CC37.NMES87.PUF141.SRC
ABSTRACT
1987 National Medical Expenditure Survey:
Public Use Tape 14.1
Household Survey
Prescribed Medicine Data, Calendar Year 1987
File Documentation
December, 1991
Agency for Health Care
Policy and Research
Center for General Health Services Intramural Research
2101 E. Jefferson Street, Suite 500
Rockville, Maryland 20852
(301) 227-8400
Data Purchase and Use Agreement
Individual identifiers have been removed from the micro-data tapes available
from the Agency for Health Care Policy and Research through NTIS. Neverthe-
less, under sections 308(d) and 903(c) of the Public Health Service Act (42
U.S.C. 242m and 42 U.S.C. 299 a-1), data collected by the Agency for Health
Care Policy and Research may not be used for any purpose other than the
purpose for which it was supplied. The information on the micro- data tapes
available for purchase was supplied to the Agency for statistical summaries
and health services research. It is necessary, therefore, that the indivi-
dual using such micro-data tapes agree to the following:
By using this data and documentation the user gives assurance that individual
elementary unit data on the micro-data tapes will be used solely for
statistical summaries and health services research.
BACKGROUND
This documentation describes one in a series of public use tapes
issued by the Agency for Health Care Policy and Research with data
from the National Medical Expenditure Survey. This survey provides
extensive information on health expenditures by or on behalf of
families and individuals, the financing of these expenditures, and
each person's use of services. The National Medical Expenditure
Survey (NMES) is a research project of the Center for General
Health Services Intramural Research, Agency for Health Care Policy
and Research.
Since the 1970s the intramural research program has given
particular emphasis to studies of the use and financing of health
services. The first series of studies (NMES-1) employed data
collected in the 1977 National Medical Care Expenditure Survey.
NMES-1 produced information on a broad range of issues such as the
number and characteristics of the uninsured and the underinsured,
the tax implications of excluding employer-paid premiums for health
insurance from employee income, and the differences among
socioeconomic and demographic groups with respect to the use of
health services.
A new series of studies (NMES-2) was initiated in the 1980s.
These studies also involve a major data collection effort - the
1987 National Medical Expenditure Survey. Like its predecessor,
NMES-2 provides information about the non-institutionalized
population. In addition and in contrast to the earlier studies,
NMES-2 also provides extensive information on the population
residing in or admitted to nursing homes and facilities for the
mentally retarded.
The Household Component of NMES-2 is based on a national
probability sample of the civilian, noninstitutionalized population
living in the community. The sample is designed to provide a
larger representation of population groups of special policy
interest to the Federal Government than would have been obtained
from a random sample. These groups include poor and low income
families, the elderly, the functionally impaired, and black and
Hispanic minorities. A Survey of American Indians and Alaska
Natives includes a separate sample of American Indians and Alaska
Natives living on or near Federal reservations and eligible to
receive care provided or supported by the Indian Health Service.
The Institutional Population Component includes a sample of persons
residing in or admitted to nursing and personal care homes and
facilities for the mentally retarded during 1987. A separate
Medicare Records Component provides claims data on all Medicare
beneficiaries included in the household and institutional samples.
Together, the major components of NMES-2 contain information to
make national estimates of health status, use of health services,
insurance coverage, expenditures, and sources of payment for the
civilian population of the United States during the period from
January 1 to December 31, 1987. Oversampling of population groups
of special interest makes possible in-depth studies of these
groups. The database can also be used to assess the implications
of recent or proposed changes in public or private health care
benefits, methods of financing both health care and insurance
coverage, various public and private subsidies for health care, and
employee compensation arrangements.
Household Survey
Each family in the Household Survey was interviewed four times
over a period of 16 months to obtain information about the family's
health and health care during calendar year 1987. Baseline data on
household composition, employment, and insurance were updated at
each interview, and information was obtained on illnesses, use of
health services, and health expenditures for each family member.
A fifth round of interviews was conducted in the spring of 1988 to
obtain information on the tax filing and medical deductions of each
household. A long term care supplement was administered during the
first and fourth rounds of interviewing to permit estimates of
persons with functional disabilities and the use of formal services
or long term care provided by family or friends.
In order to verify and supplement the information provided by
household respondents, the Household Component of NMES-2 included
two additional surveys. The Medical Provider Survey obtained
information from the physicians, hospitals, outpatient clinics,
emergency rooms, and home health agencies used by the household
sample during 1987. The Health Insurance Plan Survey obtained
information on the private insurance of persons in the household
sample, including premiums paid by all sources and the provisions
of their coverage.
Survey of American Indians and Alaska Natives (SAIAN)
This component was conducted with the same data collection
instruments and interview procedures as the Household Component and
covered the same reference period, calendar year 1987. SAIAN also
included follow-up surveys to medical providers and health
insurers. Consequently, the data can be used to compare American
Indians and Alaska Natives eligible for care from the Indian Health
Service and the general U.S. population with regard to such issues
as health status, use of health services, and access to care.
Information was obtained on services provided outside the Indian
Health Service and on other sources of health care financing
available for persons eligible for care from the IHS.
Institutional Population Component
The Institutional Population Component of NMES-2 included persons
resident in or admitted to nursing and personal care homes and
facilities for the mentally retarded at any time in calendar year
1987. This survey provides information on the functional status,
use of services, and health expenditures of the institutionalized
population. The Survey in Institutions (SII) collected data from
facility administrators and designated staff on the characteristics
of facilities and charges. The Survey of Next of Kin (SNK)
obtained data from the respondent's next-of-kin or other
knowledgeable persons in the community on the financial status,
insurance coverage, and personal history of the institutionalized
person.
Survey Samples
All survey components were designed to provide statistically
unbiased estimates that are representative of the civilian
population of the United States in 1987. The Household Survey
sample is a stratified multistage area probability design with a
total sample of roughly 35,000 individuals, in 14,000 households,
who completed all rounds of data collection. Oversampling of the
population subgroups of special policy interest was based on a
separate screening interview conducted in the fall of 1986 with a
sample of 36,000 addresses.
The Survey of American Indians and Alaska Natives adopted a
multistage area probability sample design using an IHS-constructed
frame of counties with individuals eligible for services provided
or supported by the Indian Health Service and living on or near
Federally recognized reservations or in Alaska. An initial
screening interview was completed in approximately 13,700 dwelling
units to identify the eligible sample. The screening yielded
approximately 1,950 households responding for the full year and
approximately 6,500 SAIAN persons responded for their entire period
of eligibility in 1987.
The institutional population sample was based on a three stage
probability design. The first two stages were used to select
facilities; the final stage sampled facility residents present on
January 1, 1987. These facilities were also used to obtain a
sample of admissions between January 1, 1987, and December 31,
1987. Based on sampling specifications the Institutional
Population Component includes 1,500 facilities, 800 nursing homes
and 700 facilities for the mentally retarded. There is a total of
approximately 10,100 persons in the sample including both residents
and new admissions. This includes 5,700 persons in nursing homes
and 4,400 persons in facilities for the mentally retarded. The
sample frame for facilities in the Institutional Population
Component was derived from the 1986 Inventory of Long-Term Care
Places.
Taken in conjunction, the NMES-2 surveys yield comprehensive,
population-based information that will support studies of most
population groups of policy interest, including those presently
outside the scope of various public and private financing
mechanisms. In contrast to information obtained from program or
provider statistics, NMES-2 data can be used to analyze all public
and private sources of coverage for health care services and out-
of-pocket payments by individuals and families.
The Agency for Health Care Policy and Research sponsored the
NMES-2 data collection activities. A substantial part of the
support for the Survey of American Indians and Alaska Natives was
provided by the Indian Health Service. The Health Care Financing
Administration, the National Center for Health Statistics, and the
Office of the Assistant Secretary for Planning and Evaluation
provided extensive technical assistance during the development of
the survey design and instruments. Interviews were conducted by
the primary contractor, Westat, Inc., Rockville, Maryland and by
the National Opinion Research Center at the University of Chicago;
the Council of Energy Resource Tribes, Denver, Colorado; and
Stephen R. Braund and Associates, Anchorage Alaska. Data
processing during the analysis stage of the project is being
provided by Social and Scientific Systems, Inc., Bethesda,
Maryland.
The data were collected under the authorities of the Public
Health Service Act and are being edited and published in accordance
with the confidentiality provisions of that Act and the Privacy
Act.
Additional information on NMES-2 is available from Daniel C.
Walden, Ph.D., Director of the Division of Medical Expenditure
Studies; Center for General Health Services Intramural Research,
Agency for Health Care Policy and Research; 2101 E. Jefferson
Street, Suite 500, Rockville , Maryland, 20852 (301/227-8400).
TECHNICAL AND PROGRAMMING INFORMATION
General Information
This documentation describes one in a series of public use tapes
from the Household Survey of the 1987 National Medical Expenditure
Survey (NMES). The tape provides information and related
documentation on the use of and expenditures for prescribed
medicines for calendar year 1987. The data file contains one
record per unique medication per reference period for each eligible
person in the Household Survey who reported having purchased or
otherwise obtained a prescribed medication during that reference
period. In addition, each record contains selected person-level
demographic information for the respective user, medical conditions
associated with the prescription, frequency of purchase, dates of
use, and a range of medication-specific variables such as
controlled substance status. Thus, the set of records included in
this file for each person, when taken together, represent all
instances in which the person purchased or obtained a medication
(including refills) during 1987.
The records on this file represent all persons in the civilian
noninstitutionalized population who used prescribed medicines for
one or more reference periods during calendar year 1987. The file
can be used to construct summary variables of expenditures, sources
of payment, and other aspects of utilization of prescribed
medicines. Aggregate annual person-level information on prescribed
medicine and other health services use as well as detailed
demographic, employment, insurance, round specific eligibility
status indicators and reference period dates is provided on Public
Use Tape 13 for the entire civilian noninstitutionalized
population represented by the NMES household survey including those
without use of prescribed medicines in 1987.
The data on this tape are being released as EBCDIC files only.
The tape also includes an EBCDIC file containing programming
statements required to create a SAS data set and a SAS format
library for the data file on the tape.
The following documentation offers a brief overview of the type
and level of data provided, the content and structure of the data
file and the codebook, and programming information. It contains
the following sections:
Data File Structure and Contents
Variable Naming and Codebook Conventions
Sample Design, Estimation and Sampling Weights, and Variance
Estimation
Programming Information
References
Data Dictionary
Alphabetical and Position Listing of Variables
Codebook
Codebook Notes
More detailed information on NMES survey instruments and data
collection procedures, variance estimation programs, and coding and
related information are in Attachments 1 to 5, which are provided
as hard-copy attachments to the documentation. Attachment 6
contains a catalogue of data items released on this and other NMES
public use tapes. It is supplied to guide the user to the
appropriate public use tape for the data items of interest in the
NMES Household Survey.
Data File Structure and Contents
General Information
This public use tape contains one data file. To expedite release
of prescribed medicine data and ensure data processing efficiency,
the file structure generally reflects the structure of the
household questionnaire (see Attachments 1 and 3 for the
instruments used). The file contains information on prescribed
medicines and conditions related to their prescription, the number
of purchases and refills, and expenses and sources of payment,
obtained in four rounds of interviews covering calendar year 1987.
The persons represented in this file include all sampled users of
prescribed medicines in calendar year 1987 who responded for their
entire period of NMES eligibility. The file contains 110,080
records, or one record for each specific prescribed medicine
purchased or otherwise obtained by a sample person during a
reference period. A record can represent one or more purchases or
refills of a particular medication within the reference period, and
a variable identifying the number of times the medication was
obtained within the period is provided on each record (PRCHDATX).
In order to account for all instances in 1987 of use and related
expenses for one medication by one person, it is necessary to link
all records with the same medication code and the same person
identifier.
Each record on the data file contains the following information:
Unique person and record identifiers
Indicator of the round of data collection
Selected demographic variables
Prescription medication codes for all records based on
question N10 in the prescribed medicine booklet (see
Attachment 4.B for codes)
Additional medication information including generic
descriptor and code, federal controlled substance and over-
the-counter status, and composition (single versus multiple
ingredient indicator)
Variables based on questionnaire items N11 through N32 in the
prescribed medicines booklet. These include the ICD-9 codes
representing the conditions for which the medication was
prescribed, the number of times it was obtained, dates when
it was first and last taken during the reference period, and
total expense and sources of payment across all purchases of
that medication per record per reference period as well as
associated imputation flags.
Weight and variance estimation variables
Detailed information on coding of prescribed medicines and
medical conditions is provided in Section 1.1. The construction of
the expense and source of payment variables and imputation
procedures are described in Section 1.2.
Coding
Prescribed Medicine Coding and Imputation Procedures
Each record on this file contains a unique code (MEDCODER)
corresponding to a medication name reported by the household
respondent in a given reference period. The codes and the
associated medication names are provided in Attachment 4.B.
As in most household surveys of health care use and
expenditures, it was not possible to code medicines according to
the National Drug Code (NDC), which is the pharmaceutical industry
standard. The level of detail necessary for NDC coding is
difficult to obtain with sufficient reliability from household
respondents. The system adopted for processing prescribed medicine
data in NMES was originally developed as part of the 1980 National
Ambulatory Medical Care Survey (NAMCS) by the National Center for
Health Statistics (Koch, 1980). The NCHS system uses as its
primary data source the Drug Product Information File (DPIF), a
computerized data base of commercially available drugs developed,
maintained and continually updated by the American Society of
Hospital Pharmacists. The coding of drug names involved the
assignment of a code to each reported medication. Coding included
a two-stage verification procedure performed by the coding
supervisor and, for randomly selected batches of cases, by
registered pharmacists.
The MEDCODER variable was then used to link the coded NMES
prescribed medicines with the DPIF in order to obtain additional
information about the medicine. The information was merged onto
this file and includes information on generic equivalents (generic
names and associated codes can be found in Attachment 4.C), Federal
controlled substance status, composition, and prescription status.
Medical Conditions
This file contains up to four condition codes per medication
record. For each prescribed medicine reported in the Household
Survey, information on reasons for use in terms of related medical
conditions, medical diagnosis, if any, related history, and parts
of the body affected by the condition were obtained. This
information was used to code each condition related to the reported
purchase of a prescribed medicine into one of the codes of the 9th
Revision of the International Classification of Diseases, revised
for use in the National Health Interview Survey (NHIS; NCHS, 1979).
The revision takes into account the experience of the National
Center for Health Statistics, the sponsor of the NHIS, in coding
household reported conditions. One major revision of the ICD-9
coding procedure in surveys using the NHIS system is the
introduction of X-codes. These codes represent impairments such as
blindness, deafness, and paralysis. Coding instructions directed
coders to favor X-codes over other ICD-9 codes that could be
applied to a particular condition. Attachment 4.A provides details
of the NHIS coding scheme for X-codes.
Coding was conducted by trained medical coders. Two-stage
verification of the coding was performed first by supervisors and
then by trained nosologists. Coders were required to maintain an
error rate at or below 2 percent throughout the coding process.
Prescribed Medicine Expenses,Sources of Payment,Imputation Proc.
Total Expense
The file contains both the unedited and edited total expenses
for each medication obtained by the sample person during the
reference period. The total expense variables sum all amounts paid
out-of-pocket and by third party payers for all prescribed medicine
purchases, including refills, over the reference period covered by
the record. For the unedited total (EXPTOT), the amount reflects
the unedited charge reported per prescription multiplied by the
unedited number of purchases of the medication on the record.
The edited total expense variable (EXPTOTX), adjusts for
inconsistent or missing values by a series of edits and
imputations. Only prescriptions reported as free from provider
(e.g., samples) were assigned to the zero expense category in the
edited variable.
Data editing addressed the following: (1) copayment amounts
reported by respondents as the total expense for the prescription;
(2) expenses reported by respondents that excluded refill expenses;
(3) extreme values; and (4) missing data. Logical edits were
performed in those cases where enough information existed to
construct a total value for the prescribed medicine expense. Where
the reported expense appeared to be a copayment rather than the
total, the total expense was set to missing and imputed to reflect
the total prescribed medicine expense rather than the copayment
amount. This appeared in particular in settings that do not
normally specify a total charge, such as HMOs or other prepaid
providers.
Missing values were imputed based on known values for similar
prescription purchases. A weighted sequential hot-deck procedure
was used which imputes data for prescription purchases with
complete information to cases with missing data but similar
characteristics. Variables with known values were used to form
groups of donors and groups of recipients. Within such groups,
data from donors were assigned to recipients, taking into account
the weights associated with the complex NMES household survey
design. Prescription code was used as a classification variable in
this imputation and frequency of purchase, region of the country,
and respondent's health status were used as sort variables.
The remaining cases with missing expense data involved
instances where there were only a few responses corresponding to a
given medication code. Here, the hot deck procedure could not be
used and missing values were replaced by the median value of the
cost per prescription within a prescription code classification.
In total, approximately 25 percent of expense information was
edited or imputed. All imputations were performed at the level of
a single purchase and then, where applicable, multiplied by the
number of purchases. Each record with imputed values contains a
corresponding imputation flag.
Sources of Payment
Each record on the file contains 9 constructed variables which
sum to 100 percent, corresponding to the percent of the total
prescription expense paid by each of the following sources:
Out of pocket by user or family;
Private insurance including any prescription plans;
Medicaid;
Medicare (this variable contains only zero values because
Medicare did not pay for prescription medicines in 1987);
Other Federal programs, which include CHAMPUS, CHAMPVA,
Supplemental Security Income (SSI), Indian Health Service
facility or contract, Intertribal Council, Alaska Native
Corporation, Veteran's Administration, any military and other
federal programs such as free government screening services
and NIH care;
Other State and local medical assistance such as community
health centers (excludes local and state employment related
insurance and welfare programs);
Workman's compensation;
Free from provider, including professional courtesy and bad
debt; and
Other, which includes automobile and car insurance, other
kinds of insurance not specified, company where the company
is not the insurer, school where the school is not the
insurer or employer, union where the union is not the insurer
or employer, charity, friend, foreign government or not
otherwise specified.
Based on respondent reports of the percent or amount paid by each
of these sources of payment, the source of payment variables were
edited and, where necessary, imputed to correct for the following:
(1) the household reported payer was incompatible with enrollment
in public and private insurance programs reported for the person;
(2) the person was not billed for the prescription so no expense or
sources of payment were reported; (3) the sum of the reported
sources did not equal 100 percent; or (4) the sources of payment or
the amounts or proportions of the payment were partially or
completely missing. Logical edits for sources of payment were
performed in those cases where enough information existed. When
only a partial source of payment was available and no logical edit
was possible, the total distribution of sources of payment was
imputed.
As for total expenses, the general imputation strategy for
sources of payment used a weighted sequential hotdeck procedure.
The classification variables used in the source of payment
imputation included insurance coverage, region, month of purchase,
whether the respondent had reported a specific charge, and union
membership status of the primary insured. Each record with imputed
values contains a corresponding imputation flag.
Other Edits and Omissions
Some data items from the prescription medicine booklet were
omitted from this file because they were components or probes used
to construct the summary variables provided. Omitted variables
were not considered to be of independent analytic interest but
rather, were methodological probes to insure that the respondent
had provided complete utilization and expenditure information. The
summary variables included on this file reflect all of the
components collected in the prescribed medicine section of the
questionnaire.
This file does not allow for the direct linkage of prescribed
medicine events to other medical events. Specifically, respondents
were not asked to identify the physician visit where a prescription
was obtained. To obtain this linkage indirectly, matching of
medical condition codes associated with the use of prescribed
medicines with corresponding codes for other medical events is
required, in conjunction with person identifiers and dates of
purchase or service.
Data were missing on the frequency of purchase variable
(PRCHDATX) for 1.43 percent of the prescriptions reported during
the four interviewing rounds of NMES. Regression models developed
to predict the frequency of purchase indicated that the only
significant predictor within a reference period was the
prescription code or the prescription medicine name. Data for
cases where frequency of purchase was missing were replaced with
the median value of frequency of purchase within a specific
prescription code.
Each medication record can have up to four ICD-9 condition codes
attached to it. The first condition on the record does not
necessarily reflect the primary condition for the sampled person
and, more generally, the order of the conditions on the record does
not reflect importance or severity. In addition, there is a small
probability that duplicate conditions appear for the same record.
No editing for these duplications was done.
No editing was performed on the round indicator that is on this
file.
Variable Naming and Codebook Conventions
The codebook provides unweighted and weighted frequencies for
all variables on the file. The codebook contains variable
information and frequency distributions for a total of 110,080
records. Weighted, these records represent 1,231,005,708 purchases
of prescription medications (196,180 unweighted) by 138,958,096
people (20,000 unweighted).
Complete variable listings in alphabetical order and by file
position are provided for cross-reference. Most variable
descriptors in the codebooks are abbreviated versions of
questionnaire items, preceded by indicators of item number. A copy
of the round one prescribed medicines booklet is included as
Attachment 3 to this public use tape to permit a full understanding
of the content and wording of each item, the structure of
questionnaire sections, skip patterns and administrative
information.
The codebook describes an EBCDIC data set and provides the
following programming identifiers for each variable:
IDENTIFIER DESCRIPTION
NAME Variable name (maximum of 8 characters)
DESCRIPTION Variable descriptor (maximum of 40
characters)
FORMAT Number of bytes and decimal places
TYPE Type of data: numeric (indicated by
NUM) or character (indicated by CHAR)
START Beginning column position of variable
in the record
END Ending column position of variable in
the record
NOTES Indicator of an explanatory note(s)
corresponding to the variable
In general, variable names reflect the content of the variable,
with an 8 character limitation. For edited versions of original
variables, the edited variable name is identical to the original
variable with an "X" appended (and truncated when necessary to
comply with the 8 character limitation). For variables
corresponding to specific questionnaire items, the question number
is included in the variable label.
The following reserved code values are used:
VALUE DEFINITION
-1 INAPPLICABLE Question was not asked due to skip
pattern
-5 NEVER KNOW Question was asked and respondent did
not know and never will know the answer
-7 REFUSED Question was asked and respondent
refused to answer the question
-8 DK Question was asked and respondent did
not know the answer
-9 NOT ASCERTAINED Interviewer did not record the data
Sample Design, Estimation & Sampling Wts., Variance Estimation
Sample Design and Response Rates
The NMES household survey was designed to produce national
estimates representative of the civilian noninstitutionalized
population of the United States as of 1987. For sample selection,
the household component of NMES used two independent national
multistage area samples from Westat, Inc. and NORC. To improve the
quality of the data and to allow for analysis of trends during
1987, it was conducted as a panel survey over four core rounds of
interviewing.
Sampling specifications required the selection of about 17,500
households for the first core household interview. Data were
obtained for about 86 percent of eligible households in the first
interview and 80 percent by the fourth interview. Approximately 6
percent of all survey participants provided data for only some of
the time in which they were eligible to respond. These persons
were considered total nonrespondents and a standard nonresponse
weight adjustment was used to account for possible selection bias
in this respect. For a detailed description of the survey design
and of sampling, estimation, and adjustment methods see Cohen,
DiGaetano, and Waksberg (1991).
Estimation and Sampling Weights
General Information
The application of appropriate sampling weights is essential to
the derivation of estimates when using this public use file. The
weight provided for use with prescribed medicine data, INCALPER,
reflects adjustments for complete nonresponse to the NMES survey
and poststratification to the Census Bureau 1987 Current Population
Survey (CPS) cross-classified by age, race/ethnicity, gender and
poverty status. All persons who were eligible at any time during
1987 and responded for the entire period of their eligibility have
positive INCALPER weights. Only the 20,000 persons who have
positive INCALPER weights and who purchased or otherwise obtained
a prescribed medicine are represented on this file. In order to
produce national estimates related to the types, frequency of use,
expenses and sources of payment for prescribed medicines, the value
in each record contributing to the estimates must be multiplied by
the weight (INCALPER) contained on that record. It should be noted
that the weight, INCALPER, can also serve as a person-level
estimation weight (see NMES Public Use Tape 13). The variable
containing the number of times the prescribed medicine was obtained
during the reference period must be used as an additional factor in
most estimates of utilization.
For estimates involving persons in the Household Survey not on
this file (e.g., persons without use of prescribed medicines in
1987) or for detailed person-level characteristics of users,
including round specific eligibility status and reference period
dates, the data on this file should be merged with NMES Public Use
Tape 13 (see section 3.3) using the person-level identification
variable PIDX.
Basic Estimates of Utilization and Expenditures
This file is constructed for efficient estimation of utilization
and expenditures for medications. Such estimates include the total
number of and expenses for prescribed medicines. The mean
prescription expense, for instance, should be calculated as the
weighted sum of the total expense across all records in the file
(sum of EXPTOTX x INCALPER) divided by the weighted sum of the
number of purchases (sum of PRCHDATX x INCALPER). Thus, the
numerator is the national estimate for total prescribed medicine
expenses and the denominator is the population estimate for the
total number of prescribed medication purchases (including
refills).
Subsetting to records based on characteristics of interest
expands the scope of potential estimates. For example, the number
of prescriptions paid for, totally or in part, by Medicaid is
estimated by summing (PRCHDATX x INCALPER) across all records where
the Medicaid source of payment variable (SOPTMCD) is greater than
zero.
Person-Based Ratio Estimates
Persons with Prescription Medication Use
Person-Based Ratio Estimates for Persons with Prescription
Medication Use
When calculating ratio estimates where the denominator is
persons, not prescribed medicines, care should be taken to properly
define and estimate this denominator. If the estimate of interest,
for example, is the mean prescribed medicine expense across all
medication users, the following strategy should be considered: All
expenses for a person on this file should be summed and a person-
level total expense variable created (e.g., EXPALL). The mean
national estimate would then be derived by obtaining the ratio of
the weighted sum of total expense per person across all unique
persons on the file (sum of INCALPER X EXPALL) divided by the
weighted number of unique persons on the file (sum of INCALPER).
Only one INCALPER value for each PIDX should contribute to the
calculation of the sum for both the denominator and numerator.
Relative to the Entire Population
Person-Based Ratio Estimates Relative to the Entire
Population
If the ratio relates to the entire population, this file cannot
be used to calculate the denominator, as only those persons with at
least one instance of prescribed medicine use are represented on
it. In this case, Public Use Tape 13 -- Rounds 1-4 Household
Survey: Population Characteristics and Person-level Utilization --
which has data for all sampled people, must be used to estimate
persons. For example, to estimate the proportion of persons 65 and
over with at least one purchase or other acquisition of a
particular prescribed medication, the current file is used to
calculate the numerator and NMES Tape 13 is used to calculate the
denominator.
Sampling Wts for Merging Previous Releases of NMES w/Current Tap
Sampling Weights for Merging Previous Releases of NMES
Household Data with the Current Tape
There have been several previous releases of NMES Household
Survey public use data (see Attachment 5). Unless a variable name
common to several tapes is provided, the sampling weights contained
on these tapes are tape-specific. The tape-specific sampling
weights reflect minor adjustments to eligibility and response
indicators due, among other factors, to birth, death, or
institutionalization among respondents. Adjustments to the weights
have also included post-stratification adjustments to control for
the distribution of the U.S. noninstitutionalized population by
poverty status and, where appropriate, nonresponse adjustments for
round-specific supplemental questionnaires (e.g., the health status
questionnaires).
For estimates from a NMES data file that do not require merging
with variables on other NMES files, the sampling weights provided
on that tape are the appropriate weights. When merging a NMES
household survey tape to another NMES household survey tape, the
major analytical variable (i.e., the dependent variable) determines
the correct sampling weight to use. For example, for 1987
estimates of prescribed medicine use or expenditures (from the
present household survey tape) using health status variables from
NMES Public Use Tape 9, the full-year weight, INCALPER, on the
present tape should be used. By contrast, the weight HSQACCWT from
Public Use Tape 9 should be used when the major dependent variable
is health status and prescribed medicines is an independent
variable. Three exceptions to this general sampling weight and
merger rule are noted below. For details concerning the
appropriate weight specific to each tape, see the hard copy
information specific to each tape.
(1) For estimates of round one data from NMES Public Use Tape
3 (preliminary round one person characteristics and
functional health status data), the round one weight
(WGTR1PER) provided on NMES Public Use Tape 13 should be
used. It should be noted that as a result of further
response and eligibility edits, not all persons with
positive round one weights on Tape 3 will link when merging
it to Tape 13. In those instances, an imputation or
weighting strategy can be developed to adjust for all
persons with positive WGTR1PER weights. The preferred
approach is to use the round one data and the round one
weight released on NMES Tape 13.
(2) For point in time estimates of persons with activity of
daily living and instrumental activity of daily living
difficulties (Tape 10), the round one or four weight
(WGTR1PER and WGTR4PER, respectively), provided on NMES
Tape 13 should be used in all instances of merged data
regardless of the type of analysis.
(3) NMES Public Use Tape 4, which contains prescribed medicine
data for the Medicare beneficiary population, should not
be merged with the current tape because of adjustments to
the sampling weights made after the release of Tape 4.
It should be noted that the sampling weight provided on NMES
Tape 9 (HSQACCWT) reflects nonresponse adjustments specific to the
health status questionnaire and access to care supplement data on
that tape. This further nonresponse adjustment requires additional
considerations in merging Tape 9 with the current tape.
(1) When making estimates for data in the current tape, the
sampling weights provided on the current tape should be
used. Since this would include persons not on Tape 9 in
the analysis, data items from Tape 9 will have missing
values for these persons.
(2) When making estimates of health status or access to care
indicators, the Tape 9 weight, HSQACCWT, should be used.
This weight adjusts for the exclusion of persons included
on the present tape.
Variance Estimation
Variance estimates of sample statistics require that the complex
nature of the NMES Household Survey design be taken into account
for hypothesis testing and for the construction of confidence
intervals. To obtain variance estimates of statistics by means of
statistical programs that use the Taylor series method of variance
estimation, variables must be used that denote the strata and the
primary sampling unit (PSU) within a given stratum. The variables
STRATUMX and SPSU are these variables, respectively and are
included on the data file.
There are variance estimation programs that account for the
complex survey design. A list of available variance estimation
programs is produced in Attachment 2.
Programming Information
These files are contained on a standard label, 9 track 6250 bpi
tape. The specifications for each file on the tape are as follows:
FILE 1:
Description: NMES Household Survey Prescribed Medicine Data File
Dataset Name: NMES.PUF141.DATA
Number of Observations: 110,080
Number of Variables: 45
Record Length: 260
Block Size: 13,000
Record Format: FB
FILE 2:
Description: NMES HS Prescribed Medicine Data: Technical and
Programming Information and Data Dictionary
Dataset Name: NMES.PUF141.DOC
Record Length: 133
Block Size: 19,950
Record Format: FB
FILE 3:
Description: NMES HS Prescribed Medicine Data: Additional
Documentation for SAS Users
Dataset Name: NMES.PUF141.SRC
Record Length: 80
Block Size: 800
Record Format: FB
File 1 was created using the SAS version 5.18) computer
software, and converted to EBCDIC format. File 2 contains the
technical documentation stored as an Operating System (OS) EBCDIC
file containing ASA carriage control characters in the first byte
in each record, which will direct the line printer to skip lines,
begin a new page, etc. This technical documentation can be copied
to disk and retrieved on-line to view, to modify with a text editor
program such as WYLBUR, or to make additional copies. File 3 is an
EBCDIC file containing the following additional documentation for
SAS users: INPUT statement to create the SAS file, including a
LABEL statement; SAS statements which assign a format name to each
variable; and SAS statements describing formats.
References
1. Cohen, S.B., DiGaetano, R. and Waksberg, J. (1991). National
Medical Expenditure Survey: Sample Design of the 1987 Household
Survey, Methods 3. AHCPR Pub. No. 91-0037. DHHS: U.S. Public
Health Service.
2. Koch, H.K. (1980). The collection and processing of drug
information: National Ambulatory Medical Care Survey. Vital and
Health Statistics, Series 2, No. 90, DHHS Pub. No. (PHS)82-1364.
3. National Center for Health Statistics (1979). Medical Coding
Manual: National Health Interview Survey. DHHS: U.S. Public
Health Service.
DATA DICTIONARY
Alphabetical and Position Listing of Variables
NMES EVENTS -- PRESCRIBED MEDICINES CODEBOOK
ALPHABETICAL AND POSITIONAL LISTING OF VARIABLES
DATE: DECEMBER 19, 1991
-----ALPHABETICAL LISTING OF VARIABLES-----
START END NAME DESCRIPTION
_____ ___ ____ ___________
123 123 CNTRLSUB N10 FEDERAL CONTROLLED SBSTNCE STATUS CO
124 124 COMPSTAT N10 COMPOSITION STATUS CODE
143 144 DATEBDD N12 DATE MEDICATION FIRST TAKEN - DAY
141 142 DATEBMM N12 DATE MEDICATION FIRST TAKEN - MONTH
147 148 DATEEDD N13 DATE MEDICATION LAST TAKEN - DAY
145 146 DATEEMM N13 DATE MEDICATION LAST TAKEN - MONTH
9 12 EN RECORD NUMBER
21 32 EVENTIDX RECORD ID (ODUX+PN+EN)
170 170 EXPTFLG IMPUTATION FLAG FOR EDITED TOTAL EXPENSE
154 161 EXPTOT N18/N30 ORIGINAL TOTAL CHARGE
162 169 EXPTOTX EDITED TOTAL EXPENSE
117 121 GENCODE N10 PRESCRIBED MED GENERIC NAME CODE
77 116 GENNAME N10 PRESCRIBED MED GENERIC NAME
125 128 ICD1 N11 ICD9 CODE - CONDITION 1
129 132 ICD2 N11 ICD9 CODE - CONDITION 2
133 136 ICD3 N11 ICD9 CODE - CONDITION 3
137 140 ICD4 N11 ICD9 CODE - CONDITION 4
242 253 INCALPER FULL-YEAR WEIGHT
35 37 LASTAGE ED PID AGE AT END OF LAST ELIGIBLE ROUND
149 149 LTINTFLG MONTH LAST TAKEN IS IN 1988
42 46 MEDCODER N10 PRESCRIBED MEDICINE CODE
47 76 MEDNAME N10 PRESCRIBED MEDICINE NAME
1 5 ODUX ORIGINAL DWELLING UNIT
13 20 PIDX PERSON IDENTIFIER (ODUX+PN)
6 8 PN PERSON NUMBER
150 151 PRCHDATT N14 NUM OF TIMES MEDICINE WAS OBTAIND/RD
152 153 PRCHDATX N14ED NUM OF TIMES MEDICINE OBTAIND/RD
40 40 RACE3 PID RACE/ETHNICITY
39 39 RACE6 ED PID RACE
33 34 ROUND DATA COLLECTION ROUND
122 122 SCRPSTAT N10 PRESCRIPTION/OTC STATUS CODE
38 38 SMPSEXR PID SEX
233 240 SOPTFFP PCT PAYMENT FREE FROM PROVIDER
241 241 SOPTFLG IMPUTATION FLG FOR SOURCE OF PAYM INFO
193 200 SOPTMCD PCT PAYMENT FROM MEDICAID
187 192 SOPTMCR PCT PAYMENT FROM MEDICARE
201 208 SOPTOTFD PCT PAYMENT FROM OTHER FEDERAL
225 232 SOPTOTHR PCT PAYMENT FROM OTHER
209 216 SOPTOTST PCT PAYMENT FROM OTHER STATE
179 186 SOPTPRVT PCT PAYMENT FROM PRIVATE
171 178 SOPTSELF PCT PAYMENT FROM SELF OR FAMILY
217 224 SOPTWC PCT PAYMENT FROM WORKERS COMP
257 257 SPSU PSEUDO PSU
41 41 SREGION CENSUS REGION
254 256 STRATUMX SAMPLING STRATUM
NMES EVENTS -- PRESCRIBED MEDICINES CODEBOOK
ALPHABETICAL AND POSITIONAL LISTING OF VARIABLES
DATE: DECEMBER 19, 1991
-----POSITIONAL LISTING OF VARIABLES-----
START END NAME DESCRIPTION
_____ ___ ____ ___________
1 5 ODUX ORIGINAL DWELLING UNIT
6 8 PN PERSON NUMBER
9 12 EN RECORD NUMBER
13 20 PIDX PERSON IDENTIFIER (ODUX+PN)
21 32 EVENTIDX RECORD ID (ODUX+PN+EN)
33 34 ROUND DATA COLLECTION ROUND
35 37 LASTAGE ED PID AGE AT END OF LAST ELIGIBLE ROUND
38 38 SMPSEXR PID SEX
39 39 RACE6 ED PID RACE
40 40 RACE3 PID RACE/ETHNICITY
41 41 SREGION CENSUS REGION
42 46 MEDCODER N10 PRESCRIBED MEDICINE CODE
47 76 MEDNAME N10 PRESCRIBED MEDICINE NAME
77 116 GENNAME N10 PRESCRIBED MED GENERIC NAME
117 121 GENCODE N10 PRESCRIBED MED GENERIC NAME CODE
122 122 SCRPSTAT N10 PRESCRIPTION/OTC STATUS CODE
123 123 CNTRLSUB N10 FEDERAL CONTROLLED SBSTNCE STATUS CO
124 124 COMPSTAT N10 COMPOSITION STATUS CODE
125 128 ICD1 N11 ICD9 CODE - CONDITION 1
129 132 ICD2 N11 ICD9 CODE - CONDITION 2
133 136 ICD3 N11 ICD9 CODE - CONDITION 3
137 140 ICD4 N11 ICD9 CODE - CONDITION 4
141 142 DATEBMM N12 DATE MEDICATION FIRST TAKEN - MONTH
143 144 DATEBDD N12 DATE MEDICATION FIRST TAKEN - DAY
145 146 DATEEMM N13 DATE MEDICATION LAST TAKEN - MONTH
147 148 DATEEDD N13 DATE MEDICATION LAST TAKEN - DAY
149 149 LTINTFLG MONTH LAST TAKEN IS IN 1988
150 151 PRCHDATT N14 NUM OF TIMES MEDICINE WAS OBTAIND/RD
152 153 PRCHDATX N14ED NUM OF TIMES MEDICINE OBTAIND/RD
154 161 EXPTOT N18/N30 ORIGINAL TOTAL CHARGE
162 169 EXPTOTX EDITED TOTAL EXPENSE
170 170 EXPTFLG IMPUTATION FLAG FOR EDITED TOTAL EXPENSE
171 178 SOPTSELF PCT PAYMENT FROM SELF OR FAMILY
179 186 SOPTPRVT PCT PAYMENT FROM PRIVATE
187 192 SOPTMCR PCT PAYMENT FROM MEDICARE
193 200 SOPTMCD PCT PAYMENT FROM MEDICAID
201 208 SOPTOTFD PCT PAYMENT FROM OTHER FEDERAL
209 216 SOPTOTST PCT PAYMENT FROM OTHER STATE
217 224 SOPTWC PCT PAYMENT FROM WORKERS COMP
225 232 SOPTOTHR PCT PAYMENT FROM OTHER
233 240 SOPTFFP PCT PAYMENT FREE FROM PROVIDER
241 241 SOPTFLG IMPUTATION FLG FOR SOURCE OF PAYM INFO
242 253 INCALPER FULL-YEAR WEIGHT
254 256 STRATUMX SAMPLING STRATUM
257 257 SPSU PSEUDO PSU
Codebook
General Information
THIS CODEBOOK PROVIDES UNWEIGHTED AND WEIGHTED FREQUENCIES FOR
THE USE OF PRESCRIBED MEDICINES FOR CALENDAR YEAR 1987. THE DATA FILE
CONTAINS ONE RECORD PER UNIQUE MEDICATION PER REFERENCE PERIOD FOR EACH
PERSON IN THE HOUSEHOLD SURVEY WHO REPORTED HAVING PURCHASED OR
OTHERWISE OBTAINED A PRESCRIBED MEDICATION DURING THAT ROUND. ADDITIONAL
INFORMATION ABOUT THE PERSON, THE MEDICATION AND ASSOCIATED EXPENSES
ARE ALSO INCLUDED ON EACH RECORD. TO OBTAIN NATIONAL ESTIMATES
FOR THE VARIABLES ON THIS FILE, THE WEIGHT DESCRIBED AT THE END OF
THIS CODEBOOK MUST BE USED. INFORMATION CONCERNING THE SAMPLE DESIGN,
THE EXPENSE AND SOURCE OF PAYMENT VARIABLES IS PROVIDED IN THE FILE
DOCUMENTATION. FOR VARIABLES CORRESPONDING DIRECTLY TO QUESTIONNAIRE
ITEMS, THE QUESTIONNAIRE ITEM NUMBER IS PROVIDED IN THE VARIABLE
DESCRIPTOR. FOR VARIABLES WITH AN ASTERISK IN THE RIGHTMOST COLUMN,
EXPLANATORY NOTES ARE PROVIDED AT THE END OF THE CODEBOOK IN
ALPHABETICAL ORDER OF VARIABLE NAME.
Locations 1-123
NAME DESCRIPTION FORMAT TYPE START END
________ ___________ ______ ____ _____ _____
ODUX ORIGINAL DWELLING UNIT 5.0 NUM 1 5
________ _____________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
20001-37614 110,080 707,098,251
TOTAL 110,080 707,098,251
PN PERSON NUMBER 3.0 NUM 6 8
________ _____________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
10-266 110,080 707,098,251
TOTAL 110,080 707,098,251
EN RECORD NUMBER 4.0 NUM 9 12
________ _____________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
11-1318 110,080 707,098,251
TOTAL 110,080 707,098,251
NAME DESCRIPTION FORMAT TYPE START END
________ ___________ ______ ____ _____ _____
PIDX PERSON IDENTIFIER (ODUX+PN) 8.0 CHAR 13 20
________ _____________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
VALID PERSON ID 110,080 707,098,251
TOTAL 110,080 707,098,251
EVENTIDX RECORD ID (ODUX+PN+EN) 12.0 CHAR 21 32
________ _____________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
VALID RECORD ID 110,080 707,098,251
TOTAL 110,080 707,098,251
*ROUND DATA COLLECTION ROUND 2.0 NUM 33 34
________ _____________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
-9 NOT ASCERTAIN 509 2,844,042
1 29,410 189,846,174
2 34,587 223,222,149
3 25,152 162,256,777
4 20,422 128,929,109
TOTAL 110,080 707,098,251
*LASTAGE ED PID AGE AT END OF LAST ELIGIBLE ROUND 3.0 NUM 35 37
________ _____________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
0-17 12,563 92,011,740
18-44 23,912 189,472,573
45-64 27,634 203,165,614
65+ 45,971 222,448,324
TOTAL 110,080 707,098,251
SMPSEXR PID SEX 1.0 NUM 38 38
________ _____________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
1 MALE 39,694 267,502,085
2 FEMALE 70,386 439,596,166
TOTAL 110,080 707,098,251
NAME DESCRIPTION FORMAT TYPE START END
________ ___________ ______ ____ _____ _____
*RACE6 ED PID RACE 1.0 NUM 39 39
________ _____________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
1 AMER INDIAN 691 4,586,416
2 ALASKA NATIVE 64 601,738
3 ASIAN/PACIFIC 770 6,947,195
4 BLACK 18,488 69,011,745
5 WHITE 87,663 612,990,253
6 OTHER 2,404 12,960,904
TOTAL 110,080 707,098,251
*RACE3 PID RACE/ETHNICITY 1.0 NUM 40 40
________ _____________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
1 HISPANIC 6,617 32,157,045
2 BLACK NON-HISP 18,342 68,221,805
3 OTHER 85,121 606,719,401
TOTAL 110,080 707,098,251
SREGION CENSUS REGION 1.0 NUM 41 41
________ _____________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
1 NORTHEAST 19,860 135,326,850
2 MIDWEST 27,519 180,786,169
3 SOUTH 42,887 261,241,632
4 WEST 19,814 129,743,599
TOTAL 110,080 707,098,251
*MEDCODER N10 PRESCRIBED MEDICINE CODE 5.0 NUM 42 46
_________ _____________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
1-61625 108,725 698,306,442
88888 MED DEVICE 102 622,863
99980 OTHER 166 1,100,364
99999 ILLEGIBLE 1,087 7,068,581
TOTAL 110,080 707,098,251
NAME DESCRIPTION FORMAT TYPE START END
________ ___________ ______ ____ _____ _____
*MEDNAME N10 PRESCRIBED MEDICINE NAME 30.0 CHAR 47 76
________ _____________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
-1 INAPPLICABLE 107,813 691,919,229
A-ZZZZZZZZZZ 2,267 15,179,022
TOTAL 110,080 707,098,251
*GENNAME N10 PRESCRIBED MED GENERIC NAME 40.0 CHAR 77 116
________ _____________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
A-ZZZZZZZZZZ 110,080 707,098,251
TOTAL 110,080 707,098,251
*GENCODE N10 PRESCRIBED MED GENERIC NAME CODE 5.0 NUM 117 121
________ _____________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
50000 UNDETERMIN 5,297 36,407,258
50004-60014 CODE 104,783 670,690,993
TOTAL 110,080 707,098,251
*SCRPSTAT N10 PRESCRIPTION/OTC STATUS CODE 1.0 NUM 122 122
_________ _____________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
1 PRESCRIPTION 96,802 622,025,166
2 NON-PRESCRIPTN 7,496 45,145,687
3 UNDETERMINED 5,782 39,927,398
TOTAL 110,080 707,098,251
*CNTRLSUB N10 FEDERAL CONTROLLED SBSTNCE STATUS CO 1.0 NUM 123 123
_________ _____________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
2 SCHED 2 DRUG 966 6,886,422
3 SCHED 3 DRUG 2,686 18,439,923
4 SCHED 4 DRUG 5,539 35,100,685
5 SCHED 5 DRUG 917 6,239,504
6 UNCONTRLLD DRU 94,670 603,987,442
7 UNDETERMINED 5,302 36,444,275
TOTAL 110,080 707,098,251
Locations 124-170
NAME DESCRIPTION FORMAT TYPE START END
________ ___________ ______ ____ _____ _____
*COMPSTAT N10 COMPOSITION STATUS CODE 1.0 NUM 124 124
_________ _____________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
1 SINGLE INGRED 83,439 527,466,060
2 COMBNED INGRDS 21,344 143,224,932
3 UNDETERMINED 5,297 36,407,258
TOTAL 110,080 707,098,251
ICD1 N11 ICD9 CODE - CONDITION 1 4.0 CHAR 125 128
________ _____________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
-9 NOT ASCERTAIN 1,253 8,095,933
V001-V999 7,270 49,424,227
X00X-X999 849 4,701,657
0001-9999 100,708 644,876,435
TOTAL 110,080 707,098,251
ICD2 N11 ICD9 CODE - CONDITION 2 4.0 CHAR 129 132
________ _____________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
-1 INAPPLICABLE 106,622 685,027,989
-9 NOT ASCERTAIN 1,253 8,095,933
V001-V999 38 243,738
X00X-X999 23 149,764
0001-9999 2,144 13,580,827
TOTAL 110,080 707,098,251
ICD3 N11 ICD9 CODE - CONDITION 3 4.0 CHAR 133 136
________ _____________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
-1 INAPPLICABLE 108,779 698,750,777
-9 NOT ASCERTAIN 1,253 8,095,933
V001-V999 5 19,783
X00X-X999 1 10,742
0001-9999 42 221,015
TOTAL 110,080 707,098,251
NAME DESCRIPTION FORMAT TYPE START END
________ ___________ ______ ____ _____ _____
ICD4 N11 ICD9 CODE - CONDITION 4 4.0 CHAR 137 140
________ _____________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
-1 INAPPLICABLE 108,821 698,975,564
-9 NOT ASCERTAIN 1,253 8,095,933
V001-V999 1 4,953
0001-9999 5 21,801
TOTAL 110,080 707,098,251
DATEBMM N12 DATE MEDICATION FIRST TAKEN - MONTH 2.0 NUM 141 142
________ _______________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
-9 NOT ASCERTAIN 1,393 8,482,314
-8 DON'T KNOW 948 5,265,945
-7 REFUSED 4 14,581
-5 NEVER KNOW 555 3,483,973
1 JANUARY 21,121 131,734,756
2 FEBRUARY 10,814 68,463,455
3 MARCH 12,126 77,168,166
4 APRIL 6,994 47,511,507
5 MAY 4,007 27,802,482
6 JUNE 7,070 45,393,121
7 JULY 11,007 69,290,523
8 AUGUST 8,321 53,463,536
9 SEPTEMBER 4,581 30,615,143
10 OCTOBER 5,846 39,558,028
11 NOVEMBER 9,044 57,577,819
12 DECEMBER 6,249 41,272,900
TOTAL 110,080 707,098,251
DATEBDD N12 DATE MEDICATION FIRST TAKEN - DAY 2.0 NUM 143 144
________ _____________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
-9 NOT ASCERTAIN 1,952 11,908,089
-8 DON'T KNOW 2,161 12,885,514
-7 REFUSED 4 14,581
-5 NEVER KNOW 1,085 6,777,762
1-31 104,878 675,512,305
TOTAL 110,080 707,098,251
NAME DESCRIPTION FORMAT TYPE START END
________ ___________ ______ ____ _____ _____
DATEEMM N13 DATE MEDICATION LAST TAKEN - MONTH 2.0 NUM 145 146
________ ______________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
-9 NOT ASCERTAIN 1,598 10,142,206
-8 DON'T KNOW 1,055 5,881,177
-7 REFUSED 5 24,999
-5 NEVER KNOW 462 3,066,425
1 JANUARY 4,354 29,264,053
2 FEBRUARY 14,538 91,053,739
3 MARCH 15,719 98,736,902
4 APRIL 7,576 50,516,589
5 MAY 3,390 23,628,678
6 JUNE 6,999 45,046,195
7 JULY 11,679 73,631,830
8 AUGUST 9,100 58,234,605
9 SEPTEMBER 3,781 25,376,309
10 OCTOBER 5,630 37,810,331
11 NOVEMBER 11,785 75,240,106
12 DECEMBER 12,409 79,444,108
TOTAL 110,080 707,098,251
DATEEDD N13 DATE MEDICATION LAST TAKEN - DAY 2.0 NUM 147 148
________ _____________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
-9 NOT ASCERTAIN 2,226 14,025,416
-8 DON'T KNOW 2,429 14,393,602
-7 REFUSED 4 14,581
-5 NEVER KNOW 842 5,581,855
1-31 104,579 673,082,796
TOTAL 110,080 707,098,251
*LTINTFLG MONTH LAST TAKEN IS IN 1988 1.0 NUM 149 149
_________ _____________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
0 NO 98,310 636,320,420
1 YES 11,770 70,777,831
TOTAL 110,080 707,098,251
NAME DESCRIPTION FORMAT TYPE START END
________ ___________ ______ ____ _____ _____
PRCHDATT N14 NUM OF TIMES MEDICINE WAS OBTAIND/RD 2.0 NUM 150 151
________ ______________________________________________ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
-9 NOT ASCERTAIN 746 4,225,473
-8 DON'T KNOW 984 5,253,865
-7 REFUSED 3 11,497
-5 NEVER KNOW 304 1,557,577
1 65,780 437,433,121
2 20,251 125,001,366
3 10,164 62,526,196
4 6,192 36,875,665
5-9 5,253 31,775,072
10-19 345 2,043,790
20 OR MORE 58 394,630
TOTAL 110,080 707,098,251
PRCHDATX N14ED NUM OF TIMES MEDICINE OBTAIND/RD 2.0 NUM 152 153
________ ______________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
1 67,991 449,935,812
2 20,577 126,640,649
3 10,077 61,876,282
4 6,101 36,495,337
5-9 5,006 30,156,377
10-19 304 1,812,729
20 OR MORE 24 181,065
TOTAL 110,080 707,098,251
NAME DESCRIPTION FORMAT TYPE START END
________ ___________ ______ ____ _____ _____
EXPTOT N18/N30 ORIGINAL TOTAL CHARGE 8.2 NUM 154 161
________ _____________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
-9 NOT ASCERTAIN 905 5,781,471
-8 DON'T KNOW 10,137 63,686,736
-7 REFUSED 1 11,384
-5 NEVER KNOW 6,254 41,258,317
-1 INAPPLICABLE 22,302 129,642,984
0.01- 4.99 7,675 51,926,854
5.00- 9.99 14,104 95,597,575
10.00- 24.99 24,823 165,417,945
25.00- 49.99 13,599 88,391,021
50.00- 74.99 4,915 31,421,258
75.00- 99.99 2,244 14,291,413
100.00-499.99 3,063 19,249,504
500.00-HIGH 58 421,788
TOTAL 110,080 707,098,251
*EXPTOTX EDITED TOTAL EXPENSE 8.2 NUM 162 169
________ _____________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
0.00 1,756 11,718,473
0.01- 4.99 6,837 44,139,832
5.00- 9.99 20,469 134,989,838
10.00- 24.99 40,499 262,696,664
25.00- 49.99 22,979 145,222,871
50.00- 74.99 8,347 51,746,242
75.00- 99.99 3,732 23,150,016
100.00-499.99 5,351 32,658,490
500.00-HIGH 110 775,825
TOTAL 110,080 707,098,251
*EXPTFLG IMPUTATION FLAG FOR EDITED TOTAL EXPENSE 1.0 NUM 170 170
________ ______________________________________________ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
1 EXP FROM HS 82,322 513,186,507
3 EXP IMPUTED 27,758 193,911,744
TOTAL 110,080 707,098,251
Locations 171-257
NAME DESCRIPTION FORMAT TYPE START END
________ ___________ ______ ____ _____ _____
SOPTSELF PCT PAYMENT FROM SELF OR FAMILY 8.4 NUM 171 178
________ _____________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
0 24,776 143,192,559
GT 0 TO 20 16,335 118,332,440
GT 20 TO 40 5,908 41,531,238
GT 40 TO 60 2,148 14,637,314
GT 60 TO 80 1,031 6,704,820
GT 80 TO LT 100 649 3,893,722
100 59,233 378,806,158
TOTAL 110,080 707,098,251
SOPTPRVT PCT PAYMENT FROM PRIVATE 8.4 NUM 179 186
________ _____________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
0 80,346 495,803,651
GT 0 TO 20 563 3,431,910
GT 20 TO 40 843 5,799,979
GT 40 TO 60 2,135 14,516,093
GT 60 TO 80 12,541 91,777,883
GT 80 TO LT 100 8,431 60,395,984
100 5,221 35,372,750
TOTAL 110,080 707,098,251
SOPTMCR PCT PAYMENT FROM MEDICARE 6.4 NUM 187 192
________ _____________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
0 110,080 707,098,251
TOTAL 110,080 707,098,251
NAME DESCRIPTION FORMAT TYPE START END
________ ___________ ______ ____ _____ _____
SOPTMCD PCT PAYMENT FROM MEDICAID 8.4 NUM 193 200
________ _____________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
0 97,058 641,394,989
GT 0 TO 20 5 57,941
GT 20 TO 40 15 87,322
GT 40 TO 60 22 106,226
GT 60 TO 80 38 162,727
GT 80 TO LT 100 131 627,649
100 12,811 64,661,397
TOTAL 110,080 707,098,251
SOPTOTFD PCT PAYMENT FROM OTHER FEDERAL 8.4 NUM 201 208
________ _____________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
0 105,525 678,182,855
GT 0 TO 20 5 57,941
GT 20 TO 40 13 127,784
GT 40 TO 60 8 66,202
GT 60 TO 80 111 980,714
GT 80 TO LT 100 13 98,217
100 4,405 27,584,538
TOTAL 110,080 707,098,251
SOPTOTST PCT PAYMENT FROM OTHER STATE 8.4 NUM 209 216
________ _____________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
0 109,189 702,101,195
GT 0 TO 20 22 159,888
GT 20 TO 40 30 180,675
GT 40 TO 60 73 419,332
GT 60 TO 80 227 1,276,708
GT 80 TO LT 100 383 2,092,771
100 156 867,681
TOTAL 110,080 707,098,251
NAME DESCRIPTION FORMAT TYPE START END
________ ___________ ______ ____ _____ _____
SOPTWC PCT PAYMENT FROM WORKERS COMP 8.4 NUM 217 224
________ _____________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
0 109,808 705,024,593
GT 0 TO 20 5 57,941
GT 20 TO 40 1 10,197
GT 80 TO LT 100 1 10,356
100 265 1,995,164
TOTAL 110,080 707,098,251
SOPTOTHR PCT PAYMENT FROM OTHER 8.4 NUM 225 232
________ _____________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
0 109,734 704,920,706
GT 0 TO 20 27 159,117
GT 20 TO 40 9 53,217
GT 40 TO 60 9 51,000
GT 60 TO 80 94 669,804
GT 80 TO LT 100 75 420,247
100 132 824,160
TOTAL 110,080 707,098,251
SOPTFFP PCT PAYMENT FREE FROM PROVIDER 8.4 NUM 233 240
________ _____________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
0 109,764 705,255,983
GT 0 TO 20 218 1,155,183
GT 20 TO 40 14 88,690
GT 40 TO 60 12 103,432
GT 60 TO 80 36 269,045
GT 80 TO LT 100 21 140,040
100 15 85,878
TOTAL 110,080 707,098,251
NAME DESCRIPTION FORMAT TYPE START END
________ ___________ ______ ____ _____ _____
SOPTFLG IMPUTATION FLG FOR SOURCE OF PAYM INFO 1.0 NUM 241 241
________ _______________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
0 NO EXPENSES 1,756 11,718,473
1 ALL SOP FRM HS 98,219 633,207,538
2 ALL SOP IMPUTD 10,105 62,172,240
TOTAL 110,080 707,098,251
*INCALPER FULL-YEAR WEIGHT 12.6 NUM 242 253
_________ _____________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
780.924-27174.39 110,080 N/A
TOTAL 110,080 N/A
*STRATUMX SAMPLING STRATUM 3.0 NUM 254 256
_________ _____________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
301-401 110,080 707,098,251
TOTAL 110,080 707,098,251
*SPSU PSEUDO PSU 1.0 NUM 257 257
________ _____________________________________ ______ ____ _____ _____
VALUE UNWEIGHTED WEIGHTED BY INCALPER
_____ __________ ____________________
1 54,365 349,965,762
2 55,715 357,132,489
TOTAL 110,080 707,098,251
Codebook Notes
An asterisk before the variable name indicates that an explanatory note
provides greater detail on information necessary for the use of
this variable. These notes are listed below in alphabetical order
by variable name.
VARIABLE NOTE
CNTRLSUB A variable based on the U.S. Drug Enforcement
Administration Code. It denotes the degree of
potential abuse and Federal control of the drug.
The categories range from Schedule II - most
abused (e.g. morphine, amphetamines) to Schedule V
- controlled by pharmacy only. Classification
merged from NCHS data source (Koch, 1980).
COMPSTAT This variable is used to distinguish between
single and multiple entity drugs. Single
ingredient or single-entity drugs have only one
active ingredient. Classification merged from
NCHS data source (Koch, 1980).
EXPTFLG A flag indicating whether the total prescription
expense for that record was imputed or provided
directly from the household respondent.
EXPTOTX The edited total expense variable. A dollar value
was assigned to all prescribed medicines except
for prescriptions reported as free from provider
(e.g., samples); these were assigned to the zero
expense category. For edit and imputation
procedures, see section C.1.2 of the
documentation.
GENCODE Text description and corresponding code describing
GENNAME the generic equivalent of the medication, when
applicable. Classification merged from NCHS data
source (Koch, 1980). See Attachment 4C for
specific codes.
INCALPER Weight adjusted for nonresponse and poststratified
to U.S. census data. Estimates of use and
expenditures for prescribed medicines require the
use of weighted data. For details on this weight,
see the Technical and Programming section on this
file and the hard copy documentation accompanying
this tape.
LASTAGE An edited variable which identifies person age, in
years, as of the end of the last round in 1987 for
which the person was eligible. Less than 0.1
percent of the cases were edited.
LTINTFLG A flag indicating whether the last date the
medication was purchased or obtained extended into
1988. If the variable DATEEMM was January through
June and this information was collected in the
last round then it was determined that purchases
exceeded the end of the reference period (December
31, 1987). The number of purchases in 1987
(PRCHDATX) reflects an adjustment based on such
cases flagged with this variable.
MEDCODER A code associated with unique prescribed medicine
name. Classification based on NCHS NHIS data base
on medications (Koch, 1980). See Attachment 4B
for specific codes.
MEDNAME The text associated with the variable MEDCODER is
provided only in cases where a person had multiple
records within a round for a medication with the
same MEDCODER. The text field provides additional
information to distinguish the same, similar or
different medication types; e.g., inhalers versus
tablets both containing the same active
ingredient.
RACE3 A person-level variable constructed to facilitate
the poststratification of the NMES person-level
sampling weights by race and ethnicity,
considering three mutually exclusive
classifications: Hispanic, Black--non-Hispanic,
and white or other.
RACE6 An edited variable indicating race. Less than 0.2
percent of cases were edited.
ROUND Indicator of the round of data collection.
Reference period dates specific to each person's
rounds of data collection are provided on NMES
Public Use Tape 13.
SCRPSTAT Variable which classifies Federal drug products.
Classification merged from NCHS data source (Koch,
1980).
SOPTFLG A flag indicating whether all the source of
payment percentages for that record were imputed
or provided by the household respondent.
SPSU To obtain variance estimates of sample statistics
STRATUMX by means of standard statistical programs that use
the Taylor series linearization method of variance
estimation, variables must be used that denote the
strata and primary sampling unit (PSU) within a
given strata. The variables STRATUMX and SPSU are
these variables, respectively. For details on
sampling weights and variance estimation, see the
Technical and Programming section on this file and
the hard copy documentation accompanying this
tape.
