Early Childhood Chronic

Illness: Comparability of

Maternal Reports and

Medical Records

Series 2:

Data Evaluation and

Methods Research

No. 131

Hyattsville, Maryland

April 2001

DHHS Publication No. (PHS) 2001-1331

Vital and

Health Statistics

DEPARTMENT OF HEALTH AND HUMAN SERVICES

Centers for Disease Control and Prevention

National Center for Health Statistics
Copyright information

All material appearing in this report is in the public domain and may be

reproduced or copied without permission; citation as to source, however, is

appreciated.

Suggested citation

Miller JE, Gaboda D, Davis D. Early childhood chronic illness: Comparability of

maternal reports and medical records. National Center for Health Statistics. Vital

Health Stat 2(131). 2001.

Library of Congress Cataloging-in-Publication Data

Miller, Jane E.

Early childhood chronic illness: Comparability of maternal reports and

medical records. / Jane E. Miller, Dorothy Gaboda, and Diane Davis.

p. cm. — (Vital and health statistics. Series 2, Data evaluation and

methods research ; no. 2-131) (DHHS publication ; no. (PHS) 2001-1331)

Includes bibliographical references.

ISBN 0-8406-0568-4

1. Chronically ill children—Medical care. 2. Medical records. I. Gaboda,

Dorothy. II. Davis, Diane. III. Title. IV. Vital and health statistics. Series 2, Data

evaluation and methods research ; no. 131. V. DHHS publication ; no. (PHS)

2001-1331.

RJ380.M55 2001

618.92—dc21 2001030121

For sale by the U.S. Government Printing Office

Superintendent of Documents

Mail Stop: SSOP

Washington, DC 20402-9328

Printed on acid-free paper.
National Center for Health Statistics

Edward J. Sondik, Ph.D., Director

Jack R. Anderson, Deputy Director

Jack R. Anderson, Acting Associate Director for

International Statistics

Jennifer H. Madans, Ph.D., Associate Director for Science

Lawrence H. Cox, Ph.D., Associate Director for Research

and Methodology

Jennifer H. Madans, Ph.D., Acting Associate Director for

Analysis, Epidemiology, and Health Promotion

P. Douglas Williams, Acting Associate Director for Data

Standards, Program Development, and Extramural Programs

Edward L. Hunter, Associate Director for Planning, Budget,

and Legislation

Jennifer H. Madans, Ph.D., Acting Associate Director for

Vital and Health Statistics Systems

Douglas L. Zinn, Acting Associate Director for

Management

Charles J. Rothwell, Associate Director for Information

Technology and Services

Division of Vital Statistics

Mary Anne Freedman, Director

James A. Weed, Ph.D., Deputy Director
Contents

Abstract .................................................................................................. 1

Introduction ............................................................................................... 1

Data and Methods ......................................................................................... 2

Results ................................................................................................... 2

Provider Response Patterns................................................................................. 2

Measures of Intersource Agreement .......................................................................... 4

Weighted Prevalence Estimates ............................................................................. 5

Characteristics Associated With Discordance Between Sources ................................................... 6

Discussion and Conclusions ................................................................................. 7

References ............................................................................................... 9

Text Tables

A. Concurrence of maternal report and medical records on specified chronic health conditions, by completeness

of provider response: 1991 Longitudinal Followup to 1988 National Maternal and Infant Health Survey ............. 3

B. Weighted prevalence (percent) of specified chronic health conditions from National Maternal and Infant Health

Survey/Longitudinal Followup Mother’s Questionnaire and Medical Provider Survey and 1988 National Health

Interview Survey .................................................................................... 3

C. Response rates to Medical Provider Survey by characteristics of provider: 1991 Longitudinal Followup to

1988 National Maternal and Infant Health Survey ......................................................... 4

D. Odds ratios for no provider responses to Medical Provider Survey by characteristics of the mother, family, and

provider: 1991 Longitudinal Followup to 1988 National Maternal and Infant Health Survey ....................... 5

E. Odds ratios from multinomial logistics model of discordance in reporting of asthma between mother and medical

provider, by sociodemographic, health, and insurance characteristics for children with complete provider information:

1991 Longitudinal Followup to 1988 National Maternal and Infant Health Survey ............................... 6

iii
Objectives

The research reported here seeks to

quantitatively assess the comparability

of two widely used sources of

information on child health: maternal

reports and medical records. The

analysis provides a comparison of how

well maternal reports and medical

provider data agree on 15 types of

chronic health conditions, ranging from

specific illnesses such as asthma or

sickle cell anemia to broader categories

such as chronic heart or orthopedic

conditions, to impairments such as

vision, hearing, or speech problems.

Methods

This study uses data on a nationally

representative sample of 6,201

preschool aged children whose mothers

participated in the 1991 Longitudinal

Followup (LF) to the 1988 National

Maternal and Infant Health Survey

(NMIHS) and whose identified health

care providers supplied medical visit

data for the children. The LF and

NMIHS were conducted by the National

Center for Health Statistics (NCHS),

Centers for Disease Control and

Prevention (CDC), in collaboration with

a number of other agencies of the

Federal Government. In the 1991 LF

survey, the mothers’ questionnaire

included questions on chronic and

acute health conditions, accidents

requiring medical attention, and hospital

admissions for their children that had

been included in the 1988 NMIHS. The

mothers were also asked to grant

NCHS permission to request medical

records from the children’s medical

providers.

Results

Kappa statistics reveal poor

agreement for 12 of 15 conditions

studied. Weighted prevalence estimates

vary widely across sources. For some

conditions, despite apparently similar

prevalence estimates from the two

sources, mothers’ reports and medical

records identified very different groups

of children as ‘‘cases.’’ Concurrence

rates were lower for children from poor,

less educated, and Hispanic families.

Reasons for and implications of these

findings are also discussed.

Early Childhood Chronic Illness:

Comparability of Maternal

Reports and Medical Records

by Jane E. Miller, Ph.D., Dorothy

Gaboda, Ph.D., and Diane Davis,

Rutgers University

Introduction

Most population-based studies of

child health and health care

utilization rely on parents’

retrospective reports of health

conditions (1–3). Because that approach

to data collection depends on the ability

of a parent to recall and report on a

wide range of medical conditions with

which they may have little familiarity,

the quality of those reports is variable

and uncertain. Another common source

of information on child health is records

of medical care providers. Medical

records avoid some of the shortcomings

associated with parental reports because

information is filled out by trained

medical providers using standard forms,

and the information is typically recorded

shortly after a visit. However, such

information is typically available only

for children who visit a particular care

provider or are within one insurance

plan, and cannot be used to generalize

patterns of health or health care

utilization in the general population (4).

The objective of this analysis is to

compare maternal reports of chronic

health conditions with records from

pediatric providers for a nationally

representative sample of preschool-aged

children. Although the reliability and

validity of these sources have been

evaluated for many dimensions of health

and health care utilization among adults

(5,6) or for pregnancy (7), there have

been few evaluations of data quality for

children’s health conditions (8). This

study uses data on approximately 6,200

children who participated in the 1991

Longitudinal Followup (LF) to the 1988

National Maternal and Infant Health

Survey (NMIHS), conducted by the

National Center for Health Statistics

(NCHS), Centers for Disease Control

and Prevention (CDC).

The core of our analysis is a

comparison of how well maternal

reports and medical provider data agree

on 15 types of chronic health conditions,

ranging from specific illnesses such as

asthma or sickle cell anemia to broader

categories such as chronic heart or

orthopedic conditions, to impairments

such as vision, hearing, or speech

problems. This comparison is complex

because in most instances it is not

possible to say which (if either) source

is correct. There is no clear ‘‘gold

standard’’ against which to base

Keywords: child health, evaluation,

data quality, longitudinal survey

Funding for this project was provided by the Faculty Scholars Program of the William T. Grant

Foundation. We would like to thank Stephen Marcella, Hema Kannan, Tyleisha Rainey and Michael Kogan

for assistance, and Michael Stoto, David Mechanic, Richard Lau, and Mark Schlesinger for advice.

Address correspondence to Jane Miller at: Institute for Health, Health Care Policy and Aging Research,

Rutgers University, 30 College Ave., New Brunswick, NJ 08901–5070. Phone: (732) 932–6730. Fax: (732)

932–6872; E-mail: jemrci.rutgers.edu. This report was edited by Thelma W. Sanders and typeset by

Jacqueline M. Davis of the Publications Branch, Division of Data Services

Page 1
comparisons. For example, if mothers

report a certain condition more often

than doctors, that inequality alone does

not demonstrate conclusively whether

mothers are overreporting, doctors are

underreporting, or both. By

systematically evaluating the

completeness and reliability of data

from two of the most commonly used

types of data sources on child health,

these analyses provide insight into the

accuracy of each source for describing

levels and differentials in health

conditions among young children.

Data and Methods

Data were extracted from the 1988

NMIHS and its companion 1991

LF. The live-birth component of

NMIHS was based on a sample of

13,417 birth certificates for children

born in 1988 and included an

oversample of black infants and of

low-birthweight infants. A total of 9,953

mothers, or 74 percent, responded to

NMIHS. In 1988 and 1991 mothers of

sampled children were sent

questionnaires that requested

information about demographic

background, socioeconomic

characteristics, and the child’s health

(9,10). Overall, 8,285, or 83 percent, of

the mothers of live births responding to

NMIHS participated in LF. The 1991

mothers’ questionnaire section on child

health comprised questions on chronic

and acute health conditions, accidents

requiring medical attention, and hospital

admissions. Specific wording and

methodology are described elsewhere

(11). Mothers were asked whether they

had ever been told by a doctor, nurse, or

other medical provider that the sampled

child had any of the health conditions

shown in table A.

At followup, mothers were asked to

list the names and addresses of all

medical care providers visited by the

sampled child since birth and were

asked to grant consent for NCHS to

request all medical records for that child

from those providers. In the 94 percent

of cases for whom consent was

obtained, a letter was sent to each

named provider asking for information

on diagnoses and procedures that

occurred at each visit (see below for

details). Consent rates did not differ

significantly according to

sociodemographic characteristics of the

mother or child. The Medical Provider

Survey data file comprises 99,117

records, one for each reported visit to a

medical provider. Seventy-seven percent

of the nominated providers supplied

medical visit data, covering a total of

6,201 children.

For each visit, medical providers

were asked to fill in a checklist of

possible conditions as well as an

open-ended section of diagnoses for that

visit. Fifty-five percent of medical

providers filled out the information for

each visit into the NCHS form, while

the remaining 45 percent of providers

attached a copy of the medical records

for the sampled child. All reported

conditions were categorized according to

the corresponding ICD–9–CM codes

(12) into categories that matched those

listed on the mother’s questionnaires.

The visit information was then

aggregated to yield measures of whether

each child had ever been diagnosed with

each of the specified conditions.

Classification of health conditions into

these categories was reviewed for

accuracy and plausibility with a

practicing pediatrician. The choice of

which conditions to group under each of

the headings is deliberately rather broad,

keeping in mind that the mothers had

neither special medical training nor

prompts or definitions of the terms used

on the questionnaire.

To analyze consistency of reporting

across sources, Cohen’s kappa was

calculated for each of the chronic

conditions. Cohen’s kappa is based on

the difference between the observed

proportion of subjects upon whom the

two informants (sources) agree, and the

proportion expected by chance, given

the marginal distributions (13). The

kappa statistic takes into account

concurrence on the noncases as well as

the cases (6). Cutoffs for extent of

agreement are from Landis and Koch

(14).

The authors used multinomial

logistic regression models of

discordance in asthma reporting to

assess the extent to which characteristics

of the mother or child account for the

variation in concurrence rates across

sources. Children for whom the mother

and medical record agree are compared

with those for whom the mother but not

a medical provider mentioned asthma,

and those for whom a medical provider

but not the mother mentioned the

condition. Asthma was chosen because

of its importance as a child health

problem (1, 15) and because it provided

an adequate number of cases to support

multivariate analysis (N = 665). The

model includes only the children for

whom at least one of the sources

reported asthma, and is further restricted

to those children for whom all

nominated providers responded to the

survey, to rule out incomplete response

as an explanation for the discordance.

All analyses of study participation

and consistency of reporting across

sources are unweighted so that each

sampled child represents only him

and/or her. Prevalence rates in table B

are weighted to the population level

using the sampling weights provided by

NCHS for LF, which are calibrated to

be representative of children born in the

United States in 1988 who were alive at

the time of the followup, and

incorporate adjustments for the initial

sampling design as well as

loss-to-followup (11).

Results

Provider Response

Patterns

To provide insight into how

representative the medical records

data are of all children in NMIHS

and LF, the authors analyzed the

availability of information from medical

providers and how it differs according to

socioeconomic and other characteristics

of the child and type of medical

provider. This analysis defines the

subgroup of children in the sample for

whom information from medical records

and parents is available. As shown in

table C, hospital-based providers

(80 percent responding), physician’s

offices or HMO’s (71 percent), and

Page 2 [ Series 2, No. 131
Table A. Concurrence of maternal report and medical records on specified chronic health conditions, by completeness of provider

response: 1991 Longitudinal Followup to 1988 National Maternal and Infant Health Survey

Condition as worded on mother’s questionnaire1

Corresponding

ICD–9–CM code(s)2

Number of cases

with condition3 Kappa4

Percent of cases5

mentioned by:

Complete

(N = 3910)

Partial or

complete

(N = 6183) Complete

Partial or

complete

Mother

only

Medical

provider only

Mother and

medical

provider

Deafness/other hearing problems ......... 388, 389, 744.0 273 435 0.07 0.09 32 61 7

Delayed speech/other problems with

speaking or understanding ............. 315.3, 784.5 208 377 0.03 0.02 97 2 1

Problems with sight6 ................. 368, 369, 743 114 195 0.18 0.19 64 25 11

Food allergy ...................... 693.1 213 362 0.11 0.11 79 14 7

Other allergies/hay fever ............... 477, 495.9 544 895 0.09 0.08 77 16 7

Asthma . . . . . . . . . . . . . . . . . . . . . . . . . 493 665 1,098 0.48 0.47 22 41 37

Other chronic respiratory condition6 ........ 491, 496, 506.4,

519.9, 770.7, V17.6 347 584 0.19 0.17 62 25 13

Chronic heart condition . . . . . . . . . . . . . . . 393–459 136 217 0.12 0.16 40 51 9

Sickle cell anemia ................... 282.4–282.6 65 113 0.19 0.14 74 18 8

Spina bifida . . . . . . . . . . . . . . . . . . . . . . . 741 9 17 0.50 0.58 53 6 36

Eating or swallowing problems6 .......... 307.59, 779.3,

787.2, V41.6 65 111 –0.01 0.03 73 25 2

Developmentally delayed or

mentally retarded . . . . . . . . . . . . . . . . . . 317–319 123 223 0.19 0.22 51 35 13

Epilepsy/convulsions/seizures6 ........... 345, 779.0,780.3 124 225 0.35 0.36 14 63 23

Chronic orthopedic problems6 . . . . . . . . . . . 710–739 259 398 0.09 0.15 27 63 10

Cerebral palsy . . . . . . . . . . . . . . . . . . . . . 333.7, 343 42 81 0.60 0.56 40 21 40

NOTES: Statistics are presented aggregated to the child level (for example, across all visits to providers) Table A includes the 6,183 children for whom at least one medical provider mentioned by the

mother responded to the LF. ‘‘Partial’’ provider response indicates that at least one of the nominated providers for the child responded; ‘‘complete’’ provider response indicates that all of the nominated

providers for the child responded (N = 3,910).

1Sequence and categories of conditions from the mother’s questionnaire to the LF. Medical provider classifications based on ICD–9–CM codes from medical records.

2Corresponding ICD–9–CM codes used to classify information from medical records. For each child, condition could have been mentioned by an ambulatory, emergency room, or inpatient provider, or

more than once by a provider.

3Number of children for whom the condition was mentioned by mother, medical provider, or both.

4The kappa score is a measure of the strength of agreement between the two measures. Kappa < 0.40 represents fair-to-poor agreement, 0.40–0.60 moderate agreement, and 0.60–0.80 substantial

agreement. (14)

5Percent of cases based on sample of children for whom at least one provider responded.

6Wording from the mother’s questionnaire: a) Problems with sight even when wearing glasses; b) Any other chronic respiratory, lung, or breathing condition; c) Eating or swallowing problems that affect

his/her growth; d) Epilepsy or convulsions or seizures without fever; 4) Chronic orthopedic, bone, or joint problems.

Table B. Weighted prevalence (percent) of specified chronic health conditions from National Maternal and Infant Health Survey/Longitudinal

Followup Mother’s Questionnaire and Medical Provider Survey and 1988 National Health Interview Survey

Chronic health conditions

Prevalence

either1 report

Prevalence

mother’s report

Prevalence

provider report

Prevalence

both report

1988 NHIS2

(less than 10 years of age)

Deafness/other hearing problems . . . . . . . . . 7.5 2.5 5.5 0.5 1.4

Delayed speech/other problems with

speaking or understanding . . . . . . . . . . . . . 3.8 3.7 0.2 0.1 3.2

Problems with sight3 . . . . . . . . . . . . . . . . . 2.2 1.3 1.0 0.2 1.1

Food allergy . . . . . . . . . . . . . . . . . . . . . . 6.5 5.6 1.5 0.6 2.3

Other allergies/hay fever . . . . . . . . . . . . . . . 15.5 12.9 3.7 1.1 - - -

Asthma . . . . . . . . . . . . . . . . . . . . . . . . . 13.6 7.7 10.0 4.1 3.9

Other chronic respiratory condition3 . . . . . . . . 7.3 5.3 2.3 0.3 - - -

Chronic heart condition . . . . . . . . . . . . . . . 2.5 1.4 1.5 0.4 1.4

Sickle cell anemia . . . . . . . . . . . . . . . . . . . 0.9 0.8 0.2 0.1 0.1

Spina bifida . . . . . . . . . . . . . . . . . . . . . . . 0.2 0.2 0.1 0.1 - - -

Eating or swallowing problems3 . . . . . . . . . . 1.2 0.8 0.4 0.0 - - -

Developmentally delayed

or mentally retarded . . . . . . . . . . . . . . . . . 1.9 0.8 1.3 0.2 - - -

Epilepsy/convulsions/seizures3 . . . . . . . . . . . 2.9 1.0 2.5 0.6 0.2

Chronic orthopedic problems3 . . . . . . . . . . . 6.1 2.0 4.6 0.5 - - -

Cerebral palsy . . . . . . . . . . . . . . . . . . . . . 0.5 0.4 0.3 0.2 0.2

NOTES: Weighted to population levels using final sampling weight from LF. Statistics are presented aggregated to the child level (for example, across all visits to all providers). Table includes all 6,183

children for whom at least one medical provider responded to the LF.

- - - Data not available.

0.0 Quantity more than zero but less than 0.05.

1Condition mentioned by mother or medical provider.

2Based on maternal responses to a checklist of conditions for children under age 10, weighted to national levels (15).

3Wording from the mother’s questionnaire: a) Problems with sight even when wearing glasses; b) Any other chronic respiratory, lung, or breathing condition; c) Eating or swallowing problems that affect

his/her growth; d) Epilepsy or convulsions or seizures without fever; e) Chronic orthopedic, bone, or joint problems.

Series 2, No. 131 [ Page 3
emergency rooms (79 percent) were

nearly twice as likely as health centers

or clinics (40 percent) to respond to the

survey. From the perspective of the

sampled children, 82 percent of children

who participated in LF had information

from one or more of their medical

providers. For some 37 percent of

children with medical provider

information, at least one provider

mentioned by the mother did not

respond to the survey.

Factors associated with a greater

chance of having no medical provider

data include minority race (odds-ratios

(OR) = 1.65 and 1.58 for non-Hispanic

black and Hispanic children,

respectively, both p < .0001), residence

in an urban area (OR = 1.61, p < .0001),

low maternal education attainment

(OR = 1.17, p < .05), Medicaid insurance

(OR = 1.28, p < .0001) or no insurance

(OR = 1.17, p < .05), as shown in

table D. The same characteristics were

associated with having information from

some but not all providers (‘‘partial

provider response’’; not shown).

Measures of Intersource

Agreement

Table A presents two different

measures of extent of agreement

between maternal reports and medical

records on each of the 15 chronic health

conditions, along with the definitions of

each condition according to the mother’s

questionnaire and the ICD–9–CM codes

from the medical records. Results are

shown separately for children with

complete provider response and for

children with any provider response (for

example, partial or complete provider

response). Of the children with partial

provider response, three-quarters were

missing records from only one provider.

Inclusion of all children for whom at

least one provider responded increases

the number of children in the analysis

from 3,910 to 6,183, and increases the

number of cases of each condition by

50 percent or more.

Kappa statistics for each of the

conditions are quite similar for children

with complete provider response as for

children with any provider response,

indicating little if any loss of precision

from including the cases for whom

some providers did not respond. The

analyses described below are based on

the larger, more representative sample of

children for whom at least one provider

responded. There is moderate agreement

between sources for only three

conditions—cerebral palsy (kappa =

0.56), spina bifida (kappa = .58), and

asthma (kappa = 0.48)— although

cerebral palsy and spina bifida approach

the cutoff for ‘‘substantial agreement.’’

To test for the possibility that

terminology was not well understood,

the combined category of maternal

reports of ‘‘other chronic respiratory

condition’’ or asthma was compared to a

medical record indication of asthma, but

the kappa statistic remained low (0.44).

The remaining 12 conditions exhibit

fair-to-poor agreement, many of them

with kappa values at the extreme low

end of its calculable range, including

values close to zero for delayed speech

and eating and swallowing problems.

To show in more detail where the

disagreement lies for each condition, the

right-hand columns of table A list the

percent of cases (that were mentioned

by at least one source) that were

reported only by the mother, only by a

medical provider, or by both sources.

For most of the conditions shown, the

percent of cases reported only by the

mother was slightly higher in the sample

that included partial and complete

provider responses, while the percent

reported only by a medical provider was

slightly lower (not shown). The only

condition for which the pattern of

discordance was notably different for the

full sample than for children with

complete providers was asthma.

Consistent with the kappa statistics,

the level of agreement between sources

is extremely low for most conditions.

The percent of cases upon which the

two sources agree ranges from zero

concurrence for eating and swallowing

problems that affect growth to

40 percent for cerebral palsy. For some

conditions, including delayed speech,

allergies, and sickle cell anemia, more

than three-quarters of the ‘‘cases’’ are

reported by the mother only. For other

conditions, including chronic heart or

orthopedic conditions and epilepsy/

convulsions/seizures, and deafness or

other hearing problems, at least one-half

of the cases are reported only by a

medical provider.

Mothers were more likely to recall

and report serious cases (as measured by

Table C. Response rates to Medical Provider Survey by characteristics of provider: 1991 Longitudinal Followup to 1988 National

Maternal and Infant Health Survey

Type of provider

Providers (N = 10,627) Children (N = 6,159)

Percent

responding

Eligible

number1

Percent with

provider

responding

Eligible

number2

Ambulatory or Emergency Room . . . . . . . . . . . . . 71 13,099 82 7,409

Physician’s office or HMO3 . . . . . . . . . . . . . . . 71 6,627 81 5,037

Health center or clinic . . . . . . . . . . . . . . . . . . 40 3,508 46 2,990

Emergency department or hospital service . . . . . 79 3,602 88 2,906

Inpatient hospital . . . . . . . . . . . . . . . . . . . . . . 80 1,700 81 1,521

Overall . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 14,799 82 7,499

1The eligible number of providers of each type was based on the number of providers of that type reported by mothers responding to the Longitudinal Followup. If a given child was reported to have

visited more than one provider of the same type, those providers were each counted in the total. In 925 cases, a provider supplied both outpatient and inpatient hospital care to a child and is counted

in both categories.

2The number of eligible children for each type of provider was based on the number of children for whom that provider type was mentioned. Each child could have visited more than one type of

provider and could have visited more than one provider of the same type.

3HMO is health maintenance organization.

Page 4 [ Series 2, No. 131
instances when the child was

hospitalized for the condition), however,

there is surprisingly poor concurrence

between maternal reports and medical

records even for relatively severe cases.

For example, of the 190 children with

medical records indicating

hospitalization for asthma, only

79 percent had their mother report that

their child had ever been diagnosed with

asthma. Agreement barely rises to

80 percent for the combined groups of

children hospitalized for asthma or other

serious respiratory conditions (N = 241

children).

Weighted Prevalence

Estimates

To illustrate how these levels and

patterns of disagreement affect the

estimated prevalence of each condition,

table B presents the percent of children

with that condition according to four

possible definitions of a case: 1) based

on a report in either source (the most

inclusive definition); 2) the maternal

report, regardless of the medical record;

3) the medical record, regardless of

maternal report; and 4) both sources (the

most restrictive definition). Statistics in

table B are weighted to the population

level using the LF sampling weights.

The prevalence estimates vary

widely depending on the choice of

Table D. Odds ratios for no provider responses to Medical Provider Survey by characteristics of the mother, family, and provider:

1991 Longitudinal Followup to 1988 National Maternal and Infant Health Survey

Characteristic

Sample

number Odds-ratio P-value

Age of mother

Under 20 years . . . . . . . . . . . . . . . . . . . . 933 1.02 0.81

20–24 years . . . . . . . . . . . . . . . . . . . . . . 1,722 1.00 . . .

25–29 years . . . . . . . . . . . . . . . . . . . . . . 1,740 1.03 0.74

30–34 years . . . . . . . . . . . . . . . . . . . . . . 1,163 1.05 0.57

35 years and over . . . . . . . . . . . . . . . . . . . 443 @1.25 0.04

Education of mother

Less than high school . . . . . . . . . . . . . . . . 1,282 @1.16 0.04

High school graduate . . . . . . . . . . . . . . . . . 2,528 1.00 . . .

Some college . . . . . . . . . . . . . . . . . . . . . 2,057 1.06 0.42

College graduate or more . . . . . . . . . . . . . . 292 0.81 0.16

Family income level

Less than 1.00 x poverty . . . . . . . . . . . . . . 2,145 0.97 0.77

1.00–1.84 x poverty . . . . . . . . . . . . . . . . . . 1,132 1.01 0.94

1.85–2.99 x poverty . . . . . . . . . . . . . . . . . . 1,188 0.93 0.42

3.00 x poverty or more . . . . . . . . . . . . . . . . 1,676 1.00 . . .

Race and Hispanic origin

Non-Hispanic black . . . . . . . . . . . . . . . . . . 2,769 @@1.65 0.0001

Non-Hispanic white or other race . . . . . . . . . 2,908 1.00 . . .

Hispanic . . . . . . . . . . . . . . . . . . . . . . . . . 482 @@1.58 0.0001

Type of insurance

Private1 . . . . . . . . . . . . . . . . . . . . . . . . . 3,136 1.00 . . .

Medicaid . . . . . . . . . . . . . . . . . . . . . . . . 2,416 @@1.28 0.0001

Medicare . . . . . . . . . . . . . . . . . . . . . . . . 208 0.94 0.13

None . . . . . . . . . . . . . . . . . . . . . . . . . . . 722 @1.17 0.04

Mother’s marital history

Never married . . . . . . . . . . . . . . . . . . . . . 1,670 1.01 0.92

Married by date of LF . . . . . . . . . . . . . . . . 183 1.02 0.83

Marital disruption . . . . . . . . . . . . . . . . . . . 338 0.88 0.34

Married at NMIHS and LF dates . . . . . . . . . . 3,436 1.00 . . .

Other . . . . . . . . . . . . . . . . . . . . . . . . . . . 532 1.02 0.93

Residence

Rural . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,831 1.00 . . .

Urban . . . . . . . . . . . . . . . . . . . . . . . . . . 6,454 @@1.61 0.0001

Test of fit -2log likelihood = 197.6 with 19 degrees of freedom; p < .0001

@Denotes p < .05 compared with reference category within the column.

@@Denotes p < .01 compared with reference category within the column.

. . . Category not applicable.

NOTE: ‘‘No provider response’’ indicates that none of the medical providers listed by the mother responded to the survey.

1Private insurance includes group coverage, private insurance, or health maintenance organization.

Series 2, No. 131 [ Page 5
source. Asthma rates, for example, range

from 13.6 percent (either source reports

the disease) to 4.1 percent (both sources

report it), with rates based on medical

records slightly higher than those based

on maternal report (10.0 percent and

7.7 percent, respectively). The most

extreme variation in rates based on the

different sources is observed for

conditions such as delayed speech or

epilepsy for which one source reported

the majority of the cases.

Characteristics Associated

With Discordance Between

Sources

To assess whether agreement

between sources varies according to the

characteristics of the mother or child,

table E presents results of multinomial

logistic regression models of

discordance in asthma reporting. The

first column contains odds-ratios for

children for whom the mother but not a

medical provider mentioned asthma,

while the second column contains

odds-ratios for whom a medical provider

but not the mother mentioned the

condition, when each is compared with

children for whom both sources reported

asthma (the omitted category).

The odds that either informant

reports asthma decline as mother’s

educational attainment increases

(p < .05). The pattern for income is

Table E. Odds ratios from multinomial logistics model of discordance in reporting of asthma between mother and medical provider, by

sociodemographic, health, and insurance characteristics for children with complete provider information: 1991 Longitudinal Followup to

1988 National Maternal and Infant Health Survey

[N = 655]

Characteristic

I II

Mother only1 Medical provider only1

Odds-ratio p-value Odds-ratio p-value

Age of mother

Under 20 years . . . . . . . . . . . . . . . . . . . . 0.90 0.71 0.69 0.13

20–24 years . . . . . . . . . . . . . . . . . . . . . . 1.00 . . . 1.00 . . .

25–29 years . . . . . . . . . . . . . . . . . . . . . . 0.83 0.42 0.79 0.19

30–34 years ...................... @@2.32 <0.01 @@1.80 0.01

35 years and over . . . . . . . . . . . . . . . . . . . 0.54 0.16 1.03 0.91

Education of mother

Less than high school . . . . . . . . . . . . . . . . 1.59 0.14 1.46 0.10

High school graduate . . . . . . . . . . . . . . . . . 1.00 . . . 1.00 . . .

Some college . . . . . . . . . . . . . . . . . . . . . 0.88 0.63 0.91 0.61

College graduate or more . . . . . . . . . . . . . . 0.35 0.10 0.85 0.67

Family income level

Less than 0.50 x poverty# .............. @1.76 0.03 @@0.57 0.01

0.50–0.99 x poverty . . . . . . . . . . . . . . . . . . 1.09 0.72 1.17 0.45

1.00–1.84 x poverty . . . . . . . . . . . . . . . . . . 1.20 0.44 1.03 0.89

1.85–2.99 x poverty .................. @@0.48 0.01 1.09 0.67

3.00 x poverty or more . . . . . . . . . . . . . . . . 1.00 . . . 1.00 . . .

Race and Hispanic origin

Non-Hispanic white . . . . . . . . . . . . . . . . . . 1.00 . . . 1.00 . . .

Non-Hispanic black . . . . . . . . . . . . . . . . . . 0.93 0.81 0.77 0.20

Hispanic . . . . . . . . . . . . . . . . . . . . . . . . . 1.68 0.22 1.77 0.09

Other . . . . . . . . . . . . . . . . . . . . . . . . . . . 0.47 0.23 0.71 0.40

Type of insurance2

Private only . . . . . . . . . . . . . . . . . . . . . . . 1.00 . . . 1.00 . . .

Public only . . . . . . . . . . . . . . . . . . . . . . . 0.79 0.31 1.26 0.22

Private and public . . . . . . . . . . . . . . . . . . . 0.75 0.35 0.73 0.24

No insurance . . . . . . . . . . . . . . . . . . . . . . 1.35 0.29 1.17 0.55

Mother’s rating of child health

Excellent, very good, good . . . . . . . . . . . . . 1.00 . . . 1.00 . . .

Fair, poor# . . . . . . . . . . . . . . . . . . . . . . . 0.76 0.12 @@0.36 <0.001

Likelihood ratio . . . . . . . . . . . . . . . . . . . . . ... ... 599.1 . . .

Degrees of freedom . . . . . . . . . . . . . . . . . . . . . . . . 566 . . .

#Denotes that the difference between coefficient on this variable for the mother-only versus provider-only is statistically significant at the p < .05 level.

@Denotes p < .05 compared with the reference category within the column.

@@Denotes p < .01 compared with the reference category within the column.

. . . Category not applicable.

NOTE: Analysis includes only children for whom asthma was reported by the mother or medical provider.

1Odds-ratios and p-value are for the category shown compared with children for whom mother and medical provider reported asthma.

2Private insurance includes group coverage, private insurance, or health maintenance organization; public insurance includes Medicare, Medicaid, and military insurance.

Page 6 [ Series 2, No. 131
quite different: For the poorest children

there is a direct tradeoff between a

greater likelihood that only the mother

will report asthma (OR = 1.76, p < .03)

and a smaller likelihood that only a

medical provider will report it

(OR = 0.57, p < .01). In other words,

with increasing educational attainment,

the overall level of agreement increases,

with little shift between the two sources

of information, whereas for poor

children, the most important pattern is in

the type of disagreement. These patterns

suggest that the educational attainment

pattern may relate more to

understanding the symptoms and

terminology of asthma, whereas the

income pattern may predominantly

reflect differences in access to or usage

of health care.

Hispanic children are more likely

than other children to have asthma

reported by either of the two sources

alone (OR = 1.68 for mother-only

reports, and OR = 1.77 for provider-only

reports, when each is compared with

only non-Hispanic persons). Although

they do not quite reach standard

significance levels, in part because there

were fewer than 50 Hispanic children in

this analysis, these results are suggestive

of problems with understanding and

communicating the symptoms of the

disease between the two types of

informants. Given the small number of

Hispanic children, this analysis did not

differentiate between those who use

English versus Spanish as their primary

language. Finally, agreement levels in

asthma reporting are higher among

children whose mothers report that the

child is in fair or poor health than for

those in better health, perhaps because

these children are taken to the doctor

more often so that both parties are

familiar with the child’s health

conditions. Maternal age, race, and

insurance do not relate to risk of

discordance in asthma reporting in the

multivariate model.

Discussion and

Conclusions

Analysis of a nationally

representative, population-based

sample survey of preschool-aged

children reveals substantial

inconsistency in information about

children’s chronic health conditions

based on medical records data and

maternal reports. Our findings concur

with those of studies such as those from

the Health Interview Evaluation Survey,

which also demonstrated that ‘‘surveys

and medical records often provide

different pictures of the prevalence of

chronic conditions in a population’’ (5).

In our data, levels of agreement between

the sources range from moderate for

conditions including asthma, cerebral

palsy, and spina bifida, to nearly

complete discordance for eating or

swallowing problems and delayed

speech. In a comparison of maternal

reports and physical examination results

using data from the National Health and

Nutrition Examination Survey, Jessop

and Stein (8) also found little overlap in

the identification of children’s chronic

health conditions in the two sources, and

hence wide variation in prevalence

estimates depending upon which source

or sources were used. Consistent with

our results, they also found that parents

often did not report chronic heart,

neuromuscular or joint conditions that

were diagnosed in the medical

examination, and that maternallyreported asthma was often not

mentioned in the clinical findings;

similar patterns across conditions have

also been observed for adult health (6).

The importance of a comparison of

parental reports and medical records for

a uniform nationally representative

sample is driven home in our findings

about chronic heart conditions. Although

at first glance the two sources appear to

yield quite similar estimates of the

prevalence of this condition in the

NMIHS/LF sample (1.4 percent and

1.5 percent for maternal reports and

medical records, respectively), closer

examination reveals that the two sources

are reporting almost completely

nonoverlapping groups. Fewer than

16 percent of the cases reported by

either source are reported by both

sources. This type of information cannot

be obtained from a comparison of

estimates drawn from different studies

or samples as in a review of published

literature (8). For example, prevalence

estimates from a study of maternal

reports might or might not be close to

prevalence estimates from a study of

medical records, but it is not possible to

determine whether the different sources

are capturing similar groups of children,

and hence, to assess the reliability of

either source.

Our finding that there is substantial

disagreement between maternal reports

and medical records of children’s

chronic illness has important

repercussions for the practice of

medicine. First, there is evidence of

considerable misunderstanding among

parents about their children’s health

conditions, with some cases of serious

health conditions overlooked while

others appear to be overstated. For

example, nearly two-thirds of mothers

whose children had been diagnosed with

epilepsy, convulsions, or seizures by a

medical provider did not report that

health condition even when specifically

asked about it on a questionnaire. The

lack of awareness or poor understanding

of these conditions among these

children’s primary care givers is likely

to pose a significant hindrance to

appropriate preventive care and

treatment for these conditions. On the

other hand, parents were much more

likely than physicians to report problems

with eating or speech that may have had

little clinical significance and could lead

to inappropriate focus of resources on

those perceived problems.

Also disconcerting is the relatively

high proportion of cases of some

conditions that were not reported by a

medical provider. Although this could be

the result of overreporting on the part of

the mother, there is evidence that even

fairly serious conditions may not be

diagnosed in some children. For

example, one study showed that a

substantial share of school-aged children

had clinical evidence of asthma despite

a lack of a physician’s diagnosis of the

condition (16). This problem is likely to

occur disproportionately among poor

Series 2, No. 131 [ Page 7
children and others who lack health

insurance. Particularly for chronic

conditions that require regular

management and monitoring of

symptoms, the absence of a medical

diagnosis raises the risk that the severity

of these conditions will escalate,

increasing the financial, health, and

developmental costs of the illness.

In addition to the clinical

consequences, there are several

implications of these findings for the

interpretation and collection of data on

children’s health conditions. First, any

estimates based on information from the

medical records in NMIHS/LF must be

corrected for selective nonresponse. In

our data, for example, because medical

provider’s response rates are lower for

children served by clinics, who are

typically from lower socioeconomic

groups, differentials across income

groups may be understated by medical

records data. These differentials will be

further exaggerated by the fact that

children from low income or minority

families are medically underserved—an

issue that will affect many other medical

records databases (5).

Another important consideration for

interpretation of data from NMIHS/LF

is that estimates of prevalence rates

from one or the other source may be

either high or low, depending on the

condition and the source. Previous

studies of other data sets, age groups,

and health conditions have also

concluded that agreement between

medical records and self-reports varies

across conditions or for different levels

of severity and should not be

generalized (6). Hence, it is important to

assess which source is likely to provide

more valid data for a given condition,

and to understand the types of biases

that affect reporting of that condition by

each source before analyzing

information from any of these types of

sources.

Our analyses suggest that access to

care, communication issues,

understanding about children’s health

conditions, and recall problems all

contribute to the observed discrepancies

between maternal reports and medical

records of those conditions. The

evidence regarding access to care comes

from results of our models of asthma

reporting, which showed that among

poor children, there was a much higher

likelihood of parent-only reports and a

much lower likelihood of medical

records-only reports than among higher

income children. Data from the 1987

National Medical Expenditures Survey

showed that children who lack health

insurance are 80 percent to 100 percent

more likely than children with medical

insurance to go without a visit to a

physician for asthma and other health

problems even when medical care is

indicated (17). Children who do not

receive adequate ‘‘well-baby’’ care or

visit a doctor only when ill are

especially unlikely to be screened for

normal development because ‘‘sick

visits’’ tend to focus on a specific

complaint rather than the child’s overall

health and development. Furthermore, it

is more difficult to assess other

conditions when a child is not feeling

well.

Because low-income children had

fewer health care visits, particularly

well-child visits, they are less likely to

have existing conditions diagnosed. In

their assessment of questionnaire and

medical records data for adults, Harlow

and Linet (6) concluded that use of

medical records for assessing accuracy

of adults’ self-reports of medical

conditions is not appropriate for

conditions that depend on

self-identification of health problems

and care-seeking behavior on the part of

the patient, and these concerns should

be applicable to children’s health

conditions as well.

Results of other studies suggest that

differences in usage of care for different

conditions may affect reporting patterns

(5). Statistics from the 1988 National

Health Interview Survey show that

physician contacts and hospitalization

rates were quite low for impairments

such as hearing, vision, and speech

problems, but were more common and

more variable for diseases and other

health conditions (15). Statistics from

the survey showed that 5 out of 6

asthmatic or epileptic children, but only

one out of six children with heart

disease, were reported as using

medication, while hospitalization was

relatively frequent for diabetes and

epilepsy. These statistics are consistent

with reporting patterns observed in the

NMIHS data, such as the fact that

mothers were far more likely than

medical providers to report perceptual

impairments, but that medical providers

were more likely to report epilepsy.

Poor communication or a lack of

understanding of medical terminology

also appears to affect concurrence

between sources (8, 18). If a medical

provider uses technical jargon to

describe a child’s health condition, that

wording may match the questionnaire

used in a survey like NMIHS/LF but

may not be understood or remembered

by the mother. Conversely, if a medical

provider uses lay terms to describe a

condition, the mother might understand

and recall those terms, but they will not

match the wording on the questionnaire.

Whether the average parent of a 3-yearold is familiar with the clinical meaning

of words such as ‘‘chronic,’’

‘‘orthopedic,’’ or ‘‘developmental delay’’

is unclear. If they use some other

definition, the likelihood of discordance

increases. Further, physicians may be

more likely to name specific conditions

than to describe the broad category to

which a condition belongs, so that a

parent may not know that arthritis is an

orthopedic condition, for example. The

lack of clear, unambiguous diagnostic

criteria and wording on questionnaires

increases the chance of inconsistent and

incorrect reporting on health conditions

(6) because the definitions are likely to

have differed across mothers, although

the extent of this problem cannot be

assessed by the data at hand.

A related concern is that the terms

used to name or describe a given health

condition vary across providers or

among people of different language,

cultural, or educational backgrounds

(7,19). For example, asthma is called

‘‘wheezy bronchitis’’ by some doctors

and is referred to as ‘‘fatiga’’ among

Latinos (20). In our study, Hispanic

children and those with low maternal

educational attainment were more likely

to have asthma and other health

conditions reported by their mother but

not a medical provider, even for

children with complete medical records.

Issues of parental vigilance and the

absence of specified criteria by which

the mother could evaluate clinical

Page 8 [ Series 2, No. 131
significance may also help to explain the

apparent overreporting of some

conditions by mothers. Two conditions

for which there were much higher rates

of reporting by mothers are eating

and/or swallowing problems that

interfere with growth, and delayed

speech and/or problems speaking or

understanding, both of which are

prominent issues in the daily lives of

parents of preschool-aged children such

as those in the NMIHS/LF sample.

Many 3-year-old children have not yet

achieved clear speech, and there is

considerable variation in how well

children in this age group can speak and

understand speech. Although physicians

are trained to distinguish normal speech

and language development from that

which is truly delayed, most parents are

not. It may be that some of the mothers

who are reporting delayed speech are

being hypervigilant in evaluating their

child’s speech and are overreporting

problems, whereas medical providers

may be reporting only clinically

meaningful speech problems. The

mothers’ questionnaire does not offer

any definition of these terms or basis

upon which to assess the child’s

performance. The fact that there may

not have been formal, systematic

screening of all children for some

conditions (such as speech delay) by age

3 years, may also explain some

discrepancies in mother’s and doctor’s

diagnoses. For example, if formal

screening for language problems does

not occur until a child is 4 years old,

mothers may be correct in their

assessment, but there may be no basis

for verification in the child’s medical

records.

Other studies provide indirect

evidence about the possible importance

of parental assessment of the salience of

other types of health conditions. In an

analysis of data from NHIS, Newacheck

and Taylor (15) showed that only

10 percent of mothers reported that their

asthmatic children were never bothered

by their condition, compared with

75 percent of those with heart disease.

Given these differences in the frequency

and severity of current consequences of

these two conditions, it makes sense that

mothers would be more likely to recall

asthma than a chronic, but minor, heart

condition. Conditions that have only

occasional, mild symptoms may not be

reported in the survey (21). Illnesses

that have current consequences are also

more likely to be recalled (18), hence a

health condition that has been corrected

may not be reported. Other studies have

also shown that people are more

reluctant to report conditions that are

stigmatizing (6,21), such as mental

retardation.

Recall issues may also be

responsible for poor concurrence

between sources. Because patient or

proxy (such as parent) reports in many

health surveys are collected

retrospectively for health histories that

span several years, they may suffer from

various types of recall bias. Mothers

may be more likely to omit less serious

health conditions (18), to misplace dates

of occurrence (22), or to incorrectly

remember the names of health

conditions that were diagnosed several

months or years in the past (23,24). In

contrast, information is typically

recorded in the medical record shortly

after the time of a visit. However, if

records for one or more visits are

omitted or miscoded, it is possible that a

particular diagnosis may be overlooked.

As a consequence of these issues, we

expect that estimates based on maternal

reports will be lower than those based

on medical records because of the

longer recall period pertaining to

maternal reports.

A final note of caution is that some

of our findings may not be generalizable

to data from other studies because of

differences in target populations and

other design factors. NMIHS/LF

included only preschool aged children,

which means that some conditions may

not have onset or have yet been

diagnosed. An older expected onset will

also affect whether children have been

screened for some conditions by age 3.

For example, older children may be

more likely to have been screened for

conditions such as vision or speech, so

that maternal reports on those conditions

might be more accurate for older

children. On the other hand, other age

groups may lead to different types of

data collection problems such as a

longer recall period for older children.

Another design factor is that the

NMIHS/LF data were collected with

self-administered, written questionnaires

that did not contain prompts or

definitions of terms, so our findings may

not be generalizable to other data

collection methods, such as those that

are symptom- or consequences-based

(5). However, surveys such as the

National Health Interview Survey or the

medical history portion of the National

Health and Nutrition Examination

Survey that use a checklist to collect

information on chronic conditions often

do not include definitions of the terms

or lists of related symptoms to provide a

consistent definition across subjects,

hence maternal report information from

those studies may well be subject to

similar types of biases.

Finally, the reliability and validity

of information from other studies of

medical provider data may also differ

from that collected in NMIHS/LF

because of methodological differences.

For example, data collected by physical

examination at some specified point in

the child’s lifetime (for example, the

NHES or examination data from the

NHANES) will differ from that

abstracted directly from a child’s

medical records (for example,

NMIHS/LF) because the latter yields

estimates of lifetime prevalence, while

the former yields estimates of current

prevalence. The conclusions drawn in

this paper should be generalizable to

other studies of children’s chronic

illness based on a series of medical

records, but may not apply to health

examination surveys.

References

1. Coiro MJ, Zill N, Bloom B. Health of

our nation’s children: United States,

1988. National Center for Health

Statistics. Vital Health Stat 10(191).

1994.

2. Kogan MD, Alexander GT, Teitelbaum

MA, et al. The effect of gaps in health

insurance on continuity of a regular

source of care among preschool-aged

children in the United States. JAMA

274(18):1429–35. 1995.

3. Mustin HD, Holt VL, Connell FA.

Adequacy of well-child care and

Series 2, No. 131 [ Page 9
immunizations in U.S. infants born in

1988. JAMA 272(14):1111–5. 1994.

4. Marquis K. Record checks for sample

surveys. In: Jabine T, Loftus E, Straf

M, et al., eds. Cognitive aspects of

survey methodology: Building a bridge

between disciplines. Washington, D.C.:

National Academy Press. 1984.

5. Edwards WS, Winn DM, Kurlantzick

V, et al. Evaluation of National Health

Interview Survey diagnostic reporting.

National Center for Health Statistics.

Vital Health Stat 2(120). 1994.

6. Harlow SD, Linet MS. Agreement

between questionnaire data and medical

records: The evidence for accuracy of

recall. Am J Epidemiol 129(2):223–48.

1989.

7. Githens PB, Glass CA, Sloan FA,

Entman SS. Maternal recall and

medical records: An examination of

events during pregnancy, childbirth, and

early infancy. Birth 20(3):136–41.

1993.

8. Jessop DJ, Stein REK. Consistent but

not the same: Effect of method on

chronic condition rates. Arch Pediatr

Adolesc Med 149:1105–10. 1995.

9. Sanderson M, Placek PJ, Keppel KG.

The 1988 National Maternal and Infant

Health Survey: Design, content and

data availability. Birth 18:26–32. 1991.

10. U.S. Department of Health and Human

Services, National Center for Health

Statistics. National Maternal and Infant

Health Survey, 1988: Longitudinal

Followup, 1991. Volume II: Medical

Provider Survey. Ann Arbor, MI:

Inter-university Consortium for Political

and Social Research. 1995.

11. U.S. Department of Health and Human

Services, National Center for Health

Statistics. National Maternal and Infant

Health Survey, 1988: Longitudinal

Followup, 1991. Volume I: Live Births.

Ann Arbor, MI: Inter-university

Consortium for Political and Social

Research. 1995.

12. National Center for Health Statistics.

Ninth Revision International

Classification of Diseases, Clinical

Modification. 5th ed. NCHS CD-ROM

No. 1. DHHS Pub. No. (PHS)

95–1260. 1995.

13. Stokes ME, Davis CS, Koch G.

Categorical data analysis using the SAS

System. Research Triangle Park, North

Carolina: SAS Institute Inc. 1995.

14. Landis JR, Koch GG. The measurement

of observed agreement for categorical

data. Biometrics 33:159–74. 1977.

15. Newacheck PW, Taylor WR. Childhood

chronic illness: Prevalence, severity,

and impact. AJPH 82:364–71. 1992.

16. Joseph CL, Foxman B, Leickly FE, et

al. Prevalence of possible undiagnosed

asthma and associated morbidity among

urban schoolchildren. J Pediatr

129(5):735–42. 1996.

17. Stoddard JJ, St. Peter RF, Newacheck

P. Health insurance status and

ambulatory care for children. New Engl

J Med 330(20):1421–5. 1994.

18. Perrin EC, Newacheck P, Pless B, et al.

Issues involved in the definition and

classification of chronic health

conditions. Pediatrics 91(4):787–93.

1993.

19. Stein REK, Jessop DJ. Measuring

health variables among Hispanic and

non-Hispanic children with health

conditions. Current population reports ;

104(4):377–84. 1989.

20. Guarnaccia PJ, Pelto PJ, Schensul SL.

Family health culture, ethnicity and

asthma: Coping with illness. Med

Anthropol 9(3):203–24. 1985.

21. Lilienfeld DE, Stolley PD. Foundations

of epidemiology. 3rd ed. New York:

Oxford University Press. 1994.

22. Fienberg SE, Loftus EF, Tanur JM.

Cognitive aspects of health survey

methodology: An overview. Milbank

Memorial Fund Quarterly

63(3):547–64. 1985.

23. Majnemer A, Rosenblatt B. Reliability

of parental recall of developmental

milestones. Pediatr Neurol 10(4):304–8.

1994.

24. Olson JE, Shu XO, Ross JA, et al.

Medical record validation of maternally

reported birth characteristics and

pregnancy-related events: A report from

the Children’s Cancer Group. Am J

Epidemiol 145(1):58–67. 1997.

Page 10 [ Series 2, No. 131
Vital and Health Statistics

series descriptions

SERIES 1. Programs and Collection Procedures—These reports

describe the data collection programs of the National Center

for Health Statistics. They include descriptions of the methods

used to collect and process the data, definitions, and other

material necessary for understanding the data.

SERIES 2. Data Evaluation and Methods Research—These reports are

studies of new statistical methods and include analytical

techniques, objective evaluations of reliability of collected data,

and contributions to statistical theory. These studies also

include experimental tests of new survey methods and

comparisons of U.S. methodology with those of other

countries.

SERIES 3. Analytical and Epidemiological Studies—These reports

present analytical or interpretive studies based on vital and

health statistics. These reports carry the analyses further than

the expository types of reports in the other series.

SERIES 4. Documents and Committee Reports—These are final

reports of major committees concerned with vital and health

statistics and documents such as recommended model vital

registration laws and revised birth and death certificates.

SERIES 5. International Vital and Health Statistics Reports—These

reports are analytical or descriptive reports that compare U.S.

vital and health statistics with those of other countries or

present other international data of relevance to the health

statistics system of the United States.

SERIES 6. Cognition and Survey Measurement—These reports are

from the National Laboratory for Collaborative Research in

Cognition and Survey Measurement. They use methods of

cognitive science to design, evaluate, and test survey

instruments.

SERIES 10. Data From the National Health Interview Survey—These

reports contain statistics on illness; unintentional injuries;

disability; use of hospital, medical, and other health services;

and a wide range of special current health topics covering

many aspects of health behaviors, health status, and health

care utilization. They are based on data collected in a

continuing national household interview survey.

SERIES 11. Data From the National Health Examination Survey, the

National Health and Nutrition Examination Surveys, and

the Hispanic Health and Nutrition Examination Survey—

Data from direct examination, testing, and measurement on

representative samples of the civilian noninstitutionalized

population provide the basis for (1) medically defined total

prevalence of specific diseases or conditions in the United

States and the distributions of the population with respect to

physical, physiological, and psychological characteristics, and

(2) analyses of trends and relationships among various

measurements and between survey periods.

SERIES 12. Data From the Institutionalized Population Surveys—

Discontinued in 1975. Reports from these surveys are

included in Series 13.

SERIES 13. Data From the National Health Care Survey—These reports

contain statistics on health resources and the public’s use of

health care resources including ambulatory, hospital, and longterm care services based on data collected directly from

health care providers and provider records.

SERIES 14. Data on Health Resources: Manpower and Facilities—

Discontinued in 1990. Reports on the numbers, geographic

distribution, and characteristics of health resources are now

included in Series 13.

SERIES 15. Data From Special Surveys—These reports contain statistics

on health and health-related topics collected in special

surveys that are not part of the continuing data systems of the

National Center for Health Statistics.

SERIES 16. Compilations of Advance Data From Vital and Health

Statistics—Advance Data Reports provide early release of

information from the National Center for Health Statistics’

health and demographic surveys. They are compiled in the

order in which they are published. Some of these releases

may be followed by detailed reports in Series 10–13.

SERIES 20. Data on Mortality—These reports contain statistics on

mortality that are not included in regular, annual, or monthly

reports. Special analyses by cause of death, age, other

demographic variables, and geographic and trend analyses

are included.

SERIES 21. Data on Natality, Marriage, and Divorce—These reports

contain statistics on natality, marriage, and divorce that are

not included in regular, annual, or monthly reports. Special

analyses by health and demographic variables and

geographic and trend analyses are included.

SERIES 22. Data From the National Mortality and Natality Surveys—

Discontinued in 1975. Reports from these sample surveys,

based on vital records, are now published in Series 20 or 21.

SERIES 23. Data From the National Survey of Family Growth—These

reports contain statistics on factors that affect birth rates,

including contraception, infertility, cohabitation, marriage,

divorce, and remarriage; adoption; use of medical care for

family planning and infertility; and related maternal and infant

health topics. These statistics are based on national surveys

of women of childbearing age.

SERIES 24. Compilations of Data on Natality, Mortality, Marriage,

Divorce, and Induced Terminations of Pregnancy—

These include advance reports of births, deaths, marriages,

and divorces based on final data from the National Vital

Statistics System that were published as supplements to the

Monthly Vital Statistics Report (MVSR). These reports provide

highlights and summaries of detailed data subsequently

published in Vital Statistics of the United States. Other

supplements to the MVSR published here provide selected

findings based on final data from the National Vital Statistics

System and may be followed by detailed reports in Series 20

or 21.

For answers to questions about this report or for a list of reports published in

these series, contact:

Data Dissemination Branch

National Center for Health Statistics

Centers for Disease Control and Prevention

6525 Belcrest Road, Room 1064

Hyattsville, MD 20782-2003

(301) 458–4636

E-mail: nchsquery@cdc.gov

Internet: www.cdc.gov/nchs/
DEPARTMENT OF

HEALTH & HUMAN SERVICES

Centers for Disease Control and Prevention

National Center for Health Statistics

6525 Belcrest Road

Hyattsville, Maryland 20782-2003

OFFICIAL BUSINESS

PENALTY FOR PRIVATE USE, $300

DHHS Publication No. (PHS) 2001-1331, Series 2, No. 131

1-0139 (04/2001)

STANDARD MAIL (A)

POSTAGE & FEES PAID

CDC/NCHS

PERMIT NO. G-284