Epidemiology and Health

Original article

Unraveling trends in schistosomiasis: deep learning insights into national control programs in China: Qing Su, Cici Xi Chen Bauer, Robert Bergquist, Zhiguo Cao, Fenghua Gao, Yi Hu, Zhijie Zhang; Epidemiol Health. 2024;e2024039. Published online March 13, 2024; DOI: https://doi.org/10.4178/epih.e2024039 [Accepted]

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Abstract PDF: Abstract
OBJECTIVES
To achieve the ambitious goal of eliminating schistosome infections, the Chinese government has implemented diverse control strategies. This study explored the progress of the 2 most recent national schistosomiasis control programs in an endemic area along the Yangtze River in China.
METHODS
We obtained village-level parasitological data from cross-sectional surveys combined with environmental data in Anhui Province, China from 1997 to 2015. A convolutional neural network (CNN) based on a hierarchical integro-difference equation (IDE) framework (i.e., CNN-IDE) was used to model spatio-temporal variations in schistosomiasis. Two traditional models were also constructed for comparison with 2 evaluation indicators: the mean-squared prediction error (MSPE) and continuous ranked probability score (CRPS).
RESULTS
The CNN-IDE model was the optimal model, with the lowest overall average MSPE of 0.04 and the CRPS of 0.19. From 1997 to 2011, the prevalence exhibited a notable trend: it increased steadily until peaking at 1.6 per 1000 in 2005, then gradually declined, stabilizing at a lower rate of approximately 0.6 per 1000 in 2006, and approaching zero by 2011. During this period, noticeable geographic disparities in schistosomiasis prevalence were observed; high-risk areas were initially dispersed, followed by contraction. Predictions for the period 2012 to 2015 demonstrated a consistent and uniform decrease.
CONCLUSIONS
The proposed CNN-IDE model captured the intricate and evolving dynamics of schistosomiasis prevalence, offering a promising alternative for future risk modeling of the disease. The comprehensive strategy is expected to help diminish schistosomiasis infection, emphasizing the necessity to continue implementing this strategy.; Summary

Original Article

Spatial analysis of tuberculosis treatment outcomes in Shanghai: implications for tuberculosis control: Jing Zhang, Xin Shen, Chongguang Yang, Yue Chen, Juntao Guo, Decheng Wang, Jun Zhang, Henry Lynn, Yi Hu, Qichao Pan, Zhijie Zhang; Epidemiol Health. 2022;44:e2022045. Published online May 1, 2022; DOI: https://doi.org/10.4178/epih.e2022045

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Abstract Summary PDF Supplementary Material: Abstract
OBJECTIVES
Tuberculosis (TB) treatment outcomes are a key indicator in the assessment of TB control programs. We aimed to identify spatial factors associated with TB treatment outcomes, and to provide additional insights into TB control from a geographical perspective.
METHODS
We collected data from the electronic TB surveillance system in Shanghai, China and included pulmonary TB patients registered from January 1, 2009 to December 31, 2016. We examined the associations of physical accessibility to hospitals, an autoregression term, and random hospital effects with treatment outcomes in logistic regression models after adjusting for demographic, clinical, and treatment factors.
RESULTS
Of the 53,475 pulmonary TB patients, 49,002 (91.6%) had successful treatment outcomes. The success rate increased from 89.3% in 2009 to 94.4% in 2016. The successful treatment outcome rate varied among hospitals from 78.6% to 97.8%, and there were 12 spatial clusters of poor treatment outcomes during the 8-year study period. The best-fit model incorporated spatial factors. Both the random hospital effects and autoregression terms had significant impacts on TB treatment outcomes, ranking 6th and 10th, respectively, in terms of statistical importance among 14 factors. The number of bus stations around the home was the least important variable in the model.
CONCLUSIONS
Spatial autocorrelation and hospital effects were associated with TB treatment outcomes in Shanghai. In highly-integrated cities like Shanghai, physical accessibility was not related to treatment outcomes. Governments need to pay more attention to the mobility of patients and different success rates of treatment among hospitals.; Summary

Key Message
Tuberculosis treatment outcomes, a key indicator in the assessment of TB control programs, were associated with spatial autocorrelation and hospital effects in Shanghai; however, they were not associated with physical accessibility to hospitals.

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