Trend of the Tuberculous Pleurisy Notification Rate in Eastern China During 2017-2021: Spatiotemporal Analysis (Preprint)

Author:

Zhou YingORCID,Luo DanORCID,Liu KuiORCID,Chen BinORCID,Chen SonghuaORCID,Pan JunhangORCID,Liu ZhengweiORCID,Jiang JianminORCID

Abstract

BACKGROUND

Tuberculous pleurisy (TP) presents a serious allergic reaction in the pleura caused by <i>Mycobacterium tuberculosis</i>; however, few studies have described its spatial epidemiological characteristics in eastern China.

OBJECTIVE

This study aimed to determine the epidemiological distribution of TP and predict its further development in Zhejiang Province.

METHODS

Data on all notified cases of TP in Zhejiang Province, China, from 2017 to 2021 were collected from the existing tuberculosis information management system. Analyses, including spatial autocorrelation and spatial-temporal scan analysis, were performed to identify hot spots and clusters, respectively. The prediction of TP prevalence was performed using the seasonal autoregressive integrated moving average (SARIMA), Holt-Winters exponential smoothing, and Prophet models using R (The R Foundation) and Python (Python Software Foundation).

RESULTS

The average notification rate of TP in Zhejiang Province was 7.06 cases per 100,000 population, peaking in the summer. The male-to-female ratio was 2.18:1. In terms of geographical distribution, clusters of cases were observed in the western part of Zhejiang Province, including parts of Hangzhou, Quzhou, Jinhua, Lishui, Wenzhou, and Taizhou city. Spatial-temporal analysis identified 1 most likely cluster and 4 secondary clusters. The Holt-Winters model outperformed the SARIMA and Prophet models in predicting the trend in TP prevalence.

CONCLUSIONS

The western region of Zhejiang Province had the highest risk of TP. Comprehensive interventions, such as chest x-ray screening and symptom screening, should be reinforced to improve early identification. Additionally, a more systematic assessment of the prevalence trend of TP should include more predictors.

Publisher

JMIR Publications Inc.

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