Workforce Allocation in Urban Community Mental Health Services: GIS-Based Analytical Insights for Policy and Planning

<b>Background/Objectives:</b> This study aims to provide a comprehensive understanding of the current mental health workforce and the factors influencing its distribution within adult community mental health services in Western Australia's North Metropolitan Health Service. <b>Methods:</b> Mental health workforce supply across North Metropolitan Statistical Area Level 2 (SA2-Australian Statistical Geography Standard) was estimated using the Geographically-adjusted Index of Relative Supply (GIRS) and categorised as low (0-3) or moderate-to-high (4-8) for analysis and testing associations with multiple covariates. Population, clinic, and individual-level data were analysed using principal component analysis and logistic regression to identify the factors associated with workforce distribution. <b>Results:</b> Of the 68 SA2s analysed, 25 SA2s (representing 45 suburbs) were identified as having a low workforce supply, defined by a GIRS score of ≤3. These areas were compared to those with a moderate-to-high supply (GIRS > 3) to assess the differences in service performance. A principal component analysis identified three key components within the data: service usage, health service providers, and service efficiency. A logistic regression analysis revealed that areas with a low workforce supply were significantly more likely to experience reduced service usage (OR = 3.3, <i>p</i> = 0.037, CI [0.09-0.92]), indicating fewer patient interactions and lower engagement with mental health services. In addition, these areas demonstrated a lower service efficiency as evidenced by longer wait times (OR = 3.7, <i>p</i> = 0.002, CI [1.62-8.50]), suggesting that workforce shortages directly impact timely access to health care. <b>Conclusions:</b> The findings revealed disparities in workforce supply across different urban locations, with low-supply areas facing tangible challenges in service accessibility and operational efficiency. These findings highlight the need for targeted mental health workforce planning. Developing and implementing best practice guidelines is essential to effectively manage service demands and reduce waitlists.