Local Geographical Distribution of Acute Involuntary Psychiatric Admissions in Subdistricts In and Around Utrecht, the Netherlands

Braam, Arjan W.; Ommeren, Omar W.H.R. van; Buuren, Melissa L. van; Laan, Wijnand; Smeets, Hugo M.; Engelhard, Iris M.

doi:10.1016/j.jemermed.2015.06.057

Cited by 13 publications

(13 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our findings are in line with previous research that identified unemployment as a risk factor for involuntary psychiatric hospitalization not only at the individual level, but also at an environmental, population-based level [7,9,10,22,23,34]. Economic deprivation has also been identified as a risk factor both on the individual and the population level [10,35].…”

Section: Discussionsupporting

confidence: 92%

“…Concerning generalizability, there is some overlap between our findings and those from previous studies from Germany [ 11 , 27 ] and other metropolitan regions in Europe [ 7 , 22 , 23 ]. Of note, a study from Switzerland which included both urban and rural regions also found that people with dementia and those with schizophrenia and related disorders were at the highest risk of involuntary psychiatric hospitalization [ 49 ].…”

Section: Discussioncontrasting

confidence: 51%

“…On the level of social environment, living in urban regions with high population density and living in socially deprived areas with a high unemployment rate, small household size and high percentage of immigrants were identified as risk factors for detention [7,9,22,23].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Identification of risk factors for involuntary psychiatric hospitalization: using environmental socioeconomic data and methods of machine learning to improve prediction

et al. 2020

View full text Add to dashboard Cite

Background The purpose of this study was to identify factors associated with a high risk of involuntary psychiatric in-patient hospitalization both on the individual level and on the level of mental health services and the socioeconomic environment that patients live in. Methods The present study expands on a previous analysis of the health records of 5764 cases admitted as in-patients in the four psychiatric hospitals of the Metropolitan City of Cologne, Germany, in the year 2011 (1773 cases treated under the Mental Health Act and 3991 cases treated voluntarily). Our previous analysis had included medical, sociodemographic and socioeconomic data of every case and used a machine learning-based prediction model employing chi-squared automatic interaction detection (CHAID). Our current analysis attempts to improve the previous one through (1) optimizing the machine learning procedures (use of a different type of decision-tree prediction model (Classification and Regression Trees (CART) and application of hyperparameter tuning (HT)), and (2) the addition of patients’ environmental socioeconomic data (ESED) to the data set. Results Compared to our previous analysis, model fit was improved. Main diagnoses of an organic mental or a psychotic disorder (ICD-10 groups F0 and F2), suicidal behavior upon admission, admission outside of regular service hours and absence of outpatient treatment prior to admission were confirmed as powerful predictors of detention. Particularly high risks were shown for (1) patients with an organic mental disorder, specifically if they were retired, admitted outside of regular service hours and lived in assisted housing, (2) patients with suicidal tendencies upon admission who did not suffer from an affective disorder, specifically if it was unclear whether there had been previous suicide attempts, or if the affected person lived in areas with high unemployment rates, and (3) patients with psychosis, specifically those who lived in densely built areas with a large proportion of small or one-person households. Conclusions Certain psychiatric diagnoses and suicidal tendencies are major risk factors for involuntary psychiatric hospitalization. In addition, service-related and environmental socioeconomic factors contribute to the risk for detention. Identifying modifiable risk factors and particularly vulnerable risk groups should help to develop suitable preventive measures.

show abstract

Section: Discussionsupporting

confidence: 92%

Section: Discussioncontrasting

confidence: 51%

See 1 more Smart Citation

Identification of risk factors for involuntary psychiatric hospitalization: using environmental socioeconomic data and methods of machine learning to improve prediction

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Inpatients. Linked to other national registers Haukka et al [ 100 ] Mid-Netherlands Psychiatric Care Register Yes Regional—Utrecht and surrounding areas, population 760 k. Secondary care psychiatry Braam et al [ 101 ] Norwegian Patient Register Yes National. Secondary care psychiatry.…”

Section: Resultsmentioning

confidence: 99%

‘Big data’ in mental health research: current status and emerging possibilities

Stewart

Davis

2016

Soc Psychiatry Psychiatr Epidemiol

View full text Add to dashboard Cite

Purpose‘Big data’ are accumulating in a multitude of domains and offer novel opportunities for research. The role of these resources in mental health investigations remains relatively unexplored, although a number of datasets are in use and supporting a range of projects. We sought to review big data resources and their use in mental health research to characterise applications to date and consider directions for innovation in future.MethodsA narrative review.Results Clear disparities were evident in geographic regions covered and in the disorders and interventions receiving most attention.Discussion We discuss the strengths and weaknesses of the use of different types of data and the challenges of big data in general. Current research output from big data is still predominantly determined by the information and resources available and there is a need to reverse the situation so that big data platforms are more driven by the needs of clinical services and service users.

show abstract

“…Moreover, asylum seekers in Switzerland invariably are not allowed to work. As a result, we were not able to control for the effects of this variable, although the role of unemployment for compulsory admissions as well as for the psychopathology already has been demonstrated [ 29 , 36 ]. In addition, no data regarding the length of asylum procedure were provided; nevertheless, this parameter has been associated with an increased risk of mental disorders [ 37 , 38 , 39 ].…”

Section: Discussionmentioning

confidence: 99%

Psychiatric Emergencies of Asylum Seekers; Descriptive Analysis and Comparison with Immigrants of Warranted Residence

Schoretsanitis

Eisenhardt

Ricklin

et al. 2018

IJERPH

View full text Add to dashboard Cite

Background: The aim of our study was to assess utilization patterns of psychiatric services by asylum seekers. Methods: We included 119 adults who presented themselves at the University Emergency Department between 1 March 2012 and 1 January 2017 for psychiatric consultation. Descriptive data were compared with a control group of non-Swiss individuals with warranted residence permits using Mann-Whitney-U and chi square (χ2) tests. Results: Patients were mainly single, male, residing in reception centers, and presented themselves most frequently due to suicidal ideation. Almost 60% of the patients were assigned to inpatient treatments, with 28 involuntary cases. Compared to the control group, asylum seekers were younger and more often men (p < 0.001 for both). Further, they less often had family in Switzerland (χ2 = 9.91, p = 0.007). The proportion of patients coming in as walk-ins was significantly higher in the control group than in asylum seekers (χ2 = 37.0, p < 0.001). Asylum seekers were more frequently referred due to suicidal ideation and aggressive behavior than participants in the control group (χ2 = 80.07, p < 0.001). Diagnoses for asylum seekers infrequently included mood, as they often reported stress-related disorders (χ2 = 19.6, p = 0.021) and they were infrequently released home (χ2 = 9.19, p = 0.027). Conclusion: Asylum seekers more frequently demonstrated severe symptoms such as suicidal ideation and aggressive behavior and they were mainly treated as inpatients, potentially due to minimal social resources.

show abstract

Local Geographical Distribution of Acute Involuntary Psychiatric Admissions in Subdistricts In and Around Utrecht, the Netherlands

Cited by 13 publications

References 24 publications

Identification of risk factors for involuntary psychiatric hospitalization: using environmental socioeconomic data and methods of machine learning to improve prediction

Identification of risk factors for involuntary psychiatric hospitalization: using environmental socioeconomic data and methods of machine learning to improve prediction

‘Big data’ in mental health research: current status and emerging possibilities

Psychiatric Emergencies of Asylum Seekers; Descriptive Analysis and Comparison with Immigrants of Warranted Residence

Contact Info

Product

Resources

About