Variables associated with variations in compulsory community treatment by health districts: A meta-regression analysis across four Australian jurisdictions.

The use of compulsory community treatment in Australia varies widely within, and across, jurisdictions for reasons that are not fully understood. This study, therefore, used meta-regression to explore possible explanations for this variation in the following four Australian states: Queensland, South Australia, New South Wales and Victoria.

Aggregated administrative data from the Australian Institute of Health and Welfare for 2018 were analysed, encompassing 402,060 individuals in contact with community mental health services. Meta-regression using logit-transformed proportions was employed to assess associations between compulsory community treatment rates and district-level sociodemographic variables, including age, sex, marital status, region of birth and metropolitan residence. Workforce data were also examined in New South Wales.

Compulsory community treatment use varied significantly by jurisdiction and by health district within jurisdictions. Higher rates were associated with districts having more individuals aged 25-44 years, males (in South Australia), metropolitan residents, and those born outside Oceania. These associations persisted after adjustment. There was no statistically significant association between workforce levels and compulsory community treatment use in New South Wales.

Our findings suggest that sociodemographic factors such as age, sex and place of residence or birth may explain some of the variation in compulsory community treatment use between health districts. The influence of sociodemographic variables and geographical location in influencing compulsory community treatment use at the health district level needs further investigation.

Australian and New Zealand Clinical Trials Registry (https://www.anzctr.org.au/; ACTRN12624000153516).
Mental Health
Care/Management

Authors

Kisely Kisely, Bull Bull, Zirnsak Zirnsak, Edan Edan, Lawn Lawn, Light Light, Maylea Maylea, Ryan Ryan, Sansanwal Sansanwal, Weller Weller, Brophy Brophy
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