Mental Health Evacuation Rate in USCENTCOM.

BACKGROUND: Mental health diagnosis requiring further treatment is one of the top reasons for medical evacuation in the U.S. Central Command (USCENTCOM) area of responsibility (AOR) as of 2022. This study establishes a baseline in which the effectiveness of medical interventions can be measured to determine if they have an impact on the rate of evacuation out of USCENTCOM. MATERIALS AND METHODS: The study period was January 1, 2017 to December 31, 2021. Individual evacuation data including date of initial movement and necessary specialty care requirements originating from the USCENTCOM AOR were acquired via U.S. Transportation Command's Regulating and Command & Control Evacuation System. The base evacuation rate was calculated for each month, and evacuation rates were analyzed for variations. RESULTS: For the entire study period, the mean monthly evacuation rate was 0.44 evacuations per 1,000 people in the AOR (95% CI, 0.41-0.47). There was no statistically significant difference between any monthly evacuation rate (P = .505). There is a statistically significant difference in the mean evacuation rates for calendar years (P = .003). The highest evacuation rate occurred in 2021. CONCLUSIONS: The study establishes a benchmark mental health evacuation rate. This rate will be useful for assessing mental health evacuation reduction initiatives in the USCENTCOM AOR.

Workforce Planning for Embedded Mental Health Care in the U.S. Navy.

INTRODUCTION: Embedding mental health providers directly into operational units provides opportunities for holistic individual and population focused mental health support. To effectively provide clinical mental health care to a large number of Sailors and Marines while supporting the larger command, it is crucial to arrive at an optimal number of mental health (MH) care staff. In response to an increasing demand for MH care by operational units distributed globally, the U.S. Navy (USN) critically analyzed the current MH staffing levels, estimated future demand for MH care providers, and evaluated several staffing options. The following article illustrates a case study of workforce planning for the USN's embedded MH delivery model. MATERIALS AND METHODS: Several existing data sources were used to calculate current number of MH care staff across all USN platforms and to estimate demand for MH care. An open source Linear Programming application was used to estimate staffing solutions that meet business requirements in the most efficient manner possible. RESULTS: Results suggested different conclusions for embedded mental health staffing across USN communities. Depending on existing staffing levels and the number of Sailors or Marines anticipated to require care, the Linear Programming algorithm estimated needed staffing levels to address demand. CONCLUSION: The current project represents the first systematic workforce planning initiative designed to help staff the USN's global demand for community focused MH care. The results of this project have identified areas where additional embedded mental health resources should be made available. By systematically documenting all services and capabilities and carefully examining the operational demands of each community, the current solution was able to identify precisely what type of MH resources should be allocated to a given community.