Psychiatric hospital admission and later crime, mental health, and labor market outcomes

Abstract Most OECD countries have downsized treatment capacity at psychiatric hospitals substantially. We investigate consequences of these reductions by studying how the decision whether to admit individuals in mental distress to a psychiatric hospital affects their subsequent crime, treatment trajectories, and labor market outcomes. To circumvent nonrandom selection into admission, we use a proxy of occupancy rates prior to a patient's first contact with a psychiatric hospital as an instrument. We find that admissions reduce criminal behavior, likely due to incapacitation, and predominantly for males and those with a criminal record. Furthermore, admission lowers patients' subsequent labor market attachment, likely because a psychiatric hospital admission is an eligibility criterion for welfare benefits.


Trends in Psychiatric Hospital Treatments
beds resembles that of many Western countries (WHO, 2011). The reductions to inpatient capacities relative to the number of treated patients moved treatment from a hospital setting to the patients' home environments while also reducing the costs of treatment.

Construction of Mental Health Data
We constructed the mental health data using the Danish national psychiatric register (a subset of the Danish patient register (LPR)) made available by Statistics Denmark. We period. This yields 50,439 patients. Next, we only include individuals who had their first contact between the ages of 18 and 45 as we do not wish to focus on child or geriatric psychiatric treatment as admission or non-admission of these patients is not likely to influence the outcomes we are interested in. Also, once in contact with a psychiatric care facility, different conditions apply for these two groups relative to the average adult population. We define the outcome of a contact on the basis of a patient's experiences across an entire day. I.e., if a patient contacts a psychiatric emergency ward and later in the day is admitted to a normal psychiatric ward, that is treated as one incident (contact) leading to an admission. In order not to exclude people who show up late in the day, we use the same procedure for patient who have their first contact at an emergency ward at day one and are admitted to a normal word at day two. We distinguish between admission to inpatient care, admission into outpatient care, and no admission. We define the treatment variable as admission to inpatient care.

Construction of the Instrument
We used the Danish National Psychiatric Register to obtain the number of unique individual contacts to each hospital each day for the period 1998-2001 for all ages and types of diagnoses.
We included the year 1998 in order to construct the instrument as a contact intensity measure as shown in Equation 1. We then computed the number of weekly contacts separately for the two seven day periods prior to a person's first contact by aggregating the number across day -1 to -7, and day -8 to -14. This gave us our numerators. In order to obtain the denominator, which measures the seven day period with the highest number of contacts within the last 365 days, we calculated the aggregated number for all successive seven days combination from the day prior to an individual's contact and going backward 365 days. We then used the highest number of contacts measured across a seven day period as the denominator. The two fractions made up the instruments.

Construction of Covariates
We constructed covariate data using a number of databases. Unique individual identification numbers allowed us to directly link observations across registers, and also link information on patients with information on their parents and potential spouse. We used the following databases:

Construction of Outcomes
We measured all outcomes for the following three years after first contact an in three month windows from month 1-3, 4-6, 10-12, 22-24, 34-36 from first contact. The following paragraphs provide details on how we constructed the different measures.

Crime Using the criminal justice databases on convictions (KRAF) and indictments (KRIN)
we constructed the crime variable as a quarterly count variable that aggregated all criminal convictions received for crimes committed within three month windows (as detailed above) and accumulated from first contact and the following 36 months. We excluded traffic violations.

Contacts and Admissions
We followed each patient in the Danish national psychiatric register data for the 36 subsequent months after first contact. Contact was constructed as a dummy, indicating if the patient had new subsequent contacts to a psychiatric hospital within three month windows (as detailed above). Admission was constructed as a subset of the contact dummy, indicating if the patient was admitted as an inpatient following a contact during the periods in question.
Labor Market Outcomes We use the register based labor force database (RAS) to obtain annual information on individual's labor market position for the three years after contact.
We define three categories: employed, unemployed, and outside the labor force. Employed entails any form of paid work, either as an employee or as self-employed. Unemployed entails receiving either social assistance or unemployment insurance while being available for the labor market or undertaking workfare. Outside the labor force entails receiving public welfare without work or workfare requirements. We also obtain the same information for patients' spouses. The information is obtained at the end of November for each year.

Counterfactual Outcomes
First, let z be the lowest observed values of instrument (the contact ratio where the likelihood of admission is lowest) andz denote the highest observed values of the instrument (the contact ratio where the likelihood of admission is highest). Define the share of always-takers π a as p(D = 1|z = z) and the share of never-takers π n p(D = 0|z =z). From monotonicity it follows that the share of compliers π c = 1 − π a − π n . The outcomes of those who are admitted at the lowest likelihood of admission z are a weighted average of always-takers' and compliers' outcomes (Y 1 ). Likewise, the outcomes of those who are not admitted at highest likelihood of admissionz are a weighted average of never-takers and compliers (Y 0 ). We can rearrange this to give us: 6 We observe all of the moments on the RHS in equation (1) directly in the data. 24,277 + p < .10; * p < .05; * * p < .01; * * * p < .001 Note: Table shows OLS regression results of the intrumental variable (hospital specific contact intensity the week prior to the individual's initial contact) on number of visits to general practitioners (panel A-B) and specialist practitioners (panel C-D) the year of and the year prior to initial contact, and whether the patient contacted his/her default hospital or not (panel E-F). Panel G shows regression of difference in contact intensity between the actual contacted hospital and the default hospital on whether the patient contacted his/her default hospital or not. Standard errors clustered by hospital and month in parentheses. SES and demographic controls include: Gender (dummy), age at adm., mother's age at birth, mothers years of schooling, father' age at birth, father's years of schooling, mother has prior psych. history (dummy), admitted in own municipality (dummy), greater CPH area (dummy), other metropolitan area (dummy), year dummies. Diagnosis controls include: Dummies for each F. diagnosis category from ICD-10. A : N excl. Copenhagen = 19595. Source: Own calculations on data from Statistics Denmark. Observations 24,277 + p < .10; * p < .05; * * p < .01; * * * p < .001 Note: Table shows OLS regression results of the intrumental variable (hospital specific contact intensity the week prior to the individual's initial contact) on gender (dummy), age at adm., mother's age at birth, mothers years of schooling, father' age at birth, father's years of schooling, mother has prior psych. history (dummy), admitted in own municipality (dummy), greater CPH area (dummy), other metropolitan area (dummy), in regions Funen or Jutland (dummy), year dummies, and dummies for each F. diagnosis category form ICD-10. Standard errors clustered by hospital in parentheses. Source: Own calculations on data from Statistics Denmark.  and month in parentheses. SES and demographic controls include: Gender (dummy), age at adm., mother's age at birth, mothers years of schooling, father' age at birth, father's years of schooling, mother has prior psych. history (dummy), admitted in own municipality (dummy), greater CPH area (dummy), in regions Funen or Jutland (dummy), other metropolitan area (dummy), year dummies. Diagnosis controls include: Dummies for each F. diagnosis category from ICD-10. Source: Own calculations on data from Statistics Denmark.  Table shows reduced form estimates of the instrument on probability of subsequently contacting a psychiatric hospital again, the probability of subsequently being admitted to a psychiatric hospital again, and the number of contacts / treatments at GPs or specialist practitioner. Time 0 is month of initial contact. Table shows results for quarterly contact and admission rates, i.e. from month 0-3, 3-6, 9-12, and for GPs and specialists the first two years following first contact to a psychiatric hospital. Standard errors clustered by hospital in parentheses. SES and demographic controls include: Gender (dummy), age at adm., mother's age at birth, mothers years of schooling, father' age at birth, father's years of schooling, mother has prior psych. history (dummy), admitted in own municipality (dummy), greater CPH area (dummy), in regions Funen or Jutland (dummy), other metropolitan area (dummy), year dummies. Diagnosis controls include: Dummies for each F. diagnosis category from ICD-10. Source: Own calculations on data from Statistics Denmark.  + p < .10; * p < .05; * * p < .01; * * * p < .001 Note: Standard errors and asymptotically correct standard errors in parentheses. AME: Average marginal effect. Time 0 is month of initial contact. Standard errors clustered by hospital and month in parentheses. SES and demographic controls include: Gender (dummy), age at adm., mother's age at birth, mothers years of schooling, father' age at birth, father's years of schooling, mother has prior psych. history (dummy), admitted in own municipality (dummy), greater CPH area (dummy), other metropolitan area (dummy), year dummies. Other crime includes drugs-related and weapons-related crime. Diagnosis controls include: Dummies for each F. diagnosis category from ICD-10. Source: Own calculations on data from Statistics Denmark.  Table B.9: Effect of admission on labor earnings, extensive and intensive margin effects Note: Figures show estimated potential outcomes for compliers the year before and three years after first contact to a psychiatric hospital following Dahl et al. (2014), Appendix B. Potential outcomes are estimated using both instruments (hospital specific contact intensity the weeks prior to contact) but not any covariates. Source: Own calculations on data from Statistics Denmark.