Hierarchical modeling to assess geographic disparities is an important tool for policymakers trying to decide where to focus limited resources. With our model, it was possible to use national survey data to produce statewide mammography estimates based on county-level occupation and education. These estimates enabled us to identify states that warrant concentrated efforts to improve breast cancer screening rates. The residual variation estimated by our model suggested that geographic locations merit consideration, in their own right, when planning intervention research.
Our methodology does not require the use of any particular data set. We chose the NHIS because data are uniformly collected and yield comparable measures across states. However, data from the Behavioral Risk Factor Surveillance System or the Centers for Disease Control (CDC) Breast and Cervical Cancer Early Detection Program could also be usefully incorporated into a hierarchal model for screening. It would be useful to validate our findings with one or more of these other data sets. Other approaches to hierarchical modeling that also yield estimates of geographic variation could be employed. We used a FORTRAN program to fit a hierarchical Bayesian model (Ghosh and Rao 1994
; Rao 1999
). However, a number of alternative approaches and computational resources are now accessible (e.g., SAS PROC NLMIXED [Littell et al. 1996
] and WINBUGS [Spiegelhalter 1999
Other studies using the NHIS show that a minority of women were being screened in 1987. The subsequent rapid increase in use of mammography (Breen and Kessler 1994
) reflected changed public awareness of the need for early detection and the acceptability of mammography. The mammography estimates from our fitted model reflect this strong secular trend. Our results confirm previous findings that the determinants of underutilizers changed over time as mammography became widely recommended by health care providers and adopted by women (Makuc et al. 1999
; Anderson et al. 1995
What is new about our findings is that where people live may be an independent factor in mammography use. Several authors have reported associations between neighborhood and disease, for example, see Roux et al. (2001)
for results concerning coronary heart disease and neighborhood of residence. However, our findings show that health behaviors may also be related to particular locales. Other studies are needed to confirm this. Collaborations among behavioral and social scientists and statisticians are needed to elucidate what it is about locality that influences mammography use above and beyond the individual characteristics of underutilizers. These results are needed to inform policy concerning where and how programs are implemented.
The Intervention Study Data Base we used allowed us to identify states where published intervention research took place. Using it and the results from our analysis of the NHIS data, we inferred that eighteen states have both low utilization and no published intervention research. These eighteen states represent untapped opportunities for expanding the current knowledge base of intervention research.
Most published studies showed mammography use increased significantly in the populations they targeted. However, we cannot infer how these studies affected mammography rates in the broader populations where interventions took place. Many interventions focused on only a limited area within a state. Moreover, local interventions are unlikely to dramatically impact county or state mammography rates. It is possible that states that hosted studies but continued to exhibit low utilization would have fared even more poorly in the absence of intervention research. In addition to scientific research, government sponsored demonstration programs and other aspects of health care delivery have promoted use of mammography.
Nonresearch programs serving poor and uninsured women play an important part in promoting mammography use and their impact is not captured in this analysis. The widespread CDC National Breast and Cervical Cancer Early Detection Program (NBCCEDP) is a good example. Nevertheless, our results are consistent with the level of nonresearch programmatic activity taking place at the time. Using NHIS data, we found that the proportion of black women reporting mammograms in the southeastern United States was low and, in Illinois, it was low for black women 65 and older. Not one study in our Intervention Research Database specifically targeted women 65 years or older in any of these states between 1984 and 1994. Of the southeastern states, no intervention research targeting blacks occurred in Tennessee, Mississippi, Louisiana, Alabama, South Carolina, or Florida at that time either. Half of the states without CDC comprehensive screening programs in 1995 also did not have intervention research studies identified using the Intervention Study Data Base. (These states include, NV, ID, MT, WY, ND, SD, KT, MS, and AL [Henson et al. 1996
]). These results strongly suggest opportunities for intervention research in states where it has not yet occurred.
The Health Plan Employer Data and Information Set (HEDIS) was introduced between the study periods (1987 and 1993–94). The HEDIS, which provides an indication of mammography use in managed care settings, created an incentive for managed care organizations to promote screening mammography among their enrollees. This increase, which affects only insured women, is reflected in the average screening prevalence we estimated in and we expect it to continue to increase.
Medicare began covering biennial routine screening mammography in 1991. Our findings using NHIS reflect increased use of the benefit by women 65 years and older who reported higher rates in 1993–94 than in 1987. In 1998, Medicare began covering annual mammography and removed the deductible for screening mammography. This improvement in the Medicare benefit is expected to further boost mammography screening rates for women 65 and older.
In order to build on existing research findings and more effectively use programs that deliver health services, we need to develop a collaborative infrastructure for disseminating and diffusing effective interventions in areas where need is identified. States without studies are largely rural and have relatively little research infrastructure. Conducting scientific interventions in areas without research infrastructure poses special difficulties. However, it is possible to do so with creativity and commitment. Government agencies, other funders, and researchers need to work together to overcome obstacles posed by limited infrastructure in these geographic areas.
The independent variables for our model were originally chosen on statistical grounds; however there is strong justification in economic theory for using the predictors of occupation and education. Previous studies have shown that white-collar workers are more likely to have insurance. Conversely, areas with a low percentage of white-collar workers may have fewer health services because effective demand is reduced since less of the population is insured. Interventions to promote mammography would be especially useful in these areas. Mammography services could be located in public clinics or delivered using mobile vans, for example.
Our objective for this analysis was to provide a scientific basis for planning future mammography interventions. To do this, we quantified geographic differences in mammography utilization and compared them to intervention research. Our approach of taking into account geographic context and location is even more critical now, in an era of widespread adoption, because hard-to-reach populations may remain outside the focus of intervention research. We encourage investigators to tailor proven research to populations in locations where it is most needed. We also encourage funders to explicitly solicit such research projects. Though our paper addresses interventions to promote mammography, this approach can be generalized to other health services.