Poverty Analysis Toolkit

A case study on the use of Stata.

Stata makes a difference at the Health Policy Institute of Ohio

The Health Policy Institute of Ohio (HPIO) is an independent, nonpartisan, statewide center that fosters sound health policy within the state by forecasting health trends, analyzing key health issues, and communicating current research to policymakers, state agencies, and other decision makers. HPIO promotes and facilitates health policy research among research centers, universities, and other organizations. It identifies gaps in health policy research and data; designs studies; leads the development of a statewide health policy research agenda; promotes collaboration among researchers; develops research projects to address health problems; and, as necessary, undertakes research directly. In addition, HPIO assists researchers in presenting important findings and serves as a network facilitator among health researchers and practitioners.

HPIO primarily depends upon Stata as its analytical weapon of choice

Examples of research topics include the uninsured and underinsured, health systems capacities, health safety net capacities, determinants of health, health disparities, health care reform, public health systems, family violence prevention, poverty, community health status, health information technologies, and behavioral health. Findings from various research topics are readily available at HPIO’s website, in public presentations, and by request. For all this work, HPIO primarily depends upon Stata as its analytical weapon of choice.

The main reasons HPIO uses Stata are its highly intuitive interface, its support for complex survey data, its epidemiology commands, and its support for various types of biostatistical, social-science, and econometric analyses. Some examples of how Stata has helped HPIO in its analytic needs are in analysis of the 2008 Ohio Family Health Survey (OFHS)—a complex, dual-framed survey of health systems, behaviors, and demographics of 50,944 Ohio adults—and in the Medicaid Atlas Project, which uses approximately 2,200,000 cases to examine Medicaid use in Ohio’s 88 counties.

For both projects, the expanded datasets are very large—the OFHS is approximately 300 megabytes and the Medicaid dataset is approximately 1.3 gigabytes. In the 1990s, analyzing such datasets was difficult because of software and equipment limitations. With the prerequisite of needing to allocate a large amount of memory at program startup, Stata/MP 11.1 easily handles the analysis of such datasets. For the OFHS, programming code to model the uninsured in Ohio is easily done using ado-files and do-files. The OFHS is the main source of Ohio-specific population-based health system information provided to the state’s legislators, agency heads, and health system stakeholders. Analysis of the OFHS provides Ohioans with information relating to how federal health reform will affect Ohio. Areas of interest include:

Examination and modeling of these types of issues relies on Stata’s survey commands, which allow us to incorporate the design characteristics of the survey.

The Medicaid Atlas Project analyzes Medicaid billing information to determine issues such as total Medicaid use per county and the number of physicians serving Medicaid patients in each county. The project also uses this data to monitor expenditures per Medicaid utilization category and to project the growth in average expenditure per category. Additionally, procedures are used to model relative-risk profiles of Medicaid enrollees versus nonenrollees and to model relative-risk profiles of Medicaid managed-care enrollees versus fee-for-service enrollees. Because health policy stakeholders are large contributors of health services in the state, determining the overall populationbased health impact of Ohio Medicaid is very important to them. For example, Ohio has experienced a prolonged economic downturn, having lost over 560,000 jobs since 2000.

During this period, because of the State Children’s Health Insurance Program (SCHIP), Ohio’s rate of uninsured children actually decreased while the adult rate increased. Using internal data from Medicaid mixed with state-specific external data from surveys allowed us to estimate the risk buffering of children’s access to health care that is attributable to Medicaid in hard economic times.

Finally, Stata’s web-enabled interactive search capacities are often indispensable for figuring out complex data setup and analysis issues.

“The main reasons HPIO uses Stata are its highly intuitive interface, its support for complex survey data, its epidemiology commands, and its support for various types of biostatistical, social-science, and econometric analyses.”

The Stata community, including researchers at universities, research institutes, and government agencies, is an excellent resource for figuring out problems. As an example, HPIO is participating in a project to test a concept for examining simulated benefit models for dual-frame surveys—surveys where samples are drawn independently from two overlapping sampling frames to cover the population of interest (e.g., respondents to a survey of households with landline telephones and households with both cell phones and landline telephones). The research team intends to develop a program in Stata that will enable survey researchers to determine whether to develop dual-frame or single-frame surveys rather than making sampling decisions based upon convenience.

In summary, Stata allows the Health Policy Institute of Ohio and its partners to keep an analytical edge on very complex health issues. The program is robust enough to handle very large datasets, fast enough in its MP versions to use high-end computers, and thorough enough to address epidemiology, social-science, and econometric analyses.

Timothy R. Sahr, Director of Research, The Health Policy Institute of Ohio

Director of Research, The Ohio Colleges of Medicine Government Resource Center

Reproduced with permission from The Stata News Vol 25, No 3, September 2010