Predicting Patterns of Mammography Use: A Geographic Perspective on National Needs for Intervention Research


  • Julie Legler,

  • Nancy Breen,

  • Helen Meissner,

  • Don Malec,

  • Cathy Coyne

Julie Legler, Sc.D., Mathematical Statistician, Statistical Research and Applications Branch, Surveillance Research Program, National Cancer Institute, EPN Room 4094, 6130 Executive Blvd., MSC 7359, Bethesda, MD 20892. Nancy Breen, Ph.D., is an Economist, Health Services and Economics Branch, Applied Research Program, National Cancer Institute, Bethesda, MD. Helen Meissner, Sc.M., is Chief, Applied Cancer Screening Research Branch, Behavioral Research Program, National Cancer Institute, Bethesda, MD. Don Malec, Ph.D., is Principal Researcher, Statistical Research Division, U.S. Census Bureau, Washington, DC. Cathy Coyne, Ph.D., is Assistant Professor, West Virginia University, Morgantown, WV.


Objective. To introduce a methodology for planning preventive health service research that takes into account geographic context.

Data Sources. National Health Interview Survey (NHIS) self-reports of mammography within the past two years, 1987, and 1993–94. Area Resource File (ARF), 1990. Database of mammography intervention research studies conducted from 1984 to 1994.

Design. Bayesian hierarchical modeling describes mammography as a function of county-level socioeconomic data and explicitly estimates the geographic variation unexplained by the county-level data. This model produces county use estimates (both NHIS-sampled and unsampled), which are aggregated for entire states. The locations of intervention research studies are examined in light of the statewide mammography utilization estimates.

Data Extraction. Individual level NHIS data were merged with county-level data from the ARF.

Principal Findings. State maps reveal the estimated distribution of mammography utilization and intervention research. Eighteen states with low mammography use reported no intervention research activity. County-level occupation and education were important predictors for younger women in 1993–94. In 1987, they were not predictive for any demographic group.

Conclusions. Opportunities exist to improve the planning of future intervention research by considering geographic context. Modeling results suggest that the choice of predictors be tailored to both the population and the time period under study when planning interventions.