{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T14:58:51Z","timestamp":1791471531622,"version":"4.3.4"},"reference-count":27,"publisher":"PAGEPress Publications","issue":"2","license":[{"start":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T00:00:00Z","timestamp":1791417600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Geospat Health"],"abstract":"<jats:p>This study aims to analyze the temporal trends, interprovincial differences, and key predictors of Tuberculosis Outpatient Visits (TOV) in China. It provides empirical evidence for understanding regional differences in tuberculosis-related outpatient service uti- lization. Panel data from 30 Chinese provinces from 2012 to 2024 were used. The study included variables related to economic development, health resources, education resources, environmental governance, pollutant emissions, and technological innovation. Correlation analysis, model comparison, and XGBoost-SHAP analysis were applied. From 2012 to 2024, TOV showed a gradual upward trend and clear interprovincial differences. Shanghai, Fujian, Guangdong, and Zhejiang had relatively higher total recorded outpatient volumes. XGBoost showed the best predictive performance among all candidate models (R2 = 0.8154). SHAP results identified Disposable Income Per Capita (DIPC), University Staff (USTF), Domestic Patent Applications (DPA), Hospital Beds (HBED), and Technology Market Turnover (TMT) as the main predictors. Environmental governance and pollutant emission vari- ables had lower overall contributions, but they still showed nonlinear predictive contributions in some value ranges. The findings suggest that interprovincial differences in TOV are jointly associated with economic development, health resources, education resources, environmental governance, and technological innovation. TOV mainly reflects outpatient service utilization and case detection capacity. It should not be interpreted as a direct measure of tuberculosis incidence risk.<\/jats:p>","DOI":"10.4081\/gh.2026.1535","type":"journal-article","created":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T14:33:19Z","timestamp":1791469999000},"source":"Crossref","is-referenced-by-count":0,"title":["Nonlinear analysis of factors influencing tuberculosis outpatient visits in China: an explainable machine learning approach"],"prefix":"10.4081","volume":"21","author":[{"given":"Hui","family":"Wang","sequence":"first","affiliation":[{"name":"School of Information Engineering, Minnan University of Science and Technology, Quanzhou"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dehong","family":"Sun","sequence":"additional","affiliation":[{"name":"Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macao"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shanjie","family":"Sui","sequence":"additional","affiliation":[{"name":"School of Electronic and Electrical Engineering, Minnan University of Science and Technology, Quanzhou"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanhong","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine, Quanzhou Third Hospital, Quanzhou"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2549","published-online":{"date-parts":[[2026,10,8]]},"reference":[{"key":"39986","doi-asserted-by":"crossref","unstructured":"Chen J, Jiang Y, Li Z, Zhang M, Liu L, Li A, Lu H, 2024. Predictive machine learning models for anticipating loss to follow-up in tuberculosis patients throughout anti-TB treatment journey. Sci Rep 14;24685.","DOI":"10.1038\/s41598-024-74942-z"},{"key":"39987","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C, 2016. XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785\u2013794). Association for Computing Machinery, New York, NY, USA, 785\u201394.","DOI":"10.1145\/2939672.2939785"},{"key":"39988","doi-asserted-by":"crossref","unstructured":"Chen X, Zhou J, Yuan Q, Zhang R, Huang C, Li Y, 2024. Challenge of ending TB in China: Tuberculosis control in primary healthcare sectors under integrated TB control model\u2014A systematic review and meta-analysis. BMC Public Health 24;163.","DOI":"10.1186\/s12889-023-16292-5"},{"key":"39989","doi-asserted-by":"crossref","unstructured":"Deng L, Zhao F, Li Z, Zhang W, He G, Ren X, 2024. Epidemiological characteristics of tuberculosis incidence and its macro-influence factors in Chinese mainland during 2014\u20132021. Infect Dis Poverty 13:34.","DOI":"10.1186\/s40249-024-01203-6"},{"key":"39990","doi-asserted-by":"crossref","unstructured":"Dimala CA, Kadia BM, 2022. A systematic review and meta-analysis on the association between ambient air pollution and pulmonary tuberculosis. Sci Rep 12;11282.","DOI":"10.1038\/s41598-022-15443-9"},{"key":"39991","doi-asserted-by":"crossref","unstructured":"Fan M, Liu Y, Liu K, Liu X, Li Y, Li T, Zhang C, Zhang H, Cheng J, 2025. Health system delay and risk factors in pulmonary tuberculosis diagnosis before and during the COVID-19 epidemic: A multi-center survey in China. Front Public Health 13;1526774.","DOI":"10.3389\/fpubh.2025.1526774"},{"key":"39992","doi-asserted-by":"crossref","unstructured":"Ferreira MRL, Bonfim RO, Bossonario PA, Maurin VP, Valen\u00e7a ABM, Abreu,PD de, Andrade RL de P, Fronteira I, Monroe AA, 2023. Social protection as a right of people affected by tuberculosis: A scoping review and conceptual framework. Infect Dis Poverty 12;104.","DOI":"10.1186\/s40249-023-01157-1"},{"key":"39993","doi-asserted-by":"crossref","unstructured":"Guo J, Liu C, Liu F, Zhou E, Ma R, Zhang L, Luo B, 2025. Tuberculosis disease burden in China: A spatio-temporal clustering and prediction study. Front Public Health 12:1436515.","DOI":"10.3389\/fpubh.2024.1436515"},{"key":"39994","doi-asserted-by":"crossref","unstructured":"Huang G, Xu Z, Bai L, Liu J, Yu S, Yao H, 2024. Spatiotemporal analysis of tuberculosis in the Hunan Province, China, 2014\u20132022. Front Public Health 12:1426503.","DOI":"10.3389\/fpubh.2024.1426503"},{"key":"39995","unstructured":"Jiang W, Chen J, Lou Z, Jia Y, Tang S, Xiao X, Shen X, Chen X, Long, Q, 2025. Differences between the local and migrant populations in healthcare service use and direct cost of tuberculosis treatment\u2014Shanghai Municipality, China, 2020\u20132021. China CDC Weekly 7;428\u2013434."},{"key":"39996","unstructured":"Lundberg SM, Lee S-I, 2017. A unified approach to interpreting model predictions. Advances in Neural Information Processing Syst 30."},{"key":"39997","doi-asserted-by":"crossref","unstructured":"Ma K, Fang FM, Lin YS, Yao YR, Tong F, 2026. Spatiotemporal inequalities in the burden of tuberculosis attributable to long-term particulate matter exposure in mainland of China. GeoHealth 10:e2025GH001481.","DOI":"10.1029\/2025GH001481"},{"key":"39998","doi-asserted-by":"crossref","unstructured":"Pan S, Chen L, Xin X, Li S, Zhang Y, Chen Y, Xiao S, 2024. Spatiotemporal analysis and seasonality of tuberculosis in Pudong New Area of Shanghai, China, 2014\u20132023. BMC Infect Dis 24;761.","DOI":"10.1186\/s12879-024-09645-x"},{"key":"39999","doi-asserted-by":"crossref","unstructured":"Peng A, Kong X, Liu S, Zhang H, Xie L, Ma L, Zhang Q, Chen Y, 2024. Explainable machine learning for early predicting treatment failure risk among patients with TB-diabetes comorbidity. Sci Reports 14;6814.","DOI":"10.1038\/s41598-024-57446-8"},{"key":"40000","doi-asserted-by":"crossref","unstructured":"Sun Q, Zhang K, Xu Y, Luo M, Yang Z, Liu Q, Liu S, Liu A, 2025. Explainable machine learning for predicting clinical outcomes in HIV\/TB co-infection: A comparative retrospective study. BMC Infect Dis 25;1589.","DOI":"10.1186\/s12879-025-11998-w"},{"key":"40001","doi-asserted-by":"crossref","unstructured":"Sun R, Wang L, Xia H, 2025. Disease burden of tuberculosis in China from 1990 to 2021 and its prediction to 2036. Front Public Health 12;1506266.","DOI":"10.3389\/fpubh.2024.1506266"},{"key":"40002","doi-asserted-by":"crossref","unstructured":"Sun R, Wu Z, Zhang H, Huang J, Liu Y, Chen M, Lv Y, Zhao F, Zhang Y, Li M, Yan J, Jiang H, Zhan Y, Xu J, Xu Y, Yuan J, Zhao Y, Shen X, Yang C, 2024. Assessing heterogeneity of patient and health system delay among TB in a population with internal migrants in China. Front Public Health 12;1354515.","DOI":"10.3389\/fpubh.2024.1354515"},{"key":"40003","doi-asserted-by":"crossref","unstructured":"Tao Y, Zhao J, Cui H, Liang Z, Li J, Ren J, Zhu H, 2026. Data-driven model analysis of the impact of environmental and socioeconomic factors on tuberculosis incidence. Infect Dis Modelling 11;930\u201346.","DOI":"10.1016\/j.idm.2026.02.002"},{"key":"40004","doi-asserted-by":"crossref","unstructured":"Wang L, Li Y, Chen J, Zhang D, Zhang Z, Li X, 2025. Spatiotemporal distribution and detection of spatial clusters of tuberculosis in Hubei Province, China using FleXScan (2017\u20132023). BMC Public Health 25;2758.","DOI":"10.1186\/s12889-025-23620-4"},{"key":"40005","doi-asserted-by":"crossref","unstructured":"Wang Y, Zhu W, Li T, Chen W, Wang W, 2021. Changes in newly notified cases and control of tuberculosis in China: Time-series analysis of surveillance data. Infect Dis Poverty 10:16.","DOI":"10.1186\/s40249-021-00806-7"},{"key":"40006","unstructured":"World Health Organization, 2024. Global tuberculosis report 2024. World Health Organization. Available from: https:\/\/www.who.int\/teams\/global-programme-on-tuberculosis-and-lung-health\/tb-reports\/global-tuberculosis-report-2024"},{"key":"40007","doi-asserted-by":"crossref","unstructured":"Wu S, Litvinjenko S, Magwood O, Wei X, 2023. Defining tuberculosis vulnerability based on an adapted social determinants of health framework: A narrative review. Global Public Health 18:2221729.","DOI":"10.1080\/17441692.2023.2221729"},{"key":"40008","doi-asserted-by":"crossref","unstructured":"Xiao Y, Chen Y, Huang R, Jiang F, Zhou J, Yang T, 2024. Interpretable machine learning in predicting drug-induced liver injury among tuberculosis patients: Model development and validation study. BMC Med Res Methodol 24;92.","DOI":"10.1186\/s12874-024-02214-5"},{"key":"40009","unstructured":"Xu C, Xia Y, Hu D, Zhang X, Zhao Y, 2023. Financial burden of tuberculosis patients\u2014China, 2020. China CDC Weekly 5;266\u201370."},{"key":"40010","doi-asserted-by":"crossref","unstructured":"Zhang J, Zhong M, Huang J, Deng W, Li P, Yao Z, Ye X, Zhong X, 2024. Spatiotemporal patterns and socioeconomic determinants of pulmonary tuberculosis in Dongguan city, China, during 2011\u20132020: An ecological study. BMJ Open 14;e085733.","DOI":"10.1136\/bmjopen-2024-085733"},{"key":"40011","doi-asserted-by":"crossref","unstructured":"Zhang K, Yang Z, Sun J, Liu K, Lu, Q, 2025. Spatiotemporal analysis of pulmonary tuberculosis in the central region of the Zhejiang Province, China (2016\u20132024). Front Public Health 13;1599274.","DOI":"10.3389\/fpubh.2025.1599274"},{"key":"40012","doi-asserted-by":"crossref","unstructured":"Zhang L, Ma X, Gao H, Bao C, Wu Y, Wu S, Liu M, Liu Y, Li L, 2024. Analysis of care-seeking and diagnosis delay among pulmonary tuberculosis patients in Beijing, China. Front Public Health 12;1369541.","DOI":"10.3389\/fpubh.2024.1369541"}],"container-title":["Geospatial Health"],"original-title":[],"link":[{"URL":"https:\/\/www.geospatialhealth.net\/gh\/article\/download\/1535\/1619","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.geospatialhealth.net\/gh\/article\/download\/1535\/1619","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T14:33:33Z","timestamp":1791470013000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.geospatialhealth.net\/gh\/article\/view\/1535"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,8]]},"references-count":27,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,7,23]]}},"URL":"https:\/\/doi.org\/10.4081\/gh.2026.1535","relation":{},"ISSN":["1970-7096","1827-1987"],"issn-type":[{"value":"1970-7096","type":"electronic"},{"value":"1827-1987","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10,8]]}}}