Pharmaceutical Data Validity in Estimating Rural Population Health

Lead researcher:
Project completed:
December 2006
Chronic diseases account for more than 60% of total medical expenditures in the U.S., and 70% of all deaths. Yet, morbidity rates for the vast majority of chronic illnesses are not reported at the county level and even those diseases with county-level reports are typically not collected nationwide (e.g., the cancer registries). We use a database of drug prescriptions filled in the U.S., as a proxy for nationwide county-level prevalence of three top causes of death: heart disease, stroke and diabetes. We address three separate research issues. First, are the data a valid proxy? We tested the correlation between our data set of prescriptions filled against the gold standard, the state-level Behavioral Risk Factor Surveillance System (BRFSS). The statistically significant correlations ranged from a low of 0.406 (stroke in 1999) to a high of 0.733 (heart disease in 2003) for years in which all states reported, thus validating the prescription data as a proxy measure for chronic disease prevalence. Next we address the geographic patterns revealed using the sub-state or county-level prevalence maps. The third research issue links chronic illness (morbidity) with death (mortality). In some cases the mortality rate from heart disease is high, but the prescription rate for heart disease medications is low, possibly suggesting under-diagnosis or under-treatment. Likewise, some places have relatively low cause-specific mortality and high cause-specific prescription rates, suggesting over-treatment, or effective use of pharmaceuticals to reduce mortality rates. Finally, we test the role that rurality plays in prescriptions-filled rates among the adult population. Across the rural-urban continuum, heart disease Rx rates are higher (14% to 16% of the adult population in metro areas, higher (14% to 18%) in urban areas and relatively lower in rural areas (8% to 10%). We test the explanation that rural counties have lower prescriptions-filled rates because there are fewer or no pharmacies in some rural counties. Through an analysis of County Business Patterns, we determined that an existing lack of pharmacies in rural counties (Rural Urban Continuum Codes 8 & 9) may account for fewer or zero prescriptions filled, while the addition of a pharmacy results in a statistically significant increase in prescriptions-filled in those rural category counties. In sum, county rates of prescription drugs are shown to be a valid measure with mapping quality that can be used to identify prescription concentrations-both high and low, likely also indicating concentrations of chronic disease prevalence. This information can then be used to target the delivery of needed health resources to all counties in need, both urban and rural. However, this methodology particularly targets rural areas whose prevalence rates cannot be estimated with national surveys.

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