Objectives To analyse the relation between geographical inequalities in income and the prevalence of common chronic medical conditions and mental health disorders, and to compare it with the relation between family income and these health problems.

We recently documented plasma lipid dysregulation in preclinical late-onset Alzheimer's disease (LOAD). A 10 plasma lipid panel, predicted phenoconversion and provided 90% sensitivity and 85% specificity in differentiating an at-risk group from those that would remain cognitively intact. Despite these encouraging results, low positive predictive values limit the clinical usefulness of this panel as a screening tool in subjects aged 70-80 years or younger. In this report, we re-examine our metabolomic data, analyzing baseline plasma specimens from our group of phenoconverters (n = 28) and a matched set of cognitively normal subjects (n = 73), and discover and internally validate a panel of 24 plasma metabolites. The new panel provides a classifier with receiver operating characteristic area under the curve for the discovery and internal validation cohort of 1.0 and 0.995 (95% confidence intervals of 1.0-1.0, and 0.981-1.0), respectively. Twenty-two of the 24 metabolites were significantly dysregulated lipids. While positive and negative predictive values were improved compared to our 10-lipid panel, low positive predictive values provide a reality check on the utility of such biomarkers in this age group (or younger). Through inclusion of additional significantly dysregulated analyte species, our new biomarker panel provides greater accuracy in our cohort but remains limited by predictive power. Unfortunately, the novel metabolite panel alone may not provide improvement in counseling and management of at-risk individuals but may further improve selection of subjects for LOAD secondary prevention trials. We expect that external validation will remain challenging due to our stringent study design, especially compared with more diverse subject cohorts. We do anticipate, however, external validation of reduced plasma lipid species as a predictor of phenoconversion to either prodromal or manifest LOAD.

Attempts to explain disparities in access to health care faced by racial and ethnic minorities and other underserved populations often focus on individual-level factors such as demographics, personal health beliefs, and health insurance status. This article proposes an examination of these disparities-and an effort to redress them-through the lens of public health. Public health agencies can link people to needed services such as immunizations, testing, and treatment; ensure the availability of health care; ensure the competency of the public health and personal health care workforce; and evaluate the effectiveness, accessibility, and quality of personal and population-based services. Approaching disparities through a public health framework can provide the foundation for developing more robust evidence to inform additional policies for improving access and reducing disparities.

The results suggest that characteristics of the local population, including language and nativity, play an important role in access to health care among U.S. Hispanics, and point to the need for further study, including analyses of other racial and ethnic groups, using different geographic constructs for describing the local population, and, to the extent possible, more specific exploration of the mechanisms through which these characteristics may influence access to care.

Objective. To calculate variable‐radius measures of hospital market size and create measures of competition for hospitals' markets. Data Sources. Discharge abstracts from the 1997 State Inpatient Databases of the Healthcare Cost and Utilization Project (HCUP) linked with the American Hospital Association (AHA) Annual Survey, Area Resource File (ARF), InterStudy Regional Market Analysis database, and Medicare's Prospective Payment System Impact Files. Study Design. Hospital radii capturing 75 and 90 percent of hospital admissions regressed against hospital and health care market characteristics and other local area characteristics, where the specification was designed to maximize predictive ability. The number of competing hospitals and the Herfindahl‐Hirschman index (HHI) of competition were calculated for each hospital's market. Data Collection Methods. Discharge abstracts were used to create actual radii for hospitals in nine states. These data were linked with other data describing hospital, health care market, and other characteristics. Principal Findings. We explained 44.7 and 9.6 percent of the variation among urban and rural hospitals, respectively, in radii that capture 90 percent of patients, and slightly less of the variation in radii that capture 75 percent of patients. Population density; number of other hospitals in the local area; and hospital characteristics such as medical school affiliation, percentage of admissions that are Medicaid, case mix, and service offerings are important correlates of a hospital's market size. Conclusions. Predicted radii and associated competition measures were created (matched to AHA hospital identifiers) for all nonfederal, short‐term, general medical/surgical hospitals in the continental United States for which complete data were available in 1997 (N=4,806) and are available from the authors.