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Development, validation, and cost-effectiveness analysis of
a prediction model for community-based systematic screening of active tuberculosis
active tuberculosis,case finding,systematic screening,prediction model,scoring system,cost-effectiveness analysis,
|Publication Year :||2019|
Undetected tuberculosis (TB) cases were a challenge to global TB control. Active case finding could effectively detect TB patients but costly. For those resource-constrained countries, symptom-based screening algorithms were applicable, but at the same time resulted in a massive burden to the screening program due to the low sensitivity or low specificity of the screening tools. This study aimed to develop and validate a score-based screening algorithm for active TB case finding in the community. Furthermore, through cost-effectiveness analysis, we aimed to determine the cost-effective strategies for active TB case finding among the competing algorithms including the model developed in this study and the symptom-based tools proposed by the World Health Organization (WHO).
The study population was based on a 2010 TB prevalence survey in the Zambia/South Africa Tuberculosis and AIDS Reduction (ZAMSTAR) trial. We developed separate prediction models for HIV-positive and HIV-negative/unknown populations. The dataset was divided into two parts according to participants' countries for model development (South Africa) and external validation (Zambia). The outcome was prevalent culture-confirmed TB. The potential predictors included TB symptoms, TB risk factors, and previous TB history. The models were built on multivariable logistic regression and selected through stepwise backward elimination based on Akaike Information Criterion. The final model was converted to a scoring system. Secondly, the scoring system was compared with any TB symptom and prolonged cough, through the cost0effectiveness analysis. The Average Cost Effectiveness Ratio (ACER) and Incremental Cost Effectiveness Ratio (ICER) were computed to evaluate the cost-effective strategies among all competing choices.
The predictors selected in the final model for HIV population and the corresponding scores are shown as follows: gender (1), weight loss (1), ever drink (2), current cough (2), and chest pain (1). The model was presented better AUC than any TB symptoms in both South African and Zambian dataset. On the other hand, the model for non-HIV population included the predictors: weight loss (3), night sweats (2), cough less than two weeks (3), prolonged (more than two weeks) cough (7), ever drink (2), ever smoke (1), gender (1), and household TB history (2). The model also presented the higher AUC than any TB symptoms and prolonged cough among the participants from two different countries. Moreover, through cost-effectiveness analysis, twenty score cutoff combinations were selected as the cost-effective strategies among the tools proposed by WHO and other score cutoff combinations in the South African dataset. The similar results were presented in Zambian dataset, in which sisteen score cutoff combinations were cost-effective in comparison to other screening strategies. When the cost-effective score cutoff combinations were applied for TB screening, the ACER ranged from 246 USD to 1670 USD in the South African dataset, and ranged from 186 USD to 6796 USD in the Zambian dataset.
The scoring system for active TB case finding presented higher performance than any TB symptoms and prolonged cough among study population. The results of cost-effectiveness analysis showed that both the scoring systems for HIV and the non-HIV population were cost-effective at some score cutoff combinations in comparison to the tools proposed by WHO, and therefore provided new strategies for active TB case finding with multiple options under budget consideration.
|Appears in Collections:||流行病學與預防醫學研究所|
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