Geospatial analysis reveals distinct hotspots of severe mental illness

BackgroundThe identification of geographic variation in incidence can be an important step in the delineation of disease risk factors, but has mostly been undertaken in upper-income countries. Here, we use Electronic Health Records (EHR) from a middle-income country, Colombia, to characterize geographic variation in major mental disorders. MethodWe leveraged geolocated EHRs of 16,295 patients at a psychiatric hospital serving the entire state of Caldas, all of whom received a primary diagnosis of bipolar disorder, schizophrenia, or major depressive disorder at their first visit. To identify the relationship between travel time and incidence of mental illness we used a zero-inflated negative binomial regression model. We used spatial scan statistics to identify clusters of patients, stratified by diagnosis and severity: mild (outpatients) or severe (inpatients). ResultsWe observed a significant association between incidence and travel time for outpatients (N = 11,077, relative risk (RR) = 0.80, 95% confidence interval (0.71, 0.89)), but not inpatients (N = 5,218). We found seven clusters of severe mental illness: the cluster with the most extreme overrepresentation of bipolar disorder (RR = 5.83, p < 0.001) has an average annual incidence of 8.7 inpatients per 10,000 residents, among the highest frequencies worldwide. ConclusionsThe hospital database reflects the geographic distribution of severe, but not mild, mental illness within Caldas. Each hotspot is a candidate location for further research to identify genetic or environmental risk factors for severe mental illness. Our analyses highlight how existing infrastructure from middle-income countries can be extraordinary resources for population studies.