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Using Spatial Disease Patterns and Patient-Level Characteristics to Describe Prevalence Elastic Behavior in Treatment
Kyle R Fluegge1,2,3
1Agricultural, Environmental, and Development Economics, College of Food, Agriculture and Environmental Sciences and Division of Epidemiology, College of Public Health, Ohio State University, Columbus, Ohio.
Objective:
Individual adherence to a 9-month regimen of isoniazid (9INH) for treatment of latent tuberculosis infection (LTBI) was hypothesized to reflect a prevalent elastic health behavior pattern, or prevention behavior correlated with relevant disease burden.
Method:
Log-rank tests were used to compare survival functions among raw prevalence tertiles for diseases including TB, diabetes, and obesity. Own and cross-prevalence elasticities were calculated and spatially characterized behavioral response to diseases that may impact TB re-infection and/or re-activation. Discrete choice models were used to assess the significance of the spatial elasticities among an ethnically diverse clinic population of 552 patients in an urban American county in 2010.
Results:
Log-rank results revealed a statistical association between dropout and chronic disease prevalence (p < .01), but not TB prevalence (p = .13). Discrete choice models incorporating spatial elasticities and controlling for patient- and treatment-level characteristics demonstrated significant associations with adherence (p < .01), an effect robust to various alternative treatment definitions.
Conclusion:
Individual LTBI adherence tracks a prevalence elastic pattern that may represent a potential risk for re-infection and re-activation.
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