The Effect of Medicaid Disease Management Programs on Medicaid Expenditures

Disease Management (DM) programs for Medicaid patients with chronic diseases have become very popular, with a majority of states having introduced some type of DM program in the last decade. These programs provide interventions designed to assist patients and their health care providers appropriately manage their chronic health condition(s) according to established clinical guidelines. Cost-containment has been a key justification for the creation of DM programs, despite mixed evidence that DM actually saves money for the Medicaid program or for society as a whole. While most studies on the impact of DM focus on estimating the impact of a single DM program, Chapter 2 estimates the average, national impact of state Medicaid DM programs by linking a detailed survey of state Medicaid programs to the nationally representative Medical Panel Expenditure Survey. Difference-in-difference models are used to test the hypothesis that medical expenditures change after a DM program is implemented, exploiting variation in the timing at which state Medicaid programs implemented DM programs. DM coverage also varies within states over time due to variation in program eligibility by disease, insurance category, and/or county of residence. Although the models estimate the effect of DM imprecisely, point estimates are stable across multiple specifications and indicate that DM programs for common chronic diseases may decrease total medical expenditures, potentially by 10 percent or more. Chapter 3 evaluates one DM program in the state of Georgia using a proprietary data set. By exploiting a natural experiment that delayed the introduction of high-intensity services for several thousand high and moderate risk patients, the research identifies the causal impacts of the program's interventions on total Medicaid expenditures, categories of health care utilization, and other indicators. These patients are observationally similar to those who received interventions at the beginning of the program. For example, I find the interventions lowered health costs and hospital utilization, after controlling for unobservable individual characteristics. Health expenditures were lowered about 4.4 percent for patients with positive expenditures. Heterogeneous treatment effect analysis indicates that the savings were largest at the most expensive tail of the distribution

Estimating the incidence of unintended births and pregnancies at the sub-state level to inform program design.

ObjectivesUnintended (mistimed or unwanted) pregnancies occur frequently in the United States and have negative effects. When designing prevention programs and intervention strategies for the provision of comprehensive birth control methods, it is necessary to identify (1) populations at high risk of unintended pregnancy, and (2) geographic areas with a concentration of need.MethodsTo estimate the proportion and incidence of unintended births and pregnancies for regions in Missouri, two machine-learning prediction models were developed using data from the National Survey of Family Growth and the Missouri Pregnancy Risk Assessment Monitoring System. Each model was applied to Missouri birth certificate data from 2014 to 2016 to estimate the number of unintended births and pregnancies across regions in Missouri. Population sizes from the American Community Survey were incorporated to estimate the incidence of unintended births and pregnancies.ResultsAbout 24,500 (34.0%) of the live births in Missouri each year were estimated to have resulted from unintended pregnancies: about 25 per 1,000 women (ages 15 to 45) annually. Further, 40,000 pregnancies (39.7%) were unintended each year: about 41 per 1,000 women annually. Unintended pregnancy was concentrated in Missouri's largest urban areas, and annual incidence varied substantially across regions.ConclusionsOur proposed methodology was feasible to implement. Random forest modeling identified factors in the data that best predicted unintended birth and pregnancy and outperformed other approaches. Maternal age, marital status, health insurance status, parity, and month that prenatal care began predict unintended pregnancy among women with a recent live birth. Using this approach to estimate the rates of unintended births and pregnancies across regions within Missouri revealed substantial within-state variation in the proportion and incidence of unintended pregnancy. States and other agencies could use this study's results or methods to better target interventions to reduce unintended pregnancy or address other public health needs