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Involuntary Psychiatric Admissions and Development of Psychiatric Services as an Alternative to Full-Time Hospitalization in France

Published Online:https://doi.org/10.1176/appi.ps.201600453

Abstract

Objective:

The development of alternatives to full-time hospitalization in psychiatry is limited because consensus about the benefits of such alternatives is lacking. This study assessed whether the development of such alternatives in French psychiatric sectors was associated with a reduction in involuntary inpatient care, taking into account other factors that are potentially associated with involuntary admission.

Methods:

Data on whether a patient had at least one involuntary full-time admission in 2012 were extracted from the French national discharge database for psychiatric care. The development of alternatives to full-time hospitalization was estimated as the percentage of human resources allocated to these alternatives out of all human resources allocated to psychiatry, measured at the level of the hospital hosting each sector. Other factors potentially associated with involuntary admission (characteristics of patients, health care providers, and the environment) were extracted from administrative databases, and a multilevel logistic model was carried out to account for the nested structure of the data.

Results:

Significant variations were observed between psychiatric sectors in rates of involuntary inpatient admissions. A large portion of the variation was explained by characteristics of the sectors. A significant negative association was found between involuntary admissions and the development of alternatives to full-time hospitalization, after adjustment for other factors associated with involuntary admissions.

Conclusions:

Findings suggest that the development of alternatives to full-time hospitalization is beneficial for quality of care, given that it is negatively associated with involuntary full-time admissions. The reduction of such admissions aligns with international recommendations for psychiatric care.

Increasing the quality of care is the goal of any health system. In psychiatry, the development of alternatives to full-time hospitalization is supported by international recommendations for mental health care (15) as a way to increase this quality. Such alternatives encompass full-time care outside inpatient settings as well as part-time hospitalization and ambulatory care. Research has demonstrated that such alternatives are associated with a reduction in hospitalizations (6,7) and improvements in quality of life, clinical outcomes, adherence to treatment, service accessibility, continuity of care, and patients’ satisfaction (6,811). In France, few alternatives to full-time hospitalization have been developed (12,13), compared with other European countries (1417). A recent assessment showed that development has been impeded by resistance from health professionals (18), despite support from policy makers (19). Resistance is attributed to a lack of consensus among various schools of thought in the mental health field regarding the benefits of alternatives to full-time hospitalization (18).

Therefore, additional evidence of the benefits of alternatives to full-time hospitalization is needed (20). In particular, little substantial research has examined the impact of deinstitutionalization and the development of such alternatives on involuntary treatment (21), which is still used in psychiatry worldwide (22,23), despite its controversial nature and the implementation of reforms to reduce its frequency (21,22). Involuntary treatment is often considered an indicator of the quality of prior care (2427). Indeed, if patients receive adequate care, they are less likely to experience a crisis and to require compulsory admission, and patients’ consent to treatment has been promoted as a key component of the quality of the care (28,29).

Moreover, difficulties in applying the results of mental health services research that originates from other contexts have been underscored (7). The transferability of results from other countries is limited by the particularities of the French public psychiatric system, which is territorially organized into geodemographic areas in which multidisciplinary teams hosted by hospitals (sectors) coordinate the delivery of outpatient and inpatient services necessary to cover the mental health needs of their population (30,31). Between-country comparisons also require caution because of the specificity of involuntary admissions, which are regulated by different legislative frameworks worldwide (32).

It is thus necessary to assess whether the development of alternatives to full-time hospitalization is related to a decrease in involuntary admissions in French public psychiatry. However, several studies have shown that a number of variables, unrelated to the development of alternatives, could also be associated with involuntary admission. Such variables include patient, health care provider, and environmental characteristics (3337).

In this context, the objective of our study was to assess whether the development of alternatives to full-time hospitalization provided by French psychiatric sectors was associated with a reduction in involuntary full-time admissions, taking into account other factors that may be associated with involuntary care.

Methods

Scope of the Study

French psychiatric sectors represent the cornerstone of the organization of public mental health care delivery, and they account for nearly 70% of the costs of mental health care in France (38). They are hosted by public and private nonprofit hospitals, which are either general hospitals with an activity in psychiatry or psychiatric hospitals. On average, a hospital hosts four sectors of adult psychiatry (39), which were initially created to serve a population of around 70,000 inhabitants, with some variation in population size and overall characteristics (18,31). Only sectors hosted by hospitals specifically mandated by regional health agencies can provide involuntary care. Given these factors and to ensure comparability, our study was carried out in psychiatric sectors of public and private nonprofit hospitals that provide involuntary care in mainland France and that treat adult patients.

We conducted a retrospective study for the year 2012 using administrative databases and included only psychiatric sectors hosted by hospitals for which the data were of good quality to enable analysis. Consequently, we excluded sectors hosted by hospitals that did not consistently report full-time inpatient activity or the number of psychiatric sectors in both the French national psychiatric discharge database (RIM-P) and the annual national survey on health care providers (SAE). In addition, we excluded sectors hosted by hospitals that did not report the data necessary to assess the development of alternatives to full-time hospitalization.

Population

We included patients with a diagnosis of a mental disorder from ICD-10 chapter 5 (40). We excluded patients with organic mental disorders, mental retardation, and disorders of psychological development (apart from pervasive developmental disorders) because of the specificity of the care they require (41,42).

Variables and Data Sources

Our variable of interest—whether a patient had at least one involuntary full-time admission in a psychiatric sector in 2012—was extracted from the RIM-P database. There is no direct measure of the development of alternatives to full-time hospitalization in psychiatric sectors. One way to assess this development is to determine the share of human resources allocated to such alternatives compared with the total human resources allocated to psychiatry in the hospital hosting each sector. Human resources indeed represent 70% of the hospital budget for the treatment of somatic illnesses (43,44), and this percentage is estimated to be even higher for psychiatric care (4547). The development of alternatives was measured as the ratio of the number of full-time equivalents (FTEs) working in departments that provide alternatives to full-time hospitalizations to the total number of FTEs allocated to psychiatry in the hospital hosting each sector. These data were extracted from the SAE database. Considering the overall proportion of FTEs allocated to alternatives to full-time hospitalization allowed comparability of data across sectors by adjusting for their overall capacity.

Adjustment factors were also considered. Because medical practice is a complex decision-making process, we used a conceptual framework that grouped potential factors associated with practice into three categories: patient characteristics (the demand side), health care provider characteristics (the supply side), and characteristics of the practice context (the environment) (4851). [A diagram of the framework is included in an online supplement to this article.]

We first considered factors that are expected to influence involuntary admission through mechanisms similar to alternatives to full-time hospitalization by directly affecting a patient’s health status. Diagnoses, which were classified into broader diagnostic groups (Table 1), were extracted from the RIM-P database. Because the case-mix characteristics of sectors can influence their practice, the repartition of diagnostic groups by sector was also considered. Variables related to the supply of additional medical and social care, which are likely to increase continuity of care in ways similar to alternatives to full-time hospitalization, included care provided by the private sector or through social care institutions. Information regarding the availability of these sources of care was extracted from administrative databases (52), and their density was calculated for the catchment area of each sector. These catchment areas, defined as the geographic zone from which patients of psychiatric sectors originate, were built for each sector by extracting patients’ zip codes from the RIM-P and by using a geographic information system (Geoconcept software).

TABLE 1. Characteristics of patients with at least one full-time psychiatric hospitalization in 2012 (N=473,587)

CharacteristicN%
Demographic
 Age (M±SD)47.42±16.82
 Female251,96953.2
Diagnostic (ICD-10 code)a
 Addiction (F10–F19)b58,22012.3
 Schizophrenia (F20)63,54313.4
 Other psychotic disorder (F21–F29)c42,7529.0
 Bipolar disorder (F31)29,6766.3
 Other mood disorder (F30, F32–34, F38, F39)117,24824.8
 Anxiety disorder (F40–F48)d117,26024.8
 Other mental or behavioral disorder (F50–F69, F84, F90–F99)e83,07717.5

aPatients could have diagnoses in more than one diagnostic group.

bMental and behavioral disorders due to psychoactive substance abuse

cSchizotypal and delusional disorders

dNeurotic, stress-related, and somatoform disorders

eBehavioral syndromes associated with physiological disturbances and physical factors, disorders of adult personality and behavior, pervasive developmental disorders, behavioral and emotional disorders with onset usually occurring in childhood and adolescence, and unspecified mental disorder

TABLE 1. Characteristics of patients with at least one full-time psychiatric hospitalization in 2012 (N=473,587)

Enlarge table

Second, additional adjustment factors were considered in the analysis. They included demographic characteristics extracted from the RIM-P database. To overcome the lack of data on patients’ socioeconomic characteristics, we calculated a proxy index of deprivation based on patients’ residential zip codes by using a validated composite index specifically developed for the French context, the FDep (5355). Institutional and organizational characteristics of sectors were extracted from the SAE database. Finally, we included characteristics of the overall health status of the population (5658) as well as the level of urbanization, assessed by the density of inhabitants in the zip codes of the sectors’ catchment areas (59).

We obtained the authorization to access the databases used for this research from the French data protection authority (Decision DE-2013–077). No informed consent was required from patients because data were entirely anonymized.

Analysis

Descriptive analysis.

Characteristics of the study population were described either by the mean and standard deviation or by number and percentage. We computed the involuntary full-time admission rate per 1,000 patients by dividing the number of patients admitted involuntarily to full-time hospitalization at least once during the study period (2012) by the total number of patients in each sector. To describe variations in this rate across sectors and in the development of alternatives to full-time hospitalization across hospitals, we computed the mean, SD, median, interquartile range, range, coefficient of variation (60), and the ratio of the 90th to the 10th percentiles of the distribution (61,62).

We then assessed the association between the development of alternatives and rates of involuntary admission to full-time hospitalization by sector through the calculation of the Spearman correlation coefficient.

Multivariate analysis.

To assess the association between the development of alternatives to full-time hospitalization and involuntary admission after adjustment for other potentially associated factors, we carried out a multivariate analysis by using a logistic model in which the binary dependent variable was equal to 1 for patients admitted involuntarily at least once and equal to 0 for other patients. To account for the nested structure of the data (63), we ran a multilevel model. Because the mean number of adult psychiatric sectors per hospital was low (6468), we considered only two levels: the patient (level 1) and the sector (level 2).

The development of alternatives to full-time hospitalization was introduced in the model as an explanatory variable, along with associated patient, sector, and environmental characteristics for which there were strong hypotheses in regard to their association with involuntary admissions. When explanatory variables were highly correlated or associated, only one of them was kept in the model. Because strong hypotheses about interactions between explanatory variables were lacking in the literature, we did not introduce interaction terms in the final model.

To confirm the existence of a random effect at the sector level, we first ran a null model without any explanatory variables (model 1). Second, we introduced patients’ characteristics in the model (model 2). Third, we added the variables calculated at the sector level (characteristics of the sectors and their environment) (model 3).

The analysis was performed with SAS software, version 9. The acceptable type I error rate was set at .05.

Results

Scope of the Study

A total of 229 hospitals meeting our inclusion criteria were reported in the RIM-P in 2012. Among those hospitals, 117 (51%) were included in the analysis on the basis of data quality. These hospitals were divided into 399 sectors of adult psychiatry with involuntary care activity [see online supplement], which accounted for 51% of all sectors of adult psychiatry in mainland France providing involuntary care. Included and excluded hospitals did not differ significantly in terms of main organizational and institutional characteristics or case mix.

Descriptive Analysis

A total of 473,587 patients matching our diagnostic criteria were treated in the selected sectors, representing 56.5% of all patients within the scope of our study seen in adult psychiatric sectors providing involuntary care. The mean age of patients was 47.4, and 53% were women. The two most common diagnoses were mood disorders, not including bipolar disorders, and anxiety disorders (Table 1).

A total of 105,059 (22.2%) had at least one full-time hospitalization, and 31,062 (6.6%) had at least one involuntary full-time hospitalization. Most patients admitted were men (N=18,595, 60%), and the most frequent diagnosis among the involuntarily admitted patients was schizophrenia (N=9,267, 30%) or other psychotic disorders (N=7,771, 25%).

Considerable variations between psychiatric sectors were observed, both for involuntary full-time admissions and the development of alternatives to full-time hospitalization. The mean involuntary admission rate was 70.1 per 1,000 patients per sector (Table 2) and varied between .4 and 366.1. The ratio between the 90th and the 10th percentiles of the distribution was close to 9. The ratio of FTEs allocated to alternatives to full-time hospitalization to the total number of FTEs by hospital had a mean value of .33, and the coefficient of variation reached 32.5%. This ratio was not significantly associated with any institutional or organizational characteristics of the hospital hosting each sector.

TABLE 2. Involuntary admission and development of alternatives to full-time hospitalization in 399 psychiatric sectors

VariableMSDMedianIQRaRangeCV (%)b90th/10th percentiles
Involuntary admissions per 1,000 patients per sector70.0950.0061.2655.87365.7771.338.96
Development of alternatives in hospital hosting each sectorc.33.11.35.13.5132.482.51

aInterquartile range

bCV, coefficient of variation

cN=117 hosting hospitals. Development was measured as the ratio of full-time equivalents (FTEs) in departments that provide alternatives to full-time hospitalization to the total number of FTEs allocated to psychiatry in the hospital hosting each sector.

TABLE 2. Involuntary admission and development of alternatives to full-time hospitalization in 399 psychiatric sectors

Enlarge table

In the bivariate analysis, we found a decrease in the rate of involuntary full-time admissions per 1,000 patients per sector when the level of development of alternatives increased; however, it was not statistically significant.

Multivariate Analysis

In the multivariate analysis, we introduced ten individual patient characteristics at level 1. Ten characteristics of psychiatric sectors and 14 characteristics of the environment were introduced at level 2 (Table 3). Results of the null model showed statistically significant variations between sectors (p<.001), which confirmed the need to take into account the hierarchical structure of the data and to run a random-intercept model (Table 4). Twenty-eight percent of the total variation in involuntary admission rates was related to practice differences between sectors (intersector variations), and 72% resulted from differences within sectors linked to case mix (intrasector variations).

TABLE 3. Adjustment factors in the multivariate analysis of the association between development of alternatives to full-time hospitalization and involuntary admissions, by type of factor (patient, psychiatric sector, and environmental)

CategoryVariable
Patient
Demographic characteristicsAge, sex
Clinical characteristicPresence of each diagnostic group (N=7)
Socioeconomic characteristicDeprivation indexa
Psychiatric sector
Case-mix characteristics (percentage of patients)bFemale patients, patients with addictive disorders, patients with schizophrenia, patients with anxiety disorders, patients with bipolar disorders
Institutional characteristicscLegal status of the hospital, specialization in psychiatry of the hospital (psychiatric vs. general hospital), participation of the hospital in teaching activities, participation of the hospital in emergency care
Organizational characteristicscN of inpatient psychiatric beds per 1,000 inhabitantsd
Environmental
Overall health status of the populationeAcute admission rate for general medical illnesses, mortality rate, percentage of deaths by suicide among total deaths, N of individuals with chronic general medical illnesses, percentage of individuals with chronic mental illnesses among those with chronic general medical illnesses
Availability of medical and social careeN of general practitioners; N of community-based private psychiatrists; N of psychologists; N of nonpsychiatric inpatient beds; N of inpatient beds for private psychiatry; capacity of housing institutions for disabled individuals, centers providing care through employment, and housing and social rehabilitation centers
OtherLevel of urbanization

aTo overcome a lack of data on patients’ socioeconomic characteristics, we calculated a proxy index of deprivation based on patients’ residential zip codes by using a validated composite index specifically developed for the French context.

bFor patients seen in full-time hospitalization in the sector

cFor the hospital hosting the psychiatric sector

dThe number of inpatient beds and the total number of sectors were highly correlated (ρ=.90; p<.001); thus only the number of beds was introduced into the model.

eComputed per 1,000 inhabitants of a sector’s catchment area

TABLE 3. Adjustment factors in the multivariate analysis of the association between development of alternatives to full-time hospitalization and involuntary admissions, by type of factor (patient, psychiatric sector, and environmental)

Enlarge table

TABLE 4. Estimation of random effects in three models assessing the association between development of alternatives to full-time hospitalization and involuntary admissionsa

EffectModel 1Model 2Model 3
Intersector variance1.2931.203.925
p<.001<.001<.001
Standard error.091.086.070
Intraclass correlation coefficient (%)28.21926.77221.953
Change in variance (%)7.00523.063

aModel 1 is the null model with no explanatory variables, model 2 includes patient characteristics, and model 3 includes patient and psychiatric sector characteristics.

TABLE 4. Estimation of random effects in three models assessing the association between development of alternatives to full-time hospitalization and involuntary admissionsa

Enlarge table

Patients’ individual characteristics explained 7% of the variations between sectors, and sector characteristics explained 23%. The level of development of alternatives to full-time hospitalization was significantly and negatively associated with involuntary admission (odds ratio=.99, p=.007) (Table 5). For each 10% increase in the level of development of alternatives, the probability of a patient’s being involuntarily admitted to full-time hospitalization decreased by 12%.

TABLE 5. Estimation of fixed effects in the final model (model 3) assessing the association between development of alternatives to full-time hospitalization and involuntary admissions

VariableEstimated value of coefficientOR95% CIp
Intercept1.273.344
Patient level (level 1)
 Age–.019.98.98–.98<.001
 Diagnosis (reference: no indicated diagnosis)
  Anxiety disorder –.057.95.91–.99.009
  Schizophrenia1.3023.683.55–3.81<.001
  Other psychotic disorder1.5754.834.66–5.00<.001
  Other mental or behavioral disorder.6101.841.77–1.91<.001
  Addictive disorder.9052.472.37–2.58<.001
  Bipolar disorder1.3553.883.70–4.06<.001
  Other mood disorder.4711.601.54–1.66<.001
 Quintile of deprivation index (from lower to higher deprivation) (reference: 5th quintile)
  1.0601.061.01–1.12.028
  2.0561.061.01–1.11.021
  3.0201.02.97–1.07.419
  4–.0011.00.95–1.05.953
 Male (reference: female).2431.281.24–1.31<.001
Psychiatric-sector level (level 2)
 Characteristic of patients hospitalized full-time in sector
  % female–.037.96.95–.98<.001
  % with addictive disorders–.026.98.97–.99<.001
  % with schizophrenia.0111.011.00–1.02.048
  % with anxiety disorders.0011.00.99–1.01.820
  % with bipolar disorders.0411.041.01–1.07.005
 Characteristic of psychiatric sector
  Institutional characteristic of hospital hosting sector
  Private nonprofit (reference: public).0581.06.59–1.90.845
  Psychiatric (reference: general).5111.671.29–2.16<.001
  Participates in teaching activities (reference: no)–.096.91.66–1.25.554
  Participates in emergency care (reference: no)–.089.92.65–1.29.613
 Organizational characteristic of hospital hosting sector
  N of inpatient psychiatric beds per 1,000 inhabitants–.225.80.68–.93.005
  Level of development of alternatives–.013.99.98–1.00.007
 Characteristic of environment
  Overall health status of population (per 1,000 sector inhabitants in catchment area)
   Acute admission rate for general medical disorders–.0041.00.99–1.00.054
   Mortality rate–.360.70.52–.94.018
   % of deaths by suicide among total deaths–.045.96.87–1.05.354
   N with chronic general medical illnesses–.0001.00.99–1.01.965
   % with chronic mental illnesses among those with chronic general medical illnesses.0561.06.97–1.16.233
  Availability of medical care in catchment area per 1,000 inhabitants
   N of community-based private psychiatrists–2.261.10.02–.71.021
   N of psychologists–.079.92.71–1.20.549
   N of general practitioners.5031.65.80–3.44.177
   N of nonpsychiatric inpatient beds.0181.02.99–1.05.273
   N of inpatient beds for private psychiatry–.615.54.28–1.05.069
  Availability of social care in catchment area per 1,000 inhabitants
   Capacity of housing institutions for disabled individuals–.300.74.62–.89.002
   Capacity of centers providing care through employment.2211.25.98–1.60.079
   Capacity of housing and social rehabilitation centers–.167.85.62–1.16.299
  Level of urbanization (from lower to higher) (reference: level 6)
   1.2251.25.91–1.71.161
   2.3271.39.88–2.18.157
   3.4241.53.38–6.21.553
   4.6301.88.45–7.88.388
   5–.030.97.69–1.36.864

TABLE 5. Estimation of fixed effects in the final model (model 3) assessing the association between development of alternatives to full-time hospitalization and involuntary admissions

Enlarge table

Other factors were also significantly associated with involuntary admission, in particular patients’ age, sex, and diagnostic group; the specialization in psychiatry and the number of inpatient beds of the hospital hosting each sector; and the mortality rate, the number of community-based private psychiatrists, and the capacity of housing institutions for disabled individuals per 1,000 inhabitants of a sector’s catchment area (Table 5).

Discussion

Significant variations were observed in involuntary full-time admission rates between psychiatric sectors in France, even though they are governed by the same legislation regarding this type of care. A large part of the variation was explained by characteristics of the sectors. Our results showed a significant negative association between the development of alternatives to full-time hospitalization and involuntary admissions to full-time hospitalization after adjustment for other factors associated with involuntary care. For each 10% increase in the level of development of alternatives, the probability that a patient would be involuntarily admitted decreased by 12%.

Our findings are consistent with previous work that has shown large variations between psychiatric services in involuntary admission rates (25,33,36). In particular, a study in Northern Europe showed a 13-fold difference in compulsory admission rates between psychiatric departments (36), a figure in the same order of magnitude as our results. Prior research on factors associated with such variations, albeit limited, has also shown results similar to ours; studies found that the psychiatric department where the patient was seen was one of the most important predictors of involuntary admission and that patients’ characteristics accounted for only a limited part of the variations (33,36). To our knowledge, no study has specifically focused on the association between involuntary admissions and the development of alternatives to full-time hospitalization.

Our findings suggest that the development of alternatives to full-time hospitalization provided by psychiatric sectors improved the quality of care. It was indeed negatively associated with involuntary admissions whose reductions align with international recommendations for mental health care (29). Our main hypothesis in regard to the underlying mechanisms is that alternatives to full-time hospitalization can facilitate continuity of care by providing more therapeutic options and can limit the negative consequences associated with inpatient care, such as loss of autonomy and lack of socialization. The presence of alternatives has the potential to lead to fewer crisis situations and to guarantee that patients in need of treatment can provide consent for it.

Nevertheless, some variables that can affect involuntary admissions through similar mechanisms, such as the availability of community-based psychiatrists in the catchment area, appeared to have a stronger effect than the alternatives to full-time hospitalization provided by the psychiatric sectors. Moreover, a portion of the variation remained unexplained, suggesting a greater need for implementation of evidence-based practices to avert crisis situations and for standardized legislation on involuntary admissions. The unexplained variation may also partly result from sectors hosted by hospitals that are not mandated to provide involuntary care and whose practice affects neighboring sectors; however, such effects are likely to be limited because they represent only 7.7% of hospitals in public psychiatry. Finally, since 2012, involuntary care can be provided in France by other means than full-time hospitalization; however, this change was made primarily to allow patients with long involuntary admissions to be treated outside of full-time hospitalization.

Our findings should be interpreted in light of several limitations. First, no conclusions about a causal relationship between alternatives to full-time hospitalization and involuntary full-time admissions can be made because we did not conduct a longitudinal study. Second, the study data were extracted from administrative databases and may be less precise than prospective data (69), and data on some characteristics, such as patients’ symptoms and their severity and patients’ relationship with their family, were not available. Furthermore, no information was available on the distribution of FTEs between the different types of alternatives to full-time hospitalization in the hospital hosting them. Information was also lacking on the distribution of FTEs between the different forms of care at the sector level. Differences between the sectors hosted by the same hospital could exist, even if they follow the same general policy. Moreover, even though it has been widely demonstrated that supply influences practice (7072), use of the overall proportion of FTEs allocated to alternatives as a measure of the level of development of these alternatives did not allow us to directly determine whether practitioners referred their patients to those types of services.

Finally, some sectors were excluded from the analysis because of poor data quality. Sectors hosted by hospitals that did not report data on alternatives to full-time hospitalization may have been less likely to develop such alternatives. However, these data are not used for financial or certification purposes and excluded and included hospitals did not differ in terms of main organizational and institutional characteristics or case mix.

Conclusions

Our study is the first to focus on the association between involuntary full-time admissions and the development of alternatives to full-time hospitalization in the French context. It was carried out on a national scale, thus limiting selection bias, and results were adjusted on the basis of a wide range of characteristics of patients, health care providers, and the environment. It is our belief that our results, usefully supplemented by research carried out in other settings and on other aspects of psychiatric care, can be used by researchers, policy makers, health professionals, and patients alike to support the development of alternatives to full-time hospitalization worldwide.

Ms. Gandré, Ms. Gervaix, Mr. Thillard, Dr. Roelandt, and Pr. Chevreul are with the Épidémiologie Clinique et Évaluation Économique Appliquées aux Populations Vulnérables team, Unité Mixte de Recherche, Université Paris Diderot, Sorbonne Paris Cité, Institut National de la Santé et de la Recherche Médicale, Paris. With the exception of Dr. Roelandt, they are also with the Parisian Health Economics and Health Services Research Unit, URC Eco, AP-HP, Paris. Dr. Roelandt is also with the World Health Organization Collaborative Centre, Lille, France. Pr. Macé is with the Laboratoire Interdisciplinaire de Recherches en Sciences de l'Action, National Conservatory of Arts and Crafts, Paris.
Send correspondence to Ms. Gandré (e-mail: ).

This study was funded by the Directorate for Research, Studies, Evaluation, and Statistics of the French Ministry of Health.

The funding source had no role in study design; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the report for publication.

The authors report no financial relationships with commercial interests.

The authors are grateful to Morgane Michel, M.D., M.Sc., for comments on the manuscript.

References

1 Thornicroft G, Tansella M: What Are the Arguments for Community-Based Mental Health Care? Geneva, World Health Organization, 2003Google Scholar

2 Mental Health Action Plan 2013–2020. Geneva, World Health Organization, 2013Google Scholar

3 The World Health Report 2001: Mental Health: New Understanding, New Hope. Geneva, World Health Organization, 2001Google Scholar

4 The European Mental Health Action Plan 2013–2020. Geneva, World Health Organization, 2015Google Scholar

5 Improving the Mental Health of the Population: Towards a Strategy on Mental Health for the European Union. Brussels, European Commission, 2005Google Scholar

6 Johnson S, Needle J, Bindman J, et al. (eds): Crisis Resolution and Home Treatment in Mental Health. Cambridge, United Kingdom, Cambridge University Press, 2008Google Scholar

7 Killaspy H, Johnson S, King M, et al.: Developing mental health services in response to research evidence. Epidemiologia e Psichiatria Sociale 17:47–56, 2008Crossref, MedlineGoogle Scholar

8 Creed F, Black D, Anthony P, et al.: Randomised controlled trial of day patient versus inpatient psychiatric treatment. BMJ 300:1033–1037, 1990Crossref, MedlineGoogle Scholar

9 Ryu Y, Mizuno M, Sakuma K, et al.: Deinstitutionalization of long-stay patients with schizophrenia: the 2-year social and clinical outcome of a comprehensive intervention program in Japan. Australian and New Zealand Journal of Psychiatry 40:462–470, 2006Crossref, MedlineGoogle Scholar

10 Schene AH, van Wijngaarden B, Poelijoe NW, et al.: The Utrecht comparative study on psychiatric day treatment and inpatient treatment. Acta Psychiatrica Scandinavica 87:427–436, 1993Crossref, MedlineGoogle Scholar

11 Sharifi V, Tehranidoost M, Yunesian M, et al.: Effectiveness of a low-intensity home-based aftercare for patients with severe mental disorders: a 12-month randomized controlled study. Community Mental Health Journal 48:766–770, 2012Crossref, MedlineGoogle Scholar

12 Coldefy M: The evolution of the organization of psychiatric care in Germany, the UK, France and Italy: similarities and differences [in French]. Questions D’économie de la Santé 180:1–8, 2012Google Scholar

13 Taylor Salisbury T, Killaspy H, King M: An international comparison of the deinstitutionalisation of mental health care: development and findings of the Mental Health Services Deinstitutionalisation Measure (MENDit). BMC Psychiatry 16:54, 2016Crossref, MedlineGoogle Scholar

14 Health at a Glance 2013. Paris, Organisation for Economic Co-operation and Development, 2013Google Scholar

15 Jacobs R, Barrenho E: Impact of crisis resolution and home treatment teams on psychiatric admissions in England. British Journal of Psychiatry 199:71–76, 2011Crossref, MedlineGoogle Scholar

16 Vázquez-Bourgon J, Salvador-Carulla L, Vázquez-Barquero JL: Community alternatives to acute inpatient care for severe psychiatric patients. Actas Españolas de Psiquiatría 40:323–332, 2012MedlineGoogle Scholar

17 Becker I, Vázquez-Barquero JL: The European perspective of psychiatric reform. Acta Psychiatrica Scandinavica Supplementum 410:8–14, 2001CrossrefGoogle Scholar

18 The Organization of Psychiatric Care, the Impacts of the Psychiatry and Mental Health Plan 2005–2010 [in French]. Paris, National Court of Auditors, 2011. https://www.ccomptes.fr/Publications/Publications/L-organisation-des-soins-psychiatriquesGoogle Scholar

19 Psychiatry and Mental Health Plan, 2011–2015 [in French]. Paris, Minister of Labor, Employment and Health, 2012Google Scholar

20 Lloyd-Evans B, Slade M, Jagielska D, et al.: Residential alternatives to acute psychiatric hospital admission: systematic review. British Journal of Psychiatry 195:109–117, 2009Crossref, MedlineGoogle Scholar

21 Salize HJ, Dressing H: Coercion, involuntary treatment and quality of mental health care: is there any link? Current Opinion in Psychiatry 18:576–584, 2005Crossref, MedlineGoogle Scholar

22 Salize H, Drebing H, Peitz M: Compulsory Admission and Involuntary Treatment of Mentally Ill Patients: Legislation and Practice in EU-member states. Brussels, European Commission, 2002Google Scholar

23 Fistein EC, Holland AJ, Clare ICH, et al.: A comparison of mental health legislation from diverse Commonwealth jurisdictions. International Journal of Law and Psychiatry 32:147–155, 2009Crossref, MedlineGoogle Scholar

24 Hermann C: Improving Mental Healthcare. A Guide to Measurement-Based Quality Improvement. Arlington, Va, American Psychiatric Publishing, 2006Google Scholar

25 Donisi V, Tedeschi F, Salazzari D, et al.: Differences in the use of involuntary admission across the Veneto Region: which role for individual and contextual variables? Epidemiology and Psychiatric Sciences 25:49–57, 2016Crossref, MedlineGoogle Scholar

26 The National Inventory of Mental Health Quality Measures. Cambridge, MA, Harvard University, Center for Quality Assessment and Improvement in Mental Health. http://www.cqaimh.org/NIMHQM.htmGoogle Scholar

27 Accountability and Performance Indicators for Mental Health Services and Supports. Ottawa, Canadian Federal/Provincial/Territorial Advisory Network on Mental Health, 2001Google Scholar

28 World Health Organization Assessment Instrument for Mental Health Systems: WHO-AIMS version 2.2. Geneva, World Health Organization, 2005Google Scholar

29 WHO QualityRights Tool Kit: Assessing and Improving Quality and Human Rights in Mental Health and Social Care Facilities. Geneva, World Health Organization, 2012Google Scholar

30 Circular of the 15 March 1960 Regarding the Organizational and Equipment Program of French Departments for the Fight Against Mental Disorders [in French]. Paris, Legifrance, 1960. http://www.chameaupsy.com/images/stories/systeme/doc-archives/circulaires/circulaire-15-mars-1960.pdfGoogle Scholar

31 Verdoux H, Tignol J: Focus on psychiatry in France. British Journal of Psychiatry 183:466–471, 2003Crossref, MedlineGoogle Scholar

32 Riecher-Rössler A, Rössler W: Compulsory admission of psychiatric patients: an international comparison. Acta Psychiatrica Scandinavica 87:231–236, 1993Crossref, MedlineGoogle Scholar

33 Lay B, Nordt C, Rössler W: Variation in use of coercive measures in psychiatric hospitals. European Psychiatry 26:244–251, 2011Crossref, MedlineGoogle Scholar

34 Emons B, Haussleiter IS, Kalthoff J, et al.: Impact of social-psychiatric services and psychiatric clinics on involuntary admissions. International Journal of Social Psychiatry 60:672–680, 2014Crossref, MedlineGoogle Scholar

35 Hattori I, Higashi T: Socioeconomic and familial factors in the involuntary hospitalization of patients with schizophrenia. Psychiatry and Clinical Neurosciences 58:8–15, 2004Crossref, MedlineGoogle Scholar

36 Hansson L, Muus S, Saarento O, et al.: The Nordic comparative study on sectorized psychiatry: rates of compulsory care and use of compulsory admissions during a 1-year follow-up. Social Psychiatry and Psychiatric Epidemiology 34:99–104, 1999Crossref, MedlineGoogle Scholar

37 Kokkonen P: Coercion and legal protection in psychiatric care in Finland. Medicine and Law 12:113–124, 1993MedlineGoogle Scholar

38 Chevreul K, Prigent A, Bourmaud A, et al.: The cost of mental disorders in France. European Neuropsychopharmacology 23:879–886, 2013Crossref, MedlineGoogle Scholar

39 Base Nationale RIM-P. Lyon, France, Agence Technique de l'Information sur l'Hospitalisation, 2014. http://www.atih.sante.fr/base-nationale-rim-pGoogle Scholar

40 International Statistical Classification of Diseases and Related Health Problems 10th Revision. Geneva, World Health Organization, 2010. http://apps.who.int/classifications/icd10/browse/2010/enGoogle Scholar

41 Haro JM, Ayuso-Mateos JL, Bitter I, et al.: ROAMER: roadmap for mental health research in Europe. International Journal of Methods in Psychiatric Research 23(suppl 1):1–14, 2014Crossref, MedlineGoogle Scholar

42 Prigent A, Auraaen A, Kamendje-Tchokobou B, et al.: Health-related quality of life and utility scores in people with mental disorders: a comparison with the non-mentally ill general population. International Journal of Environmental Research and Public Health 11:2804–2817, 2014Crossref, MedlineGoogle Scholar

43 Shulenburg JMGVD: The Influence of Economic Evaluation Studies on Health Care Decision Making: A European Survey. Amsterdam, IOS Press, 2000Google Scholar

44 Thampi N, Showler A, Burry L, et al.: Multicenter study of health care cost of patients admitted to hospital with Staphylococcus aureus bacteremia: impact of length of stay and intensity of care. American Journal of Infection Control 43:739–744, 2015Crossref, MedlineGoogle Scholar

45 Ezenduka C, Ichoku H, Ochonma O: Estimating the costs of psychiatric hospital services at a public health facility in Nigeria. Journal of Mental Health Policy and Economics 15:139–148, 2012MedlineGoogle Scholar

46 Wolff J, McCrone P, Berger M, et al.: A work time study analysing differences in resource use between psychiatric inpatients. Social Psychiatry and Psychiatric Epidemiology 50:1309–1315, 2015Crossref, MedlineGoogle Scholar

47 Cromwell J, Maier J, Gage B, et al.: Characteristics of high staff intensive Medicare psychiatric inpatients. Health Care Financing Review 26:103–117, 2004MedlineGoogle Scholar

48 Mercuri M, Gafni A: Medical practice variations: what the literature tells us (or does not) about what are warranted and unwarranted variations. Journal of Evaluation in Clinical Practice 17:671–677, 2011Crossref, MedlineGoogle Scholar

49 Chevreul K: General practitioners’ prescribing decision-making: the case of proton pump inhibitors in France. Doctoral dissertation, London, London School of Economics and Political Science, 2010Google Scholar

50 Cutler D, Skinner J, Stern AD, et al: Physician Beliefs and Patient Preferences: A New Look at Regional Variation in Health Care Spending. Working paper 15-090. Cambridge, Mass, Harvard University, 2015. http://www.hbs.edu/faculty/Publication%20Files/15-090_bfbc1026-8cd3-497a-845a-f57b382a348a.pdfGoogle Scholar

51 Long MJ: An explanatory model of medical practice variation: a physician resource demand perspective. Journal of Evaluation in Clinical Practice 8:167–174, 2002Crossref, MedlineGoogle Scholar

52 Permanent Equipment Base [in French]. Paris, French Public Data Platform, 2010. https://www.data.gouv.fr/fr/datasets/base-permanente-des-equipements-1/Google Scholar

53 Rey G, Jougla E, Fouillet A, et al.: Ecological association between a deprivation index and mortality in France over the period 1997–2001: variations with spatial scale, degree of urbanicity, age, gender and cause of death. BMC Public Health 9:33, 2009Crossref, MedlineGoogle Scholar

54 Rey G, Rican S, Jougla E: Measuring social inequalities in mortality by cause of death: ecological approach based on a social deprivation index [in French]. Bulletin Epidémiologique Hebdomadaire 8–9:87–90, 2011Google Scholar

55 Windenberger F, Rican S, Jougla E, et al.: Spatiotemporal association between deprivation and mortality: trends in France during the nineties. European Journal of Public Health 22:347–353, 2012Crossref, MedlineGoogle Scholar

56 Causes of Death, 2012 [in French]. Paris, Centre d'Epidémiologie sur les Causes Médicales de Décès, 2012. http://www.cepidc.inserm.fr/inserm/html/index2.htmGoogle Scholar

57 Eco-Health Databases [in French]. Paris, Eco-Santé France, Régions and Départements, 2012. http://www.ecosante.fr/Google Scholar

58 Results of Population Censuses [in French]. Paris, Institut National de la Statistique et des Études Économiques, 2012. http://www.insee.fr/fr/bases-de-donnees/default.asp?page=recensements.htmGoogle Scholar

59 Definitions, Methods, and Quality: Base of Urban Units [in French]. Paris, Institut National de la Statistique et des Études Économiques, 2010. http://www.insee.fr/fr/methodes/default.asp?page=zonages/unites_urbaines.htmGoogle Scholar

60 Abdi H: Coefficient of variation; in Encyclopedia of Research Design. Edited by Salkind N. Thousand Oaks, CA, Sage, 2010Google Scholar

61 Geographic Variations in Health Care. What Do We Know and What Can Be Done to Improve Health System Performance? Paris, Organisation for Economic Co-operation and Development, 2014Google Scholar

62 Hollingworth W, Rooshenas L, Busby J, et al.: Using clinical practice variations as a method for commissioners and clinicians to identify and prioritise opportunities for disinvestment in health care: a cross-sectional study, systematic reviews and qualitative study. Health Services and Delivery Research, 2015 (doi org/10.3310/hsdr03130)Google Scholar

63 Merlo J, Chaix B, Ohlsson H, et al.: A brief conceptual tutorial of multilevel analysis in social epidemiology: using measures of clustering in multilevel logistic regression to investigate contextual phenomena. Journal of Epidemiology and Community Health 60:290–297, 2006Crossref, MedlineGoogle Scholar

64 Kreft I: Are Multilevel Techniques Necessary? An Overview, Including Simulation Studies. Los Angeles, California State University, 1996Google Scholar

65 Hox J: Multilevel modeling: when and why; in Classification, Data Analysis, and Data Highways. Edited by Balderjahn I, Mathar R, Schader M. Berlin, Springer, 1998Google Scholar

66 Maas CJM, Hox JJ: Robustness issues in multilevel regression analysis. Statistica Neerlandica 58:127–137, 2004CrossrefGoogle Scholar

67 Maas CJM, Hox JJ: Sufficient sample sizes for multilevel modeling. Methodology 1:86–92, 2005CrossrefGoogle Scholar

68 Clarke P: When can group level clustering be ignored? Multilevel models versus single-level models with sparse data. Journal of Epidemiology and Community Health 62:752–758, 2008Crossref, MedlineGoogle Scholar

69 Gandhi S, Warren Salmon J, Kong S, et al.: Administrative databases and outcomes assessment: an overview of issues and potential utility. Journal of Managed Care and Specialty Pharmacy 5:215–222, 1999CrossrefGoogle Scholar

70 Wennberg J: Time to tackle unwarranted variations in practice. BMJ 342:d1513, 2011Crossref, MedlineGoogle Scholar

71 Supply-Sensitive Care. Lebanon, NH, Dartmouth Atlas of Health Care. http://www.dartmouthatlas.org/keyissues/issue.aspx?con=2937Google Scholar

72 Wennberg JE, Fisher ES, Skinner JS: Geography and the debate over Medicare reform. Health Affairs (suppl Web exclusives):W96-114, 2002Google Scholar