The effect of disease management on utilization of services by race/ethnicity: evidence from the Florida Medicaid program.

Objective: To measure possible racial/ethnic differences in a Florida disease management (DM) program on utilization of selected healthcare services for 4 chronic illnesses.

Study Design: Pre-post comparison of utilization among 15,275 high-risk beneficiaries enrolled in DM in the Florida Medicaid program between October 2001 and October 2003.

Methods: Two-part regression analyses of the effect of DM on annualized inpatient days, emergency department (ED) visits, and outpatient visits, controlling for relevant covariates.

The effects of a disease management program on self-reported health behaviors and health outcomes: evidence from the "Florida: a healthy state (FAHS)" Medicaid program.

Premature morbidity and mortality from chronic diseases account for a major proportion of expenditures for health care cost in the United States. The purpose of this study was to measure the effects of a disease management program on physiological and behavioral health indicators for Medicaid patients in Florida. A two-year prospective study of 15,275 patients with one or more chronic illnesses (congestive heart failure, hypertension, diabetes, or asthma) was undertaken.

Impact of disease management on health care utilization: evidence from the "Florida: A Healthy State (FAHS)" Medicaid Program.

Objective: To examine the impact of disease management on utilization of selected health care services.

Method: Prospective observational population-based study comparing Florida Medicaid patients who elected to participate in disease management (DM, N=15,275) with a usual-care (UC, N=32,034) group who elected not to participate in the program. Patients had at least one of four chronic diseases (diabetes, asthma, congestive heart failure, and hypertension) and all received standard health care.

Methods for improving regression analysis for skewed continuous or counted responses.

Standard inference procedures for regression analysis make assumptions that are rarely satisfied in practice. Adjustments must be made to insure the validity of statistical inference. These adjustments, known for many years, are used routinely by some health researchers but not by others.