Background
Identifying which factors contribute to involuntary psychiatric hospitalization may support initiatives to reduce its frequency. This study examines the sociodemographic, clinical, and contextual factors associated with involuntary hospitalization of patients from five Portuguese psychiatric departments in 2002, 2007 and 2012.
Methods
Data from all admissions were extracted from clinical files. A Poisson generalized linear model estimated the association between the number of involuntary hospitalizations per patient in one year and sociodemographic, clinical, and contextual factors.
Results
An increment of involuntary hospitalizations was associated with male gender [exp($$\widehat{\upbeta }$$
β
^
) = 1.31; 95%CI 1.06–1.62, p < 0.05], having secondary and higher education [exp($$\widehat{\upbeta }$$
β
^
) = 1.45; 95%CI 1.05–2.01, p < 0.05, and exp($$\widehat{\upbeta }$$
β
^
) = 1.89; 95%CI 1.38–2.60, p < 0.001, respectively], a psychiatric diagnosis of psychosis [exp($$\widehat{\upbeta }$$
β
^
) = 2.02; 95%CI 1.59–2.59, p < 0.001], and being admitted in 2007 and in 2012 [exp($$\widehat{\upbeta }$$
β
^
) = 1.61; 95%CI 1.21–2.16, p < 0.01, and exp($$\widehat{\upbeta }$$
β
^
) = 1.73; 95%CI 1.31–2.32, p < 0.001, respectively]. A decrease in involuntary hospitalizations was associated with being married/cohabitating [exp($$\widehat{\upbeta }$$
β
^
) = 0.74; 95%CI 0.56–0.99, p < 0.05], having experienced a suicide attempt [exp($$\widehat{\upbeta }$$
β
^
) = 0.26; 95%CI 0.15–0.42, p < 0.001], and belonging to the catchment area of three of the psychiatric services evaluated [exp($$\widehat{\upbeta }$$
β
^
) = 0.65; 95%CI 0.49–0.86, p < 0.01, exp($$\widehat{\upbeta }$$
β
^
) = 0.67; 95%CI 0.49–0.90, p < 0.01, and exp($$\widehat{\upbeta }$$
β
^
) = 0.67; 95%CI 0.46–0.96, p < 0.05 for Hospital de Magalhães Lemos, Centro Hospitalar Psiquiátrico de Lisboa and Unidade Local de Saúde do Baixo Alentejo, respectively].
Conclusions
The findings suggest that involuntary psychiatric hospitalizations in Portugal are associated with several sociodemographic, clinical, and contextual factors. This information may help identify high-risk patients and inform the development of better-targeted preventive interventions to reduce these hospitalizations.
