To describe changes in the organizational structure of state health-related departments/agencies between 1990 and 2009; to identify factors associated with key organizational structures; and to investigate their relationship with different resource allocations across health policy areas, as represented by state budgets.
Original data collection on the organization of state health-related departments/agencies from 1990 to 2009. Analyses included descriptive statistics, logistic regression, and time-series regression modeling.
All 50 states.
Organizational structure of state government related to health in 4 areas (Medicaid, public health, mental health, human services); coupling of Medicaid and public health in the same agency; state budget changes in health policy areas, including Medicaid, public health, and hospitals.
The housing of 2 or more health-related functions in the same unit was common, with 21 states combining public health and Medicaid at 1 or more points in time. Eighteen states (36%) reorganized their health agencies/departments during the study period. Controlling for numerous economic, social, and political factors, when the state agency responsible for public health is consolidated with Medicaid, the share of the state budget allocated to Medicaid declined significantly, while public health allocations were unchanged. However, consolidating Medicaid with other services did not impact state Medicaid spending.
Government organizational structure related to health varies greatly across states and is somewhat dynamic. When Medicaid and public health functions are consolidated in the same stage agency, public health does not “lose” in terms of its share of the state budget. However, this could change as Medicaid costs continue to grow and with the implementation the Patient Protection and Affordable Care Act of 2010.
This article analyzes the changes in the organizational structure of state health-related departments/agencies between 1990 and 2009, which included descriptive statistics, logistic regression, and time-series regression modeling.
Department of Health Policy, School of Public Health and Health Services at George Washington University, Washington (Dr Lantz); Department of Health Management and Policy (Dr Alexander) and Department of Epidemiology (Dr Montgomery), School of Public Health, University of Michigan, Ann Arbor, Michigan; Department of Political Science, and Statistics, and Center for Statistics and the Social Sciences, University of Washington (Dr Adolph).
Correspondence: Paula M. Lantz, PhD, MS, Department of Health Policy, 2021 K St, Suite 800, George Washington University, Washington, DC 20006 (firstname.lastname@example.org.).
This research was supported by a grant from the Center for Healthcare Research and Transformation, Ann Arbor, Michigan. The study was approved by the Health Sciences institutional review board at the University of Michigan. The authors thank Phillip Stadler for research assistance and Marianne Udow, MHSA, and Matthew Boulton, MD, MPH, for their helpful contributions to the research objectives and design.
The authors declare no conflicts of interest.