Public health nursing (Boston, Mass.) · 2015
Using Spatial Disease Patterns and Patient-Level Characteristics to Describe Prevalence Elastic Behavior in Treatment for Latent Tuberculosis Infection (LTBI).
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Fluegge Kyle R
Аннотация
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.
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.