-Child. 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 85

10. Assessment of Total Survey Error 

Assessing the validity of the NIS-Child estimates of vaccination coverage is a critical and ongoing aspect 

of the NIS-Child surveillance program. CDC frequently conducts evaluation studies and controlled 

experiments to understand the causes and impacts of sampling and nonsampling errors on the estimates 

and to enable formulation of methodological refinements that have the demonstrated capacity to improve 

data quality. As landline phone use decreased and cellular phone use increased dramatically over the past 

decade, and the NIS-Child transitioned first from a single-frame landline RDD sampling design to a dualframe landline and cellular phone RDD design and then to a single-frame cellular phone RDD design, 

CDC has monitored the NIS-Child estimates utilizing a Total Survey Error (TSE) approach.

TSE is the sum of the errors that arise at every step of a survey, including both sampling error and 

nonsampling errors such as coverage, nonresponse, and measurement errors (Mulry and Spencer, 1991). 

Pooling information from multiple evaluations of their precision and accuracy, we have conducted TSE

analyses for the 2009-2013 NIS-Child and NIS-Teen data (Molinari et al. 2011; NORC 2011; Pineau et 

al. 2012; Pineau et al. 2013; Skalland et al. 2016; Wolter et al. 2017b) and for the 2018-2021 NIS-Child 

and NIS-Teen data (see the Data User’s Guides for the 2018-2021 NIS-Child and NIS-Teen public use 

data files). 

An assessment based on 2022 NIS-Child data was conducted in 2023, with results summarized 

below. 

Comparison of Demographic Distributions. Demographic distributions (age, sex, mother’s race and 

ethnicity, mother’s education, mother’s age) among children with adequate provider data were 

compared to benchmark values derived from natality data supplied by the National Vital Statistics 

System (NCHS 2019, 2020). When using design weights that have not been adjusted for 

nonresponse or calibrated to external population totals, demographic distributions as estimated in 

the NIS-Child were generally close to the benchmark distributions. Nonetheless, before calibration 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 86

of the weights to external population totals, the 2022 NIS-Child somewhat over-represented 

children whose mothers are college graduates, non-Hispanic White, or age 30 or greater, as well as 

children 30 to 35 months old. The survey somewhat under-represented children whose mothers are 

not college graduates, are Hispanic or non-Hispanic Black, or are age 20 to 29 years, as well as 

children 24 to 29 months old. When using the final weights that have been adjusted for nonresponse 

and calibrated to external population totals, the differences between survey and population 

proportions are reduced, but the 2022 NIS-Child still overrepresented children whose mothers are 

four-year college graduates (40.2% in survey, 33.7% in population) or are age 30 or greater (67.2% 

in survey, 62.5% in population). 

Comparison to IISAR Vaccination Coverage Rates. NIS-Child vaccination coverage rate estimates 

were compared to vaccination coverage rates reported in the Immunization Information Systems 

Annual Report (IISAR). Sponsored and conducted by NCIRD, the IISAR is an annual assessment 

of Immunization Information Systems (IIS)2 activity among the 64 immunization program 

awardees, which include the 50 states, 6 cities (Chicago, District of Columbia, Houston, New York 

City, Philadelphia, and San Antonio), and 8 U.S. territories. To evaluate each awardee’s 

performance, the immunization program manager in the awardee area is asked to complete a selfadministered, web-based questionnaire asking for demographic and immunization information, 

public and private provider site participation levels, and information about achievement of IIS 

functional standards. NCIRD provides competitive supplemental funds to awardees that have met 

high data timeliness and participation (child and adolescent) in the IIS. During the period 2013-

2017, six awardees were recognized as IIS sentinel sites, including Michigan, Minnesota, North 

Dakota, New York City, Oregon, and Wisconsin. Because of increased timeliness and higher child 

and adolescent saturation levels in the IIS, vaccination coverage rates reported in IISAR by sentinel 

2 State IISs are computer databases that attempt to compile information about all of the doses of all vaccines 

administered to all children resident within the state. State IISs vary in their completeness of both children and the 

doses they received.
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 87

sites are thought to be relatively more accurate than vaccination rates reported by non-sentinel 

sites.

NIS-Child vaccination coverage rate estimates for 2021 were compared to 2021 IISAR vaccination 

coverage rates. Because 2022 IISAR vaccination coverage rates were not available at the time of the 

assessment, the 2021 comparison served as the most current information available about the 

relative accuracy of the 2022 NIS-Child. NIS-Child vaccination coverage rate estimates were found 

to be generally higher than IISAR vaccination coverage rates. For the six sentinel sites, reasonably 

good agreement was observed between the NIS-Child and IISAR vaccination coverage rates, with 

the absolute difference in rates in 2021 falling within 5 percentage points for the combined 7-

vaccine series for Minnesota, Michigan, and Wisconsin; for Oregon and New York City, the 

absolute differences were larger, but still within 10 percentage points; for North Dakota, the 

difference was slightly greater than 10 percentage points. Further, the child participation rate – the 

proportion of children in the IIS jurisdiction with two or more vaccine doses in the IIS database3

 – 

was determined to be a reasonable indicator of the quality of the corresponding IIS database, 

because the IIS vaccination coverage rate was found to increase as the child participation rate 

increased. It was also observed (Figure 1) that the difference between NIS-Child and IISAR 

vaccination coverage rates for the combined 7-vaccine series declines as the child participation rate 

increases (i.e., as the quality of the IIS increases). These findings are consistent with the view that 

IIS vaccination coverage rates converge towards NIS-Child vaccination coverage rates as the 

quality of the IIS increases.

3 When setting the denominator for the participation rate calculation, some IIS use an external estimate of the 

number of children living in the jurisdiction rather than a count of children in the IIS itself; this results in some IIS 

reporting a participation rate of over 100 percent.
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 88

Figure 1: Scatter Plot of Percentage Point Difference between 2021 NIS-Child and 

Immunization Information Systems Annual Report (IISAR) Vaccination Coverage 

Rates for the Combined 7-Vaccine Series vs. Immunization Information Systems (IIS) 

Child Participation Rate (CPR) with Regression Line: 56 Estimation Areas

Note for Figure 1: A positive difference indicates the NIS-Child vaccination coverage rate estimate was 

higher than the corresponding IISAR estimate, and a negative difference indicates the NIS-Child vaccination 

coverage rate estimate was lower than the corresponding IISAR estimate.

Comparison of Health Insurance Distributions. NIS-Child health insurance distributions were compared

to similar distributions produced by the Current Population Survey Annual Social and Economic 

Supplements (CPS ASEC) (https://www.census.gov/data/datasets/time-series/demo/cps/cps-asec.html), 

the National Health Interview Survey (NHIS) (https://www.cdc.gov/nchs/nhis/index.htm), and the 

American Community Survey (ACS) (https://www.census.gov/programs-surveys/acs/data.html). All of 

these surveys use somewhat different definitions of insurance status and report for different age ranges of 

children. Nevertheless, we found the NIS-Child distributions to be broadly similar to those from the 

CPS, NHIS, and ACS, but with some differences. NIS-Child estimates of percent of children with 

any public insurance (57.5% in 2021; 56.1% in 2022) were higher than the corresponding 

benchmark estimates (46.2% (NHIS), 42.1% (CPS ASEC), and 47.1% (ACS) in 2021; 42.7% (CPS

ASEC), 51.0% (NHIS) in 2022), and the NIS-Child estimates of uninsured children (1.9% in 2021; 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 89

1.9% in 2022) were lower than the estimates from the benchmark surveys (2.8% (NHIS), 5.0% 

(CPS ASEC), and 4.2% (ACS) in 2021; 4.7% (CPS ASEC), and 4.3% (NHIS) in 2022). 

Comparison to State Immunization Surveys. A comparison was undertaken of NIS-Child vaccination 

coverage rate estimates to estimates from eight state immunization surveys4

: Florida (Blackmore, 

2022), Georgia (Machado et al., n.d.), Kans, as (Gillespie, 2019), Michigan (Michigan Department 

of Health and Human Services, 2022), Tennessee (Tennessee Department of Health, 2022), Virginia 

(Virginia Department of Health, n.d.), Washington (Washington State Department of Health, n.d.), 

and Wisconsin (Wisconsin Department of Health Services, n.d.). Some aspects of NIS-Child and 

state survey practices are not comparable and limit the importance of the results. That said, there is 

reasonable agreement between NIS-Child and state vaccination rates in many of the states. Where 

NIS-Child and state practices were comparable, similar vaccination rates were found. For state 

surveys that rely on IIS vaccination histories, NIS-Child rates were generally found to be higher 

than the corresponding states’ rates. While this work did not provide specific measures of error in 

NIS-Child statistics, it did provide general support for the accuracy of the vaccination coverage 

rates.

Next, an assessment of all sources of error in the 2022 NIS-Child was conducted, including sampleframe coverage error, nonresponse error, and measurement error; the component errors were then

combined to assess TSE. The change in total survey error between the 2021 NIS-Child and 2022 

NIS-Child was also estimated.

Coverage Error. The NIS-Child cellular phone RDD sampling frame fails to cover the landline only 

and phoneless populations; vaccination coverage rates in the former were estimated using data 

collected in the 2017 NIS-Child (the last year a dual-frame landline and cellular phone sample was 

4 For each state, the most recent year of data available from that state was compared to the corresponding year of 

NIS-Child data, which was 2015 for Kansas, 2020 for Florida, 2021 for Virginia, and 2022 for Georgia, Michigan, 

Tennessee, Washington, and Wisconsin.
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 90

fielded for the NIS) and vaccination coverage rates in the latter were estimated using data collected 

in the 2012 NHIS Provider Record Check. The vaccination coverage rates in the population covered 

by the sampling-frame were found to be higher than the vaccination rates in the uncovered 

population. Because the sampling-frame uncovered population is so small relative to the covered 

population, however, mean sampling-frame coverage error was estimated to be very minor, at 0.3 

percentage points or less for all vaccines/series examined. 

Nonresponse Error. Nonresponse error in the 2022 NIS-Child was assessed through comparison to 

the cellular phone domain within the combined 2021 and 2022 NHIS. NHIS does not offer direct 

estimates of vaccination coverage rates. Instead, a model-based technique was used to impute NHIS 

vaccination status, and then the resulting NHIS vaccination coverage rates (treated as vaccination 

coverage rates void of nonresponse error) were compared to NIS-Child vaccination coverage rates, 

with the difference treated as nonresponse error in the NIS-Child. Despite nonresponse in the 2022 

NIS-Child, including household nonresponse, non-consent to contact vaccination providers, and 

provider nonresponse, mean nonresponse error in vaccination rates was estimated to be modest at 

most (0.1 to 1.6 percentage points in absolute value across the four vaccines/series examined when 

using design weights, and 0.2 to 1.4 percentage points in absolute value when using final weights 

that account for the survey’s nonresponse adjustments). Of the vaccines/series examined, none of 

the nonresponse errors were found to be significantly different from zero.

Measurement Error. A form of measurement error called “provider under-reporting” error was 

assessed. Sometimes called “under-ascertainment,” provider under-reporting error arises when a 

child with adequate provider data is truly vaccinated but is reported as unvaccinated for one or 

more recommended doses in the child’s provider-reported vaccination history. Under-reporting 

error can occur if the household respondent fails to nominate all of the child’s vaccination 

providers, if one or more of the child’s nominated vaccination providers fails to report a 

vaccination history for the child, or if one or more of the child’s nominated providers reports a 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 91

vaccination history but fails to report all of the vaccinations the child has received. Underreporting 

error was estimated using data from projects sponsored by CDC in which the 2017 NIS-Child 

sample of children in 20 jurisdictions and the 2019 NIS-Child sample of children in 5 jurisdictions

was matched to the state or local IIS for the jurisdiction. In this work, the standard of truth for a 

given child is taken to be the synthesis of the NIS-Child and IIS vaccination histories. In prior 

studies conducted in 2012 and 2013 using similar methods, measurement error was found to be by 

far the largest component of error in the NIS-Child vaccination rates. Similar conclusions were 

reached for the 2022 NIS-Child, where it was estimated that measurement error depressed 

observed vaccination rates by about 2 to 8 percentage points.

Total Survey Error. Finally, all of the component errors were combined to assess the distribution of 

total error in the NIS-Child vaccination coverage rates, using a Monte Carlo technique. The mean 

of the distribution is an estimate of the total survey error, and the 2.5 and 97.5 percentiles of the 

distribution form a 95% credible interval for the total error. The estimated component errors and 

total survey errors are presented in Table 12. For the 4+ DTaP vaccination coverage rate, the mean 

of the TSE distribution was found to be -4.0 percentage points with a 95% credible interval of (-6.9, 

-0.6) percentage points. That is, the NIS-Child vaccination coverage rate was on average about 4.0

percentage points too low. For the 1+ MMR vaccination coverage rate, the mean of the TSE 

distribution was found to be -1.7 percentage points with a 95% credible interval of (-3.8, 1.3) 

percentage points; for the Hepatitis B birth dose rate, the mean of the TSE distribution was 

estimated at -3.3 percentage points with a 95% credible interval of (-6.4, 0.3) percentage points; and 

the mean of the TSE distribution for the combined 7-vaccine series was estimated to be -9.2

percentage points with a 95% credible interval of (-13.1, -4.8) percentage points. These results 

suggest that, according to best estimates, the 2022 NIS-Child underestimated vaccination coverage 

rates. As in prior NIS-Child total survey error assessments, under-ascertainment of the providerreported vaccination history was found to be the main source of total survey error.
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 92

Table 12: Mean and 95% Credible Interval for the Estimated Total Survey Error (TSE) 

Distribution and Component Error Distributions for National Vaccination Coverage 

Rate Estimates, National Immunization Survey - Child, 2022 

Vaccine or 

Series Component Mean TSE(pct points)

95% Credible Interval

(pct points)

4+ DTaP

TSE (final weighted) -4.0 (-6.9, -0.6)*

TSE (design weighted) -4.2 (-7.1, -0.8)*

Noncoverage error 0.2 (-0.1, 0.7)

Nonresponse error 0.2 (-3.2, 4.1)

Measurement error -4.7 (-6.3, -2.8)*

Sampling error 0.0 (-1.7, 2.0)

1+ MMR

TSE (final weighted) -1.7 (-3.8, 1.3)

TSE (design weighted) -2.4 (-4.5, 0.6)

Noncoverage error 0.1 (-0.1, 0.4)

Nonresponse error 0.1 (-2.4, 3.3)

Measurement error -2.6 (-3.8, -1.3)*

Sampling error 0.1 (-1.2, 1.5)

HepB Birth 

Dose 

TSE (final weighted) -3.3 (-6.4, 0.3)

TSE (design weighted) -4.6 (-7.8, -1.1)*

Noncoverage error 0.1 (-0.1, 0.4)

Nonresponse error -1.5 (-5.1, 2.5)

Measurement error -3.3 (-4.9, -1.6)*

Sampling error 0.0 (-1.7, 1.8)

Combined 7-

vaccine series†

TSE (final weighted) -9.2 (-13.1, -4.8)*

TSE (design weighted) -7.7 (-11.5, -3.2)*

Noncoverage error 0.3 (0.0, 0.7)

Nonresponse error 0.3 (-4.1, 5.1)

Measurement error -8.3 (-10.1, -6.3)*

Sampling error 0.0 (-2.0, 2.2)

* 95% credible interval excludes zero. † The combined 7-vaccine series (4:3:1:3*:3:1:4) includes ≥4 doses of DTaP, ≥3 doses of poliovirus vaccine, ≥1 

dose of measles-containing vaccine, the full Hib series (≥3 or ≥4 doses, depending on product type), ≥3 doses of 

HepB, ≥1 dose of VAR, and ≥4 doses of PCV.

Change in Total Survey Error. Change in TSE between the 2021 and 2022 NIS-Child was measured 

using the bridging cohort method introduced by NCIRD (Yankey, Hill, Elam-Evans, et al. 2015). 

Each survey quarter includes children born in 20 monthly birth cohorts. Every pair of adjacent 

survey quarters spans 23 monthly birth cohorts, of which 17 are in common and 6 are not in 

common. In turn, every survey year represents 29 monthly birth cohorts. Every pair of adjacent 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 93

survey years spans 39 monthly birth cohorts, of which 17 are in common and 22 are not in 

common. The 17 common months comprise the bridging cohort, and for 2021 and 2022, the 

bridging cohort extends from children born in January 2019 through children born in May 2020. 

Consider a vaccination coverage rate estimated from the bridging cohort as of a given child age, 

such as 19 months or 24 months. Two estimates are possible, one using the sample of children in the 

bridging cohort within the 2021 NIS-Child sample and the second using the corresponding sample 

of children within the 2022 NIS-Child sample. Ideally, the two estimators should exhibit the same 

statistical expectation (i.e., average value in hypothetical repeated sampling). A large difference 

between the two estimates may signal a change in the statistical expectation of total survey error

from one survey year to the next, which could result from a change in the distribution of samplingframe coverage error, nonresponse error, or measurement error. Differences may also result 

simply from the effects of random sampling error.

Table 13 presents the two estimates of vaccination coverage for children as of 19 months of age for 

the 2021-2022 bridging cohort. None of the differences between the 2021 and 2022 national-level 

vaccination coverage rates for the bridging cohort were found to be statistically significant at the 

0.05 level. That is, no evidence was found of a change in the statistical expectation of total survey 

error between the 2021 NIS-Child and the 2022 NIS-Child.
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 94

Table 13: Difference Between the Estimates* for the Bridging Birth Cohort† by Age 19 Months, 

National Immunization Survey - Child, 2021 vs. 2022 

Description

2021 2022 Difference

Est Std Error Est Std Error Est Std Error pp-value for Test 

of No Difference

3+ DTaP/DTP/DT by 19 months 93.5 0.46 93.1 0.59 -0.4 0.75 0.596

4+ DTaP/DTP/DT by 19 months 72.5 0.94 72.3 0.95 -0.2 1.34 0.873

3+ Polio by 19 months 92.5 0.49 91.7 0.63 -0.8 0.80 0.321

1+ MMR by 19 months 88.7 0.66 89.8 0.62 1.1 0.91 0.220

3+ Hib by 19 months 90.7 0.56 90.1 0.64 -0.7 0.86 0.445

Hib partial series by 19 months 93.1 0.49 93.0 0.57 -0.1 0.75 0.903

Hib full series by 19 months 74.2 0.91 74.4 0.92 0.2 1.30 0.885

1+ Varicella by 19 months, 

excluding shots before 12 months 88.0 0.67 88.4 0.68 0.3 0.96 0.718

3+ Hepatitis B by 19 months 92.1 0.48 91.4 0.62 -0.7 0.78 0.377

3+ Pneumococcal by 19 months 92.3 0.54 92.2 0.63 -0.1 0.83 0.873

4+ Pneumococcal by 19 months 80.0 0.85 80.1 0.86 0.2 1.21 0.883

1+ Hepatitis A by 19 months 84.3 0.74 83.9 0.79 -0.4 1.08 0.729

2+ Hepatitis A by 19 months 29.9 0.96 28.1 0.88 -1.9 1.30 0.148

2+ or 3+ Rotavirus depending on 

type by 19 months 78.8 0.85 77.6 0.89 -1.2 1.23 0.331

7-series by 19 months 62.2 1.02 62.0 1.02 -0.1 1.44 0.920

1+ Hepatitis B-containing on day 

of birth or on day 1, 2 or 3 

following birth

82.5 0.78 82.9 0.71 0.5 1.05 0.660

Unvaccinated children 0.78 0.13 0.75 0.11 0.0 0.17 0.871

2+ Flu by 19 months, doses at least 

24 days apart 61.6 1.01 60.9 1.01 -0.7 1.42 0.611

* National-level estimates computed among children with adequate provider data, excluding children from U.S. territories.

† The bridging birth cohort used for this analysis of the 2021 and 2022 NIS-Child includes children born between January 2019 and May 

2020. 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 95

11. Limitations 

The findings in this report are subject to at least four limitations. First, because NIS-Child is a telephone 

survey, results are weighted to be representative of all children aged 19 through 35 months. Although 

statistical adjustments were made to account for non-response and households without cellular phones, 

some bias might remain. Second, underestimates of vaccination coverage might have resulted from the 

exclusive use of provider-reported vaccination histories because completeness of these records is 

unknown. Third, although national estimates of vaccination coverage are precise, estimates for state and 

local areas should be interpreted with caution because their sample sizes are smaller and their confidence 

intervals generally are wider than those for national estimates. Finally, analysis of trends across data years 

that span from 2010 and earlier to 2011-2017 and from 2011-2017 to 2018-2022 are subject to potential 

bias that may remain after weighting adjustments because of the switch from landline to dual landline and 

cellular phone frames in 2011, and from dual landline and cellular phone frames to a single cellular phone

frame in 2018 (Hill et al., 2019). In addition, analysis of trends across data years that span from 2011 to 

2017 are subject to potential bias that may remain after weighting adjustments because of the expansions 

and reductions of the share of the total sample that came from the cellular phone frame across these years. 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 96

12. Citations for NIS-Child Data 

In publications, please acknowledge the original data source. The citation for the 2022 NIS-Child publicuse data file is:

U.S. Department of Health and Human Services (DHHS). National Center for Immunization and 

Respiratory Diseases . The 2022 National Immunization Survey-Child, Atlanta, GA: Centers for Disease 

Control and Prevention, 2023. 

Information about the NIS-Child is located at http://www.cdc.gov/vaccines/imz-managers/nis/about.html. 

The NIS-Child public-use data files are located at http://www.cdc.gov/vaccines/imzmanagers/nis/datasets.html. 

Please place the acronym “NIS-Child” in the titles, keywords, or abstracts of journal articles and other 

publications in order to facilitate retrieval of such materials in bibliographic searches. 

The following publications use NIS-Child data, published from 2010 or later: 

2023

Butler, M. S., Smart, B. P., Watson, E. J., Narla, S .S., and Keenan-Devlin, L. (2023). U.S. Breastfeeding 

Outcomes at the Intersection: Differences in Duration Among Racial and Ethnic Groups with Varying 

Educational Attainment in a Nationally Representative Sample. Journal of Human Lactation, 39(4), 722-

732. https://doi.org/10.1177/08903344231186786

Hill, H. A., Chen, M., Elam-Evans, L. D,. Yankey, D., and Singleton, J. A. (2023). Vaccination Coverage 

by Age 24 Months Among Children Born During 2018-2019 – National Immunization Survey - Child, 

United States, 2019-2021. MMWR Morb Mortal Wkly Rep, 72(2), 33-38.

https://doi.org/10.15585/mmwr.mm7202a3 

Hill, H. A., Yankey, D., Elam-Evans, L. D., Chen, M., and Singleton, J. A. (2023). Vaccination Coverage 

by Age 24 Months Among Children Born in 2019 and 2020 – National Immunization Survey - Child, 

United States, 2020-2022. MMWR Morb Mortal Wkly Rep, 72(44), 1190-1196.

https://doi.org/10.15585/mmwr.mm7244a3

Michels, S. Y., Niccolai, L. M., Hadler, J. L., Freeman, R. E., Albers, A. N., Glanz, J. M., Daley, M. F., 

and Newcomer, S. R. (2023). Failure to Complete Multidose Vaccine Series in Early Childhood, 

Pediatrics, 152(2), e2022059844. https://doi.org/10.1542/peds.2022-059844

Newcomer, S. R., Glanz, J. M., and Daley, M. F. (2023). Beyond Vaccination Coverage: PopulationBased Measurement of Early Childhood Immunization Schedule Adherence. Academic Pediatrics, 23(1),

24-34. https://doi.org/10.1016/j.acap.2022.08.003

Nguyen, K. H., Zhao, R., Mullins, C., Corlin, L., Beninger, P., and Bednarczyk, R. A. (2023). Trends in 

Vaccination Schedules and Up-to-date Status of Children 19-35 Months, United States, 2015-2020. 

Vaccine, 41, 467-475. https://doi.org/10.1016/j.vaccine.2022.11.023
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 97

Raju, T. N. K. (2023). Achieving Healthy People 2030 Breastfeeding Targets in the United States: 

Challenges and Opportunities. Journal of Perinatology, 43, 74-80. https://doi.org/10.1038/s41372-022-

01535-x

2022

Elam-Evans, L. D., Valier, M. R., Fredua, B., Zell, E., Murthy, B. P., Sterrett, N., Harris, L. Q., Leung, J., 

Singleton, J. A., and Marin, M. (2002). Celebrating 25 Years of Varicella Vaccination Coverage for 

Children and Adolescents in the United States: A Success Story. Journal of Infectious Diseases, 226(4 

Suppl), S416-24. https://doi.org/10.1093/infdis/jiac337

Freeman, R., Thaker, J., Daley, M. F., Glanz, J. M., and Newcomer, S. R. (2022). Vaccine Timeliness and 

Prevalence of Undervaccination Patterns in Children ages 0-19 Months, U.S., National Immunization 

Survey - Child 2017. Vaccine, 40, 765-773. https://doi.org/10.1016/j.vaccine.2021.12.037

Hong, K., Hill, H. A., Tsai, Y., Lindley, M. C., and Zhou, F. (2022). Vaccination Coverage of Privately 

Insured Children: Comparing U.S. Survey and Administrative Data. American Journal of Preventive 

Medicine, 63(1), 107-110. https://doi.org/10.1016/j.amepre.2022.01.020

Kirtland, K. A., Raghunathan, T., Murthy, B. P., Li, J., White, K., Gibbs-Scharf, L., Harris, L., and Zell, 

E. R. (2022). Estimating Vaccination Coverage for Routinely Recommended Vaccines Among Children 

Aged 24 Months and Adolescents Aged 13 through 17 Years Using Data from Immunization Information 

Systems in the United States. Vaccine, 40, 7559-7570. https://doi.org/10.1016/j.vaccine.2022.10.070

Nguyen, K. H., Srivastav, A., Lindley, M. C., Fisher, A., Kim, D., Greby, S. M., Lee, J., and Singleton, J. 

A. (2022). Parental Vaccine Hesitancy and Association with Childhood Diphtheria, Tetanus Toxoid, and 

Acellular Pertussis; Measles, Mumps, and Rubella; Rotavirus; and Combined 7-series Vaccination. 

American Journal of Preventive Medicine, 62(3), 367-376. https://doi.org/10.1016/j.amepre.2021.08.015

Yoo, S., Dhingra, M., Gaughan, J., Danespooy, S., Bhana, N. B., Bartick, M. C., and Feldman-Winter, L. 

(2022). Challenges and Opportunities of Using a National Database to Evaluate Racial/Ethnic Disparities 

and Breastfeeding Effects on Sudden Unexpected Infant Death. Breastfeeding Medicine, 17(11), 964-969.

https://doi.org/10.1089/bfm.2022.0097

2021

Choudhury, A. R. and Polachek, S. W. (2021). The Impact of Paid Family Leave on the Timely 

Vaccination of Infants. Vaccine, 39, 2886–2893. https://doi.org/10.1016/j.vaccine.2021.03.087

Doll, M. K., Weitzen, S. D., and Morrison, K. T. (2021). Trends in the Uptake of Pediatric Measlescontaining Vaccine in the United States: A Disneyland Effect? Vaccine, 39(2), 357-363.

https://doi.org/10.1016/j.vaccine.2020.11.048

Fu, L. Y., Torres, R., Caleb, S., Cheng, Y. I., Gennaro, E., Thoburn, E., McLaughlin, J., AlexanderParrish, R., and Wang, J. (2021) Vaccination Coverage Among Young Homeless Children Compared to 

US National Immunization Survey Data. Vaccine, 39(45), 6637-6643. 

https://doi.org/10.1016/j.vaccine.2021.09.073

Hill, H. A., Yankey, D., Elam-Evans, L. D., Singleton, J. A., and Sterrett, N. (2021) Vaccination 

Coverage by Age 24 Months Among Children Born in 2017 and 2018 – National Immunization Survey - 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 98

Child, United States, 2018-2020. MMWR Morb Mortal Wkly Rep, 70(41), 1435-1440. https//doi 

10.15585/mmwr/mm7041a1

Kulkarni, A. A., Desai, R. P., Alcala, H. E., and Balkrishnan, R. (2021). Persistent Disparities in 

Immunization Rates for the Seven-Vaccine Series Among Infants 19–35 Months in the United States. 

Health Equity, 5(1), 135–139. https://doi.org/10.1089/heq.2020.0127

2020

Adebanjo, T. A., Pondo, T., Yankey, D., Hill, H. A., Gierke, R., Apostol, M., Barnes, M., Petit, S., Farley, 

M., Harrison, L. H., Holtzman, C., Baumbach, J., Bennett, N., McGuire, S., Thomas, A., Schaffner, W., 

Beall, B., Whitney, C. G., and Pilishvili, T. (2020). Pneumococcal Conjugate Vaccine Breakthrough 

Infections: 2001-2016. Pediatrics, 145(3), e20190836. https://doi.org/10.1542/peds.2019-0836

Bleser, W. K., Salmon, D. A., and Miranda, P.Y. (2020). A Hidden Vulnerable Population: Young 

Children Up-to-date on Vaccine Series Recommendations Except Influenza Vaccines. PLoS One, 15(6),

e0234466. https://doi.org/10.1371/journal.pone.0234466

Hill, H. A., Yankey, D., Elam-Evans, L. D., Singleton, J. A., Pingali, S. C., and Santibanez, T. A. (2020). 

Vaccination Coverage by Age 24 Months Among Children Born in 2016 and 2017 — National 

Immunization Survey - Child, United States, 2017–2019. MMWR Morb Mortal Wkly Rep, 69, 1505–1511. 

http://dx.doi.org/10.15585/mmwr.mm6942a1

Vader, D. T., Lee, B. K., and Evans, A. A. (2020). Hepatitis B Birth Dose Effects on Childhood 

Immunization in the U.S. American Journal of Preventive Medicine, 58(2), 208-215.

https://doi.org/10.1016/j.amepre.2019.10.007

2019

Beauregard, J. L., Hamner, H. C., Chen, J., Avila-Rodriguez, W., Elam-Evans, L. D., and Perrine, C. G.

(2019). Racial Disparities in Breastfeeding Initiation and Duration Among U.S. Infants Born in 2015. 

MMWR Morb Mortal Wkly Rep, 68, 745–748. http://dx.doi.org/10.15585/mmwr.mm6834a3

Grubesic, T. H. and Durbin, K. M. (2019). A Spatial Analysis of Breastfeeding and Breastfeeding 

Support in the United States: The Leaders and Laggards Landscape. J Hum Lact, 35(4), 790-800.

https://doi.org/10.1177/0890334419856615 

Hamad, R., Modrek, S., and White, J. S. (2019). Paid Family Leave Effects on Breastfeeding: A QuasiExperimental Study of US Policies. Am J Public Health, 109, 164-166.

https://doi.org/10.2105/AJPH.2018.304693

Hill, H. A., Singleton, J. A., Yankey, D., Elam-Evans, L. D., Pingali, S. C., and Kang, Y. (2019). 

Vaccination Coverage by Age 24 Months Among Children Born in 2015 and 2016 — National 

Immunization Survey - Child, United States, 2016–2018. MMWR Morb Mortal Wkly Rep, 68, 913–918.

https://doi.org/10.15585/mmwr.mm6841e2 

Li, R., Perrine, C. G., Anstey, E. H., Chen, J., MacGowen, C. A., and Elam-Evans, L. D. (2019). 

Breastfeeding Trends by Race and Ethnicity Among US Children Born from 2009 to 2015. JAMA 

Pediatr, 173(12), e193319. https://doi.org/10.1001/jamapediatrics.2019.3319
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 99

Sederdahl, B.K., Orenstein, W. A., Yi, J., Anderson, E. J., and Bednarczyk, R. A. (2019). Missed 

Opportunities for Rotavirus Vaccination. Pediatrics, 143(5), e20182498. 

https://doi.org/10.1542/peds.2018-2498 

Wolter, K. M., Ganesh, N., Copeland, K. R., Singleton, J. A., and Khare, M. (2019). Estimation Tools for 

Reducing the Impact of Sampling and Nonresponse Errors in Dual-Frame RDD Telephone Surveys. Stat 

Med, 38(23), 4718-4732. https://doi.org/10.1002/sim.8329

2018

Hill, H. A., Elam-Evans, L. D., Yankey, D., Singleton, J. A., and Kang, Y. (2018). Vaccination Coverage 

Among Children Aged 19–35 Months — United States, 2017. MMWR Morb Mortal Wkly Rep, 67(40), 

1123-1128. http://doi.org/10.15585/mmwr.mm6740a4

Lavrakas, P. J., Skalland, B., Ward, C., Geng, C., Welch, V., Jeyarajah, J., and Knighton, C. (2018). 

Testing the Effects of Envelope Features on Survey Response in a Telephone Survey Advance Letter 

Mailing Experiment. Journal of Survey Statistics and Methodology, 6, 262-283.

https://doi.org/10.1093/jssam/smx023

Mulligan, K., Snider, J. T., Arthur, P., Frank, G., Tebeka, M., Walker, A., and Abrevaya, J. (2018).

Examination of Universal Purchase Programs as a Driver of Vaccine Uptake Among US States, 1995-

2014. Vaccine, 36, 4032-4038. https://doi.org/10.1016/j.vaccine.2018.05.103

Zhao, Z., Smith, P. J., and Hill, H. A. (2018). Factors Associated with Missed Opportunities for 

Simultaneous Administration of the Fourth Dose of Pneumococcal Conjugate Vaccine for Children in the 

United States. International Journal of Science and Research Methodology, 9(1), 149-162. PMCID: 

PMC7008703

2017

Anstey, E. H., Chen, J., Elam-Evans, L. D., and Perrine, C. G. (2017). Racial and Geographic Differences 

in Breastfeeding — United States, 2011–2015. MMWR Morb Mortal Wkly Rep, 66, 723–727.

https://doi.org/10.15585/mmwr.mm6627a3

Casillas, S. M. and Bednarczyk, R. A. (2017). Missed Opportunities for Hepatitis A Vaccination, 

National Immunization Survey - Child, 2013. J Pediatr, 187, 265-71.

https://doi.org/10.1016/j.jpeds.2017.04.001

Chen, W., Elam-Evans, L. D., Hill, H. A., and Yankey, D. (2017). Employment and Socioeconomic 

Factors Associated with Children’s Up-to-date Vaccination Status. Clinical Pediatrics, 56(4), 348-356.

https://doi.org/10.1177/0009922816660540

Childs, L. and Bednarczyk, R. A. (2017). Estimating Pertussis Susceptibility Among 0-23-month-old 

Children in the United States. Pediatr Infect Dis J, 36, 705-711.

https://doi.org/10.1097/INF.0000000000001537

Hill, H. A., Elam-Evans, L. D., Yankey, D., Singleton, J. A., and Kang, Y. (2017). Vaccination Coverage 

Among Children Aged 19-35 Months – United States, 2016. MMWR Morb Mortal Wkly Rep, 66, 1171-

1177. https://doi.org/10.15585/mmwr.mm6643a3 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 100

Kurosky, S. K., Davis, K. L., and Galindo, C. M. (2017). Effect of Combination Vaccines on Hepatitis B 

Vaccine Compliance in Children in the United States. Pediatr Infect Dis J, 36, e179-e196.

https://doi.org/10.1097/INF.0000000000001548

Kurosky, S. K., Davis, K. L., and Krishnarajah, G. (2017). Effect of Combination Vaccines on 

Completion and Compliance of Childhood Vaccinations in the United States. Human Vaccines & 

Immunotherapeutics, 13(11), 2494-2502. https://doi.org/10.1080/21645515.2017.1362515

Lo, N. C. and Hotez, P. J. (2017). Public Health and Economic Consequences of Vaccine Hesitancy for 

Measles in the United States. JAMA Pediatrics, 17(9), 887-892.

https://doi.org/10.1001/jamapediatrics.2017.1695

Varan, A. K., Rodriguez-Lainz, A., Hill, H. A., Elam-Evans, L. D., Yankey, D., and Li, Q. (2017).

Vaccination Coverage Disparities Between Foreign-born and U.S.-born Children Aged 19-35 Months, 

United States, 2010-2012. J Immigrant Minority Health, 19, 779-789. https://doi.org/10.1007/s10903-

016-0465-4

Zhao, Z., Smith, P. J., and Hill, H. A. (2017). Missed Opportunities for Simultaneous Administration of 

the Fourth Dose of DTaP Among Children in the United States. Vaccine, 35, 3191-3195.

https://doi.org/10.1016/j.vaccine.2017.04.070 

2016

Cardemil, C. V., Cullen, K. A., Harris, L., Greby, S. M., and Santibanez, T. A. (2016). Factors Associated 

with Provider Reporting of Child and Adolescent Vaccination History to Immunization Information 

Systems: Results from the National Immunization Survey, 2006-2012. J Public Health Management 

Practice, 22(3), 245-254. https://doi.org/10.1097/PHH.0000000000000278

Curran, D., Terlinden, A., Poirrier, J-E, Masseria, C., and Krishnarajah, G. (2016). Vaccine Timeliness: A 

Cost Analysis of the Potential Implications of Delayed Pertussis Vaccination in the US. Pediatr Infect Dis 

J, 35(5), 542-547. https://doi.org/10.1097/INF.0000000000001071

Gilkey, M. B., McRee, A-L, Magnus, B. E., Reiter, P. L., Dempsey, A. F., and Brewer, N. T. (2016).

Vaccination Confidence and Parental Refusal/Delay of Early Childhood Vaccines. PLoS One, 11(7), 

e0159087. https://doi: 10.1371/journal.pone.0159087 

Hill, H. A., Elam-Evans, L. D., Yankey, D., Singleton, J. A., and Dietz, V. (2016). Vaccination Coverage 

Among Children Aged 19–35 Months — United States, 2015. MMWR Morb Mortal Wkly Rep, 65, 1065–

1071. https://doi.org/10.15585/mmwr.mm6539a4

Hu, T., Decker, S. L., and Chou, S. Y. (2016). Medicaid Pay for Performance Programs and Childhood 

Immunization Status. Am J Prev Med, 50(5S1), S51-S57. https://doi.org/10.1016/j.amepre.2016.01.012

Kurosky, S. K., Davis, K. L., and Krishnarajah, G. (2016). Completion and Compliance of Childhood 

Vaccinations in the United States. Vaccine, 34(3), 387-394. https://doi.org/10.1016/j.vaccine.2015.11.011

Murphy, T. V., Denniston, M. M., Hill, H. A., McDonald, M., Klevens, M., Elam-Evans, L. D., Nelson, 

N. P., Iskander, J., and Ward, J. D. (2016). Progress Toward Eliminating Hepatitis A Disease in the 

United States. MMWR Suppl, 65(1), 29-41. https://doi.org/10.15585/mmwr.su6501a6
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 101

Santibanez, T. A., Grohskopf, L. A., Zhai, Y., and Kahn, K. E. (2016). Complete Influenza Vaccination 

Trends for Children Six to Twenty-three Months. Pediatrics, 137(3), e20153280.

https://doi.org/10.1542/peds.2015-3280

Walsh, B., Doherty, E., and O’Neill, C. (2016). Since the Start of the Vaccines for Children Program, 

Uptake has Increased, and Most Disparities have Decreased. Health Affairs, 35(2), 356-364.

https://doi.org/10.1377/hlthaff.2015.1019

Zhao, Z., Smith, P. J., and Hill, H. A. (2016). Evaluation of Potentially Achievable Vaccination Coverage 

with Simultaneous Administration of Vaccines Among Children in the United States. Vaccine, 34, 3030-

3036. https://doi.org/10.1016/j.vaccine.2016.04.097 

2015

Crouch, E. and Dickes, L. A. (2015). A Prediction Model of Childhood Immunization Rates. Appl Health

Econ Health Policy, 13(2), 243-251. https://doi.org/10.1007/s40258-015-0157-6

Dunn, A. C., Black, C. L., Arnold, J., Brodine, S., Waalen, J., and Binkin, N. (2015). Childhood 

Vaccination Coverage Rates Among Military Dependents in the United States. Pediatrics, 135(5), e1148-

56. https://doi.org/10.1542/peds.2014-2101

Hill, H. A., Elam-Evans, L. D., Yankey, D., Singleton, J. A., and Kolasa, M. (2015). National, State, and 

Selected Local Area Vaccination Coverage Among Children Aged 19-35 Months – United States, 2014. 

MMWR Morb Mortal Wkly Rep, 64(33), 889-896. https://doi.org/10.15585/mmwr.mm6433a1

Joyce, T. and Reeder, J. (2015). Changes in Breastfeeding Among WIC Participants Following 

Implementation of the New Food Package. Matern Child Health J, 19(4), 868-76.

https://doi.org/10.1007/s10995-014-1588-7

Smith, P. J., Marcuse, E. K., Seward, J. F., Zhao, Z., and Orenstein, W. A. (2015). Children and 

Adolescents Unvaccinated Against Measles: Geographic Clustering, Parents’ Beliefs, and Missed 

Opportunities. Public Health Rep, 130(5), 485-504. https://doi.org/10.1177/003335491513000512

Srivastav, A., Zhai, Y., Santibanez, T. A., Kahn, K. E., Smith, P. J., and Singleton, J. A. (2015). Influenza 

Vaccination Coverage of Vaccine for Children (VFC)-entitled versus Privately Insured Children, United 

States, 2011-2013. Vaccine, 33(27), 3114-21. https://doi.org/10.1016/j.vaccine.2015.04.098 

Wolter, K. M., Tao, X., Montgomery, R., and Smith, P. J. (2015). Optimum Allocation for a Dual-Frame 

Telephone Survey. Survey Methodology, 41(2), 389-401.

http://www.ncbi.nlm.nih.gov/pmc/articles/pmc5839168

2014 

Elam-Evans, L. D., Yankey, D., Singleton, J. A., and Kolasa, M. (2014). National, State, and Selected 

Local Area Vaccination Coverage Among Children aged 19-35 Months - United States, 2013. MMWR 

Morb Mortal Wkly Rep, 63(34), 741-8. http://www.ncbi.nlm.nih.gov/pmc/articles/pmc5779444

Johnson, N. B., Hayes, L. D., Brown, K., Hoo, E. C., and Ethier, K. A. (2014). CDC National Health 

Report: Leading Causes of Morbidity and Mortality and Associated Behavioral Risk and Protective 

Factors - United States, 2005-2013. MMWR Morb Mortal Wkly Rep, 63(Suppl-4), 1-27.

https://pubmed.ncbi.nlm.nih.gov/25356673/
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 102

Santibanez, T. A., Lu, P. J., O’Halloran, A., Meghani, A., Grabowsky, M., and Singleton, J. A. (2014). 

Trends in Childhood Influenza Vaccination Coverage—US, 2004-2012. Public Health Rep, 129(5), 417-

27. https://doi.org/10.1177/003335491412900505

Thomas, T. N., Kolasa, M. S., Zhang, F., and Shefer, A. M. (2014). Assessing Immunization

Interventions in the Women, Infants, and Children (WIC) Program. Am J Prev Med, 47(5), 624-628. 

https://doi.org/10.1016/j.amepre.2014.06.017 

Walker, A. T., Smith, P. J., and Kolasa, M. (2014). Reduction of Racial/Ethnic Disparities in Vaccination

Coverage, 1995-2011. MMWR Suppl, 63(1), 7-12. https://pubmed.ncbi.nlm.nih.gov/24743661

Whitney, C. G., Zhou, F., Singleton, J., and Schuchat, A. (2014) Benefits from Immunization During the 

Vaccines for Children Program Era - United States, 1994-2013. MMWR Morb Mortal Wkly Rep, 63(16), 

352-355.

Yang, Y. T. and Debold, V. (2014). A Longitudinal Analysis of the Effect of Nonmedical Exemption Law 

and Vaccine Uptake on Vaccine-Targeted Disease Rates. Am J Public Health, 104, 371-377.

https://doi.org/10.2105/AJPH.2013.301538

Zhao, Z., Smith, P. J., Yankey, D., and Copeland, K. R. (2014) Calculating Adjusted Survival Functions 

for Complex Sample Survey Data and Application to Vaccination Coverage Studies with National 

Immunization Survey. British Journal of Mathematics & Computer Science, 4(18), 2686-2698.

2013 

Allen, J. A., Li, R., Scanlon, K. S., Perrine, C. G., Chen, J., Odom, E., and Black, C. (2013). Progress in 

Increasing Breastfeeding and Reducing Racial/Ethnic Differences — United States, 2000–2008 Births. 

MMWR Morb Mortal Wkly Rep, 62(5), 77-80.

https://www.cdc.gov/mmwr/preview/mmwrhtml/mm6205a1.htm?s_cid=mm6205a1_w

Black, C. L., Yankey, D., and Kolasa, M. (2013). National, State, and Local Area Vaccination Coverage 

Among Children Aged 19–35 Months — United States, 2012. MMWR Morb Mortal Wkly Rep, 62(36),

733-740. https://www.cdc.gov/mmwr/preview/mmwrhtml/mm6236a1.htm?s_cid=mm6236a1_w 

Schuller, K. A. and Probst, J. C. (2013). Factors Associated with Influenza Vaccination Among US 

Children in 2008. J Infect Public Health, 6(2), 80-88. https://doi.org/10.1016/j.jiph.2012.12.001

Zhao, Z. and Murphy, T. V. (2013). Which Newborns Missed the Hepatitis B Birth Dose Vaccination 

Among U.S. Children? Preventive Medicine, 57, 613-617. https://doi.org/10.1016/j.ypmed.2013.08.012

Zhao, Z. and Smith, P. (2013). Trends in Vaccination Coverage Disparities Among Children, United 

States, 2001-2010. Vaccine, 31(19), 2324-2327. https://doi.org/10.1016/j.vaccine.2013.03.018

2012

Black, C. L., Yankey, D., and Kolasa, M. (2012). National, State, and Local Area Vaccination Coverage 

Among Children Aged 19-35 Months – United States, 2011. MMWR Morb Mortal Wkly Rep, 61(35), 

689-696. https://www.cdc.gov/mmwr/pdf/wk/mm6135.pdf
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 103

Bundy, D. G., Solomon, B. S., Kim, J. M., and Miller, M. R. (2012). Accuracy and Usefulness of the 

HEDIS Childhood Immunization Measures. Pediatrics, 129(4), 648-656.

https://doi.org/10.1542/peds.2011-3073

Groom, A. V., Santibanez, T. A., and Bryan, R. T. (2012). Vaccination Coverage Among American 

Indian and Alaska Native Children, 2006 – 2010. Pediatrics, 130(6), e1592-e1599.

https://doi.org/10.1542/peds.2012-1001 

Jensen, E. (2012). Participation in the Supplemental Nutrition Program for Women, Infants, and Children 

(WIC) and Breastfeeding: National, Regional, and State Level Analyses. Matern Child Health J, 16, 624-

631. https://doi.org/10.1007/s10995-011-0796-7

Ransom, J., Schaff, K., and Kan, L. (2012). Is there an Association between Local Health Department 

Organizational and Administrative Factors and Childhood Immunization Coverage Rates? J Health Hum 

Serv Adm, 34(4), 418-455. https://pubmed.ncbi.nlm.nih.gov/22530285

Santibanez, T. A., Shefer, A., Briere, E. C., Cohn, A. C., and Groom, A. V. (2012). Effects of a 

Nationwide Hib Vaccine Shortage on Vaccination Coverage. Vaccine, 30, 941-947.

https://doi.org/10.1016/j.vaccine.2011.11.075

Thompson, K. M., Wallace, G. S., Tebbens, R. J. D., Smith, P. J., Barskey, A. E., Pallansch, M. A., 

Gallagher, K. M., Alexander, J. P., Armstrong, G. L., Cochi, S. L., and Wassilak, S. G. F. (2012). Trends 

in the Risk of U.S. Polio Outbreaks and Poliovirus Vaccine Availability for Response. Public Health Rep, 

127, 23-37. https://doi.org/10.1177/003335491212700104

Zhao, Z. and Murphy, T. V. (2012). The Association of Hepatitis B Vaccine Supply Policy with Timing 

of Receipt of the First Dose of Hepatitis B Vaccination. Open Journal of Statistics, 2, 429-434.

https://doi.org/10.4236/ojs.2012.24053

2011

Black, C. L., Wooten, K. G., Yankey, D., and Kolasa, M. (2011). National and State Vaccination 

Coverage Among Children Aged 19-35 Months – United States, 2010. MMWR Morb Mortal Wkly Rep, 

60(34), 1157-1163. https://www.cdc.gov/mmwr/pdf/wk/mm6034.pdf

Byrd, K. K., Santibanez, T. A., and Chaves, S. S. (2011). Predictors of Hepatitis A Vaccination Among 

Young Children in the United States. Vaccine, 29, 3254-3259.

https://doi.org/10.1016/j.vaccine.2011.02.028

Dozier, A. M. and McKee, K. S. (2011). State Breastfeeding Worksite Statutes... Breastfeeding Rates ... 

and... Breastfeed Med, 6(5), 319-324. https://doi.org/10.1089/bfm.2011.0082

Flaherman, V. J., Chien, A. T., McCulloch, C. E., and Dudley, R. A. (2011). Breastfeeding Rates Differ 

Significantly by Method Used: A Cause for Concern for Public Health Measurement. Breastfeed Med, 

6(1), 31-35. https://doi.org/10.1089/bfm.2010.0021

Molinari, N. M., Wolter, K. M., Skalland, B., Montgomery, R., Khare, M., Smith, P. J., Barron, M. L., 

Copeland, K., Santos, K., and Singleton, J. A. (2011). Quantifying Bias in a Health Survey: Modeling 

Total Survey Error in the National Immunization Survey. Stat Med, 30, 505-514.

https://doi.org/10.1002/sim.3911
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 104

Smith, P. J., Humiston, S. G., Marcuse, E. K., Zhao, Z., Dorell, C. G., Howes, C., and Hibbs, B. (2011).

Parental Delay or Refusal of Vaccine Doses, Childhood Vaccination Coverage at 24 Months of Age, and 

the Health Belief Model. Public Health Rep, 126(2 Suppl), 135-146. 

https://doi.org/10.1177/00333549111260S215

Smith, P, J., Lindley, M. C., and Rodewald, L. E. (2011). Vaccination Coverage Among U.S. Children 

Aged 19-35 Months Entitled by the Vaccines for Children Program, 2009. Public Health Rep, 126(2 

Suppl), 109-23. https://doi.org/10.1177/00333549111260S213

Smith, P. J. and Singleton, J. A. (2011). County-level Trends in Vaccination Coverage Among Children 

Aged 19-35 Months – United States, 1995 – 2008. MMWR Morb Mortal Wkly Rep , 60(4), 1-86.

https://www.cdc.gov/mmwr/pdf/ss/ss6004.pdf

Smith, P. J., Wood, D., and Darden, P. M. (2011). Highlights of Historical Events Leading to National 

Surveillance of Vaccination Coverage in the United States. Public Health Reports, 125(2 Suppl), 3-12.

https://doi.org/10.1177/00333549111260S202

Zhao, Z. (2011). Power of Tests for Comparing Trend Curves with Application to National Immunization 

Survey (NIS). Stat Med, 30, 531-540. https://doi.org/10.1002/sim.3898

Zhao, Z., Murphy, T. V., and Jacques-Carroll, L. (2011). Progress in Newborn Hepatitis B Vaccination by 

Birth Year Cohorts – 1998-2007, USA. Vaccine, 30, 14-20. https://doi.org/10.1016/j.vaccine.2011.10.076

2010

Cohen, S. A., Ahmed, S., Klassen, A. C., Agree, E. M., Louis, T. A., and Naumova, E. N. (2010).

Childhood Hib Vaccination and Pneumonia and Influenza Burden in US Seniors. Vaccine, 28, 4462-4469.

https://doi.org/10.1016/j.vaccine.2010.04.035

Committee on Practice and Ambulatory Medicine and Council on Community Pediatrics (2010). 

Increasing immunization coverage. Pediatrics, 125, 1295-1304. https://doi.org/10.1542/peds.2010-0743

Groom, H., Kennedy, A., Evans, V., and Fasano, N. (2010). Qualitative Analysis of Immunization 

Programs with Most Improved Childhood Vaccination Coverage from 2001 to 2004. J Public Health 

Management Practice, 16(1), E1-E8. https://doi: 10.1097/PHH.0b013e3181b0b8bc

Kennedy, A., Groom, H., Evans, V., and Fasano, N. (2010). A Aualitative Analysis of Immunization 

Programs with Sustained High Coverage, 200-2005. J Public Health Management Practice, 16(1), E9-

E17. https://doi.org/10.1097/PHH.0b013e3181c7e053

McElligott, J. T. and Darden, P. M. (2010). Are Patient-held Vaccination Records Associated with 

Improved Vaccination Coverage Rates? Pediatrics, 125(3), e467-e472.

https://doi.org/10.1542/peds.2009-0835

Mennito, S. H. and Darden, P. M. (2010). Impact of Practice Policies on Pediatric Immunization Rates. J 

Pediatr, 156, 618-622. https://doi.org/10.1016/j.jpeds.2009.10.046

Santibanez, T. A., Singleton, J.A., Shefer, A., and Cohn, A. (2010). Changes in Measurement of 

Haemophilus influenzae serotype b (Hib) Vaccination Coverage – National Immunization Survey, United 

States, 2009. MMWR Morb Mortal Wkly Rep, 59(33), 1069-1072.

https://www.cdc.gov/mmwr/pdf/wk/mm5933.pdf
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 105

Scanlon, K. S., Grummer-Strawn, L., Li, R., and Chen, J. (2010). Racial and Ethnic Differences in 

Breastfeeding Initiation and Duration, by State – National Immunization Survey, United States, 2004-

2008. MMWR Morb Mortal Wkly Rep, 59(11), 327-334. https://www.cdc.gov/mmwr/pdf/wk/mm5911.pdf

Smith, P. J., Humiston, S. G., Parnell, T., Vannice, K. S., and Salmon, D. A. (2010). The Association 

Between Intentional Delay of Vaccine Administration and Timely Childhood Vaccination Coverage. 

Public Health Rep, 125, 534-541. https://doi.org/10.1177/003335491012500408

Wooten, K. G., Kolasa, M., Singleton, J. A., and Shefer, A. (2010). National, State, and Local Area 

Vaccination Coverage Among Children Aged 19-35 Months – United States, 2009. MMWR Morb Mortal 

Wkly Rep ,59(36), 1171-1177. https://www.cdc.gov/mmwr/pdf/wk/mm5936.pdf

Zhao, Z. and Luman, E. T. (2010). Progress Toward Eliminating Disparities in Vaccination Coverage 

Among U.S. Children, 2000–2008. Am J Prev Med, 38(2), 127–137.

https://doi.org/10.1016/j.amepre.2009.10.035
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 106

13. References 

American Association for Public Opinion Research (AAPOR) (2016). Standard Definitions: Final 

Dispositions of Case Codes and Outcome Rates for Surveys. 9th edition. https://aapor.org/wpcontent/uploads/2022/11/Standard-Definitions20169theditionfinal.pdf

Blackmore, C. (2022). Results of the Survey of Immunization Levels in 2-Year-Old Children (2020). 

https://www.floridahealth.gov/programs-andservices/immunization/resources/surveys/_documents/2yo2020.pdf

Blumberg, S. J., Luke, J. V., Ganesh, N., Davern, M. E., Boudreaux, M. H. and Soderberg, K. (2011). 

Wireless Substitution: State-level Estimates from the National Health Interview Survey, January 2007–

June 2010. National Health Statistics Report, 39, 1-28. http://www.cdc.gov/nchs/data/nhsr/nhsr039.pdf

Brick, J. M. and Kalton, G. (1996). Handling Missing Data in Survey Research. Statistical Methods in 

Medical Research, 5, 215–238. https://doi.org/10.1177/096228029600500302

Centers for Disease Control and Prevention (CDC) (1994). Reported Vaccine-Preventable Diseases - 

United States, 1993, and the Childhood Immunization Initiative. MMWR Morb Mortal Wkly Rep, 43(4), 

57-60.

Centers for Disease Control and Prevention (CDC) (2002). National Immunization Survey: Guide to 

Quality Control Procedures. http://www.cdc.gov/nchs/data/nis/qcman.pdf

Centers for Disease Control and Prevention (CDC) (2010). Changes in measurement of Haemophilus 

influenzae serotype b (Hib) vaccination coverage—National Immunization Survey, United States, 2009. 

MMWR Morb Mortal Wkly Rep, 59(33), 1069-1072.

http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5933a3.htm

Centers for Disease Control and Prevention (CDC) (2015). National Immunization Survey - Child: A 

User’s Guide for the 2014 Public-Use Data File. https://www.cdc.gov/vaccines/imzmanagers/nis/downloads/NIS-PUF15-DUG.pdf

Council of American Survey Research Organizations (CASRO) (1982). On the Definition of Response 

Rates: A Special Report of the CASRO Task Force on Completion Rates. Council of American Survey 

Research Organizations: http://www.casro.org. 

Coronado, V. G., Maes, E. F., Rodewald, L. E., Chu, S., Battaglia, M. P., Hoaglin, D. C., Merced, N. L., 

Yusuf, H., Cordero, J. F., and Orenstein, W. A. (2000). Risk Factors for Underimmunization Among 19-

35 Month-Old Children in the United States: National Immunization Survey, July 1996-June 1998.

Unpublished manuscript, Centers for Disease Control and Prevention, Atlanta.

Deming, W. E. (1943). Statistical Adjustment of Data. New York: Wiley.

Ezzati-Rice, T. M., Zell, E. R., Battaglia, M. P., Ching, P. L. Y. H., and Wright, R. A. (1995). The Design 

of the National Immunization Survey. 1995 Proceedings of the Section on Survey Research Methods,

Alexandria, VA: American Statistical Association, 668-672.

https://www.cdc.gov/nchs/data/nis/sample_design/ezzati1995.pdf
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 107

Ford, B. L. (1983). An Overview of Hot-Deck Procedures, in: Incomplete Data in Sample Surveys, 

Madow W. G., Olkin I., Rubin D. B. (Eds.), Academic Press, New York, 185-207. 

Gillespie, K. (2019). Retrospective Vaccination Coverage Survey 2013-2014 Results (School Year 2017-

2018). Kansas Department of Health and Environment, Topeka, KS. 

https://www.kdheks.gov/immunize/download/Retrospective_2017-2018.pdf

Hill, H. A., Chen, M., Elam-Evans, L. D., Yankey, D., and Singleton, J. A. (2023). Vaccination Coverage 

by Age 24 Months Among Children Born in 2019 and 2020 — National Immunization Survey - Child, 

United States, 2020–2022. MMWR Morb Mortal Wkly Rep, 72(44), 1190–1196. 

http://dx.doi.org/10.15585/mmwr.mm7244a3

Hill, H. A., Singleton, J. A., Elam-Evans, L. D., Nguyen, K., Pingali, S., Walker, T., et al. (2019). 

Transition from a Dual-Frame (Cell-Phone and Landline) to a Single-Frame (Cell-Phone) Sample Design: 

Impact on Vaccination Coverage Estimates, National Immunization Survey - Child, 2014-2018. 

https://www.cdc.gov/vaccines/imz-managers/coverage/childvaxview/pubs-presentations/NIS-child-vaccoverage-estimates-2014-2018.html. 

Khare, M., Battaglia, M. P., Huggins, V. J., Stokley, S., Hoaglin, D. C., Wright, R. A., and Rodén, A. S. 

(2000). Accuracy of Vaccination Dates Reported by Immunization Providers in the National 

Immunization Survey. 2000 Proceedings of the Section on Survey Research Methods. Alexandria, VA:

American Statistical Association, 665-670.

https://www.cdc.gov/nchs/data/nis/data_collection/khare2000.pdf

Khare, M., Battaglia, M. P., Stokley, S., Wright, R. A., and Huggins, V. J. (2001). Quality of 

Immunization Histories Reported in the National Immunization Survey. Proceedings of the International 

Conference on Quality in Official Statistics (CD-ROM). Stockholm: Statistics Sweden.

Lumley, T. (2022). Survey Analysis in R. http://r-survey.r-forge.r-project.org/survey/index.html 

Machado, F. R., Tuttle, J., Drenzek, C., and Lovett, S. (n.d.). Georgia Immunization Study 2017, edited 

by Tuttle, J., Drenzek, C., and Lovett, S. Georgia Department of Public Health, Immunization program / 

Acute Disease Epidemiology Section, Atlanta, GA. 

https://dph.georgia.gov/sites/dph.georgia.gov/files/Immunizations/GIS_2017_web.pdf

Michigan Department of Health & Human Services (2022). Michigan’s Statewide Quarterly 

Immunization Report Card. https://www.michigan.gov/mdhhs/-/media/Project/Websites/mdhhs/Adultand-Childrens-Services/Children-and-Families/Immunization-Information/LHD/Immunization-ReportCards/State_Level_ReportCard_2022Q2.pdf

Molinari, N., Wolter, K., Skalland, B., Montgomery, R., Khare, M., Smith, P., and Singleton, J. (2011). 

Quanitfying Bias in a Health Surey: Modeling Total Surey Error in the National Immunization Survey. 

Statistics in Medicine, 30, 505-515. https://doi.org/10.1002/sim.3911

Mulry, M. H. and Spencer, B. C. (1991). Total Error in PES Estimates of Population. Journal of the 

American Statistical Association, 86(416), 839-863. https://doi.org/10.1080/01621459.1991.10475122

National Center for Health Statistics (NCHS) (1999). National Health Interview Survey: Research for the 

1995-2004 Redesign. Vital and Health Statistics, Series 2, Data Evaluation and Methods Research, (126), 

1-119. https://www.cdc.gov/nchs/data/series/sr_02/sr02_126.pdf
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 108

National Center for Health Statistics (NCHS) (2019). Natality Data, Public-Use Data Files. 

http://www.cdc.gov/nchs/data_access/vitalstatsonline.htm. 

National Center for Health Statistics (NCHS) (2020). Natality Data, Public-Use Data Files.

http://www.cdc.gov/nchs/data_access/vitalstatsonline.htm.

National Center for Immunization and Respiratory Diseases (NCIRD) (2023). National Immunization 

Survey - Child 2022 Public-Use Data File: Documentation, Codebook and Frequencies. Atlanta, GA. 

https://www.cdc.gov/vaccines/imz-managers/nis/datasets.html

NORC at the University of Chicago (NORC) (2011). Modeling Total Survey Error in the 2009 and 2010 

NIS: Young Children and Teens. Report submitted to the Centers for Disease Control and Prevention. 

Chicago, IL: NORC.

Parker, J. D., Talih, M., Malec, D. J., Beresovsky, V., Carroll, M., Gonzalez, J. F., Hamilton, B. E., 

Ingram, D. D., Kochanek, K., McCarty, F., Moriarity, C., Shimizu, I., Strashny, A., and Ward, B. W. 

(2017). National Center for Health Statistics Data Presentation Standards for Proportions. Vital and 

Health Statistics. Series 2, Data Evaluation and Methods Research, (175), 1–22.

Pineau, V., Wolter, K., Skalland, B., Zeng, W., Black, C., Dorell, C., Khare, M., and Yankey, D. (2013). 

Modeling Total Surey Error in the 2011 National Immunization Survey (NIS): Pre-School Children and 

Teens. Presented at the 2013 American Statistical Association (ASA) Joint Statistical Meetings, Montreal, 

Canada. https://www.cdc.gov/vaccines/imz-managers/coverage/downloads/total-survey-error.pdf

Pineau, V., Wolter, K., Skalland, B., Zeng, W., Zhao, Z. and Khare, M. (2012). Modeling Total Survey 

Error in the 2010 National Immunization Survey (NIS): Pre-School Children and Teens. Presented at the 

2012 American Statistical Association (ASA) Joint Statistical Meetings, San Diego, CA. 

Research Triangle Institute (2008). SUDAAN Language Manual, Release 10.0. Research Triangle Park, 

NC: Research Triangle Institute.

Rosenbaum, P. R. (1987). Model-Based Direct Adjustment. Journal of the American Statistical 

Association, 82, 387-394. https://doi.org/10.1080/01621459.1987.10478441

Rosenbaum, P. R. and Rubin, D. B. (1983). The Central Role of the Propensity Score in Observational 

Studies for Causal Effects. Biometrika, 70(1), 41-55. https://doi.org/10.1093/biomet/70.1.41

Rosenbaum, P. R. and Rubin, D. B. (1984). Reducing Bias in Observational Studies Using 

Subclassification on the Propensity Score. Journal of the American Statistical Association, 79(387), 516-

534. https://doi.org/10.1080/01621459.1984.10478078

SAS Institute Inc. (2009). SAS/STAT 9.2 User’s Guide, Second Edition. Cary, NC: SAS Institute Inc. 

https://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm

Singleton, J. A. (2019). Evolving Approaches for Vaccination Coverage Assessment of Young Children. 

Presented at the American Immunization Registry Association National Meeting, Indianapolis, IN, 

August, 2019. 

Singleton, J. A., Hill, H. A., Yankey, D., Zhao, Z., Fredua, B., Li, Q., Elam-Evans, L., Ma, Q., Skalland, 

B., Tao, X., and Wolter, K. (2019). Monitoring Vaccination Coverage by Annual Birth Cohort: A 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 109

Paradigm Shift. Poster presented at the conference of the American Association for Public Opinion 

Research, Toronto, May, 2019. 

Skalland, B., Wolter, K., Ma, Q., Pineau, V., Singleton, J., Yankey, D., and Smith, P. (2016). A Total 

Survey Error Framework and Assessment for the 2013 National Immunization Survey. Presented at the 

International Total Survey Error Workshop, Sydney, Australia, October, 2016.

Smith, P. J., Battaglia, M. P., Huggins, V. J., Hoaglin, D. C., Rodén, A. S., Khare, M., Ezzati-Rice, T. M., 

and Wright, R. A. (2001a). Overview of the Sampling Design and Statistical Methods Used in the 

National Immunization Survey. American Journal of Preventive Medicine, 20(4 Suppl), 17-24.

https://doi.org/10.1016/S0749-3797(01)00285-9

Smith, P. J., Hoaglin, D. C., Battaglia, M. P., Khare, M., and Barker, L. E. (2005), Statistical 

Methodology of the National Immunization Survey: 1994-2002. Vital and Health Statistics, Series 2,

Data Evaluation and Methods Research, 138, 1-64. 

https://www.cdc.gov/nchs/data/series/sr_02/sr02_138.pdf

Smith, P. J., Rao, J. N. K., Battaglia, M. P., Ezzati-Rice, T. M., Daniels, D., and Khare, M. (2001b). 

Compensating for Provider Non-response Using Response Propensities to Form Adjustment Cells: The 

National Immunization Survey. Vital and Health Statistics, Series 2, Data Evaluation and Methods 

Research, 133, 1-17.

StataCorp (2009). Stata Statistical Software: Release 9. College Station, TX: StataCorp LP. 

Tennessee Department of Health (2022). Results of the 2021 Immunization Status Survey of 24 Month 

Old Children in Tennessee. https://www.tn.gov/content/dam/tn/health/documents/cedepweeklyreports/2021-24-Month-Old-Survey.pdf

U.S. Census Bureau (2020). American Community Survey: 1-Year Public Use Microdata Sample 

(PUMS) (2019). Retrieved from http://www.census.gov/programs-surveys/acs/data/pums.html 

U.S. Census Bureau (2021). American Community Survey: 1-Year Public Use Microdata Sample 

(PUMS) (2020). Retrieved from http://www.census.gov/programs-surveys/acs/data/pums.html 

U.S. Census Bureau (2022). American Community Survey: 1-Year Public Use Microdata Sample 

(PUMS) (2021). Retrieved from http://www.census.gov/programs-surveys/acs/data/pums.html 

Virginia Department of Health. (n.d.). Virginia Immunization Survey, SY 2020 Single Antigen Coverage 

Rates (%) at 24 Months of Age for PubKG, PrivKG, Combined KG & DC. 

https://www.vdh.virginia.gov/content/uploads/sites/11/2016/04/VAISAgRates.pdf

Wall, T. P., Kochanek, K. M., Fitti, J. E., and Zell, E. R. (1995). The Use of Real Time Translation 

Services in RDD Telephone Surveys. Presented at the 1995 Conference of the American Association for 

Public Opinion Research, Fort Lauderdale, FL.

Washington State Department of Health (n.d.). Immunization Measures by County Dashboard. Retrieved 

from 

https://doh.wa.gov/data-and-statistical-reports/washington-tracking-network-wtn/immunizationdata/county-public-health-measures-dashboard
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Page 110

Wisconsin Department of Health Services (n.d.). Immunization Rate Data.

https://www.dhs.wisconsin.gov/immunization/data.htm

Wodi, A. P., Murthy, N., Bernstein, H., McNally, V., Cineas, S., and Ault, K. (2022). Advisory 

Committee on Immunization Practices Recommended Immunization Schedule for Children and 

Adolescents Aged 18 Years or Younger – United States, 2022. MMWR Morb Mortal Wkly Rep , 71(7), 

234-237. http://dx.doi.org/10.15585/mmwr.mm7107a2

Wolter, K. M. (2007). Introduction to Variance Estimation. New York, NY: Springer-Verlag.

Wolter, K., Smith, P., Khare, M., Welch, B., Copeland, K., Pineau, V., and Davis, N. (2017a). Statistical 

Methodology of the National Immunization Survey, 2005-2014. Vital and Health Statistics. Ser. 1, 

Programs and Collection Procedures, (61), 1–107.

Wolter, K., Pineau, V., Skalland, B., Zeng, W., Singleton, J., Khare, M., Zhao, Z., Yankey, D., and Smith, 

P. (2017b). Total Survey Error Assessment for Socio-Demographic Subgroups in the 2012 U.S. National 

Immunization Survey. In Biemer, P., De Leeuw, E., Edwards, B., Kreuter, F., Lyberg, L., Tucker, C., and 

West, B. (Eds.) Total Survey Error in Practice, John Wiley & Sons, Inc., Hoboken, NJ, USA.

https://doi.org/10.1002/9781119041702.CH20

Yankey, D., Hill, H. A., Elam-Evans, L. D., Khare, M., Singleton, J.A., Pineau, V., and Wolter, K. (2015). 

Estimating Change in Telephone Survey Bias in an Era of Declining Response Rates and Transition to 

Wireless Telephones – Evidence from the National Immunization Survey (NIS), 1995-2013. Presented at the 

annual conference of the American Association for Public Opinion Research, Hollywood, FL.

Zell, E. R., Ezzati-Rice, T. M., Battaglia, M. P., and Wright, R. A. (2000). National Immunization 

Survey: The Methodology of a Vaccination Surveillance System. Public Health Reports, 115(1), 65-77.

https://doi.org/10.1093/phr/115.1.65
Data User’s Guide for the 2022 NIS-Child Public-Use Data File APPENDIX A 

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Appendix A: Glossary of Abbreviations and Terms 

3:3:1 The series of 3 or more DTaP vaccinations, 3 or more polio vaccinations, and 1 or 

more MCV vaccinations

4:3:1 The series of 4 or more DTaP vaccinations, 3 or more polio vaccinations, and 1 or 

more MCV vaccinations

4:3:1:3 The series of 4 or more DTaP vaccinations, 3 or more polio vaccinations, 1 or more 

MCV vaccinations, and 3 or more Hib vaccinations of any type

4:3:1:3* The series of 4 or more DTaP vaccinations, 3 or more polio vaccinations, 1

(routine Hib) or more MCV vaccinations, and 3 or 4 Hib vaccinations depending on manufacturer 

(routine recommendation)

4:3:1:3:3 The series of 4 or more DTaP vaccinations, 3 or more polio vaccinations, 1 or more 

MCV vaccinations, 3 or more Hib vaccinations of any type, and 3 or more hepatitis B 

vaccinations

4:3:1:3*:3 The series of 4 or more DTaP vaccinations, 3 or more polio vaccinations, 1

(routine Hib) or more MCV vaccinations, 3 or 4 Hib vaccinations depending on manufacturer 

(routine recommendation), and 3 or more hepatitis B vaccinations

4:3:1:3:3:1 The series of 4 or more DTaP vaccinations, 3 or more polio vaccinations, 1 or more 

MCV vaccinations, 3 or more Hib vaccinations of any type, 3 or more hepatitis B 

vaccinations, and 1 or more varicella vaccinations given at age 12 months or older

4:3:1:3*:3:1 The series of 4 or more DTaP vaccinations, 3 or more polio vaccinations, 1 or

(routine Hib) more MCV vaccinations, 3 or 4 Hib vaccinations depending on manufacturer (routine 

recommendation), 3 or more hepatitis B vaccinations, and 1 or more varicella 

vaccinations given at age 12 months or older

4:3:1:3:3:1:3 The series of 4 or more DTaP vaccinations, 3 or more polio vaccinations, 1 or more 

MCV vaccinations, 3 or more Hib vaccinations of any type, 3 or more hepatitis B 

vaccinations, 1 or more varicella vaccinations given at age 12 months or older, and 3 

or more pneumococcal vaccinations

4:3:1:3*:3:1:3 The series of 4 or more DTaP vaccinations, 3 or more polio vaccinations, 1 or

(routine Hib) more MCV vaccinations, 3 or 4 Hib vaccinations depending on manufacturer (routine 

recommendation), 3 or more hepatitis B vaccinations, 1 or more varicella 

vaccinations given at age 12 months or older, and 3 or more pneumococcal 

vaccinations

4:3:1:3:3:1:4 The series of 4 or more DTaP vaccinations, 3 or more polio vaccinations, 1 or more 

MCV vaccinations, 3 or more Hib vaccinations of any type, 3 or more hepatitis B 

vaccinations, 1 or more varicella vaccinations given at age 12 months or older, and 4 

or more pneumococcal vaccinations
Data User’s Guide for the 2022 NIS-Child Public-Use Data File APPENDIX A 

Page 112

4:3:1:3*:3:1:4 The series of 4 or more DTaP vaccinations, 3 or more polio vaccinations, 1 or

(routine Hib) more MCV vaccinations, 3 or 4 Hib vaccinations depending on manufacturer (routine 

recommendation), 3 or more hepatitis B vaccinations, 1 or more varicella 

vaccinations given at age 12 months or older, and 4 or more pneumococcal 

vaccinations

CATI Computer-assisted telephone interviewing

CDC Centers for Disease Control and Prevention

CII Childhood Immunization Initiative 

DOB Date of birth

DTaP Diphtheria and tetanus toxoids and acellular pertussis vaccine adsorbed

DTP Diphtheria and tetanus toxoids and pertussis vaccine

DT Diphtheria and tetanus toxoids adsorbed

H1N Monovalent 2009 H1N1 influenza 

Hep A Hepatitis A vaccine

Hep B Hepatitis B vaccine

Hib Haemophilus influenzae type b conjugate vaccine 

Hib routine Four or more doses of Hib vaccine of any type, or two or more doses of Hib

recommendation vaccine of Merck types followed by one dose of Hib vaccine of any type

Hib shortage Three or more doses of Hib vaccine of any type or two or more doses of Hib

recommendation vaccine of Merck types

 

IAP Immunization Action Plan 

IHQ Immunization history questionnaire

IIS Immunization Information System

IPV Inactivated poliovirus vaccine

MCV Measles-containing vaccine

MMR Measles, mumps, and rubella vaccine

NCHS National Center for Health Statistics

NCIRD National Center for Immunization and Respiratory Diseases

NIS National Immunization Surveys 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File APPENDIX A 

Page 113

NIS-Child National Immunization Survey-Child

NHIS National Health Interview Survey

NIP National Immunization Program

OPV Oral poliovirus vaccine

PCV Pneumococcal conjugate vaccine

PRC Provider Record Check 

PUF Public-use (Data) File

RDD Random digit dialing

RV Rotavirus 

SC Shot card

UTD Up-to-date

VFC Vaccines for Children 

VAR Varicella vaccine
Data User’s Guide for the 2022 NIS-Child Public-Use Data File APPENDIX B 

114

Appendix B: Summary Statistics for Sampling Weights by 

Estimation Area

Table B.1: Distribution of Sampling Weights* for Children with Completed Household 

Interviews, National Immunization Survey - Child, 2022 

State/Estimation Area n Sum§ Minimum Maximum Mean

Coefficient of 

Variation

U.S. National† 34,675 5,336,063.38 1.32 2,547.06 153.89 148.73

Alabama 515 82,735.48 70.23 457.54 160.65 53.88

Alaska 488 12,467.50 3.20 79.93 25.55 75.76

Arizona 547 116,378.01 4.24 612.09 212.76 65.58

Arkansas 620 50,238.18 16.57 280.39 81.03 83.99

California 982 626,308.78 4.97 2,485.98 637.79 90.03

Colorado 625 89,130.76 20.45 369.34 142.61 51.74

Connecticut 464 50,597.01 6.43 306.19 109.05 67.59

Delaware 371 14,978.06 12.16 119.32 40.37 62.60

District of Columbia 677 10,978.97 3.61 47.33 16.22 78.86

Florida 879 319,081.17 10.17 1,431.75 363.00 102.00

Georgia 652 182,636.18 2.22 1,057.40 280.12 100.53

Hawaii 471 23,293.89 15.42 115.16 49.46 44.23

Idaho 497 32,236.40 1.32 219.95 64.86 80.83

Illinois 1,247 194,051.25 15.84 472.04 155.61 68.35

 IL-City of Chicago 415 45,045.19 15.84 307.85 108.54 66.78

 IL-Rest of State 832 149,006.06 29.21 472.04 179.09 62.89

Indiana 452 115,122.15 47.26 689.42 254.70 58.89

Iowa 513 52,281.02 5.06 366.24 101.91 86.24

Kansas 699 51,093.40 4.27 239.58 73.09 73.31

Kentucky 650 74,286.90 4.04 401.09 114.29 79.94

Louisiana 951 82,224.07 7.05 305.55 86.46 93.50

Maine 420 16,882.91 7.74 97.85 40.20 47.87

Maryland 910 102,002.09 3.06 344.33 112.09 81.83

Massachusetts 515 99,378.62 6.70 499.91 192.97 68.86

Michigan 847 151,176.40 7.74 635.22 178.48 82.52

Minnesota 541 93,692.24 92.85 377.58 173.18 38.98

Mississippi 650 52,113.32 4.63 278.30 80.17 91.15

Missouri 724 101,311.14 5.17 446.61 139.93 80.15

Montana 411 16,837.53 11.91 94.69 40.97 45.42

Nebraska 431 34,208.10 4.17 306.49 79.37 84.08

Nevada 734 52,639.87 4.04 239.63 71.72 78.18

New Hampshire 394 17,693.65 13.30 103.88 44.91 56.79

New Jersey 630 146,739.72 5.64 780.12 232.92 78.74

New Mexico 705 30,759.86 1.75 161.92 43.63 90.92

New York 1,323 300,643.89 4.57 813.02 227.24 77.69

 NY-City of New York 747 142,275.45 4.57 646.64 190.46 78.89

 NY-Rest of State 576 158,368.43 4.70 813.02 274.95 71.21

North Carolina 891 171,736.80 4.07 702.55 192.75 94.80

North Dakota 497 13,829.26 7.22 87.14 27.83 73.63
Data User’s Guide for the 2022 NIS-Child Public-Use Data File APPENDIX B 

115

State/Estimation Area n Sum§ Minimum Maximum Mean

Coefficient of 

Variation

Ohio 841 190,418.93 7.13 725.06 226.42 79.70

Oklahoma 550 71,210.67 7.71 445.06 129.47 79.43

Oregon 425 58,779.72 4.11 327.22 138.31 54.82

Pennsylvania 1,344 191,305.14 7.35 819.16 142.34 104.92

 PA-Philadelphia County 596 29,442.02 8.92 149.53 49.40 60.59

 PA-Rest of State 748 161,863.11 7.35 819.16 216.39 75.93

Rhode Island 585 14,608.06 5.03 80.51 24.97 72.91

South Carolina 596 83,051.17 42.28 416.77 139.35 69.62

South Dakota 535 15,522.20 1.99 97.12 29.01 92.05

Tennessee 668 118,361.97 2.20 616.89 177.19 88.13

Texas 1,732 554,170.75 5.64 2,547.06 319.96 167.62

 TX-Bexar County 428 38,228.96 23.26 235.20 89.32 57.36

 TX-City of Houston 493 68,069.68 16.94 411.88 138.07 69.59

 TX-Rest of State 811 447,872.10 5.64 2,547.06 552.25 128.73

Utah 563 66,482.46 13.23 312.04 118.09 53.96

Vermont 521 7,273.67 3.03 37.34 13.96 59.00

Virginia 1,065 141,389.40 3.81 377.36 132.76 77.30

Washington 710 120,679.52 2.07 497.70 169.97 66.49

West Virginia 535 25,538.45 16.00 124.40 47.74 52.28

Wisconsin 564 86,620.03 19.05 381.38 153.58 55.07

Wyoming 518 8,886.66 1.65 51.14 17.16 68.52

 

Puerto Rico 1,028 25,564.14 1.56 76.15 24.87 71.97

Guam 216 4,368.70 4.75 47.65 20.23 48.56

* Distribution of RDDWT_C_TERR. 

† Excludes U.S. territories. 

§ The sum of the weights is an estimate of the total number of children age 19-36 months in the population.
Data User’s Guide for the 2022 NIS-Child Public-Use Data File APPENDIX B 

116

Table B.2: Distribution of Sampling Weights* for Children with Adequate Provider Data, 

National Immunization Survey - Child, 2022 

State/Estimation Area n Sum Minimum Maximum Mean

Coefficient 

of Variation

U.S. National† 17,232 5,336,063.38 2.50 6,168.52 309.66 165.59

Alabama 229 82,735.48 70.23 1,031.99 361.29 68.88

Alaska 285 12,467.50 7.04 152.61 43.75 87.09

Arizona 278 116,378.01 7.17 1,345.68 418.63 81.96

Arkansas 291 50,238.18 36.18 628.09 172.64 91.42

California 438 626,308.78 8.05 6,168.52 1429.93 103.04

Colorado 308 89,130.76 30.55 759.90 289.39 62.75

Connecticut 233 50,597.01 14.87 599.18 217.15 63.95

Delaware 194 14,978.06 20.25 246.86 77.21 69.32

District of Columbia 328 10,978.97 2.50 158.97 33.47 113.53

Florida 374 319,081.17 27.35 3,311.63 853.16 104.40

Georgia 304 182,636.18 4.98 2,501.51 600.78 114.58

Hawaii 220 23,293.89 17.69 318.34 105.88 64.28

Idaho 278 32,236.40 6.62 366.23 115.96 83.97

Illinois 604 194,051.25 19.35 1,155.86 321.28 79.93

 IL-City of Chicago 196 45,045.19 19.35 788.94 229.82 89.76

 IL-Rest of State 408 149,006.06 36.67 1,155.86 365.21 73.14

Indiana 224 115,122.15 76.61 1,447.99 513.94 60.16

Iowa 271 52,281.02 21.76 762.87 192.92 103.54

Kansas 382 51,093.40 7.20 524.36 133.75 89.47

Kentucky 310 74,286.90 10.21 915.18 239.64 102.24

Louisiana 430 82,224.07 11.47 731.86 191.22 105.47

Maine 221 16,882.91 19.84 217.67 76.39 52.92

Maryland 481 102,002.09 5.68 699.78 212.06 86.58

Massachusetts 265 99,378.62 9.06 1,189.01 375.01 79.65

Michigan 457 151,176.40 10.08 1,210.11 330.80 98.84

Minnesota 266 93,692.24 108.37 864.83 352.23 45.79

Mississippi 297 52,113.32 22.71 665.09 175.47 104.47

Missouri 390 101,311.14 6.46 907.02 259.77 91.62

Montana 192 16,837.53 19.37 246.56 87.70 55.80

Nebraska 242 34,208.10 5.03 484.44 141.36 87.45

Nevada 324 52,639.87 4.25 620.21 162.47 98.42

New Hampshire 201 17,693.65 22.48 258.44 88.03 67.82

New Jersey 277 146,739.72 20.48 1,758.80 529.75 78.15

New Mexico 375 30,759.86 3.25 286.42 82.03 92.45

New York 634 300,643.89 8.38 1,855.17 474.20 88.22

 NY-City of New York 355 142,275.45 14.21 1,410.69 400.78 90.54

 NY-Rest of State 279 158,368.43 8.38 1,855.17 567.63 81.72

North Carolina 441 171,736.80 8.72 1,485.42 389.43 98.78

North Dakota 244 13,829.26 11.93 185.08 56.68 87.33

Ohio 424 190,418.93 32.82 1,534.76 449.10 85.36

Oklahoma 257 71,210.67 13.98 1,046.89 277.08 89.89

Oregon 215 58,779.72 6.73 759.43 273.39 63.83

Pennsylvania 668 191,305.14 12.94 1,558.10 286.38 115.44

 PA-Philadelphia County 306 29,442.02 12.94 310.50 96.22 83.63
Data User’s Guide for the 2022 NIS-Child Public-Use Data File APPENDIX B 

117

State/Estimation Area n Sum Minimum Maximum Mean

Coefficient 

of Variation

 PA-Rest of State 362 161,863.11 20.51 1,558.10 447.14 83.66

Rhode Island 327 14,608.06 7.80 145.71 44.67 80.72

South Carolina 266 83,051.17 70.57 1,021.81 312.22 80.73

South Dakota 284 15,522.20 3.10 199.60 54.66 103.92

Tennessee 347 118,361.97 7.71 1,261.34 341.10 101.41

Texas 793 554,170.75 8.54 5,803.91 698.83 171.03

 TX-Bexar County 206 38,228.96 37.76 659.85 185.58 81.47

 TX-City of Houston 226 68,069.68 44.99 1,012.97 301.19 85.27

 TX-Rest of State 361 447,872.10 8.54 5,803.91 1240.64 128.57

Utah 335 66,482.46 14.30 546.11 198.46 71.20

Vermont 317 7,273.67 4.64 64.33 22.95 67.25

Virginia 504 141,389.40 4.82 878.49 280.53 89.74

Washington 394 120,679.52 6.96 889.28 306.29 77.14

West Virginia 260 25,538.45 23.63 280.05 98.22 62.04

Wisconsin 278 86,620.03 25.57 885.31 311.58 68.51

Wyoming 275 8,886.66 3.46 109.40 32.32 82.68

 

Puerto Rico 343 25,564.14 12.38 246.22 74.53 79.71

Guam 99 4,368.70 7.12 137.35 44.13 79.36

* Distribution of PROVWT_C_TERR. 

† Excludes U.S. territories. 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File APPENDIX C 

118

Appendix C: Flags for Inconsistent Values in the 

Breastfeeding Data 

Two different types of inconsistency can arise in breastfeeding data. The first is that the duration of any 

breastfeeding can exceed the age of the child, and the second is that the age of the child when first fed 

formula can exceed the age of child. BF_ENDR06 stores the duration of any breastfeeding, and 

BF_ENDFL06 flags the inconsistency; BF_FORMR20 stores the age of the child when first fed formula, 

and BF_FORMFL06 flags the inconsistency. 

1. Both BF_ENDR06 and BF_FORMR08 are formulated using the following conversion 

factors: 

if unit=1(days) then BF_ENDR06 = number x 1 

if unit=2(weeks) then BF_ENDR06 = number x 7 

if unit=3(months) then BF_ENDR06 = number x 30.4375 

if unit=4(years) then BF_ENDR06 = number x 365.25 

if unit=1(days) then BF_FORMR20 = number x 1 

if unit=2(weeks) then BF_FORMR20 = number x 7 

if unit=3(months) then BF_FORMR20 = number x 30.4375 

if unit=4(years) then BF_FORMR20 = number x 365.25 

2. Flagging BF_ENDR06 when the duration of any breastfeeding exceeds the age in days with a 

buffer for different units: 

if unit=1(days) flag when BF_ENDR06 > age + 1 

if unit=2(weeks) flag when BF_ENDR06 > age + 3 

if unit=3(months) flag when BF_ENDR06 > age + 15 

if unit=4(years) flag when BF_ENDR06 > age + 182 

The different buffers allow for the impact of rounding durations upward in the specified units (for 

example, 50 days might be reported as 2 months). 

3. Flagging BF_FORMR20 when the age when first fed formula exceeds the age in days with a 

buffer for different units: 

if unit=1(days) flag when BF_FORMR20 > age + 1 

if unit=2(weeks) flag when BF_FORMR20 > age + 3 

if unit=3(months) flag when BF_FORMR20 > age + 15 

if unit=4(years) flag when BF_FORMR20 > age + 182

The different buffers allow for the impact of rounding durations upward in the specified units (for 

example, 50 days might be reported as 2 months).
Data User’s Guide for the 2022 NIS-Child Public-Use Data File APPENDIX D 

119

Appendix D: Programs for Estimation: Examples of the Use 

of SUDAAN, SAS, and R to Estimate Vaccination Coverage 

Rates and Their Standard Errors, and an Example of the 

Production of a Cross-Tabulation and Chart 

I. SUDAAN (RTI, 2008) Page 120

II. SAS (SAS, 2009) Page 133

III. ‘R’ (Lumley, 2022) Page 144
Data User’s Guide for the 2022 NIS-Child Public-Use Data File APPENDIX D 

120

I. SUDAAN

**********************;

title1 'SUD_IAP.SAS'; 

**********************************************************************

THIS PROGRAM WILL PRODUCE ESTIMATION AREA ESTIMATES AND STANDARD ERRORS FOR 

P_UTD431H314_ROUT_S USING SAS CALLABLE SUDAAN. 

SUDAAN NOTES:

1. ALL VARIABLES USED MUST BE NUMERIC.

2. VARIABLES IN THE SUBGROUP STATEMENT MUST HAVE VALUES 1,2,..K

WHERE K IS THE NUMBER OF LEVELS FOR EACH VARIABLE.

3. DATA MUST BE SORTED ACCORDING TO THE SAMPLE DESIGN VARIABLES

(STRATUM AND PRIMARY SAMPLING UNIT), SPECIFIED IN THE

NEST STATEMENT.

**********************************************************************;

options ps=78 ls=90 obs= max;

libname dd 'c:\nispuf22'; *--- SPECIFY PATH TO SAS DATASET ---*;

libname library 'c:\nispuf22'; *--- IF DATASET WAS CREATED WITH FORMATS

STORED ---*;

*--- PERMANENTLY SPECIFY PATH TO LIBRARY ---*;

*--- OTHERWISE COMMENT THIS STATEMENT OUT ---*;

%let in_file=dd.nispuf22; *--- NAME OF SAS DATASET ---*;

%let estiap=estiap22; * --- ESTIMATION AREA VARIABLE TO USE ---*;

%let wt=provwt_c; * --- WEIGHT TO USE (PROVWT_C is the single-frame cellular phone weight excluding territories. 

Use PROVWT_C_TERR to include territories) --*;

%let strat=stratum; * --- STRATUM VARIABLE TO USE FOR VARIANCE ESTIMATION;

proc format; 

/*

THE FOLLOWING FORMAT WILL BE USED FOR P_UTD431H314_ROUT_S. 

ORIGINAL VALUES OF P_UTD431H314_ROUT_S ARE 1,0.

MUST BE CONVERTED TO 1,2 IN SUDAAN.

*/

value putd431h314f 

1='4:3:1:3:3:1:4 Up-to-Date'

2='Not 4:3:1:3:3:1:4 Up-to-Date'; 

value estiapf

. = "Missing"

0 = "US Total"

1 = "CT"

2 = "MA"

4 = "ME"

5 = "NH"

6 = "RI"

7 = "VT"

8 = "NJ"

10 = "NY-Rest of State"

11 = "NY-City of New York"

12 = "DC"
Data User’s Guide for the 2022 NIS-Child Public-Use Data File APPENDIX D 

121

13 = "DE"

14 = "MD"

16 = "PA-Rest of State"

17 = "PA-Philadelphia County"

18 = "VA"

19 = "WV"

20 = "AL"

22 = "FL"

25 = "GA"

27 = "KY"

28 = "MS"

29 = "NC"

30 = "SC"

31 = "TN"

34 = "IL-Rest of State"

35 = "IL-City of Chicago"

36 = "IN"

38 = "MI"

40 = "MN"

41 = "OH"

44 = "WI"

46 = "AR"

47 = "LA"

49 = "NM"

50 = "OK"

51 = "TX-Rest of State"

54 = "TX-City of Houston"

55 = "TX-Bexar County"

56 = "IA"

57 = "KS"

58 = "MO"

59 = "NE"

60 = "CO"

61 = "MT"

62 = "ND"

63 = "SD"

64 = "UT"

65 = "WY"

66 = "AZ"

68 = "CA"

72 = "HI"

73 = "NV"

74 = "AK"

75 = "ID"

76 = "OR"

77 = "WA"

106 = "Puerto Rico" 

; 

run; 

data sud_file;

set &in_file(keep= seqnumhh seqnumc P_UTD431H314_ROUT_S &estiap &wt &strat);
Data User’s Guide for the 2022 NIS-Child Public-Use Data File APPENDIX D 

122

if P_UTD431H314_ROUT_S=0 then P_UTD431H314_ROUT_S=2; *--- CONVERT P_UTD431H314_ROUT_S=0 TO 

P_UTD431H314_ROUT_S=2 ---*;

nseqnumh=1*seqnumhh; *---CONVERT HOUSEHOLD ID SEQNUMHH FROM CHARACTER TO NUMERIC ---*;

run; 

*=== SORT BY NEST VARIABLES: STRATUM (STRATUM) NSEQNUMH (PRIMARY SAMPLING UNIT) ===*;

proc sort; 

by &strat nseqnumh;

run; 

proc crosstab data=sud_file filetype=sas design=wr;

weight &wt;

nest &strat nseqnumh;

subgroup &estiap P_UTD431H314_ROUT_S ; 

levels 106 2 ; 

tables &estiap * P_UTD431H314_ROUT_S ; 

print nsum wsum rowper serow/style=nchs ;

rtitle "4:3:1:3:3:1:4 ESTIMATES BY ESTIMATION AREA"; 

rformat &estiap estiapf.; 

rformat P_UTD431H314_ROUT_S putd431h314f.; 

output rowper serow/filename=sud_est filetype=sas replace; 

run; 

proc print data=sud_est(where=(P_UTD431H314_ROUT_S=1 and rowper ne .)) noobs label; 

format &estiap estiapf.; 

var &estiap rowper serow ;

label

rowper='Percent 4:3:1:3:3:1:4 Up-to-Date'

serow='Standard Error'

; 

title "4:3:1:3:3:1:4 ESTIMATES BY ESTIMATION AREA"; 

run; 

**********************;

title1 'SUDSTATE.SAS'; 

*************************************************************************

THIS PROGRAM WILL PRODUCE STATE ESTIMATES AND STANDARD ERRORS

FOR P_UTD431H314_ROUT_S USING SAS CALLABLE SUDAAN.

NOTE: THE STATE VARIABLE IS BASED ON FIPSTATE CODES, THERE ARE

NO STATES WITH FIPS CODES 3,7,14,43,52,57-71,73-78. 

SUDAAN NOTES:

1. ALL VARIABLES USED MUST BE NUMERIC.

2. VARIABLES IN THE SUBGROUP STATEMENT MUST HAVE VALUES 1,2,..K

WHERE K IS THE NUMBER OF LEVELS FOR EACH VARIABLE.

3. DATA MUST BE SORTED ACCORDING TO THE SAMPLE DESIGN VARIABLES

(STRATUM AND PRIMARY SAMPLING UNIT), SPECIFIED IN THE

NEST STATEMENT.

*************************************************************************;

options ps=78 ls=90 obs= max;

libname dd 'c:\nispuf22'; *--- SPECIFY PATH TO SAS DATASET ---*;

libname library 'c:\nispuf22'; *--- IF DATASET WAS CREATED WITH FORMATS STORED ---*;
Data User’s Guide for the 2022 NIS-Child Public-Use Data File APPENDIX D 

123

*--- PERMANENTLY SPECIFY PATH TO LIBRARY ---*;

*--- OTHERWISE COMMENT THIS STATEMENT OUT ---*;

%let in_file=dd.nispuf22; *--- NAME OF SAS DATASET ---*;

%let wt=provwt_c; *--- WEIGHT TO USE (PROVWT_C is the single-frame cellular phone weight excluding territories. 

Use PROVWT_C_TERR to include territories) --*;

%let strat=stratum; * --- STRATUM VARIABLE TO USE FOR VARIANCE ESTIMATION;

proc format; 

/*

THE FOLLOWING FORMAT WILL BE USED FOR P_UTD431H314_ROUT_S. 

ORIGINAL VALUES OF P_UTD431H314_ROUT_S ARE 1,0.

MUST BE CONVERTED TO 1,2 IN SUDAAN.

*/

value putd431h314f 

1='4:3:1:3:3:1:4 Up-to-Date'

2='Not 4:3:1:3:3:1:4 Up-to-Date'

; 

value statef

0 ='U.S. Total'

1 ='Alabama '

2 ='Alaska '

4 ='Arizona '

5 ='Arkansas '

6 ='California '

8 ='Colorado '

9 ='Connecticut '

10 ='Delaware '

11 ='District of Columbia'

12 ='Florida '

13 ='Georgia '

15 ='Hawaii '

16 ='Idaho '

17 ='Illinois '

18 ='Indiana '

19 ='Iowa '

20 ='Kansas '

21 ='Kentucky '

22 ='Louisiana '

23 ='Maine '

24 ='Maryland '

25 ='Massachusetts '

26 ='Michigan '

27 ='Minnesota '

28 ='Mississippi '

29 ='Missouri '

30 ='Montana '

31 ='Nebraska '

32 ='Nevada '

33 ='New Hampshire '

34 ='New Jersey '

35 ='New Mexico '

36 ='New York '
Data User’s Guide for the 2022 NIS-Child Public-Use Data File APPENDIX D 

124

37 ='North Carolina '

38 ='North Dakota '

39 ='Ohio '

40 ='Oklahoma '

41 ='Oregon '

42 ='Pennsylvania '

44 ='Rhode Island '

45 ='South Carolina '

46 ='South Dakota '

47 ='Tennessee '

48 ='Texas '

49 ='Utah '

50 ='Vermont '

51 ='Virginia '

53 ='Washington '

54 ='West Virginia '

55 ='Wisconsin '

56 ='Wyoming '

72 ='Puerto Rico ' 

; 

run; 

data sud_file;

set &in_file.(keep= seqnumhh seqnumc P_UTD431H314_ROUT_S state &wt. &strat.); 

if P_UTD431H314_ROUT_S=0 then P_UTD431H314_ROUT_S=2; *** CONVERT P_UTD431H314_ROUT_S=0 TO 

P_UTD431H314_ROUT_S=2 ***;

nseqnumh=1*seqnumhh; *** CONVERT HOUSEHOLD ID SEQNUMH FROM CHARACTER TO NUMERIC ***;

run; 

*=== SORT BY NEST VARIABLES: STRATUM (STRATUM) NSEQNUMH (PRIMARY SAMPLING UNIT) ===*;

proc sort; 

by &strat. nseqnumh;

run; 

proc crosstab data=sud_file filetype=sas design=wr;

weight &wt.; 

nest &strat nseqnumh;

subgroup state P_UTD431H314_ROUT_S ; 

levels 72 2 ; 

tables state * P_UTD431H314_ROUT_S ; 

print nsum wsum rowper serow/style=nchs ;

rtitle "4:3:1:3:3:1:4 ESTIMATES BY STATE"; 

rformat state statef.; 

rformat P_UTD431H314_ROUT_S putd431h314f.; 

output rowper serow / filename=sud_est2 filetype=sas replace;

run; 

*** EXCLUDE 3,7,14,43,52, 57-71, 73-78 THERE ARE NO STATES WITH THESE FIPS CODES *** ;

option spool; 

proc print data=sud_est2(where=(P_UTD431H314_ROUT_S=1 and rowper ne . 

& state notin (3,7,14,43,52) and not(57<=state<=71) and not (73<=state<=78)))label noobs; 

format state statef.; 

var state rowper serow ;
Data User’s Guide for the 2022 NIS-Child Public-Use Data File APPENDIX D 

125

label

rowper='Percent 4:3:1:3:3:1:4 Up-to-Date'

serow='Standard Error'

; 

title "4:3:1:3:3:1:4 ESTIMATES BY STATE"; 

run; 

option nospool; 

**********************;

title1 'PROG_3.SAS'; 

*************************************************************************

THIS PROGRAM WILL PRODUCE A TABLE OF HAD_CPOX BY STATE FOR ALL RDD

COMPLETES USING RDDWT_C. THE PROGRAM USES SAS CALLABLE SUDAAN.

SUDAAN NOTES:

1. ALL VARIABLES USED MUST BE NUMERIC.

2. VARIABLES IN THE SUBGROUP STATEMENT MUST HAVE VALUES 1,2,..K

WHERE K IS THE NUMBER OF LEVELS FOR EACH VARIABLE.

3. DATA MUST BE SORTED ACCORDING TO THE SAMPLE DESIGN VARIABLES

(STRATUM AND PRIMARY SAMPLING UNIT), SPECIFIED IN THE

NEST STATEMENT.

*************************************************************************;

options ps=78 ls=90 obs= max;

libname dd 'c:\nispuf22'; *--- SPECIFY PATH TO SAS DATASET ---*;

libname library 'c:\nispuf22'; *--- IF DATASET WAS CREATED WITH FORMATS STORED ---*;

*--- PERMANENTLY SPECIFY PATH TO LIBRARY ---*;

*--- OTHERWISE COMMENT THIS STATEMENT OUT ---*;

%let in_file=dd.nispuf22; *--- NAME OF SAS DATASET ---*;

%let wt=rddwt_c; * --- WEIGHT TO USE (RDDWT_C is the single-frame cellular phone weight excluding territories. 

Use RDDWT_C_TERR to include territories) ---*;

%let strat=stratum; * --- STRATUM VARIABLE TO USE FOR VARIANCE ESTIMATION;

proc format; 

/*

THE FOLLOWING FORMAT WILL BE USED FOR HAD_CPOX. 

*/

value hadcpoxf

1='Yes'

2='No'

; 

value statef

0 ='U.S. Total '

1 ='Alabama '

2 ='Alaska '

4 ='Arizona '

5 ='Arkansas '

6 ='California '

8 ='Colorado '

9 ='Connecticut '

10 ='Delaware '

11 ='District of Columbia'
Data User’s Guide for the 2022 NIS-Child Public-Use Data File APPENDIX D 

126

12 ='Florida '

13 ='Georgia '

15 ='Hawaii '

16 ='Idaho '

17 ='Illinois '

18 ='Indiana '

19 ='Iowa '

20 ='Kansas '

21 ='Kentucky '

22 ='Louisiana '

23 ='Maine '

24 ='Maryland '

25 ='Massachusetts '

26 ='Michigan '

27 ='Minnesota '

28 ='Mississippi '

29 ='Missouri '

30 ='Montana '

31 ='Nebraska '

32 ='Nevada '

33 ='New Hampshire '

34 ='New Jersey '

35 ='New Mexico '

36 ='New York '

37 ='North Carolina '

38 ='North Dakota '

39 ='Ohio '

40 ='Oklahoma '

41 ='Oregon '

42 ='Pennsylvania '

44 ='Rhode Island '

45 ='South Carolina '

46 ='South Dakota '

47 ='Tennessee '

48 ='Texas '

49 ='Utah '

50 ='Vermont '

51 ='Virginia '

53 ='Washington '

54 ='West Virginia '

55 ='Wisconsin '

56 ='Wyoming '

72 ='Puerto Rico ' 

; 

run; 

data sud_file;

set &in_file.(keep= seqnumhh seqnumc state had_cpox &wt. &strat.); 

nseqnumh=1*seqnumhh; *** CONVERT HOUSEHOLD ID SEQNUMH FROM CHARACTER TO NUMERIC ***;

run; 

*=== SORT BY NEST VARIABLES: STRATUM (STRATUM) NSEQNUMH (PRIMARY SAMPLING UNIT) ===*;

proc sort; 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File APPENDIX D 

127

by &strat. nseqnumh;

run; 

proc crosstab data=sud_file filetype=sas design=wr;

weight &wt.; 

nest &strat. nseqnumh;

subgroup state had_cpox ;

levels 72 2 ; 

tables state * had_cpox ;

print nsum wsum rowper serow/style=nchs ;

rtitle "HAD_CPOX ESTIMATES BY STATE"; 

rtitle "WEIGHT = &WT"; 

rformat state statef.; 

rformat had_cpox hadcpoxf.; 

output rowper serow / filename=sud_est3 filetype=sas replace; 

run; 

*** EXCLUDE 3,7,14,43,52, 57-71, 73-78 THERE ARE NO STATES WITH THESE FIPS CODES *** ;

option spool; 

proc print data=sud_est3(where=(had_cpox=1 and rowper ne .

& state notin (3,7,14,43,52) and not (57<=state<=71) and not (73<=state<=78))) label noobs; 

format state statef.; 

var state rowper serow ;

label

rowper='Percent HAD_CPOX = Yes'

serow='Standard Error'

; 

title "CHILD HAD CHICKEN POX BY STATE"; 

run; 

option nospool; 

**********************;

title1 'PROG_4.SAS'; 

*************************************************************************

TABLE OF P_UTD431H314_ROUT_S BY INCPOV1 BY RACE_K. SAVE % UTD

ESTIMATES (NOT S.E.'S) FOR USE IN THE PROGRAM CHART_4.

THIS PROGRAM WILL PRODUCE ESTIMATES USING SAS CALLABLE SUDAAN.

SUDAAN NOTES:

1. ALL VARIABLES USED MUST BE NUMERIC.

2. VARIABLES IN THE SUBGROUP STATEMENT MUST HAVE VALUES 1,2,..K

WHERE K IS THE NUMBER OF LEVELS FOR EACH VARIABLE.

3. DATA MUST BE SORTED ACCORDING TO THE SAMPLE DESIGN VARIABLES

(STRATUM AND PRIMARY SAMPLING UNIT), SPECIFIED IN THE

NEST STATEMENT.

*************************************************************************;

options ps=78 ls=90 obs= max;

libname dd 'c:\nispuf22'; *--- SPECIFY PATH TO SAS DATASET ---*;

libname library 'c:\nispuf22'; *--- IF DATASET WAS CREATED WITH FORMATS

STORED ---*;

*--- PERMANENTLY SPECIFY PATH TO LIBRARY ---*;
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128

*--- OTHERWISE COMMENT THIS STATEMENT OUT ---*;

libname out 'c:\nispuf22'; *--- SPECIFY THE PATH FOR WHERE YOU WANT THE CHART OUTPUT TO GO ---*;

%let in_file=dd.nispuf22; *--- NAME OF SAS DATASET ---*;

%let wt=provwt_c; *--- WEIGHT TO USE (PROVWT_C is the single-frame cellular phone weight excluding territories. 

Use PROVWT_C_TERR to include territories) ---*;

%let strat=stratum; * --- STRATUM VARIABLE TO USE FOR VARIANCE ESTIMATION;

%let qtr_lab=Q1/2022 - Q4/2022; *NIS 4 QUARTER PERIOD*;

proc format; 

/*

THE FOLLOWING FORMAT WILL BE USED FOR P_UTD431H314_ROUT_S. 

ORIGINAL VALUES OF P_UTD431H314_ROUT_S ARE 1,0.

MUST BE CONVERTED TO 1,2 IN SUDAAN.

*/

value putd431h314f

1='4:3:1:3:3:1:4 Up-to-Date'

2='Not 4:3:1:3:3:1:4 Up-to-Date'

; 

VALUE race_kf

1 = "WHITE ONLY"

2 = "BLACK ONLY"

3 = "OTHER AND MULTIPLE RACE"

; 

VALUE incpvr2f

1 = "ABOVE, > $75,000"

2 = "ABOVE, <= $75,000"

3 = "BELOW"

4 = "UNKNOWN"

; 

value statef

0 ='U.S. Total '

1 ='Alabama '

2 ='Alaska '

4 ='Arizona '

5 ='Arkansas '

6 ='California '

8 ='Colorado '

9 ='Connecticut '

10 ='Delaware '

11 ='District of Columbia '

12 ='Florida '

13 ='Georgia '

15 ='Hawaii '

16 ='Idaho '

17 ='Illinois '

18 ='Indiana '

19 ='Iowa '

20 ='Kansas '

21 ='Kentucky '

22 ='Louisiana '

23 ='Maine '
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24 ='Maryland '

25 ='Massachusetts '

26 ='Michigan '

27 ='Minnesota '

28 ='Mississippi '

29 ='Missouri '

30 ='Montana '

31 ='Nebraska '

32 ='Nevada '

33 ='New Hampshire '

34 ='New Jersey '

35 ='New Mexico '

36 ='New York '

37 ='North Carolina '

38 ='North Dakota '

39 ='Ohio '

40 ='Oklahoma '

41 ='Oregon '

42 ='Pennsylvania '

44 ='Rhode Island '

45 ='South Carolina '

46 ='South Dakota '

47 ='Tennessee '

48 ='Texas '

49 ='Utah '

50 ='Vermont '

51 ='Virginia '

53 ='Washington '

54 ='West Virginia '

55 ='Wisconsin '

56 ='Wyoming '

72 ='Puerto Rico ' 

; 

run; 

data sud_file;

set &in_file(keep= seqnumhh seqnumc P_UTD431H314_ROUT_S race_k incpov1 &wt &strat);

nseqnumh=1*seqnumhh; *** CONVERT HOUSEHOLD ID SEQNUMH FROM CHARACTER TO NUMERIC ***;

if P_UTD431H314_ROUT_S=0 then P_UTD431H314_ROUT_S=2; *** CONVERT P_UTD431H314_ROUT_S=0 TO 

P_UTD431H314_ROUT_S=2 ***;

run; 

*=== SORT BY NEST VARIABLES: STRATUM (STRATUM) NSEQNUMH (PRIMARY SAMPLING UNIT) ===*;

proc sort; 

by &strat nseqnumh;

run; 

proc freq; 

tables P_UTD431H314_ROUT_S incpov1 race_k;

title1 "Table 4A. &qtr_lab: Unweighted Frequencies"; 

run; 

proc crosstab data=sud_file filetype=sas design=wr;
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weight &wt;

nest &strat nseqnumh;

subgroup incpov1 race_k P_UTD431H314_ROUT_S ; 

levels 4 3 2 ; 

tables (incpov1 * race_k * P_UTD431H314_ROUT_S) ;

print nsum wsum rowper="4:3:1:3:3:1:4 Up-to-Date (ROWPER)"

serow="Standard Error (SEROW)" /style=nchs ;

rtitle "Table 4B. &qtr_lab, Percent 4:3:1:3:3:1:4 Up-to-Date and Estimated Standard Errors"; 

rtitle "WEIGHT = &WT"; 

rformat P_UTD431H314_ROUT_S putd431h314f.; 

rformat incpov1 incpvr2f.; 

rformat race_k race_kf.; 

output rowper serow / filename=sud_est4 filetype=sas replace; 

run; 

data out.sud_est4;

set sud_est4(where=(P_UTD431H314_ROUT_S=1 & incpov1 > 0 & race_k > 0)); 

keep incpov1 race_k rowper serow;

label rowper='4:3:1:3:3:1:4 Up-to-Date'; 

format rowper 5.2; 

format serow 5.2; 

run; 

proc print data=out.sud_est4 label; 

format race_k race_kf.; 

format incpov1 incpvr2f.; 

title "Table 4B. &qtr_lab, 4:3:1:3:3:1:4 ESTIMATES AND STANDARD ERRORS BY INCPOV1 BY RACE_K"; 

run; 

**********************;

title1 'SAS_GRAPH_4.SAS'; 

*************************************************************************

THIS PROGRAM BUILDS OFF OF THE PROGRAM SAS_PROG_4. IT PRODUCES A CHART OF

P_UTD431H314_ROUT_S BY INCPOV1 BY RACE_K. IT CREATES A BAR CHART IN SAS GRAPH FOR

THE 4X3 = 12 CELLS. THE OUTPUT OF THE FOLLOWING EXAMPLE IS ATTACHED AT THE

END.

*************************************************************************;

options ps=78 ls=90 obs= max;

libname dd 'c:\nispuf22'; *--- SPECIFY PATH TO SAS DATASET ---*;

%let out='c:\nispuf22'; *--- SPECIFY THE PATH FOR WHERE YOU WANT THE CHART

OUTPUT TO GO ---*;

%let in_file=dd.sud_est4; *--- NAME OF SAS DATASET OUTPUT FROM PROG_4 ---*;

%let qtr_lab=Q1/2022 - Q4/2022; *NIS 4 QUARTER PERIOD*;

proc format; 

value incpvr2f

1 = "ABOVE, > $75,000"

2 = "ABOVE, <= $75,000"

3 = "BELOW"
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4 = "UNKNOWN"

; 

value race_kf

1 = "WHITE ONLY"

2 = "BLACK ONLY"

3 = "OTHER/MULT RACE"

; 

run; 

data sud_est4;

set &in_file.; 

format rowper 3.

RACE_K race_kf.

INCPOV1 incpvr2f.

; 

label

RACE_K = 'Race of Child'

INCPOV1 = 'Poverty Status'

; 

filename odsout &out.; 

ods listing close; 

/* SET THE GRAPHICS ENVIRONMENT */

goptions reset=global gunit=pct border

ftext=swissb htitle=4 htext=1.5

device=gif

; 

ods html body='graph_4_sud.html' path=odsout;

run; 

title1 h=12pt "Percentage of Children Up-to-Date with Vaccine Series 4:3:1:3:3:1:4"; 

title2 h=12pt "by Race and Poverty Status, National Immunization Survey - Child, 2022"; 

footnote j=r 'graph_4sud'; 

proc sgplot data=sud_est4;

styleattrs datacolors=(wheat lightpink forestgreen) datacontrastcolors=(wheat lightpink forestgreen) 

datalinepatterns=(solid);

vbar INCPOV1 / response=rowper group=RACE_K groupdisplay=cluster stat=mean barwidth=0.8; 

xaxis display=(noticks);

yaxis grid; 

keylegend / across=1 position=top;

run; 

ods html close; 

ods listing; 
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Data User’s Guide for the 2022 NIS-Child Public-Use Data File APPENDIX D 

133

II. SAS

**********************;

title1 'SAS_IAP.SAS'; 

**********************************************************************

THIS PROGRAM WILL PRODUCE ESTIMATION AREA ESTIMATES AND STANDARD ERRORS

FOR P_UTD431H314_ROUT_S USING SAS.

**********************************************************************;

options ps=78 ls=90 obs= max;

libname dd 'c:\nispuf22'; *--- SPECIFY PATH TO SAS DATASET ---*;

libname library 'c:\nispuf22'; *--- IF DATASET WAS CREATED WITH FORMATS STORED ---*;

*--- PERMANENTLY SPECIFY PATH TO LIBRARY ---*;

*--- OTHERWISE COMMENT THIS STATEMENT OUT ---*;

%let in_file=dd.nispuf22; *--- NAME OF SAS DATASET ---*;

%let estiap=estiap22; * --- ESTIMATION AREA VARIABLE TO USE ---*;

%let wt=provwt_c; * --- WEIGHT TO USE (PROVWT_C is the single-frame cellular phone weight excluding territories. 

Use PROVWT_C_TERR to include territories) --*;

%let strat=stratum; * --- STRATUM VARIABLE TO USE FOR VARIANCE ESTIMATION;

proc format; 

value putd431h314f

0='Not 4:3:1:3:3:1:4 Up-To-Date'

1='4:3:1:3:3:1:4 Up-To-Date'; 

value estiapf

. = "Missing"

0 = "US Total"

1 = "CT"

2 = "MA"

4 = "ME"

5 = "NH"

6 = "RI"

7 = "VT"

8 = "NJ"

10 = "NY-Rest of State"

11 = "NY-City of New York"

12 = "DC"

13 = "DE"

14 = "MD"

16 = "PA-Rest of State"

17 = "PA-Philadelphia County"

18 = "VA"

19 = "WV"

20 = "AL"

22 = "FL"

25 = "GA"

27 = "KY"

28 = "MS"

29 = "NC"
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30 = "SC"

31 = "TN"

34 = "IL-Rest of State"

35 = "IL-City of Chicago"

36 = "IN"

38 = "MI"

40 = "MN"

41 = "OH"

44 = "WI"

46 = "AR"

47 = "LA"

49 = "NM"

50 = "OK"

51 = "TX-Rest of State"

54 = "TX-City of Houston"

55 = "TX-Bexar County"

56 = "IA"

57 = "KS"

58 = "MO"

59 = "NE"

60 = "CO"

61 = "MT"

62 = "ND"

63 = "SD"

64 = "UT"

65 = "WY"

66 = "AZ"

68 = "CA"

72 = "HI"

73 = "NV"

74 = "AK"

75 = "ID"

76 = "OR"

77 = "WA"

106 = "Puerto Rico" 

; 

run; 

data sas_file;

set &in_file.(keep= seqnumhh seqnumc P_UTD431H314_ROUT_S &estiap. &wt. &strat.);

run; 

proc sort data = sas_file;

by &estiap.; 

run; 

title1 '4:3:1:3:3:1:4 ESTIMATES BY ESTIMATION AREA'; 

ods output Statistics=sas_est;

proc surveymeans data = sas_file nobs sum mean stderr;

stratum &strat.; 

cluster seqnumhh;

weight &wt.; 

class P_UTD431H314_ROUT_S; 
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135

var P_UTD431H314_ROUT_S; 

by &estiap.; 

format P_UTD431H314_ROUT_S putd431h314f.; 

format &estiap. estiapf.; 

run; 

data sas_est;

set sas_est;

mean = mean*100; *CONVERT TO PERCENT ESTIMATES;

stderr = stderr*100; 

run; 

proc print data=sas_est(where=(varlevel='4:3:1:3:3:1:4 Up-To-Date')) noobs

label; 

format &estiap. estiapf.; 

format mean stderr 5.2; 

var &estiap. mean stderr;

label

mean='Percent 4:3:1:3:3:1:4 Up-to-Date'

stderr='Standard Error'; 

title "4:3:1:3:3:1:4 ESTIMATES BY ESTIMATION AREA"; 

run; 

**********************;

title1 'SASSTATE.SAS'; 

**********************************************************************

THIS PROGRAM WILL PRODUCE STATE ESTIMATES AND STANDARD ERRORS

FOR P_UTD431H314_ROUT_S USING SAS.

NOTE: THE STATE VARIABLE IS BASED ON FIPSTATE CODES, THERE ARE

NO STATES WITH FIPS CODES 3,7,14,43,52,57-71,73-78. 

**********************************************************************;

options ps=78 ls=90 obs= max;

libname dd 'c:\nispuf22'; *--- SPECIFY PATH TO SAS DATASET ---*;

libname library 'c:\nispuf22'; *--- IF DATASET WAS CREATED WITH FORMATS

STORED ---*;

*--- PERMANENTLY SPECIFY PATH TO LIBRARY ---*;

*--- OTHERWISE COMMENT THIS STATEMENT OUT ---*;

%let in_file=dd.nispuf22; *--- NAME OF SAS DATASET ---*;

%let wt=provwt_c; * --- WEIGHT TO USE (PROVWT_C is the single-frame cellular phone weight excluding territories. 

Use PROVWT_C_TERR to include territories) --*;

%let strat=stratum; * --- STRATUM VARIABLE TO USE FOR VARIANCE ESTIMATION;

proc format; 

value putd431h314f

0='Not 4:3:1:3:3:1:4 Up-To-Date'

1='4:3:1:3:3:1:4 Up-To-Date'; 

value statef

. ="Missing"

0 ='U.S. Total '

1 ='Alabama '
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2 ='Alaska ' 4 ='Arizona ' 5 ='Arkansas ' 6 ='California ' 8 ='Colorado ' 9 ='Connecticut '

10 ='Delaware '

11 ='District of Columbia '

12 ='Florida '

13 ='Georgia '

15 ='Hawaii '

16 ='Idaho '

17 ='Illinois '

18 ='Indiana '

19 ='Iowa '

20 ='Kansas '

21 ='Kentucky '

22 ='Louisiana '

23 ='Maine '

24 ='Maryland '

25 ='Massachusetts '

26 ='Michigan '

27 ='Minnesota '

28 ='Mississippi '

29 ='Missouri '

30 ='Montana '

31 ='Nebraska '

32 ='Nevada '

33 ='New Hampshire '

34 ='New Jersey '

35 ='New Mexico '

36 ='New York '

37 ='North Carolina '

38 ='North Dakota '

39 ='Ohio '

40 ='Oklahoma '

41 ='Oregon '

42 ='Pennsylvania '

44 ='Rhode Island '

45 ='South Carolina '

46 ='South Dakota '

47 ='Tennessee '

48 ='Texas '

49 ='Utah '

50 ='Vermont '

51 ='Virginia '

53 ='Washington '

54 ='West Virginia '

55 ='Wisconsin '

56 ='Wyoming '

72 ='Puerto Rico ' 

; 

run; 
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data sas_file;

set &in_file(keep= seqnumhh seqnumc P_UTD431H314_ROUT_S state &wt &strat);

run; 

proc sort data = sas_file;

by state;

title1 '4:3:1:3:3:1:4 ESTIMATES BY STATE'; 

ods output Statistics=sas_est2;

run; 

proc surveymeans data = sas_file nobs sum mean stderr;

stratum &strat;

cluster seqnumhh;

weight &wt;

class P_UTD431H314_ROUT_S; 

var P_UTD431H314_ROUT_S; 

by state;

format P_UTD431H314_ROUT_S putd431h314f.; 

format state statef.; 

run; 

data sas_est2;

set sas_est2;

mean = mean*100; *CONVERT TO PERCENT ESTIMATES;

stderr = stderr*100; 

run; 

proc print data=sas_est2(where=(varlevel='4:3:1:3:3:1:4 Up-To-Date')) noobs

label; 

format state statef.; 

format mean stderr 5.2; 

var state mean stderr;

label

mean='Percent 4:3:1:3:3:1:4 Up-to-Date'

stderr='Standard Error'; 

title "4:3:1:3:3:1:4 ESTIMATES BY STATE"; 

run; 

**********************;

title1 'SAS_PROG_3.SAS'; 

*************************************************************************

THIS PROGRAM WILL PRODUCE A TABLE OF HAD_CPOX BY STATE FOR ALL RDD

COMPLETES USING RDDWT. THE PROGRAM USES SAS.

*************************************************************************;

options ps=78 ls=90 obs= max;

libname dd 'c:\nispuf22'; *--- SPECIFY PATH TO SAS DATASET ---*;

libname library 'c:\nispuf22'; *--- IF DATASET WAS CREATED WITH FORMATS

STORED ---*;

*--- PERMANENTLY SPECIFY PATH TO LIBRARY ---*;

*--- OTHERWISE COMMENT THIS STATEMENT OUT ---*;
Data User’s Guide for the 2022 NIS-Child Public-Use Data File APPENDIX D 

138

%let in_file=dd.nispuf22; *--- NAME OF SAS DATASET ---*;

%let wt=rddwt_c; *--- WEIGHT TO USE (RDDWT_C is the single-frame cellular phone weight excluding territories) ---

*;

%let strat=stratum; * --- STRATUM VARIABLE TO USE FOR VARIANCE ESTIMATION;

proc format; 

value hadcpoxf

1='Yes'

2='No'

; 

value statef

0 ='U.S. Total '

1 ='Alabama '

2 ='Alaska '

4 ='Arizona '

5 ='Arkansas '

6 ='California '

8 ='Colorado '

9 ='Connecticut '

10 ='Delaware '

11 ='District of Columbia '

12 ='Florida '

13 ='Georgia '

15 ='Hawaii '

16 ='Idaho '

17 ='Illinois '

18 ='Indiana '

19 ='Iowa '

20 ='Kansas '

21 ='Kentucky '

22 ='Louisiana '

23 ='Maine '

24 ='Maryland '

25 ='Massachusetts '

26 ='Michigan '

27 ='Minnesota '

28 ='Mississippi '

29 ='Missouri '

30 ='Montana '

31 ='Nebraska '

32 ='Nevada '

33 ='New Hampshire '

34 ='New Jersey '

35 ='New Mexico '

36 ='New York '

37 ='North Carolina '

38 ='North Dakota '

39 ='Ohio '

40 ='Oklahoma '

41 ='Oregon '

42 ='Pennsylvania '

44 ='Rhode Island '
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139

45 ='South Carolina '

46 ='South Dakota '

47 ='Tennessee '

48 ='Texas '

49 ='Utah '

50 ='Vermont '

51 ='Virginia '

53 ='Washington '

54 ='West Virginia '

55 ='Wisconsin '

56 ='Wyoming '

72 ='Puerto Rico '

; 

run; 

data sas_file;

set &in_file.(keep= seqnumhh seqnumc state had_cpox &wt. &strat.); 

run; 

proc sort data = sas_file;

by state;

title1 'HAD_CPOX ESTIMATES BY STATE'; 

ods output Statistics=sas_est3;

run; 

proc surveymeans data = sas_file nobs sum mean stderr;

stratum &strat.; 

cluster seqnumhh;

weight &wt.; 

class had_cpox;

var had_cpox;

by state;

format had_cpox hadcpoxf.; 

format state statef.; 

run; 

data sas_est3;

set sas_est3;

mean = mean*100; *CONVERT TO PERCENT ESTIMATES;

stderr = stderr*100; 

run; 

proc print data=sas_est3(where=(varlevel='Yes')) noobs label; 

format state statef.; 

format mean stderr 5.2; 

var state mean stderr;

label

mean='Percent HAD_CPOX = Yes'

stderr='Standard Error'; 

title "CHILD HAD CHICKEN POX BY STATE"; 

run; 
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140

**********************;

title1 'SAS_PROG_4.SAS'; 

*************************************************************************

TABLE OF P_UTD431H314_ROUT_S BY INCPOV1 BY RACE_K. SAVE % UTD

ESTIMATES (NOT S.E.'S) FOR USE IN THE PROGRAM SAS_GRAPH_4.

THIS PROGRAM WILL PRODUCE ESTIMATES USING SAS.

*************************************************************************;

options ps=78 ls=90 obs= max;

libname dd 'c:\nispuf22'; *--- SPECIFY PATH TO SAS DATASET ---*;

libname library 'c:\nispuf22'; *--- IF DATASET WAS CREATED WITH FORMATS

STORED ---*;

*--- PERMANENTLY SPECIFY PATH TO LIBRARY ---*;

*--- OTHERWISE COMMENT THIS STATEMENT OUT ---*;

libname out 'c:\nispuf22'; *--- SPECIFY THE PATH FOR 

WHERE YOU WANT THE CHART OUTPUT TO GO ---*;

%let in_file=dd.nispuf22; *--- NAME OF SAS DATASET ---*;

%let wt=provwt_c; *--- WEIGHT TO USE (PROVWT_C is the single-frame cellular phone weight excluding territories. 

Use PROVWT_C_TERR to include territories) --*;

%let strat=stratum; * --- STRATUM VARIABLE TO USE FOR VARIANCE ESTIMATION;

%let qtr_lab=Q1/2022 - Q4/2022; *NIS 4 QUARTER PERIOD*;

proc format; 

value putd431h314f

0='Not 4:3:1:3:3:1:4 Up-To-Date'

1='4:3:1:3:3:1:4 Up-To-Date'

; 

value race_kf

1 = "WHITE ONLY"

2 = "BLACK ONLY"

3 = "OTHER AND MULTIPLE RACE"

; 

value incpvr2f

1 = "ABOVE, > $75,000"

2 = "ABOVE, <= $75,000"

3 = "BELOW"

4 = "UNKNOWN"

; 

run; 

data sas_file;

set &in_file.(keep= seqnumhh seqnumc P_UTD431H314_ROUT_S race_k incpov1 &wt. &strat.); 

run; 

proc sort data = sas_file;

by incpov1 race_k;

run; 

proc freq; 

tables P_UTD431H314_ROUT_S incpov1 race_k;

title1 "Table 4A. &qtr_lab: Unweighted Frequencies"; 
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141

run; 

data sas_file;

set sas_file;

if P_UTD431H314_ROUT_S < 0 | incpov1 < 0 | race_k < 0 | &wt. < 0 then delete; 

run; 

proc surveymeans data = sas_file nobs sum mean stderr;

ods output Domain=sas_est4;

stratum &strat.; 

cluster seqnumhh;

weight &wt.; 

class P_UTD431H314_ROUT_S; 

var P_UTD431H314_ROUT_S; 

domain incpov1*race_k;

format P_UTD431H314_ROUT_S putd431h314f.; 

format incpov1 incpvr2f.; 

format race_k race_kf.; 

run; 

data sas_est4;

set sas_est4;

mean = mean*100; *CONVERT TO PERCENT ESTIMATES;

stderr = stderr*100; 

run; 

proc print data=sas_est4(where=(varlevel='4:3:1:3:3:1:4 Up-To-Date')) noobs

label; 

format incpov1 incpvr2f.; 

format race_k race_kf.; 

format mean stderr 5.2; 

var incpov1 race_k mean stderr;

label

mean='4:3:1:3:3:1:4 Up-To-Date'

stderr='Standard Error'; 

title1 "Table 4B. &qtr_lab, 4:3:1:3:3:1:4 ESTIMATES AND STANDARD ERRORS BY INCPOV1 BY RACE_K"; 

run; 

data out.sas_est4;

set sas_est4(where=(varlevel='4:3:1:3:3:1:4 Up-To-Date')); 

keep incpov1 race_k mean;

label mean='4:3:1:3:3:1:4 Up-to-Date'; 

format mean 5.2; 

run; 

**********************;

title1 'SAS_GRAPH_4.SAS'; 

*************************************************************************

THIS PROGRAM BUILDS OFF OF THE PROGRAM SAS_PROG_4. IT PRODUCES A CHART OF

P_UTD431H314_ROUT_S BY INCPOV1 BY RACE_K. IT CREATES A BAR CHART IN SAS GRAPH FOR

THE 4X3 = 12 CELLS. THE OUTPUT OF THE FOLLOWING EXAMPLE IS ATTACHED AT THE

END.
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142

*************************************************************************;

options ps=78 ls=90 obs= max;

libname dd 'c:\nispuf22'; *--- SPECIFY PATH TO SAS DATASET ---*;

%let out='c:\nispuf22'; *--- SPECIFY THE PATH FOR WHERE YOU WANT THE CHART

OUTPUT TO GO ---*;

%let in_file=dd.sas_est4; *--- NAME OF SAS DATASET OUTPUT FROM PROG_4 ---*;

%let qtr_lab=Q1/2022 - Q4/2022; *NIS 4 QUARTER PERIOD*;

proc format; 

value incpvr2f

1 = "ABOVE, > $75,000"

2 = "ABOVE, <= $75,000"

3 = "BELOW"

4 = "UNKNOWN"

; 

value race_kf

1 = "WHITE ONLY"

2 = "BLACK ONLY"

3 = "OTHER/MULT RACE"

; 

run; 

data sas_est4;

set &in_file.; 

format mean 3.

race_k race_kf.

incpov1 incpvr2f.

; 

label

race_k = 'Race of Child'

incpov1 = 'Poverty Status'

; 

filename odsout &out.; 

ods listing close; 

/* SET THE GRAPHICS ENVIRONMENT */

goptions reset=global gunit=pct border

ftext=swissb htitle=4 htext=1.5

device=gif

; 

ods html body='graph_4.html' path=odsout;

run; 

title1 h=12pt "Percentage of Children Up-to-Date with Vaccine Series 4:3:1:3:3:1:4 "; 

title2 h=12pt "by Race and Poverty Status, National Immunization Survey - Child, 2022"; 

footnote j=r 'graph_4'; 

proc sgplot data=sas_est4;

styleattrs datacolors=(wheat lightpink forestgreen) datacontrastcolors=(wheat lightpink forestgreen) 

datalinepatterns=(solid);

vbar INCPOV1 / response=mean group=RACE_K groupdisplay=cluster stat=mean barwidth=0.8; 
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143

xaxis display=(noticks);

yaxis grid; 

keylegend / across=1 position=top;

run; 

ods html close; 

ods listing; 
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144

III. ‘R’ 

###################### 

title <- "R_IAP.R" 

###################################################################### 

#THIS PROGRAM WILL PRODUCE ESTIMATION AREA ESTIMATES AND STANDARD ERRORS

#FOR P_UTD431H314_ROUT_S USING R.

# 

#R NOTES:

#1. R IS CASE SENSITIVE.

#2. A FILE PATH IS SEPERATED BY SLASH(/)

###################################################################### 

library(survey) #TO USE svydesign(), svymean(), and svyby()

library(Hmisc) #TO USE prn()

dd <- "c:/nispuf22" #"path-to-dataset"

#--- NAME OF R DATASET ---# 

in.file <- paste(dd,"/NISPUF22.RData",sep="")

#---READ R DATASET---# 

load(in.file) 

#---FORMAT---# 

UTD431H314levels=c(0,1)

UTD431H314labels=c("NOT 4:3:1:3:3:1:4 UTD", "4:3:1:3:3:1:4 UTD")

ESTIAPlevels=c(0, 1, 2, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 25, 27, 28, 29, 30, 31, 34, 35, 36, 38, 40, 41, 44, 46, 47, 49, 50, 51, 54, 

55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 68, 72, 73, 74, 75, 76, 77, 106) 

ESTIAPlabels=c("US Total", "CT", "MA", "ME", "NH", "RI", "VT", "NJ", "NY-Rest of State", "NY-City of New York", "DC", "DE", "MD", "PARest of State", "PA-Philadelphia County", "VA", "WV", "AL", "FL", "GA", "KY", "MS", "NC", "SC", "TN", "IL-Rest of State", "IL-City of 

Chicago", "IN", "MI", "MN", "OH", "WI", "AR", "LA", "NM", "OK", "TX-Rest of State", "TX-City of Houston", "TX-Bexar County", "IA", "KS", 

"MO", "NE", "CO", "MT", "ND", "SD", "UT", "WY", "AZ", "CA", "HI", "NV", "AK", "ID", "OR", "WA", "Puerto Rico") 

#---PROVWT_C WILL BE USED AS A WEIGHT (PROVWT_C IS THE SINGLE-FRAME CELLULAR PHONE WEIGHT EXCLUDING 

TERRITORIES; USE PROVWT_C_TERR TO INCLUDE TERRITORIES)---# 

#---STRATUM WILL BE USED AS A STRATUM VARIABLE FOR VARIANCE ESTIMATION ---# 

R_FILE <- subset(NISPUF22, select=c(SEQNUMHH, SEQNUMC, P_UTD431H314_ROUT_S, ESTIAP22, 

PROVWT_C, STRATUM))

names(R_FILE) <- c("SEQNUMHH", "SEQNUMC", "P_UTD431H314_ROUT_S", "ESTIAP", "WT", "STRATUM")

R_FILE <- na.omit(R_FILE) 

#---ASSIGN LABELS---# 

R_FILE$P_UTD431H314_ROUT_S <- factor(R_FILE$P_UTD431H314_ROUT_S, levels=UTD431H314levels,

labels=UTD431H314labels)

R_FILE$ESTIAP <- factor(R_FILE$ESTIAP, levels=ESTIAPlevels,

labels=ESTIAPlabels)

#---SPECIFY A SAMPLING DESIGN AND FORCE WT AS NUMERIC---# 

svydsg <- svydesign(id=~SEQNUMHH, strata=~STRATUM, weights=~(as.numeric(WT)),

data=R_FILE) 

#---U.S. TOTAL ESTIMATES AND STANDARD ERRORS---# 

r_nation <- svymean(~P_UTD431H314_ROUT_S, svydsg)

PERCENT_UTD <- round(r_nation*100,2) #CONVERT INTO PERCENT ESTIMATES(MEAN)

SE_UTD <- round(SE(r_nation)*100,2) #CONVERT INTO PERCENT ESTIMATES(SE)

r_nation_est <- cbind(PERCENT_UTD, SE_UTD)

title <- "PERCENT 4:3:1:3:3:1:4 ESTIMATES AT A NATIONAL LEVEL"

prn(r_nation_est, title)

#---ESTIMATION AREA ESTIMATES AND STANDARD ERRORS---# 

r_est <- svyby(~P_UTD431H314_ROUT_S, ~ESTIAP, svydsg, svymean)

r_est[,-c(1)] <- round(r_est[,-c(1)]*100,2) #CONVERT INTO PERCENT ESTIMATES

r_est <- subset(r_est, select=c(1,3,5))
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145

#SELECT ESTIMATES FOR UP-TO-DATE CASES

names(r_est) <- c("ESTIMATION AREA", "PERCENT 4:3:1:3:3:1:4 UTD", "STANDARD ERROR UTD")

title <- "PERCENT 4:3:1:3:3:1:4 ESTIMATES BY ESTIMATION AREA"

prn(r_est, title)

###################### 

title <- "R_STATE.R"

###################################################################### 

#THIS PROGRAM WILL PRODUCE STATE ESTIMATES AND STANDARD ERRORS

#FOR P_UTD431H314_ROUT_S USING R.

# 

#NOTE : THE STATE VARIABLE IS BASED ON FIPSTATE CODES ,THERE ARE

#NO STATES WITH FIPS CODES 3,7,14,43,52,57-71,73-78. 

# 

#R NOTES:

#1. R IS CASE SENSITIVE.

#2. A FILE PATH IS SEPERATED BY SLASH(/)

###################################################################### 

library(survey) #TO USE svydesign(), svymean(), and svyby()

library(Hmisc) #TO USE prn()

dd <- "c:/nispuf22" #"path-to-data"

#--- NAME OF R DATASET ---# 

in.file <- paste(dd,"/NISPUF22.RData",sep="")

#---READ R DATASET---# 

load(in.file) 

#---FORMAT---# 

UTD431H314levels=c(0,1)

UTD431H314labels=c("NOT 4:3:1:3:3:1:4 UTD", "4:3:1:3:3:1:4 UTD")

STATElevels=c(1, 2, 4, 5, 6, 8, 9, 10, 11, 12, 13, 15, 16, 17,

18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,

36, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 53,

54, 55, 56, 72) 

STATElabels=c(

"ALABAMA",

"ALASKA",

"ARIZONA",

"ARKANSAS",

"CALIFORNIA",

"COLORADO",

"CONNECTICUT",

"DELAWARE",

"DISTRICT OF COLUMBIA",

"FLORIDA",

"GEORGIA",

"HAWAII",

"IDAHO",

"ILLINOIS", 

"INDIANA",

"IOWA",

"KANSAS",

"KENTUCKY",

"LOUISIANA",

"MAINE",

"MARYLAND",

"MASSACHUSETTS",

"MICHIGAN",

"MINNESOTA",

"MISSISSIPPI",

"MISSOURI",

"MONTANA",
Data User’s Guide for the 2022 NIS-Child Public-Use Data File APPENDIX D 

146

"NEBRASKA",

"NEVADA",

"NEW HAMPSHIRE",

"NEW JERSEY",

"NEW MEXICO",

"NEW YORK",

"NORTH CAROLINA",

"NORTH DAKOTA",

"OHIO",

"OKLAHOMA",

"OREGON",

"PENNSYLVANIA",

"RHODE ISLAND",

"SOUTH CAROLINA",

"SOUTH DAKOTA",

"TENNESSEE",

"TEXAS",

"UTAH",

"VERMONT",

"VIRGINIA", 

"WASHINGTON",

"WEST VIRGINIA",

"WISCONSIN",

"WYOMING", 

"PUERTO RICO" 

) 

#---PROVWT_C WILL BE USED AS A WEIGHT (PROVWT_C IS THE SINGLE-FRAME CELLULAR PHONE WEIGHT EXCLUDING 

TERRITORIES; USE PROVWT_C_TERR TO INCLUDE TERRITORIES)---# 

#---STRATUM WILL BE USED AS A STRATUM VARIABLE FOR VARIANCE ESTIMATION ---# 

R_FILE <- subset(NISPUF22, select=c(SEQNUMHH, SEQNUMC, P_UTD431H314_ROUT_S, 

STATE, PROVWT_C, STRATUM))

names(R_FILE) <- c("SEQNUMHH", "SEQNUMC", "P_UTD431H314_ROUT_S", "STATE",

"WT","STRATUM")

R_FILE <- na.omit(R_FILE) 

#---ASSIGN LABELS---# 

R_FILE$P_UTD431H314_ROUT_S <- factor(R_FILE$P_UTD431H314_ROUT_S, levels=UTD431H314levels,

labels=UTD431H314labels)

R_FILE$STATE <- factor(R_FILE$STATE, levels=STATElevels,

labels=STATElabels)

#---SPECIFY A SAMPLING DESIGN AND FORCE WT AS NUMERIC---# 

svydsg <- svydesign(id=~SEQNUMHH, strata=~STRATUM, weights=~(as.numeric(WT)),

data=R_FILE) 

#---STATE ESTIMATES AND STANDARD ERRORS---# 

r_est2 <- svyby(~P_UTD431H314_ROUT_S, ~STATE, svydsg, svymean)

r_est2[,-c(1)] <- round(r_est2[,-c(1)]*100,2) #CONVERT INTO PERCENT ESTIMATES

r_est2 <- subset(r_est2, select=c(1,3,5)) #SELECT ESTIMATES FOR UP-TO-DATE CASES

names(r_est2) <- c("STATE", "PERCENT 4:3:1:3:3:1:4 UTD", "STANDARD ERROR UTD")

prn(r_est2, '4:3:1:3:3:1:4 ESTIMATES BY STATE')

###################### 

title <- "R_PROG_3.R" 

###################################################################### 

#THIS PROGRAM WILL PRODUCE A TABLE OF HAD_CPOX BY STATE FOR ALL RDD

#COMPLETES USING RDDWT_C. THE PROGRAM USES R.

# 

#R NOTES:

#1. R IS CASE SENSITIVE.

#2. A FILE PATH IS SEPERATED BY SLASH(/)

###################################################################### 

library(survey) #TO USE svydesign(), svymean(), and svyby()

library(Hmisc) #TO USE prn()

library(prettyR) #TO USE freq()

dd <- "c:/nispuf22" #"path-to-dataset"
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-Child Public-Use Data FileAPPENDIX D 

147

#--- NAME OF R DATASET ---# 

in.file <- paste(dd,"/NISPUF22.RData",sep="")

#---READ R DATASET---# 

load(in.file) 

#---FORMAT---# 

HAD_CPOXlevels=c(1,2,77,99) 

HAD_CPOXlabels=c("YES", "NO", "DON'T KNOW", "REFUSED")

STATElevels=c(1, 2, 4, 5, 6, 8, 9, 10, 11, 12, 13, 15, 16, 17,

18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,

36, 37, 38, 39, 40, 41, 42, 44, 45, 46, 47, 48, 49, 50, 51, 53,

54, 55, 56, 72) 

STATElabels=c(

"ALABAMA",

"ALASKA",

"ARIZONA",

"ARKANSAS",

"CALIFORNIA",

"COLORADO",

"CONNECTICUT",

"DELAWARE",

"DISTRICT OF COLUMBIA",

"FLORIDA",

"GEORGIA",

"HAWAII",

"IDAHO",

"ILLINOIS", 

"INDIANA",

"IOWA",

"KANSAS",

"KENTUCKY",

"LOUISIANA",

"MAINE",

"MARYLAND",

"MASSACHUSETTS",

"MICHIGAN",

"MINNESOTA",

"MISSISSIPPI",

"MISSOURI",

"MONTANA",

"NEBRASKA",

"NEVADA",

"NEW HAMPSHIRE",

"NEW JERSEY",

"NEW MEXICO",

"NEW YORK",

"NORTH CAROLINA",

"NORTH DAKOTA",

"OHIO",

"OKLAHOMA",

"OREGON",

"PENNSYLVANIA",

"RHODE ISLAND",

"SOUTH CAROLINA",

"SOUTH DAKOTA",

"TENNESSEE",

"TEXAS",

"UTAH",

"VERMONT",

"VIRGINIA", 

"WASHINGTON",

"WEST VIRGINIA",

"WISCONSIN",

"WYOMING", "PUERTO RICO" 
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148

) 

#---RDDWT_C WILL BE USED AS A WEIGHT (RDDWT_C IS THE SINGLE-FRAME CELLULAR PHONE WEIGHT EXCLUDING 

TERRITORIES; USE RDDWT_C_TERR TO INCLUDE TERRITORIES)---# 

#---STRATUM WILL BE USED AS A STRATUM VARIABLE FOR VARIANCE ESTIMATION ---# 

R_FILE <- subset(NISPUF22, select=c(SEQNUMHH, SEQNUMC, STATE,

HAD_CPOX, RDDWT_C, STRATUM))

names(R_FILE) <- c("SEQNUMHH", "SEQNUMC", "STATE", "HAD_CPOX",

"WT", "STRATUM")

#---ASSIGN LABELS---# 

R_FILE$HAD_CPOX <- factor(R_FILE$HAD_CPOX, levels=HAD_CPOXlevels,

labels=HAD_CPOXlabels)

R_FILE$STATE <- factor(R_FILE$STATE, levels=STATElevels,

labels=STATElabels)

R_FILE <- na.omit(R_FILE) 

summary(R_FILE$HAD_CPOX) 

#---SPECIFY A SAMPLING DESIGN AND FORCE WT AS NUMERIC---# 

svydsg <- svydesign(id=~SEQNUMHH, strata=~STRATUM, weights=~(as.numeric(WT)),

data=R_FILE) 

#---U.S. TOTAL ESTIMATES AND STANDARD ERRORS---# 

r_nation <- svymean(~HAD_CPOX, svydsg)

PERCENT_UTD <- round(r_nation*100,2) #CONVERT INTO PERCENT ESTIMATES(MEAN)

SE_UTD <- round(SE(r_nation)*100,2) #CONVERT INTO PERCENT ESTIMATES(SE)

r_nation_est3 <- cbind(PERCENT_UTD, SE_UTD)

prn(r_nation_est3, "PERCENT HAD_CPOX = YES ESTIMATES AT A NATIONAL

LEVEL\n")

#---HAD_CPOX = YES ESTIMATES BY STATE---# 

r_est3 <- svyby(~HAD_CPOX, ~STATE, svydsg, svymean)

r_est3[,-c(1)] <- round(r_est3[,-c(1)]*100,2) #CONVERT INTO PERCENT ESTIMATES

r_est3 <- subset(r_est3, select=c(1,2,6)) #SELECT ESTIMATES FOR HAD_CPOX=YES

names(r_est3) <- c("STATE", "PERCENT HAD_CPOX=YES", "STANDARD ERROR

HAD_CPOX=Y")

prn(r_est3, 'PERCENT HAD_CPOX ESTIMATES BY STATE')

###################### 

title <- "PROG_4.R" 

######################################################################### 

#TABLE OF P_UTD431H314_ROUT_S BY INCPOV1 BY RACE_K. SAVE % UTD

#ESTIMATES (NOT S.E.'S) FOR USE IN THE PROGRAM GRAPH_4.

# 

#THIS PROGRAM WILL PRODUCE ESTIMATES USING R.

# 

#R NOTES:

#1. R IS CASE SENSITIVE.

#2. A FILE PATH IS SEPERATED BY SLASH(/)

######################################################################### 

library(survey) #TO USE svydesign(), svymean(), and svyby()

library(Hmisc) #TO USE prn()

dd <- "c:/nispuf22" #"path-to-dataset"

out <-"c:/nispuf22" #"path-to-output" 

#--- NAME OF R DATASET ---# 

in.file <- paste(dd,"/NISPUF22.RData",sep="")

#---READ R DATASET---# 

load(in.file) 

#---FORMAT---# 
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149

UTD431H314levels=c(0,1)

UTD431H314labels=c("NOT 4:3:1:3:3:1:4 UTD", "4:3:1:3:3:1:4 UTD")

RACE_PUFlevels=c(1,2,3) 

RACE_PUFlabels=c("WHITE ONLY", "BLACK ONLY", "OTHER + MULTIPLE RACE")

INCPOVlevels=c(1,2,3,4) 

INCPOVlabels=c("ABOVE POVERTY, > $75K", "ABOVE POVERTY, <= $75K", "BELOW POVERTY", "UNKNOWN")

#---PROVWT_C WILL BE USED AS A WEIGHT (PROVWT_C IS THE SINGLE-FRAME CELLULAR PHONE WEIGHT EXCLUDING 

TERRITORIES; USE PROVWT_C_TERR TO INCLUDE TERRITORIES)---# 

#---STRATUM WILL BE USED AS A STRATUM VARIABLE FOR VARIANCE ESTIMATION ---# 

R_FILE <- subset(NISPUF22, select=c(SEQNUMHH, SEQNUMC, P_UTD431H314_ROUT_S, RACE_K, INCPOV1, PROVWT_C, STRATUM))

names(R_FILE) <- c("SEQNUMHH", "SEQNUMC", "P_UTD431H314_ROUT_S", "RACE_K", "INCPOV1", "WT", "STRATUM")

#---ASSIGN LABELS---# 

R_FILE$P_UTD431H314_ROUT_S <- factor(R_FILE$P_UTD431H314_ROUT_S, levels=UTD431H314levels, labels=UTD431H314labels, 

exclude=NULL)

R_FILE$RACE_K <- factor(R_FILE$RACE_K, levels=RACE_PUFlevels, labels=RACE_PUFlabels, exclude=NULL)

R_FILE$INCPOV1 <- factor(R_FILE$INCPOV1, levels=INCPOVlevels,labels=INCPOVlabels, exclude=NULL)

#---UNWEIGHTED FREQUENCIES---# 

unwt_freq <- function(UNWT.VAR){#FUNCTION TO PRINT UNWEIGHTED FREQUENCIES

unwt.tab <- wtd.table(UNWT.VAR, weights= NULL, type='table')

unwtd.freq <- data.frame(cbind( 

unwt.tab, round(unwt.tab/sum(unwt.tab)*100,2),

cumsum(unwt.tab), cumsum(round(unwt.tab/sum(unwt.tab)*100,2))))

names(unwtd.freq) <- c("Frequency", "Percent", "Cumulative Frequency", "Cumulative Percent")

unwtd.title <- paste('Table 4A. Q1/2022 - Q4/2022', 'UNWEIGHTED FREQUENCIES', label(UNWT.VAR), sep="\n")

label(unwtd.freq) <- unwtd.title 

print(unwtd.freq) 

} 

unwt_freq(R_FILE$P_UTD431H314_ROUT_S) 

unwt_freq(R_FILE$INCPOV1) 

unwt_freq(R_FILE$RACE_K) 

R_FILE <- na.omit(R_FILE) 

#---SPECIFY A SAMPLING DESIGN AND FORCE WT AS NUMERIC---# 

svydsg <- svydesign(id=~SEQNUMHH, strata=~STRATUM, weights=~(as.numeric(WT)),

data=R_FILE) 

#---PERCENT 4:3:1:3:3:1:4 UP-TO-DATE AND ESTIMATED STANDARD ERRORS---# 

r_est4 <- svyby(~P_UTD431H314_ROUT_S, ~RACE_K+INCPOV1, svydsg, svymean)

r_est4[,-c(1,2)] <- round(r_est4[,-c(1,2)]*100,2) #CONVERT INTO PERCENT ESTIMATES

r_est4 <- subset(r_est4, select=c(1,2,4,6)) #SELECT ESTIMATES FOR UP-TODATE CASES

names(r_est4) <- c("RACE", "INCOME", "PERCENT_UTD", "STANDARD_ERROR_UTD")

title <- "Table 4B. Q1/2022 - Q4/2022, Percent 4:3:1:3:3:1:4 UTD and Estimated Standard Errors"

prn(r_est4, title)

#---SAVE PERCENT UP-TO-DATE ESTIMATES FOR USE IN THE PROGRAM GRAPH_4---# 

r_est4 <- subset(r_est4, select=c(RACE, INCOME, PERCENT_UTD))

save(r_est4, file=paste(out, "/r_est4", sep=""))

###################### 

title <- "GRAPH_4.R" 

######################################################################### 

#THIS PROGRAM BUILDS OFF OF THE PROGRAM PROG_4. IT PRODUCES A CHART OF

#P_UTD431H314_ROUT_S BY INCPOV1 BY RACE_K. IT CREATES A BAR CHART IN R GRAPH FOR

#THE 4X3 = 12 CELLS.

#R NOTES:

#1. R IS CASE SENSITIVE.

#2. A FILE PATH IS SEPERATED BY SLASH(/)

######################################################################### 

library(survey) #TO USE svydesign(), svymean(), and svyby()

library(Hmisc) #TO USE prn()

dd <- "c:/nispuf22" #---SPECIFY PATH TO R DATASET THAT WAS THE OUTPUT OF R_PROG_4---# 

out <- "c:/nispuf22" #---SPECIFY THE PATH FOR WHERE YOU WANT THE CHART OUTPUT TO GO---# 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File APPENDIX D 

150

#---NAME OF R DATASET OUTPUT FROM R_PROG_4---# 

in.file <- paste(dd,"/r_est4",sep="")

#---READ R DATASET---# 

load(in.file) 

#---BARCHART---# 

#NOTE:R DOES NOT SUPPORT CREATING A HTML FILE CONTAINING A BARCHART#

#CREATE A DATA MATRIX FOR DRAWING A BARCHART#

utd431H314 <- matrix(r_est4$PERCENT_UTD, nrow=3, ncol=4, byrow=F, dimnames=list(levels(r_est4$RACE), levels(r_est4$INCOME)))

#CREATE GRAPH_4_R # 

png(file=paste(out,"/GRAPH_4_R.png",sep=""), width=1000, height=700)

barplot(utd431H314, beside=TRUE, space=c(0.2,1),

 col = c("wheat", "lightpink2", "forestgreen"),

 axis.lty = 1,

 sub="(Graph 4 using 'R')", cex.sub=1, ylim=c(0,120),

 xlab="Poverty Status",

 ylab="4:3:1:3:3:1:4 Up-To-Date (%)", cex=1.1, cex.names=1, cex.lab=1.5, border=NA)

legend("top", rownames(utd431H314), col=c("wheat", "lightpink2",

 "forestgreen"), title="Race of Child", pch=10, cex=1.5)

title1 <- "Percentage of Children Up-to-Date with Vaccine Series 4:3:1:3:3:1:4 \n" 

title2 <- "by Race and Poverty Status, National Immunization Survey - Child, 2022\n" 

mtext(paste(title1,title2), cex=1.5)

dev.off() 
Data User’s Guide for the 2022 NIS

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151
Data User’s Guide for the 2022 NIS-Child Public-Use Data File APPENDIX E 

152

Appendix E: Alphabetical Listing of Variables that are in the 2004-2022 Public-Use Data 

Files

Table E.1 Alphabetical Listing of Variables that are in the 2004-2022 Public-Use Data Files*

Year of Data Collection

Variable Name Variable Label† 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Notes§

AGECPOXR AGE IN MONTHS AT CHICKEN POX DISEASE 

(RECODE) Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Replaced IAGECPXR starting 2005. 

This version is not imputed.

AGEGRP AGE CATEGORY OF CHILD (19-23, 24-29, 30-35 MO) 

(RECODE) Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

ALL4SHOT HH REPORT OF 4:3:1:3 UP-TO-DATE Y Y 

Dropped starting in 2006 because no 

longer possible to derive due to 

questionnaire change.

BF_ENDR DURATION OF BREASTFEEDING IN DAYS 

(TOPCODE) Y Y 

Dropped starting in 2006 because of 

question wording change. Replaced by 

BF_ENDR06.

BF_ENDR06 DURATION OF BREASTFEEDING IN DAYS 

(RECODE) Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Replaced BF_ENDR starting 2006.

BF_EXCLR DURATION OF EXCLUSIVE BREASTFEEDING IN 

DAYS (TOPCODE) Y Y 

Dropped starting in 2006 because of 

question wording change. Replaced by 

BF_EXCLR06.

BF_EXCLR06 DURATION OF EXCLUSIVE BREAST/FORMULA 

FEEDING IN DAYS (RECODE) Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Replaced BF_EXCLR starting 2006.

BF_FORMR06 AGE IN DAYS WHEN CHILD FIRST FED FORMULA 

(TOPCODE) Y Y 

Question CBF_03_X added starting 

2006. Replaced by BF_FORMR06 

starting 2008.

BF_FORMR08 AGE IN DAYS WHEN CHILD FIRST FED FORMULA 

(RECODE) Y Y Y Y Y Y Y Y Y Y Y Y 

Replaced BF_FORMR06 to add a 

"never fed formula" code. Replaced by 

BF_FORMR20 starting in 2020.

BF_FORMR20 AGE IN DAYS WHEN CHILD FIRST FED FORMULA 

(RECODE) Y Y Y Replaced BF_FORMR08 due to a change in the top-coding cutoff.

BFENDFL DURATION OF BREAST FEEDING EXCEEDS CHILD 

AGE IN DAYS, WITH BUFFER Y Y 

Dropped starting in 2006 because of 

question wording change. Replaced by 

BFENDFL06.

BFENDFL06 DURATION OF BREAST FEEDING EXCEEDS CHILD 

AGE IN DAYS, WITH BUFFER Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Replaced BFENDFL starting 2006.

BFEXCLFL DURATION OF EXCLUSIVE BREAST FEEDING 

EXCEEDS TOTAL BREASTFEEDING, WITH BUFFER Y Y 

Dropped starting in 2006 because 

question wording change does not 

allow it to be derived.

BFFORMFL06 AGE IN DAYS WHEN CHILD FIRST FED FORMULA 

EXCEEDS CHILD AGE IN DAYS, WITH BUFFER Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Question CBF_03_X added starting 

2006.

C_431 HH REPORT OF 4:3:1 UP-TO-DATE BY SHOT CARD 

USE Y Y 

Dropped starting in 2006 because no 

longer possible to derive due to 

questionnaire change.

C_4313 HH REPORT OF 4:3:1:3 UP-TO-DATE BY SHOT CARD 

USE Y Y 

Dropped starting in 2006 because no 

longer possible to derive due to 

questionnaire change.
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153

Variable Name Variable Label† 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Notes§

C_DTP HH REPORT OF 4+ DT-CONTAINING UP-TO-DATE 

BY SHOT CARD USE Y Y 

Dropped starting in 2006 because no 

longer possible to derive due to 

questionnaire change.

C_HEP HH REPORT OF 3+ HEPATITIS B-CONTAINING UPTO-DATE BY SHOT CARD USE Y Y 

Dropped starting in 2006 because no 

longer possible to derive due to 

questionnaire change.

C_HIB HH REPORT OF 3+ HIB-CONTAINING UP-TO-DATE 

BY SHOT CARD USE Y Y 

Dropped starting in 2006 because no 

longer possible to derive due to 

questionnaire change.

C_MMR HH REPORT OF 1+ MEASLES-CONTAINING UP-TODATE BY SHOT CARD USE Y Y 

Dropped starting in 2006 because no 

longer possible to derive due to 

questionnaire change.

C_POL HH REPORT OF 3+ POLIO-CONTAINING UP-TODATE BY SHOT CARD USE Y Y 

Dropped starting in 2006 because no 

longer possible to derive due to 

questionnaire change.

C_VRC HH REPORT OF 1+ VARICELLA-CONTAINING UPTO-DATE BY SHOT CARD USE Y Y 

Dropped starting in 2006 because no 

longer possible to derive due to 

questionnaire change.

C1R NUMBER OF PEOPLE IN HOUSEHOLD (RECODE) Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

C5R RELATIONSHIP OF RESPONDENT TO CHILD 

(RECODE) Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

CBF_01 WAS CHILD EVER BREASTFED OR FED BREAST 

MILK? Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

CEN_REG CENSUS REGION BASED ON TRUE STATE OF 

RESIDENCE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

CHILDNM NUMBER OF CHILDREN LESS THAN 18 YEARS IN 

HH (RECODE) Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

CWIC_01 CHILD EVER RECEIVED WIC BENEFITS? Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

CWIC_02 CHILD CURRENTLY RECEIVING WIC BENEFITS? Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

D6R NUMBER OF VACCINATION PROVIDERS 

IDENTIFIED BY RESPONDENT (RECODE) Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

D7 CONSENT TO OBTAIN CHILD'S IMMUNIZATION 

RECORDS FROM PROVIDERS Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DDTP1 AGE IN DAYS OF PROV-REPTD DT-CONTAINING 

SHOT #1 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DDTP2 AGE IN DAYS OF PROV-REPTD DT-CONTAINING 

SHOT #2 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DDTP3 AGE IN DAYS OF PROV-REPTD DT-CONTAINING 

SHOT #3 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DDTP4 AGE IN DAYS OF PROV-REPTD DT-CONTAINING 

SHOT #4 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DDTP5 AGE IN DAYS OF PROV-REPTD DT-CONTAINING 

SHOT #5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DDTP6 AGE IN DAYS OF PROV-REPTD DT-CONTAINING 

SHOT #6 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DDTP7 AGE IN DAYS OF PROV-REPTD DT-CONTAINING 

SHOT #7 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DDTP8 AGE IN DAYS OF PROV-REPTD DT-CONTAINING 

SHOT #8 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 
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Variable Name Variable Label† 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Notes§

DDTP9 AGE IN DAYS OF PROV-REPTD DT-CONTAINING 

SHOT #9 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

DFLU1 AGE IN DAYS OF PROV-REPTD SEASONAL FLUCONTAINING VACCINATION #1 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DFLU2 AGE IN DAYS OF PROV-REPTD SEASONAL FLUCONTAINING VACCINATION #2 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DFLU3 AGE IN DAYS OF PROV-REPTD SEASONAL FLUCONTAINING VACCINATION #3 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DFLU4 AGE IN DAYS OF PROV-REPTD SEASONAL FLUCONTAINING VACCINATION #4 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DFLU5 AGE IN DAYS OF PROV-REPTD SEASONAL FLUCONTAINING VACCINATION #5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DFLU6 AGE IN DAYS OF PROV-REPTD SEASONAL FLUCONTAINING VACCINATION #6 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DFLU7 AGE IN DAYS OF PROV-REPTD SEASONAL FLUCONTAINING VACCINATION #7 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DFLU8 AGE IN DAYS OF PROV-REPTD SEASONAL FLUCONTAINING VACCINATION #8 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DFLU9 AGE IN DAYS OF PROV-REPTD SEASONAL FLUCONTAINING VACCINATION #9 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

DH1N1 AGE IN DAYS OF PROV-REPTD MONOVALENT 2009 

H1N1 INFLUENZA VACCINATION #1 Y Y Y 

H1N1 influenza added to the IHQ 

shotgrid starting in late 2009, and 

introduced in the PUF in 2010. 

Removed from the IHQ and the PUF 

in 2013.

DH1N2 AGE IN DAYS OF PROV-REPTD MONOVALENT 2009 

H1N1 INFLUENZA VACCINATION #2 Y Y Y 

H1N1 influenza added to the IHQ 

shotgrid starting in late 2009, and

introduced in the PUF in 2010. 

Removed from the IHQ and the PUF 

in 2013.

DH1N3 AGE IN DAYS OF PROV-REPTD MONOVALENT 2009 

H1N1 INFLUENZA VACCINATION #3 Y Y Y 

H1N1 influenza added to the IHQ 

shotgrid starting in late 2009, and 

introduced in the PUF in 2010. 

Removed from the IHQ and the PUF 

in 2013.

DH1N4 AGE IN DAYS OF PROV-REPTD MONOVALENT 2009 

H1N1 INFLUENZA VACCINATION #4 Y Y Y 

H1N1 influenza added to the IHQ 

shotgrid starting in late 2009, and

introduced in the PUF in 2010. 

Removed from the IHQ and the PUF 

in 2013.

DH1N5 AGE IN DAYS OF PROV-REPTD MONOVALENT 2009 

H1N1 INFLUENZA VACCINATION #5 Y Y Y 

H1N1 influenza added to the IHQ 

shotgrid starting in late 2009, and 

introduced in the PUF in 2010. 

Removed from the IHQ and the PUF 

in 2013.

DH1N6 AGE IN DAYS OF PROV-REPTD MONOVALENT 2009 

H1N1 INFLUENZA VACCINATION #6 Y Y Y 

H1N1 influenza added to the IHQ 

shotgrid starting in late 2009, and

introduced in the PUF in 2010. 

Removed from the IHQ and the PUF 

in 2013.
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Variable Name Variable Label† 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Notes§

DH1N7 AGE IN DAYS OF PROV-REPTD MONOVALENT 2009 

H1N1 INFLUENZA VACCINATION #7 Y Y Y 

H1N1 influenza added to the IHQ 

shotgrid starting in late 2009, and 

introduced in the PUF in 2010. 

Removed from the IHQ and the PUF 

in 2013.

DH1N8 AGE IN DAYS OF PROV-REPTD MONOVALENT 2009 

H1N1 INFLUENZA VACCINATION #8 Y Y Y 

H1N1 influenza added to the IHQ 

shotgrid starting in late 2009, and

introduced in the PUF in 2010. 

Removed from the IHQ and the PUF 

in 2013.

DH1N9 AGE IN DAYS OF PROV-REPTD MONOVALENT 2009 

H1N1 INFLUENZA VACCINATION #9 Y Y Y 

H1N1 influenza added to the IHQ 

shotgrid starting in late 2009, and 

introduced in the PUF in 2010. 

Removed from the IHQ and the PUF 

in 2013.

DHEPA1 AGE IN DAYS OF PROV-REPTD HEPATITIS ACONTAINING SHOT #1 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DHEPA2 AGE IN DAYS OF PROV-REPTD HEPATITIS ACONTAINING SHOT #2 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DHEPA3 AGE IN DAYS OF PROV-REPTD HEPATITIS ACONTAINING SHOT #3 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DHEPA4 AGE IN DAYS OF PROV-REPTD HEPATITIS ACONTAINING SHOT #4 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DHEPA5 AGE IN DAYS OF PROV-REPTD HEPATITIS ACONTAINING SHOT #5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DHEPA6 AGE IN DAYS OF PROV-REPTD HEPATITIS ACONTAINING SHOT #6 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DHEPA7 AGE IN DAYS OF PROV-REPTD HEPATITIS ACONTAINING SHOT #7 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DHEPA8 AGE IN DAYS OF PROV-REPTD HEPATITIS ACONTAINING SHOT #8 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DHEPA9 AGE IN DAYS OF PROV-REPTD HEPATITIS ACONTAINING SHOT #9 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

DHEPB1 AGE IN DAYS OF PROV-REPTD HEPATITIS BCONTAINING SHOT #1 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DHEPB2 AGE IN DAYS OF PROV-REPTD HEPATITIS BCONTAINING SHOT #2 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DHEPB3 AGE IN DAYS OF PROV-REPTD HEPATITIS BCONTAINING SHOT #3 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DHEPB4 AGE IN DAYS OF PROV-REPTD HEPATITIS BCONTAINING SHOT #4 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DHEPB5 AGE IN DAYS OF PROV-REPTD HEPATITIS BCONTAINING SHOT #5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DHEPB6 AGE IN DAYS OF PROV-REPTD HEPATITIS BCONTAINING SHOT #6 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DHEPB7 AGE IN DAYS OF PROV-REPTD HEPATITIS BCONTAINING SHOT #7 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DHEPB8 AGE IN DAYS OF PROV-REPTD HEPATITIS BCONTAINING SHOT #8 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 
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Variable Name Variable Label† 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Notes§

DHEPB9 AGE IN DAYS OF PROV-REPTD HEPATITIS BCONTAINING SHOT #9 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

DHIB1 AGE IN DAYS OF PROV-REPTD HIB-CONTAINING 

SHOT #1 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DHIB2 AGE IN DAYS OF PROV-REPTD HIB-CONTAINING 

SHOT #2 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DHIB3 AGE IN DAYS OF PROV-REPTD HIB-CONTAINING 

SHOT #3 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DHIB4 AGE IN DAYS OF PROV-REPTD HIB-CONTAINING 

SHOT #4 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DHIB5 AGE IN DAYS OF PROV-REPTD HIB-CONTAINING 

SHOT #5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DHIB6 AGE IN DAYS OF PROV-REPTD HIB-CONTAINING 

SHOT #6 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DHIB7 AGE IN DAYS OF PROV-REPTD HIB-CONTAINING 

SHOT #7 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DHIB8 AGE IN DAYS OF PROV-REPTD HIB-CONTAINING 

SHOT #8 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DHIB9 AGE IN DAYS OF PROV-REPTD HIB-CONTAINING 

SHOT #9 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

DISPCODE NIS PROVIDER RECORD-CHECK DISPOSITION 

CODE Y Y Y Y Y Y Y Y 

Dropped starting in 2012 because no 

longer possible to derive due to 

questionnaire change

DMMR1 AGE IN DAYS OF PROV-REPTD MEASLESCONTAINING SHOT #1 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DMMR2 AGE IN DAYS OF PROV-REPTD MEASLESCONTAINING SHOT #2 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DMMR3 AGE IN DAYS OF PROV-REPTD MEASLESCONTAINING SHOT #3 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DMMR4 AGE IN DAYS OF PROV-REPTD MEASLESCONTAINING SHOT #4 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DMMR5 AGE IN DAYS OF PROV-REPTD MEASLESCONTAINING SHOT #5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

DMMR6 AGE IN DAYS OF PROV-REPTD MEASLESCONTAINING SHOT #6 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

DMMR7 AGE IN DAYS OF PROV-REPTD MEASLESCONTAINING SHOT #7 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

DMMR8 AGE IN DAYS OF PROV-REPTD MEASLESCONTAINING SHOT #8 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

DMMR9 AGE IN DAYS OF PROV-REPTD MEASLESCONTAINING SHOT #9 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

DMP1 AGE IN DAYS OF PROV-REPTD MUMPS-ONLY SHOT 

#1 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 
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Variable Name Variable Label† 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Notes§

DMP2 AGE IN DAYS OF PROV-REPTD MUMPS-ONLY SHOT 

#2 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DMP3 AGE IN DAYS OF PROV-REPTD MUMPS-ONLY SHOT 

#3 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DMP4 AGE IN DAYS OF PROV-REPTD MUMPS-ONLY SHOT 

#4 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DMP5 AGE IN DAYS OF PROV-REPTD MUMPS-ONLY SHOT 

#5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

DMP6 AGE IN DAYS OF PROV-REPTD MUMPS-ONLY SHOT 

#6 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

DMP7 AGE IN DAYS OF PROV-REPTD MUMPS-ONLY SHOT 

#7 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

DMP8 AGE IN DAYS OF PROV-REPTD MUMPS-ONLY SHOT 

#8 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

DMP9 AGE IN DAYS OF PROV-REPTD MUMPS-ONLY SHOT 

#9 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

DMPRB1 AGE IN DAYS OF PROV-REPTD (MUMPS/RUBELLA)-

ONLY SHOT #1 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DMPRB2 AGE IN DAYS OF PROV-REPTD (MUMPS/RUBELLA)-

ONLY SHOT #2 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DMPRB3 AGE IN DAYS OF PROV-REPTD (MUMPS/RUBELLA)-

ONLY SHOT #3 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DMPRB4 AGE IN DAYS OF PROV-REPTD (MUMPS/RUBELLA)-

ONLY SHOT #4 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DMPRB5 AGE IN DAYS OF PROV-REPTD (MUMPS/RUBELLA)-

ONLY SHOT #5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

DMPRB6 AGE IN DAYS OF PROV-REPTD (MUMPS/RUBELLA)-

ONLY SHOT #6 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

DMPRB7 AGE IN DAYS OF PROV-REPTD (MUMPS/RUBELLA)-

ONLY SHOT #7 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

DMPRB8 AGE IN DAYS OF PROV-REPTD (MUMPS/RUBELLA)-

ONLY SHOT #8 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

DMPRB9 AGE IN DAYS OF PROV-REPTD (MUMPS/RUBELLA)-

ONLY SHOT #9 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

DPCV1 AGE IN DAYS OF PROV-REPTD PNEUMOCOCCALCONTAINING SHOT #1 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DPCV2 AGE IN DAYS OF PROV-REPTD PNEUMOCOCCALCONTAINING SHOT #2 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DPCV3 AGE IN DAYS OF PROV-REPTD PNEUMOCOCCALCONTAINING SHOT #3 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 
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Variable Name Variable Label† 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Notes§

DPCV4 AGE IN DAYS OF PROV-REPTD PNEUMOCOCCALCONTAINING SHOT #4 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DPCV5 AGE IN DAYS OF PROV-REPTD PNEUMOCOCCALCONTAINING SHOT #5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DPCV6 AGE IN DAYS OF PROV-REPTD PNEUMOCOCCALCONTAINING SHOT #6 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DPCV7 AGE IN DAYS OF PROV-REPTD PNEUMOCOCCALCONTAINING SHOT #7 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DPCV8 AGE IN DAYS OF PROV-REPTD PNEUMOCOCCALCONTAINING SHOT #8 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DPCV9 AGE IN DAYS OF PROV-REPTD PNEUMOCOCCALCONTAINING SHOT #9 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

DPOLIO1 AGE IN DAYS OF PROV-REPTD POLIO-CONTAINING 

SHOT #1 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DPOLIO2 AGE IN DAYS OF PROV-REPTD POLIO-CONTAINING 

SHOT #2 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DPOLIO3 AGE IN DAYS OF PROV-REPTD POLIO-CONTAINING 

SHOT #3 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DPOLIO4 AGE IN DAYS OF PROV-REPTD POLIO-CONTAINING 

SHOT #4 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DPOLIO5 AGE IN DAYS OF PROV-REPTD POLIO-CONTAINING 

SHOT #5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DPOLIO6 AGE IN DAYS OF PROV-REPTD POLIO-CONTAINING 

SHOT #6 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DPOLIO7 AGE IN DAYS OF PROV-REPTD POLIO-CONTAINING 

SHOT #7 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DPOLIO8 AGE IN DAYS OF PROV-REPTD POLIO-CONTAINING 

SHOT #8 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DPOLIO9 AGE IN DAYS OF PROV-REPTD POLIO-CONTAINING 

SHOT #9 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

DRB1 AGE IN DAYS OF PROV-REPTD RUBELLA-ONLY 

SHOT #1 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DRB2 AGE IN DAYS OF PROV-REPTD RUBELLA-ONLY 

SHOT #2 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DRB3 AGE IN DAYS OF PROV-REPTD RUBELLA-ONLY 

SHOT #3 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DRB4 AGE IN DAYS OF PROV-REPTD RUBELLA-ONLY 

SHOT #4 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DRB5 AGE IN DAYS OF PROV-REPTD RUBELLA-ONLY 

SHOT #5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DRB6 AGE IN DAYS OF PROV-REPTD RUBELLA-ONLY 

SHOT #6 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DRB7 AGE IN DAYS OF PROV-REPTD RUBELLA-ONLY 

SHOT #7 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DRB8 AGE IN DAYS OF PROV-REPTD RUBELLA-ONLY 

SHOT #8 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 
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Variable Name Variable Label† 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Notes§

DRB9 AGE IN DAYS OF PROV-REPTD RUBELLA-ONLY 

SHOT #9 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

DROT1 AGE IN DAYS OF PROV-REPTD ROTAVIRUSCONTAINING SHOT #1 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DROT2 AGE IN DAYS OF PROV-REPTD ROTAVIRUSCONTAINING SHOT #2 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DROT3 AGE IN DAYS OF PROV-REPTD ROTAVIRUSCONTAINING SHOT #3 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DROT4 AGE IN DAYS OF PROV-REPTD ROTAVIRUSCONTAINING SHOT #4 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DROT5 AGE IN DAYS OF PROV-REPTD ROTAVIRUSCONTAINING SHOT #5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DROT6 AGE IN DAYS OF PROV-REPTD ROTAVIRUSCONTAINING SHOT #6 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DROT7 AGE IN DAYS OF PROV-REPTD ROTAVIRUSCONTAINING SHOT #7 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DROT8 AGE IN DAYS OF PROV-REPTD ROTAVIRUSCONTAINING SHOT #8 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DROT9 AGE IN DAYS OF PROV-REPTD ROTAVIRUSCONTAINING SHOT #9 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

DTP_SOUR SHOT CARD USED FOR DTP REPORTING Y 

Dropped starting in 2005 because this 

variable is redundant with variable 

SHOTCARD.

DTP1_AGE AGE IN MONTHS OF PROV-REPTD DT-CONTAINING 

SHOT #1 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DTP2_AGE AGE IN MONTHS OF PROV-REPTD DT-CONTAINING 

SHOT #2 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DTP3_AGE AGE IN MONTHS OF PROV-REPTD DT-CONTAINING 

SHOT #3 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DTP4_AGE AGE IN MONTHS OF PROV-REPTD DT-CONTAINING 

SHOT #4 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DTP5_AGE AGE IN MONTHS OF PROV-REPTD DT-CONTAINING 

SHOT #5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DTP6_AGE AGE IN MONTHS OF PROV-REPTD DT-CONTAINING 

SHOT #6 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DTP7_AGE AGE IN MONTHS OF PROV-REPTD DT-CONTAINING 

SHOT #7 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DTP8_AGE AGE IN MONTHS OF PROV-REPTD DT-CONTAINING 

SHOT #8 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DTP9_AGE AGE IN MONTHS OF PROV-REPTD DT-CONTAINING 

SHOT #9 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

DVRC1 AGE IN DAYS OF PROV-REPTD VARICELLACONTAINING SHOT #1 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DVRC2 AGE IN DAYS OF PROV-REPTD VARICELLACONTAINING SHOT #2 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 
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160

Variable Name Variable Label† 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Notes§

DVRC3 AGE IN DAYS OF PROV-REPTD VARICELLACONTAINING SHOT #3 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DVRC4 AGE IN DAYS OF PROV-REPTD VARICELLACONTAINING SHOT #4 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

DVRC5 AGE IN DAYS OF PROV-REPTD VARICELLACONTAINING SHOT #5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

DVRC6 AGE IN DAYS OF PROV-REPTD VARICELLACONTAINING SHOT #6 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

DVRC7 AGE IN DAYS OF PROV-REPTD VARICELLACONTAINING SHOT #7 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

DVRC8 AGE IN DAYS OF PROV-REPTD VARICELLACONTAINING SHOT #8 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

DVRC9 AGE IN DAYS OF PROV-REPTD VARICELLACONTAINING SHOT #9 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

EDUC1 EDUCATION OF MOTHER CATEGORIES: IMPUTED 

(RECODE) Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

ENTRY2

CHILD LIVES IN STATE WITH HEPATITIS B STATE 

ENTRY LAW FOR DAY CARE/HEAD START (2001-

2002 SCHOOL YEAR)

Y Dropped starting in 2005.

EST_GRANT AREA OF RESIDENCE ACCORDING TO THE 56 

ORIGINAL CORE GRANTEE AREAS Y Y Y Y Y Y Y Y Y Y Y Added in 2012 to facilitate production of estimates for the 56 core areas.

ESTIAP ESTIMATION IAP AREA OF RESIDENCE Y 

Replaced ITRUEIAP in 2005 because 

estimation areas were modified. 

Replaced by ESTIAP06 in 2006.

ESTIAP06 ESTIMATION IAP AREA OF RESIDENCE Y 

Replaced ESTIAP in 2006 because 

estimation areas were modified. 

Replaced by ESTIAP07 in 2007.

ESTIAP07 ESTIMATION AREA OF RESIDENCE Y 

Replaced ESTIAP06 in 2007 because 

estimation areas were modified. 

Replaced by ESTIAP08 in 2008.

ESTIAP08 ESTIMATION AREA OF RESIDENCE Y 

Replaced ESTIAP07 in 2008 because 

estimation areas were modified. 

Replaced by ESTIAP09 in 2009.

ESTIAP09 ESTIMATION AREA OF RESIDENCE Y 

Replaced ESTIAP08 in 2009 because 

estimation areas were modified. 

Replaced by ESTIAP10 in 2010.

ESTIAP10 ESTIMATION AREA OF RESIDENCE Y 

Replaced ESTIAP09 in 2010 because 

estimation areas were modified. 

Replaced by ESTIAP11 in 2011.

ESTIAP11 ESTIMATION AREA OF RESIDENCE Y 

Replaced ESTIAP10 in 2011 because 

estimation areas were modified. 

Replaced by ESTIAP12 in 2012.

ESTIAP12 ESTIMATION AREA OF RESIDENCE Y 

Replaced ESTIAP11 in 2012 because 

estimation areas were modified. 

Replaced by ESTIAP13 in 2013.
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Variable Name Variable Label† 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Notes§

ESTIAP13 ESTIMATION AREA OF RESIDENCE Y 

Replaced ESTIAP12 in 2013 because 

estimation areas were modified. 

Replaced by ESTIAP14 in 2014.

ESTIAP14 ESTIMATION AREA OF RESIDENCE Y 

Replaced ESTIAP13 in 2014 because 

estimation areas were modified. 

Replaced by ESTIAP15 in 2015.

ESTIAP15 ESTIMATION AREA OF RESIDENCE Y 

Replaced ESTIAP14 in 2015 because 

estimation areas were modified. 

Replaced by ESTIAP16 in 2016.

ESTIAP16 ESTIMATION AREA OF RESIDENCE Y 

Replaced ESTIAP15 in 2016 because 

estimation areas were modified. 

Replaced by ESTIAP17 in 2017.

ESTIAP17 ESTIMATION AREA OF RESIDENCE Y 

Replaced ESTIAP16 in 2017 because 

estimation areas were modified. 

Replaced by ESTIAP18 in 2018.

ESTIAP18 ESTIMATION AREA OF RESIDENCE Y 

Replaced ESTIAP17 in 2018 because 

estimation areas were modified. 

Replaced by ESTIAP19 in 2019. 

ESTIAP19 ESTIMATION AREA OF RESIDENCE Y 

Replaced ESTIAP18 in 2019 because 

estimation areas were modified. 

Replaced by ESTIAP20 in 2020.

ESTIAP20 ESTIMATION AREA OF RESIDENCE Y 

Replaced ESTIAP19 in 2020 because 

estimation areas were modified.

Replaced by ESTIAP21 in 2021.

ESTIAP21 ESTIMATION AREA OF RESIDENCE Y 

Replaced ESTIAP20 in 2021 

because estimation areas were 

modified. Replaced by ESTIAP22 in 

2022.

ESTIAP22 ESTIMATION AREA OF RESIDENCE Y 

Replaced ESTIAP21 in 2022 

because estimation areas were 

modified.

FLU1_AGE AGE IN MONTHS OF PROV-REPTD SEASONAL FLUCONTAINING VACCINATION #1 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

FLU2_AGE AGE IN MONTHS OF PROV-REPTD SEASONAL FLUCONTAINING VACCINATION #2 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

FLU3_AGE AGE IN MONTHS OF PROV-REPTD SEASONAL FLUCONTAINING VACCINATION #3 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

FLU4_AGE AGE IN MONTHS OF PROV-REPTD SEASONAL FLUCONTAINING VACCINATION #4 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

FLU5_AGE AGE IN MONTHS OF PROV-REPTD SEASONAL FLUCONTAINING VACCINATION #5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

FLU6_AGE AGE IN MONTHS OF PROV-REPTD SEASONAL FLUCONTAINING VACCINATION #6 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

FLU7_AGE AGE IN MONTHS OF PROV-REPTD SEASONAL FLUCONTAINING VACCINATION #7 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

FLU8_AGE AGE IN MONTHS OF PROV-REPTD SEASONAL FLUCONTAINING VACCINATION #8 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

FLU9_AGE AGE IN MONTHS OF PROV-REPTD SEASONAL FLUCONTAINING VACCINATION #9 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

FRSTBRN FIRST BORN STATUS OF CHILD: IMPUTED Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 
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162

Variable Name Variable Label† 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Notes§

FUL2_MMR HOUSEHOLD REPORT OF 1+ MMR AT ANY AGE Y Replaced by FULL_MMR starting in 

2005.

FULL_CPO HH REPORT OF 1+ VARICELLA-CONTAINING SHOT 

AT ANY AGE Y Y 

Starting 2005, a code of 88 added for 

children with unknown UTD status. 

Dropped starting in 2006 because no 

longer possible to derive due to 

questionnaire change.

FULL_DTP HH REPORT OF 4+ DT-CONTAINING SHOT Y Y 

Starting 2005, a code of 88 added for 

children with unknown UTD status. 

Dropped starting in 2006 because no 

longer possible to derive due 

questionnaire change.

FULL_HEP HH REPORT OF 3+ HEPATITIS B-CONTAINING 

SHOTS Y Y 

Starting 2005, a code of 88 added for 

children with unknown UTD status. 

Dropped starting in 2006 because no 

longer possible to derive due to 

questionnaire change.

FULL_HIB HH REPORT OF 3+ HIB-CONTAINING SHOTS Y Y 

Starting 2005, a code of 88 added for 

children with unknown UTD status. 

Dropped starting in 2006 because no 

longer possible to derive due to 

questionnaire change.

FULL_MMR HH REPORT OF 1+ MEASLES-CONTAINING SHOT 

AT ANY AGE Y 

Replaced FUL2_MMR starting in 

2005. A code of 88 added for children 

with unknown UTD status. Dropped 

starting in 2006 because no longer 

possible to derive due to questionnaire 

change. 

FULL_POL HH REPORT OF 3+ POLIO-CONTAINING SHOTS Y Y 

Starting 2005, a code of 88 added for 

children with unknown UTD status. 

Dropped starting in 2006 because no 

longer possible to derive due to 

questionnaire change.

H1N1_AGE AGE IN MONTHS OF PROV-REPTD MONOVALENT 

2009 H1N1 INFLUENZA VACCINATION #1 Y Y Y 

H1N1 influenza added to the IHQ 

shotgrid starting in late 2009, and 

introduced in the PUF in 2010. 

Removed from the IHQ and the PUF 

in 2013.

H1N2_AGE AGE IN MONTHS OF PROV-REPTD MONOVALENT 

2009 H1N1 INFLUENZA VACCINATION #2 Y Y Y 

H1N1 influenza added to the IHQ 

shotgrid starting in late 2009, and 

introduced in the PUF in 2010. 

Removed from the IHQ and the PUF 

in 2013.

H1N3_AGE AGE IN MONTHS OF PROV-REPTD MONOVALENT 

2009 H1N1 INFLUENZA VACCINATION #3 Y Y Y 

H1N1 influenza added to the IHQ 

shotgrid starting in late 2009, and

introduced in the PUF in 2010. 

Removed from the IHQ and the PUF 

in 2013.

H1N4_AGE AGE IN MONTHS OF PROV-REPTD MONOVALENT 

2009 H1N1 INFLUENZA VACCINATION #4 Y Y Y 

H1N1 influenza added to the IHQ 

shotgrid starting in late 2009, and 

introduced in the PUF in 2010. 

Removed from the IHQ and the PUF 

in 2013.
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Variable Name Variable Label† 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Notes§

H1N5_AGE AGE IN MONTHS OF PROV-REPTD MONOVALENT 

2009 H1N1 INFLUENZA VACCINATION #5 Y Y Y 

H1N1 influenza added to the IHQ 

shotgrid starting in late 2009, and

introduced in the PUF in 2010. 

Removed from the IHQ and the PUF 

in 2013.

H1N6_AGE AGE IN MONTHS OF PROV-REPTD MONOVALENT 

2009 H1N1 INFLUENZA VACCINATION #6 Y Y Y 

H1N1 influenza added to the IHQ 

shotgrid starting in late 2009, and 

introduced in the PUF in 2010. 

Removed from the IHQ and the PUF 

in 2013.

H1N7_AGE AGE IN MONTHS OF PROV-REPTD MONOVALENT 

2009 H1N1 INFLUENZA VACCINATION #7 Y Y Y 

H1N1 influenza added to the IHQ 

shotgrid starting in late 2009, and

introduced in the PUF in 2010. 

Removed from the IHQ and the PUF 

in 2013.

H1N8_AGE AGE IN MONTHS OF PROV-REPTD MONOVALENT 

2009 H1N1 INFLUENZA VACCINATION #8 Y Y Y 

H1N1 influenza added to the IHQ 

shotgrid starting in late 2009, and 

introduced in the PUF in 2010. 

Removed from the IHQ and the PUF 

in 2013.

H1N9_AGE AGE IN MONTHS OF PROV-REPTD MONOVALENT 

2009 H1N1 INFLUENZA VACCINATION #9 Y Y Y 

H1N1 influenza added to the IHQ 

shotgrid starting in late 2009, and

introduced in the PUF in 2010. 

Removed from the IHQ and the PUF 

in 2013.

HAD_CPOX CHILD EVER HAD CHICKEN POX DISEASE? Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Replaced I_HADCPX starting in 

2005. This version is not imputed.

HEA1_AGE AGE IN MONTHS OF PROV-REPTD HEPATITIS ACONTAINING SHOT #1 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

HEA2_AGE AGE IN MONTHS OF PROV-REPTD HEPATITIS ACONTAINING SHOT #2 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

HEA3_AGE AGE IN MONTHS OF PROV-REPTD HEPATITIS ACONTAINING SHOT #3 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

HEA4_AGE AGE IN MONTHS OF PROV-REPTD HEPATITIS ACONTAINING SHOT #4 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

HEA5_AGE AGE IN MONTHS OF PROV-REPTD HEPATITIS ACONTAINING SHOT #5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

HEA6_AGE AGE IN MONTHS OF PROV-REPTD HEPATITIS ACONTAINING SHOT #6 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

HEA7_AGE AGE IN MONTHS OF PROV-REPTD HEPATITIS ACONTAINING SHOT #7 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

HEA8_AGE AGE IN MONTHS OF PROV-REPTD HEPATITIS ACONTAINING SHOT #8 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

HEA9_AGE AGE IN MONTHS OF PROV-REPTD HEPATITIS ACONTAINING SHOT #9 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

HEP_BRTH HEPATITIS B-CONTAINING SHOT GIVEN AT BIRTH 

FLAG Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

HEP_FLAG HEPATITIS B BIRTH SHOT DATE IMPUTATION 

FLAG Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 
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Variable Name Variable Label† 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Notes§

HEP1_AGE AGE IN MONTHS OF PROV-REPTD HEPATITIS BCONTAINING SHOT #1 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

HEP2_AGE AGE IN MONTHS OF PROV-REPTD HEPATITIS BCONTAINING SHOT #2 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

HEP3_AGE AGE IN MONTHS OF PROV-REPTD HEPATITIS BCONTAINING SHOT #3 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

HEP4_AGE AGE IN MONTHS OF PROV-REPTD HEPATITIS BCONTAINING SHOT #4 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

HEP5_AGE AGE IN MONTHS OF PROV-REPTD HEPATITIS BCONTAINING SHOT #5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

HEP6_AGE AGE IN MONTHS OF PROV-REPTD HEPATITIS BCONTAINING SHOT #6 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

HEP7_AGE AGE IN MONTHS OF PROV-REPTD HEPATITIS BCONTAINING SHOT #7 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

HEP8_AGE AGE IN MONTHS OF PROV-REPTD HEPATITIS BCONTAINING SHOT #8 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

HEP9_AGE AGE IN MONTHS OF PROV-REPTD HEPATITIS BCONTAINING SHOT #9 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

HH_DTP HH REPORT OF NUMBER OF DT-CONTAINING 

SHOTS RECEIVED Y Y Y Y Y Y 

Added in 2006 as a partial 

replacement for the "FULL" and "C_" 

variables. Dropped in 2012 due to 

questionnaire changes.

HH_FLU

HH REPORT OF NUMBER OF SEASONAL 

INFLUENZA VACCINATIONS RECEIVED IN THE 12 

MONTHS PRIOR TO INTERVIEW

Y Y Y 

Influenza questions added to the HH 

questionnaire starting in 2007. 

Dropped in 2009 due to mid-year 

questionnaire changes. Reinstated in 

2010. Dropped again in 2011 due to 

mid-year questionnaire changes.

HH_H1N

HH REPORT OF NUMBER OF MONOVALENT 2009 

H1N1 INFLUENZA VACCINATIONS RECEIVED IN 

THE 12 MONTHS PRIOR TO INTERVIEW

Y 

H1N1 influenza questions added to the 

HH questionnaire starting in 2009. 

Introduced in the PUF in 2010. 

Dropped in 2011 due to mid-year 

questionnaire changes.

HH_HEPB HH REPORT OF NUMBER OF HEPATITIS BCONTAINING SHOTS RECEIVED Y Y Y Y Y Y 

Added in 2006 as a partial 

replacement for the "FULL" and "C_" 

variables. Dropped in 2012 due to 

questionnaire changes.

HH_HIB HH REPORT OF NUMBER OF HIB-CONTAINING 

SHOTS RECEIVED Y Y Y Y Y Y 

Added in 2006 as a partial 

replacement for the "FULL" and "C_" 

variables. Dropped in 2012 due to 

questionnaire changes.

HH_MCV HH REPORT OF NUMBER OF MEASLESCONTAINING SHOTS RECEIVED Y Y Y Y Y Y 

Added in 2006 as a partial 

replacement for the "FULL" and "C_" 

variables. Dropped in 2012 due to 

questionnaire changes.

HH_POL HH REPORT OF NUMBER OF POLIO-CONTAINING 

SHOTS RECEIVED Y Y Y Y Y Y 

Added in 2006 as a partial 

replacement for the "FULL" and "C_" 

variables. Dropped in 2012 due to 

questionnaire changes.
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Variable Name Variable Label† 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Notes§

HH_VRC HH REPORT OF NUMBER OF VARICELLACONTAINING SHOTS RECEIVED Y Y Y Y Y Y 

Added in 2006 as a partial 

replacement for the "FULL" and "C_" 

variables. Dropped in 2012 due to 

questionnaire changes.

HIB1_AGE AGE IN MONTHS OF PROV-REPTD HIBCONTAINING SHOT #1 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

HIB2_AGE AGE IN MONTHS OF PROV-REPTD HIBCONTAINING SHOT #2 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

HIB3_AGE AGE IN MONTHS OF PROV-REPTD HIBCONTAINING SHOT #3 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

HIB4_AGE AGE IN MONTHS OF PROV-REPTD HIBCONTAINING SHOT #4 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

HIB5_AGE AGE IN MONTHS OF PROV-REPTD HIBCONTAINING SHOT #5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

HIB6_AGE AGE IN MONTHS OF PROV-REPTD HIBCONTAINING SHOT #6 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

HIB7_AGE AGE IN MONTHS OF PROV-REPTD HIBCONTAINING SHOT #7 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

HIB8_AGE AGE IN MONTHS OF PROV-REPTD HIBCONTAINING SHOT #8 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

HIB9_AGE AGE IN MONTHS OF PROV-REPTD HIBCONTAINING SHOT #9 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

HUTD4313 HOUSEHOLD REPORT OF 4:3:1:3 UTD (UP-TO-DATE) Y 

Dropped starting in 2005 because this 

variable is redundant with variable 

ALL4SHOT.

I_HADCPX DID CHILD EVER HAVE CHICKEN POX? Y Replaced by HAD_CPOX starting in 

2005.

I_HISP_K HISPANIC ORIGIN OF CHILD: IMPUTED Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

IAGECPXR AGE IN MONTHS WHEN CHILD HAD CHICKEN POX 

(RECODE) Y Replaced by AGECPOXR starting in 

2005.

INCPORAR INCOME TO POVERTY RATIO (RECODE) Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Replaced INCPORAT starting in 

2005. INCPORAT used categories 

whereas INCPORAR is continuous. 

INCPORAR has been top- and 

bottom-coded.

INCPORAR_I INCOME TO POVERTY RATIO: IMPUTED (RECODE) Y Y Y Y Y Y Y Imputed version of INCPORAR added 

in 2016.

INCPORAT INCOME TO POVERTY RATIO Y Replaced by INCPORAR starting in 

2005.

INCPOV1 POVERTY STATUS Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Replaced INCPOV1R starting in 

2005. INCPOV1R used two categories 

whereas INCPOV1 uses three.

INCPOV1R POVERTY STATUS (RECODE) Y Replaced by INCPOV1 starting in 

2005.

INCQ298A FAMILY INCOME CATEGORIES (RECODE) Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Replaced INCQ298R starting in 2005. 

INCQ298A uses different categories 

than were used by INCQ298R.

INCQ298R FAMILY INCOME CATEGORIES (RECODE) Y Replaced by INCQ298A starting in 

2005.
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Variable Name Variable Label† 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Notes§

INOPHONR LENGTH OF INTERRUPTION IN TELEPHONE 

SERVICE IN DAYS (RECODE) Y Y Y Y Y Y Dropped starting in 2010 due to 

questionnaire change.

INS_1 IS CHILD COVERED BY HEALTH INSURANCE 

PROVIDED THROUGH EMPLOYER OR UNION? Y Y Y Y Y Y Y Y Y 

Health insurance questions were added 

to the questionnaire in 2006 and first 

included on the PUF in 2007. 

Replaced with INS_STAT_I and 

INS_BREAK_I in 2016.

INS_11 ANY TIME WHEN CHILD WAS NOT COVERED BY 

ANY HEALTH INSURANCE? Y Y Y Y Y Y Y Y Y 

Health insurance questions were added 

to the questionnaire in 2006 and first 

included on the PUF in 2007. 

Replaced with INS_STAT_I and 

INS_BREAK_I in 2016.

INS_2 IS CHILD COVERED BY ANY MEDICAID PLAN? Y Y Y Y Y Y Y Y Y 

Health insurance questions were added 

to the questionnaire in 2006 and first 

included on the PUF in 2007. 

Replaced with INS_STAT_I and 

INS_BREAK_I in 2016.

INS_3 IS CHILD COVERED BY S-CHIP? Y Y Y Y Y Y Y Y Y 

Health insurance questions were added 

to the questionnaire in 2006 and first 

included on the PUF in 2007. 

Replaced with INS_STAT_I and 

INS_BREAK_I in 2016.

INS_3A IS CHILD COVERED BY ANY MEDICAID PLAN OR SCHIP? Y Y Y Y Y Y Y Y Y 

Health insurance questions were added 

to the questionnaire in 2006 and first 

included on the PUF in 2007. 

Replaced with INS_STAT_I and 

INS_BREAK_I in 2016.

INS_4 IS CHILD COVERED BY INDIAN HEALTH SERVICE? Y Y 

Health insurance questions were added 

to the questionnaire in 2006 and first 

included on the PUF in 2007. 

Replaced by INS_4_5 starting in 2009.

INS_4_5

IS CHILD COVERED BY INDIAN HEALTH SERVICE, 

MILITARY HEALTH CARE, TRICARE, CHAMPUS, OR 

CHAMP-VA?

Y Y Y Y Y Y Y 

Replaced INS_4 and INS_5 starting in 

2009. Replaced with INS_STAT_I 

and INS_BREAK_I in 2016.

INS_5 IS CHILD COVERED BY MILITARY HEALTH CARE, 

TRICARE, CHAMPUS, OR CHAMP-VA? Y Y 

Health insurance questions were added 

to the questionnaire in 2006 and first 

included on the PUF in 2007. 

Replaced by INS_4_5 starting in 2009.

INS_6 IS CHILD COVERED BY ANY OTHER HEALTH 

INSURANCE OR HEALTH CARE PLAN? Y Y Y Y Y Y Y Y Y 

Health insurance questions were added 

to the questionnaire in 2006 and first 

included on the PUF in 2007. 

Replaced with INS_STAT_I and 

INS_BREAK_I in 2016.

INS_BREAK_I CONTINUITY OF INSURANCE COVERAGE SINCE 

BIRTH: IMPUTED Y Y Y Y Y Y Y Replaced INS_1-INS_11 starting in 

2016.

INS_STAT_I INSURANCE STATUS: IMPUTED Y 

Replaced INS_1-INS_11 starting in 

2016. Replaced by INS_STAT2_I 

starting in 2017.

INS_STAT2_I

INSURANCE STATUS (PRIVATE ONLY/ANY 

MEDICAID/OTHER INSURANCE/UNINSURED): 

IMPUTED

Y Y Y Y Y Y Replaced INS_STAT_I starting in 

2017.

INTRP PHONE INTERRUPTION OF 7 DAYS OR MORE IN 

PAST YEAR? Y Y Y Y Y Y Y Y Y Y Y Y Dropped starting in 2016 due to 

questionnaire change.
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Variable Name Variable Label† 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Notes§

ITRUEIAP IAP AREA OF CURRENT RESIDENCE Y Replaced by ESTIAP in 2005 because 

estimation areas were modified.

LANGUAGE LANGUAGE IN WHICH INTERVIEW WAS 

CONDUCTED Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

M_AGEGRP AGE OF MOTHER CATEGORIES (RECODE) Y Y Y Y Y Y Y Y Y Y Y Y Replaced by M_AGEGRP2 starting in 

2016.

M_AGEGRP2 AGE OF MOTHER CATEGORIES: IMPUTED 

(RECODE) Y Y Y Y Y Y Y Replaced M_AGEGRP starting in 

2016.

MARITAL MARITAL STATUS OF MOTHER CATEGORIES 

(RECODE) Y Y Y Y Y Replaced by MARITAL2 starting in 

2009.

MARITAL2 MARITAL STATUS OF MOTHER: IMPUTED 

(RECODE) Y Y Y Y Y Y Y Y Y Y Y Y Y Y Replaced MARITAL starting in 2009.

MMR1_AGE AGE IN MONTHS OF PROV-REPTD MEASLESCONTAINING SHOT #1 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

MMR2_AGE AGE IN MONTHS OF PROV-REPTD MEASLESCONTAINING SHOT #2 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

MMR3_AGE AGE IN MONTHS OF PROV-REPTD MEASLESCONTAINING SHOT #3 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

MMR4_AGE AGE IN MONTHS OF PROV-REPTD MEASLESCONTAINING SHOT #4 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

MMR5_AGE AGE IN MONTHS OF PROV-REPTD MEASLESCONTAINING SHOT #5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

MMR6_AGE AGE IN MONTHS OF PROV-REPTD MEASLESCONTAINING SHOT #6 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

MMR7_AGE AGE IN MONTHS OF PROV-REPTD MEASLESCONTAINING SHOT #7 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

MMR8_AGE AGE IN MONTHS OF PROV-REPTD MEASLESCONTAINING SHOT #8 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

MMR9_AGE AGE IN MONTHS OF PROV-REPTD MEASLESCONTAINING SHOT #9 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

MOBIL 

GEOGRAPHIC MOBILITY STATUS: STATE OF 

RESIDENCE OF CHILD AT BIRTH VERSUS CURRENT 

STATE

Y Replaced by MOBIL_I starting in 

2005.

MOBIL_I 

GEOGRAPHIC MOBILITY STATUS: STATE OF 

RESIDENCE OF CHILD AT BIRTH VERSUS CURRENT 

STATE: IMPUTED

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Replaced MOBIL starting in 2005. 

This version is imputed.

MP1_AGE AGE IN MONTHS OF PROV-REPTD MUMPS-ONLY 

SHOT #1 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

MP2_AGE AGE IN MONTHS OF PROV-REPTD MUMPS-ONLY 

SHOT #2 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

MP3_AGE AGE IN MONTHS OF PROV-REPTD MUMPS-ONLY 

SHOT #3 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

MP4_AGE AGE IN MONTHS OF PROV-REPTD MUMPS-ONLY 

SHOT #4 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 
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Variable Name Variable Label† 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Notes§

MP5_AGE AGE IN MONTHS OF PROV-REPTD MUMPS-ONLY 

SHOT #5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

MP6_AGE AGE IN MONTHS OF PROV-REPTD MUMPS-ONLY 

SHOT #6 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

MP7_AGE AGE IN MONTHS OF PROV-REPTD MUMPS-ONLY 

SHOT #7 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

MP8_AGE AGE IN MONTHS OF PROV-REPTD MUMPS-ONLY 

SHOT #8 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

MP9_AGE AGE IN MONTHS OF PROV-REPTD MUMPS-ONLY 

SHOT #9 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

MPR1_AGE AGE IN MONTHS OF PROV-REPTD 

(MUMPS/RUBELLA)-ONLY SHOT #1 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

MPR2_AGE AGE IN MONTHS OF PROV-REPTD 

(MUMPS/RUBELLA)-ONLY SHOT #2 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

MPR3_AGE AGE IN MONTHS OF PROV-REPTD 

(MUMPS/RUBELLA)-ONLY SHOT #3 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

MPR4_AGE AGE IN MONTHS OF PROV-REPTD 

(MUMPS/RUBELLA)-ONLY SHOT #4 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

MPR5_AGE AGE IN MONTHS OF PROV-REPTD 

(MUMPS/RUBELLA)-ONLY SHOT #5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

MPR6_AGE AGE IN MONTHS OF PROV-REPTD 

(MUMPS/RUBELLA)-ONLY SHOT #6 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

MPR7_AGE AGE IN MONTHS OF PROV-REPTD 

(MUMPS/RUBELLA)-ONLY SHOT #7 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

MPR8_AGE AGE IN MONTHS OF PROV-REPTD 

(MUMPS/RUBELLA)-ONLY SHOT #8 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

MPR9_AGE AGE IN MONTHS OF PROV-REPTD 

(MUMPS/RUBELLA)-ONLY SHOT #9 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

N_PRVR NUMBER OF PROVIDERS RESPONDING WITH 

VACCINATION DATA FOR CHILD (RECODE) Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

NUM_CELLS_HH NUMBER OF WORKING CELL PHONES HOUSEHOLD 

MEMBERS HAVE AVAILABLE FOR PERSONAL USE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Question added to household 

questionnaire in 2009.

NUM_CELLS_PARENTS NUMBER OF WORKING CELL PHONES USUALLY 

USED BY PARENTS OR GUARDIANS Y Y Y Y Y Y Y Y Y Y Y Y Y Y Question added to household 

questionnaire in 2009.

NUM_PHONE NUMBER OF RESIDENTIAL TELEPHONE NUMBERS 

IN HOUSEHOLD (EXCLUDING CELL PHONES) Y Y Y Y Y Y Y Y Y Y Y Y Y Y Question added to household 

questionnaire in 2009.

P_NUHEPX

NUMBER OF HEPATITIS B-ONLY SHOTS BY 36 

MONTHS OF AGE DETERMINED FROM PROVIDER 

INFO, EXCLUDING ANY VACCINATIONS AFTER 

THE HH INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 
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Variable Name Variable Label† 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Notes§

P_NUHIBX

NUMBER OF HIB-ONLY SHOTS BY 36 MONTHS OF 

AGE DETERMINED FROM PROVIDER INFO, 

EXCLUDING ANY VACCINATIONS AFTER THE HH 

INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Added in 2006 so that each vaccine 

type on the IHQ has a corresponding 

shot count variable.

P_NUHPHB

NUMBER OF HEPATITIS B/HIB COMBO SHOTS BY 

36 MONTHS OF AGE DETERMINED FROM 

PROVIDER INFO, EXCLUDING ANY VACCINATIONS 

AFTER THE HH INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

P_NUM1L

NUMBER OF MONOVALENT 2009 H1N1 INFLUENZA 

VACCINATIONS OF UNKNOWN TYPE BY 36 

MONTHS OF AGE DETERMINED FROM PROVIDER 

INFO, EXCLUDING ANY VACCINATIONS AFTER 

THE HH INTERVIEW DATE.

Y Y Y 

H1N1 influenza added to the IHQ 

shotgrid starting in late 2009, and 

introduced in the PUF in 2010. 

Removed from the IHQ and the PUF 

in 2013.

P_NUM1M

NUMBER OF MONOVALENT 2009 H1N1 INFLUENZA 

SPRAY VACCINATIONS BY 36 MONTHS OF AGE 

DETERMINED FROM PROVIDER INFO, EXCLUDING 

ANY VACCINATIONS AFTER THE HH INTERVIEW 

DATE.

Y Y Y 

H1N1 influenza added to the IHQ 

shotgrid starting in late 2009, and 

introduced in the PUF in 2010. 

Removed from the IHQ and the PUF 

in 2013.

P_NUM1N

NUMBER OF INJECTED MONOVALENT 2009 H1N1 

INFLUENZA VACCINATIONS BY 36 MONTHS OF 

AGE DETERMINED FROM PROVIDER INFO, 

EXCLUDING ANY VACCINATIONS AFTER THE HH 

INTERVIEW DATE.

Y Y Y 

H1N1 influenza added to the IHQ 

shotgrid starting in late 2009, and 

introduced in the PUF in 2010. 

Removed from the IHQ and the PUF 

in 2013.

P_NUMDAH

NUMBER OF DTAP/HIB COMBO SHOTS BY 36 

MONTHS OF AGE DETERMINED FROM PROVIDER 

INFO, EXCLUDING ANY VACCINATIONS AFTER 

THE HH INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

P_NUMDHB

NUMBER OF DTP/HIB CONTAINING SHOTS 

DETERMINED FROM PROVIDER INFO, EXCLUDING 

ANY VACCINATIONS AFTER THE HH INTERVIEW 

DATE.

Y Y Y Y Y Dropped in 2009 due to change to 

IHQ shotgrid.

P_NUMDHI

NUMBER OF DTAP/HEPB/IPV COMBO SHOTS BY 36 

MONTHS OF AGE DETERMINED FROM PROVIDER 

INFO, EXCLUDING ANY VACCINATIONS AFTER 

THE HH INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Added in 2006 so that each vaccine 

type on the IHQ has a corresponding 

shot count variable.

P_NUMDHM

NUMBER OF DTP/HIB COMBO SHOTS DETERMINED 

FROM PROVIDER INFO, EXCLUDING ANY 

VACCINATIONS AFTER THE HH INTERVIEW DATE.

Y Y Y Y Y Dropped in 2009 due to change to 

IHQ shotgrid.

P_NUMDIH

NUMBER OF DTAP/IPV/HIB COMBO SHOTS BY 36 

MONTHS OF AGE DETERMINED FROM PROVIDER 

INFO, EXCLUDING ANY VACCINATIONS AFTER 

THE HOUSEHOLD INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Added in 2009 due to change to IHQ 

shotgrid.

P_NUMDIHB

NUMBER OF DTAP/IPV/HIB/HEPB SHOTS BY 19-35 

MONTHS OF AGE DETERMINED FROM PROVIDER 

INFO, EXCLUDING ANY VACCINATIONS AFTER 

THE MAX OF HOUSEHOLD INTERVIEW DATE AND 

ELIGIBILITY DATE.

Y Y Added in 2021 to reflect new IHQ grid 

shot type.

P_NUMDTA

NUMBER OF DTAP-ONLY SHOTS BY 36 MONTHS OF 

AGE DETERMINED FROM PROVIDER INFO, 

EXCLUDING ANY VACCINATIONS AFTER THE HH 

INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 
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P_NUMDTM

NUMBER OF DT-ONLY SHOTS DETERMINED FROM 

PROVIDER INFO, EXCLUDING ANY VACCINATIONS 

AFTER THE HH INTERVIEW DATE.

Y Y Y Y Y Dropped in 2009 due to change to 

IHQ shotgrid.

P_NUMDTP

NUMBER OF DT-CONTAINING SHOTS BY 36 

MONTHS OF AGE DETERMINED FROM PROVIDER 

INFO, EXCLUDING ANY VACCINATIONS AFTER 

THE HH INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

P_NUMFLU

NUMBER OF SEASONAL FLU-CONTAINING 

VACCINATIONS BY 36 MONTHS OF AGE 

DETERMINED FROM PROVIDER INFO, EXCLUDING 

ANY VACCINATIONS AFTER THE HH INTERVIEW 

DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

P_NUMFLUL

NUMBER OF SEASONAL FLU-CONTAINING 

VACCINATIONS OF UNKNOWN TYPE BY 36 

MONTHS OF AGE DETERMINED FROM PROVIDER 

INFO, EXCLUDING ANY VACCINATIONS AFTER 

THE HH INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Starting in 2008, influenza type boxes 

were added to the IHQ shot grid.

P_NUMFLUM

NUMBER OF SEASONAL FLU SPRAY 

VACCINATIONS BY 36 MONTHS OF AGE 

DETERMINED FROM PROVIDER INFO, EXCLUDING 

ANY VACCINATIONS AFTER THE HH INTERVIEW 

DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Starting in 2008, influenza type boxes 

were added to the IHQ shot grid.

P_NUMFLUN

NUMBER OF INJECTED SEASONAL FLU 

VACCINATIONS BY 36 MONTHS OF AGE 

DETERMINED FROM PROVIDER INFO, EXCLUDING 

ANY VACCINATIONS AFTER THE HH INTERVIEW 

DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Starting in 2008, influenza type boxes 

were added to the IHQ shot grid.

P_NUMH1N

NUMBER OF MONOVALENT 2009 H1N1 INFLUENZA 

VACCINATIONS BY 36 MONTHS OF AGE 

DETERMINED FROM PROVIDER INFO, EXCLUDING 

ANY VACCINATIONS AFTER THE HH INTERVIEW 

DATE.

Y Y Y 

H1N1 influenza added to the IHQ 

shotgrid starting in late 2009, and

introduced in the PUF in 2010. 

Removed from the IHQ and the PUF 

in 2013.

P_NUMH2

NUMBER OF HIB-SANOFI or HIBGLAXOSMITHKLINE SHOTS BY 36 MONTHS OF 

AGE DETERMINED FROM PROVIDER INFO, 

EXCLUDING ANY VACCINATIONS AFTER THE 

HOUSEHOLD INTERVIEW DATE.

Y 

Added in 2009 due to change to IHQ 

shotgrid. Replaced by P_NUMHG and 

P_NUMHS starting in 2010.

P_NUMHEA

NUMBER OF HEPATITIS A-CONTAINING SHOTS BY 

36 MONTHS OF AGE DETERMINED FROM 

PROVIDER INFO, EXCLUDING ANY VACCINATIONS 

AFTER THE HH INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

P_NUMHEN

NUMBER OF HEPATITIS B-CONTAINING SHOTS OF 

UNKNOWN TYPE BY 36 MONTHS OF AGE 

DETERMINED FROM PROVIDER INFO, EXCLUDING 

ANY VACCINATIONS AFTER THE HH INTERVIEW 

DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Added in 2006 so that each vaccine 

type on the IHQ has a corresponding 

shot count variable.

P_NUMHEP

NUMBER OF HEPATITIS B-CONTAINING SHOTS BY 

36 MONTHS OF AGE DETERMINED FROM 

PROVIDER INFO, EXCLUDING ANY VACCINATIONS 

AFTER THE HH INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

P_NUMHG

NUMBER OF HIB-GLAXOSMITHKLINE SHOTS BY 36 

MONTHS OF AGE DETERMINED FROM PROVIDER 

INFO, EXCLUDING ANY VACCINATIONS AFTER 

THE HOUSEHOLD INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Replaced P_NUMH2 starting in 2010 

due to a change to the IHQ shotgrid.
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P_NUMHHY

NUMBER OF HIB-MENCY SHOTS BY 36 MONTHS OF 

AGE DETERMINED FROM PROVIDER INFO, 

EXCLUDING ANY VACCINATIONS AFTER THE 

HOUSEHOLD INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Added in 2014 due to change in IHQ 

shotgrid.

P_NUMHIB

NUMBER OF HIB-CONTAINING SHOTS BY 36 

MONTHS OF AGE DETERMINED FROM PROVIDER 

INFO, EXCLUDING ANY VACCINATIONS AFTER 

THE HH INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

P_NUMHIN

NUMBER OF HIB-CONTAINING SHOTS OF 

UNKNOWN TYPE BY 36 MONTHS OF AGE 

DETERMINED FROM PROVIDER INFO, EXCLUDING 

ANY VACCINATIONS AFTER THE HH INTERVIEW 

DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Added in 2006 so that each vaccine 

type on the IHQ has a corresponding 

shot count variable.

P_NUMHION

NUMBER OF HIB-ONLY SHOTS OF UNKNOWN TYPE 

BY 36 MONTHS OF AGE DETERMINED FROM 

PROVIDER INFO, EXCLUDING ANY VACCINATIONS 

AFTER THE HOUSEHOLD INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Added in 2009 due to change to IHQ 

shotgrid.

P_NUMHM

NUMBER OF HIB-MERCK SHOTS BY 36 MONTHS OF 

AGE DETERMINED FROM PROVIDER INFO, 

EXCLUDING ANY VACCINATIONS AFTER THE 

HOUSEHOLD INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Added in 2009 due to change to IHQ 

shotgrid.

P_NUMHS

NUMBER OF HIB-SANOFI SHOTS BY 36 MONTHS OF 

AGE DETERMINED FROM PROVIDER INFO, 

EXCLUDING ANY VACCINATIONS AFTER THE 

HOUSEHOLD INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Replaced P_NUMH2 starting in 2010 

due to a change in the IHQ shotgrid.

P_NUMIPV

NUMBER OF IPV-ONLY SHOTS BY 36 MONTHS OF 

AGE DETERMINED FROM PROVIDER INFO, 

EXCLUDING ANY VACCINATIONS AFTER THE HH 

INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

P_NUMMCN

NUMBER OF MEASLES-CONTAINING SHOTS OF 

UNKNOWN TYPE BY 36 MONTHS OF AGE 

DETERMINED FROM PROVIDER INFO, EXCLUDING 

ANY VACCINATIONS AFTER THE HH INTERVIEW 

DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Added in 2006 so that each vaccine 

type on the IHQ has a corresponding 

shot count variable.

P_NUMMMR 

NUMBER OF MEASLES-CONTAINING SHOTS BY 36 

MONTHS OF AGE DETERMINED FROM PROVIDER 

INFO, EXCLUDING ANY VACCINATIONS AFTER 

THE HH INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

P_NUMMMRX 

NUMBER OF MMR-ONLY SHOTS BY 36 MONTHS OF 

AGE DETERMINED FROM PROVIDER INFO, 

EXCLUDING ANY VACCINATIONS AFTER THE HH 

INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Added in 2006 so that each vaccine 

type on the IHQ has a corresponding 

shot count variable.

P_NUMMMX

NUMBER OF MMR-CONTAINING SHOTS BY 36 

MONTHS OF AGE DETERMINED FROM PROVIDER 

INFO, EXCLUDING ANY VACCINATIONS AFTER 

THE HH INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

P_NUMMP

NUMBER OF MUMPS-ONLY SHOTS BY 36 MONTHS 

OF AGE DETERMINED FROM PROVIDER INFO, 

EXCLUDING ANY VACCINATIONS AFTER THE HH 

INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 
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P_NUMMPR

NUMBER OF (MUMPS/RUBELLA)-ONLY SHOTS BY 

36 MONTHS OF AGE DETERMINED FROM 

PROVIDER INFO, EXCLUDING ANY VACCINATIONS 

AFTER THE HH INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

P_NUMMRV

NUMBER OF MMR/VARICELLA COMBO SHOTS BY 

36 MONTHS OF AGE DETERMINED FROM 

PROVIDER INFO, EXCLUDING ANY VACCINATIONS 

AFTER THE HH INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Added in 2006 so that each vaccine 

type on the IHQ has a corresponding 

shot count variable.

P_NUMMS

NUMBER OF MEASLES-ONLY SHOTS BY 36 

MONTHS OF AGE DETERMINED FROM PROVIDER 

INFO, EXCLUDING ANY VACCINATIONS AFTER 

THE HH INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

P_NUMMSM

NUMBER OF MEASLES/MUMPS COMBO SHOTS BY 

36 MONTHS OF AGE DETERMINED FROM 

PROVIDER INFO, EXCLUDING ANY VACCINATIONS 

AFTER THE HH INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

P_NUMMSR

NUMBER OF MEASLES/RUBELLA COMBO SHOTS 

BY 36 MONTHS OF AGE DETERMINED FROM 

PROVIDER INFO, EXCLUDING ANY VACCINATIONS 

AFTER THE HH INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

P_NUMOLN

NUMBER OF POLIO SHOTS OF UNKNOWN TYPE BY 

36 MONTHS OF AGE DETERMINED FROM 

PROVIDER INFO, EXCLUDING ANY VACCINATIONS 

AFTER THE HH INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

P_NUMOPV

NUMBER OF OPV-ONLY SHOTS BY 36 MONTHS OF 

AGE DETERMINED FROM PROVIDER INFO, 

EXCLUDING ANY VACCINATIONS AFTER THE HH 

INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

P_NUMPCC

NUMBER OF PNEUMOCOCCAL CONJUGATE SHOTS 

BY 36 MONTHS OF AGE DETERMINED FROM 

PROVIDER INFO, EXCLUDING ANY VACCINATIONS 

AFTER THE HOUSEHOLD INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

P_NUMPCC13

NUMBER OF PNEUMOCOCCAL CONJUGATE-13 

SHOTS BY 36 MONTHS OF AGE DETERMINED 

FROM PROVIDER INFO, EXCLUDING ANY 

VACCINATIONS AFTER THE HOUSEHOLD 

INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Added in 2010 due to new PCV 

vaccination recommendations.

P_NUMPCC15

NUMBER OF PNEUMOCOCCAL CONJUGATE-15 

SHOTS BY 36 MONTHS OF AGE DETERMINED 

FROM PROVIDER INFO, EXCLUDING ANY 

VACCINATIONS AFTER THE HOUSEHOLD 

INTERVIEW DATE.

Y New vaccine subtype added in 2022.

P_NUMPCC7

NUMBER OF PNEUMOCOCCAL CONJUGATE-7 

SHOTS BY 36 MONTHS OF AGE DETERMINED 

FROM PROVIDER INFO, EXCLUDING ANY 

VACCINATIONS AFTER THE HOUSEHOLD 

INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Added in 2010 due to new PCV 

vaccination recommendations.

P_NUMPCCN

NUMBER OF PNEUMOCOCCAL CONJUGATE SHOTS 

OF UNKNOWN TYPE BY 36 MONTHS OF AGE 

DETERMINED FROM PROVIDER INFO, EXCLUDING 

ANY VACCINATIONS AFTER THE HOUSEHOLD 

INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Added in 2010 due to new PCV 

vaccination recommendations.
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Variable Name Variable Label† 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Notes§

P_NUMPCN

NUMBER OF PNEUMOCOCCAL SHOTS OF 

UNKNOWN TYPE BY 36 MONTHS OF AGE 

DETERMINED FROM PROVIDER INFO, EXCLUDING 

ANY VACCINATIONS AFTER THE HOUSEHOLD 

INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

P_NUMPCP

NUMBER OF PNEUMOCOCCAL POLYSACCHARIDE 

SHOTS BY 36 MONTHS OF AGE DETERMINED 

FROM PROVIDER INFO, EXCLUDING ANY 

VACCINATIONS AFTER THE HOUSEHOLD 

INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

P_NUMPCV

NUMBER OF PNEUMOCOCCAL-CONTAINING 

SHOTS BY 36 MONTHS OF AGE DETERMINED 

FROM PROVIDER INFO, EXCLUDING ANY 

VACCINATIONS AFTER THE HOUSEHOLD 

INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

P_NUMPOL

NUMBER OF POLIO-CONTAINING SHOTS BY 36 

MONTHS OF AGE DETERMINED FROM PROVIDER 

INFO, EXCLUDING ANY VACCINATIONS AFTER 

THE HH INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

P_NUMRB

NUMBER OF RUBELLA-ONLY SHOTS BY 36 

MONTHS OF AGE DETERMINED FROM PROVIDER 

INFO, EXCLUDING ANY VACCINATIONS AFTER 

THE HH INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

P_NUMRG

NUMBER OF ROTARIX-GSK SHOTS BY 36 MONTHS 

OF AGE DETERMINED FROM PROVIDER INFO, 

EXCLUDING ANY VACCINATIONS AFTER THE 

HOUSEHOLD INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Starting in 2009, rotavirus type boxes 

were added to the IHQ shot grid.

P_NUMRM

NUMBER OF ROTATEQ-MERCK SHOTS BY 36 

MONTHS OF AGE DETERMINED FROM PROVIDER 

INFO, EXCLUDING ANY VACCINATIONS AFTER 

THE HOUSEHOLD INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Starting in 2009, rotavirus type boxes 

were added to the IHQ shot grid.

P_NUMRO

NUMBER OF ROTAVIRUS SHOTS OF UNKNOWN 

TYPE BY 36 MONTHS OF AGE DETERMINED FROM 

PROVIDER INFO, EXCLUDING ANY VACCINATIONS 

AFTER THE HOUSEHOLD INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Starting in 2009, rotavirus type boxes 

were added to the IHQ shot grid.

P_NUMROT

NUMBER OF ROTAVIRUS-CONTAINING SHOTS BY 

36 MONTHS OF AGE DETERMINED FROM 

PROVIDER INFO, EXCLUDING ANY VACCINATIONS 

AFTER THE HH INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

P_NUMTPM

NUMBER OF DTP-ONLY SHOTS DETERMINED 

FROM PROVIDER INFO, EXCLUDING ANY 

VACCINATIONS AFTER THE HH INTERVIEW DATE.

Y Y Y Y Y Dropped in 2009 due to change to 

IHQ shotgrid.

P_NUMTPN

NUMBER OF DT-CONTAINING SHOTS OF 

UNKNOWN TYPE BY 36 MONTHS OF AGE 

DETERMINED FROM PROVIDER INFO, EXCLUDING 

ANY VACCINATIONS AFTER THE HH INTERVIEW 

DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

P_NUMVRC

NUMBER OF VARICELLA-CONTAINING SHOTS BY 

36 MONTHS OF AGE DETERMINED FROM 

PROVIDER INFO, EXCLUDING ANY VACCINATIONS 

AFTER THE HH INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 
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P_NUMVRN

NUMBER OF VARICELLA-CONTAINING SHOTS OF 

UNKNOWN TYPE BY 36 MONTHS OF AGE 

DETERMINED FROM PROVIDER INFO, EXCLUDING 

ANY VACCINATIONS AFTER THE HH INTERVIEW 

DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Added in 2006 so that each vaccine 

type on the IHQ has a corresponding 

shot count variable.

P_NUMVRX

NUMBER OF VARICELLA-ONLY SHOTS BY 36 

MONTHS OF AGE DETERMINED FROM PROVIDER 

INFO, EXCLUDING ANY VACCINATIONS AFTER 

THE HH INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Added in 2006 so that each vaccine 

type on the IHQ has a corresponding 

shot count variable.

P_U12VRC

UTD (UP-TO-DATE) FLAG FOR PROVIDER 1+ 

VARICELLA-CONTAINING SHOT AT 12+ MONTHS 

OF AGE, BY 36 MONTHS OF AGE, EXCLUDING ANY 

VACCINATIONS AFTER THE HOUSEHOLD 

INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

P_UTD331

UTD (UP-TO-DATE) FLAG FOR PROVIDER 3:3:1 BY 

36 MONTHS OF AGE, EXCLUDING ANY 

VACCINATIONS AFTER THE HOUSEHOLD 

INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

P_UTD431

UTD (UP-TO-DATE) FLAG FOR PROVIDER 4:3:1 BY 

36 MONTHS OF AGE, EXCLUDING ANY 

VACCINATIONS AFTER THE HOUSEHOLD 

INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

P_UTD431H_ROUT_S

UTD (UP-TO-DATE) FLAG FOR PROVIDER 4:3:1:3 BY 

36 MONTHS OF AGE, USING THE ROUTINE, STRICT 

DEFINITION OF HIB UTD, EXCLUDING ANY 

VACCINATIONS AFTER THE HOUSEHOLD 

INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Added in 2009 due to new Hib 

vaccination recommendations.

P_UTD431H3_ROUT_S

UTD (UP-TO-DATE) FLAG FOR PROVIDER 4:3:1:3:3 

BY 36 MONTHS OF AGE, USING THE ROUTINE, 

STRICT DEFINITION OF HIB UTD, EXCLUDING ANY 

VACCINATIONS AFTER THE HOUSEHOLD 

INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Added in 2009 due to new Hib 

vaccination recommendations.

P_UTD431H31_ROUT_S

UTD (UP-TO-DATE) FLAG FOR PROVIDER 4:3:1:3:3:1 

BY 36 MONTHS OF AGE (INCLUDES 1+ VARICELLACONTAINING AT AGE 12+ MTHS) USING THE 

ROUTINE, STRICT DEFINITION OF HIB UTD, 

EXCLUDING ANY VACCINATIONS AFTER THE 

HOUSEHOLD INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Added in 2009 due to new Hib 

vaccination recommendations.

P_UTD431H313_ROUT_S

UTD (UP-TO-DATE) FLAG FOR PROVIDER 

4:3:1:3:3:1:3 BY 36 MONTHS OF AGE (INCLUDES 1+ 

VARICELLA-CONTAINING AT AGE 12+ MTHS) 

USING THE ROUTINE, STRICT DEFINITION OF HIB 

UTD, EXCLUDING ANY VACCINATIONS AFTER THE 

HOUSEHOLD INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Added in 2009 due to new Hib 

vaccination recommendations.

P_UTD431H314_ROUT_S

UTD (UP-TO-DATE) FLAG FOR PROVIDER 

4:3:1:3:3:1:4 BY 36 MONTHS OF AGE (INCLUDES 1+ 

VARICELLA-CONTAINING AT AGE 12+ MTHS) 

USING THE ROUTINE, STRICT DEFINITION OF HIB 

UTD, EXCLUDING ANY VACCINATIONS AFTER THE 

HOUSEHOLD INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Added in 2009 due to new Hib 

vaccination recommendations.
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Variable Name Variable Label† 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Notes§

P_UTDFL1

UTD (UP-TO-DATE) FLAG FOR PROVIDER 

SEASONAL INFLUENZA VARIABLE 1 BY 36 

MONTHS OF AGE, EXCLUDING ANY 

VACCINATIONS AFTER THE HOUSEHOLD 

INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Dropped starting in 2014 due to 

change in ACIP recommendations.

P_UTDFL2

UTD (UP-TO-DATE) FLAG FOR PROVIDER 

SEASONAL INFLUENZA VARIABLE 2 BY 36 

MONTHS OF AGE, EXCLUDING ANY 

VACCINATIONS AFTER THE HOUSEHOLD 

INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Dropped starting in 2014 due to 

change in ACIP recommendations.

P_UTDFL3

UTD (UP-TO-DATE) FLAG FOR PROVIDER 

SEASONAL INFLUENZA VARIABLE 3 BY 36 

MONTHS OF AGE, EXCLUDING ANY 

VACCINATIONS AFTER THE HOUSEHOLD 

INTERVIEW DATE.

Y Y Y Y Y Y Y 

Added in 2007 due to new influenza 

vaccination recommendations. 

Dropped starting in 2014 due to 

change in ACIP recommendations.

P_UTDH1N_1

UTD (UP-TO-DATE) FLAG FOR PROVIDER 1+ 

MONOVALENT 2009 H1N1 INFLUENZA 

VACCINATION BY 36 MONTHS OF AGE, 

EXCLUDING ANY VACCINATIONS AFTER THE 

HOUSEHOLD INTERVIEW DATE AND EXCLUDING 

VACCINATIONS GIVEN PRIOR TO 10/5/2009.

Y Y Y 

H1N1 influenza added to the IHQ 

shotgrid starting in late 2009, and

introduced in the PUF in 2010. 

Removed from the IHQ and the PUF 

in 2013.

P_UTDH1N_2

UTD (UP-TO-DATE) FLAG FOR PROVIDER 2+ 

MONOVALENT 2009 H1N1 INFLUENZA 

VACCINATIONS BY 36 MONTHS OF AGE, 

EXCLUDING ANY VACCINATIONS AFTER THE 

HOUSEHOLD INTERVIEW DATE AND EXCLUDING 

VACCINATIONS GIVEN PRIOR TO 10/5/2009.

Y Y Y 

H1N1 influenza added to the IHQ 

shotgrid starting in late 2009, and

introduced in the PUF in 2010. 

Removed from the IHQ and the PUF 

in 2013.

P_UTDHEP

UTD (UP-TO-DATE) FLAG FOR PROVIDER 3+ 

HEPATITIS B-CONTAINING SHOTS BY 36 MONTHS 

OF AGE, EXCLUDING ANY VACCINATIONS AFTER 

THE HOUSEHOLD INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

P_UTDHEPA1

UTD (UP-TO-DATE) FLAG FOR PROVIDER 1+ 

HEPATITIS A-CONTAINING SHOTS BY 36 MONTHS 

OF AGE, EXCLUDING ANY VACCINATIONS AFTER 

THE HOUSEHOLD INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Added in 2011 to aid analysis.

P_UTDHEPA2

UTD (UP-TO-DATE) FLAG FOR PROVIDER 2+ 

HEPATITIS A-CONTAINING SHOTS BY 36 MONTHS 

OF AGE, EXCLUDING ANY VACCINATIONS AFTER 

THE HOUSEHOLD INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Added in 2010 to aid analysis.

P_UTDHIB

UTD (UP-TO-DATE) FLAG FOR PROVIDER 3+ HIBCONTAINING SHOTS BY 36 MONTHS OF AGE, 

EXCLUDING ANY VACCINATIONS AFTER THE 

HOUSEHOLD INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

P_UTDHIB_ROUT_S

UTD (UP-TO-DATE) FLAG FOR PROVIDER 3+ HIB 

DOSES BY 36 MONTHS OF AGE, BASED ON THE 

ROUTINE (NON-SHORTAGE) HIB 

RECOMMENDATIONS AND A STRICT TREATMENT 

OF HIB SHOTS OF UNKNOWN TYPE, EXCLUDING 

ANY VACCINATIONS AFTER THE HOUSEHOLD 

INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Added in 2009 due to new Hib 

vaccination recommendations.
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176

Variable Name Variable Label† 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Notes§

P_UTDHIB_SHORT_S

UTD (UP-TO-DATE) FLAG FOR PROVIDER 3+ HIB 

DOSES BY 36 MONTHS OF AGE, BASED ON THE HIB 

SHORTAGE RECOMMENDATIONS AND A STRICT 

TREATMENT OF HIB SHOTS OF UNKNOWN TYPE, 

EXCLUDING ANY VACCINATIONS AFTER THE 

HOUSEHOLD INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Added in 2009 due to new Hib 

vaccination recommendations.

P_UTDMCV

UTD (UP-TO-DATE) FLAG FOR PROVIDER 1+ 

MEASLES-CONTAINING SHOT BY 36 MONTHS OF 

AGE, EXCLUDING ANY VACCINATIONS AFTER THE 

HOUSEHOLD INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

P_UTDMMX

UTD (UP-TO-DATE) FLAG FOR PROVIDER 1+ MMR 

COMBO SHOT BY 36 MONTHS OF AGE, EXCLUDING 

ANY VACCINATIONS AFTER THE HOUSEHOLD 

INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

P_UTDPC3

UTD (UP-TO-DATE) FLAG FOR PROVIDER 3+ 

PNEUMOCOCCAL-CONTAINING SHOTS BY 36 

MONTHS OF AGE, EXCLUDING ANY 

VACCINATIONS AFTER THE HOUSEHOLD 

INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

P_UTDPCV

UTD (UP-TO-DATE) FLAG FOR PROVIDER 4+ 

PNEUMOCOCCAL-CONTAINING SHOTS BY 36 

MONTHS OF AGE, EXCLUDING ANY 

VACCINATIONS AFTER THE HOUSEHOLD 

INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

P_UTDPCVB13

UTD (UP-TO-DATE) INDICATOR FOR PROVIDER 1+ 

PNEUMOCOCCAL VACCINATIONS OF TYPE 

CONJUGATE-13, GIVEN 4+ DOSES OF TYPE 

CONJUGATE-7, BY 36 MONTHS OF AGE, 

EXCLUDING ANY VACCINATIONS AFTER THE 

HOUSEHOLD INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Added in 2010 due to new PCV 

vaccination recommendations.

P_UTDPOL

UTD (UP-TO-DATE) FLAG FOR PROVIDER 3+ POLIOCONTAINING SHOTS BY 36 MONTHS OF AGE, 

EXCLUDING ANY VACCINATIONS AFTER THE 

HOUSEHOLD INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

P_UTDROT_S

UTD (UP-TO-DATE) FLAG FOR PROVIDER 3+ 

ROTAVIRUS DOSES BY 36 MONTHS OF AGE, BASED 

ON A STRICT TREATMENT OF ROTAVIRUS 

VACCINATIONS OF UNKNOWN TYPE, EXCLUDING 

ANY VACCINATIONS AFTER THE HOUSEHOLD 

INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Added in 2009 to aid analysis.

P_UTDTP3

UTD (UP-TO-DATE) FLAG FOR PROVIDER 3+ DTCONTAINING SHOTS BY 36 MONTHS OF AGE, 

EXCLUDING ANY VACCINATIONS AFTER THE 

HOUSEHOLD INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

P_UTDTP4

UTD (UP-TO-DATE) FLAG FOR PROVIDER 4+ DTCONTAINING SHOTS BY 36 MONTHS OF AGE, 

EXCLUDING ANY VACCINATIONS AFTER THE 

HOUSEHOLD INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

PCV1_AGE AGE IN MONTHS OF PROV-REPTD 

PNEUMOCOCCAL-CONTAINING SHOT #1 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

PCV2_AGE AGE IN MONTHS OF PROV-REPTD 

PNEUMOCOCCAL-CONTAINING SHOT #2 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 
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Variable Name Variable Label† 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Notes§

PCV3_AGE AGE IN MONTHS OF PROV-REPTD 

PNEUMOCOCCAL-CONTAINING SHOT #3 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

PCV4_AGE AGE IN MONTHS OF PROV-REPTD 

PNEUMOCOCCAL-CONTAINING SHOT #4 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

PCV5_AGE AGE IN MONTHS OF PROV-REPTD 

PNEUMOCOCCAL-CONTAINING SHOT #5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

PCV6_AGE AGE IN MONTHS OF PROV-REPTD 

PNEUMOCOCCAL-CONTAINING SHOT #6 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

PCV7_AGE AGE IN MONTHS OF PROV-REPTD 

PNEUMOCOCCAL-CONTAINING SHOT #7 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

PCV8_AGE AGE IN MONTHS OF PROV-REPTD 

PNEUMOCOCCAL-CONTAINING SHOT #8 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

PCV9_AGE AGE IN MONTHS OF PROV-REPTD 

PNEUMOCOCCAL-CONTAINING SHOT #9 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

PDAT CHILD HAS ADEQUATE PROVIDER DATA Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

POL1_AGE AGE IN MONTHS OF PROV-REPTD POLIOCONTAINING SHOT #1 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

POL2_AGE AGE IN MONTHS OF PROV-REPTD POLIOCONTAINING SHOT #2 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

POL3_AGE AGE IN MONTHS OF PROV-REPTD POLIOCONTAINING SHOT #3 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

POL4_AGE AGE IN MONTHS OF PROV-REPTD POLIOCONTAINING SHOT #4 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

POL5_AGE AGE IN MONTHS OF PROV-REPTD POLIOCONTAINING SHOT #5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

POL6_AGE AGE IN MONTHS OF PROV-REPTD POLIOCONTAINING SHOT #6 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

POL7_AGE AGE IN MONTHS OF PROV-REPTD POLIOCONTAINING SHOT #7 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

POL8_AGE AGE IN MONTHS OF PROV-REPTD POLIOCONTAINING SHOT #8 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

POL9_AGE AGE IN MONTHS OF PROV-REPTD POLIOCONTAINING SHOT #9 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

PROV_FAC PROVIDER FACILITY TYPES: IMPUTED Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

PROVWT

WEIGHT FOR CHILDREN WITH ADEQUATE 

PROVIDER DATA AND UNVACCINATED CHILDREN 

(EXCLUDING U.S. VIRGIN ISLANDS)

Y Y Y Y Y Y 

Added in 2005 to replace WGT. 

Replaced by PROVWT_LL in 2011 

due to addition of dual-frame weights.

PROVWT_C FINAL SINGLE-FRAME CELL-PHONE PROVIDERPHASE WEIGHT (EXCLUDES TERRITORIES) Y Y Y Y Y Replaced PROVWT_D in 2018 due to 

removal of the landline sample.

PROVWT_C_TERR FINAL SINGLE-FRAME CELL-PHONE PROVIDERPHASE WEIGHT INCLUDING TERRITORIES Y Y Y Y 

Added in 2019 to replace 

PROVWT_D_TERR due to removal 

of the landline sample

PROVWT_D FINAL DUAL-FRAME PROVIDER-PHASE WEIGHT 

(EXCLUDES TERRITORIES) Y Y Y Y Y Y Y 

Added in 2011 as dual-frame weight. 

Replaced by PROVWT_C in 2018 due 

to removal of the landline sample.
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Variable Name Variable Label† 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Notes§

PROVWT_LL

LANDLINE-FRAME WEIGHT FOR CHILDREN WITH 

ADEQUATE PROVIDER DATA AND 

UNVACCINATED CHILDREN (EXCLUDING U.S. 

VIRGIN ISLANDS)

Y 

Replaced PROVWT in 2011 to 

distinguish from new dual-frame 

weight PROVWT_D. Removed in 

2012.

PROVWTVI

WEIGHT FOR CHILDREN WITH ADEQUATE 

PROVIDER DATA AND UNVACCINATED CHILDREN 

(INCLUDING U.S. VIRGIN ISLANDS)

Y Y 

Added in 2009 to include U.S. Virgin 

Island sample. Replaced by 

PROVWTVI_LL in 2011 due to 

addition of dual-frame weights. 

PROVWTVI_D

COMBINATION OF THE DUAL-FRAME WEIGHT FOR 

CHILDREN IN THE U.S. PROPER AND LANDLINE 

WEIGHT FOR CHILDREN IN THE U.S. VIRGIN 

ISLANDS FOR CHILDREN WITH ADEQUATE 

PROVIDER DATA AND UNVACCINATED CHILDREN

Y 

Replaced PROVWTVI_LL in 2012.

Replaced with PROVWTVIGU_D in 

2013 due to the addition of Guam 

sample.

PROVWTVIGU_D

THE DUAL-FRAME WEIGHT FOR CHILDREN IN THE 

U.S. PROPER, THE U.S. VIRGIN ISLANDS AND 

GUAM FOR CHILDREN WITH ADEQUATE 

PROVIDER DATA AND UNVACCINATED CHILDREN

Y 

Replaced PROVWTVI_D in 2013 due 

to the addition of Guam sample. 

Replaced with PROVWT_D_TERR in 

2014 due to addition of Puerto Rico 

sample.

PROVWT_D_TERR FINAL DUAL-FRAME PROVIDER-PHASE WEIGHT 

INCLUDING TERRITORIES Y Y Y 

Replaced PROVWTVIGU_D in 2014 

due to addition of Puerto Rico sample. 

Not available on the 2017 PUF as no 

data from U.S. territories were 

included.

PROVWTVI_LL

LANDLINE-FRAME WEIGHT FOR CHILDREN WITH 

ADEQUATE PROVIDER DATA AND 

UNVACCINATED CHILDREN (INCLUDING U.S. 

VIRGIN ISLANDS)

Y 

Replaced PROVWTVI in 2011. 

Replaced with dual-frame weight 

PROVWTVI_D in 2012.

PU431_31

UTD (UP-TO-DATE) FLAG FOR PROVIDER 4:3:1::3:1 

(4:3:1:3:3:1 EXCLUDING HIB; INCLUDES 1+ 

VARICELLA AT AGE 12+ MTHS) BY 36 MONTHS OF 

AGE, EXCLUDING ANY VACCINATIONS AFTER THE 

HOUSEHOLD INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Added in 2010 to aid analysis. 

Dropped in 2019.

PU431_314

UTD (UP-TO-DATE) FLAG FOR PROVIDER 4:3:1::3:1:4 

(4:3:1:3:3:1:4 EXCLUDING HIB; INCLUDES 1+ 

VARICELLA AT AGE 12+ MTHS) BY 36 MONTHS OF 

AGE, EXCLUDING ANY VACCINATIONS AFTER THE 

HOUSEHOLD INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Added in 2010 to aid analysis. 

Dropped in 2019.

PU431331

UTD (UP-TO-DATE) FLAG FOR PROVIDER 4:3:1:3:3:1 

(INCLUDES 1+ VARICELLA AT AGE 12+ MTHS) BY 

36 MONTHS OF AGE, EXCLUDING ANY 

VACCINATIONS AFTER THE HOUSEHOLD 

INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

PU4313313

UTD (UP-TO-DATE) FLAG FOR PROVIDER 

4:3:1:3:3:1:3 (INCLUDES 1+ VARICELLA AT AGE 12+ 

MTHS) BY 36 MONTHS OF AGE, EXCLUDING ANY 

VACCINATIONS AFTER THE HOUSEHOLD 

INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Added in 2007 to aid analysis.

PU4313314

UTD (UP-TO-DATE) FLAG FOR PROVIDER 

4:3:1:3:3:1:4 (INCLUDES 1+ VARICELLA AT AGE 12+ 

MTHS) BY 36 MONTHS OF AGE, EXCLUDING ANY 

VACCINATIONS AFTER THE HOUSEHOLD 

INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Added in 2007 to aid analysis.
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Variable Name Variable Label† 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Notes§

PUT43133

UTD (UP-TO-DATE) FLAG FOR PROVIDER 4:3:1:3:3 

BY 36 MONTHS OF AGE, EXCLUDING ANY 

VACCINATIONS AFTER THE HOUSEHOLD 

INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

PUTD4313

UTD (UP-TO-DATE) FLAG FOR PROVIDER 4:3:1:3 BY 

36 MONTHS OF AGE, EXCLUDING ANY 

VACCINATIONS AFTER THE HOUSEHOLD 

INTERVIEW DATE.

Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Q5WEB1 INTEREST IN IHQ ON WEBSITE PROVIDER #1 Y Question was not asked starting in 

2005.

Q5WEB2 INTEREST IN IHQ ON WEBSITE PROVIDER #2 Y Question was not asked starting in 

2005.

Q5WEB3 INTEREST IN IHQ ON WEBSITE PROVIDER #3 Y Question was not asked starting in 

2005.

Q5WEB4 INTEREST IN IHQ ON WEBSITE PROVIDER #4 Y Question was not asked starting in 

2005.

Q5WEB5 INTEREST IN IHQ ON WEBSITE PROVIDER #5 Y Question was not asked starting in 

2005.

RACE_K RACE OF CHILD: IMPUTED (RECODE) Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

RACEETHK RACE AND ETHNICITY OF CHILD: IMPUTED 

(RECODE) Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

RB1_AGE AGE IN MONTHS OF PROV-REPTD RUBELLA-ONLY 

SHOT #1 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

RB2_AGE AGE IN MONTHS OF PROV-REPTD RUBELLA-ONLY 

SHOT #2 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

RB3_AGE AGE IN MONTHS OF PROV-REPTD RUBELLA-ONLY 

SHOT #3 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

RB4_AGE AGE IN MONTHS OF PROV-REPTD RUBELLA-ONLY 

SHOT #4 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

RB5_AGE AGE IN MONTHS OF PROV-REPTD RUBELLA-ONLY 

SHOT #5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

RB6_AGE AGE IN MONTHS OF PROV-REPTD RUBELLA-ONLY 

SHOT #6 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

RB7_AGE AGE IN MONTHS OF PROV-REPTD RUBELLA-ONLY 

SHOT #7 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

RB8_AGE AGE IN MONTHS OF PROV-REPTD RUBELLA-ONLY 

SHOT #8 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

RB9_AGE AGE IN MONTHS OF PROV-REPTD RUBELLA-ONLY 

SHOT #9 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

RDDWT HH-PHASE CHILD INTERVIEW WEIGHT 

(EXCLUDING U.S. VIRGIN ISLANDS) Y Y Y Y Y Y 

Added in 2005 to replace WGT_RDD. 

Replaced by RDDWT_LL in 2011 due 

to addition of dual-frame weights.

RDDWT_C FINAL SINGLE-FRAME CELLULAR PHONERDDPHASE WEIGHT (EXCLUDES TERRITORIES) Y Y Y Y Y Replaced RDDWT_D in 2018 due to 

removal of the landline sample.

RDDWT_C_TERR FINAL SINGLE-FRAME CELLULAR PHONERDDPHASE WEIGHT INCLUDING TERRITORIES Y Y Y Y 

Added in 2019 to replace 

RDDWT_D_TERR due to removal of 

the landline sample.
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Variable Name Variable Label† 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Notes§

RDDWT_D FINAL DUAL-FRAME RDD-PHASE WEIGHT 

(EXCLUDES TERRITORIES) Y Y Y Y Y Y Y 

Added in 2011 as dual-frame weight. 

Replaced by RDDWT_C in 2018 due 

to removal of the landline sample.

RDDWT_LL LANDLINE-FRAME HH-PHASE CHILD INTERIVEW 

WEIGHT (EXCLUDING U.S. VIRGIN ISLANDS) Y 

Replaced RDDWT in 2011 to 

distinguish from new dual-frame 

weight RDDWT_D. Removed in 

2012.

RDDWTVI HH-PHASE CHILD INTERVIEW WEIGHT 

(INCLUDING U.S. VIRGIN ISLANDS) Y Y 

Added in 2009 to include U.S. Virgin 

Island sample. Replaced by 

RDDWTVI_LL in 2011 due to 

addition of dual-frame weights.

RDDWTVI_D

COMBINATION OF THE DUAL-FRAME HH-PHASE 

WEIGHT FOR HOUSEHOLDS IN THE U.S. PROPER 

AND LANDLINE HH-PHASE WEIGHT FOR 

HOUSEHOLDS IN THE U.S. VIRGIN ISLANDS

Y 

Replaced RDDWTVI_LL in 2012. 

Replaced with RDDWTVIGU_D in 

2013 due to the addition of Guam 

sample.

RDDWTVIGU_D

THE DUAL-FRAME HH-PHASE WEIGHT FOR 

HOUSEHOLDS IN THE U.S. PROPER, THE U.S. 

VIRGIN ISLANDS AND GUAM

Y 

Replaced RDDWTVI_D in 2013 due 

to the addition of Guam sample. 

Replaced with RDDWT_D_TERR in 

2014 due to addition of Puerto Rico 

sample.

RDDWT_D_TERR FINAL DUAL-FRAME RDD-PHASE WEIGHT 

INCLUDING TERRITORIES Y Y Y 

Replaced RDDWTVIGU_D in 2014 

due to addition of Puerto Rico sample. 

Not available on the 2017 PUF as no 

data from U.S. territories were 

included.

RDDWTVI_LL LANDLINE-FRAME HH-PHASE CHILD INTERIVEW 

WEIGHT (INCLUDING U.S. VIRGIN ISLANDS) Y 

Replaced RDDWTVI in 2011. 

Replaced with dual-frame weight 

RDDWTVI_D in 2012.

REGISTRY CHILD'S PROVIDERS REPORTED CHILD'S 

VACCINATIONS TO IMMUNIZATION REGISTRY Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

RENT_OWN IS HOME OWNED/BEING BOUGHT, RENTED, OR 

OCCUPIED BY SOME OTHER ARRANGEMENT? Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Question added to the questionnaire 

starting in late 2008, and introduced in 

the PUF in 2009.

ROT1_AGE AGE IN MONTHS OF PROV-REPTD ROTAVIRUSCONTAINING SHOT #1 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

ROT2_AGE AGE IN MONTHS OF PROV-REPTD ROTAVIRUSCONTAINING SHOT #2 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

ROT3_AGE AGE IN MONTHS OF PROV-REPTD ROTAVIRUSCONTAINING SHOT #3 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

ROT4_AGE AGE IN MONTHS OF PROV-REPTD ROTAVIRUSCONTAINING SHOT #4 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

ROT5_AGE AGE IN MONTHS OF PROV-REPTD ROTAVIRUSCONTAINING SHOT #5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

ROT6_AGE AGE IN MONTHS OF PROV-REPTD ROTAVIRUSCONTAINING SHOT #6 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

ROT7_AGE AGE IN MONTHS OF PROV-REPTD ROTAVIRUSCONTAINING SHOT #7 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

ROT8_AGE AGE IN MONTHS OF PROV-REPTD ROTAVIRUSCONTAINING SHOT #8 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 
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Variable Name Variable Label† 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Notes§

ROT9_AGE AGE IN MONTHS OF PROV-REPTD ROTAVIRUSCONTAINING SHOT #9 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

SC_431 HH SHOT CARD REPORT OF 4:3:1 UP-TO-DATE Y Y Y Y Y Y 

Added in 2006 as a partial 

replacement for the "FULL" and "C_" 

variables. Dropped in 2012 due to 

questionnaire changes.

SC_4313 HH SHOT CARD REPORT OF 4:3:1:3 UP-TO-DATE Y Y Y Y Y Y 

Added in 2006 as a partial 

replacement for the "FULL" and "C_" 

variables. Dropped in 2012 due to 

questionnaire changes.

SC_43133 HH SHOT CARD REPORT OF 4:3:1:3:3 UP-TO-DATE Y Y Y Y Y Y 

Added in 2006 as a partial 

replacement for the "FULL" and "C_" 

variables. Dropped in 2012 due to 

questionnaire changes.

SC_DTP HH SHOT CARD REPORT OF 4+ DT-CONTAINING 

UP-TO-DATE Y Y Y Y Y Y 

Added in 2006 as a partial 

replacement for the "FULL" and "C_" 

variables. Dropped in 2012 due to 

questionnaire changes.

SC_HEPB HH SHOT CARD REPORT OF 3+ HEPATITIS BCONTAINING UP-TO-DATE Y Y Y Y Y Y 

Added in 2006 as a partial 

replacement for the "FULL" and "C_" 

variables. Dropped in 2012 due to 

questionnaire changes.

SC_HIB HH SHOT CARD REPORT OF 3+ HIB-CONTAINING 

UP-TO-DATE Y Y Y Y Y Y 

Added in 2006 as a partial 

replacement for the "FULL" and "C_" 

variables. Dropped in 2012 due to 

questionnaire changes.

SC_MCV HH SHOT CARD REPORT OF 1+ MEASLESCONTAINING UP-TO-DATE Y Y Y Y Y Y 

Added in 2006 as a partial 

replacement for the "FULL" and "C_" 

variables. Dropped in 2012 due to 

questionnaire changes.

SC_POL HH SHOT CARD REPORT OF 3+ POLIO-CONTAINING 

UP-TO-DATE Y Y Y Y Y Y 

Added in 2006 as a partial 

replacement for the "FULL" and "C_" 

variables. Dropped in 2012 due to 

questionnaire changes.

SC_VRC HH SHOT CARD REPORT OF 1+ VARICELLACONTAINING UP-TO-DATE Y Y Y Y Y Y 

Added in 2006 as a partial 

replacement for the "FULL" and "C_" 

variables. Dropped in 2012 due to 

questionnaire changes.

SEQNUMC UNIQUE CHILD IDENTIFIER Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

SEQNUMHH UNIQUE HOUSEHOLD IDENTIFIER Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

SEX SEX OF CHILD: IMPUTED Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

SHORT Q1/2004 SHORT QUESTIONNAIRE STUDY FLAG Y There was no short questionnaire 

study starting in 2005.

STATE TRUE STATE OF RESIDENCE (STATE FIPS CODE) Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

STRATUM STRATUM VARIABLE FOR VARIANCE ESTIMATION Y Y Y Y Y Y Y Y Y Y Y 

Replaced STRATUM_D in 2012. 

Equal to sample frame by estimation 

area.

STRATUM_D STRATUM VARIABLE FOR DUAL-FRAME 

VARIANCE ESTIMATION Y 

Added in 2011. Equal to sample frame 

by estimation area. Replaced by 

STRATUM in 2012.
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182

Variable Name Variable Label† 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Notes§

TEL_SAMPFRAME SAMPLE FRAME INDICATOR Y Added in 2011. Dropped in 2012 due 

to use of only dual-frame weights.

U19_FLU_24D

UTD (UP-TO-DATE) FLAG FOR PROVIDER 2+ 

INFLUENZA VACCINATIONS AT LEAST 4 WEEKS-4 

DAYS APART BY 19 MONTHS OF AGE, EXCLUDING 

ANY VACCINATIONS AFTER THE HOUSEHOLD 

INTERVIEW DATE.

Y Y Y New up-to-date variable added in 

2020.

U1D_HEP BIRTH DOSE HEPATITIS B-CONTAINING GIVEN 

FROM BIRTH TO DAY 1 FLAG Y Y Y Y Y Y Y Y Y Y Y Y Added 2011 to aid analysis.

U24_FLU_24D

UTD (UP-TO-DATE) FLAG FOR PROVIDER 2+ 

INFLUENZA VACCINATIONS AT LEAST 4 WEEKS-4 

DAYS APART BY 24 MONTHS OF AGE, EXCLUDING 

ANY VACCINATIONS AFTER THE HOUSEHOLD 

INTERVIEW DATE.

Y Y Y Y Y New up-to-date variable added in 

2018.

U24_FLU3_24D

UTD (UP-TO-DATE) FLAG FOR PROVIDER 3+ 

INFLUENZA VACCINATIONS AT LEAST 4 WEEKS-4 

DAYS APART BY 24 MONTHS OF AGE, EXCLUDING 

ANY VACCINATIONS AFTER THE HOUSEHOLD 

INTERVIEW DATE.

Y Y Y New up-to-date variable added in 

2020.

U2D_HEP BIRTH DOSE HEPATITIS B-CONTAINING GIVEN 

FROM BIRTH TO DAY 2 FLAG Y Y Y Y Y Y Y Y Y Y Y Y Added 2011 to aid analysis.

U3D_HEP BIRTH DOSE HEPATITIS B-CONTAINING GIVEN 

FROM BIRTH TO DAY 3 FLAG Y Y Y Y Y Y Y Y Y Y Y Y Added 2011 to aid analysis.

VFC_I DERIVED: IS CHILD VFC ELIGIBLE? Y Y Y 

Added in 2009 to aid analysis. 

Dropped starting in 2012 due to a 

change in the IHQ.

VFC_ORDER DO CHILD'S PROVIDERS ORDER VACCINES FROM 

STATE/LOCAL HEALTH DEPT? Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Added in 2006 due to a change in the 

IHQ.

VFC_PRO PARTICIPATION OF CHILD'S PROVIDERS IN 

VACCINES FOR CHILDREN PROGRAM Y Y Question was not asked starting in 

2006.

VRC1_AGE AGE IN MONTHS OF PROV-REPTD VARICELLACONTAINING SHOT #1 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

VRC2_AGE AGE IN MONTHS OF PROV-REPTD VARICELLACONTAINING SHOT #2 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

VRC3_AGE AGE IN MONTHS OF PROV-REPTD VARICELLACONTAINING SHOT #3 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

VRC4_AGE AGE IN MONTHS OF PROV-REPTD VARICELLACONTAINING SHOT #4 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

VRC5_AGE AGE IN MONTHS OF PROV-REPTD VARICELLACONTAINING SHOT #5 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

VRC6_AGE AGE IN MONTHS OF PROV-REPTD VARICELLACONTAINING SHOT #6 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

VRC7_AGE AGE IN MONTHS OF PROV-REPTD VARICELLACONTAINING SHOT #7 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

VRC8_AGE AGE IN MONTHS OF PROV-REPTD VARICELLACONTAINING SHOT #8 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.
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183

Variable Name Variable Label† 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Notes§

VRC9_AGE AGE IN MONTHS OF PROV-REPTD VARICELLACONTAINING SHOT #9 Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

WGT NEW WEIGHT FOR CHILDREN WITH ADEQUATE 

PROVIDER DATA AND UNVACCINATED CHILDREN Y Replaced by PROVWT starting in 

2005.

WGT_RDD RDD CHILD INTERVIEW WEIGHT Y Replaced by RDDWT starting in 

2005.

XDTPTY1 DT-CONTAINING VACCINATION #1 TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XDTPTY2 DT-CONTAINING VACCINATION #2 TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XDTPTY3 DT-CONTAINING VACCINATION #3 TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XDTPTY4 DT-CONTAINING VACCINATION #4 TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XDTPTY5 DT-CONTAINING VACCINATION #5 TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XDTPTY6 DT-CONTAINING VACCINATION #6 TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XDTPTY7 DT-CONTAINING VACCINATION #7 TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XDTPTY8 DT-CONTAINING VACCINATION #8 TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XDTPTY9 DT-CONTAINING VACCINATION #9 TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

XFLUTY1 SEASONAL FLU-CONTAINING VACCINATION #1 

TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Starting in 2008, influenza type boxes 

were added to the IHQ shot grid.

XFLUTY2 SEASONAL FLU-CONTAINING VACCINATION #2 

TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Starting in 2008, influenza type boxes were added to the IHQ shot grid.

XFLUTY3 SEASONAL FLU-CONTAINING VACCINATION #3 

TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Starting in 2008, influenza type boxes were added to the IHQ shot grid.

XFLUTY4 SEASONAL FLU-CONTAINING VACCINATION #4 

TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Starting in 2008, influenza type boxes were added to the IHQ shot grid.

XFLUTY5 SEASONAL FLU-CONTAINING VACCINATION #5 

TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Starting in 2008, influenza type boxes were added to the IHQ shot grid.

XFLUTY6 SEASONAL FLU-CONTAINING VACCINATION #6 

TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Starting in 2008, influenza type boxes were added to the IHQ shot grid.

XFLUTY7 SEASONAL FLU-CONTAINING VACCINATION #7 

TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Starting in 2008, influenza type boxes were added to the IHQ shot grid.

XFLUTY8 SEASONAL FLU-CONTAINING VACCINATION #8 

TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Starting in 2008, influenza type boxes 

were added to the IHQ shot grid.

XFLUTY9 SEASONAL FLU-CONTAINING VACCINATION #9 

TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Starting in 2008, influenza type boxes 

were added to the IHQ shot grid.

XH1NTY1 MONOVALENT 2009 H1N1 INFLUENZA 

VACCINATION #1 TYPE CODE Y Y Y 

H1N1 influenza added to the IHQ 

shotgrid starting in late 2009, and

introduced in the PUF in 2010. 

Removed from the IHQ and the PUF 

in 2013.

XH1NTY2 MONOVALENT 2009 H1N1 INFLUENZA 

VACCINATION #2 TYPE CODE Y Y Y 

H1N1 influenza added to the IHQ 

shotgrid starting in late 2009, and 

introduced in the PUF in 2010. 

Removed from the IHQ and the PUF 

in 2013.
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184

Variable Name Variable Label† 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Notes§

XH1NTY3 MONOVALENT 2009 H1N1 INFLUENZA 

VACCINATION #3 TYPE CODE Y Y Y 

H1N1 influenza added to the IHQ 

shotgrid starting in late 2009, and 

introduced in the PUF in 2010. 

Removed from the IHQ and the PUF 

in 2013.

XH1NTY4 MONOVALENT 2009 H1N1 INFLUENZA 

VACCINATION #4 TYPE CODE Y Y Y 

H1N1 influenza added to the IHQ 

shotgrid starting in late 2009, and

introduced in the PUF in 2010. 

Removed from the IHQ and the PUF 

in 2013.

XH1NTY5 MONOVALENT 2009 H1N1 INFLUENZA 

VACCINATION #5 TYPE CODE Y Y Y 

H1N1 influenza added to the IHQ 

shotgrid starting in late 2009, and 

introduced in the PUF in 2010. 

Removed from the IHQ and the PUF 

in 2013.

XH1NTY6 MONOVALENT 2009 H1N1 INFLUENZA 

VACCINATION #6 TYPE CODE Y Y Y 

H1N1 influenza added to the IHQ 

shotgrid starting in late 2009, and

introduced in the PUF in 2010. 

Removed from the IHQ and the PUF 

in 2013.

XH1NTY7 MONOVALENT 2009 H1N1 INFLUENZA 

VACCINATION #7 TYPE CODE Y Y Y 

H1N1 influenza added to the IHQ 

shotgrid starting in late 2009, and 

introduced in the PUF in 2010. 

Removed from the IHQ and the PUF 

in 2013.

XH1NTY8 MONOVALENT 2009 H1N1 INFLUENZA 

VACCINATION #8 TYPE CODE Y Y Y 

H1N1 influenza added to the IHQ 

shotgrid starting in late 2009, and

introduced in the PUF in 2010. 

Removed from the IHQ and the PUF 

in 2013.

XH1NTY9 MONOVALENT 2009 H1N1 INFLUENZA 

VACCINATION #9 TYPE CODE Y Y Y 

H1N1 influenza added to the IHQ 

shotgrid starting in late 2009, and 

introduced in the PUF in 2010. 

Removed from the IHQ and the PUF 

in 2013.

XHEPTY1 HEPATITIS B-CONTAINING VACCINATION #1 TYPE 

CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XHEPTY2 HEPATITIS B-CONTAINING VACCINATION #2 TYPE 

CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XHEPTY3 HEPATITIS B-CONTAINING VACCINATION #3 TYPE 

CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XHEPTY4 HEPATITIS B-CONTAINING VACCINATION #4 TYPE 

CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XHEPTY5 HEPATITIS B-CONTAINING VACCINATION #5 TYPE 

CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XHEPTY6 HEPATITIS B-CONTAINING VACCINATION #6 TYPE 

CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XHEPTY7 HEPATITIS B-CONTAINING VACCINATION #7 TYPE 

CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XHEPTY8 HEPATITIS B-CONTAINING VACCINATION #8 TYPE 

CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 
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Variable Name Variable Label† 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Notes§

XHEPTY9 HEPATITIS B-CONTAINING VACCINATION #9 TYPE 

CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

XHIBTY1 HIB-CONTAINING VACCINATION #1 TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XHIBTY2 HIB-CONTAINING VACCINATION #2 TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XHIBTY3 HIB-CONTAINING VACCINATION #3 TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XHIBTY4 HIB-CONTAINING VACCINATION #4 TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XHIBTY5 HIB-CONTAINING VACCINATION #5 TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XHIBTY6 HIB-CONTAINING VACCINATION #6 TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XHIBTY7 HIB-CONTAINING VACCINATION #7 TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XHIBTY8 HIB-CONTAINING VACCINATION #8 TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XHIBTY9 HIB-CONTAINING VACCINATION #9 TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

XMMRTY1 MEASLES-CONTAINING VACCINATION #1 TYPE 

CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XMMRTY2 MEASLES-CONTAINING VACCINATION #2 TYPE 

CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XMMRTY3 MEASLES-CONTAINING VACCINATION #3 TYPE 

CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XMMRTY4 MEASLES-CONTAINING VACCINATION #4 TYPE 

CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XMMRTY5 MEASLES-CONTAINING VACCINATION #5 TYPE 

CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

XMMRTY6 MEASLES-CONTAINING VACCINATION #6 TYPE 

CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

XMMRTY7 MEASLES-CONTAINING VACCINATION #7 TYPE 

CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

XMMRTY8 MEASLES-CONTAINING VACCINATION #8 TYPE 

CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

XMMRTY9 MEASLES-CONTAINING VACCINATION #9 TYPE 

CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

XPCVTY1 PNEUMOCOCCAL-CONTAINING VACCINATION #1 

TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XPCVTY2 PNEUMOCOCCAL-CONTAINING VACCINATION #2 

TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XPCVTY3 PNEUMOCOCCAL-CONTAINING VACCINATION #3 

TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XPCVTY4 PNEUMOCOCCAL-CONTAINING VACCINATION #4 

TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XPCVTY5 PNEUMOCOCCAL-CONTAINING VACCINATION #5 

TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 
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Variable Name Variable Label† 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Notes§

XPCVTY6 PNEUMOCOCCAL-CONTAINING VACCINATION #6 

TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XPCVTY7 PNEUMOCOCCAL-CONTAINING VACCINATION #7 

TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XPCVTY8 PNEUMOCOCCAL-CONTAINING VACCINATION #8 

TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XPCVTY9 PNEUMOCOCCAL-CONTAINING VACCINATION #9 

TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

XPOLTY1 POLIO-CONTAINING VACCINATION #1 TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XPOLTY2 POLIO-CONTAINING VACCINATION #2 TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XPOLTY3 POLIO-CONTAINING VACCINATION #3 TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XPOLTY4 POLIO-CONTAINING VACCINATION #4 TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XPOLTY5 POLIO-CONTAINING VACCINATION #5 TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XPOLTY6 POLIO-CONTAINING VACCINATION #6 TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XPOLTY7 POLIO-CONTAINING VACCINATION #7 TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XPOLTY8 POLIO-CONTAINING VACCINATION #8 TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

XPOLTY9 POLIO-CONTAINING VACCINATION #9 TYPE CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

Starting in 2005, nine shot variables 

are included for each vaccine 

category.

XROTTY1 ROTAVIRUS-CONTAINING VACCINATION #1 TYPE 

CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Rotavirus vaccination types were added to the IHQ starting 2009.

XROTTY2 ROTAVIRUS-CONTAINING VACCINATION #2 TYPE 

CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Rotavirus vaccination types were added to the IHQ starting 2009.

XROTTY3 ROTAVIRUS-CONTAINING VACCINATION #3 TYPE 

CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Rotavirus vaccination types were added to the IHQ starting 2009.

XROTTY4 ROTAVIRUS-CONTAINING VACCINATION #4 TYPE 

CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Rotavirus vaccination types were added to the IHQ starting 2009.

XROTTY5 ROTAVIRUS-CONTAINING VACCINATION #5 TYPE 

CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Rotavirus vaccination types were added to the IHQ starting 2009.

XROTTY6 ROTAVIRUS-CONTAINING VACCINATION #6 TYPE 

CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Rotavirus vaccination types were added to the IHQ starting 2009.

XROTTY7 ROTAVIRUS-CONTAINING VACCINATION #7 TYPE 

CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Rotavirus vaccination types were 

added to the IHQ starting 2009.

XROTTY8 ROTAVIRUS-CONTAINING VACCINATION #8 TYPE 

CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Rotavirus vaccination types were 

added to the IHQ starting 2009.

XROTTY9 ROTAVIRUS-CONTAINING VACCINATION #9 TYPE 

CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Rotavirus vaccination types were 

added to the IHQ starting 2009.

XVRCTY1 VARICELLA-CONTAINING VACCINATION #1 TYPE 

CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Varicella vaccination types were 

added to the IHQ starting 2006.

XVRCTY2 VARICELLA-CONTAINING VACCINATION #2 TYPE 

CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Varicella vaccination types were 

added to the IHQ starting 2006.

XVRCTY3 VARICELLA-CONTAINING VACCINATION #3 TYPE 

CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Varicella vaccination types were 

added to the IHQ starting 2006.

XVRCTY4 VARICELLA-CONTAINING VACCINATION #4 TYPE 

CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Varicella vaccination types were 

added to the IHQ starting 2006.

XVRCTY5 VARICELLA-CONTAINING VACCINATION #5 TYPE 

CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Varicella vaccination types were 

added to the IHQ starting 2006.
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187

Variable Name Variable Label† 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Notes§

XVRCTY6 VARICELLA-CONTAINING VACCINATION #6 TYPE 

CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Varicella vaccination types were 

added to the IHQ starting 2006.

XVRCTY7 VARICELLA-CONTAINING VACCINATION #7 TYPE 

CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Varicella vaccination types were 

added to the IHQ starting 2006.

XVRCTY8 VARICELLA-CONTAINING VACCINATION #8 TYPE 

CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Varicella vaccination types were 

added to the IHQ starting 2006.

XVRCTY9 VARICELLA-CONTAINING VACCINATION #9 TYPE 

CODE Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Varicella vaccination types were 

added to the IHQ starting 2006.

YEAR YEAR OF INTERVIEW Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y 

* For a list of variables that appeared in one or more (but not all) NIS-Child public-use data files from 1995-2004, see "Alphabetical Listing of Variables that are Not Available in All Public-Use Data 

Files, National Immunization Survey, 1995-2004": http://www.cdc.gov/nchs/data/nis/pufvariables1995to2004.pdf

† If the variable appeared in the 2022 NIS-Child public-use data file, then the 2022 label is given; otherwise the label from the most recent NIS-Child public-use data file in which the variable 

appeared is given.

§ Starting in 2005, a code of 77 is used for "Don't Know" responses and a code of 99 is used for "Refused" responses.
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188

Appendix F: Summary Tables 

Table F.1: Estimated Population Totals and Sample Sizes of Children 19 through 35 Months of 

Age by State and Estimation Area, National Immunization Survey - Child, 2022 

State/Estimation Area

Estimation 

Area Number 

(ESTIAP22) 

Estimated 

Population 

Total of 

Children

Number of 

Children 

with 

Complete 

Household 

Interviews

Number of 

Children 

with 

Adequate 

Provider 

Data

Percent of 

Children with 

Adequate 

Provider Data

U.S. National† 5,336,063 34,675 17,232 49.7

Alabama 20 82,735 515 229 44.5

Alaska 74 12,468 488 285 58.4

Arizona 66 116,378 547 278 50.8

Arkansas 46 50,238 620 291 46.9

California 68 626,309 982 438 44.6

Colorado 60 89,131 625 308 49.3

Connecticut 1 50,597 464 233 50.2

Delaware 13 14,978 371 194 52.3

District of Columbia 12 10,979 677 328 48.4

Florida 22 319,081 879 374 42.5

Georgia 25 182,636 652 304 46.6

Hawaii 72 23,294 471 220 46.7

Idaho 75 32,236 497 278 55.9

Illinois 194,051 1,247 604 48.4

 IL-City of Chicago 35 45,045 415 196 47.2

 IL-Rest of State 34 149,006 832 408 49.0

Indiana 36 115,122 452 224 49.6

Iowa 56 52,281 513 271 52.8

Kansas 57 51,093 699 382 54.6

Kentucky 27 74,287 650 310 47.7

Louisiana 47 82,224 951 430 45.2

Maine 4 16,883 420 221 52.6

Maryland 14 102,002 910 481 52.9

Massachusetts 2 99,379 515 265 51.5

Michigan 38 151,176 847 457 54.0

Minnesota 40 93,692 541 266 49.2

Mississippi 28 52,113 650 297 45.7

Missouri 58 101,311 724 390 53.9

Montana 61 16,838 411 192 46.7

Nebraska 59 34,208 431 242 56.1

Nevada 73 52,640 734 324 44.1
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189

State/Estimation Area

Estimation 

Area Number 

(ESTIAP22) 

Estimated 

Population 

Total of 

Children

Number of 

Children 

with 

Complete 

Household 

Interviews

Number of 

Children 

with 

Adequate 

Provider 

Data

Percent of 

Children with 

Adequate 

Provider Data

New Hampshire 5 17,694 394 201 51.0

New Jersey 8 146,740 630 277 44.0

New Mexico 49 30,760 705 375 53.2

New York 300,644 1,323 634 47.9

 NY-City of New York 11 142,275 747 355 47.5

 NY-Rest of State 10 158,368 576 279 48.4

North Carolina 29 171,737 891 441 49.5

North Dakota 62 13,829 497 244 49.1

Ohio 41 190,419 841 424 50.4

Oklahoma 50 71,211 550 257 46.7

Oregon 76 58,780 425 215 50.6

Pennsylvania 191,305 1,344 668 49.7

 PA-Philadelphia County 17 29,442 596 306 51.3

 PA-Rest of State 16 161,863 748 362 48.4

Rhode Island 6 14,608 585 327 55.9

South Carolina 30 83,051 596 266 44.6

South Dakota 63 15,522 535 284 53.1

Tennessee 31 118,362 668 347 51.9

Texas 554,171 1,732 793 45.8

 TX-Bexar County 55 38,229 428 206 48.1

 TX-City of Houston 54 68,070 493 226 45.8

 TX-Rest of State 51 447,872 811 361 44.5

Utah 64 66,482 563 335 59.5

Vermont 7 7,274 521 317 60.8

Virginia 18 141,389 1,065 504 47.3

Washington 77 120,680 710 394 55.5

West Virginia 19 25,538 535 260 48.6

Wisconsin 44 86,620 564 278 49.3

Wyoming 65 8,887 518 275 53.1

Puerto Rico 106 25,564 1,028 343 33.4

*Excludes U.S. territories. 
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190

Table F.2: Estimated Population Totals and Sample Sizes for Age Group by Maternal Education, 

National Immunization Survey - Child, 2022 

Age Group in 

Months Maternal Education

Children with 

Completed 

Household 

Interviews* 

Unweighted 

Completes

Children with 

Completed 

Household 

Interviews*

Weighted 

Completes†

Children with 

Adequate 

Provider 

Data*

Unweighted 

Completes

Children with 

Adequate 

Provider 

Data*

Weighted 

Completes§

19-23 Months <12 Years 540 159,514 267 183,955

19-23 Months 12 Years 1,689 440,120 779 441,079

19-23 Months

>12, Non College 

Graduate 2,585 336,598 1,235 314,076

19-23 Months College Graduate 5,494 628,017 2,920 625,139

24-29 Months <12 Years 567 184,604 250 174,778

24-29 Months 12 Years 1,735 499,275 758 455,626

24-29 Months

>12, Non College 

Graduate 2,589 389,463 1,303 447,599

24-29 Months College Graduate 5,619 736,375 2,965 731,715

30-35 Months <12 Years 754 205,662 331 206,499

30-35 Months 12 Years 2,314 567,649 1,002 594,715

30-35 Months

>12, Non College 

Graduate 3,337 393,181 1,534 370,581

30-35 Months College Graduate 7,452 795,606 3,888 790,301

Total 34,675 5,336,063 17,232 5,336,063

* Excludes U.S. territories. 

† Weighted by RDDWT_C. 

§ Weighted by PROVWT_C. 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Appendix F

191

Table F.3: Estimated Population Totals and Sample Sizes for Age Group by Poverty Status, 

National Immunization Survey - Child, 2022 

Age Group in 

Months Poverty Status

Children 

with 

Completed 

Household 

Interviews*

Unweighted 

Completes

Children 

with 

Completed 

Household 

Interviews*

Weighted 

Completes†

Children

with 

Adequate 

Provider 

Data*

Unweighted 

Completes

Children 

with 

Adequate 

Provider 

Data*

Weighted 

Completes§

19-23 Months Above poverty, > $75K 5,255 626,491 2,780 615,760

19-23 Months Above poverty, <= $75K 2,877 472,400 1,450 478,800

19-23 Months Below poverty 1,578 352,857 799 351,940

19-23 Months Unknown 598 112,500 172 117,748

24-29 Months Above poverty, > $75K 5,401 767,376 2,862 797,795

24-29 Months Above poverty, <= $75K 3,015 534,911 1,505 531,771

24-29 Months Below poverty 1,562 398,546 761 405,084

24-29 Months Unknown 532 108,884 148 75,068

30-35 Months Above poverty, > $75K 7,127 800,625 3,650 782,190

30-35 Months Above poverty, <= $75K 3,983 597,734 1,945 593,327

30-35 Months Below poverty 2,075 431,664 957 445,271

30-35 Months Unknown 672 132,075 203 141,309

Total 34,675 5,336,063 17,232 5,336,063

* Excludes U.S. territories. 

† Weighted by single-frame cellular phone weight RDDWT_C.

§ Weighted by single-frame cellular phone weight PROVWT_C
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Appendix F

192

Table F.4: Estimated Population Totals and Sample Sizes for Race and Ethnicity by Poverty 

Status, National Immunization Survey - Child, 2022

Race and Ethnicity† Poverty Status

Children with 

Completed 

Household 

Interviews*

Unweighted 

Completes

Children with 

Completed 

Household 

Interviews*

Weighted 

Completes§

Children with 

Adequate 

Provider 

Data*

Unweighted 

Completes

Children with 

Adequate 

Provider 

Data*

Weighted 

Completes¶

Hispanic Above poverty, > $75K 2,189 371,050 1,037 352,028

Hispanic Above poverty, <= $75K 2,120 496,807 1,032 467,738

Hispanic Below poverty 1,684 512,157 832 544,039

Hispanic Unknown 467 129,627 155 145,302

Non-Hispanic White Only Above poverty, > $75K 11,768 1,263,697 6,407 1,269,023

Non-Hispanic White Only Above poverty, <= $75K 5,035 628,746 2,581 642,145

Non-Hispanic White Only Below poverty 1,737 296,559 849 301,134

Non-Hispanic White Only Unknown 796 121,130 214 96,594

Non-Hispanic Black Only Above poverty, > $75K 1,062 178,411 426 169,950

Non-Hispanic Black Only Above poverty, <= $75K 1,204 257,205 519 266,400

Non-Hispanic Black Only Below poverty 932 236,761 408 222,265

Non-Hispanic Black Only Unknown 216 50,624 50 39,163

Non-Hispanic Other & 

Multiple Races Above poverty, > $75K 2,764 381,335 1,422 404,744

Non-Hispanic Other & 

Multiple Races Above poverty, <= $75K 1,516 222,288 768 227,615

Non-Hispanic Other & 

Multiple Races Below poverty 862 137,590 428 134,857

Non-Hispanic Other & 

Multiple Races Unknown 323 52,079 104 53,065

Total 34,675 5,336,063 17,232 5,336,063

* Excludes U.S. territories.

† Race and ethnicity categories are self-reported and mutually exclusive.

§ Weighted by RDDWT_C. 

¶ Weighted by PROVWT_C. 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Appendix F

193

Table F.5: Estimated Population Totals and Sample Sizes for Age Group by Race and Ethnicity, 

National Immunization Survey - Child, 2022 

Age Group in 

Months Race and Ethnicity of Child†

Children with 

Completed 

Household 

Interviews*

Unweighted 

Completes

Children with 

Completed 

Household 

Interviews*

Weighted 

Completes§

Children with 

Adequate 

Provider 

Data*

Unweighted 

Completes

Children with 

Adequate 

Provider 

Data*

Weighted 

Completes¶

19-23 Months Hispanic 1,934 435,739 937 427,573

19-23 Months Non-Hispanic White Only 5,761 676,475 3,028 677,926

19-23 Months Non-Hispanic Black Only 1,054 219,269 448 214,336

19-23 Months Non-Hispanic Other & Multiple 

Races 1,559 232,765 788 244,413

24-29 Months Hispanic 1,959 511,100 935 523,246

24-29 Months Non-Hispanic White Only 5,843 795,110 3,076 784,200

24-29 Months Non-Hispanic Black Only 1,038 232,966 427 224,919

24-29 Months Non-Hispanic Other & Multiple 

Races 1,670 270,542 838 277,353

30-35 Months Hispanic 2,567 562,801 1,184 558,288

30-35 Months Non-Hispanic White Only 7,732 838,547 3,947 846,770

30-35 Months Non-Hispanic Black Only 1,322 270,765 528 258,524

30-35 Months Non-Hispanic Other & Multiple 

Races 2,236 289,984 1,096 298,515

Total 34,675 5,336,063 17,232 5,336,063

* Excludes U.S. territories.

† Race and ethnicity categories are self-reported and mutually exclusive.

§ Weighted by RDDWT_C. 

¶ Weighted by PROVWT_C. 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Appendix F

194

Table F.6: Estimated Population Totals and Sample Sizes for Age Group by Sex, National 

Immunization Survey - Child, 2022 

Age Group in 

Months Sex 

Children with 

Completed 

Household 

Interviews*

Unweighted 

Completes

Children with 

Completed 

Household 

Interviews*

Weighted 

Completes†

Children with 

Adequate 

Provider 

Data*

Unweighted 

Completes

Children with 

Adequate 

Provider 

Data*

Weighted 

Completes§

19-23 Months Male 5,336 815,197 2,701 800,001

19-23 Months Female 4,972 749,052 2,500 764,247

 

24-29 Months Male 5,382 921,304 2,722 929,013

24-29 Months Female 5,128 888,413 2,554 880,705

 

30-35 Months Male 7,069 991,701 3,434 999,189

30-35 Months Female 6,788 970,396 3,321 962,909

Total 34,675 5,336,063 17,232 5,336,063

* Excludes U.S. territories. 

† Weighted by RDDWT_C. 

§ Weighted by PROVWT_C. 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File Appendix F

195

Table F.7: Estimated Vaccination Coverage* with Individual Vaccines and Selected Vaccination Series Among Children 19-35 Months 

of Age by State and Estimation Area, National Immunization Survey - Child, 2022†

 ≥4 DTaP§ ≥3 Polio¶ ≥1 MMR** Hib-FS†† ≥3 HepB¶¶ HepB Birth 

Dose ≥1 HepA ≥1 Var*** ≥4 PCV††† Rotavirus 4:3:1:3*:3:1:4§§§

U.S. National¶¶¶ 84.5 ± 1.2 93.5 ± 0.8 93.0 ± 0.7 81.3 ± 1.2 92.9 ± 0.8 81.6 ± 1.1 90.5 ± 0.8 92.6 ± 0.8 83.8 ± 1.2 77.4 ± 1.3 72.1 ± 1.4

Alabama 88.2 ± 5.1 95.8 ± 3.1 92.5 ± 4.7 76.8 ± 7.1 93.9 ± 2.9 80.3 ± 6.3 90.3 ± 4.7 95.7 ± 3.1 82.1 ± 6.5 73.6 ± 7.2 66.3 ± 7.9

Alaska 78.4 ± 6.8 88.6 ± 5.6 86.3 ± 5.9 75.0 ± 7.3 89.0 ± 5.4 81.9 ± 5.3 83.9 ± 6.3 83.1 ± 6.3 79.0 ± 6.8 68.1 ± 7.7 64.3 ± 7.7

Arizona 81.3 ± 6.1 89.8 ± 5.0 89.9 ± 5.0 78.2 ± 6.5 89.2 ± 4.9 77.7 ± 6.5 89.6 ± 5.1 91.0 ± 4.8 79.8 ± 7.0 71.5 ± 7.5 69.2 ± 7.4

Arkansas 81.5 ± 6.0 91.0 ± 4.2 92.2 ± 4.2 83.3 ± 5.5 90.6 ± 4.3 81.4 ± 6.0 90.9 ± 4.2 92.3 ± 4.1 78.3 ± 6.5 77.5 ± 6.4 72.5 ± 7.0

California 84.0 ± 5.5 94.6 ± 3.5 94.8 ± 3.2 83.1 ± 5.5 93.5 ± 3.4 78.8 ± 5.7 94.3 ± 3.3 93.3 ± 3.5 83.0 ± 5.5 81.5 ± 5.6 69.5 ± 6.6

Colorado 85.5 ± 4.7 94.2 ± 3.1 92.9 ± 3.5 83.8 ± 4.9 93.3 ± 3.3 81.0 ± 5.6 88.6 ± 4.1 91.6 ± 3.9 84.6 ± 5.0 74.6 ± 5.8 75.1 ± 5.9

Connecticut 90.6 ± 4.8 96.6 ± 2.4 96.9 ± 1.8 89.0 ± 5.0 93.8 ± 3.6 81.2 ± 6.3 97.2 ± 2.2 97.0 ± 2.2 92.8 ± 4.1 88.9 ± 5.2 82.2 ± 6.0

Delaware 85.9 ± 6.4 93.5 ± 4.4 94.4 ± 4.2 83.8 ± 6.9 92.0 ± 4.7 81.5 ± 7.1 93.0 ± 4.6 92.0 ± 4.6 88.3 ± 5.9 82.6 ± 7.0 80.0 ± 7.2

Dist. of Columbia 84.0 ± 6.1 92.4 ± 4.0 88.2 ± 5.9 80.9 ± 6.5 91.7 ± 4.1 79.2 ± 6.7 92.3 ± 4.2 88.8 ± 5.6 87.2 ± 5.0 75.3 ± 7.6 74.2 ± 7.4

Florida 83.6 ± 5.5 88.3 ± 5.2 92.7 ± 3.8 80.0 ± 5.8 91.1 ± 4.1 79.3 ± 5.7 84.4 ± 5.4 93.3 ± 3.6 79.2 ± 6.5 66.7 ± 7.3 68.2 ± 7.1

Georgia 86.3 ± 5.9 94.8 ± 3.7 93.4 ± 4.5 76.9 ± 7.6 94.7 ± 3.7 83.5 ± 5.8 93.7 ± 4.5 93.1 ± 4.5 85.3 ± 6.5 78.9 ± 7.1 73.6 ± 7.9

Hawaii 84.1 ± 5.7 89.7 ± 4.7 90.5 ± 4.3 83.4 ± 5.7 90.8 ± 4.3 82.5 ± 5.7 90.5 ± 4.2 92.8 ± 3.5 83.4 ± 5.9 78.5 ± 6.8 77.6 ± 6.6

Idaho 83.1 ± 6.0 89.9 ± 5.4 90.8 ± 4.7 84.0 ± 6.0 92.5 ± 4.8 80.6 ± 5.9 87.9 ± 5.6 88.4 ± 5.5 87.3 ± 5.4 82.9 ± 6.1 74.3 ± 7.0

Illinois 87.4 ± 3.6 95.2 ± 2.3 92.2 ± 3.0 79.9 ± 4.3 92.7 ± 2.9 78.9 ± 4.3 89.0 ± 3.4 91.8 ± 3.1 87.0 ± 3.6 79.0 ± 4.4 72.7 ± 4.8

IL-City of Chicago 85.2 ± 7.4 93.1 ± 5.0 88.3 ± 6.6 74.5 ± 8.8 91.9 ± 5.3 80.6 ± 7.9 90.8 ± 5.2 90.9 ± 5.8 82.2 ± 7.9 71.3 ± 9.3 67.4 ± 9.4

IL-Rest of State 88.0 ± 4.1 95.8 ± 2.6 93.4 ± 3.4 81.5 ± 5.0 92.9 ± 3.5 78.5 ± 5.1 88.5 ± 4.2 92.0 ± 3.7 88.5 ± 4.1 81.3 ± 4.9 74.2 ± 5.5

Indiana 86.8 ± 5.6 93.1 ± 4.0 92.0 ± 4.2 85.4 ± 5.6 92.9 ± 4.0 85.8 ± 5.1 92.8 ± 4.0 92.1 ± 4.1 84.4 ± 6.1 74.9 ± 6.8 79.4 ± 6.5

Iowa 84.3 ± 6.9 94.5 ± 4.1 92.3 ± 5.0 81.6 ± 7.3 93.8 ± 4.3 84.6 ± 6.3 86.9 ± 6.2 88.5 ± 6.4 88.4 ± 5.7 83.0 ± 6.4 77.4 ± 7.7

Kansas 82.5 ± 5.3 93.2 ± 3.3 91.2 ± 3.8 77.8 ± 6.0 91.2 ± 4.1 81.7 ± 5.2 92.7 ± 3.4 91.4 ± 3.8 80.2 ± 5.9 79.4 ± 5.4 68.8 ± 6.6

Kentucky 85.4 ± 6.3 94.2 ± 4.2 93.5 ± 4.3 79.3 ± 6.9 93.7 ± 4.3 82.9 ± 5.9 91.8 ± 4.8 92.2 ± 4.8 82.9 ± 6.5 69.8 ± 7.7 70.7 ± 7.7

Louisiana 85.1 ± 5.0 96.0 ± 2.6 94.2 ± 3.2 83.0 ± 5.5 96.4 ± 2.3 78.6 ± 6.1 90.6 ± 4.2 94.4 ± 3.2 81.4 ± 5.5 78.1 ± 6.1 73.1 ± 6.3

Maine 91.1 ± 4.2 97.6 ± 2.0 97.2 ± 2.2 88.8 ± 4.8 93.7 ± 4.0 85.8 ± 5.2 95.2 ± 3.1 97.2 ± 2.2 91.2 ± 4.3 85.7 ± 5.3 80.4 ± 6.2

Maryland 88.8 ± 3.8 94.4 ± 2.8 93.5 ± 3.0 83.6 ± 4.3 91.9 ± 3.8 77.8 ± 5.0 90.1 ± 3.6 93.7 ± 2.9 89.6 ± 3.4 81.0 ± 4.7 76.9 ± 5.2

Massachusetts 90.7 ± 4.4 97.1 ± 2.0 97.1 ± 2.0 92.3 ± 3.6 95.5 ± 2.7 82.5 ± 5.9 94.1 ± 3.4 97.0 ± 2.1 91.3 ± 4.3 85.8 ± 5.7 87.1 ± 4.9

Michigan 87.6 ± 4.6 96.6 ± 2.4 96.7 ± 2.3 87.5 ± 4.4 95.6 ± 2.6 82.2 ± 5.1 96.0 ± 2.6 97.1 ± 2.2 87.3 ± 4.8 83.6 ± 5.1 80.5 ± 5.4

Minnesota 89.8 ± 4.1 94.0 ± 3.2 93.9 ± 3.4 83.0 ± 5.4 92.5 ± 3.6 81.8 ± 5.2 94.2 ± 3.1 94.1 ± 3.0 88.0 ± 4.8 82.0 ± 5.8 76.7 ± 6.3

Mississippi 84.2 ± 5.8 94.2 ± 3.6 93.4 ± 3.7 78.8 ± 6.4 92.6 ± 4.3 79.3 ± 6.6 72.9 ± 7.4 92.7 ± 3.9 83.2 ± 7.0 64.4 ± 8.4 71.6 ± 7.8

Missouri 84.8 ± 5.3 93.6 ± 3.6 92.6 ± 3.8 80.9 ± 5.4 95.1 ± 3.1 83.3 ± 5.2 84.5 ± 5.7 93.8 ± 3.4 84.9 ± 5.2 76.6 ± 5.9 70.7 ± 6.3

Montana 73.8 ± 7.5 90.3 ± 4.3 85.2 ± 6.0 73.4 ± 7.5 89.1 ± 4.9 75.3 ± 6.8 84.7 ± 6.1 85.1 ± 6.0 75.5 ± 7.5 73.9 ± 7.3 62.8 ± 8.1

Nebraska 88.9 ± 4.8 95.5 ± 2.9 95.5 ± 2.9 73.0 ± 7.3 95.1 ± 3.2 86.3 ± 6.0 93.3 ± 3.5 93.8 ± 3.5 90.5 ± 4.2 84.5 ± 5.7 67.1 ± 7.7

Nevada 79.5 ± 6.6 91.2 ± 4.7 88.4 ± 5.2 75.8 ± 6.9 91.1 ± 4.8 81.1 ± 6.2 90.3 ± 4.7 88.7 ± 5.2 80.6 ± 6.4 73.4 ± 7.0 71.2 ± 7.1

New Hampshire 89.3 ± 5.4 95.2 ± 4.0 92.5 ± 4.6 87.6 ± 5.9 93.2 ± 4.9 84.5 ± 6.4 91.1 ± 4.8 90.6 ± 5.0 88.6 ± 5.7 83.7 ± 6.3 81.0 ± 6.7

New Jersey 86.7 ± 5.6 96.2 ± 3.1 96.3 ± 2.9 84.0 ± 5.8 95.2 ± 3.3 81.7 ± 6.3 89.1 ± 4.7 93.4 ± 3.7 81.2 ± 6.4 75.8 ± 6.7 70.4 ± 7.1

New Mexico 85.9 ± 4.9 96.1 ± 2.2 92.3 ± 3.6 87.8 ± 4.0 95.1 ± 2.8 75.8 ± 6.1 91.9 ± 3.4 92.9 ± 3.4 86.9 ± 4.3 82.6 ± 5.4 77.7 ± 5.6

New York 83.7 ± 4.1 92.1 ± 3.2 91.2 ± 3.0 80.4 ± 4.4 92.2 ± 3.0 82.9 ± 4.0 86.1 ± 3.7 92.8 ± 2.6 82.1 ± 4.3 77.5 ± 4.5 72.6 ± 4.8

NY-City of New York 87.3 ± 4.8 93.6 ± 3.5 91.3 ± 3.9 85.0 ± 5.1 93.5 ± 3.3 84.2 ± 4.9 88.7 ± 4.3 93.4 ± 3.5 84.0 ± 5.3 78.6 ± 5.8 78.1 ± 5.8

NY-Rest of State 80.6 ± 6.4 90.7 ± 5.1 91.2 ± 4.4 76.3 ± 6.9 91.1 ± 4.9 81.7 ± 6.1 83.7 ± 5.9 92.3 ± 3.9 80.5 ± 6.7 76.5 ± 6.8 67.6 ± 7.4
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196

 ≥4 DTaP§ ≥3 Polio¶ ≥1 MMR** Hib-FS†† ≥3 HepB¶¶ HepB Birth 

Dose ≥1 HepA ≥1 Var*** ≥4 PCV††† Rotavirus 4:3:1:3*:3:1:4§§§

North Carolina 88.3 ± 4.3 98.3 ± 1.2 95.5 ± 2.7 86.1 ± 4.5 93.1 ± 3.5 85.3 ± 4.4 90.1 ± 4.1 94.5 ± 2.9 92.5 ± 3.4 82.9 ± 4.9 74.6 ± 5.9

North Dakota 84.9 ± 6.4 93.8 ± 4.4 92.6 ± 4.8 82.6 ± 6.7 96.9 ± 2.7 92.0 ± 3.7 90.5 ± 5.4 90.5 ± 5.3 83.3 ± 6.8 79.9 ± 7.0 74.6 ± 7.7

Ohio 84.5 ± 5.1 94.6 ± 2.9 92.0 ± 3.5 77.1 ± 5.6 92.8 ± 3.3 84.9 ± 4.5 90.0 ± 4.0 90.2 ± 4.3 84.1 ± 5.0 78.3 ± 5.6 70.7 ± 6.1

Oklahoma 75.8 ± 7.7 92.2 ± 5.1 88.6 ± 6.1 72.6 ± 7.9 90.4 ± 5.6 75.4 ± 7.6 89.5 ± 5.7 86.3 ± 6.5 76.0 ± 7.5 70.0 ± 7.8 61.2 ± 8.3

Oregon 76.8 ± 7.9 89.8 ± 5.0 91.1 ± 4.7 76.8 ± 7.9 88.9 ± 5.1 85.6 ± 5.4 89.3 ± 5.4 90.4 ± 6.2 77.1 ± 7.8 70.7 ± 8.4 67.1 ± 8.2

Pennsylvania 83.6 ± 4.3 91.4 ± 3.6 91.0 ± 3.6 78.6 ± 4.8 90.7 ± 3.7 82.7 ± 4.6 90.0 ± 3.8 90.0 ± 3.8 84.6 ± 4.5 78.7 ± 4.9 71.5 ± 5.2

PA-Philadelphia County 78.6 ± 6.5 90.4 ± 4.5 91.1 ± 4.2 77.4 ± 6.2 90.9 ± 4.3 82.0 ± 5.8 91.7 ± 4.0 91.4 ± 4.1 83.3 ± 5.8 74.1 ± 6.9 67.1 ± 7.2

PA-Rest of State 84.5 ± 4.9 91.6 ± 4.2 91.0 ± 4.2 78.9 ± 5.6 90.7 ± 4.3 82.9 ± 5.3 89.6 ± 4.4 89.8 ± 4.4 84.8 ± 5.2 79.6 ± 5.7 72.3 ± 6.0

Rhode Island 91.4 ± 3.9 97.4 ± 2.3 97.7 ± 1.9 88.7 ± 4.8 97.7 ± 2.1 79.3 ± 5.5 96.7 ± 2.7 97.1 ± 2.0 92.9 ± 4.0 94.3 ± 2.9 82.2 ± 5.5

South Carolina 83.1 ± 6.2 93.8 ± 3.9 89.3 ± 5.1 76.8 ± 6.6 95.5 ± 3.1 85.2 ± 5.9 89.0 ± 5.3 90.0 ± 4.8 82.7 ± 6.3 81.8 ± 6.3 72.1 ± 7.0

South Dakota 79.8 ± 7.1 91.7 ± 5.0 89.9 ± 5.3 77.3 ± 7.1 93.2 ± 4.5 84.0 ± 7.1 85.2 ± 6.3 89.1 ± 5.4 79.7 ± 7.1 79.5 ± 7.1 69.6 ± 7.7

Tennessee 85.0 ± 5.6 95.7 ± 3.5 94.0 ± 4.2 77.7 ± 6.5 96.6 ± 2.8 73.3 ± 7.5 92.1 ± 4.6 94.3 ± 4.0 83.7 ± 6.2 73.1 ± 7.0 71.2 ± 7.1

Texas 79.8 ± 5.8 90.7 ± 4.3 91.4 ± 3.6 79.3 ± 5.9 90.5 ± 3.8 83.2 ± 4.8 91.7 ± 3.8 91.1 ± 3.6 80.8 ± 5.8 74.4 ± 6.4 69.0 ± 6.4

TX-Bexar County 78.8 ± 7.4 89.5 ± 5.3 86.9 ± 6.2 77.1 ± 7.7 87.5 ± 5.5 77.8 ± 7.2 88.4 ± 5.6 86.2 ± 6.3 80.9 ± 7.0 75.0 ± 7.9 69.9 ± 8.2

TX-City of Houston 79.1 ± 7.3 88.8 ± 5.8 86.6 ± 5.7 80.8 ± 6.6 88.2 ± 6.0 78.1 ± 7.5 88.5 ± 5.2 85.2 ± 6.0 79.8 ± 7.1 77.3 ± 7.3 68.1 ± 8.2

TX-Rest of State 80.0 ± 7.1 91.1 ± 5.3 92.6 ± 4.3 79.2 ± 7.2 91.1 ± 4.5 84.4 ± 5.7 92.4 ± 4.6 92.5 ± 4.4 81.0 ± 7.1 73.9 ± 7.8 69.1 ± 7.8

Utah 89.5 ± 4.2 97.3 ± 1.9 96.9 ± 2.1 86.5 ± 4.7 93.5 ± 3.2 84.6 ± 4.9 94.0 ± 3.1 95.0 ± 2.7 89.4 ± 4.4 81.3 ± 5.3 77.7 ± 5.7

Vermont 88.3 ± 4.6 97.2 ± 2.4 94.8 ± 3.4 87.1 ± 5.1 96.1 ± 2.8 79.5 ± 5.3 88.6 ± 4.6 92.6 ± 3.9 91.7 ± 4.3 85.7 ± 5.0 78.5 ± 5.9

Virginia 86.2 ± 4.2 93.2 ± 3.2 93.0 ± 3.0 81.2 ± 4.7 92.3 ± 3.2 81.7 ± 4.6 90.5 ± 3.5 94.1 ± 2.8 85.8 ± 4.3 79.7 ± 5.0 73.4 ± 5.4

Washington 87.1 ± 4.4 94.7 ± 2.9 92.6 ± 4.5 85.4 ± 4.5 97.4 ± 1.4 84.9 ± 4.5 91.0 ± 3.9 92.2 ± 4.5 86.9 ± 4.3 75.5 ± 5.6 75.9 ± 5.9

West Virginia 80.3 ± 5.9 91.0 ± 4.4 89.3 ± 4.6 73.7 ± 6.5 89.1 ± 4.7 79.7 ± 6.0 88.9 ± 4.7 88.2 ± 4.8 79.5 ± 5.8 72.2 ± 6.7 63.2 ± 7.0

Wisconsin 83.9 ± 5.7 93.4 ± 4.0 95.3 ± 3.0 84.1 ± 5.5 94.3 ± 3.5 86.7 ± 5.1 89.8 ± 4.4 92.8 ± 3.8 82.9 ± 6.0 74.7 ± 6.9 71.1 ± 6.8

Wyoming 81.1 ± 6.4 91.4 ± 4.5 89.6 ± 5.3 78.9 ± 6.7 90.3 ± 4.7 80.6 ± 6.6 84.4 ± 5.9 88.0 ± 5.5 79.7 ± 6.8 75.8 ± 7.1 70.2 ± 7.4

Puerto Rico 64.8 ± 6.6 78.0 ± 5.8 75.0 ± 6.1 64.6 ± 6.6 74.2 ± 6.1 68.7 ± 6.5 78.7 ± 5.9 76.7 ± 6.0 64.3 ± 6.5 57.5 ± 6.9 51.6 ± 6.9

Guam 67.9 ± 12.4 83.9 ± 9.7 79.9 ± 11.5 67.3 ± 12.3 79.0 ± 11.6 87.0 ± 8.7 81.8 ± 10.9 79.5 ± 11.5 71.4 ± 12.2 57.2 ± 12.9 54.0 ± 12.7

* Estimates presented as point estimate (%) ± 95% Confidence Interval half width. Estimate=NA (Not Available) if the unweighted sample size for the denominator was < 30, or 

(CI half width)/Estimate > 0.588, or (CI half width) >10.

† Children in the 2022 NIS-Child were born between January 2019 through May 2021. Vaccination coverage estimates include only children who had adequate provider-reported 

immunization records.

§ 4 or more doses of diphtheria and tetanus toxoids and acellular pertussis vaccine adsorbed, diphtheria and tetanus toxoids and pertussis vaccine, or diphtheria and tetanus toxoids 

vaccine adsorbed (DTaP/DTP/DT). 

¶ 3 or more doses of any poliovirus vaccine.

** 1 or more doses of measles-mumps-rubella vaccine

†† 4 or more doses of Haemophilus influenzae type b (Hib) vaccine of any type or 2 doses of Hib of Merck types followed by 1+ dose of Hib of any type.

¶¶ 3 or more doses of hepatitis B vaccine.

*** 1 or more doses of varicella at or after child’s first birthday, unadjusted for history of varicella illness.

††† 4 or more doses of pneumococcal conjugate vaccine (PCV). §§§ 4+ diphtheria and tetanus toxoids and acellular pertussis vaccine adsorbed, diphtheria and tetanus toxoids and pertussis vaccine, or diphtheria and tetanus toxoids vaccine adsorbed (DTaP/DTP/DT); 

3+ poliovirus vaccine; 1+ measles-containing vaccine (MCV); full series Haemophilus influenzae type b conjugate vaccine (Hib), i.e., 3 or 4 doses depending on type of vaccine received; 3+ hepatitis B 

vaccine (Hep B); 1+ varicella at or after 12 months of age; and 4+ pneumococcal conjugate vaccine (PCV).

¶¶¶ U.S. national estimates exclude U.S. territories.
Data User’s Guide for the 2022 NIS-Child Public-Use Data File APPENDIX G 

197

Appendix G: Trends in NIS-Child Response Rates and 

Vaccination Coverage Rates, 1995-2022 

Table G.1: Key Indicators* from Landline Sample Household and Provider Data Collection by 

Survey Year, National Immunization Survey - Child, 1995-2017†

Survey 

Year

Resolution 

Rate (%)

Screener 

Completion 

Rate (%)

Interview 

Completion 

Rate (%)

CASRO 

Response Rate 

(%) 

Children with 

Adequate 

Provider Data 

(%) 

1995 96.5 96.4 93.5 87.1 50.6

1996 94.3 96.8 94.0 85.8 63.4

1997 92.1 97.9 93.8 84.6 69.7

1998 90.4 97.8 93.6 82.7 67.1

1999 88.6 97.0 93.4 80.2 65.4

2000 88.1 96.0 93.1 78.7 67.4

2001 86.8 96.2 91.1 76.1 70.4

2002 84.8 96.6 90.6 74.2 67.6

2003 83.6 94.0 88.7 69.8 68.9

2004 83.8 94.8 92.0 73.1 71.0

2005 83.3 92.8 84.2 65.1 63.6

2006 83.3 90.5 85.6 64.5 70.4

2007 82.9 90.2 86.8 64.9 68.6

2008 82.3 90.3 85.1 63.2 71.0

2009 82.9 92.4 83.2 63.8 68.7

2010 83.3 91.5 83.6 63.8 71.2

2011 83.0 90.7 81.7 61.6 72.3

2012 84.1 90.7 84.6 64.5 67.9

2013 83.2 91.0 82.3 62.3 63.5

2014 82.7 92.2 82.1 62.6 63.3

2015 81.9 89.9 80.3 59.1 59.7

2016 81.6 88.4 77.2 55.7 58.6

2017 80.8 84.4 76.1 51.9 57.2

* For the definition of the key indicators see Table 1 of NIS-Child Data User’s Guide for the survey year of interest.

† Excludes U.S. territories. 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File APPENDIX G 

 198

Figure G.1: Trends in Landline Sample Key Indicators from Household and Provider Data 

Collection by Survey Year, National Immunization Survey - Child, 1995-2017*

* Excludes U.S. territories. 

Figure G.1 provides a graphical representation of the data contained in Table G.1. It shows how selected 

landline sample key indicators from the household and provider data collection performed throughout the 

years, from 1995 to 2017. (The NIS utilized a single-frame cellular phone design beginning in 2018.) We 

observe that the trend in the data collection rates was downward, with the exception of the percentage of 

children with adequate provider data, which had been essentially flat from 1997-2012, but also trended 

downward from 2013-2017. Note that this chart reflects the landline sample only. 

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015

Percent

Year

Resolution Rate (%)

Screener Completion Rate (%)

Interview Completion Rate (%)

CASRO Response Rate (%)

Children with Adequate Provider Data (%)
Data User’s Guide for the 2022 NIS-Child Public-Use Data File APPENDIX G 

 199

Table G.2: Key Indicators* from Cellular Phone Sample Household and Provider Data Collection 

by Survey Year, National Immunization Survey - Child, 2011-2022†

Survey 

Year§

Resolution 

Rate (%)

Screener 

Completion 

Rate (%)

Interview 

Completion 

Rate (%)

CASRO 

Response Rate 

(%) 

Children with 

Adequate 

Provider Data 

(%) 

2011 47.0 76.2 70.4 25.2 66.7

2012 52.4 77.5 75.5 30.6 63.9

2013 53.8 79.3 71.6 30.5 59.8

2014 58.7 78.5 72.6 33.5 58.9

2015 56.3 79.3 72.2 32.2 55.5

2016 54.0 83.9 70.9 32.1 54.0

2017 43.0 83.7 69.5 25.0 53.6

2018 41.2 84.9 70.3 24.6 54.0

2019 35.2 84.4 71.1 21.1 49.4

2020 36.8 85.7 71.8 22.5 54.2

2021 39.4 84.5 68.8 22.9 51.5

2022 43.5 84.8 68.1 25.1 49.7

*For the definition of the key indicators see Table 1 of NIS-Child Data User’s Guide for the survey year of interest.

† Excludes U.S. territories. 

§ Cellular phone sample was added to the NIS-Child in 2011.
Data User’s Guide for the 2022 NIS-Child Public-Use Data File APPENDIX G 

 200

Figure G.2: Trends in Cellular Phone Sample Key Indicators from Household and Provider Data 

Collection by Survey Year, National Immunization Survey - Child, 2011-2022*

* Excludes U.S. territories. 

Figure G.2 provides a graphical representation of the data contained in Table G.2. It shows how selected 

cellular phone sample key indicators from the household and provider data collection performed from 

2011 to present. We observe that the rates since the inception of the cellular phone sample were fairly flat

from 2011 to 2016, with the exception of the percent of children with adequate provider data, which fell 

throughout this period. There was a decline in the resolution rate beginning in 2017, leading to a decline 

in the CASRO response rate, but both have been gradually rising again since 2019.

The response rate is the number of households with a completed household interview divided by the 

estimated number of eligible households in the sample. Within each sample type (landline or cellular

phone), the number of eligible households was estimated using the CASRO assumptions; these 

0

10

20

30

40

50

60

70

80

90

100

2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022

Percent

Year

Resolution Rate (%) Screener Completion Rate (%)

Interview Completion Rate (%) CASRO Response Rate (%)

Children with Adequate Provider Data (%)
Data User’s Guide for the 2022 NIS-Child Public-Use Data File APPENDIX G 

 201

assumptions are that the rate of households among the unresolved telephone numbers is the same as the 

observed rate of households among the resolved telephone numbers, and the rate of eligible households 

among unscreened households is the same as the observed rate of eligible households among screened 

households. Under these assumptions, within each sample type the CASRO response rate is equal to the 

product of the resolution rate, the screener completion rate, and the interview completion rate. For the 

combined samples, we have defined the CASRO response rate as the total number of households with a 

completed interview divided by the estimated total number of eligible households across both sample 

types, where the estimated total number of eligible households is equal to the sum of the estimated 

number of eligible households in the landline sample (using CASRO assumptions) and the estimated 

number of eligible households in the cellular phone sample (using CASRO assumptions). Table G.3 

presents the CASRO response rate calculated in this way for the combined landline and cellular phone

samples, by survey year, and Figure G.3 presents a graphical representation. Because the CASRO 

response rate is lower for the cellular phone sample than for the landline sample, the CASRO response 

rate for the combined landline and cellular phone samples was lower in years with a larger cellular phone

sample and higher in years with a smaller cellular phone sample.
Data User’s Guide for the 2022 NIS-Child Public-Use Data File APPENDIX G 

 202

Table G.3: CASRO Response Rate for the Combined Landline and Cellular Phone Samples by 

Survey Year, National Immunization Survey - Child, 2011-2017*

Survey Year†

CASRO Response Rate 

(%) 

2011 52.2

2012 41.2

2013 34.4

2014 42.5

2015 34.9

2016 33.9

2017 26.1

* Excludes U.S. territories. 

† Cellular phone sample was added to the NIS-Child in 2011. The NIS-Child transitioned from a dual-frame landline and cellular 

phone RDD sample design to a single-frame cellular phone RDD sample design beginning in 2018.

Figure G.3: Trend in CASRO Response Rate for the Combined Landline and Cellular Phone

Samples by Survey Year, National Immunization Survey - Child, 2011-2017*

* Excludes U.S. territories.

0

10

20

30

40

50

60

70

80

90

100

2011 2012 2013 2014 2015

Percent

Year

CASRO Response Rate (%)
Data User’s Guide for the 2022 NIS-Child Public-Use Data File APPENDIX G 

 203

Table G.4: Vaccine-Specific Coverage Levels Among Children Age 19-35 Months in the United 

States by Survey Year, National Immunization Survey - Child, 1995-2022*

Survey 

Year†

4+ 

DTaP

3+ 

Polio

1+ 

MMR

3+ 

Hib§

3+ 

Hep B

1+ 

Varicella¶

4+ 

PCV 4:3:1** 4:3:1:3††

1995 78.4 87.8 89.8 91.2 67.9 N.A. N.A. 76.0 73.7

1996 81.1 91.0 90.6 91.4 81.8 12.0 N.A. 78.4 76.4

1997 81.5 90.7 90.4 92.5 83.6 25.8 N.A. 77.9 76.2

1998 83.9 90.8 92.0 93.4 87.0 43.2 N.A. 80.6 79.2

1999 83.3 89.6 91.5 93.5 88.1 57.5 N.A. 79.9 78.4

2000 81.7 89.5 90.5 93.4 90.3 67.8 N.A. 77.6 76.2

2001 82.1 89.4 91.4 93.0 88.9 76.3 N.A. 78.6 77.2

2002 81.6 90.2 91.6 93.1 89.9 80.6 N.A. 78.5 77.5

2003 84.8 91.6 93.0 93.9 92.4 84.8 N.A. 82.2 81.3

2004 85.5 91.6 93.0 93.5 92.4 87.5 N.A. 83.5 82.5

2005 85.7 91.7 91.5 93.9 92.9 87.9 53.7 83.1 82.4

2006 85.2 92.8 92.3 93.4 93.3 89.2 68.4 83.1 82.2

2007 84.5 92.6 93.2 92.6 92.7 90.0 75.3 82.8 80.1

2008 84.6 93.6 92.1 90.9 93.5 90.7 80.1 82.5 79.6

2009 83.9 92.8 90.0 83.6 92.4 89.6 80.4 81.5 73.4

2010 84.4 93.3 91.5 90.4 91.8 90.4 83.3 82.0 78.8

2011 84.6 93.9 91.6 94.0 91.1 90.8 84.4 82.6 81.9

 2012§§ 82.5 92.8 90.8 93.0 89.7 90.2 81.9 80.5 80.0

2013 83.1 92.7 91.9 92.8 90.8 91.2 82.0 81.5 81.1 

2014 84.2 93.3 91.5 92.6 91.6 91.0 82.9 82.6 82.0

2015 84.6 93.7 91.9 93.2 92.6 91.8 84.1 83.2 82.6

2016 83.4 91.9 91.1 91.6 90.5 90.6 81.8 81.9 81.2

2017 83.2 92.7 91.5 91.8 91.4 91.0 82.4 81.7 80.9

2018 83.8 93.6 92.1 91.6 92.1 92.0 83.3 82.5 81.7

2019 83.3 92.3 91.3 90.9 91.3 90.9 82.5 82.3 81.7

2020 84.2 93.9 92.9 92.4 92.8 92.7 83.2 83.3 82.8

2021 84.4 93.8 92.1 92.6 93.2 91.6 84.6 83.3 82.8

2022 84.5 93.5 93.0 92.4 92.9 92.6 83.9 83.6 83.2

* Excludes U.S. territories. 

† Prior to 2011, estimates are single-frame, landline-sample estimates. From 2011-2017, estimates are dual-frame (landline plus 

cellular phone) estimates. From 2018 onward, estimates are single-frame, cellular phone estimates.

§ Beginning in 2009, the number of doses required to be up-to-date on Hib depends on the manufacturer of the vaccine. However, 

the figures shown here refer to 3 or more doses of Hib vaccine regardless of manufacturer.

¶ Varicella was added to the NIS-Child in 1996.

** Four or more doses of DTaP, three or more doses of poliovirus vaccine, and one or more doses of MCV.

†† Four or more doses of DTaP, three or more does of poliovirus vaccine, one or more doses of MCV, and three or more doses of 

Hib.

§§ Revised definition of adequate provider data (APD) implemented. 
Data User’s Guide for the 2022 NIS-Child Public-Use Data File APPENDIX G 

 204

Figure G.4: Trends in Vaccine-Specific Coverage Levels among Children 19 through 35 Months of 

Age in the United States by Survey Year, National Immunization Survey - Child, 1995-

2022*†¶

* Excludes U.S. territories. 

† Prior to 2011, estimates are single-frame, landline-sample estimates. From 2011-2017, estimates are dual-frame (landline plus 

cellular phone) estimates. From 2018 onward, estimates are single-frame, cellular phone estimates.

¶ Revised definition of adequate provider data (APD) implemented in 2012. 

Figure G.4 provides a graphical representation of the data contained in Table G.4. It displays the trend in 

vaccine-specific coverage levels among children aged 19 through 35 months from 1995 to present. We 

observe that the trend in the vaccination coverage levels is stable or slightly upward for the longerestablished vaccines, while the early trends for new vaccines are strongly upward. Note that this chart

reflects the landline sample prior to 2011, the dual-frame sample in 2011-2017, and the single-frame 

cellular phone sample thereafter. For more information on interpreting trends in vaccination coverage, see 

online reports at https://www.cdc.gov/vaccines/imz-managers/coverage/childvaxview/pubspresentations/NIS-vax-trends-2012-2016.html.

0

10

20

30

40

50

60

70

80

90

100

1995199619971998199920002001200220032004200520062007200820092010201120122013201420152016201720182019202020212022

Percent

Year

Survey Year† 4+ DTaP 3+ Polio

1+ MMR 3+ Hib 3+ Hep B

1+ Varicella 4+ PCV 4:3:1**
Data User’s Guide for the 2022 NIS-Child Public-Use Data File APPENDIX H 

205

Appendix H: Vaccine Type Codes 

Table H.1: 2022 NIS-Child Vaccine Type Codes

Vaccine Code Description

03 DTaP/DTP/DT-containing, unknown type

04 DTaP/DTP/DT-only

07 DTaP-Hib

08 DTaP-HepB-IPV

20 OPV

21 IPV

22 Polio-containing, unknown type

30 Measles-mumps-rubella

31 Measles-only

32 Measles-mumps

33 Measles-rubella

43 HepB-Hib

44 Hib-only, unknown type

60 HepB-only

70 Pneumococcal conjugate, unknown type

71 Pneumococcal polysaccharide

72 Pneumococcal-containing, unknown type

73 Pneumococcal conjugate-7

74 Pneumococcal conjugate-13

75 Pneumococcal conjugate-15

D3 DTaP-IPV-Hib

D4 DTaP-IPV-Hib-HepB

FL Seasonal influenza, unknown type

FM Seasonal influenza spray

FN Injected seasonal influenza

HB HepB-containing, unknown type

HG Hib-only (GSK)

HI Hib-containing, unknown type

HM Hib-only (Merck)

HS Hib-only (Sanofi)

HY Hib-MenCY

MM Measles-containing, unknown type

RG Rotarix (GSK)

RM Rotateq (Merck)

RO Rotavirus-containing, unknown type

VA Varicella-containing, unknown type

VM MMR-varicella

VO Varicella-only
Data User’s Guide for the 2022 NIS-Child Public-Use Data File APPENDIX I 

206

Appendix I: Key NIS-Child Response Rates by Area

Table I.1: Key Indicators* for the Cellular Phone Sample by Estimation Area, National 

Immunization Survey - Child, 2022 

Area Resolution 

Rate (%)

Screener 

Completion 

Rate (%)

Interview 

Completion 

Rate (%)

CASRO 

Response 

Rate (%)

Children with 

Adequate Provider 

Data (%)

U.S. National† 43.5 84.8 68.1 25.1 49.7

Alabama 47.8 85.0 63.1 25.6 44.5

Alaska 50.6 82.5 75.6 31.5 58.4

Arizona 38.9 86.1 64.3 21.6 50.8

Arkansas 52.1 83.4 64.5 28.0 46.9

California 38.2 86.0 61.7 20.3 44.6

Colorado 37.2 88.3 71.1 23.3 49.3

Connecticut 33.8 85.1 72.1 20.8 50.2

Delaware 41.2 83.5 63.5 21.9 52.3

District of Columbia 42.3 82.4 72.6 25.3 48.4

Florida 36.2 81.5 68.8 20.3 42.5

Georgia 42.4 84.4 63.6 22.8 46.6

Hawaii 33.1 84.3 60.8 17.0 46.7

Idaho 33.8 83.6 77.9 22.0 55.9

Illinois 46.7 85.5 67.9 27.2 48.4

 IL-City of Chicago 47.9 84.2 68.5 27.7 47.2

 IL-Rest of State 46.0 86.4 67.5 26.8 49.0

Indiana 43.8 86.1 64.6 24.4 49.6

Iowa 47.5 85.8 72.1 29.4 52.8

Kansas 46.9 86.2 67.9 27.4 54.6

Kentucky 44.7 84.0 70.5 26.5 47.7

Louisiana 47.8 83.0 62.5 24.8 45.2

Maine 42.1 86.4 66.3 24.1 52.6

Maryland 35.8 79.5 74.1 21.1 52.9

Massachusetts 45.6 87.4 65.8 26.2 51.5

Michigan 46.8 84.4 74.4 29.4 54.0

Minnesota 39.8 88.5 70.9 25.0 49.2

Mississippi 48.2 81.0 63.6 24.8 45.7

Missouri 46.4 84.3 74.7 29.2 53.9

Montana 41.0 85.7 70.1 24.7 46.7

Nebraska 41.6 85.2 72.3 25.6 56.1

Nevada 37.3 84.2 63.6 19.9 44.1

New Hampshire 40.0 85.7 66.0 22.6 51.0
Data User’s Guide for the 2022 NIS-Child Public-Use Data File APPENDIX I 

207

Area Resolution 

Rate (%)

Screener 

Completion 

Rate (%)

Interview 

Completion 

Rate (%)

CASRO 

Response 

Rate (%)

Children with 

Adequate Provider 

Data (%)

New Jersey 41.9 85.8 63.7 22.9 44.0

New Mexico 40.5 85.7 71.5 24.8 53.2

New York 41.6 85.0 66.6 23.5 47.9

 NY-City of New York 38.5 82.8 66.8 21.3 47.5

 NY-Rest of State 43.3 86.4 66.2 24.8 48.4

North Carolina 39.3 84.9 70.4 23.5 49.5

North Dakota 46.7 83.6 76.0 29.6 49.1

Ohio 42.2 82.7 74.1 25.9 50.4

Oklahoma 51.6 84.3 67.6 29.4 46.7

Oregon 38.9 88.4 71.4 24.5 50.6

Pennsylvania 41.2 84.8 63.3 22.1 49.7

 PA-Philadelphia County 41.0 83.2 61.1 20.8 51.3

 PA-Rest of State 41.7 87.1 66.6 24.1 48.4

Rhode Island 38.8 84.4 68.9 22.6 55.9

South Carolina 41.2 86.0 62.4 22.1 44.6

South Dakota 45.9 82.7 73.1 27.8 53.1

Tennessee 40.6 82.6 70.6 23.6 51.9

Texas 40.2 84.3 63.4 21.5 45.8

 TX-Bexar County 37.9 83.9 63.5 20.2 48.1

 TX-City of Houston 40.7 84.3 62.7 21.5 45.8

 TX-Rest of State 41.9 84.8 65.0 23.1 44.5

Utah 37.5 83.1 76.6 23.9 59.5

Vermont 38.5 82.0 73.8 23.3 60.8

Virginia 42.8 87.2 70.0 26.2 47.3

Washington 33.3 82.5 75.0 20.6 55.5

West Virginia 54.5 84.7 63.8 29.5 48.6

Wisconsin 43.4 87.6 64.9 24.7 49.3

Wyoming 64.7 81.9 72.7 38.5 53.1

Puerto Rico 44.2 89.4 59.1 23.4 33.4

Guam 37.0 76.0 46.6 13.1 45.8

* For the definition of the key indicators see Table 1 of NIS-Child Data User’s Guide. 

† Excludes U.S. territories.