/* SHORT TITLE: Users Manual */
AIDS Cost and Services
Utilization Survey
Public Use Tapes #4 and #5
Users Manual
Submitted to:
Agency for Health Care Policy and Research
2101 East Jefferson Street
Rockville, Maryland 20852
Submitted by:
Westat, Inc.
1650 Research Boulevard
Rockville, Maryland 20850-3129
Contract No: 282-89-0020
Deliverable No.: 49
June 30, 1994
TABLE OF CONTENTS
Chapter Page
1 INTRODUCTION 1-1
2 RESEARCH DESIGN AND ANALYSIS OBJECTIVES 2-1
2.1 Objectives 2-1
2.2 Study Design 2-2
2.3 Strengths and Limitations of Data 2-7
3 DATA STRUCTURE OVERVIEW 3-1
3.1 Content of Public Use Volumes 3-1
3.1.1 Adult Public Use Volume (Tape #4) 3-2
3.1.2. Pediatric Public Use Volume (Tape #5) 3-5
3.2 ACSUS Public Use File Data Structure 3-8
3.2.1 Patient Reported Data 3-14
3.2.2 Medical Records Abstract Data 3-14
3.2.3 Provider Billing Data 3-15
3.2.4 General Analytic Usage Guidance 3-16
4 DATA PREPARATION, TYPES OF VARIABLES,
AND DATA ANOMALIES 4-1
4.1 Data Preparation 4-1
4.1.1 Range Specifications 4-1
4.1.2 Consistency Checks 4-1
4.1.3 Frequency Review 4-2
4.2 Types of Variables 4-2
4.2.1 Edited Variables 4-2
4.2.2 Derived Variables 4-2
4.2.3 Imputed Variables 4-3
4.2.4 Flag Variables 4-3
4.3 Data Anomalies 4-3
4.3.1 Self-Reported Marital Status 4-4
4.3.2 Utilization Information Reported During the
Wrong Reference Period 4-4
4.3.3 Dates of Service Utilization After the
Subject's Date of Death 4-5
4.3.4 Loss of Medicare Coverage 4-5
4.3.5 Self-Reported Date of Positive HIV Test 4-6
4.3.6 Self-Reported Conditions of HIV Illness 4-6
5 GUIDE TO THE DATA FILES, SPECIFIC VARIABLES,
CODEBOOKS, AND ANNOTATED QUESTIONNAIRES 5-1
5.1 Patient Data Files 5-1
5.1.1 Patient Level Files 5-2
5.1.2 Service Utilization Files 5-8
5.2 Medical Record Abstract Data Files 5-12
5.2.1 Overview 5-12
5.2.2 Patient Level File 5-13
5.2.3 Medical Abstract Repeating Record Files 5-14
5.3 Provider Billing Survey Data Files 5-17
5.4 Guide to Codebooks and Annotated
Questionnaires 5-18
6 DATA IMPUTATION 6-1
6.1 Introduction to Imputation Strategies 6-1
6.2 General Imputation Strategy for ACSUS Data 6-2
6.2.1 Auxiliary Variables Used to Define Imputation
Cells 6-5
6.2.2 Group 1 Item Nonresponse Imputation Procedures 6-6
6.3 Limitations of the Imputed ACSUS Data 6-7
6.4 Imputation Variables 6-8
6.4.1 Original Variables 6-8
6.4.2 Imputed Variables 6-8
6.4.3 Flag Variables 6-9
List of Tables
Table
2-1 Number of Adult and Pediatric Patients:
AIDS Cost and Services Utilization Survey 2-4
3-1 Contents of Adult Public Use Volume (Tape # 4) 3-2
3-2 Contents of Pediatric Public Use Volume
(Tape # 5) 3-5
5-1 Categories of Information on Adult Time
Specific Files 5-6
5-2 Categories of Information on Pediatric Time
Specific Files 5-6
6-1 Imputed Charge Components by Care Type 6-4
List of Figures
Figure
3-1 Patient Files 3-9
3-2 Medical Record Abstract Files 3-12
3-3 Provider Billing Files 3-13
5-1 Example of the Codebook Format 5-20
5-2 Example of the Annotated Questionnaire
Format 5-21
List of Appendices
Appendix
A PATIENT INTERVIEW SURVEY INSTRUMENTS
(Annotated) A-1
A-1 Adult/Adolescent Screener Questionnaire
A-2 Time 1 Adult/Adolescent Patient Questionnaire
A-3 Time 2 Adult/Adolescent Patient Questionnaire
A-4 Time 3 Adult/Adolescent Patient Questionnaire
A-5 Time 4 Adult/Adolescent Patient Questionnaire
A-6 Time 5 Adult/Adolescent Patient Questionnaire
A-7 Time 6 Adult/Adolescent Patient Questionnaire
A-8 Pediatric Screener Questionnaire
A-9 Time 1 Pediatric Patient Questionnaire
A-10 Time 2 Pediatric Patient Questionnaire
A-11 Time 3 Pediatric Patient Questionnaire
A-12 Time 4 Pediatric Patient Questionnaire
A-13 Time 5 Pediatric Patient Questionnaire
A-14 Time 6 Pediatric Patient Questionnaire
B PROVIDER SURVEY MEDICAL RECORD ABSTRACT FORM
(Annotated) B-1
C PROVIDER SURVEY BILLING FORMS (Annotated) C-1
C-1 Inpatient Stay Form
C-2 Ambulatory Billing Form
C-3 Home Health Form
C-4 Prescribed Medicine Form
D CODEBOOK APPENDICES D-1
D-1 Name of Physician/Doctor Specialty Codes
D-2 Prescription Drug Codes
D-3 Nonprescription Drug/Non-Traditional Substance Codes
D-4 Relationship to Patient Codes
1. INTRODUCTION
This manual provides documentation and guidance for users
of the public release of the complete data files for the AIDS
Cost and Services Utilization Survey (ACSUS). These data files
are contained in two volumes: ACSUS Public Use Tape #4 (Complete
Adult Patient Questionnaires, Adult Provider Billing
Questionnaires, and Adult Medical Record Abstracts) and ACSUS
Public Use Tape #5 (Complete Pediatric Patient Questionnaires,
Pediatric Provider Billing Questionnaires and Pediatric Medical
Record Questionnaires).
Partial information from the study was released
previously in ACSUS Public Use Tapes 1-3. The ACSUS study data
have undergone subsequent editing and some changes may have been
made to the data since the publication of Tapes 1-3. Therefore,
Tape 4 and Tape 5 should be considered the complete and final set
of public use data from the ACSUS study. It should also be noted
that Tape 4 and Tape 5 are self-contained and cannot be linked
with previous tapes since patient and provider identifiers are
not consistent with Tapes 1-3.
ACSUS was a national survey of persons infected with HIV
that was conducted by Westat, Inc., for the Agency for Health
Care Policy and Research (Contract No. 282-89-0020). The study
1-1
was designed to provide estimates of the use of health care,
social and support services, and the costs of services by a
sample of HIV-infected persons in ten geographic locations in the
United States.
The chapters that follow provide information about the
research design (Chapter 2), structure of the public use data
files (Chapter 3), data preparation (Chapter 4), and the use of
the ACSUS data files and codebooks (Chapter 5). Imputation
techniques are described in Chapter 6. The appendices provide
copies of all data collection instruments and codebook
appendices.
The main body of this users manual (Chapters 1-6) is also
contained as a text file on the public use tapes. All figures,
tables, and appendices are available in hard copy only.
1-2
2. RESEARCH DESIGN AND ANALYSIS OBJECTIVES*
2.1 Objectives
The increase in the number of reported cases of AIDS
(acquired immunodeficiency syndrome) and the expanded demands on
the health care system for treating persons with illness related
to HIV (human immunodeficiency virus) have been widely reported.
Despite the growing problem, data on use of and expenditures for
health care services for this population are surprisingly scarce.
Thus, public policy related to the HIV epidemic has often been
formulated on the basis of limited case studies and cross-
sectional research.
ACSUS is the largest data collection effort targeting the
population of persons infected with HIV. In addition, its design
addresses three major limitations of current data. First, data
from other sources do not permit examination of early stages of
HIV illness, such as periods of asymptomatic infection, or HIV
illness that does not satisfy diagnostic criteria for AIDS. The
ACSUS sample includes more than 400 adults who reported being HIV
____________________
*This chapter is excerpted from Berk, M.L., Maffeo, C. and Schur,
C.L. (1993). Research Design and Analysis Objectives, AIDS Cost
and Services Utilization Survey (ACSUS) Report No. 1 (AHCPR
Publication No. 93-0019). Rockville, MD: Agency for Health Care
Policy and Research.
2-1
positive but having no HIV-related symptoms or conditions.
Second, despite the fact that the disease is increasingly treated
in outpatient settings, current information on AIDS does not
permit analysis of the use of a wide variety of outpatient
services. ACSUS data, on the other hand, include the use of and
charges for the full range of ambulatory physician settings, use
of both formal and informal home care services, use of a number
of mental health and support services, and use of and charges
associated with various drug therapies. Finally, perhaps of
greatest importance is the longitudinal nature of ACSUS. Length
of survival after infection has been increasing and may continue
to do so as prophylactic treatment for HIV infection becomes the
norm. Yet there is little information on how levels and mix of
service use, sources of payment for care, or quality of life
change over the course of the illness. With six interviews over
the course of an 18-month period, ACSUS provides a wealth of
information on factors affecting the use of services over
different stages of the illness.
Data collected through ACSUS will be key to the
formulation of Federal strategies for the allocation of resources
to the care of persons with HIV-related illness and to a broader
understanding of the effect of HIV on segments of the U.S. health
care system. Moreover, the information needs of State and local
2-2
governments parallel those of the Federal Government as they
deliver care to persons with HIV-related illness.
2.2 Study Design
Sample Design
The ACSUS sample was drawn using a multistage design that
involved selection of geographic areas, sampling of patient care
sites in the selected areas, and a probability sample of HIV-
infected persons who sought treatment from those providers over a
study enrollment period of approximately 4 months.
During the first stage, the sample of geographic areas
was selected so as to provide regional diversity in third-party
payment mechanisms, service delivery systems, and type of HIV-
infected individuals (e.g., injecting drug users, homosexual men,
pediatric cases, and women). The first step in the geographic
area selection process was to focus on cities with the highest
number of reported AIDS cases to ensure obtaining a large enough
sample of HIV-infected persons. Using data provided by the
Centers for Disease Control (CDC), the 25 cities with the
greatest number of cases (accounting for more than 62 percent of
all cumulative AIDS cases in the United States) were identified.
2-3
From these 25 cities, 10 were selected, using the following
criteria:
§ At least six cities with a high prevalence of HIV
infection.
§ At least one city in a State with a Medicaid waiver
in place that permits reimbursement for services not
usually covered by Medicaid.
§ At least one city in a State with restrictive
Medicaid policies.
§ At least five cities with pediatric cases.
§ At least one city with a moderate HIV prevalence
rate.
The final selection of the 10 geographic areas was based
on data supplied by CDC on the race and ethnicity of persons with
AIDS, number of persons by exposure category, and number of
pediatric cases. The 10 geographic areas are New York, Newark,
Philadelphia, Baltimore, Miami, Tampa, Chicago, Houston, Los
Angeles, and San Francisco.
The second stage of sampling was selection of patient
care sites in each area. A partial listing of hospitals and
clinics (but not private practitioners) that provided services to
HIV-infected persons was obtained from CDC's National AIDS
Information Clearinghouse. Calls were made to the city or county
department of health in each of the 10 areas to update the list
2-4
of patient care sites and to attempt to obtain some information
on the number of persons treated by each.
In general, accurate information on the number of
patients treated (as distinct from the number of visits) was
unavailable. Although some hospitals made the information
public, in some cases it was deemed to be confidential. Most
health departments considered information on names of private
practitioners treating AIDS patients too sensitive to release.
However, it was possible to rank hospitals and outpatient clinics
in each area in terms of estimated caseload. In general, the
patient care sites that treated the largest number of patients in
the area were selected, although attempts were made to include
both private and public sites. In four geographic areas, private
practitioners who were affiliated with the hospitals providing
substantial amounts of inpatient care were included in the
sample. A sample of 32 patient care sites was selected; 26 of
these participated in the study.
In the third stage of sampling, a probability sample of
patients was selected from each patient care site. The sampling
frame of patients was identified through the use of a self-
administered screener questionnaire. A group of 55 site
coordinators, some of whom were clinic employees, were trained to
distribute the screening form to all patients visiting the site
2-5
for care during the sampling period of 2-4 months. Use of the
screener questionnaire allowed for the collection of information
necessary for sampling purposes without requiring review of
medical records or collection of any personal identifying
information. Coordinators were trained to provide help to
patients requiring assistance in completing the form. After the
screening form was completed, the coordinator used the
information in the form to determine whether the patient was
eligible for the study and placed the patient in the appropriate
sampling stratum. The coordinator then selected a systematic
sample within each stratum. Approximately 6,000 persons both
completed the screening form and were designated eligible for the
study; of these, 2,487 were sampled (Table 2-1).
2-6
Table 2-1. Number of Adult and Pediatric Patients: AIDS Cost
and Services Utilization Survey
The primary sampling criterion used was illness stage,
defined as AIDS, HIV-related illness, and asymptomatic. In
addition, pediatric patients and women were oversampled,
regardless of illness stage. (Pediatric patients who had
clinically defined AIDS were enrolled in the study regardless of
age; those who were HIV-positive but non-AIDS were required to be
at least 15 months of age to preclude the chance of
seroconversion.)
The secondary goal in selecting patients was to obtain a
sample whose distribution by exposure group and payment source
was roughly proportionate to that of the HIV-infected population
in the geographic areas targeted. Thus, patients were stratified
by exposure group and, in the case of homosexual or bisexual
males, by insurance status as well. This stratification not only
allowed for a more representative sample of HIV-infected patients
but also made it possible to obtain as many patients as possible
in some of the rare strata (for example, pediatrics). The study
includes some primarily public hospitals, where AIDS patients
with higher socioeconomic status may be under- represented. Male
homosexuals and bisexuals were stratified by source of payment in
an attempt to control the distribution of patients by
2-7
socioeconomic status and reduce potential bias. It was
anticipated that the number of female and pediatric cases with
private insurance would be small. It also was expected that
nearly all injecting drug users (IDUs) would be uninsured, with a
small percent receiving public assistance. Target sampling rates
were then computed for each provider.
If, after reviewing the screening form, the site
coordinator selected the patient into the sample, the coordinator
then immediately reapproached the patient to initiate formal
recruitment into the study. During recruitment, the site
coordinator explained the study purpose and requirements, and the
patient completed a consent form and patient location form.
Coordinators reported weekly on sampling activities so that
sample yield could be monitored and sampling rates adjusted.
Of the 2,487 sampled patients, 88 percent, or 2,197, were
successfully recruited, that is, they signed a consent form and
provided location information (Table 2-1). Enrollment response
rates by exposure category, illness stage, and insurance status
were rather uniform. The lowest rate was for asymptomatic
homosexual men who had private insurance, about 80 percent of
whom agreed to participate. Given the prognosis for the disease,
the stigma the disease still carries, and the socioeconomic
status of this group, this 80-percent response rate is not
2-8
unexpected. The response rates for IDUs, women (many of whom are
IDUs), and pediatric cases were higher than the mean response
rate for the entire sample. These groups were of concern not
only from the point of view of initial enrollment into the sample
but also with respect to continuing participation in the study.
Consequently, a number of procedures and incentives were
introduced in an attempt to maximize participation. These are
described in the next section.
As shown in Table 2-1, 2,090 persons, or 95 percent of
those agreeing to participate, actually completed the initial
(Time 1) patient interview. Of these, 141 were pediatric
patients. Of the adults (including 361 women), 678 were patients
with AIDS, 843 patients with other HIV-related illnesses, and 422
asymptomatic patients. The illness stage for six patients was
unknown at Time 1.
Data Collection Design
The data collection design for the patient survey
component of the study involved conducting six in-person
interviews (Time 1-Time 6) with study subjects over an 18-month
study reference period, March 1, 1991, through August 31, 1992.
Subjects were contacted every 12 to 14 weeks. It is believed
2-9
that this timeframe minimizes potential sample attrition because
of death, tracking problems, and possible recall bias in
reporting. Although information on insurance coverage,
employment, and income was collected at every interview, the
content of the interview varied somewhat over the six time
periods, with detailed segments on functional status, quality of
life, and access and barriers to care included in three periods.
During each interview the patient was asked to name every
health care provider from which a service was received during the
time since the previous interview, referred to as the interview
reference period. For the Time 1 interview, the interview
reference period begins on March 1, 1991, and for the Time 6
interview, the period ends on August 31, 1992. During each
interview the patient was asked to sign permission forms that
allowed contact with every medical provider from which the
patient received a service. Providers included private
practitioners, outpatient departments of hospitals, freestanding
clinics, pharmacies, and home health agencies. The set of signed
provider permission forms represents the sample for the provider
survey component of ACSUS. As of the first interview, almost
2,500 medical care providers had been identified. These
providers were contacted two times during the 18-month patient
survey reference period to obtain information on services
rendered, charges for services, and source of payment for
2-10
charges, as well as medical data from those providers named by
the patient as the usual source of care.
The decision to obtain charge information from providers
rather than from patients was made in recognition that data
obtained from individuals may suffer from a number of biases,
including recall error (Berk, Horgan, and Meyers, 1986; Berk,
Schur, and Mohr, 1990; Cox and Cohen, 1985; National Center for
Health Statistics, 1961). No attempt was made to collect charge
data from patients, with the exception of out-of-pocket expenses
and dental service charges. Dental providers were specifically
excluded from the list of providers from which charge data were
to be collected because of concern that awareness of a patient's
HIV status might jeopardize receipt of services.
Providers sampled for the provider survey component were
asked either to furnish a printout of the patient's bill or to
complete a data form for each reported visit made during the
reference period. The information gathered from billing records
should provide an accurate count of the services received at the
particular medical facility and the charges for those services.
It should be noted that charge data, rather than cost or
expenditure data, were collected. Charges refer to billed
amounts and may be greater than expenditures because of unpaid
bills, bad debt, or uncompensated care. Costs should measure
2-11
actual resource use but, in fact, are rarely likely to be
available or even known. Different methods of allocating fixed
costs as well as cross-subsidization among hospital departments
make cost comparisons difficult (Prospective Payment Assessment
Commission, 1985).
In addition to medical providers, approximately 2,500
nonmedical providers were identified in the first interview.
These included community-based organizations providing social
support services, podiatrists, and providers of alternative
therapy. For these providers, respondents were asked to provide
information about the type and amount of services received.
Because of concerns about maintaining patient
participation among a group of patients who are very ill and, in
many cases, very transient, data collection procedures were
designed to maximize continuing participation. Interviewers
received not only extensive training in administering the
questionnaire but also specialized instruction in understanding
the sensitive nature of the subject matter and in relating to
persons with a serious and debilitating illness. Initial and
followup interviews were conducted at a location specified by the
study subject. IDUs and homeless patients, however, completed
Time 1 interviews at the provider site because of the potential
tracking problems associated with these persons. Subsequent
2-12
interviews for these participants were also held at the patient
care site if the subject preferred that location over another.
Upon recruitment into the study, respondents were
provided with an 800 number and told to call with any questions
or problems at any time during the data collection. Participants
were given a new card with the 800 number on it each time they
were interviewed. About 15 calls a week were received on this
number. Subjects also were paid $50.00 each time they completed
an interview.
Patient location data used for tracking purposes were
collected initially when the subject consented to participate in
the study and updated during each of the Time 1-Time 5
interviews. Study subjects were asked to provide traditional
tracking information, such as the name of a person who would know
how to find them if they moved or could not be located. They
were also asked about places they frequented, names of friends,
names of social workers or parole workers, other names they use,
and nicknames or street names that they had.
Using patient location data, interviewers telephoned or
visited subjects to set up interview appointments. However, in
some cases, patients remained difficult to locate, and
interviewers obtained additional assistance from clinic staff at
2-13
the enrollment sites. When difficult-to-locate patients visited
the clinic, staff would inform them that interviewers were trying
to contact them. If these patients had scheduled a future clinic
appointment, staff would apprise interviewers of the time.
Finally, study subjects were asked to provide the name of a proxy
respondent who would know about their health care use and
expenses and could be contacted if study subjects were too ill to
complete an interview. More than 82 percent of the study
subjects provided the name of a proxy.
2.3 Strengths and Limitations of Data
ACSUS is the largest and most comprehensive study of the
cost and use of services by persons with HIV-related illness.
Although it was designed to overcome many of the data limitations
described below, the difficulties inherent in identifying persons
with HIV infection necessitated a survey design with certain
limitations. The implications of these limitations for data
analysis can be minimized, however, if the analytic plan is
designed with the particular strengths and weaknesses of the data
base in mind. Thus, analysis using the ACSUS data base should
emphasize those areas of inquiry in which it is strongest and
should not attempt to examine issues for which the survey is ill
suited.
2-14
Sources of Bias
Like all surveys, ACSUS has methodological limitations.
As Fowler (1988, p. 145) has indicated, "the cost of trying to
achieve error-free estimates is too high for most research
purposes; some potential for error exists in virtually all survey
plans. Total survey design involves considering all aspects of
the survey and choosing a level of rigor appropriate to the
purpose of the particular project."
Groves (1989) describes four major types of error:
sampling error, coverage error, nonresponse error, and
measurement error. Within each of these categories, there can be
systematic as well as random error. The former will produce
biased estimates; the latter will not. Random, or variable,
error is said to be unbiased because, although it affects the
precision of the estimate, it is as likely to produce an
overestimate as an underestimate. Thus, the estimate will be
unbiased when there is variable error, but the variance of the
estimate will be increased. Three types of potential error are
discussed here as they apply to ACSUS.
2-15
Coverage bias. Coverage bias occurs when some members of
the target population have no chance of sample selection.
Coverage bias is probably the most important source of error in
ACSUS. Because it is a sample of patients, ACSUS does not
include any persons who have not entered the medical care system.
ACSUS excludes persons who receive care only at settings where a
small number of persons with AIDS are seen. These persons may
differ systematically from those surveyed in terms of income and
source of payment for care.
Nonresponse bias. Nonresponse bias occurs as a result of
the failure to collect data from all eligible persons in the
sample. This can occur at several points in ACSUS. Some persons
refused to complete the ACSUS eligibility screener, and others
declined participation at the time of enrollment. A small number
of respondents declined to participate in later rounds of the
survey. Some declined to allow contact with their medical
providers.
Moreover, refusals are not the only source of
nonresponse. Some patients died, and it was not always possible
to locate an acceptable proxy respondent who could provide the
necessary utilization and cost information. Other patients moved
away and were not locatable. Field procedures, including efforts
to interview by telephone respondents who had relocated, were
2-16
designed to minimize nonresponse bias, and field experience
suggests it is not be an important source of error.
Measurement bias. Measurement bias can stem from at
least four different sources: the respondent, the interviewer,
the questionnaire, and the mode of interview. For example,
measurement error can occur when respondents give inaccurate
answers or when an accurate answer is incorrectly coded. Some
self-reports of illness stage, for example, may be erroneous.
Inaccurate responses may result from a respondent's inability to
correctly recall events. They also may result from a
respondent's deliberately incorrect answer, as could occur if a
patient does not wish to reveal to an interviewer that he or she
is receiving psychiatric counseling or drug detoxification.
Measurement bias also occurs as a result of interviewer
effects. Interviewers may ask questions incorrectly or may fail
to accurately record the respondent's answer. Interviewer error
tends to be variable, but if interviewers systematically probe
incorrectly or if codes fail to correctly categorize particular
kinds of responses, the error may result in biased estimates.
2-17
Implications for Analysis
In considering the analytic utility of ACSUS, several
elements of study design need to be considered. These are
reviewed with an eye to specific policy and health services
research issues.
Statistical representation of population. The most
important limitation is the lack of a national probability
sample. Given the sensitive nature of the illness as well as the
low rate of prevalence, a national probability sample of
households would have been unlikely to achieve acceptable results
in terms of participation or cost. Thus, although a national
probability sample was justifiably ruled out, the resulting
sample is not statistically representative of the U.S. population
with HIV-related illness.
Specifically, certain population subgroups are
undercounted. ACSUS is limited to major metropolitan centers and
does not include low-prevalence or rural areas. Persons who used
health care services infrequently, and thus did not use services
during the enrollment period, or who did not use services at all
are undercounted. It should be noted that the primary objective
of ACSUS is to provide data on the use and cost of services for
persons with HIV-related illness. ACSUS was not intended to
2-18
count the number of HIV-infected persons or to measure the number
of persons unable to obtain services.
Geographic diversity. Relative to other studies, ACSUS
has several strengths critical to the usefulness of the resulting
data. Although the sample population is not strictly
representative of the national population with HIV-related
illness, ACSUS is one of the few surveys of HIV-related illness
that was conducted in multiple geographic and provider sites. It
is the first multiple-site survey in which site selection was
driven primarily by analytical needs rather than by the necessity
of evaluating particular programs, as was the case in the
evaluation of the AIDS/HIV Service Demonstration Grants Program
conducted for the Health Resources and Services Administration
and the evaluation of the Robert Wood Johnson Foundation's AIDS
Health Services Program (Mor, Fleishman, Allen and Piette, 1994).
The ACSUS geographic and patient care sites were selected in
order to ensure diversity with respect to ethnicity, exposure
category, and source of payment such that there will be a
significant number of persons in each analytical cell of
interest.
Length of reference period. One of the most important
design features of ACSUS is the use of multiple contacts with
respondents over an 18-month period. This substantially adds to
2-19
the analytic utility of the ACSUS data base by allowing for the
examination of changes over time. As more effective drug
therapies are found and prophylactic treatment of HIV infection
becomes more widespread, length of survival is likely to continue
to increase. A number of critical changes take place over the
course of the disease that affect the infected person's ability
to function and use of services. For example, as the illness
progresses, individuals may become unable to work, thus losing
their means of financial support as well as their access to
private health insurance. These changes have implications for
public financing programs such as Medicaid, which bear a
disproportionately large share of the cost of care for persons
with HIV. In addition, the level of services used and the mix of
services are likely to change with the course of illness; in some
cases, caregiving arrangements may need to be supplemented with
formal home care as individuals become sicker.
2-20
REFERENCES
Berk, M. L., Horgan, C., and Meyers, S.: The reporting of
stigmatizing conditions: A comparison of proxy and self-
reporting. Journal of Economic and Social Measurement
14:197-205, 1986.
Berk, M. L., Schur, C. L., and Mohr, P.: Using survey data to
estimate prescription drug costs. Health Affairs, Fall 1990.
Cox, B., and Cohen, S.: Methodological Issues of Health Care
Surveys. New York: Marcel Dekker, 1985.
Fowler, F. J., Jr.: Survey Research Methods. Applied Social
Research Methods Series, Vol. 1. Beverly Hills: Sage
Publications, 1988.
Groves, R. M.: Survey Errors and Survey Costs. New York: John
Wiley and Sons, 1989.
Mor, V., Fleishman, J.A., Allen, S.M., and Piette, J.D.:
Networking AIDS Services. Ann Arbor: Health Administration
Press, 1994.
National Center for Health Statistics: Reporting of
hospitalization in the Health Interview Survey. Series D,
No. 4. Washington: U.S. Government Printing Office, 1961.
Prospective Payment Assessment Commission: Technical Appendixes
to the Report and Recommendations to the Secretary of the
U.S. Department of Health and Human Services by the
Prospective Payment Assessment Commission. Washington, D.C.,
1985.
2-21
3. DATA STRUCTURE OVERVIEW
As described in Chapter 2, this study involved data
collection for two groups of HIV-infected persons: adult and
pediatric. A public use volume has been created for each of
these groups -- ACSUS Public Use Tapes #4 and #5. Data collected
for each group includes up to six patient interviews, conducted
once every three months over a period of 18 months, two abstracts
of medical records data from the provider(s) the patient
indicated as his "usual source of care," and billing data
collected from medical providers identified during the patient
interviews over the 18-month period.
Confidentiality issues prohibit identification of
respondents, where respondents include providers as well as
patients. Therefore, all information on the geographic location
of respondents has been deleted from these tapes.
This chapter of the public use documentation describes
the content of the public use volumes, and provides a description
of the structure of the data files and their correspondence to
data collection instruments for each group -- adults and
pediatrics.
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3.1 Content of Public Use Volumes
Two public use volumes have been developed for the ACSUS
study. The first volume contains data collected for the adult
sample (Tape #4), and the second contains data collected for the
pediatric sample (Tape #5). Each volume is contained on a 9
track tape, recorded at 6250 bytes per inch using the EBCDIC
character set. These volumes have been recorded using IBM
standard labels. Each volume contains four types of data sets:
fixed length documentation files, fixed length EBCDIC data files,
fixed length codebook print files corresponding to the data
files, and fixed length SAS source statement files, one for each
data file.
Specific information regarding the content of each public
use volume are provided in the following subsections.
3.1.1 Adult Public Use Volume (Tape #4)
The adult public use volume contains data files,
documentation files, printable codebook files, and SAS source
code statements required to read each of the data files. SAS
source code included consists of input statements, variable
labels, and variable value format statements. Three major types
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of data were collected during the study: patient interview,
medical record abstract, and provider billing data. These data
were supplemented with vital statistics data which are included
on the patient characteristics file.
The content of the adult public use volume (Tape #4) is
provided in Table 3-1.
Table 3-1. Contents of Adult Public Use Volume (Tape #4)
Further detail regarding the content of the data files
and specific instructions for using the codebooks can be found in
Chapter 5 of this document.
3.1.2 Pediatric Public Use Volume (Tape #5)
The pediatric public use volume contains data files,
documentation files, printable codebook files, and SAS source
code statements required to read each of the data files. SAS
source code included consists of input statements, variable
labels, and variable value format statements. Three major types
of data were collected during the study: patient interview,
medical record abstract, and provider billing data. These data
were supplemented with vital statistics data which are included
on the patient characteristics file.
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The content of the pediatric public use volume (Tape #5)
is provided in Table 3-2.
Table 3-2. Contents of Pediatric Public Use Volume (Tape #5)
Further detail regarding the content of the data files
and specific instructions for using the codebooks can be found in
Chapter 5 of this document.
3.2 ACSUS Public Use File Data Structure
To facilitate analytic use of the ACSUS data, and enable
use of current microprocessor technology, we have delivered the
public use data in a relational structure as a group of fixed
length normalized data files. Figures 3-1 through 3-3 show the
relationships between these files and their correspondence to the
data collection instruments used. The figures depict both the
longitudinal and the logical relationships between the entities
represented by the study data (i.e., patient, service(s)
utilized, provider billing, and medical record(s)).
Three major types of data are represented by this
structure: patient, medical record, and provider billing. A
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brief description of each data type is presented in the following
subsections. Detailed guidance for using these data is provided
in Chapter 5 of this document.
Figure 3-1. Patient Files
Figure 3-2. Medical Record Abstract Files
Figure 3-3. Provider Billing FIles
3.2.1 Patient Reported Data
As shown on Figure 3-1, the patient data are composed of
a patient level file, interview time specific patient
questionnaire data for each interview period (Time 1 through Time
6), and service utilization files which contain data collected
during all six rounds of data collection. Service utilization
files correspond to distinct service utilization sections in the
patient questionnaire and are not included in the time specific
files.
A patient will have one record in the patient
characteristics file, uniquely identified by the patient
identifier (PATID). In addition, a patient will have one record
in each of the Time 1 through Time 6 specific data files (if they
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completed an interview in each of these periods) uniquely
identified by patient identifier (PATID), and any number of
records in each of the service utilization files, each record
representing use of specific types of services over the entire
18-month study period. Patients will have zero records in a
specific service utilization file if they did not report
receiving that type of care.
These service utilization records represent either a
visit or use of a specific provider by the patient depending on
the information required by the particular section of the
questionnaire. Each record is identified by the patient
identifier (PATID), the interview time period (1 through 6)
(SFORM), a questionnaire section identifier (SFPART), and a
service utilization sequence number (SSUBREC). For example, data
regarding inpatient hospital stays were collected for each stay,
and data regarding use of a particular physician/doctor were
collected by provider.
Further detail regarding the usage of these data files
can be found in Chapter 5 of this document.
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3.2.2 Medical Records Abstract Data
As shown on Figure 3-2, the Medical Record Abstract Data
comprise 4 separate files: patient, inpatient stay, check-list
conditions, and T-Cell reports. Twice during the study period,
medical records data were collected from providers identified by
patients as their usual source of care. More than one provider
may have been identified as the usual source of care for a
particular patient and thus data collected from more than one
provider for one patient.
The Patient Level File contains one record for each
patient containing data derived from all abstracts received for a
patient. Each record on this file is uniquely identified by a
patient identifier (PATID).
The Inpatient Stays File contains information regarding
inpatient stays reported by the usual source of care providers
identified by the patient. A record is uniquely identified by
patient identifier (PATID) plus provider identifier (USCID) plus
a record sequence number (USREC02).
The Checklist Conditions File contains information
regarding medical conditions commonly associated with HIV-
infected persons. A record on this file is uniquely identified
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by patient identifier (PATID) plus provider (USCID) identifier
plus a record sequence number (USREC03).
The T-Cell Reports File contains information abstracted
from medical records regarding laboratory tests reporting T-Cell
counts. Depending upon available information, absolute counts
and percentages were recorded for both CD4 and CD8 cells. A
record in this file is uniquely identified by patient identifier
(PATID) plus provider identifier (USCID) plus a record sequence
number (USREC04).
Further detail regarding the use of Medical Record
Abstract Data is provided in Chapter 5 of this document.
3.2.3 Provider Billing Data
Provider billing data were collected twice during the
conduct of the study for medical providers reported by the
patient during the patient interviews. As shown on Figure 3-3,
four provider billing data files are provided which contain
billing data covering the 18-month period represented by the
patient interviews: Ambulatory, Inpatient, Home Health, and
Pharmacy. Each record in the Ambulatory, Inpatient, and Pharmacy
billing data files contains one record per event: an ambulatory
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visit, an inpatient stay, or a prescription medication obtained.
A record in these files can be uniquely identified by Provider
Identifier (PROVID) plus Patient Identifier (PATID) plus the Form
Identifier (PFORM) plus the Event Sequence Number (PSUBREC). A
record in the Home Health billing data file may contain
information regarding more than one event, or visit, during a
period of time.
Further information regarding substantive usage is
presented in Chapter 5 of this document.
3.2.4 General Analytic Usage Guidance
Effective use of these data require familiarity with the
ACSUS study, data collection instruments and the structure and
content of the data files. In conducting analyses with these
data we have found it useful to begin with the data collection
instruments to identify the questions of interest and associated
variable names on the data file required to support the analysis.
Users should reference the annotated questionnaires to identify
the questions and variable names appropriate for their analyses.
Once variables and files have been identified, the codebook(s)
are used to identify variable values, variable position, length,
type, and applicable formats. Chapter 5 includes an explanation
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of how to use the codebooks and annotated questionnaires.
Appendices A-C contain copies of the annotated questionnaires.
Patient and provider identifiers included on patient
reported service utilization files are consistent with those used
in the provider billing data files. Patient reported service
utilization can be associated with provider reported service and
associated billing data through the patient and provider
identifiers. In many cases, the level and type of service
provided are inconsistent between the patient-reported data and
the provider billing data. These inconsistencies are due to
patient recall issues, provider misidentification, and the
availability of billing records at the provider for the patient.
Imputation of service and charges were performed in some cases.
Details regarding situations for which imputations were performed
are provided in Chapter 6 of this document.
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4. DATA PREPARATION, TYPES OF VARIABLES, AND DATA ANOMALIES
4.1 Data Preparation
Various data preparation and editing techniques were
employed to ensure accuracy and consistency in the ACSUS data.
These techniques are described in the following sections.
4.1.1 Range Specifications
Acceptable ranges for all data items were defined and
computerized edits conducted to identify all items falling
outside the predetermined parameters. For close-ended items the
ranges were determined by the codes available for the responses.
For open-ended items, for example, the out-of-pocket dollar
amount paid for a doctor's visit, reasonable ranges were defined.
Data items that failed the range edits were reviewed for
coding and data entry errors and corrected as necessary.
Following review by data preparation and project staff, out-of-
range values were retained if no coding and data entry errors
were found.
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4.1.2 Consistency Checks
Consistency or logic checks were conducted to examine the
relationships between responses to ensure that they did not
conflict with one another or that the response to one item did
not make the response to another unlikely. Logic checks were
conducted both within and across the various data files. Checks
within files examined the data for skip patterns and other types
of logical inconsistencies.
Logic checks across the ACSUS data files examined the
relationship between information reported during one patient
interview and similar information gathered at a subsequent
interview. Logic checks also inspected the data for
discrepancies in information across the different data collection
instruments, for example, the medical record abstract and the
patient interview.
To the extent that either type of logic check uncovered
coding or data entry errors, appropriate corrections were made to
the files. However, edit checks uncovered a variety of
inconsistent items which, following review by project staff,
could not be resolved. Such inconsistencies remain in the data
files and are discussed in Section 4.3.
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4.1.3 Frequency Review
The frequencies of responses to all data items were
reviewed to ensure that appropriate skip patterns were followed
and that the correct number of responses was represented for all
items. If a discrepancy was discovered, the problem case was
identified and hard copy of the record was reviewed to determine
the appropriate response. If the hard copy revealed no
additional information, the item was coded as "not ascertained".
4.2 Types of Variables
Data items in the ACSUS public use tapes can be
classified into four major categories: edited, derived, imputed,
and flag variables. The sections that follow describe each
variable type.
4.2.1 Edited Variables
The majority of data items on the ACSUS data files are
original variables which contain information corresponding to
individual items or questions on the data collection instruments.
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All original variables have been edited for skip pattern errors,
range outliers, and logic checks, as described in Section 4.1 and
have therefore been categorized as edited variables.
4.2.2 Derived Variables
A limited number of derived variables are included on the
files to assist the user in analyzing the data. A variable is
defined as derived if it is constructed from one or more original
data items. Because the analytic needs of each user are unique,
most analysts will choose to derive their own set of analysis
variables. Accordingly, derived data items on these tapes are
restricted to either items the user can not easily construct
without extensive knowledge of the data files, or to items
thought to be of interest to a majority of users.
4.2.3 Imputed Variables
In ACSUS, as in most surveys, the responses to some data
items were not obtained. For this study, missing item imputation
was conducted for a limited set of variables on the provider
billing survey files. In addition, entire service event records
were imputed on the provider billing survey files. As a result
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of these imputation processes, several new variables were created
and reside on the provider billing survey files. A description
of ACSUS imputation procedures is provided in Chapter 6.
4.2.4 Flag Variables
Three types of flag variables are present in the ACSUS
public use data files. First, a series of imputation flags were
created to enable users to identify imputed values and events.
Imputation flags are discussed more fully in Chapter 6. Second,
flag variables were created to identify and to alert the user to
analytic issues that might otherwise be overlooked. Third, flag
variables are used to highlight discrepant data elements. The
second and third type of flag variables are discussed in Chapter
5 in the sections corresponding to the specific data files in
which they reside.
4.3 Data Anomalies
The purpose of this section is to assist the user in
making informed decisions about conducting analyses with the
ACSUS public use data tapes. It is intended to bring to the
user's attention certain data considerations and anomalies so
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that the user may take them into account when analyzing the ACSUS
data.
Although the data have been subjected to rigorous editing
processes, not all data inconsistencies or problems could be
resolved and therefore some inconsistencies remain in the files.
Remaining inconsistencies occur primarily for two reasons.
First, much of the data from the ACSUS study is self-reported and
therefore subject to problems of recall error and so on. Second,
the ACSUS study data draw upon multiple sources to obtain similar
kinds of data and the sources sometimes report discrepant
information. Accordingly, to conduct the most meaningful
analysis for their purpose, users should read this section and
thoroughly familiarize themselves with the various data
instruments prior to undertaking any analysis.
4.3.1 Self-Reported Marital Status
In each of the six adult patient interviews, subjects
were asked to indicate their marital status. Review of self-
reported marital status across time uncovered subjects who
indicated during the current interview that they were never
married but in a previous questionnaire they reported their
marital status as divorced, separated, or married.
4-6
Investigation of these cases found no recurring patterns
-- inconsistent cases included male and female subjects,
respondents with and without children, and subject and proxy
interviews. Inconsistent marital status data were not removed
from the tapes. In view of these inconsistencies, users
interested in incorporating marital status in their analyses may
want to supplement or cross-reference marital status with the
series of questions on household composition in the time specific
patient characteristics files.
4.3.2 Utilization Information Reported During the Wrong
Reference Period
The patient files contain self-reported information on
inpatient stays, nursing home stays, and dental visits. In some
instances, respondents reported utilization for these services
that occurred in the period of time covered by a previous
reference period.
Events were examined and deleted if found to be
duplicates of previously reported events. If they were found to
be new events reported for the first time, the event was
retained. The variable which indicates the source of the
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information, SFORM, was coded to reflect the interview period in
which the information was collected; SFORM was not changed to
represent the period in which the utilization actually occurred.
This data anomaly may have a bearing on analyses that
link patient-reported utilization to the specific time period in
which they occur. Users interested in such analyses should link
utilization by comparing the dates of service to the reference
dates for each interview period rather the selecting events based
on SFORM.
4.3.3 Dates of Service Utilization After the Subject's Date of
Death
Editing procedures included a series of logical edits
which compared all dates of service utilization in the provider
billing data files for deceased individuals to the subject's
reported date of death. These edits identified all unimputed
records where the date of service was after the date of death.
Review of these cases found some coding or data entry errors
which were corrected. Investigation by project staff seems to
indicate that the remaining inconsistencies are a function of
provider billing patterns. It appears that some health care
providers generated bills following the death of a subject for
4-8
services rendered prior to death. However, the bills contain
only the billing date and do not include the actual dates of
service. Analysts may choose to remove all charge data for
events with associated dates occurring after a subject's date of
death.
Due to the imputation methods which were used to impute
events on the provider billing files, there are also situations
where an imputed event has an associated date of service that is
after a subject's date of death. This occurs because the entire
record from the donor event, including the date of service, was
imputed to the recipient event.
4.3.4 Loss of Medicare Coverage
Editing procedures uncovered cases where a subject
reported Medicare in the current questionnaire but failed to
report the coverage in a subsequent interview. Unlike private
insurance or some forms of public insurance where it is likely
that coverage may fluctuate over time, once an individual becomes
eligible for Medicare benefits they should continue to receive
this coverage. Review of these cases found several instances
where respondents confused Medicare and Medicaid coverage. In
this situation, the data were corrected to reflect the
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appropriate insurance coverage. However, the user should be
aware that a small number of these inconsistencies could not be
resolved and remain in the data files.
4.3.5 Self-Reported Date of Positive HIV Test
In the screener questionnaire, subjects were asked to
report whether they had tested positive for the HIV virus and the
date of the test. Several subjects reported HIV test dates which
are clearly incorrect (i.e., prior to 1986). Because the
screener data reflects self-reported information, these outliers
have not been removed. However, the user should be aware that
these anomalies remain in the data files and that the medical
record abstract may be a better source for determining an HIV
positive diagnosis.
4.3.6 Self-Reported Conditions of HIV Illness
Subjects were asked in the Time 1, Time 5, and Time 6
questionnaires to indicate whether they had been told they had
any of a list of conditions commonly associated with HIV illness.
Review of these data items found numerous instances where the
subject's response in Time 5 or Time 6 contradicted what was
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reported in an earlier questionnaire. These inconsistencies
remain in the files and users interested in conducting analysis
with those data are encouraged to cross-reference the self-
reported conditions with the clinical data in the medical record
abstract files.
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5. GUIDE TO THE DATA FILES, SPECIFIC VARIABLES, CODEBOOKS,
AND ANNOTATED QUESTIONNAIRES
As discussed in Chapter 3, Data Structure Overview, ACSUS
Public Use Tape #4 contains complete study data for adult ACSUS
subjects and Tape #5 includes similar information for pediatric
study subjects. The study data have been organized into three
major components: patient data files, medical record abstract
files, and provider billing survey data files. Each component
contains multiple data files.
The following sections provide a general discussion of
the type of information contained on each set of files followed
by detailed descriptions of derived, imputed, and flag variables
specific to each data set. Although the adult and pediatric
study data are on separate tapes, the two tapes are structured
similarly with only slight variations between the adult and
pediatric versions. Unless otherwise noted, the information in
this chapter is applicable to both adult and pediatric files.
5.1 Patient Data Files
The patient data files contain self-reported information
collected for each of the six interview periods for which an
5-1
interview was completed and several items collected from a
screener questionnaire completed at the time of study enrollment.
They also contain selected information from other sources, such
as death certificates.
The patient files are classified into two types: patient
level and service level. A series of 14 files (7 adult and 7
pediatric) constitute the complete set of patient level files.
There is an overall patient characteristics file and a patient
level file corresponding to each of the six interview periods.
The service level files are a series of 29 files (15 adult and 14
pediatric). Each service level file contains complete data for a
particular type of health care service, such as hospital
inpatient stays, collected across the six patient interviews.
5.1.1 Patient Level Files
5.1.1.1 Overall Patient Characteristics File
The overall patient level adult file contains data for
the 5,898 adult subjects who completed the screener questionnaire
and were eligible to participate in the study. This includes
2,327 subjects who were sampled, of which 2,050 agreed to
participate. The pediatric version of this file contains similar
5-2
information for the 224 eligible pediatric subjects. This
includes 160 subjects who were sampled, of which 146 agreed to
participate.
The overall patient characteristics files include data
items in four major categories: sociodemographics, survey
administration, clinical and vital status, and service
utilization. These four categories are described below.
Sociodemographic Variables: The overall patient
characteristics file contains four sociodemographic
variables: gender, race/ethnicity, age, and mode of
exposure.
Gender (SEX): The subject's gender was collected in
the screener and medical abstract instruments. The
variable SEX is a composite of two original variables
edited for inconsistencies across these two sources.
Race/Ethnicity (RACE): Information on race/ethnicity
was collected in the screener. RACE is a recoded
version of the original variable. It has been
collapsed into fewer categories for purposes of
confidentiality.
Age at Start of Study (AGE): This is a derived
variable which reflects the age of the subject in
years as of March 1, 1991. For purposes of
confidentiality, the small number of adult subjects
who were 60 years of age or older at the start of the
study have been grouped into one age category.
Similarly, adult subjects 15 through 19 years of age
have been grouped into one age category. Pediatric
subjects who were 10 through 12 years of age have
been grouped together.
Mode of Exposure (EXPROUTE): EXPROUTE is the
suspected mode of exposure to the HIV virus as
reported in the subject's medical record. Because
medical records were collected from multiple
providers, more than one mode of exposure may have
been reported for an individual. If multiple modes
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were reported, EXPROUTE is coded to reflect all
modes.
EXPROUTE for the 3,848 adult subjects who were
screened for participation in the study but not
enrolled is defined using self-reported information
from the screener instrument. Screener data is also
used to define EXPROUTE for the small group of adult
subjects who were enrolled in the study but for whom
no medical abstract data were collected.
The pediatric screener instrument did not collect
information on suspected mode of exposure.
Therefore, EXPROUTE is missing for 78 screened
pediatric subjects who were not enrolled.
Survey Administration Variables (T1_STAT through
T6_STAT): There are a series of six interview status
variables, one corresponding to each interview period, on
the overall patient characteristics file. These
variables indicate whether a patient interview was
completed for that time period and whether the interview
that was completed was a proxy respondent. If an
interview was not completed, the status variables
indicate the reason for nonresponse (e.g., subject
deceased with no proxy available, subject refused,
subject could not be located, etc.). Status codes are
not assigned for interview periods following the death of
a subject. Status codes are blank also when a final
nonresponse code (e.g., subject unlocateable) was
assigned at the previous interview.
The interview status variables were generated for
survey administrative purposes and are intended to
reflect a subject's status at the time of contact by
the interviewer. As a result, there are a small
number of subject's in the adult files with a Time 6
interview status that indicates the interview was
completed by proxy and the subject was deceased
(T6_STAT is coded as DD), but for whom the vital
status (VITSTAT) indicates the subject is alive.
This situation occurs because vital status is
reported as of the end of the study period (8/31/92)
but efforts to conduct Time 6 patient interviews
continued through November 1992.
Clinical/Vital Status Variables: Several derived
variables related to the subject's clinical and vital
status are present on the overall patient level file.
These are described below.
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Illness Stage (ILLSTAGE): This variable was derived
to indicate the stage of an individual's HIV
infection at the time the screener was administered.
It is based on self-reported information; it does not
reflect information from subjects' medical records.
This variable was intended to stratify subjects into
approximate illness categories for sampling purposes.
It was not an effort to apply the full CDC AIDS case
definition. Analysts interested in applying the
complete CDC classification scheme should construct
their own definitions using data from the medical
record abstract.
Subjects were grouped into three disease stages based
upon their responses regarding the type of conditions
or symptoms they had experienced. Persons were
classified as having AIDS if they had been diagnosed
with any of the following: PCP, Kaposi's sarcoma,
lymphoma, wasting syndrome, tuberculosis,
cryptococcosis, cytomegalovirus, MAI,
cryptosporidosis, dementia, histoplasmosis,
toxoplasmosis, isosporiasis, leukoencephalopathy, or
salmonellosis. In addition, some individuals
voluntarily reported that they had been diagnosed
with AIDS but had not been diagnosed with any of the
listed conditions. These persons were also
classified as having AIDS.
Subjects were classified as being HIV-ill if they
reported no AIDS qualifying conditions but did report
one of the following: swollen glands, persistent
fever, diarrhea, weight loss, candidiasis, or herpes
simplex.
Subjects who reported no AIDS or HIV-ill qualifying
conditions were considered asymptomatic and those who
did not know whether they had any of the listed
conditions were classified as unknown.
Vital Status (VITSTAT): This variable indicates a
subject's vital status (alive, dead) as of August 31,
1992.
Date Last Known Alive (VSLIVEMO, VSLIVEDY, VSLIVEYR):
These variables are coded with the most recent date
(on or before 8/31/92) that the study data confirmed
that a subject was alive. These variables are blank
if a subject died during the study period.
Date of Death (DODMO, DODDY, DODYR): These variables
are coded with the date the subject was reported
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deceased. They are blank if the subject was not
reported deceased.
Source of Death Date (DODSOURC): Three sources of
information were used to determine whether or not a
subject was deceased. A death certificate was used
as the primary source if one was obtained for the
subject. The secondary source of this information
was the medical record abstract. Finally, dates
reported by a proxy respondent were used if
information was not received from the other two
sources.
Service Utilization Variables: Aggregated patient level
self-reported counts of the major types of health care
services utilized were calculated for the 18-month study
period. Specific variables are described below.
Total Inpatient Admissions (ADMTOT): This is a count
of the total number of hospital inpatient stays
reported by the patient across the six interview
periods. In situations where a hospital stay begins
in one interview period and continues into the next
period, it is counted as one stay.
Total Inpatient Nights (IPNGTTOT): This variable
sums the total number of nights a subject spent in a
hospital during the 18-month study period. Some
subjects may have reported the number of nights they
were in the hospital for some, but not all, inpatient
stays. IPNGTTOT sums the number of nights for only
those stays with complete information.
Total Emergency Room Visits (ERVSTOT): ERVSTOT is
the total number of visits the subject reported
making to hospital emergency rooms during the study
period.
Total Hospital Clinic Visits (HCVSTOT): HCVSTOT
represents the total number of self-reported visits
by a subject to hospital clinics during the study
period.
Total Other Clinic Visits (OCVSTOT): OCVSTOT is the
number of visits the subject reported making to other
clinics during the study period.
Total Private Physician Visits (MDVSTOT): MDVSTOT
represents the total number of self-reported visits
by a subject to private physicians during the study
period.
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Total Ambulatory Visits (AMBVSTOT): AMBVSTOT
represents the sum of all visits to hospital clinics
(HCVSTOT), other clinics (OCVSTOT), and private
physicians (MDVSTOT) reported by a subject during the
study period. It excludes visits to hospital
emergency rooms.
Total Observation Days (TOBSDAYS): This variable
reflects the total number of days during the 18-month
study period that the subject was observed and for
which patient interview information was collected.
It does not include any gaps in coverage during
periods of ineligibility (i.e., periods when the
respondent was out of the country or in jail). Total
observation days can be used to standardize 18-month
utilization counts to account for variation in the
length of observation periods across individuals.
Examples:
Case 1: Time 1 and Time 2 interviews were
completed for this individual. The subject was
in jail for the entire Time 3 interview period so
no interview was completed. The subject died in
Time 4 and an interview was completed by proxy
respondent.
Time 1: covers 3/1/91-5/7/91; 67 observation
days
Time 2: covers 5/7/91-8/21/91; 106
observation days
Time 3: not completed, subject ineligible; 0
observation days
Time 4: covers 1/27/92-3/4/92 (date subject
deceased);
36 observation days
Total observation days for this individual is
equal to the sum of observation days for the
individual interview periods (209).
Case 2: Interviews were completed in each of the
six periods. The person was out of the country
for 5 days during the second interview period and
for 9 days during the third interview period.
Time 1: covers 3/1/91-6/30/91; 121
observation days
Time 2: covers 6/30/91-10/6/91 with 5 day gap
from
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8/14/91-8/18/91; 93 observation days
Time 3: covers 10/6/91-1/12/92 with 9 day gap
from 12/22/91-12/30/91; 89 observation days
Time 4: covers 1/12/92-4/15/92; 93
observation days
Time 5: covers 4/15/92-7/4/92; 79
observation days
Time 6: covers 7/4/92-8/31/92; 58
observation days
Total observation days for this individual
equals 533.
5.1.1.2 Time Specific Patient Level Files
There are six adult and six pediatric patient level files
which contain information specific to the patient for each of the
interview periods. These files include all patient interview
data contained on nonrepeating records. There is some variation
between the adult and pediatric files in the types of information
they contain. Minor differences also occur across the six times
for both adults and pediatrics. Table 5-1 outlines the general
categories of information contained on the adult files and
indicates the presence (denoted by an X) or absence of such
information in each of the time specific files. Table 5-2
presents similar information for the pediatric files.
Table 5-1. Categories of Information on Adult Time Specific
Files
5-8
Table 5-2. Categories of Information on Pediatric Time
Specific Files
Derived and flag variables in the time specific patient
level files fall into two general categories: survey
administration and service utilization. In general, the naming
convention for these variables is to use a base name followed by
an integer corresponding to the particular time specific file in
which the variable resides. For example, ADM1 resides on the
Time 1 file, ADM2 on the Time 2 file, and so on. Individual
variables are described below.
Time Specific Interview Status (T1_STAT - T6_STAT):
Unlike the overall patient characteristics file, the time
specific files contain records for only those persons
with a completed interview. Accordingly, there are three
valid status codes in these files. These include
interview completed with subject; interview completed
with proxy, subject living; and interview completed with
proxy, subject deceased.
Time Gap Flag (GAP1FLAG - GAP6FLAG): Beginning with the
Time 4 interview, adult and pediatric subjects were asked
to indicate whether they had travelled outside of the
country for a period of 2 weeks or longer. Adult
subjects were also requested to provide information about
periods of incarceration exceeding 2 weeks. In Time 4,
subjects provided information on all time gaps occurring
since the start of the study period. Subsequent
interviews gathered information on only those time gaps
occurring during the current interview period.
For study purposes, subjects were considered ineligible
for periods of travel or incarceration two weeks or
longer in duration. Information on health care
utilization was not collected for these periods. The
time gap flag was created to assist the user in
5-9
identifying interview periods in which a time gap
occurred. If a gap occurred in a particular time period,
the flag variable is set to 1. Otherwise, the flag is
set to blank.
Number of Inpatient Admissions, Per Interview (ADM1 -
ADM6): This variable contains the number of inpatient
hospital admissions reported by a subject in the specific
interview period. Inpatient admissions which begin in
one interview period and continue into the next period
are counted as a stay in each period. Therefore, if
users want to determine the total number of inpatient
admissions for a subject during the 18-month study period
they should use the variable created for this purpose
(ADMTOT), and not attempt to sum ADM1 through ADM6.
Summing ADM1 through ADM6 may result in a slightly higher
number of total admissions.
Number of Inpatient Nights, Per Interview (IPNGT1 -
IPNGT6): IPNGT1 through IPNGT6 represent the number of
nights the subject was in the hospital in each interview
period. For each hospital admission, subjects were asked
to indicate how many nights they spent in the hospital.
The IPNGT variables are calculated by summing the number
of nights across all stays reported by a patient in a
particular interview period. If an inpatient stay began
during the current interview period and continued into
the next period, only the portion of the stay which falls
in the current period is counted.
Number of Emergency Room Visits, Per Interview (ERVS1 -
ERVS6): This variable contains the number of visits a
subject made to emergency rooms in the specific interview
period.
Hospital Clinic Visits, Per Interview (HCVS1 - HCVS6):
HCVS1 through HCVS6 represent the number of visits a
subject made to hospital clinics in each interview
period.
Other Clinic Visits, Per Interview (OCVS1 - OCVS6): This
variable indicates the number of visits an individual
made to other clinics in the specific interview period.
Private Physician Visits, Per Interview (MDVS1 - MDVS6):
The number of private physician visits a subject made in
each interview period is contained in MDVS1 through
MDVS6.
Number of Ambulatory Visits, Per Interview (AMBVS1 -
AMBVS6): This variable indicates the number of visits to
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hospital clinics, other clinics, and private physicians
made by the subject in the specific interview period. It
does not include visits to emergency rooms.
Interview Specific Observation Days (OBSDAYS1 -
OBSDAYS6): This variable indicates the number of days in
a given interview period that a subject was observed and
for which patient information was collected. It is
calculated by counting the number of days elapsed from
the reference period begin date through the reference
period end date, minus any days the subject was
ineligible during this period. Observation days can be
used to standardize utilization counts by taking into
account variation in the length of observation periods
across individuals.
Examples:
Case 1:
Time 1 Begin Reference Date - 3/1/91
Time 1 End Reference Date - 5/20/91
Periods of Ineligibility - none
Time 1 Observation Days (OBSDAYS1) - 80 days
Case 2:
Time 3 Begin Reference Date - 10/20/91
Time 3 End Reference Date - 1/5/92
Periods of Ineligibility - 11/2/91-11/20/91, subject
in jail
Time 3 Observation Days (OBSDAYS3) - 57 days
5.1.2 Service Utilization Files
Self-reported information on the amount and types of
health care services received reside on the service utilization
files as a series of repeating records. Information on out-of-
pocket payments and other sources of payment for these services
is also available on these files. The service data are divided
5-11
into separate files by type of service. Each file includes
complete self-reported information on a particular service for
the entire study period for either adult or pediatric subjects.
For example, there is an adult inpatient hospital stay file which
contains all information on this type of service collected during
the six patient interviews.
With the exception of the dental file, the service
utilization files are structured such that similar data items are
assigned the same variable names across the various files. For
example, the variable SRE_DOL is always used to indicate the
dollar amount a subject paid out of pocket for a service,
regardless of the type of service. This format makes it easier
for the user to aggregate information about all services received
by a particular patient.
Derived and flag variables present on the service
utilization files are described below.
Source Questionnaire (SFORM): This variable indicates
the particular interview period in which the data were
collected and whether the questionnaire was for an adult
or pediatric case. For example, if SFORM has a value of
A, this indicates that the information was collected in
the Time 1 questionnaire, and was an adult case.
Type of Service Utilization (SFPART): SFPART is used to
distinguish the type of service utilized.
Unique Stay Flag (SHSTAYFG): Each reported inpatient
hospital and nursing home stay is assigned a unique stay
number using the variable SHSTAYFG. The stay numbers are
unique throughout all six interviews but they are not
necessarily assigned in order from the first stay
reported to the last.
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This variable is also assigned to all events in the
separately billing doctor (SBD) files. A separately
billing doctor or "separate billing doctor" is one who
provides care to a patient during an inpatient or nursing
home stay but bills the patient separately for services
rendered. Anesthesiologists and radiologists commonly
fall into this category. Although the majority of SBDs
are medical doctors, they may also include other types of
medical practitioners. Inclusion of SHSTAYFG on these
files enables the user to link an SBD event to the
inpatient hospital or nursing home stay in which the
service was provided.
For example:
Subject has 3 inpatient stays during the study
period.
The first stay is assigned an SHSTAYFG of 7.
The second stay is assigned an SHSTAYFG of 8.
The third stay is assigned an SHSTAYFG of 108.
Subject has 2 separately billing doctor (SBD) events
during the study period.
The first SBD event is assigned an SHSTAYFG of 8.
The second SBD event is assigned an SHSTAYFG of
108.
Using the values in SHSTAYFG the user can link the
first SBD event to the second inpatient stay and the
second SBD event to the third inpatient stay.
Continuous Stay Flag (ICTMFLG): This variable can be
used in conjunction with SHSTAYFG to identify the
components of stays that begin in one interview period
and continue into the next period. ICTMFLG indicates in
which interview periods the components of the stay can be
found. If useful to a particular analysis, these
variables enable the user to aggregate the component
records into one stay.
For example:
The subject was interviewed for the Time 1 interview
while in the hospital and was subsequently discharged
during the Time 2 interview period. Therefore, the
subject has a stay that spans the first and second
interview period. This stay is represented in the
inpatient stay file as follows:
5-13
First component record:
Provider ID for the Stay: Coded 3198671. This
represents the randomly assigned sequential ID
number for the hospital where the stay occurred.
Date of Discharge: Coded 95/95/95 which indicates
the subject was still in the hospital at the time
of interview.
Number of Nights in Hospital: Coded as 5. This
represents the number of nights the subject spent
in the hospital up until they completed the Time
1 interview.
Unique Stay Flag: Coded as 15.
Continuous Stay Flag: Coded as AB. A indicates
that one component of the stay was collected in
the Time 1 adult questionnaire while the B
indicates that the second component was collected
in the Time 2 questionnaire. A complete
explanation of these codes is included in the
inpatient stay file codebook.
Second component record:
Provider ID for the Stay: Coded 3198671.
Date of Discharge: Coded 8/15/91 which indicates
the date the subject was actually discharged.
Number of Nights in Hospital: Coded as 10. This
represents the number of nights the subject spent
in the hospital since the Time 1 interview.
Unique Stay Flag: Coded as 15.
Continuous Stay Flag: Coded as AB.
Therefore, if users are interested in identifying all
components of a continuous stay they should first
look at the continuous stay flag (ICTMFG) to see
whether it is set. If ICTMFG is not set (i.e.,
blank) then the stay is not a continuous stay. If
ICTMFG is set, users should examine the values in
ICTMFG to determine in which interview periods the
remaining component pieces are located. Then, the
users should search through all inpatient stays for
that subject that fall within the identified
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interview periods and that have the same unique stay
numbers (SHSTAYFG) as the initial component.
Overlapping Inpatient Hospital and Nursing Home Stay Flag
(ANOSTYF1, ANOSTYF2): These flags are used to denote
situations where subjects were admitted to a hospital
from a nursing home, residential care facility, or
hospice. In these cases, subjects were not discharged
from the long-term care facility and their bed was held
for them until they returned from the hospital stay.
This results in hospital stay dates that overlap with the
dates of stay in the nursing home or other long-term care
facility. This flag variable allows users to take this
overlap into account if appropriate for their analysis.
ANOSTYF1 and ANOSTYF2 are set only for hospital and
nursing home stays that overlap. The values in ANOSTYF1
and ANOSTYF2 correspond to the unique stay number(s)
(SHSTAYFG) of the stays with which it overlaps. In
situations where one nursing home stay overlaps with two
hospital stays, both ANOSTYF1 and ANOSTYF2 are set.
For example:
Case 1: Hospital stay number 43 overlaps with nursing
home stay number 111. ANOSTYF1 for the hospital stay
record is set to 111 to indicate that it overlaps
with nursing home stay 111. Similarly, ANOSTYF1 for
the nursing home stay record is set to 43 to indicate
that it overlaps with hospital stay 43.
Case 2: Hospital stays number 75 and 81 overlap with
nursing home stay number 26. ANOSTYF1 is set to 26
for both hospital stay records to indicate that they
overlap with nursing home stay 26. ANOSTYF2 is set
to blank for both hospital stay records. On the
nursing home record, ANOSTYF1 is set to 75 and
ANOSTYF2 is set to 81.
Discrepant Source of Payment Flag (INSURFLG): In each
interview period subjects were asked to indicate what
type of medical insurance coverage they had during the
reference period. Subjects were also asked to indicate
the source of payment for each episode of medical care
they received. Editing procedures found situations where
the subject reported that a specific type of insurance
covered a particular event but failed to report that type
of coverage in the overall set of questions on medical
insurance. Following review of these cases for coding
and data entry errors, some discrepancies remained in the
data. INSURFLG is used to highlight these discrepancies.
5-15
INSURFLG is set to 1 on the service record if the
reported source of payment for that particular service is
inconsistent with the type of medical insurance reported
at the overall patient level for the interview period in
which the service was received.
5.2 Medical Record Abstract Data Files
5.2.1 Overview
The medical abstract data files contain selected clinical
information abstracted from study subjects' medical records. The
ACSUS study design included the medical record data collection
component for the purpose of confirming specific HIV-related
conditions and diseases to be used in determining the stage of
subjects' HIV disease. Therefore, these files are intended to
provide the user with information about a subject's clinical
history and are not an appropriate source for obtaining patient
level utilization counts. Users interested in such analyses
should use the patient interview or provider billing files
instead.
During three of six patient interviews, subjects were
asked to indicate their usual source of medical care. Subjects
were asked to sign permission forms authorizing study personnel
to contact the named providers for purposes of obtaining access
to subjects' medical records. If a subject reported having more
5-16
than one usual source of care, an attempt was made to obtain
medical records from the multiple sources of care. If a subject
did not report a usual source of care, an attempt was made to
obtain the patient's medical record from the provider where they
were sampled into the study. The data contained in these files
reflect information abstracted from the subjects' medical records
and may represent data collected from more than one medical
provider.
Although the ACSUS patient interviews and provider
billing survey collected data for the 18-month period beginning
March 1, 1991, and ending August 31, 1992, medical record data
were collected for a broader interval of time. Medical
abstractors were instructed to collect clinical information for
health care services provided during the period beginning January
1, 1990, and ending August 31, 1992. In addition, abstractors
went as far back in the medical record as necessary to confirm an
AIDS diagnosis or for evidence of a positive HIV serostatus.
Four separate data files make up the complete set of
medical record abstract files. The first of these files is a
patient record file which is a compilation of information
collected from all usual sources of care which has been edited
across these multiple data sources. The three remaining files,
the inpatient stay, check-list conditions and T-cell reports
5-17
files, contain whatever information was collected from the
individual usual sources of care. Because these data have not
been compiled into one record it is possible that two different
usual sources of care may report similar information about a
subject.
5.2.2 Patient Level File
The medical abstract files contain one patient level
record. This record includes data items that were derived using
information drawn from all abstracts received for a patient.
Specific variables and how they were derived are described below.
Record Review Period (URMOF, URDYF, URYRF, URMOL, URDYL,
URYRL): This set of date variables reflects the earliest
and latest medical record reviewed for an individual. It
does not reflect gaps in medical record coverage
occurring between these dates. The review period for an
individual may span as wide a period as 1/1/90 through
8/31/92 or some portion of this period. Factors such as
the completeness of a subject's medical record, provider
response rate, and study subject survival time affect the
length of the review period. For example:
Case 1: Medical abstracts for this subject were
received from three providers covering the following
time periods:
Provider 1: 2/8/90-7/17/91
Provider 2: 1/1/90-11/20/91
Provider 3: 9/21/91-6/5/92
The record review period for this individual is set
to 1/1/90 through 6/5/92.
5-18
Case 2: Medical abstracts for this subject were
received from two providers covering the following
time periods:
Provider 1: 3/1/90-12/13/91
Provider 2: 2/3/92-8/31/92
The record review period for this individual is set
to 3/1/90 through 8/31/92. Note that the record
review dates do not take into account the 2-month gap
in coverage between providers (12/13/91 through
2/3/92).
HIV Positive Diagnosis (UHIV), Diagnosis Date (UHDIAGMO,
UHDIAGDY, UHDIAGYR) and Report Date (UHRDMO, UHRDDY,
UHRDYR): These variables were derived to represent the
best information available across all medical abstract
forms for an individual. A subject is coded as having a
positive HIV diagnosis if at least one medical abstract
confirmed a diagnosis. The corresponding diagnosis and
report dates from that abstract are coded in UHDIAGMO,
UHDIAGDY, UHDIAGYR and UHRDMO, UNRDDY, UHRDYR. If
multiple abstracts indicated a positive serostatus, then
the earliest diagnosis and report dates are coded. A
subject is coded as not having a positive HIV diagnosis
if none of the medical abstracts for that patient
confirmed a diagnosis. A subject's serostatus is unknown
if all medical abstracts reported an unknown status.
AIDS Diagnosis (UAIDS), Diagnosis Date (UADIAGMO,
UADIAGDY, UADIAGYR) and Report Date (UAIDRDMO, UAIDRDDY,
UAIDRDYR): These data items follow the same approach as
the HIV positive diagnosis and report date variables. If
at least one medical abstract stated that the individual
was diagnosed with AIDS, the subject is coded as having
an AIDS diagnosis. If multiple abstracts indicated an
AIDS diagnosis, then the earliest diagnosis and report
dates are coded in UADIAGMO, UADIAGDY, UADIAGYR and
UAIDRDMO, UAIDRDDY, UAIDRDYR. A subject is coded as not
having an AIDS diagnosis if none of the medical abstracts
confirmed a diagnosis. If all medical records stated
that it was unknown whether the subject had a diagnosis
of AIDS, UAIDS is unknown.
5-19
5.2.3 Medical Abstract Repeating Record Files
Information on inpatient stays, outpatient/clinic
checklist conditions, and laboratory reports of T-cell counts
exist in the files as a series of repeating records. Because
data for a subject may have been collected from more than one
usual source of care, each repeating record has a provider
identification number that indicates the source of the
information. The provider identification number, USCID0n,
corresponds to the provider from whom the information was
collected. This is not necessarily the provider from whom care
was received. USCID0n is a randomly assigned sequential number
where n is an integer that uniquely identifies two records from
the same provider.
Since the medical data represent information collected
from multiple providers there are situations where data overlap,
are duplicative, or discrepant across providers. For example, if
a subject reported having a hospital and a private physician as
usual sources of care, patient charts from both sources may
contain information on a single inpatient stay; however, data
collected on that stay may or may not be the same across
providers. It is possible that the hospital chart contains more
detailed information on admitting or discharge diagnoses than the
chart maintained by the private physician, which may record only
5-20
a primary diagnosis. The situation may also occur where the
admission or discharge date differ.
In cases of apparent overlaps or discrepancies, the data
have been edited for keying and coding errors only. Additional
editing, which would have involved either extensive follow-up
beyond the scope of this study, or making numerous assumptions
without sufficient supporting evidence, was not undertaken.
Discrepant or overlapping items across medical records have not
been flagged. Therefore, users should be aware that these items
exist and, if important to their analysis, they should examine
the data files for those items prior to undertaking analysis.
5.2.3.1 Inpatient Stays File
This file contains data on all inpatient stays reported
in the medical record. For each inpatient stay, information was
collected on the admission and discharge dates as well as
admitting and discharge diagnoses.
The inpatient stays file contains one flag variable which
is described below.
Inpatient Stay Flag (UIPSFLG): This is a flag variable
that is used to denote inpatient stays where the medical
record contained more than three admitting or ten
discharge diagnoses. Because the data files were
initially structured to accommodate no more than ten
codes, the inpatient stay flag was introduced to alert
5-21
the user that additional diagnoses were reported and are
contained in a "secondary" record for the stay.
The inpatient stay flag is set to 1 on the primary record
and on the secondary record so that the user can identify
both components of the stay. The secondary record
contains the same discharge and admission dates as the
primary record. If more than three admitting diagnoses
are reported, the additional diagnoses are recorded in
the secondary stay and the discharge diagnoses are set to
blank on the secondary stay. Similarly, if more than ten
discharge diagnoses are reported, the additional
discharge diagnoses are recorded in the secondary stay
and the first admitting diagnosis is set to "99999" on
the secondary stay. Subsequent admitting diagnoses are
set to blank.
For example:
Primary Record: Secondary Record:
Provider ID - 000001 Provider ID - 000001
Admission Date - 6/10/92 Admission Date - 6/10/92
Discharge Date - 7/3/92 Discharge Date - 7/3/92
Admission Diagnoses: Admission Diagnoses:
Diagnosis #1 - 486XX Diagnosis #1 - 99999
Diagnosis #2 - 1363X Diagnosis #2 - blank
Diagnoses #3 - blank Diagnoses #3 - blank
5-22
Discharge Diagnoses: Discharge Diagnoses:
Diagnosis #1 - 1120X Diagnosis #1 - 1363X
Diagnosis #2 - 7832X Diagnosis #2 - blank
Diagnosis #3 - 5589X Diagnosis #3 - blank
Diagnosis #4 - 7994X Diagnosis #4 - blank
Diagnosis #5 - 7806X Diagnosis #5 - blank
Diagnosis #6 - 2859X Diagnosis #6 - blank
Diagnosis #7 - 0389X Diagnosis #7 - blank
Diagnosis #8 - 01190 Diagnosis #8 - blank
Diagnosis #9 - 690XX Diagnosis #9 - blank
Diagnosis #10 - 0420X Diagnosis #10 -blank
UIPSFLG - 1 UIPSFLG - 1
A primary record and the secondary counterpart for that
stay can be identified in the data by searching for two
inpatient stay records with the same patient ID number,
the same provider ID number, and the same admission and
discharge dates, and where UIPSFLG is set to 1.
5.2.3.2 Checklist Conditions File
The outpatient/clinic checklist records contain
information on medical conditions commonly associated with HIV
infected persons. For purposes of this study, a checklist of 75
conditions was developed and hospital outpatient, emergency room,
clinic, and physician records were reviewed for any mention of
these conditions.
The checklist conditions file is structured such that the
earliest reported date of diagnosis is coded in the date of
diagnosis variables (UCCDXMO, UCCDXDY, UCCDXYR). Subsequent
diagnosis dates or reports of the condition are contained in
UCCRDAT1 through UCCRDAT8. If a subject's medical record
5-23
indicated numerous visits over a brief period of time for
treatment of the same medical condition, only one report date per
month may have been recorded.
Each outpatient/clinic record contains space for 8 report
dates of a particular condition. If more than 8 dates were
present in the medical data, additional dates are recorded on
another record. In addition, information on the same condition
may have been collected from multiple providers and appears on
separate records. Therefore, it is important that users search
all outpatient records for a patient if they are interested in
obtaining all information that was collected on a particular
condition.
5.2.3.3 T-Cell Reports File
Medical abstractors were instructed to search the medical
record for all laboratory reports of T-cell counts. Depending
upon the available information, absolute counts, percentages, and
date of report were recorded for monitoring both CD4 and CD8
cells.
5-24
5.3 Provider Billing Survey Data Files
In each of six patient interviews, subjects were asked to
supply the names of all medical care providers from whom they
received health care during the interview period. Subjects were
also requested to grant permission to allow study personnel
access to their billing records from the named providers.
Medical providers were contacted in two rounds of data collection
depending on whether the patient reported services from the
provider and gave consent during the round period. Providers
were contacted to obtain information on the services rendered,
charges for these services, and the source of payment for these
services. The provider billing survey files contain all
information gathered as part of this data collection effort. The
files also include imputed data for nonrespondent providers and
imputed data for charges not reported by respondent providers.
Information collected as part of the provider billing
survey is contained on four files: the ambulatory visit,
inpatient stay, home health visit, and prescription medicine
files. The file structure is one record per bill for a health
care event on the ambulatory, inpatient, and prescription files.
An event is defined as an ambulatory visit, an inpatient stay, or
a medical prescription purchase. The home health file reports
multiple events on one record which contains billing information
5-25
for one or more home health visits by a particular type of
caregiver during a period of time.
The records of the provider billing files have a master
structure in which each record contains the same set of variables
regardless of the event type. Because not all variables are
applicable to each of the files, some data fields are blank for
all records in the file. For example, the variable NUMNIGHT,
which refers to the length of an inpatient stay, is only
applicable to the inpatient stay file. Therefore, NUMNIGHT is
always blank on the ambulatory, home health, and prescription
medicine files.
The provider billing survey files contain edited,
derived, flag and imputed variables. The following paragraphs
describe all derived variables and some flag variables. All
imputed variables and any flag variables related to imputation
are highlighted in Chapter 6.
Questionnaire Form (PFORM): This variable indicates the
type of provider billing survey form on which the data
were collected, that is, the inpatient stay, the
ambulatory, home health, or pharmacy billing form.
Hospital Ownership Code (OWNSHP2): OWNSHP2 indicates
whether the hospital where the care was received is
publicly, privately, or federally owned. OWNSHP2 is
blank for all providers that are not hospitals.
Type of Care Provided (CARETYPE): Each of the four
provider billing survey data files contain records that
may represent more than one type of health care service.
5-26
For example, the inpatient stay file contains information
about both hospital inpatient and nursing home stay
events, the prescription medicine file includes data on
prescription medications as well as medical equipment and
supplies, and so on. CARETYPE is a derived variable
which is used to distinguish among the various types of
services. Nonresponses were imputed by this care type
variable.
Length of Stay (NUMNIGHT): This variable measures the
duration of an inpatient stay in nights. It is
calculated from the admission and discharge dates
reported by the provider.
Inpatient Stay Identifier (PHSTAYFG): Each inpatient
stay is assigned a unique stay number (PHSTAYFG).
Similarly, all emergency room visits that resulted in an
inpatient admission and separately billing provider
events in the ambulatory visit file are assigned a unique
stay number which corresponds to the stay number for the
associated inpatient event. If appropriate for their
analyses, users can link the inpatient, emergency room,
and separately billing provider events using PHSTAYFG.
5.4 Guide to Codebooks and Annotated Questionnaires
There are a series of codebooks which contain complete
descriptions of the contents of the data files on the tape. In
general, there is a separate codebook for each data file. Two
exceptions are the codebooks for the Provider Survey Billing
files and the Non-Medical Services file. The Provider Survey
Billing files have one master codebook that is applicable to the
four component files. Eight codebooks exist for the Non-Medical
Services file, one codebook for each type of non-medical service.
5-27
Printable codebooks are available on the tape as a series
of text data files. The users manual does not contain hard copy
versions of the codebooks. File names for the individual
codebooks are listed in Chapter 3.
Figure 5-1 provides a description of each of the items
appearing in the codebook. In addition, each codebook includes
an index of variables which alphabetically lists all variables in
the codebook and the corresponding codebook page number. The
index of variables is located at the end of the codebook.
Appendices A-C contain copies of all data collection
instruments for the ACSUS study. The instruments are annotated
with the actual variable names that appear in the data files.
Annotated variable names are listed in brackets under the
question to which they refer. Questionnaire items that are not
included in the ACSUS public use tapes are not annotated. Figure
5-2 is an example of the annotated questionnaire format.
5-28
Figure 5-1. Example of the Codebook Format
Figure 5-2. Example of the Annotated Questionnaire Format
5-29
6. DATA IMPUTATION
This chapter is an overview of the imputation procedures
associated with the ACSUS data. Section 6.1 briefly summarizes
imputation strategies for handling different types of
nonresponses in surveys. Section 6.2 outlines the general
imputation approach for the ACSUS provider survey data. Section
6.3 discusses some limitations in the use of the imputed ACSUS
data. Section 6.4 describes the types of imputation variables in
the data files.
6.1 Introduction to Imputation Strategies
The problem of missing data is pervasive in survey
research. Estimates derived from datasets that contain missing
items may be biased when respondents differ from nonrespondents
with respect to the characteristics being analyzed. By carefully
assigning responses to missing data, imputation can produce a
complete dataset which compensates for nonresponse bias,
simplifies analyses, and produces consistent results across
analyses, with minimal loss of precision.
Nonresponse in surveys is typically categorized as either
unit nonresponse or item nonresponse. A unit nonresponse occurs
6-1
when none of the survey items are obtained from a surveyed unit.
In the ACSUS study, for example, information could not be
obtained for some patient-reported events, when the provider
refused or for some other reason was unable to participate in the
survey. Item nonresponse occurs when some, but not all, of the
responses are missing from an otherwise cooperating survey
respondent. In this study, the provider data for an event was
missing because the provider omitted the data, refused to give
it, was unable to locate it, etc. The distinction between
different types of nonresponse is useful because different
imputation strategies may be required, depending on the
situation.
The most common imputation procedures utilize the survey
data as a source of information to derive the imputed values.
Surveyed units are matched by auxiliary variables that make the
events similar in respect to the data being imputed, and then the
respondents' information (known as donor data) are used to derive
the imputed result for the nonrespondents (known as recipients).
The imputation procedure can either be of a stochastic or
deterministic type. The most common of these types are,
respectively, the hot-deck imputation method and the mean
imputation method.
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Typically the procedure for both hot-deck and mean
imputation methods begins by sorting the surveyed units into a
set of imputation cells. Cells are formed by combining the
values of the auxiliary variables used to match surveyed units.
Adjacent cells may be collapsed if a cell has a deficiency in
donors. Then the value for a recipient variable may be chosen at
random from the donors within its cell (hot-deck method), or be
set to the mean value calculated across the donors in its cell
(mean method).
6.2 General Imputation Strategy for ACSUS Data
Both unit and item nonresponse imputation were performed
on the ACSUS provider data. Only billing data for medical
providers was imputated. Information collected from patients was
not imputed, due to a low nonresponse rate.
For imputation, the patient-reported health care services
and provider bills for services (events) were arranged into
imputation categories by the following.
§ Whether the data were collected in Round 1 or 2.
(Imputation was necessary when data were collected
from a provider in one round but, although requested,
data were not collected in the other.)
§ Whether the patient was adult or pediatric.
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§ Whether the records for a patient/provider pair fell
into one of the following care types:
- Inpatient Hospital and Nursing Home Stays.
- Visits by Separately Billing Doctors Associated
with Inpatient Stays. (Only for adult patients.)
- Emergency Room Visits, classified as associated
with an inpatient stay or not.
- Ambulatory Visits, including visits to Hospital
Clinics, Community Clinics, Private Medical
Doctors, Mental Health Services for Psychological
Counseling, and Medical Practitioners.
- Home Health Visits from Medical Personnel, Social
Workers/Case Managers, or Helpers and other types
of caregivers.
- Prescription Medications, classified by cost or
by the drug itself for the most frequency
purchased medications.
If a provider event did not fall into one of these care type
categories, then this event was excluded from any imputation
procedure. For instance, imputation was not performed on Medical
Equipment/Supplies data or to events for which no care type could
be assigned.
For each category, the events were further organized into
two groups based on whether or not data had been obtained from
the provider for a patient and whether imputation was to be
performed when data were not available.
§ Group 1 consisted of events where the provider had
supplied records of events reported by the patient.
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§ Group 2 consisted of events where the provider was a
unit nonresponse and met these criteria:
- The provider refused;
- The provider did not respond before data
collection was closed;
- The provider was found to be out of business at
the time of the survey;
- The provider had purged all the records for the
reference period;
- The provider had a language barrier or some other
reason existed for no data; or
- There was no permission form from the patient to
collect provider data.
If a patient/provider pair did not meet one of the criteria for
either Group 1 or Group 2, then the events for the pair were
excluded from any imputation procedure.
The ACSUS imputation process proceeded separately by
imputation category. First, imputation cells were defined by
auxiliary variables, such as, geographic region of health
provider, disease stage of the patient, etc. The choice of
variables was dependent on the care type and whether adult or
pediatric data were being imputed. See Section 6.2.1 for details
on cell definitions.
Then provider records associated with Group 1 received
item nonresponse imputation for missing charge components by a
combination of hot-deck and mean imputation methods. Section
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6.2.2 generally outlines this procedure. These methods varied by
care type, but not across rounds or between adult and pediatric
patients. Table 6.1 lists the charge components imputed for each
care type.
Table 6-1. Imputed Charge Components by Care Type
After the item nonresponse imputation was completed for
Group 1, a unit nonresponse imputation by the hot-deck method was
completed for Group 2, using Group 1 as donors. For a Group 2
patient-reported event, a similar provider survey event from
Group 1 (as defined by imputation cells) was randomly selected,
and all the billing information associated with this event was
imputed for the Group 2 patient-reported event. Events that were
imputed were added to the Group 1 event file as separate records.
This imputation strategy was used to preserve the relationships
between billing data and provider care types, as well as to
preserve the multivariate relationships within the components of
the billing information.
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6.2.1 Auxiliary Variables Used To Define Imputation Cells
The variables listed below were used to define the
imputation cells. These variables were defined by the source
data (charge information) separately within imputation
categories, and therefore, varied in use among imputation
categories. For example, for the adult Inpatient Stays, seven
different auxiliary variables were used to define similar events.
In contrast, the imputation process for pediatric patients
utilized only three auxiliary variables for the same care type.
In the imputations using the hot-deck methods, cells were
defined as having either hard or soft boundaries. A hard
boundary cell could not be collapsed with another cell during
imputation. On the other hand, a soft boundary cell variable
would be collapsed with another if, within a hard boundary cell,
there was a deficiency in donors. Cells with fewer donor events
than recipients were combined automatically with events in the
adjacent soft boundary cell until the number of donors was
sufficient or until all the events in the hard boundary cells
were used. Deficient cells with hard boundaries were collapsed
manually according to a predefined protocol.
Auxiliary variables used during ACSUS imputation were the
following:
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§ Geographic Group (classified by provider charges)
§ Type of Provider Ownership (hospitals only)
§ Patient's Insurance Status
§ Patient's Disease Stage
§ Patient's Exposure Route
§ Association of an Emergency Room Visit with an
Inpatient Stay
§ Type of Reported Ambulatory Care
§ Type of Home Health Care
§ Type of Prescription Medication
§ Length of Stay (for Inpatient Stays)
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§ Total Charge, or Total Charge per Day or per Visit
§ Number of Days of Home Health Care Reported by the
Patient
§ Number of Prescription Medication Purchases Reported
by the Patient
6.2.2 Group 1 - Item Nonresponse Imputation Procedures
Imputation for this group was broken down into cases
based on the patterns of missing information and particular care
types. The imputation methods did not differ across adult or
pediatric patients. Provider records which contained no missing
charge components were used as donors. Imputation cells were
defined according to care type.
For Inpatient Stays, Separately Billing Doctors,
Emergency Room Visits, and Ambulatory Visits the same imputation
procedures were used. If the total charge appeared to be a
copayment (zero to ten dollars), then all billing information was
set to missing for imputation purposes and all the charge
components were imputed. For inpatient stays whose total charge
was imputed, length of stay was also imputed. Three cases of
missing data were imputed as follows.
Case 1: Total and component charges missing. The hot-
deck method was utilized to impute the total and all
charge components from a single donor. Payment data were
set to missing.
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Case 2: Total charge present, but all component charges
missing. The hot-deck method was utilized to impute all
charge components from a single donor. The imputed
components were adjusted to the original nonimputed total
by keeping the same proportions of components to total
that were on the donor event. Payment data were retained
as collected.
Case 3: Total and some component charges missing. The
mean method was utilized. For each respective component
charge, the mean (or mean per diem) charge was calculated
from those donors where the component charge was greater
than zero. This mean (or mean per diem) charge was used
to impute the missing component. The total charge was
imputed by summing across components. Payment data were
set to missing.
For Home Health Visits item nonresponse imputation was
accomplished by the mean method. If the total charge was zero
dollars, then all billing information was set to missing for
imputation purposes and the total charge was imputed. The
imputed total charge was obtained by calculating the mean per
visit charge from the donors in the cell and multiplying by the
recipient's number of visits. There were no components of the
total charge.
Item nonresponse imputation for Prescription Medications
was broken down into two cases. Imputation in both cases was by
the mean method. If the total charge was zero dollars, then all
billing information was set to missing for imputation purposes
and the total charge was imputed. There were no components of
the total charge.
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Case 1: Top 30 most frequently use medications for adults
or top 10 for pediatric patients, with at least one
matching donor on dosage. If a recipient event was
matched to at least one donor with the same medication
and exact dosage, the imputed value was calculated as the
mean charge per quantity from the matching donor(s)
multiplied by the recipient's quantity. Note that donors
and recipients must have had both dosage and quantity
present. The payment data were set to missing.
Case 2: Medications not in Case 1. Within an imputation
cell the recipient events received the mean charge across
the donors. The dosage and quantity of the drug were set
to missing as well as the payment.
6.3 Limitations of the Imputed ACSUS Data
The imputation methods for the ACSUS data attempted to
preserve the relationships between response items subject to
imputation by sorting records in such a way as to group records
by characteristics related to the item being imputed.
Furthermore, attempts were made to check for outliers in the data
before and after imputation.
However, if provider survey data are to be used to
explore relationships between charge and payment data, caution
should be exercised. The imputation process considered only the
charge components as described in Section 6.2. Billing
information, other than charges, was imputed to missing during
the ACSUS item nonresponse imputation process. This imputation
took place because the imputed charges did not have any real
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relationship to other original billing information. Original
charge and payment data would be more appropriate to use in
exploring these relationships.
The assumption behind the imputation schemes used here is
that after controlling for available auxiliary information, the
missing values are missing at random. If this assumption does
not hold, then estimates using the imputed values could still be
biased due to nonresponse.
6.4 Imputation Variables
The provider billing files contain three types of
variables: original, imputed, and imputation flag variables.
This section describes each of these variable types and is
intended to provide the user with an understanding of how to
conduct analyses with the imputed data.
6.4.1 Original Variables
Original variables are defined as the original data
elements as they appear in the data collection instruments.
Original variables do not contain any imputed data. Users
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interested in examining only unimputed charge data should use
these variables for their analyses.
The provider billing survey collected information on
total charges for a particular type of service event and, for
some services, it also collected information on component
charges. Total charges for an event are contained in the
variable PTE_CHRG. Component charges reside in a series of
variables with the naming convention PEC_*, where * refers to the
specific charge component. For example, PEC_LAB is the amount
charged for laboratory services and PEC_RM is the room charge.
The provider billing survey also collected information on
total payments and, for some services, the source of the payment
and the reason that no payment was received. The amount coded in
variable PTE_PAY represents the total payment for an event. The
amounts paid by specific sources are contained in a series of
variables with the naming convention PEP_*, where * refers to the
source. For example, PEP_PRVI is the amount paid by private
insurance and PEP_MED is the amount paid by Medicare/Medicaid.
The reason that no payment was received is represented by a
series of variables PEN_*, where * refers to the reason. For
example, PEN_CARE represents Medicare assignment.
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6.4.2 Imputed Variables
For each original variable which may have been imputed,
the provider billing files have a counterpart variable which
contains either the imputed or original value. For example,
PTE_ICHR is the counterpart of PTE_CHRG. If total charges were
imputed for an event then the total imputed charge is coded in
PTE_ICHR. If total charges were not imputed for an event then
the total charge in PTE_CHRG is copied to PTE_ICHR and the two
variables have the same value.
Imputed charge components reside in a series of variables
with the naming convention PEC_I*, where * refers to the specific
charge component. For example, PEC_ILAB is the imputed/original
amount charged for laboratory services and PEC_IRM is the
imputed/original room charge. Similar to PTE_CHRG, the PEC_I*
variables contain the same as the original variable if no
imputation was done.
The imputed amounts paid by specific sources are
contained in a series of variables with the naming convention
PEP_I*, where * refers to the source. For example, PEP_IPRV is
the imputed/original amount paid by private insurance and
PEP_IMED is the imputed/original amount paid by
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Medicare/Medicaid. The imputed reason that no payment was
received is represented by the series of variables PEN_I*.
The provider billing files contain a series of
miscellaneous imputation variables. These include an imputed
length of stay (ILOS2) for inpatient stays and imputed drug type
(IDRUGCD), quantity (IQTY) and dose (IDOSE) for pharmacy events.
6.4.3 Flag Variables
Each imputed variable has a counterpart flag variable
which is coded to reflect the type of imputation that was done.
The imputation flag variables use four different naming
conventions, as follows.
FGT_I* - These are a series of imputation flags that are
assigned to total amount variables. For example,
FGT_ICHR is the flag variable assigned to the imputed
total charge (PTE_ICHR), FGT_IPAY is assigned to the
imputed total amount paid (PTE_IPAY), and so on.
FGC_I* - These imputation flags are assigned to imputed
component charge variables. For example, FGC_IRM is the
imputation flag assigned to the imputed room charge
variable (PEC_IRM).
FGP_I* - These flags are assigned to imputed source of
payment variables. For example, FGP_IPRV is assigned to
the imputed amount paid by private insurance (PEP_IPRV).
FGN_I* - These are a series of imputation flags that are
assigned to the imputed reason that no payment was
received. For example, FGN_ICAR is assigned to the
imputed reason for non-payment due to Medicare assignment
variable (PEN_ICARE).
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FLG_I* - These flags are assigned to a series of
miscellaneous imputed variables. For example, FLG_IDOS
is assigned to the imputed drug dosage variable (IDOSE).
Each of the imputation flag variables described above is
coded using a series of alpha and numeric codes that reflect the
type of imputation that was conducted. Valid codes for each flag
variable and their meanings are described in the provider billing
codebook. An imputation flag is set to zero if no imputation was
done for a particular variable. An imputation flag variable may
contain multiple codes if more than one type of imputation was
conducted (e.g., both missing item and event imputation) for a
particular variable.
In addition to the imputation flag variables described
above, there is an overall event flag (IEVENTFG) which indicates
whether or not the entire unit nonresponse record was imputed.
IEVENTFG is coded Y when the record was imputed and is blank
otherwise. Therefore, analysts interested in excluding imputed
events from their analyses can use this variable to identify
which records to remove.