Spatiotemporal Patterns of Tuberculosis in Hunan Province, China

Abstract

Tuberculosis (TB) is the leading cause of death from a bacterial pathogen worldwide. China has the third highest TB burden in the world, with a high reported burden in Hunan Province (amongst others). This study aimed to investigate the spatial distribution of TB and identify socioeconomic, demographic, and environmental drivers in Hunan Province, China. Numbers of reported cases of TB were obtained from the Tuberculosis Control Institute of Hunan Province, China. A wide range of covariates were collected from different sources, including from the Worldclim database, and the Hunan Bureau of Statistics. These variables were summarized at the county level and linked with TB notification data. Spatial clustering of TB was explored using Moran’s I statistic and the Getis–Ord statistic. Poisson regression models were developed with a conditional autoregressive (CAR) prior structure, and with posterior parameters estimated using a Bayesian approach with Markov chain Monte Carlo (MCMC) simulation. A total of 323,340 TB cases were reported to the Hunan TB Control Institute from 2013 to 2018. The mean age of patients was 51.7 years (SD + 17.6 years). The majority of the patients were male (72.6%, n = 234,682) and had pulmonary TB (97.5%, n = 315,350). Of 319,825 TB patients with registered treatment outcomes, 306,107 (95.7%) patients had a successful treatment outcome. The annual incidence of TB decreased over time from 85.5 per 100,000 population in 2013 to 76.9 per 100,000 population in 2018. TB case numbers have shown seasonal variation, with the highest number of cases reported during the end of spring and the beginning of summer. Spatial clustering of TB incidence was observed at the county level, with hotspot areas detected in the west part of Hunan Province. The spatial clustering of TB incidence was significantly associated with low sunshine exposure (RR: 0.86; 95% CrI: 0.74, 0.96) and a low prevalence of contraceptive use (RR: 0.88; 95% CrI: 0.79, 0.98). Substantial spatial clustering and seasonality of TB incidence were observed in Hunan Province, with spatial patterns associated with environmental and health care factors. This research suggests that interventions could be more efficiently targeted at locations and times of the year with the highest transmission risk.

Changes in tuberculosis burden and its associated risk factors in Guizhou Province of China during 2006–2020: an observational study

“…As PTB accounts for more than 90% of TB patients in China [ 7 ], our results could represent the overall epidemic of TB in Guizhou. Second, underreporting of TB cases and missing data were inevitable when using notification data collected from the existing surveillance system [ 7 , 59 , 60 ]. In our study, after 348 patients are excluded, the remaining 557,476 cases are still representative of the TB status for this province.…”

Geo-spatial high-risk clusters of Tuberculosis in the global general population: a systematic review

“…From studies in Ethiopia, Generally, the TB high-risk cluster in the country over the study period appeared in rural Ethiopia and in more urbanized areas such as Addis Ababa, and other parts of Northwest Ethiopia further emphasizing the localization of TB high-risk, effect of high population density and poor socioeconomic condition on TB. The study also recommended that ensuring access to TB diagnosis and treatment could aid in TB incidence diminution, improve adherence to therapy, and strengthen the community's TB control program [ 16 ].…”

Spatio‐Temporal Pattern of Tuberculosis Distribution in Romania and Particulate Matter Pollution Associated With Risk of Infection

“…The frequency of pulmonary TB in Romania differs from region to region, making interdisciplinary research necessary to understand the determinants of these disparities. The literature highlights these geographical differentiations in numerous tuberculosis studies (Alene et al., 2017 , 2021 ; Chen et al., 2021 ; Gao & Du, 2018 ; X. Li et al., 2021 ; Y. W. Li et al., 2016 ; Sun et al., 2022 ). Spatial heterogeneity points to the need for interdisciplinary analyses of disease transmission hubs in the community.…”

“…Knowledge of the spatial distribution of TB has an important role to play in developing strategies to eliminate TB from affected communities, and integrating the spatial dimension into public policy‐making in this area can help to increase the effectiveness of public policies. Thus, spatial analyses should distinguish between dispersion and diffusion patterns, an important differentiation in identifying spatial hotspots and the best TB management systems (Alene et al., 2021 ; Oxlade et al., 2015 ; Sattenspiel, 2009 ; Shaweno et al., 2018 ; Sotgiu et al., 2021 ).…”