Can routine Primary Care Records Help in Detecting Suicide Risk? A Population-Based Case-Control Study in Barcelona.

OBJECTIVES

To characterize people who died by suicide after having previous contacts with public health system using the data available in a primary care setting.

METHOD

A retrospective, population-based case-control study identified through autopsy reports subjects who died from suicide between 2010 and 2015 in Barcelona province. Those who had previous interaction with primary healthcare services were selected, and 20 controls per case with similar age, sex and residence area were randomly sampled from a primary healthcare medical database; the available medical data derived from routine primary care records were anonymised and retrieved for analysis in all subjects.

RESULTS

Of 2109 people deceased by suicide, 971 had previous primary healthcare interactions; 33.8% aged over 65 and 74% men. Of those with previous contact, 33% died during the week after and 58.5% within one month. A multivariable analysis identified several significant risk factors in the database, including previous suicide attempts, psychiatric diagnosis, number of primary healthcare visits, referrals to mental health, number of drugs prescribed, recent psychoactive drug prescriptions, and being on sick leave due to mental conditions in the previous year.

CONCLUSIONS

Nearly half of suicide subjects contacted primary healthcare before dying but remained either undetected or not effectively managed for prevention. In cases who interacted, available data on risk factors included high frequentation and being on sick leave due to mental health disorders, in addition to other well-known factors. Routine electronic medical records represent an opportunity to provide clinical-decision support tools that could be implemented through automatized risk calculation.HIGHLIGHTSMost of suicide patients had a recent interaction with the healthcare systemWell known risk factors were available in the routine electronic medical recordsAdditional alerting tools could be implemented through automatized risk calculation.