From the aDivision of Health Policy & Management, University of Minnesota and bMinnesota Population Center, University of Minnesota, Minneapolis, MN.
Submitted 22 August 2007, accepted 18 March 2008.
Supported by Grant Number R01HD046697 from the National Institute of Child Health and Human Development.
Supplemental material for this article is available with the online version of the journal at www.epidem.com; click on “Article Plus.”
Correspondence: Pamela Jo Johnson, Division of Health Policy & Management, University of Minnesota, School of Public Health, 2221 University Ave SE, Suite 345, Minneapolis, MN 55414. E-mail: firstname.lastname@example.org
The National Health Interview Survey (NHIS) is a leading source of data on the health of the American population.1 These data are used to monitor the health of the US population,2 track progress toward Healthy People 2010 objectives,3 and evaluate the quality of health care in the United States4 The rich array of data have also been valuable for a broad range of population health research. NHIS data allow examination of conditions such as cancer, diabetes, hypertension, asthma, and functional limitations. Data are also available on preventive care utilization, including cancer screening5–7 and immunization,8,9 and on health behaviors such as diet,10,11 physical activity,12–14 and tobacco use.15–17 A wealth of sociodemographic information permits examination of social disparities in access to care, morbidity, and mortality.18–22 Moreover, pooling multiple years of data provides sufficient sample size for analysis of subpopulations of interest.23,24
The NHIS is the longest-running US health survey, with annual microdata from 1963 to the present. This broad chronologic coverage makes these data uniquely suited for studying changes in health over time. Yet, cross-temporal analyses of these important data have been uncommon, particularly by researchers outside the National Center for Health Statistics.25 Use of NHIS data has increased in recent years, most notably after the 2001 release of data files on the Internet. However, use of these complex data in time-series analyses remains rare. The purpose of this paper is to introduce this resource to the epidemiologic community.
The Data Integration Project
The Integrated Health Interview Series (IHIS) project is a well-documented cross-sectional time series based on the National Health Interview Survey. We make these data freely available through a user-friendly Web-based data dissemination system (available at http://www.ihis.us) to facilitate informed analysis of this valuable source of information about the nation's health. The IHIS builds on the model of the Integrated Public Use Microdata Series (IPUMS), a harmonized set of US Census data from 1850 to 2000.26 There are 3 components to the IHIS data integration project: (1) harmonization, (2) documentation, and (3) dissemination.
Discontinuities in National Health Interview Survey variables complicate analysis of change over time. Harmonization is the process of taking original variables with different coding schemes and creating a new variable that is comparable over time. The “translation table” is the foundation of this harmonization work. It is a tool (in spreadsheet format) for laying out the various coding schemes for each year and then aligning the coding schemes into a single integrated scheme.
In some cases, the original variables are completely or nearly compatible, and recoding them into a common classification is relatively straightforward. For example, marital status is nearly identical over time, although with small differences. Table 1 shows selected sections of the marital status translation table. In the first 2 columns, the final integrated coding scheme and value labels are listed with the original (unharmonized) codes for each year in the remaining columns.
For other variables, it is impossible to construct a single uniform classification without losing information. Some years provide more detail than others, and using the “lowest common denominator” of all years would discard important information. In these cases, we construct composite coding schemes. The first digit(s) of the code provide information available across all years. The next digits provide additional information available in a broad subset of years. For example, the variable for self-reported main race uses a variety of coding schemes over time. Table 2 shows selected sections of the translation table, and eFigure 1 (available with the online version of this article) shows a partial screenshot of the IHIS codes available for this variable. The utility of composite coding can be seen in the case of Asian or Pacific Islanders. Codes 400 through 430 are all subclassifications of this group. Using the first digit (4) provides the broadest comparable grouping over time. Using the second digit distinguishes Asian (41) from Pacific Islander (42). Researchers interested in even finer distinctions can use the 3-digit values.
Harmonization also allows us to address sample design discontinuities. We constructed the IHIS survey design variables to be usable when examining data from 1 year or from many years. We employed the concatenated design period pooling approach suggested by Korn and Graubard27 for pooling data from 1 survey over multiple years and sample designs. Strata and primary sampling unit (PSU) variables are constructed so researchers need do no additional recoding of these variables, regardless of which years of data are analyzed.
The Integrated Health Interview Series provides documentation designed to enhance researchers’ ability to work with the data as a cross-sectional time series. Along with detailed descriptions of each variable, the IHIS also includes general documentation (such as user notes about the original NHIS source data, sample design, and sampling weights) and guidance on analysis and variance estimation.
For each variable, we consulted the survey descriptions, codebooks, questionnaires, and interviewer instructions for each year, as well as documented discussions of survey methodology, concepts, and sample selection.28–33 We reorganized this information by putting all essential facts relating to one variable over time into a single narrative.
Variable-specific documentation covers the meaning of a variable, years available, universe definitions, codes, and frequency distributions. We also provide discussions of cross-temporal comparability for each variable. We have noted potential problems in combining multiple years of the variable and offer suggestions for maximizing comparability and for choosing appropriate weights. The variable descriptions also reference related variables, with information accessible via hyperlinks. When variables cannot be fully harmonized, the documentation explains the limitations of comparability.
Response category codes and frequencies can be accessed from the variable availability grid or from a link on each variable description. Codes and frequencies are displayed so researchers can see which categories are represented in each available year. Codes can also be viewed in “case count” format, with unweighted sample size for each response category displayed by year. eFigure 2 in the online appendix shows both the category availability view and the case count views for one IHIS variable.
User-friendly data dissemination is an integral component of this effort. We distribute these data and documentation through a Web-based data access system that is available free of charge (http://www.ihis.us). For each data extract, the researcher specifies the file type (hierarchical or rectangular), data format (SAS, Stata, SPSS), years to be included, and variables for analysis. The researcher can provide a short description of the extract, which is numbered and displayed for future reference in the researcher's personal download history (accessible at every subsequent log-in). When the data extract is ready for download, an e-mail is sent to alert the researcher.
For each extract submitted, the researcher downloads a compressed ASCII data file, an extract-specific codebook, and a command file with syntax to convert the ASCII data to the preferred file format. At any time, the researcher can return to the personalized data download Web page to revise or resubmit a previously created extract request. Users who encounter difficulties can e-mail IHIS user support for assistance.
Project Status and Future Plans
The Integrated Health Interview Series currently consists of more than 1000 integrated variables selected from NHIS data files for 1969 to the present. However, this is only a fraction of the total variables available in NHIS. Additional variables are steadily being added. Furthermore, users can link additional variables from the original NHIS public use files to an IHIS data extract. A user note with guidance on linking, and syntax files for merging are provided on the website.
IHIS data can be used by population health researchers in numerous ways. The data can document trends over time in the incidence of conditions such as diabetes, the prevalence of health behaviors such as smoking, or disparities in cancer screening. Exposure-outcome relationships such as socioeconomic indicators (eg, education, income, or poverty status) and cause-specific mortality can be examined for the years 1986–2000. Pooling multiple years of IHIS data can provide sufficient sample size to study small subgroups such as American Indians, farmers, or new immigrants.
We are making available links between the original NHIS survey text and each IHIS variable description. We are extending the time series backward by including new public use files for 1963–1968 (these files have recently been released by National Center for Health Statistics staff). We are also in the process of developing new features for the Web site, including on-line tabulation and a search engine to help users efficiently locate variables.
Old health survey data are not simply of historical interest; rather, they are essential tools for understanding the dynamics of population health. Our goal with IHIS is to reduce barriers to cross-temporal analysis by using 4 decades of NHIS data. These integrated, well-documented, and easily accessible health data provide an important new data resource for epidemiologic and population health research.
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