Effect of COVID-19 pandemic on utilisation of community-based mental health care in North-East of Italy: A psychiatric case register study.

AIMS: WHO declared that mental health care should be considered one essential health service to be maintained during the coronavirus disease 2019 (COVID-19) pandemic. This study aims to describe the effect of lockdown and restrictions due to the COVID-19 pandemic in Italy on mental health services' utilisation, by considering psychiatric diagnoses and type of mental health contacts. METHODS: The study was conducted in the Verona catchment area, located in the Veneto region (northeastern Italy). For each patient, mental health contacts were grouped into: (1) outpatient care, (2) social and supportive interventions, (3) rehabilitation interventions, (4) multi-professional assessments, (5) day care. A 'difference in differences' approach was used: difference in the number of contacts between 2019 and 2020 on the weeks of lockdown and intermediate restrictions was compared with the same difference in weeks of no or reduced restrictions, and such difference was interpreted as the effect of restrictions. Both a global regression on all contacts and separate regressions for each type of service were performed and Incidence Rate Ratios (IRRs) were calculated. RESULTS: In 2020, a significant reduction in the number of patients who had mental health contacts was found, both overall and for most of the patients' characteristics considered (except for people aged 18-24 years for foreign-born population and for those with a diagnosis of schizophrenia. Moreover, in 2020 mental health contacts had a reduction of 57 096 (-33.9%) with respect to 2019; such difference remained significant across the various type of contacts considered, with rehabilitation interventions and day care showing the greatest reduction. Negative Binomial regressions displayed a statistically significant effect of lockdown, but not of intermediate restrictions, in terms of reduction in the number of contacts. The lockdown period was responsible of a 32.7% reduction (IRR 0.673; p-value <0.001) in the overall number of contacts. All type of mental health contacts showed a reduction ascribable to the lockdown, except social and supportive interventions. CONCLUSIONS: Despite the access to community mental health care during the pandemic was overall reduced, the mental health system in the Verona catchment area was able to maintain support for more vulnerable and severely ill patients, by providing continuity of care and day-by-day support through social and supportive interventions.

Clinical and organizational factors predicting readmission for mental health patients across Italy.

PURPOSE: The aims of our study are: to explore rehospitalization in mental health services across Italian regions, Local Health Districts (LHDs), and hospitals; to examine the predictive power of different clinical and organizational factors. METHODS: The data set included adult patients resident in Italy discharged from a general hospital episode with a main psychiatric diagnosis in 2012. Independent variables at the individual, hospital, LHD, and region levels were used. Outcome variables were individual-level readmission and LHD-level readmission rate to any hospital at 1-year follow-up. The association with readmission of each variable was assessed through both single- and multi-level logistic regression; descriptive statistics were provided to assess geographical variation. Relevance of contextual effects was investigated through a series of random-effects regressions without covariates. RESULTS: The national 1-year readmission rate was 43.0%, with a cross-regional coefficient of variation of 6.28%. Predictors of readmission were: admission in the same LHD as residence, psychotic disorder, higher length of stay (LoS), higher rate of public beds in the LHD; protective factors were: young age, involuntary admission, and intermediate number of public healthcare staff at the LHD level. Contextual factors turned out to affect readmission only to a limited degree. CONCLUSIONS: Homogeneity of readmission rates across regions, LHDs, hospitals, and groups of patients may be considered as a positive feature in terms of equity of the mental healthcare system. Our results highlight that readmission is mainly determined by individual-level factors. Future research is needed to better explore the relationship between readmission and LoS, discharge decision, and resource availability.

Standard comparison of local mental health care systems in eight European countries.

AIMS: There is a need of more quantitative standardised data to compare local Mental Health Systems (MHSs) across international jurisdictions. Problems related to terminological variability and commensurability in the evaluation of services hamper like-with-like comparisons and hinder the development of work in this area. This study was aimed to provide standard assessment and comparison of MHS in selected local areas in Europe, contributing to a better understanding of MHS and related allocation of resources at local level and to lessen the scarcity in standard service comparison in Europe. This study is part of the Seventh Framework programme REFINEMENT (Research on Financing Systems' Effect on the Quality of Mental Health Care in Europe) project. METHODS: A total of eight study areas from European countries with different systems of care (Austria, England, Finland, France, Italy, Norway, Romania, Spain) were analysed using a standard open-access classification system (Description and Evaluation of Services for Long Term Care in Europe, DESDE-LTC). All publicly funded services universally accessible to adults (≥18 years) with a psychiatric disorder were coded. Care availability, diversity and capacity were compared across these eight local MHS. RESULTS: The comparison of MHS revealed more community-oriented delivery systems in the areas of England (Hampshire) and Southern European countries (Verona - Italy and Girona - Spain). Community-oriented systems with a higher proportion of hospital care were identified in Austria (Industrieviertel) and Scandinavian countries (Sør-Trøndelag in Norway and Helsinki-Uusimaa in Finland), while Loiret (France) was considered as a predominantly hospital-based system. The MHS in Suceava (Romania) was still in transition to community care. CONCLUSIONS: There is a significant variation in care availability and capacity across MHS of local areas in Europe. This information is relevant for understanding the process of implementation of community-oriented mental health care in local areas. Standard comparison of care provision in local areas is important for context analysis and policy planning.

Differences in the use of involuntary admission across the Veneto Region: which role for individual and contextual variables?

AIMS: The first aim of this study is to compare involuntary admissions across the Veneto Region in Italy. The second aim is to explore the relation between mental health services provision, characteristics of population, individual factors and involuntary admissions. METHODS: For 21 Mental Health Departments (MHDs) in the Veneto Region (Italy), the average population prevalence rate of involuntary admissions between 2000 and 2007 and the percentage of involuntary admissions were calculated. Chi-square tests for equality of proportions were used to test hypotheses. Variables at the individual, contextual and organisational levels were used in multiple regressions, with the involuntary admission data as dependent variables. RESULTS: The average prevalence rate of involuntary commitment was 12.75 ranging from 1.96 to 27.59 across MHDs . About 75% of the involuntary admissions referred to psychotic patients, and almost half of patients were aged 25-44. Significant differences among MHDs emerged; higher percentages of involuntary admissions were generally found in densely populated areas. Higher ageing indices and rates of social workers were found as predictors of the prevalence rate. In the multilevel regression, being males and psychotic significantly increased involuntary admissions, while the percentage of singles in population decreased it. CONCLUSIONS: This study contributes to define the specific contribution of each factor predicting the use of involuntary admission, even within areas under the same legislation. It shows how the inclusion of both individual and contextual factors may lead to better predictions and provides precious data for the services improvement.

The difficult task of predicting the costs of community-based mental health care. A comprehensive case register study.

BACKGROUND: Previous studies have attempted to forecast the costs of mental health care, using clinical and individual variables; the inclusion of ecological measures could improve the knowledge of predictors of psychiatric service utilisation and costs to support clinical and strategic decision-making. METHODS: Using a Psychiatric Case Register (PCR), all patients with an ICD-10 psychiatric diagnosis, who had at least one contact with community-based psychiatric services in the Verona Health District, Northern Italy, were included in the study (N = 4558). For each patient, one year's total cost of care was calculated by merging service contact data with unit cost estimates and clinical and socio-demographic variables were collected. A socio-economic status (SES) index was developed, as a proxy of deprivation, using census data. Multilevel multiple regression models, considering socio-demographic and clinical characteristics of patients as well as socioeconomic local characteristics, were estimated to predict costs. RESULTS: The mean annual cost for all patients was 2,606.11 Euros; patients with an ongoing episode of care and with psychosis presented higher mean costs. Previous psychiatric history represented the most significant predictor of cost (36.99% R2 increase) and diagnosis was also a significant predictor but explained only 4.96% of cost variance. Psychiatric costs were uniform throughout the Verona Health District and SES characteristics alone contributed towards less than 1% of the cost variance. CONCLUSIONS: For all patients of community-based psychiatric services, a comprehensive model, including both patients' individual characteristics and socioeconomic local status, was able to predict 43% of variance in costs of care.