Series 1, Number 47 March 2009

Collecting Medication

Data in the 2004 National

Nursing Home Survey
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Dwyer LL. Collecting medication data in the 2004 National Nursing Home

Survey. National Center for Health Statistics. Vital Health Stat 1(47). 2009.

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Collecting medication data in the 2004 National Nursing Home Survey.

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Series 1, Number 47

Collecting Medication Data

in the 2004 National Nursing

Home Survey

Programs and Collection Procedures

U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES

Centers for Disease Control and Prevention

National Center for Health Statistics

Hyattsville, Maryland

March 2009

DHHS Publication No. (PHS) 2009–1323
National Center for Health Statistics

Edward J. Sondik, Ph.D., Director

Jennifer H. Madans, Ph.D., Acting Co-Deputy Director

Michael H. Sadagursky, Acting Co-Deputy Director

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

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

Planning, Budget, and Legislation

Michael H. Sadagursky, Associate Director for

Management and Operations

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

and Methodology

Linda B. Torian, Acting Director for Information Technology

Linda B. Torian, Acting Director for Information Services

Linda T. Bilheimer, Ph.D., Associate Director for Analysis

and Epidemiology

Charles J. Rothwell, M.S., Director for Vital Statistics

Jane E. Sisk, Ph.D., Director for Health Care Statistics

Jane F. Gentleman, Ph.D., Director for Health Interview

Statistics

Clifford L. Johnson, M.S.P.H., Director for Health and

Nutrition Examination Surveys

Division of Health Care Statistics

Jane E. Sisk, Ph.D., Director

Lauren D. Harris-Kojetin, Ph.D., Chief, Long-Term Care

Statistics Branch
Contents
 

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

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

The National Nursing Home Survey (NNHS) .................................................................... 1 
 

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

Pretest. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 
 

National Response Rates..................................................................................... 2 
 

The National Ambulatory Medical Care Survey (NAMCS) Drug Database ............................................ 2 
 

Drug Characteristics ...................................................................................... 3 
 

Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 
 

Reason Why Medication Was Prescribed and Adverse Events..................................................... 4 
 

Data Processing ............................................................................................ 4 
 

File Edits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 
 

Disclosure Risk Review ..................................................................................... 5 
 

Prescribed Medications Public-use File (PM PUF) ................................................................ 5 
 

Recodes ................................................................................................ 5 
 

Design Variables ......................................................................................... 5 
 

Survey Weighting Procedures............................................................................... 6 
 

Computing Estimates...................................................................................... 6 
 

Searching for Information on Specific Medications ............................................................... 6 
 

PDF File 1: Drugs by NDC Class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 
 

PDF File 2: Drug Estimates and Characteristics ................................................................ 7 
 

Naming Conventions for Medication Variables ................................................................... 9 
 

Analyzing the Prescribed Medications (PM) Data................................................................ 11 
 

Limitations to the PM Module and CAPI Instrument ............................................................. 13 
 

Summary and Suggestions for Future Data Collection ............................................................ 14 
 

References ............................................................................................... 17 
 

Appendix I............................................................................................... 18 
 

List of Prescribed Medications (PM) Module Items ............................................................ 18 
 

Appendix II .............................................................................................. 23 
 

Summary of Key Items for Data Collection .................................................................. 23 
 

Appendix III ............................................................................................. 24 
 

1995 Version of the NDC Directory Used to Adjudicate 2004 NNHS Data ......................................... 24 
 

Text Figures

1.	 1995 National Drug Code Directory listing of therapeutic classes that were used to classify medications collected in the
 

2004 National Nursing Home Survey .................................................................... 8 
 

2.	 Drugs by National Drug Code Class file that shows where the listing of antidepressants begins..................... 8 
 

iii
3.	 Drugs by NDC Class file, highlighting listing of antidepressant Lexapro ....................................... 9 
 

4.	 Drug estimates and characteristics file, highlighting listing of Lexapro ....................................... 10 
 

5.	 Drug estimates and characteristics file, highlighting listing of Prozac ......................................... 12 
 

6.	 Drug estimates and characteristics file, highlighting listing of fluoxetine....................................... 13 
 

7.	 Drug estimates and characteristics file, highlighting listing of Sarafem ........................................ 14 
 

8.	 Drug estimates and characteristics file, highlighting listing of Symbyax ....................................... 15 
 

9.	 SAS output of resident identification numbers of those who took fluoxetine-containing medications generated from
 

Step 2 ............................................................................................ 15 
 

10.	 Temporary SAS dataset created from merging the dataset from Step 1 with the Current Resident Public-use File...... 16 
 

11.	 SAS output of PROC CROSSTAB of residents by fluoxetine-containing medication status (yes/no) generated from
 

Step 5 ............................................................................................ 16 
 

12.	 SAS output of PROC DESCRIPT of residents who took fluoxetine-containing medications by sex, generated from
 

Step 6 ............................................................................................ 17 
 

iv
Objectives

This methods report provides an

overview of how medication data were

collected and processed in the 2004

National Nursing Home Survey (NNHS)

and how analysts may use the

medication data. The 2004 survey

marked the first time that medication

data were collected on sampled nursing

home residents in the NNHS.

Information about medications, which

are an important component of nursing

home care, can help policy makers,

researchers, and members of the

long-term care community better

understand which medications are

taken by U.S. nursing home residents.

Methods

The medication data were collected

in the Prescribed Medications (PM)

module of the 2004 NNHS. The

computer-assisted personal interviewing

(CAPI) instrument, which included a

drug lookup list, allowed interviewers to

enter a maximum of 25 medications

taken on the day before the facility

interview and a maximum of 25

medications taken regularly but not on

the day before the facility interview.

This medication information, which is

documented in residents’ medication

administration records, was collected

during a face-to-face interview at the

nursing home facilities. All data were

provided by facility respondents who

used residents’ administrative and

medical records to answer the survey

items. The data were entered into the

CAPI instrument that was loaded onto

each interviewer’s computer laptop.

Results and conclusions

Data were collected on 13,507

current residents, 98.5 percent of whom

took one or more medications. A

stand-alone PM Public-use File has

been released. This file, which includes

the sampling weight and design

variables from the resident file, can be

linked to the 2004 NNHS Current

Resident Public-use File to generate

national estimates on medications by

various resident characteristics.

Keywords: medications c National

Nursing Home Survey c nursing

homes c nursing home residents

Collecting Medication Data in

the 2004 National Nursing

Home Survey

by Lisa L. Dwyer, M.P.H., Division of Health Care Statistics

Introduction
 

This methods report describes how

medication data were collected in the

2004 National Nursing Home Survey

(NNHS). The NNHS has been

conducted periodically since 1973 and

was redesigned after the 1999 survey.

Information on medications was

collected for the first time in the 2004

survey. The redesigned survey included

the collection of many new data items,

including medication information,

because medications play a critical role

in nursing home care. Many nursing

home residents, the majority of whom

are elderly and frail, take numerous

medications to treat multiple chronic

conditions. A 1991 study reported that

‘‘the highest use of medications by

Americans is in the institutionalized

elderly’’ (1). Information about

medications taken by nursing home

residents is of great interest to policy

makers, long-term care planners, and

health services researchers, particularly

as the U.S. elderly population continues

to grow. Previous federal and nonfederal

data collection initiatives have collected

information on medication use in

nursing homes. However, the results

from these efforts are dated (e.g., 1996

Medical Expenditures Panel Survey—

Nursing Home Component) or not

generalizeable to the entire U.S. nursing

home population (e.g., Medicare Current

Beneficiary Survey, National Medication

Usage Survey, Systematic Assessment of

Geriatric drug use via Epidemiology,

and the Minimum Data Set). Medication

data from the 2004 NNHS may be used

to generate more current national

estimates of medication use by U.S.

nursing home residents.

The National Nursing

Home Survey (NNHS)

The NNHS is one in a series of

surveys that makes up the National

Health Care Surveys (NHCS) conducted

through the Division of Health Care

Statistics (DHCS) by the Centers for

Disease Control and Prevention’s

National Center for Health Statistics

(NCHS). The survey used a nationally

representative sample of U.S. nursing

home facilities to collect data on

facilities’ staffing and services and on

current residents (data on discharged

residents were collected in previous

survey years including 1999), including

demographic information, health

conditions, and services received. The

redesign of the NNHS resulted in

collecting medication data, as well as

other new clinical survey items, for the

first time in NNHS history. These data

were collected in the Prescribed

Medications (PM) module (see

Appendix I).

The 2004 NNHS medication data

are available online in a public-use file.

The file includes the name and

characteristics of medications taken by

sampled nursing home residents. The

data can be linked to the Current

Resident Public-use File, which includes

residents’ demographic, health status,

and payment information and

information on services received.

Page 1
Page 2 [ Series 1, No. 47

Information on medication dosage,

strength, frequency, and route was not

collected in this survey. The data from

the 2004 NNHS are the most recent

nationally representative data available

on medications taken by nursing home

residents.

Sample Design

The 2004 NNHS used a sampling

frame of U.S. nursing homes drawn

from the Centers for Medicare &

Medicaid Services’ Provider of Services

file of U.S. nursing homes and state

licensing lists compiled by SMG

Marketing Group. The combined files

were matched and unduplicated,

resulting in a sampling frame of 16,628

nursing homes.

Sampling for the NNHS used a

stratified two-stage probability design.

The first stage was the selection of

facilities and the second stage was the

selection of residents. The primary strata

of facilities were defined by bed size

category and metropolitan statistical area

status. Within primary strata, facilities

were sorted by the following factors:

certification status; hospital-based

compared with nonhospital based status;

ownership; geographic region; and state,

county, and ZIP Code. Nursing homes

were then selected using systematic

sampling with probability proportional

to bed size. For the 2004 NNHS, 1,500

nursing homes were selected to

participate.

The second-stage sampling of

current residents was carried out by the

interviewers using a programmed

algorithm in their laptops when they

visited the facilities to conduct the

interview. The sampling frame for

current residents was the total number

of residents on the facility rolls as of

midnight on the day before the survey

interview. Residents who were

physically absent from the facility

because of an overnight leave or a

hospital visit, but had a bed maintained

for them at the facility, were included in

the sampling frame. A sample of up to

12 current residents per facility was

selected.

Pretest
 

The 2004 NNHS pretest was

administered in 75 nursing homes in

June and July 2003. The pretest allowed

NCHS to gain direct experience with the

PM module under normal interviewing

conditions (2). Interviewers did not

report any major problems in retrieving

medication information from the

residents’ medication administration

records (MARs) during the pretest.

Some respondents retrieved the MARs

from the nurses’ stations to answer

medication questions. In other cases,

because the medication questions were

mentioned in the Key Items for Data

Collection (see Appendix II) sent to

appointed facilities in the confirmation

package prior to the interview, some

nursing facilities included the MARs in

the residents’ charts, which facilitated

collecting these data.

The main problem with using the

MARs was in collecting data on the

reason why the medication was

prescribed, which is typically found with

the drug order. For these cases, the

interviewers were trained to enter ‘‘don’t

know.’’ However, some conscientious

respondents looked up information in

the Drug Handbook, called other

coworkers for drug classification

information, or answered using their

existing knowledge of the medication

indication. These extra efforts increased

administration time and were not

performed by all respondents. This

contributed to the inconsistent collection

of this information, which ultimately

was not released in the public-use file.

National Response

Rates

Of the 1,500 nursing home facilities

selected to participate in the national

survey, 283 refused to participate and 43

were considered out of scope for one or

more of the following reasons: the

nursing home had gone out of business,

it failed to meet the minimum definition

(i.e., a facility or unit licensed as a

nursing home or a nursing facility by

the state health department or some

other state agency and a facility that has

three or more beds) used in this survey,

or it was a duplicate of another facility

in the sample. A total of 1,174 nursing

homes participated at the first stage by

providing facility information, resulting

in a first-stage response rate of 81%. A

total of 14,017 residents were sampled

from the responding facilities. Of these

residents, 8 were out of scope and for

502 residents the facilities refused to

provide resident information or ran out

of time. This yielded a second-stage

response rate of 96%. The overall

response rate for the resident component

of the 2004 NNHS was 78%. Complete

information was collected on 13,507

residents.

The National

Ambulatory Medical

Care Survey

(NAMCS) Drug

Database

The 2004 NNHS medication data

were collected using the National

Ambulatory Medical Care Survey

(NAMCS) drug database. The NAMCS

is another survey within the family of

National Health Care Surveys. NAMCS

has a drug database to code and

adjudicate the medication names

collected in its survey. The 2004 NNHS

used a lookup list of medications

constructed from the NAMCS drug

database during the pilot and pretest

phases of the survey. The lookup list

contained the names of 908 medications

most frequently prescribed for nursing

home residents; these medications were

compiled from conditions most

commonly reported by physicians of

elderly patients in the NAMCS and by

facility respondents in the Medicare

Current Beneficiary Survey’s long-term

care facility component. Modeling the

collection and coding of NNHS

medication data after the NAMCS will

allow NCHS to compare medications

taken by elderly populations across

different providers surveyed in the

NHCS–nursing homes, physician

offices, hospital outpatient facilities, and

hospital emergency rooms.
Series 1, No. 47 [ Page 3

Approximately 89.8% of the

medications reported in the survey were

in the 2004 NNHS CAPI lookup list.

The NAMCS drug database, which

holds information on more than 11,000

medications (prescription and

over-the-counter (OTC) drugs) and their

characteristics, can be accessed at

http://www.cdc.gov/nchs/about/major/

ahcd/ambulatory.htm.

Drug Characteristics

The NAMCS drug database

contains the following six characteristics

for each medication:

1.	 Generic name code: A unique

five-digit code for the chemical

name of a drug (3).

2.	 Rx status code: A code that indicates

whether the medication is available

by prescription (Rx) only or as an

OTC drug in the noninstitutionalized

setting. In the nursing home setting,

all medications given to a nursing

home resident—even OTCs,

including vitamin, mineral, or herbal

supplements—must be ordered by a

physician and, therefore, are

considered prescriptions. Despite

this, the NAMCS drug database

contains information on how the

medication is available in the

noninstitutionalized setting:

+	 1 = Prescription.

+	 2 = OTC (over the counter).

+	 3 = Undetermined.

+	 4 = Illicit (although there are

some illicit drugs in the

NAMCS drug database, none

were reported in the 2004

NNHS.)

3.	 Drug Enforcement Agency (DEA)

status code: A code that describes

the abuse potential of the medication

(i.e., is a person more likely to

become addicted to the medication)

from least controlled to most

controlled:

+	 1 = Schedule I (research only);

high abuse potential.

+	 2 = Schedule II (high abuse

potential; abuse may lead to

severe psychological or physical

dependence).

+	 3 = Schedule III (lower abuse

potential).

+	 4 = Schedule IV (low abuse

potential; abuse may lead to

limited physical or psychological

dependence).

+	 5 = Schedule V (lowest abuse

potential).

+	 6 = Uncontrolled.

4.	 Composition (Comp) status code: A

code that indicates the drug makeup

as a single entity or combination

product:

+	 1 = Single entity drug.

+	 2 = Combination drug.

+	 3 = Undetermined.

5.	 Ingredient code: A unique five-digit

code for the active ingredient in a

medication. An active ingredient is

any component of a drug product

intended to furnish pharmacological

activity or other direct effect in the

diagnosis, cure, mitigation,

treatment, or prevention of disease,

or to affect the structure or any

function of the body of humans or

animals (4). The NAMCS drug

database can hold up to five active

ingredients for each medication.

6.	 Drug class code: A four-digit code

that describes the therapeutic effect

of the medication based on the organ

or system on which the medication

acts and its chemical,

pharmacological, and therapeutic

properties (5). The NAMCS drug

database allows up to three

therapeutic classes per medication,

although the majority of medications

belong to only one or two

therapeutic classes. The therapeutic

classification system used to code

new medications in the 2004 NNHS

is the 1995 National Drug Code

(NDC) Directory (see Appendix III),

which was the most current version

at the time of the survey.

Data Collection

The 2004 National Nursing Home

Survey was conducted from August

2004 through January 2005. The survey

consisted of two facility modules (the

Facility Qualifications module and the

Facility Characteristics module), two

sampling modules (for residents and

nursing assistants), and four residentlevel modules. The Prescribed

Medications (PM) module that collected

the medication data was one of the four

resident-level modules.

In the PM module, the following

questions were asked about each

sampled resident: What medications

were taken by the resident during the 24

hours of the day before the facility

interview, including standing or routine

medications or PRN (i.e., as needed)

medications? (question PM1A); and

What medications were taken regularly

by the resident but not during the 24

hours of the day before the facility

interview, including standing orders for

administration only, but excluding PRN

medications (question PM2A)? The drug

lookup list was used to enter responses

to these two questions. The type of

medication order (i.e., standing, routine,

or PRN) for each medication was not

collected; nor was dosage, strength,

route, or frequency information

collected.

The survey included these two

separate medication questions because

some medications are administered

weekly or even monthly, as in the case

of some osteoporosis drugs (e.g.,

alendronate) or commonly prescribed

supplements (e.g., vitamin B-12).

Therefore, the survey instrument was

designed to capture information on

medications taken every day through

question PM1A and on medications

taken regularly but not every day

through question PM2A. The medication

data provided by the facility respondent

were collected as brand or generic

name, whichever the facility respondent

provided. The facility respondent, who

was familiar with the residents and their

care, was selected by the nursing home

administrator to answer the data items in

the resident-level modules.
Page 4 [ Series 1, No. 47

Reason Why Medication

Was Prescribed and

Adverse Events

For each medication collected in the

2004 NNHS, information was collected

on the reason the medication was

prescribed and the adverse events

experienced by the resident. These data

were entered manually by the

interviewer; they were not collected

using a lookup list. After the data

collection period ended, a corresponding

International Classification of Diseases,

9th Revision, Clinically Modified

(ICD–9–CM) code was assigned to each

‘‘Reason why prescribed’’ entry, using a

computerized matching algorithm.

Because these data were not collected

consistently, they were not included in

the Prescribed Medications Public-use

File (PM PUF). As mentioned

previously, interviewers as well as

observers of select interviews found

that, in many cases, the reason why the

medication was prescribed was not

documented in the MARs and was

reported inconsistently across

respondents.

Data on adverse events were also

collected for each medication reported

in the 2004 NNHS. Medication errors

and adverse events are prevalent in the

U.S. health care system (6). However,

because the number of adverse events

reported in the NNHS was very low

(less than 1% of all nursing home

residents), cell sizes are small and

estimates are unreliable, particularly if

the data are analyzed by basic

demographic variables (e.g., sex and

age). As a result, NCHS omitted this

information from the PM PUF.

Data Processing

Medication names that field

interviewers did not find in the drug

lookup list at the time of interview were

entered manually into the ‘‘Other,

specify’’ field as text strings. These text

strings were compiled into drug batches

and later reviewed by the coding staff,

who determined if the text strings could

be back-coded to a pre-existing drug

name. If an exact match was found for

the text string, then it was coded

accordingly. If not, the coders searched

for the medication name (i.e., text

string) using various pharmacy

references and documented any

information found.

The next level of review was

conducted by a pharmacist who

reviewed the coders’ decisions and

coders’ notes and attempted to assign

codes to uncodeable medications. The

pharmacist was able to override the

coders’ initial decisions when

appropriate. The pharmacist also added

decision notes, particularly related to

medications’ active ingredients. All

medication text strings that were still

uncodeable after review by the coders

and the pharmacist were sent to NCHS

for review and adjudication.

Adjudication included the review of

the medication name entered into the

CAPI, the drug name code assigned by

the coders (and second drug name code

if there was a discrepancy between the

two coders’ assignments), the coders’

notes, the ‘‘Reason why prescribed’’

field, and the pharmacist’s notes. In

adjudication, NCHS researched a

medication name and its characteristics

(i.e., generic name, active ingredients,

composition status, DEA status, and

therapeutic class) to determine if it was

a valid drug. This research involved

using a combination of pharmacology

references and clinical staff or contacts

who were familiar with medications

commonly used in nursing homes. If

NCHS was able to confirm that the

medication was a valid drug, the drug

information was entered into the

NAMCS drug database, which

automatically assigned the medication a

unique five-digit drug name code. The

drug name code has a two-digit prefix

that corresponds to the year of data

collection, followed by three digits—

starting with 001—that are assigned in

sequential order. Medications that were

still ‘‘uncodeable’’ even after NCHS

review were coded as 99980. These

uncodeables resulted from incorrect

spelling or data entry (e.g., in a few

cases, the names of medical devices or

manufacturer names were entered). In

all, 6,186 unique drug entries were

coded. A total of 272 medications were

added to the NAMCS drug database; the

majority of them were vitamin or

mineral supplements.

File Edits

The majority of edits for the

medication data were for the ‘‘Reason

why prescribed’’ item. Because these

data were entered manually by

interviewers, there were varied spellings

for many medical conditions. The

ICD–9–CM was used to code the

diagnoses and medical conditions. The

coding entailed two steps, combining

the efforts of interviewers and trained

medical coders: unique conditions in

earlier batches were matched to similar

conditions in later batches to provide

suggested codes, and exact matching to

medical coding text-to-code tables from

the field test and other in-house projects

was used to provide suggested codes.

The coders reviewed the suggested

codes and the various spellings for

reported medical conditions, combining

the suggested codes into a ‘‘best’’

suggested code where possible. This

coding was then thoroughly reviewed

and finalized by the medical coders,

who were trained certified nosologists.

Three major coding issues were

identified for the ‘‘Reason why

prescribed’’ survey item:

1. Inconsistent data entries.

2. Nondescript entries.

3. Dual coding.

Although this survey item was designed

to collect information about medical

conditions, it was found that dosage

information (e.g., ‘‘bid’’ for twice daily),

symptoms, and ICD–9–CM codes were

reported as well. In other cases, the data

entered were extremely vague or

nondescript, forcing the coder to guess

the field interviewer’s entry or to leave

the corresponding ICD–9–CM field

uncoded; examples include ‘‘SUPP,’’

‘‘Mental,’’ ‘‘Blood,’’ and

‘‘Maintenance,’’ to name some. Finally,

dual coding caused the most concern. In

some cases, the ‘‘Reason why

prescribed’’ field contained two

conditions, such as ‘‘MI/CAD’’ for

myocardial infarction
Series 1, No. 47 [ Page 5

and coronary artery disease. This

resulted in assigning two ICD–9 codes

for dual conditions. Ideally, the field

interviewer would have asked the

respondent to provide the primary

reason for why a medication was taken

and then code that single condition. An

NCHS in-house medical coder reviewed

the dual coding cases and provided

direction in editing these fields. Even

after expending these resources for file

edits, NCHS decided not to release the

‘‘Reason why prescribed’’ data in the

PM PUF as these data failed to meet

data quality standards. These data are

available upon request through the

NCHS Research Data Center.

Disclosure Risk

Review

Under Section 308(d) of the Public

Health Service Act (42 U.S.C. 242m),

the confidentiality of sampled

establishments, providers, and patients

in NCHS surveys is protected. As a

result, all public-use files released by

NCHS must complete the disclosure risk

review process to protect the facilities

and patients for whom data were

collected from being identified. NCHS

staff reviewed the 2004 NNHS

medication data along with other

resident data to ensure that no drug

information could inadvertently disclose

the identity of a nursing home or any of

its residents.

Prescribed

Medications

Public-use File (PM

PUF)

The PM PUF is a stand-alone file

of 13,507 records. The file was designed

to make it easier for analysts to

manipulate the data. If the residents’

medication information were included in

the Current Resident Public-use File, the

file would have been too large, requiring

a great deal of computer memory;

therefore, the PM

PUF is a separate file that can be linked

to the Current Resident Public-use File

when it is necessary to examine other

resident variables.

Each record in the PM PUF

represents a sampled resident who has a

unique resident identification number

(RESNUM) that can be linked to its

counterpart in the Current Resident

Public-use File. The RESNUM is

randomly generated and assigned to a

unique resident. The remaining variables

in the PM PUF are for the 25

medications taken during the 24 hours

of the day before the facility interview

(i.e., PMCODE01–PMCODE25), the 15

medications taken regularly but not

during the 24 hours of the day before

the facility interview (i.e., OTHPMC01–

OTHPMC15), the drug characteristics

for each medication, the survey design

variables, and the sample weights that

are necessary to generate accurate

national estimates of medication use.

Additional information on design

variables and sample weights is found in

the following text. Each medication is

identified by a unique five-digit drug

code (unless the medication was

uncodeable, in which case it remains

coded as 99980). The drug

characteristics provide further

information about the medications as

described in the previous Drug

Characteristics section.

The PM PUF can be used to

generate national estimates of medication

use, such as the mean number of

medications per resident. Other analyses

that examine specific drugs or

therapeutic classes can also be

conducted by creating a subset of

resident records with the corresponding

drug name codes or therapeutic class

codes and linking these records to the

Current Resident Public-use File; this

process facilitates creating a more

manageable analytic file. The Current

Resident Public-use File contains

information on residents’ demographics,

health status, services used, and sources

of payment. The SAS and ASCII

versions of the PM PUF may be

accessed at ftp://ftp.cdc.gov/pub/

Health_Statistics/NCHS/Datasets/NNHS/

nnhs04/. Separate SAS, SPSS, and

STATA input statements and variable

label statements are also available at this

website. The data dictionary for the PM

PUF is available at http://www.cdc.gov/

nchs/data/nnhsd/March07_

WebPMfiledatadictionary.pdf. The data

dictionary provides the file layout with

the variable names, file positions, field

descriptions, formats (character or

numeric), lengths, and values. The

frequencies for responses to select

variables in the PM PUF may be

verified on pages 33–36 of the PM PUF

data dictionary. The data dictionary for

the Current Resident Public-use File

may be accessed at ftp://ftp.cdc.gov/pub/

Health_Statistics/NCHS/Dataset_

Documentation/NNHS/nnhs04/

2004ResidentFile_DataDictionary_

021607.pdf.

Recodes

Four additional variables were

created from the original medication

data and were added to the PM PUF:

ANYMEDS, RXMED, RXOTH, and

RXTOT. ANYMEDS shows if the

nursing home resident took any

medications. The overwhelming majority

of residents, 99%, took at least one

medication. RXMED provides the total

number of medications taken by the

resident during the 24 hours of the day

before the facility interview; the range is

0 to 25. RXOTH shows the number of

medications a resident took regularly but

not during the 24 hours of the day

before the facility interview; the range is

0 to 15. Although the CAPI instrument

was designed to collect up to 25

medications taken regularly but not

during the 24 hours of the day before

the facility interview for each resident, a

maximum of 15 medications were

reported for this item (i.e., no resident

took more than 15 medications regularly

but not on the day before the facility

interview). RXTOT is the sum of

RXMED + RXOTH; the range is 0 to

30.

Design Variables

The PM PUF and the other NNHS

public-use files include design variables

that designate each record’s stratum

marker and the first-stage unit (or

cluster) to which the record belongs.

These design variables must be used in
Page 6 [ Series 1, No. 47

statistical software programs to obtain

accurate variances of the survey

estimates. Design variables compute the

inter- and intra- cluster variances and

adjust for the complex survey design.

Survey Weighting

Procedures

Because the NNHS is designed to

produce unbiased national estimates for

medications taken by nursing home

residents, the data must have weights to

inflate the numbers of sampled residents

and their medications that would result

if all U.S. nursing home residents were

sampled; therefore, each record on the

data file is assigned a weight. By

aggregating these weights, counts can be

obtained for nationally representative

data. The weight is constructed to reflect

the design of the sample: a stratified

multistage, probability proportional to

size, systematic design. The following

are components of the sample weight:

Inverse of the probability of

selecting the nursing home

resident

The probability of selecting a

nursing home resident is a product of

the two selection probabilities: the

probability of selecting a facility in the

NNHS sample and the probability of

selecting the nursing home resident

within the sampled NNHS facility. The

inverse of the product of these

probabilities is used in weighting.

Adjustment for nonresponse

The second component for

calculating the weight is adjustment for

nonresponse. There are three types of

nonresponse. The first two types are

facility level and the third is person

level. The first type occurs when

in-scope facilities do not respond to the

NNHS. The second type occurs when an

in-scope facility does not provide the

number of nursing home residents

within the facility. The third type occurs

when the administrative and medical

records of the sampled residents are not

made available to complete the survey.

Ratio adjustment and weight

smoothing

The final components of calculating

weights involve ratio adjustment and

smoothing. Ratio adjustments are made

within each of the groups defined by

region to adjust for over- or undersampling of facilities reported in the

sampling frame. This adjustment is a

multiplicative factor whose numerator

was the number of nursing home

facilities in the sampling frame within

each region and whose denominator was

the estimated number of nursing home

facilities for that same group. Ratioadjusted weights are smoothed only if

there are disproportionately large

weights. In smoothing, totals are

preserved.

Computing Estimates

Standard errors may be calculated

for medication estimates using any

statistical software package, as long as

clustering within facilities and other

aspects of the complex sample design

are taken into account. Software

products such as SAS and STATA have

these capabilities. Tables A, B, and C

provide information to generate national

medication estimates with the 2004

NNHS data using the respective

software packages.

Searching for

Information on

Specific Medications

Medication-specific information,

such as drug characteristics and crude

estimates of use by nursing home

residents, is available at the NNHS

website, http://www.cdc.gov/nchs/about/

major/nnhsd/drugdatabase.htm. This

website has two PDF files—Drugs by

NDC Class and Drug Estimates and

Characteristics—with information on

each medication collected in the 2004

NNHS: unique drug names and codes,

preliminary drug estimates, and drug

characteristics. If an analyst wants to

examine drug use by resident

characteristics, he will need to access

the PM PUF. The starting point for this

type of analysis is the PDF files

described in the following text to

determine if there are enough cases of

the medication (i.e., enough residents

who took the medication of interest) to

conduct meaningful analyses.

PDF File 1: Drugs by NDC

Class

The Drugs by NDC Class file lists

every medication in the NAMCS drug

database by major therapeutic class code

and therapeutic subclass code in

ascending order. The 1995 National

Drug Code Directory (see Appendix

III), which was used to classify

medications

Table A. Computations using SUDAAN

File

PROC

statement

NEST

statement

TOTCNT

statement

WEIGHT

statement

Prescribed medications file .......

PROC x File=y

DESIGN=WOR;

NEST RSTRATA

FACNUM/ MISSUNIT;

TOTCNT POPFAC

NPOPRES;

WEIGHT

SAMWT;

Table B. Computations using STATA

File Design description in STATA

Svyset [pw=SAMWT], strata (RSTRATA) psu (FACNUM) fpc(POPFAC) ll_n

Prescribed medications file .......
Series 1, No. 47 [ Page 7

Table C. Computations using SAS survey procedures

File PROC STRATA CLUSTER WEIGHT

Prescribed medications file .......

PROC SURVEY_

DATA=Y; STRATA RSTRATA; CLUSTER FACNUM;

WEIGHT

SAMWT;

reported in the 2004 NNHS, has 21

major therapeutic classes and 139

therapeutic subclasses. The major

therapeutic class refers to the 21 broad

categories in the NDC Directory that

describes medications; each category

generally includes the name of the body

organ it is indicated to treat. For

example, the major therapeutic class

central nervous system (NDC code

0600) includes medications that treat

disorders of the central nervous system.

The therapeutic subclass further defines

or describes the drug; for example,

antidepressants (NDC code = 0630) is a

subcategory of the central nervous

system and is indicated to treat

depression. In the NAMCS Drug

Database that was used to code

medications collected in the 2004

NNHS, most medications are assigned a

therapeutic subclass if it is known; if

not, then it is defined by its major

therapeutic class.

The Drugs by NDC Class file

provides a user-friendly format for data

users to see which medications belong

to each therapeutic class—major class

and subclass—in the NAMCS Drug

Database. The layout of this PDF file is

as follows:

Column 1: Major therapeutic class code

Column 2: Therapeutic subclass code

Column 3: Drug name code

Column 4: Drug name

In this PDF file, generally the major

therapeutic class code is found at the

top of the page unless otherwise

indicated.

PDF File 2: Drug

Estimates and

Characteristics

All medications are identified by

their unique five-digit drug name code

only; no drug names are spelled out

anywhere in the PM PUF. Therefore, if

an analyst wants to find drug-specific

information beyond unique drug name

code and therapeutic class code, he can

find the crude estimate for the number

of residents who took a given

medication, the rate of use per 10,000

residents, and the drug characteristics

associated with a given medication in

the Drug Estimates and Characteristics

file. This file contains the following 12

columns:

Column 1: Drug name

Column 2: Drug name code

Column 3: Drug estimate

Column 4: Drug rate per 10,000 residents

Column 5: Generic name

Column 6: Generic name code

Column 7: Rx status

Column 8: DEA status

Column 9: Comp status

Column 10: Drug ingredients

Column 11: Ingredient codes

Column 12: Drug class codes

Using the Edit-Find feature in this

file, the data analyst can search for

medications by name—brand or

generic—to find the corresponding drug

name codes and characteristics. First,

enter the name of the medication in the

Edit-Find box and click the Next key. If

the drug name is in the Drug Estimates

and Characteristics file, the drug name

will be highlighted. This record will also

have the five-digit drug name code, the

weighted drug estimate, and the drug

rate per 10,000 residents; the latter

information will show if the medication

was taken by any sampled residents in

the NNHS. If these columns—drug

estimate and drug rate per 10,000

residents—are blank, then the

medication was not taken by any

sampled nursing home residents. If these

columns are populated with numbers,

then the drug name code should be

noted, as it will be used to retrieve the

residents’ records.

The remainder of this section

(Searching for Information on Specific

Medications) illustrates how to use the

two PDF files—Drugs by NDC Class

and Drug Estimates and

Characteristics—using antidepressants

as an example. An analyst who wants to

know which medications are classified

as antidepressants, can perform the

following steps:

1.	 Go to Appendix III to find the major

therapeutic class code and subclass

code for antidepressants. The major

therapeutic class code for

antidepressants, a central nervous

system drug, is 0600; the subclass

code is 0630 (Figure 1).

2.	 Go to the Drugs by NDC Class file

and type 0600 in the Edit-Find box.

Click Next or scroll through the

document to find the page where

0600 appears in the first column.

3.	 Search for therapeutic subclass code

0630 by using the Edit-Find feature

or scrolling to the page where this

code first appears. Therapeutic

subclass code 0630 starts with the

medication Sarafem on page 53

(Figure 2).

4.	 Antidepressants are listed through

to page 55. There are a total of 85

antidepressants listed in the Drugs

by NDC Class file, complete with

their unique drug name codes and

drug names.

Analysts may also want to focus on

a particular type of antidepressant, such

as serotonin-specific reuptake inhibitors

(SSRIs). (SSRIs are a newer class of

antidepressant medications that was

introduced into the market in 1987 with

fluoxetine (7). SSRIs block the reuptake

of serotonin and have fewer side effects
0628 96173 ZYPREXA 

0628 97002 OLANZAPINE 

0628 97168 SEROQUEL 

0628 98049 DONEPEZIL HCL 

0628 99101 QUETIAPINE FUMARATE 

0630 00318 SARAFEM 

0630 00510 ADAPIN 

0630 01009 LIMBITROL DS 10-25 

0630 01029 EFFEXOR XR 

0630 01064 WELLBUTRIN SR 

0630 01525 AMITRIL 

0630 01530 AMITRIPTYLINE 

0630 01535 AMITRIPTYLINE HCL W/PERPHENAZINE 

0630 01628 AMOXAPINE 

0630 02119 LEXAPRO 

0630 02256 ESCITALOPRAM OXALATE 

SOURCE: http://www.cdc.gov/nchs/data/nnhsd/DrugsbyNDCClass3.pdf. P 53. 

Figure 2. Drugs by National Drug Code Class file that shows where the listing of antidepressants begins

1995 Version of the NDC Directory Used to Adjudicate 2004 NNHS Data 

0100 

0117 

0118 

0119 

0120 

0121 

0122 

0123 

0200 

0281 

0283 

0285 

0286 

0300 

0346 

0347 

0348 

0349 

ANESTHETIC DRUGS 

Anesthetics, Local (Injectable)

Anesthetics, General 

Adjuncts to Anesthesia/Analeptics

Medicinal Gases

Anesthetics, Topical

Anesthetics, Ophthalmic

Anesthetics, Rectal 

ANTIDOTES

Antidotes, Specific

Antidotes, General

Antitoxins/Antivenins 

Anaphylaxis Treatment Kit 

ANTIMICROBIAL AGENTS

Penicillins

Cephalosporins

Erythromycins/Lincosamides/Macrolides

Polymyxins 

0509 Relaxants/Stimulants, Urinary Tract 

0510 Calcium Channel Blockers 

0511 Carbonic Anhydrase Inhibitors

 0512 Beta Blockers 

0513 Alpha Agonist/Alpha Blockers

 0514 ACE Inhibitors 

0600 CENTRAL NERVOUS SYSTEM 

0626 Sedatives and Hypnotics

 0627 Antianxiety Agents

 0628 Antipsychotic/Antimanics

 0630 Antidepressants

 0631 Anorexiants/CNS Stimulants

 0632 CNS, Miscellaneous

 0633 Alzheimer-Type Dementia 

0634 Sleep Aid Products-OTC

 0635 Antiemetics 

0700 CONTRAST MEDIA/ 

RADIOPHARMACEUTICAL 

NOTE: NDC is the National Drug Code and NNHS is the National Nursing Home Survey. 

SOURCE: http://www.cdc.gov/nchs/data/nnhsd/Technicalnotes_April_6_2007_revision.pdf. 

Figure 1. 1995 National Drug Code Directory listing of therapeutic classes that were used to classify medications collected in the 2004

National Nursing Home Survey

Page 8 [ Series 1, No. 47
0628 96173 ZYPREXA 

0628 97002 OLANZAPINE 

0628 97168 SEROQUEL 

0628 98049 DONEPEZIL HCL 

0628 99101 QUETIAPINE FUMARATE 

0630 00318 SARAFEM 

0630 00510 ADAPIN 

0630 01009 LIMBITROL DS 10-25 

0630 01029 EFFEXOR XR 

0630 01064 WELLBUTRIN SR 

0630 01525 AMITRIL 

0630 01530 AMITRIPTYLINE 

0630 01535 AMITRIPTYLINE HCL W/PERPHENAZINE 

0630 01628 AMOXAPINE 

0630 02119 LEXAPRO 

0630 02256 ESCITALOPRAM OXALATE 

SOURCE: http://www.cdc.gov/nchs/data/nnhsd/DrugsbyNDCClass3.pdf. P 53. 

Figure 3. Drugs by NDC Class file, highlighting listing of antidepressant Lexapro

Series 1, No. 47 [ Page 9

than the older antidepressants (8).) An

analyst who knows the names of

particular SSRIs can identify the drug

names among the list of antidepressants.

After finding the drug name codes for

the SSRIs (use the Drugs by NDC Class

file and the Edit-Find function to locate

the SSRI names), the analyst can check

the Drug Estimates and Characteristics

file to find out which SSRIs were taken

by the sampled nursing home residents.

The next example focuses on finding the

antidepressant Lexapro in the two PDF

files. The Drugs by NDC Class file

shows that the drug name code for

Lexapro is 02119 (Figure 3).

If the analyst then goes to the Drug

Estimates and Characteristics file and

types in 02119 in the Edit-Find search

feature, the medication will appear on

page 111 in row 11 (Figure 4). This

summary file allows analysts to obtain

crude estimates of medication use and to

easily identify drug characteristics without

downloading and manipulating the PM

PUF.

Lexapro was taken by an estimated

300 residents and the rate per 10,000

residents is 2. Additional information

shows that the generic name for

Lexapro is escitalopram oxalate; the

generic name code for escitalopram

oxalate is 70466. The Rx status field

shows that Lexapro is a prescription

medication, its DEA status is

undetermined, and its composition status

is single entity. If one scrolls across to

the last field of the Lexapro record (not

shown in Figure 4), the therapeutic class

code shows that it is 0630, an

antidepressant.

Naming Conventions

for Medication

Variables

Each medication reported in the PM

PUF has 13 variables associated with it,

which include codes for drug name,

generic name, composition status,

ingredients, DEA (or control) status, Rx

(prescription) status, and therapeutic

classes. As mentioned before, the large

number of variables led to creating the

PM PUF as a stand-alone file; the many

variable names also resulted in

developing a naming convention. The

first medication in the file has the suffix

‘‘01’’ or ‘‘1’’ to indicate that the variable

refers to the first medication collected

on each sampled resident (Table D). The

second medication in the file has the

suffix ‘‘02’’ or ‘‘2’’ to indicate that the

variable refers to the second medication

collected on the sampled residents. The

remaining medications follow a similar

pattern. Also, in the PM PUF data

dictionary, the medications taken

regularly but not on the day before the

facility interview are referred to as

‘‘Other medications.’’ The variables for

these medications start with the letter

‘‘O’’ for ‘‘Other’’ (Tables E).

The variable labels listed in

Tables D and E are provided to orient
Page 10 [ Series 1, No. 47

Rx DEA Comp

Status Status Statu

1 4 1

1 6 1

2 6 1 

2 6 1 

1 6 1

1 6 1

1 6 1 

1 6 1

1 6 1

2 6 1

1 7 1

1 6 1 

1 6 1

1 6 1

1 6 1 

1 6 1

2 6 1 

2 6 1

2 6 1

1 6 1

1 6 1 

1 6 1

1 6 1

e 

de 

15 

69

44 

87 

08

25

67 

80

97

30 

66

63 

20

07

03 

72 

75 

25 

40

20 

63 

10

43 

Nam

Co

701

539

703

704

500

555

509

531

542

506

704

561

523

700

518

529

538

524

520

535

561

549

512

ericGen

ORIDE 

E 

s

Drug Drug Rate 

Name Drug per 10,000 

Drug Name Code Estimate Residents Generic Name 

ZALEPLON 02107 304 0002 ZALEPLON 

NOROXIN 21385 304 0002 NORFLOXACIN 

BENADRYL ALLERGY 03332 303 0002 DIPHENHYDRAMINE HCL 

BILBERRY EXTRACT 02204 302 0002 BILBERRY EXTRACT 

SECTRAL 61330 302 0002 ACEBUTOLOL HYDROCHL

BLEPH-10 93030 300 0002 SULFACETAMIDE 

CEFUROXIME SODIUM 91070 300 0002 CEFUROXIME SODIUM 

ALMORA 01070 300 0002 MAGNESIUM GLUCONATE

PENTOXIL 04381 300 0002 PENTOXIFYLLINE 

BISAC-EVAC 04324 300 0002 BISACODYL 

LEXAPRO 02119 300 0002 ESCITALOPRAM OXALATE

COVERA HS 96131 300 0002 VERAPAMIL 

FML 00259 298 0002 FLUOROMETHOLONE 

OMNICEF 99014 298 0002 CEFDINIR 

CARTIA XT 00254 295 0002 DILTIAZEM 

KETOCONAZOLE 91085 295 0002 KETOCONAZOLE 

CLEAR EYES 06895 295 0002 NAPHAZOLINE 

POLYCOSE 24450 295 0002 GLUCOSE 

NEPHRON 00006 294 0002 EPINEPHRINE 

METHAZOLAMIDE 19025 294 0002 METHAZOLAMIDE 

ISOPTIN S.R. 91031 292 0002 VERAPAMIL 

PTU 41845 291 0002 PROPYLTHIOURACIL 

TEMOVATE 61440 290 0002 CLOBETASOL PROPIONAT

RCE: http://www.cdc.gov/nchs/data/nnhsd/Drugsestimatesandcharacteristics.pdf. P 111. 

111

SOU

Figure 4. Drug estimates and characteristics file, highlighting listing of Lexapro

the analyst to the PM PUF naming

conventions; this is not the actual layout

of the public-use file. The PM PUF file

layout is found in the data dictionary at

http://www.cdc.gov/nchs/data/nnhsd/

March07_WebPMfiledatadictionary.pdf.

The order of the medications in the PM

PUF does not have any significance; it

reflects only how the nursing home

respondent read the resident’s

medication information from the

medication administration record.

Table D. Variable naming convention for medications taken on the day before facility interview (Medications 1–4)

Variable description

Medication number (variable labels)

1 2 3 4

Drug name code .......................

Generic name code .....................

Composition status code ..................

Prescription status code...................

Drug Enforcement Agency (DEA) code .........

Ingredient code 1 .......................

Ingredient code 2 .......................

Ingredient code 3 .......................

Ingredient code 4 .......................

Ingredient code 5 .......................

Therapeutic class code 1 ..................

Therapeutic class code 2 ..................

Therapeutic class code 3 ..................

PMCODE01

GEN01

COMST01

RXST01

CONTR01

RX1ING1

RX1ING2

RX1ING3

RX1ING4

RX1ING5

DRG1CL1

DRG1CL2

DRG1CL3

PMCODE02

GEN02

COMST02

RXST02

CONTR02

RX2ING1

RX2ING2

RX2ING3

RX2ING4

RX2ING5

DRG2CL1

DRG2CL2

DRG2CL3

PMCODE03

GEN03

COMST03

RXST03

CONTR03

RX3ING1

RX3ING2

RX3ING3

RX3ING4

RX3ING5

DRG3CL1

DRG3CL2

DRG3CL3

PMCODE04

GEN04

COMST04

RXST04

CONTR04

RX4ING1

RX4ING2

RX4ING3

RX4ING4

RX4ING5

DRG4CL1

DRG4CL2

DRG4CL3
Series 1, No. 47 [ Page 11

Table E. Variable naming convention for medications taken regularly but not on the day before facility interview (Medications 1–4)

Medication number (variable labels)

Variable description 1 2 3 4

Drug name code ....................... OTHPMC01 OTHPMC02 OTHPMC03 OTHPMC04

Generic name code ..................... OGEN01 OGEN02 OGEN03 OGEN04

Composition status code .................. OCOMST01 OCOMST02 OCOMST03 OCOMST04

Prescription status code................... ORXST01 ORXST02 ORXST03 ORXST04

Drug Enforcement Agency (DEA) code ......... OCONTR01 OCONTR02 OCONTR03 OCONTR04

Ingredient code 1 ....................... OX1ING1 OX2ING1 OX3ING1 OX4ING1

Ingredient code 2 ....................... OX1ING2 OX2ING2 OX3ING2 OX4ING2

Ingredient code 3 ....................... OX1ING3 OX2ING3 OX3ING3 OX4ING3

Ingredient code 4 ....................... OX1ING4 OX2ING4 OX3ING4 OX4ING4

Ingredient code 5 ....................... OX1ING5 OX2ING5 OX3ING5 OX4ING5

Therapeutic class code 1 .................. ODR1CL1 ODR2CL1 ODR3CL1 ODR4CL1

Therapeutic class code 2 .................. ODR1CL2 ODR2CL2 ODR3CL2 ODR4CL2

Therapeutic class code 3 .................. ODR1CL3 ODR2CL3 ODR3CL3 ODR4CL3

Analyzing the

Prescribed

Medications (PM)

Data

Many prescription medications (i.e.,

medications that require a doctor’s

prescription in the noninstitutionalized

setting) are available in either brand

name or generic drug form. In order for

an analyst to obtain an accurate estimate

of medication use that considers both

forms, NCHS advises analysts to search

for medications by generic name code in

the Drug Estimates and Characteristics

file online. If a medication is a

single-entity drug, the generic name will

be in the generic name column with its

generic name code in the next column.

If a medication is a combination

product, the generic name is

‘‘combination product’’ (the generic

name code for combination product is

51380), but the ingredient field will list

the generic name of interest; the

corresponding generic name code will

follow in the ingredient codes column.

The ingredient code that one would find

if a medication had a single-entity

generic name should be the same as the

generic name code found in the

ingredient codes column. The drug

fluoxetine (hydrochloride) illustrates this

point in the following text. Fluoxetine

belongs to a subcategory of

antidepressants called selective serotonin

reuptake inhibitors (SSRIs). Fluoxetine

is the generic or chemical name for this

SSRI, which helps improve a person’s

mood. One of several brand names for

this medication is Prozac. Using the

Edit-Find feature in the Drug Estimates

and Characteristics file to locate

fluoxetine takes the analyst to page 98,

where the record for Prozac is found

(Figure 5). This record also shows that

the generic name code for fluoxetine is

80006.

By clicking Next in the Edit-Find

feature to find more fluoxetinecontaining medications, the analyst is

taken to page 101, where fluoxetine is

listed as a drug name (Figure 6). Here

the generic name drug code is also

80006 as found for Prozac.

By clicking Next again, the analyst

will arrive at Sarafem. This record on

page 140 in the Drug Estimates and

Characteristics file shows that

fluoxetine is the generic name for

Sarafem as well (Figure 7).

By clicking Next again, the analyst

will find that, on page 168, fluoxetine is

one of the active ingredients in

Symbyax (drug name code is 04043

(Figure 8); cross-reference this drug

name code in the Drugs by NDC Class

file to check this drug name code).

The search for fluoxetine leads the

analyst to four different records within

the Drug Estimates and Characteristics

file. To generate a nationally

representative estimate of all fluoxetinecontaining medications in the PM PUF,

the SAS-callable SUDAAN program

below is provided. This code will search

the generic name code fields (GEN_ _

and OGEN_ _) and the ingredient code

fields (RX_ ING_ and OX_ING_) for

all medications that contain fluoxetine.

This code will provide the count of

unique nursing home residents who took

one or more fluoxetine-containing

medications; a resident can be counted

only once even if he or she took more

than one fluoxetine-containing

medication.

1.	 Using the generic name code for

fluoxetine—80006—the SAS

program in the following text will

scan the generic name code fields

and the ingredient code fields for

medications taken the day before the

facility interview and medications

taken regularly but not on the day

before the facility interview of each

resident record to find fluoxetine

hydrochloride. The resident records

that contain at least one mention of

fluoxetine will be flagged.

Libname meds ’[filepath for PM

PUF]’;

data test;

set meds;

fluoxetine=0;

array meds (240) GEN01– –

GEN25 OGEN01– –OGEN15

RX1ING1– –RX25ING1

RX1ING2– –RX25ING2

RX1ING3– –RX25ING3

RX1ING4– –RX25ING4

RX1ING5– –RX25ING5

OX1ING1– –OX15ING1

OX1ING2– –OX15ING2

OX1ING3– –OX15ING3

OX1ING4– –OX15ING4

OX1ING5– –OX15ING5;

do i=1 to 240;

if meds (i)=80006 then
Page 12 [ Series 1, No. 47

Drug Drug Rate Generic 

Name Drug per 10,000 Name Rx DEA Comp 

Drug Name Code Estimate Residents Generic Name Code Status Status Status 

ACTONEL 00206 52014 0349 RISEDRONATE SODIUM 70168 1 6 1 

DULCOLAX 10575 51004 0342 BISACODYL 50630 2 6 1 

KCL 41850 49873 0334 POTASSIUM REPLACEMENT SOLUTIONS 54655 1 6 1 

GLUCOPHAGE 95111 48596 0326 METFORMIN 57192 1 6 1 

CELEXA 98115 45483 0305 CITALOPRAM HYDROBRIMIDE 59829 1 6 1 

CARDIZEM 05789 45193 0303 DILTIAZEM 51803 1 6 1 

FLOMAX 97126 44917 0301 TAMSULOSIN HYDROCHLORIDE 59731 1 6 1 

XALATAN 96167 44913 0301 LATANOPROST 59639 1 6 1 

MEGACE 18655 44810 0300 MEGESTROL 53295 1 6 1 

EFFEXOR 94070 44419 0298 VENLAFAXINE 57096 1 6 1 

PROZAC 25674 43293 0290 FLUOXETINE HYDROCHLORIDE 80006 1 6 1 

DETROL 98065 41794 0280 TOLTERODINE TARITRATE 59811 1 6 1 

DOCUSATE SODIUM 96027 38942 0261 DOCUSATE 51935 1 6 1 

DARVOCET-N 08470 38785 0260 COMBINATION PRODUCT 51380 1 4 2 

HYDROCHLOROTHIAZIDE 14930 38271 0256 HYDROCHLOROTHIAZIDE 52645 1 6 1 

FUROSEMIDE 13118 38243 0256 FUROSEMIDE 52385 1 6 1 

METAMUCIL 18930 37654 0252 PSYLLIUM 54965 2 6 1 

REMINYL 01141 36589 0245 GALANTAMINE HYDROBROMIDE 70296 1 6 1 

AMBIEN 93347 36554 0245 ZOLPIDEM TARTRATE 57048 1 4 1 

DITROPAN 09995 36232 0243 OXYBUTYNIN 54105 1 6 1 

ADVAIR DISKUS 01034 36095 0242 COMBINATION PRODUCT 51380 1 6 2 

XANAX 35023 36034 0241 ALPRAZOLAM 50113 1 4 1 

EXELON 00081 35773 0240 RIVASTIGMINE TARTRATE 70129 1 6 1 

VASOTEC 61565 35090 0235 ENALAPRIL 52028 1 6 1 

98

SOURCE: http://www.cdc.gov/nchs/data/nnhsd/Drugsestimatesandcharacteristics.pdf. P 98. 

Figure 5. Drug estimates and characteristics file, highlighting listing of Prozac

fluoxetine=1;
 

end;
 

keep resnum fluoxetine;
 

run;
 

2.	 This code will generate the

frequency (n=461) of residents who

took fluoxetine-containing

medications and their resident IDs

(Figure 9):

Proc freq data=test;
 

tables resnum;
 

where fluoxetine=1;
 

run;
 

3.	 This code will link the file produced

from the array in Step 1 with the

resident file. In this step, the

additional variables that the analyst

wants included in the analytic file

from the Current Resident Public-use

File should be indicated in the

programming code:

Libname res ’[file path for Current

Resident Public-use File]’;

Proc sort data=test nodupkey; by

resnum; run;

Proc sort data=res.resident_

04nnhs_07192006b out=res;

by resnum; run;

data merged;

merge test res(in=resident

keep=resnum sex ageatint

rstrata facnum popfac npopres

samwt [and any other

variables the analyst wants from

the Current Resident Public-use

File]);

by resnum;

if resident=1; run;

4.	 Check the newly created analytic file

to make sure that all the variables

the analyst needs for his or her

specific analysis are in the data set

(in this analysis, nine variables were

selected) (Figure 10).

5.	 The following proc crosstab allows

the analyst to obtain a more accurate

weighted estimate for residents

taking fluoxetine-containing

medications (Figure 11). It is

important to use the design variables

and sample weight to generate

precise estimates:

Proc format;

Value drgform 0=’No’ 1=’Yes’;

run;

Proc sort data=merged; by rstrata

facnum; run;

Proc crosstab data=merged

design=wor;

nest strata facnum/missunit;

totcnt popfac npopres;

weight samwt;

class fluoxetine;

table fluoxetine;

rformat fluoxetine drgform.; run;

6.	 This proc descript will allow the

analyst to determine the weighted

mean age at interview, by sex, of the

residents reported to have taken

fluoxetine-containing medications:

Proc format;
 

Value sexform
 

1=’Males’
 

2=’Females’; run;
Series 1, No. 47 [ Page 13

Drug Drug Rate Generic

ame Rx DEA Comp 

Code Status Status Status 

6145 1 6 1

4094 1 2 1 

1380 1 6 2

3475 1 2 1

3700 1 6 1

0565 1 6 1 

7124 1 6 1

2975 2 6 1

7108 1 6 1

0642 1 6 1

4795 1 6 1

0006 1 6 1

0122 1 6 1

1382 2 6 2

6290 1 6 1

2490 1 6 1 

7220 1 6 1

0113 1 4 1

2885 2 6 1

2042 1 6 1

1380 1 6 2

3975 1 6 1

1978 1 2 1

0006 1 6 1

N

5

5

5

5

5

5

5

5

5

7

5

8

7

5

5

5

5

5

5

5

5

5

5

7

Name Drug per 10,000 

Drug Name Code Estimate Residents Generic Name 

DEPAKENE 08835 5133 0034 VALPROIC ACID 

OXYCODONE HCL 22303 5116 0034 OXYCODONE 

STROVITE 95062 5050 0034 COMBINATION PRODUCT 

METHADONE 18985 5001 0034 METHADONE 

FLAGYL 12585 5000 0034 METRONIDAZOLE 

BENZTROPINE MESYLATE 60180 4999 0034 BENZTROPINE 

PULMICORT 02279 4955 0033 BUDESONIDE 

LACT-AID 16925 4882 0033 LACTASE 

LESCOL 94154 4878 0033 FLUVASTATIN 

ZELNORM 02373 4873 0033 TEGASEROD MALEATE 

MYSOLINE 20135 4868 0033 PRIMIDONE 

FLUOXETINE 91079 4856 0033 FLUOXETINE HYDROCHLORIDE 

COMTAN 00054 4842 0032 ENTACAPONE 

CERTAGEN 04031 4775 0032 MULTIVITAMIN COMBINATION 

TERAZOSIN 94133 4759 0032 TERAZOSIN HCL 

HUMIBID LA 01026 4747 0032 GUAIFENESIN 

LAMICTAL 95181 4730 0032 LAMOTRIGINE 

ALPRAZOLAM 91062 4712 0032 ALPRAZOLAM 

HEMOCYTE 93074 4709 0032 IRON PREPARATIONS 

EPOGEN 93059 4586 0031 EPOETIN ALFA 

HYZAAR 95171 4516 0030 COMBINATION PRODUCT 

NORTRIPTYLINE 21403 4512 0030 NORTRIPTYLINE 

MARINOL 60870 4509 0030 DRONABINOL 

AZMACORT 02059 4401 0029 TRIAMCINOLONE ACETON 

101

SOURCE: http://www.cdc.gov/nchs/data/nnhsd/Drugsestimatesandcharacteristics.pdf. P 101.

Figure 6. Drug estimates and characteristics file, highlighting listing of fluoxetine

Proc descript data=merged

design=wor;

nest strata facility/missunit;

totcnt popfac npopres;

weight samwt;

class sex;

subpopn fluoxetine=1;

table sex;

var ageatint;

rformat sex sexform.;

run;

The output shows that the mean age

of males who took fluoxetine-containing

medications was 74 and the mean age of

women who took fluoxetine-containing

medications was 79 (Figure 12).

A few medications in the Drug

Estimates and Characteristics file may

be incorrectly labeled as combination

products (i.e., generic name

code=51380) when, in fact, they are

single-entity medications. If the analyst

finds a medication that he or she knows

is a single-entity medication but is

labeled as a combination product (i.e.,

generic name code = 51380) in the

Drug Estimates and Characteristics file,

the analyst should use the Edit-Find

feature to find its generic name and

generic name code.

Limitations to the PM

Module and CAPI

Instrument

The CAPI instrument facilitated the

collection of medication data in the

2004 NNHS. Because the NNHS was

designed to collect up to a total of 50

medications names per resident, the

survey required a system that facilitated

quick data entry by interviewers. The

drug lookup list reduced data entry time

by allowing field interviewers to type in

the first three letters of a medication

name and automatically retrieve a list of

medications beginning with those letters.

Even with these features, there were

some limitations to the PM module and

the CAPI system. The wording of the

two questions that ask for medication

names was somewhat complex. The

drug list was not exhaustive; therefore,

many drugs had to be entered manually

into the ‘‘Other, specify’’ field. This also

increased the number of misspelled

medications and the editing time

required to correct them post-data

collection. The survey did not

specifically ask if the resident received a

brand name medication or a generic

medication. It is possible that the

resident could have taken the generic

form of a medication but the respondent

found it easier to report the brand name

(i.e., Prozac instead of fluoxetine

hydrochloride). Finally, the system did

not include a lookup list for the medical

conditions collected for the ‘‘Reason

why prescribed’’ item.
Page 14 [ Series 1, No. 47

140

 Drug Drug Rate Generic 

Name Drug per 10,000 Name Rx DEA Comp 

Drug Name Code Estimate Residents Generic Name Code Status Status Status 

ULCEREASE 00279 COMBINATION PRODUCT 51380 2 6 2 

ACETANILID 00285 ACETANILID 50015 1 6 1 

ACETATED RINGER'S 00290 COMBINATION PRODUCT 51380 1 6 2 

ZICAM 00303 ZINCUM GLUCONICUM 70198 2 6 1 

AMINO-MAG 00309 MAGNESIUM GLYCINATE 70199 2 6 1 

DILATING DROPS 00314 UNDETERMINED 50000 1 6 3 

SARAFEM 00318 FLUOXETINE HYDROCHLORIDE 80006 1 6 1 

ACHROMYCIN 00340 TETRACYCLINE 55725 1 6 1 

ACID-EZE 00370 UNDETERMINED 50000 3 7 3 

ACIDULATED PHOSPHATE FLUORIDE 00380 FLUORIDE 52318 1 6 1 

ACNE 00395 UNDETERMINED 50000 3 7 3 

ACNOMEAD 00420 UNDETERMINED 50000 3 7 3 

ACTH 00455 CORTICOTROPIN 51400 1 6 1 

ACTIDIL 00470 TRIPROLIDINE 56020 2 6 1 

ACTIFED WITH CODEINE 00481 COMBINATION PRODUCT 51380 1 5 2 

ACTOL 00485 COMBINATION PRODUCT 51380 2 6 2 

ADENOSINE 00535 ADENOSINE 50080 1 6 1 

ADRENAL CORTEX 00555 HYDROCORTISONE 52655 1 6 1 

ADRENERGIC AGENT 00563 UNDETERMINED 50000 3 7 3 

ADSORBONAC 00590 SODIUM CHLORIDE 55315 2 6 1 

AEROSEB-DEX 00610 DEXAMETHASONE 51635 1 6 1 

AFKO-LUBE SYRUP 00640 DOCUSATE 51935 2 6 1 

AHF 00668 ANTIHEMOPHILIC FACTOR HUMAN 50320 1 6 1 

AKNE DRYING LOTION 00690 COMBINATION PRODUCT 51380 2 6 2 

SOURCE: http://www.cdc.gov/nchs/data/nnhsd/Drugsestimatesandcharacteristics.pdf. P 140. 

Figure 7. Drug estimates and characteristics file, highlighting listing of Sarafem

Summary and

Suggestions for Future

Data Collection

Collecting medication data on

nursing home residents is increasingly

important. The majority of nursing home

residents are elderly and frail and

require numerous medications. The 2004

NNHS provided valuable lessons on

collecting medication data that can

enhance future surveys. Suggestions for

future surveys include:

+	 Simplifying the medication

questions.

+	 Adding a question about whether the

medication taken by the resident

was a brand name or generic drug.

+	 Adding a question about the

documentation on the reason that

medications were prescribed.

+	 Providing a lookup list of medical

conditions that facilitates data entry

for why the medications were

prescribed.

Knowledge about medications taken

by nursing home residents is useful to

the long-term care community, which is

striving to find ways to ensure quality

care while containing costs. Medication

data on other long-term care populations

will be available through NCHS in the

future; the 2007 National Home and

Hospice Care Survey collected

medication information, which will add

to our current understanding of

pharmacotherapy in long-term care

settings.

Additional information about the

2004 NNHS and the data collected is

available from the NNHS website:

http://www.cdc.gov/nchs/nnhs.htm.

Public-use data files are available for

download from the website. Questions

about these data can be directed to the

NCHS Office of Information Services,

Information Dissemination Staff at

1–800–232–4363 or at cdcinfo@cdc.gov,

or to the Long-term Care Statistics

Branch at 301–458–4747.
Drug Drug Rate Generic 

Name Drug per 10,000 Name Rx DEA Comp 

Code Estimate Residents Generic Name Code Status Status Status Drug Ingredients 

12665 4751 0032 FLUDROCORTISONE ACETATE 52295 1 6 1 

02223 4467 0030 EZETIMIBE 70494 1 6 1 

03177 3148 0021 CALCIUM CARBONATE 50785 2 6 1 

41860 2748 0018 FOLIC ACID 52355 3 6 1 

92120 2494 0017 DOXAZOSIN MESYLATE 51953 1 6 1 

21144 2162 0014 NITROGLYCERIN 53945 1 6 1 

93204 1788 0012 SELEGILINE 56630 1 6 1 

22630 1624 0011 PANCRELIPASE 54165 1 6 1 

97159 1089 0007 CALCITRIOL 59736 1 6 1 

20095 989 0007 SIMETHICONE 55240 2 6 1 

95149 883 0006 COMBINATION PRODUCT 51380 1 6 2 PIPERACILLIN SODIUM, TAZOBACTAM SODIUM 

30781 790 0005 COMBINATION PRODUCT 51380 1 6 2 ATENOLOL, CHLORTHALIDONE 

31775 600 0004 TOLAZAMIDE 55870 1 6 1 

04043 516 0003 COMBINATION PRODUCT 51380 1 6 2 OLANZAPINE, FLUOXETINE HYDROCHLORIDE 

00096 494 0003 COMBINATION PRODUCT 51380 2 6 2 CHONDROITIN SULFATE, GLUCOSAMINE 

12035 414 0003 COMBINATION PRODUCT 51380 2 6 2 UNDETERMINED, ACETAMINOPHEN 

11688 393 0003 COMBINATION PRODUCT 51380 1 6 2 ACETAMINOPHEN, BUTALBITAL, CAFFEINE 

99155 355 0002 LEVOCARNITINE 57049 2 6 1 

07740 312 0002 BISACODYL 50630 2 6 1 

93360 276 0002 ENOXACIN 56720 1 6 1 

92050 268 0002 POTASSIUM REPLACEMENT SOLUTIONS 54655 1 6 1 

02382 241 0002 DOXYCYCLINE HYCLATE 70655 1 6 1 

02148 220 0001 DICLOFENAC SODIUM 80004 1 6 1 

95101 200 0001 SALICYLIC ACID 55160 2 6 1 

168

SOURCE: http://www.cdc.gov/nchs/data/nnhsd/Drugsestimatesandcharacteristics.pdf. P 168.

Figure 8. Drug estimates and characteristics file, highlighting listing of Symbyax

The FREQ Procedure 

Cumulative Cumulative 

RESNUM Frequency Percent frequency percent 

111006 1 0.22 442 95.88 

111102 1 0.22 443 96.10 

111206 1 0.22 444 96.31 

111509 1 0.22 445 96.53 

111707 1 0.22 446 96.75 

111807 1 0.22 447 96.96 

111904 1 0.22 448 97.18 

112208 1 0.22 449 97.40 

112210 1 0.22 450 97.61 

112401 1 0.22 451 97.83 

112911 1 0.22 452 98.05 

113006 1 0.22 453 98.26 

113309 1 0.22 454 98.48 

113701 1 0.22 455 98.70 

113802 1 0.22 456 98.92 

113809 1 0.22 457 99.13 

113912 1 0.22 458 99.35 

114305 1 0.22 459 99.57 

114306 1 0.22 460 99.78 

115008 1 0.22 461 100.00 

NOTES: FREQ refers to frequency. RESNUM is the resident identification number. 

SOURCE: Generated by author for this report. 

Figure 9. SAS output of resident identification numbers of those who took fluoxetinecontaining medications generated from Step 2

Series 1, No. 47 [ Page 15
PUF resident id Fluoxetine FACNUM SEX Age at interview RSTRATA POPFAC NPOPRES SAMWT 

1204 0 12 2 91 17 372 -1 23 

1205 1 12 2 80 17 372 -1 23 

1206 0 12 1 86 17 372 -1 23 

1207 0 12 1 41 17 372 -1 23 

1208 0 12 2 77 17 372 -1 23 

1209 0 12 2 86 17 372 -1 23 

1210 0 12 2 72 17 372 -1 23 

1211 0 12 1 85 17 372 -1 23 

1212 0 12 2 86 17 372 -1 23 

1301 0 13 2 96 14 639 -1 85 

1302 0 13 2 92 14 639 -1 85 

1303 0 13 1 88 14 639 -1 85 

1304 0 13 2 90 14 639 -1 85 

1305 0 13 2 91 14 639 -1 85 

1306 0 13 1 84 14 639 -1 85 

1307 1 13 2 88 14 639 -1 85 

1308 0 13 2 87 14 639 -1 85 

1309 0 13 2 90 14 639 -1 85 

1310 0 13 1 84 14 639 -1 85 

1311 0 13 2 90 14 639 -1 85 

1312 0 13 1 77 14 639 -1 85 

1401 0 14 2 95 1 1201 -1 53

NOTES: PUF resident id is the resident ID in the Public-use File. FACNUM is the facility identification number. RSTRATA, POPFAC, and NPOPRES are sample design variables needed to generate 


accurate national estimates. SAMWT is the weight for resident estimates.
 

SOURCE: Generated by author for this report.
 

Figure 10. Temporary SAS dataset created from merging the dataset from Step 1 with the Current Resident Public-use File

2884.27 

The CROSSTAB Procedure 

Variance Estimation Method: Taylor Series (WOR)

by: FLUOXETINE. 

FLUOXETINE

 Total No Yes

Sample Size 13507 13046 4

Weighted Size 1492207.00 1443542.00 

SE Weighted 11281.95 11308.30 

Row Percent 100.00 96.74 

SE Row Percent 0.00 0.19 

Lower 95% Limit

 ROWPER 96.34 2.90 

Upper 95% Limit

ROWPER 97.10 3.66 

Col Percent 100.00 96.74 3.26 

SE Col Percent 0.00 0.19 0.19 

Lower 95% Limit

 COLPER 96.34 2.90 

Upper 95% Limit

COLPER 97.10 3.66 

Tot Percent 100.00 96.74 3.26 

SE Tot Percent 0.00 0.19 0.19 

Lower 95% Limit

 TOTPER 96.34 2.90 

Upper 95% Limit

TOTPER 97.10 3.66

61 

48665.00 

3.26 

0.1 

SOURCE: Generated by author for this report. 

Figure 11. SAS output of PROC CROSSTAB of residents by fluoxetine-containing

medication status (yes/no) generated from Step 5

Page 16 [ Series 1, No. 47
Research Triangle Institute

The DESCRIPT Procedure 

Variance Estimation Method: Taylor Series (WOR)

For Subpopulation: FLUOXETINE = 1

by: Variable, SEX.

Variable 

Sex 

Total 

Age at

Interview 

Sample Size

Weighted Size 

Total 

Lower 95% Limit

 Total 

Upper 95% Limit

Total 

Mean 

 SE Mean 

Lower 95% Limit 

Mean 

Upper 95% Limit

Mean 

461 

48665.00 

3798370.00 

3336043.76 

4260696.24 

78.05 

0.88 

76.33 

79.77 

Males 

115 

12304.00 

908630.00 

690459.74 

1.85 0.94

1126800.26 3277703.01

73.85 79.47

70.22 

77.48 

Females 

346 

36361.00

2889740.00 

2501776.99 

77.64 

81.31 

SOURCE: Generated by author for this report. 

Figure 12. SAS output of PROC DESCRIPT of residents who took fluoxetine-containing

medications by sex, generated from Step 6

Series 1, No. 47 [ Page 17

References

1.	 Beers MH, Ouslander JG, Rollinger I,

et al. Explicit criteria for determining

inappropriate medication use in nursing

home residents. Arch Intern Med

151:1825–32. 1991.

2.	 Edwards B, Tourangeau K, Branden L,

Lohr B, Vincent C. 2004 National

Nursing Home Survey: Final report.

Rockville, MD: Westat. 2005.

3.	 Definition taken from Medicinenet.com.

Information accessed at http://

www.medterms.com/script/main/

art.asp?articlekey=33074 on June 5,

2008.

4.	 U.S. Food and Drug Administration,

Center for Drug Evaluation and

Research website. Information accessed

at http://www.fda.gov/cder/iig/

iigfaqweb.htm on June 4, 2008.

5.	 World Health Organization website.

Information accessed at http://

www.who.int/classifications/atcddd/en/

on June 5, 2008.

6.	 United States General Accounting

Office. Adverse drug events: The

magnitude of health risk is uncertain

because of limited incidence data.

GAO/HEHS-00–21. 2000.

7.	 Information accessed at http://

www.mayoclinic.com/health/

antidepressants/MH00067 on June

13, 2008.

8.	 Ballington DA, Laughlin MM.

Pharmacology for Technicians. EMC

Paradigm, St. Paul. 1999.
Page 18 [ Series 1, No. 47

Appendix I

List of Prescribed Medications (PM) Module Items

PMPRE.

THIS IS THE FIRST PAGE OF THE PRESCRIBED MEDICATIONS (PM) SECTION. STATUS = {STATUS}.
 

IF THIS SECTION HAS ALREADY BEEN COMPLETED, RETURN TO THE CHOOSE PERSON SCREEN.
 

PRESS 1 AND ENTER TO CONTINUE.
 

IF RECORDS FOR THIS PERSON ARE NOT AVAILABLE (RP), PRESS 2 AND ENTER.
 

IF CONSENT REQUIRED AND NOT OBTAINED FOR THIS PERSON (CP), PRESS 3 AND ENTER.
 

PM1A.

{Please give me the names of all the medications {SP} received yesterday, that is, from 12:01 AM to 12:00

midnight {DATE BEF CURRENT INTERVIEW}. Please include all the standing or routine medications, or PRN

medications.}

{Any other medications?}

{TYPE THE FIRST 3 LETTERS OF THE MEDICATION. THEN USE ARROW KEYS TO LOCATE

MEDICATIONS, AND PRESS ENTER TO SELECT. IF NO MEDICATIONS TAKEN, ENTER AAA. IF

MEDICATION IS NOT LISTED, ENTER ZZZ.}

{ENTER 999 TO LEAVE ROSTER.}

MEDICATION MEDICATION (OTHER) REASON PRESCRIBED

PM [1–25] PM OS [1–25] WhyPM[1–25]

PM1B.

SPECIFY MEDICATION.
Series 1, No. 47 [ Page 19

PM1C.
 

Why was this medication prescribed for {SP}?
 

ENTER REASON.
 

PM2.

Does {SP} receive any medications on a regularly scheduled basis that were not administered yesterday?

Please include only medications with standing orders for administration. Please do not include PRN medications.

PRESS F1 FOR HELP SCREEN.

YES .....................................1

NO ......................................2

DK

RF

PM2A.

{Please give me the names of all of these medications.}

{Any other medications?}

{TYPE THE FIRST 3 LETTERS OF THE MEDICATION. THEN USE ARROW KEYS TO LOCATE

MEDICATIONS, AND PRESS ENTER TO SELECT. IF MEDICATION IS NOT LISTED, ENTER ZZZ.}

{ENTER 999 TO LEAVE ROSTER.}

MEDICATION MEDICATION (OTHER) REASON PRESCRIBED

OthrPM [1–15] OthrPMOS [1–15] WhyOthPM[1–15]

PM2B.

SPECIFY MEDICATION.
Page 20 [ Series 1, No. 47

PM2C.
 

Why was this medication prescribed for {SP}?
 

ENTER REASON(S).
 

PM3.

{Since admission {FAD}/During the past 30 days, that is, since {PAST 30 DAYS}, did {SP} have any type of

reaction to a drug or medication?}

PRESS F1 FOR HELP SCREEN.

YES .....................................1

NO ......................................2

DK

RF

PM3A.

{Please give me the names of all the drugs or medications to which {SP} had a reaction.}

{Any other medications?}

{TYPE THE FIRST 3 LETTERS OF THE MEDICATION. THEN USE ARROW KEYS TO LOCATE

MEDICATIONS, AND PRESS ENTER TO SELECT. IF MEDICATION IS NOT LISTED, ENTER ZZZ.}

{ENTER 999 TO LEAVE ROSTER.}

MEDICATION MEDICATION (OTHER) REACTION

Drug Name[1–5] Drug Name OS[1–5] Reaction[1–5]

PM3B.

SPECIFY MEDICATION.
Series 1, No. 47 [ Page 21

PM3C.
 

What kind of reaction did {SP} have?
 

ENTER REACTION.
 

PMEND.

YOU HAVE COMPLETED PM FOR {SP}. PRESS 1 AND ENTER TO CONTINUE.

PMNAV.

YOU HAVE COMPLETED THE PM SECTION FOR {SP}. YOUR OPTIONS ARE TO:

CONTINUE WITH PA {STATUS} FOR {SP} .........................1 
 

CHOOSE A DIFFERENT SECTION FOR {SP} .....................2 
 

CHOOSE A DIFFERENT PERSON ...............................3 
 
Page 22 [ Series 1, No. 47

Prescribed Medications Section Help Screens

PM2

The following are types of medications to include: Vitamin B-12, bisphosphonates (alendronate and risedrontate),

Lupron depot, depo provera, methotrexate.

PM3

A drug reaction includes any unexpected, unintended, undesired, or excessive response to a medication that

c Requires discontinuing the medicine (therapeutic or diagnostic)

c Requires changing the medication therapy

c Requires modifying the dose (except for minor dosage adjustments)

c Necessitates admission to a hospital (e.g., an ED visit)

c Prolongs stay in a health care facility

c Necessitates supportive treatment

c Significantly complicates diagnosis

c Negatively affects prognosis

c Results in temporary permanent harm, disability, or death
Series 1, No. 47 [ Page 23

Appendix II

Summary of Key Items for Data Collection

Facility Visit Activities

Approximately 40 minutes of the administrator’s (or designee) time will be needed to answer questions about the facility,

residents, and staff. Access to the following information will expedite the interview.

¶ Current number of residents with Medicare, Medicaid, and private pay as primary source of payment

¶ Payment rates for Medicaid and private pay residents

¶ Medicare and Medicaid facility ID numbers

¶ Number of admissions and discharges for 2003

¶ List of all current residents on the rolls of this facility as of midnight

[INSERT DATE BEFORE THE DATE OF INTERVIEW IN BOLD PRINT]

Sampled Current Residents Information

The study collects information about the sampled current residents and discharges primarily from the person’s medical record.

Approximately 25 minutes per resident is required to collect the following information:

¶ Health status information from the Minimum Data Set (MDS) assessments

¶ Medications administered the day before interview date

¶ Hospital or emergency room visits during residency

¶ Vaccination/immunization status

¶ Expenditure information including: total charges, sources of payment, and amount paid by resident/discharge for all care

during the first month/billing period and the most recent or final month/billing period

To collect this medical and billing information, we will need to work with staff familiar with the medical and billing records.
Page 24 [ Series 1, No. 47

Appendix III

1995 Version of the NDC Directory Used to Adjudicate 2004 NNHS Data

0100 ANESTHETIC DRUGS 0600 CENTRAL NERVOUS SYSTEM

0117 Anesthetics, Local (Injectable) 0626 Sedatives and Hypnotics

0118 Anesthetics, General 0627 Antianxiety Agents

0119 Adjuncts to Anesthesia/Analeptics 0628 Antipsychotic/Antimanics

0120 Medicinal Gases 0630 Antidepressants

0121 Anesthetics, Topical 0631 Anorexiants/CNS Stimulants

0122 Anesthetics, Ophthalmic 0632 CNS, Miscellaneous

0123 Anesthetics, Rectal 0633 Alzheimer-type Dementia

0200

0281

ANTIDOTES

Antidotes, Specific

0634

0635

Sleep Aid Products–OTC

Antiemetics

0283 Antidotes, General 0700 CONTRAST MEDIA/

0285 Antitoxins/Antivenins RADIOPHARMACEUTICAL

0286 Anaphylaxis Treatment Kit 0789 Diagnostics, Radiopaque & Nonradioactive

0300

0346

0347

ANTIMICROBIAL AGENTS

Penicillins

Cephalosporins

0790

0791

0792

Diagnostics–Radiopharmaceuticals

Therapeutics–Radiopharmaceuticals

Miscellaneous

0348 Erythromycins/Lincosamides/Macrolides 0800 GASTROINTESTINAL AGENTS

0349 Polymyxins 0874 Disorders, Acid/Peptic

0350 Tetracyclines 0875 Antidiarrheals

0351 Chloramphenicol/Derivatives 0876 Laxatives

0352 Aminoglycosides 0877 Miscellaneous Gastrointestinals

0353 Sulfonamides and Trimethoprim 0878 Antispasmodics/Anticholinergics

0354 Urinary Tract Antiseptics 0879 Antacids

0355

0356

0357

0358

0388

Miscellaneous Antibacterial Agents

Antimycobacterial/Anti-leprosy Agents

Quinolones/Derivatives

Antifungals

Antiviral Agents

0900

0912

0913

0914

0915

METABOLIC/NUTRIENTS

Hyperlipidemia

Vitamins/Minerals

Nutrition, Enteral/Parenteral

Repl/Regs of Electrolytes/Water Balance

0400 HEMATOLOGIC AGENTS 0916 Calcium Metabolism

0408 Deficiency Anemias 0917 Hematopoietic Growth Factor

0409

0410

0411

Anticoagulants/Thrombolytics

Blood Components/Substitutes

Hemostatics/Antihemophilics

1000

1032

1033

HORMONES/HORMONAL MECHANISMS

Adrenal Corticosteroids

Androgens/Anabolic Steroids

0500 CARDIOVASCULAR-RENAL DRUGS 1034 Estrogens/Progestins

0501 Cardiac Glycosides 1035 Anterior Pituitary/Hypothalamic Function

0502 Antiarrhythmic Agents 1036 Blood Glucose Regulators

0503 Antianginal Agents 1037 Thyroid/Antithyroid

0504 Vascular Disorders, Cerebral/Peripheral 1038 Antidiuretics

0505 Agents Used to Treat Shock/Hypotension 1039 Relaxants/Stimulants, Uterine

0506 Antihypertensive Agents 1040 Contraceptives

0507 Diuretics 1041 Infertility

0508 Coronary Vasodilators 1042 Growth Hormone Secretion Disorder

0509

0510

0511

0512

0513

0514

Relaxants/Stimulants, Urinary Tract

Calcium Channel Blockers

Carbonic Anhydrase Inhibitors

Beta Blockers

Alpha Agonist/Alpha Blockers

ACE Inhibitors

1100

1180

1181

1182

1183

IMMUNOLOGICS

Vaccines/Antisera

Immunomodulators

Allergenic extracts

Immune serums
Series 1, No. 47 [ Page 25

1200 SKIN/MUCOUS MEMBRANE 1700 RELIEF OF PAIN

1264 Antiseptics/Disinfectants 1720 Analgesics/General

1265 Dermatologics, Misc. 1721 Analgesics, Narcotic

1266 Keratolytics 1722 Analgesics, Non-narcotic

1267 Antiperspirants 1723 Antimigraine/Other Headaches

1268 Topical Steroids 1724 Antiarthritics

1269 Burn/Sunburn, Sunscreen/Suntan Products 1725 Antigout

1270 Acne Products 1726 Central Pain Syndrome

1271 Topical Anti-infectives 1727 NSAID

1272 Anorectal Products 1728 Antipyretics

1273 Personal Care (Vaginal) Products 1729 Menstrual Products

1274

1275

Dermatitis/Antipuretics

Topical Analgesics 1800

1860

ANTIPARASITICS

Antiprotozoals

1300 NEUROLOGIC DRUGS 1862 Anthelmintics

1371 Extrapyramidal Movement Disorders 1863 Scabicides/Pediculicides

1372 Myasthenia Gravis 1864 Antimalarials

1373

1374

Skeletal Muscle Hyperactivity

Anticonvulsants 1900

1940

RESPIRATORY TRACT

Antiasthmatics/Bronchodilators

1400 ONCOLYTICS 1941 Nasal Decongestants

1479 Antineoplastics, Miscellaneous 1943 Antitussives/Expectorants/Mucolytics

1480 Hormonal/Biological Response Mod. 1944 Antihistamines

1481 Antimetabolites 1945 Cold Remedies

1482 Antibiotics, Alkaloids, Enzymes 1946 Lozenge Products

1483 DNA Damaging Drugs 1947 Corticosteroid-inhalation, Nasal

1500 OPHTHALMICS 2000 UNCLASSIFIED/MISCELLANEOUS

1566 Glaucoma 2087 Unclassified

1567 Cycloplegics/Mydriatics 2095 Pharmaceutical Aids

1568 Ocular Anti-infective/Anti-inflammatory 2096 Surgical Aids

1569 Miscellaneous Ophthalmics 2097 Dental Preparation

1570 Decongestants/Antiallergy Agents 2098 Dentrifice/Denture Products

1571 Contact Lens Products 2099 Mouth Pain, Cold Sore, Canker

1600 OTOLOGICS 2100 HOMEOPATHIC PRODUCTS

1670 Otic, Topical (Misc)

1671 Vertigo/Motion Sickness/Vomiting

NOTE: NDC is the National Drug Code and NNHS is the National Nursing Home Survey.
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 long-term 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 and men of childbearing age.

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

Divorce—These include advance reports of births, deaths,

marriages, and divorces based on final data from the National

Vital Statistics System that were published as National Vital

Statistics Reports (NVSR), formerly Monthly Vital Statistics

Report. These reports provide highlights and summaries of

detailed data subsequently published in Vital Statistics of the

United States. Other special reports 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:

Information Dissemination Staff

National Center for Health Statistics

Centers for Disease Control and Prevention

3311 Toledo Road, Room 5412

Hyattsville, MD 20782

1–800–232–4636
 

E-mail: cdcinfo@cdc.gov
 

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

HEALTH & HUMAN SERVICES

Centers for Disease Control and Prevention

National Center for Health Statistics

3311 Toledo Road

Hyattsville, MD 20782

OFFICIAL BUSINESS

PENALTY FOR PRIVATE USE, $300

MEDIA MAIL
 

POSTAGE & FEES PAID
 

CDC/NCHS
 

PERMIT NO. G-284
 

CS124898

DHHS Publication No. (PHS) 2009–1323, Series 1, No. 47