  Our objective was to develop and test a new concept (affinity) analogous to
multimorbidity of chronic conditions for individuals at census tract level in
Memphis, TN. The use of affinity will improve the surveillance of multiple
chronic conditions and facilitate the design of effective interventions. We
used publicly available chronic condition data (Center for Disease Control and
Prevention 500 Cities project), socio-demographic data (US Census Bureau), and
demographic data (Environmental Systems Research Institute). A geo-distinctive
pattern of clustered chronic affinity associated with socio-economic
deprivation wasobserved. Statistical results confirmed that neighborhoods with
higher rates of crime, poverty, and unemploy-ment were associated with an
increased likelihood of having a higher affinity among major chronic
conditions.With the inclusion of smoking in the model, however, only the crime
prevalence was statistically significantlyassociated with the chronic affinity.
Chronic affinity disadvantages were disproportionately accumulated in socially
disadvantagedareas. We showed links between commonly co-observed chronic
diseases at the population level and systemat-ically explored the complexity of
affinity and socio-economic disparities. Our affinity score, based on
publiclyavailable datasets, served as a surrogate for multimorbidity at the
population level, which may assist policy-makers and public health planners to
identify urgent hot spots for chronic disease and allocate clinical, medicaland
healthcare resources efficiently.
