Article Text
Abstract
Background This study contributes robust evidence on the association between mental health and local crime rates by showing how changing exposure to small area-level crime relates to self-reported and administrative data on mental health.
Methods The study sample comprised 112 251 adults aged 16–60 years, drawn from the Scottish Longitudinal Study, a 5.3% representative sample of Scottish population followed across censuses. Outcomes were individual mental health indicators: self-reported mental illness from the 2011 Census and linked administrative data on antidepressants and antipsychotics prescribed through primary care providers in the National Health Service in 2010/2012. Crime rates at data zone level (500–1000 persons) were matched to the participants’ main place of residence, as defined by general practitioner patient registration duration during 2004/2006, 2007/2009 and 2010/12. Average neighbourhood crime exposure and change in area crime were computed. Covariate-adjusted logistic regressions were conducted, stratified by moving status.
Results In addition to average crime exposure during follow-up, recent increases in crime (2007/2009–2010/2012) were associated with a higher risk of self-reported mental illness, among ‘stayers’ aged 16–30 years (OR=1.11; 95% CI 1.00 to 1.22), and among ‘movers’ aged 31–45 years (OR=1.07; 95% CI 1.01 to 1.13). Prescribed medications reinforced these findings; worsening crime rates were linked with antidepressant prescriptions among young stayers (OR=1.09; 95% CI 1.04 to 1.14) and with antipsychotic prescriptions among younger middle-aged movers (OR=1.11; 95% CI 1.01 to 1.23).
Conclusion Changing neighbourhood crime exposure is related to individual mental health, but associations differ by psychiatric conditions, age and moving status. Crime reduction and prevention, especially in communities with rising crime rates, may benefit public mental health.
- MENTAL HEALTH
- CRIME
- NATURAL EXPERIMENT
- ANTIPSYCHOTICS
- ANTIDEPRESSANTS
- NEIGHBOURHOOD
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Footnotes
Twitter Jamie Pearce @jamie0pearce and Gergő Baranyi @GBaranyi
Contributors GB designed the study, conducted data analyses, interpreted the results and prepared the first draft. JP and CD participated in the study design, in the interpretation of the findings and commented on the manuscript. MC and SEC contributed to the interpretation and revised the manuscript.
Funding This work was supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement (LONGPOP—Methodologies and Data Mining Techniques for the Analysis of Big Data Based on Longitudinal Population and Epidemiological Registers (grant number 676060)). This research also benefitted from the support of the Economic and Social Research Council, UK (grant award ES/P008585/1).
Competing interests None declared.
Patient consent for publication Not required.
Ethics approval Ethical approval for the research was given by the SLS data governors, the Public Benefit and Privacy Panel for Health and Social Care, NHS Scotland (application number eDRIS 1516-0398) and the Research Ethics and Integrity Committee, University of Edinburgh (GeoSciences_2018_189).
Disclaimer This publication reflects only the author’s view and that the Research Executive Agency is not responsible for any use that may be made of the information it contains.
Data sharing statement The data used in this research are publicly not available, but may be accessible for scientific and statistical purposes after a successful application process (https://sls.lscs.ac.uk/). The researcher has access to anonymised data in secure settings controlled by SLS. Results released for publication are carefully monitored to avoid any risk of disclosure about individuals.
Provenance and peer review Not commissioned; externally peer reviewed.