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Sadeniemi M, Almeda N, Salinas-Pérez JA, Gutiérrez-Colosía MR, García-Alonso C, Ala-Nikkola T, Joffe G, Pirkola S, Wahlbeck K, Cid J, Salvador-Carulla L. A Comparison of Mental Health Care Systems in Northern and Southern Europe: A Service Mapping Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2018; 15:E1133. [PMID: 29857556 PMCID: PMC6024953 DOI: 10.3390/ijerph15061133] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2018] [Revised: 05/21/2018] [Accepted: 05/30/2018] [Indexed: 11/16/2022]
Abstract
Mental health services (MHS) have gone through vast changes during the last decades, shifting from hospital to community-based care. Developing the optimal balance and use of resources requires standard comparisons of mental health care systems across countries. This study aimed to compare the structure, personnel resource allocation, and the productivity of the MHS in two benchmark health districts in a Nordic welfare state and a southern European, family-centered country. The study is part of the REFINEMENT (Research on Financing Systems' Effect on the Quality of Mental Health Care) project. The study areas were the Helsinki and Uusimaa region in Finland and the Girona region in Spain. The MHS were mapped by using the DESDE-LTC (Description and Evaluation of Services and Directories for Long Term Care) tool. There were 6.7 times more personnel resources in the MHS in Helsinki and Uusimaa than in Girona. The resource allocation was more residential-service-oriented in Helsinki and Uusimaa. The difference in mental health personnel resources is not explained by the respective differences in the need for MHS among the population. It is important to make a standard comparison of the MHS for supporting policymaking and to ensure equal access to care across European countries.
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Affiliation(s)
- Minna Sadeniemi
- Department of Social Services and Health Care, City of Helsinki, Southern Psychiatric Outpatient Clinic, Työpajankatu 14, FI-00099 Helsinki, Finland.
- University of Helsinki and Helsinki University Hospital, Välskärinkatu 12, Helsinki FI-00029, Finland.
- Unit for Mental Health, National Institute for Health and Welfare (THL); Mannerheimintie 168, Helsinki FI-00270, Finland.
| | - Nerea Almeda
- PSICOST Research Association, Department of Psychology, Universidad Loyola Andalucía, C/Energía Solar 1, 41014 Sevilla, España.
| | - Jose A Salinas-Pérez
- PSICOST Research Association, Department of Quantitative Methods, Universidad Loyola Andalucía, C/Energía Solar 1, 41014 Sevilla, España.
| | - Mencía R Gutiérrez-Colosía
- PSICOST Research Association, Department of Psychology, Universidad Loyola Andalucía, C/Energía Solar 1, 41014 Sevilla, España.
| | - Carlos García-Alonso
- PSICOST Research Association, Department of Quantitative Methods, Universidad Loyola Andalucía, C/Energía Solar 1, 41014 Sevilla, España.
| | - Taina Ala-Nikkola
- University of Helsinki and Helsinki University Hospital, Välskärinkatu 12, Helsinki FI-00029, Finland.
- Unit for Mental Health, National Institute for Health and Welfare (THL); Mannerheimintie 168, Helsinki FI-00270, Finland.
| | - Grigori Joffe
- University of Helsinki and Helsinki University Hospital, Välskärinkatu 12, Helsinki FI-00029, Finland.
| | - Sami Pirkola
- University of Tampere School of Health Sciences, and Tampere University Hospital, Lääkärinkatu 1, Tampere FI-33014, Finland.
| | - Kristian Wahlbeck
- Unit for Mental Health, National Institute for Health and Welfare (THL); Mannerheimintie 168, Helsinki FI-00270, Finland.
| | - Jordi Cid
- Mental Health & Addiction Research Group, Institut d'Investigacions Biomèdiques de Girona (IdibGI)-Institut d'Assistència Sanitària, 17190 Salt Girona, Spain.
| | - Luis Salvador-Carulla
- VIDEA Lab, Centre for Mental Health Research, Australian National University, 63 Eggleston Rd, Acton ACT 2601, Australia.
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Cisse B, Moore L, Kuimi BLB, Porgo TV, Boutin A, Lavoie A, Bourgeois G. Impact of socio-economic status on unplanned readmission following injury: A multicenter cohort study. Injury 2016; 47:1083-90. [PMID: 26746984 DOI: 10.1016/j.injury.2015.11.034] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2015] [Revised: 11/11/2015] [Accepted: 11/21/2015] [Indexed: 02/02/2023]
Abstract
BACKGROUND Unplanned readmissions cost the US economy approximately $17 billion in 2009 with a 30-day incidence of 19.6%. Despite the recognised impact of socio-economic status (SES) on readmission in diagnostic populations such as cardiovascular patients, its impact in trauma patients is unclear. We examined the effect of SES on unplanned readmission following injury in a setting with universal health insurance. We also evaluated whether additional adjustment for SES influenced risk-adjusted readmission rates, used as a quality indicator (QI). STUDY DESIGN We conducted a multicenter cohort study in an integrated Canadian trauma system involving 56 adult trauma centres using trauma registry and hospital discharge data collected between 2005 and 2010. The main outcome was unplanned 30-day readmission; all cause, due to complications of injury and due to subsequent injury. SES was determined using ecological indices of material and social deprivation. Odds ratios of readmission and 95% confidence intervals adjusted for covariates were generated using multivariable logistic regression with a correction for hospital clusters. We then compared a readmission QI validated previously (original QI) to a QI with additional adjustment for SES (SES-adjusted QI) using the mean absolute difference. RESULTS The cohort consisted of 52,122 trauma admissions of which 6.5% were rehospitalised within 30 days of discharge. Compared to patients in the lowest quintile of social deprivation, those in the highest quintile had a 20% increase in the odds of all-cause unplanned readmission (95% CI=1.06-1.36) and a 27% increase in the odds of readmission due to complications of injury (95% CI=1.04-1.54). No association was observed for material deprivation or for readmissions due to subsequent injuries. We observed a strong agreement between the original and SES-adjusted readmission (mean absolute difference= 0.04%). CONCLUSIONS Patients admitted for traumatic injury who suffer from social deprivation have an increased risk of unplanned rehospitalisation due to complications of injury in the 30 days following discharge. Better discharge planning or follow up for such patients may improve patient outcome and resource use for trauma admissions. Despite observed associations, results suggest that the trauma QI based on unplanned readmission does not require additional adjustment for SES.
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Affiliation(s)
- Brahim Cisse
- Department of social and preventive medicine, Université Laval, Québec, QC, Canada; Axe Santé des Populations - Pratiques Optimales en Santé (Population Health - Practice - Changing Research Unit), Traumatologie - Urgence - Soins intensifs (Trauma - Emergency - Critical Care Medicine), Centre de Recherche du Centre Hospitalier Universitaire de Québec (CHU de Québec - Hôpital de l'Enfant-Jésus), Université Laval, Québec, QC, Canada.
| | - Lynne Moore
- Department of social and preventive medicine, Université Laval, Québec, QC, Canada; Axe Santé des Populations - Pratiques Optimales en Santé (Population Health - Practice - Changing Research Unit), Traumatologie - Urgence - Soins intensifs (Trauma - Emergency - Critical Care Medicine), Centre de Recherche du Centre Hospitalier Universitaire de Québec (CHU de Québec - Hôpital de l'Enfant-Jésus), Université Laval, Québec, QC, Canada
| | - Brice Lionel Batomen Kuimi
- Department of social and preventive medicine, Université Laval, Québec, QC, Canada; Axe Santé des Populations - Pratiques Optimales en Santé (Population Health - Practice - Changing Research Unit), Traumatologie - Urgence - Soins intensifs (Trauma - Emergency - Critical Care Medicine), Centre de Recherche du Centre Hospitalier Universitaire de Québec (CHU de Québec - Hôpital de l'Enfant-Jésus), Université Laval, Québec, QC, Canada
| | - Teegwendé Valérie Porgo
- Department of social and preventive medicine, Université Laval, Québec, QC, Canada; Axe Santé des Populations - Pratiques Optimales en Santé (Population Health - Practice - Changing Research Unit), Traumatologie - Urgence - Soins intensifs (Trauma - Emergency - Critical Care Medicine), Centre de Recherche du Centre Hospitalier Universitaire de Québec (CHU de Québec - Hôpital de l'Enfant-Jésus), Université Laval, Québec, QC, Canada
| | - Amélie Boutin
- Department of social and preventive medicine, Université Laval, Québec, QC, Canada; Axe Santé des Populations - Pratiques Optimales en Santé (Population Health - Practice - Changing Research Unit), Traumatologie - Urgence - Soins intensifs (Trauma - Emergency - Critical Care Medicine), Centre de Recherche du Centre Hospitalier Universitaire de Québec (CHU de Québec - Hôpital de l'Enfant-Jésus), Université Laval, Québec, QC, Canada
| | - André Lavoie
- Institut National d'Excellence en Santé et en Services Sociaux, Montréal, QC, Canada
| | - Gilles Bourgeois
- Institut National d'Excellence en Santé et en Services Sociaux, Montréal, QC, Canada
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Moran V, Jacobs R, Mason A. Variations in Performance of Mental Health Providers in the English NHS: An Analysis of the Relationship Between Readmission Rates and Length-of-Stay. ADMINISTRATION AND POLICY IN MENTAL HEALTH AND MENTAL HEALTH SERVICES RESEARCH 2016; 44:188-200. [PMID: 26749002 DOI: 10.1007/s10488-015-0711-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Length-of-stay (LOS) for inpatient mental health care is a major driver of variation in resource use internationally. We explore determinants of LOS in England, focusing on the impact of emergency readmission rates which can serve as a measure of the quality of care. Data for 2009/2010 and 2010/2011 are analysed using hierarchical and non-hierarchical models. Unexplained residual variation among providers is quantified using Empirical Bayes techniques. Diagnostic, treatment and patient-level demographic variables are key drivers of LOS. Higher emergency readmission rates are associated with shorter LOS. Ranking providers by residual variation reveals significant differences, suggesting some providers can improve performance.
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Affiliation(s)
- Valerie Moran
- Department of Health Services Research and Policy, Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, 15-17 Tavistock Place, London, WC1H 9SH, UK.
| | - Rowena Jacobs
- Centre for Health Economics and Department of Economics and Related Studies, University of York, Alcuin A Block, Heslington, York, YO10 5DD, UK
| | - Anne Mason
- Centre for Health Economics and Department of Economics and Related Studies, University of York, Alcuin A Block, Heslington, York, YO10 5DD, UK
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Kovess-Masfety V, Boyd A. Modelling Needs for Mental Healthcare from Epidemiological Surveys with Validation Using Sociodemographic Census Data. Clin Pract Epidemiol Ment Health 2015; 11:186-94. [PMID: 26962324 PMCID: PMC4763960 DOI: 10.2174/1745017901511010186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2015] [Revised: 07/06/2015] [Accepted: 08/08/2015] [Indexed: 11/22/2022]
Abstract
Purpose: To
develop and validate a prediction model for mental health needs (MHN) and
psychiatric needs (PN) using specific social indicators, obtainable from census
data, within low-density departments (LDD) and high- density departments (HDD). Methods: In a population-based study of 20,404 participants from
22 departments in France, mental health needs were defined into three categories
(no needs, MHN, and PN) using the Composite International Diagnosis Interview
Short-Form, Sheehan disability scale, and presence of depressive and alcohol
disorders. Within HDD (n=9) and LDD (n=13) departments, two separate logistic
regression models, using MHN or PN as an endpoint, were fitted using available
sociodemographic data. Model validation was performed using 2007 census data.
Overall accuracy was evaluated using average residuals (AR) calculated within
density stratum. Results: In LDD
and HDD respectively, 26.6% and 28.7% of persons had MHN and 9.8% and 11.3% had
PN. In LDD, housing type, age, employment, living alone, housing support, and
household size predicted MHN and PN. In HDD, housing type, living alone,
household size, living in a marriage/partnership, and duration of dwelling
habitation predicted MHN and PN. Predictions were more accurate in HDD, in which
the AR was 30% lower for MHN and 40% lower for PN. Predictions were less
accurate when using census data, yet they were consistently better in HDD. Conclusions: Sociodemographic indicators
from either survey or census data may be useful in predicting MHN and PN in
high-density settings. The ideal territorial size still needs to be evaluated
when planning psychiatric and mental health resources.
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Affiliation(s)
| | - Anders Boyd
- EA 4057 Paris Descartes University, EHESP, Paris France
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Does primary care mental health resourcing affect the use and costs of secondary psychiatric services? INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2014; 11:8743-54. [PMID: 25162710 PMCID: PMC4198988 DOI: 10.3390/ijerph110908743] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/25/2014] [Revised: 08/13/2014] [Accepted: 08/14/2014] [Indexed: 11/24/2022]
Abstract
Collaborative care models for treatment of depression and anxiety disorders in primary care have been shown to be effective. The aim of this study was to investigate at the municipal level to what extent investment in mental health personnel at primary care health centres in the study area is reflected in the costs and use of secondary psychiatric services. Furthermore, we analysed whether the service provision and use of secondary psychiatric care correlates with the socioeconomic indicators of need. We found significant variation in the amount of mental health personnel provided at the health centres, uncorrelated with the indicators of need nor with the costs of secondary psychiatric care. The amount of mental health nurses at the health centres correlated inversely with the number of secondary psychiatric outpatient visits, whereas its relation to inpatient days and admission was positive. The costs of secondary psychiatric care correlated with level of psychiatric morbidity and socioeconomic indicators of need. The results suggest that when aiming at equal access of care and cost-efficiency, the primary and secondary care should be organized and planned with integrative collaboration.
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Moss J, Li A, Tobin J, Weinstein IS, Harimoto T, Lanctôt KL. Predictors of readmission to a psychiatry inpatient unit. Compr Psychiatry 2014; 55:426-30. [PMID: 24405773 DOI: 10.1016/j.comppsych.2013.11.019] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2013] [Revised: 10/28/2013] [Accepted: 11/26/2013] [Indexed: 11/25/2022] Open
Abstract
OBJECTIVE To determine predictors of time to readmission to a general psychiatry inpatient unit. METHOD Data from the Minimum Data Set-Mental Health (MDS-MH), a standardized assessment used to collect demographic and clinical information, were retrospectively reviewed from April 2006 through October 2008. A total of 758 patients were eligible for the study. A set of clinically relevant predictors was generated based on a literature review. A Cox regression model was applied to determine which variables were most predictive of shorter time to readmission, and their respective hazard ratios (HR). RESULTS Covariates that were significantly associated with readmission (HR [95% CI]) included receiving a pass (3.48 [2.33, 5.17], p ≤ 0.0005), 1-2 psychiatric admissions in the past two years (15.63 [7.50, 32.55], p ≤ 0.0005), and more than 3 psychiatric admissions in the past two years (24.15 [11.58, 50.36], p ≤ 0.0005). Post hoc analysis indicated that those issued passes were more commonly male (57.1% vs. 43.9%, p=0.03), with a longer length of stay (25.4 ± 21.2 days vs. 18.7 ± 21.1 days, p=0.008), and higher GAF score (62.8 ± 11.1 vs. 57.8 ± 13.9, p=0.003), but were otherwise similar. CONCLUSIONS The factors that were associated with reduced time to readmission were a history of previous admissions and receipt of a pass prior to discharge. These results suggest that while physicians may be able to identify patients at high risk of early readmission, issuing a pass may not fully mitigate this risk. There is a need for critical research evaluating the potential benefits of passes.
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Affiliation(s)
- Jay Moss
- Department of Psychiatry, Faculty of Medicine, University of Toronto.
| | - Abby Li
- Department of Psychiatry and Neuropsychopharmacology Research Program, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - James Tobin
- Department of Psychiatry and Neuropsychopharmacology Research Program, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Izak S Weinstein
- Department of Psychiatry and Neuropsychopharmacology Research Program, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Tetsuhiro Harimoto
- Department of Psychiatry and Neuropsychopharmacology Research Program, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Krista L Lanctôt
- Department of Psychiatry, Faculty of Medicine, University of Toronto; Department of Pharmacology/Toxicology, Faculty of Medicine, University of Toronto; Department of Psychiatry and Neuropsychopharmacology Research Program, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
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Does hospital admission risk for depression vary across social groups? A population-based register study of 231,629 middle-aged Finns. Soc Psychiatry Psychiatr Epidemiol 2014; 49:15-25. [PMID: 23712515 DOI: 10.1007/s00127-013-0711-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2012] [Accepted: 05/15/2013] [Indexed: 10/26/2022]
Abstract
PURPOSE Evidence on social differentials in depression outcomes remains inconsistent. We assess social predictors of psychiatric admission for depression in a community setting. METHODS A register-based 14% sample of community-dwelling Finns aged 40-64 at the end of 1997 was assessed for depression and psychiatric comorbidity, using register data on psychiatric hospital care and medication purchases in 1996-1997. Those with inpatient treatment for unipolar depression (n = 846), those with antidepressant treatment (n = 8,754), and those with neither (n = 222,029) were followed for psychiatric admission with a diagnosis of unipolar depression in 1998-2003. Differentials in admission rates by socioeconomic position, employment status, and living arrangements were studied using Cox proportional hazards modelling. RESULTS Among those with prior inpatient or antidepressant treatment, the material aspects of socioeconomic position increased admission risk for depression by 20-40%, even after controlling for baseline depression severity and psychiatric comorbidities, whereas education and occupational social class were unrelated to admission risk. Among inpatients, also having no partner, and among antidepressant users, being previously married and living without co-resident children increased admission risk. However, among inpatients few excess risks reached statistical significance. Among those with no inpatient or antidepressant treatment, all measures of low social position and not living with a partner predicted admission, and the factors had more predictive power in admission than among those with prior treatment. CONCLUSIONS Further studies should disentangle the mechanisms behind the higher admission risk among those with fewer economic resources and no co-resident partner.
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Zhang J, Harvey C, Andrew C. Factors associated with length of stay and the risk of readmission in an acute psychiatric inpatient facility: a retrospective study. Aust N Z J Psychiatry 2011; 45:578-85. [PMID: 21718126 PMCID: PMC3190839 DOI: 10.3109/00048674.2011.585452] [Citation(s) in RCA: 132] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
OBJECTIVE This study was to investigate factors influencing the length of stay and predictors for the risk of readmission at an acute psychiatric inpatient unit. METHOD Two comparative studies were embedded in a retrospective cross-sectional clinical file audit. A randomly selected 226 episodes of admissions including 178 patients during a twelve-month period were reviewed. A total of 286 variables were collected and analysed. A case control study was employed in the study of length of stay. A retrospective cohort study was used to investigate the predictors for the risk of readmission. RESULTS Logistic regression analyses showed that 10 variables were associated with length of stay. Seclusion during the index admission, accommodation problems and living in an area lacking community services predicted longer stay. During the follow-up period 82 patients (46%) were readmitted. Cox regression analyses showed 9 variables were related to the risk of readmission. Six of these variables increased the risk of readmission, including history of previous frequent admission, risk to others at the time of the index admission and alcohol intoxication. More active and assertive treatment in the community post-discharge decreased the risk of readmission. CONCLUSIONS Length of stay is multifactorially determined. Behavioural manifestations of illness and lack of social support structures predicted prolonged length of stay. Good clinical practice did not necessarily translate to a shorter length of stay. Therefore, length of stay is predictable, but not readily modifiable within the clinical domain. Good clinical practice within the community following discharge likely reduces the risk of readmission. Quality of inpatient care does not influence the risk of readmission, which therefore raises a question about the validity of using the rate of readmission as an outcome measure of psychiatric inpatient care.
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Affiliation(s)
- Jianyi Zhang
- North West Area Mental Health Service, Broadmeadows, Victoria, Australia.
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