Detecting co-occurring clusters of coronary heart disease and depression in New England neighborhoods

Abstract

Background

There is increasing evidence that coronary heart disease (CHD) patients with a mental health disorder have increased risk of cardiovascular mortality. Studying this relationship beyond the individual at the neighborhood level could lead to novel public health interventions.

Methods

Spatial scan statistics were used to detect potential co-occurring geographic clusters of CHD and depression among adults in the New England region of the United States during 2019. Negative binomial regression models were used to adjust cluster analyses for census-tract level estimates of relevant risk factors, including social vulnerability, urbanicity, walkability, greenspace, healthcare utilization, and access to mental health facilities.

Results

Nine significant adjusted clusters were identified, including six multivariate clusters and three univariate clusters for depression (none for CHD). The highest multivariate relative risk (RR) was seen in the cluster around Hartford County, CT (n=234 census tracts; Depression RR=1.06; CHD RR=1.06).

Conclusions

Clusters from adjusted analyses indicate clustering that is not explained by selected covariates alone, many of which were social determinants of health. Mixed-methods, longitudinal data, and individual-level approaches could help explain remaining clustering. The spatial methods employed in this study can more effectively identify high-risk areas where interventions, such as increasing mental health care utilization or enhancing health literacy, should be implemented for both conditions.