Modelling individual vulnerability to sexually transmitted infections to optimise intervention strategies: analysis of surveillance data from Kalamazoo County, Michigan, USA

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We modelled individual vulnerability to STI using personal history of infection and neighbourhood characteristics.


Retrospective chlamydia and gonorrhoea data of reported confirmed cases from Kalamazoo County, Michigan for 2012 through 2014 were analysed. Unique IDs were generated from the surveillance data in collaboration with local health officials to track the individual STI histories. We then examine the concept that individuals with similar STI histories form a ‘peer’ group. These peer group include: (1) individuals with a single chlamydia; (2) individuals with single gonorrhoea; (3) individuals with repeated cases of one type of STI and (4) individuals that were diagnosed with both infections during the study period. Using Kernel density estimation, we generated densities for each peer group and assigned the intensity of the infection to the location of the individual. Finally, the individual vulnerability was characterised through ordinary least square regression (OLS) using demographics and socioeconomic variables.


In an OLS regression adjusted for frequency of infection, individual vulnerability to STI was only consistently significant for race and neighbourhood-level socioeconomic status (SES) in all the models under consideration. In addition, we identified six areas in three townships in Kalamazoo County that could be considered for unique interventions based on overlap patterns among peer groups.


The results provide evidence that individual vulnerability to STI has some dependency on individual contextual (race) and exogenous factors at the neighbourhood level such as SES, regardless of that individual’s personal history of infection. We suggest place-based intervention strategies be adopted for planning STI interventions instead of current universal screening of at-risk populations.

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