Using Spatial Disease Patterns and Patient-Level Characteristics to Describe Prevalence Elastic Behavior in Treatment for Latent Tuberculosis Infection (LTBI).

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.