Correlating pharmaceutical data with a national health survey as a proxy for estimating rural population health.

BACKGROUND: Chronic disease accounts for nearly three-quarters of US deaths, yet prevalence rates are not consistently reported at the state level and are not available at the sub-state level. This makes it difficult to assess trends in prevalence and impossible to measure sub-state differences. Such county-level differences could inform and direct the delivery of health services to those with the greatest need. METHODS: We used a database of prescription drugs filled in the US as a proxy for nationwide, county-level prevalence of three top causes of death: heart disease, stroke, and diabetes. We tested whether prescription data are statistically valid proxy measures for prevalence, using the correlation between prescriptions filled at the state level and comparable Behavioral Risk Factor Surveillance System (BRFSS) data. We further tested for statistically significant national geographic patterns. RESULTS: Fourteen correlations were tested for years in which the BRFSS questions were asked (1999-2003), and all were statistically significant. The correlations at the state level ranged from a low of 0.41 (stroke, 1999) to a high of 0.73 (heart disease, 2003). We also mapped self-reported chronic illnesses along with prescription rates associated with those illnesses. CONCLUSIONS: County prescription drug rates were shown to be valid measures of sub-state estimates of diagnosed prevalence and could be used to target health resources to counties in need. This methodology could be particularly helpful to rural areas whose prevalence rates cannot be estimated using national surveys. While there are no spatial statistically significant patterns nationally, there are significant variations within states that suggest unmet health needs.

Evaluating heart disease presciptions-filled as a proxy for heart disease prevalence rates.

Heart disease is the leading cause of death in the U.S. Yet, prevalence rates are not reported at the county level. Not knowing how many have the disease, and where they are, may be a knowledge barrier to effective health care interventions. We use heart disease drug prescriptions-filled as a proxy measure for prevalence of heart disease. We test the correlation to the Behavioral Risk Factor Surveillance System (BRFSS) and find positive, statistically significant correlations. Next we illustrate the geographic patterns revealed using the county-level prevalence estimate maps. This information can be used to provide a better understanding of sub-state variations in disease patterns and subsequently target the delivery of health resources to small areas in need.

Assessing the patient's mammogram experience.

This study overviews an operational blueprint that diagrams the activities and interactions of all participants in a typical screening mammography appointment in a large medical center. The blueprint is constructed from multiple sources of data collected from mammography patients, service providers in the radiology department, and medical records. The benefits from using patient perspectives, the insights gained from the blueprint development process, and the value of the resulting screening mammography appointment blueprint are included.

A method for identifying places in rural America at risk of not being able to support adequate health services.

CONTEXT: Public policymakers and their advisers struggle with the problem of specifying criteria by which health care providers in rural areas are eligible for special consideration in payment policies and for special grant programs. A means of designating places can provide a basis for assistance and can help target public resources for any providers who deliver services in those places. PURPOSE: This paper provides the details underlying a place-based approach to identifying rural areas that are at risk for not being able to provide requisite health services. METHODS: A population size criterion is utilized first to eliminate metropolitan areas and other large agglomerations from consideration. Any territory not included in a place of 3500 or more people, including a 25-mile buffer around that place, is a priori considered to be at risk. All places, including buffers, that have populations between 3500 and 100,000 are further analyzed using population compositional data and principal components analysis. FINDINGS: In 10 states and 24 bordering states selected for developing and testing the method, there were 1907 block groups outside the boundaries of any place with a population of at least 3500. In addition, the analysis suggested that 66 out of 236 places and buffers with populations between 3500 and 100,000 also should be classified as vulnerable. CONCLUSIONS: The results are discussed in regard to how a place-based approach can advance the study of rural health needs. By focusing on the needs of the people residing in a defined area, as determined from the aggregate characteristics of the population, a model is generated that can be used to predict special circumstances confronting any service provider. The public policy implications of the findings are also considered. Special payment policies could be written on the basis of place instead of provider characteristics, and grant programs providing technical assistance could be targeted to places of greatest need.

Racial and ethnic differences in health insurance for the near elderly.

Recent increases in the number of Americans without health insurance have spurred research designed to identify factors related to noncoverage. One age group receiving recent attention is the near elderly, persons 55 through 64 years of age. This paper brings together health insurance research on the near elderly with that focused on racial and ethnic differences in coverage. Logistic regression is used to study the factors that predict whether or not an individual has health insurance coverage. Results indicate that even after accounting for health insurance correlates such as education and income, non-Hispanic African Americans and Hispanics have a significantly higher probability of not having coverage than their non-Hispanic white counterparts. The results are discussed in terms of recent efforts to reshape public policy regarding health insurance, especially proposals that would affect the Medicare program.

Health insurance status of the adult, nonelderly foreign-born population.

The last two decades have been marked by substantial immigration to the United States. As a result of this movement, the foreign-born population is growing rapidly. Previous studies have shown that the foreign-born population is much more likely than the native-born one to be without health insurance. The present analysis focuses on factors that distinguish the insured from the uninsured, utilizing nativity status (foreign born versus native born) as one of the independent variables in a set of logistic regression models. Results show that even after controlling for income, employment status, and other variables known to be associated with health insurance status, the foreign born are twice as likely to be without health insurance than are their native-born counterparts. Among the foreign born, recency of arrival emerges as an important factor in distinguishing the insured from the uninsured. Public policies intended to address the problem of health insurance in the foreign-born population must go beyond being based only on economic considerations and take into account factors such as cultural background and health-related attitudes to be effective.