Application of Bayesian Poisson-logistic Joint Model in Assessing Underreporting Risk of Pulmonary Tuberculosis in Xinjiang
10.11783/j.issn.1002－3674.2025.02.012
- VernacularTitle:贝叶斯Poisson-logistic联合模型在新疆肺结核漏报风险中的应用研究
- Author:
Zhichao LIANG
1
;
Xinqi WANG
;
Wanting XU
Author Information
1. 新疆科技学院(841000)
- Publication Type:Journal Article
- Keywords:
Underreporting risk;
Pulmonary tuberculosis;
Bayesian mixed model
- From:
Chinese Journal of Health Statistics
2025;42(2):220-225
- CountryChina
- Language:Chinese
-
Abstract:
Objective A joint Poisson-logistic model in a Bayesian framework is proposed to constructed using tuberculosis(TB)reporting data from 14 prefectures in Xinjiang from 2014 to 2020 in combination with relevant social,economic,and environmental factors affecting the reported incidence rate of TB to explore potential underreporting areas of the TB reporting data,and to provide a strong evidence－based support for the subsequent decision－making on the precision prevention and control of TB.Methods Relevant factors affecting the reporting process and disease process of TB were collected,and important covariates were screened for inclusion in the model using the factor detector in the Geo-detector method,and the reported incidence model of TB and the expected incidence model of TB in Xinjiang were constructed separately,which together constituted a hybrid model of underreporting of TB(Poisson-logistic joint model).The mixed model was used to estimate the risk of TB underreporting in each prefecture of Xinjiang,and to explore the regional distribution of the potential risk of TB underreporting.Results Factor detector result pairs showed that GDP per capita was associated with the largest contribution to the risk of TB underreporting(0.5481);goodness－of－fit test showed that the data were well fitted(Bayesian P－value<0.001),and the Bayesian Poisson-logistic joint model could be applied to the study of the risk of underreporting of TB reporting data in Xinjiang from 2014 to 2020.The results showed that the risk of underreporting of TB The risk of underreporting of reported data was concentrated in the four southern Xinjiang prefectures,with the greatest risk of underreporting of TB reported data in Kashgar 0.1426(0.1403,0.1445).The lower risk of underreporting was concentrated in the eastern and central parts of Xinjiang,with the lowest risk of underreporting in the city of Karamay[0.1017(0.9983,0.1034)].In a joint Bayesian Poisson-logistic model,it was found that population density(IRR＝1.0060,95%CI:1.0059~1.0061)and average annual temperature(IRR＝1.0087,95%CI:1.0086~1.0088)were risk factors for underreporting of TB,and GDP per capita(IRR＝0.9385,95%CI:0.9365~0.9394)and an increase in the number of registered nurses(IRR＝0.9916,95%CI:0.9913 to 0.9920)reduced the risk of TB underreporting.Conclusion The Bayesian Poisson-logistic joint model estimated the potential incidence of TB in Xinjiang Uygur Autonomous Region and revealed significant discrepancies between reported and true TB incidence rates.It identified underreporting trends and localized potential underreporting risk areas,providing a theoretical basis for tailored and precise TB prevention and control strategies in Xinjiang.
