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Yu S, Pan Y, Chen Q, Liu Q, Wang J, Rui J, Guo Y, Gavotte L, Zhao Q, Frutos R, Xu M, Pu D, Chen T. Analysis of the epidemiological characteristics and influencing factors of tuberculosis among students in a large province of China, 2008-2018. Sci Rep 2024; 14:20472. [PMID: 39227742 PMCID: PMC11372133 DOI: 10.1038/s41598-024-71720-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Accepted: 08/30/2024] [Indexed: 09/05/2024] Open
Abstract
This study examines tuberculosis (TB) incidence among students in Jilin Province, China, focusing on spatial, temporal, and demographic dynamics in areas of social inequality. Variation in incidence rate of TB was analyzed using the joinpoint regression method. Spatial analyses techniques included the global and local Moran indices and Getis-Ord Gi* analysis. Demographic changes in new cases were analyzed descriptively, and the Geodetector method measured the influence of risk factors on student TB incidence. The analysis revealed a declining trend in TB cases, particularly among male students. TB incidence showed geographical heterogeneity, with lower rates in underdeveloped rural areas compared to urban regions. Significant spatial correlations were observed, with high-high clusters forming in central Jilin Province. Hotspots of student TB transmission were primarily concentrated in the southwestern and central regions from 2008 to 2018. Socio-economic factors exhibited nonlinear enhancement effects on incidence rates, with a dominant bifactor effect. High-risk zones were predominantly located in urban centers, with university and high school students showing higher incidences than other educational stages. The study revealed economic determinants as being especially important in affecting TB incidence among students, with these factors having nonlinear interacting effects on student TB incidence.
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Affiliation(s)
- Shanshan Yu
- State Key Laboratory of Vaccines for Infectious Disease, Xiang An Biomedicine Laboratory, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, National Innovation Platform for Industry-Education Intergration in Vaccine Research, School of Public Health, Xiamen University, Xiamen City, Fujian Province, People's Republic of China
| | - Yan Pan
- Jilin Scientific Research Institute of Tuberculosis Control, Changchun City, Jilin Province, People's Republic of China
| | - Qiuping Chen
- State Key Laboratory of Vaccines for Infectious Disease, Xiang An Biomedicine Laboratory, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, National Innovation Platform for Industry-Education Intergration in Vaccine Research, School of Public Health, Xiamen University, Xiamen City, Fujian Province, People's Republic of China
- CIRAD, URM 17, Intertryp, Montpellier, France
- Université de Montpellier, Montpellier, France
| | - Qiao Liu
- State Key Laboratory of Vaccines for Infectious Disease, Xiang An Biomedicine Laboratory, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, National Innovation Platform for Industry-Education Intergration in Vaccine Research, School of Public Health, Xiamen University, Xiamen City, Fujian Province, People's Republic of China
| | - Jing Wang
- State Key Laboratory of Vaccines for Infectious Disease, Xiang An Biomedicine Laboratory, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, National Innovation Platform for Industry-Education Intergration in Vaccine Research, School of Public Health, Xiamen University, Xiamen City, Fujian Province, People's Republic of China
| | - Jia Rui
- State Key Laboratory of Vaccines for Infectious Disease, Xiang An Biomedicine Laboratory, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, National Innovation Platform for Industry-Education Intergration in Vaccine Research, School of Public Health, Xiamen University, Xiamen City, Fujian Province, People's Republic of China
- CIRAD, URM 17, Intertryp, Montpellier, France
- Université de Montpellier, Montpellier, France
| | - Yichao Guo
- State Key Laboratory of Vaccines for Infectious Disease, Xiang An Biomedicine Laboratory, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, National Innovation Platform for Industry-Education Intergration in Vaccine Research, School of Public Health, Xiamen University, Xiamen City, Fujian Province, People's Republic of China
| | | | - Qinglong Zhao
- Jilin Provincial Center for Disease Control and Prevention, Changchun City, Jilin, People's Republic of China
| | | | - Mingshu Xu
- Shangrao Centre for Disease Control and Prevention, Shangrao City, Jiangxi, People's Republic of China
| | - Dan Pu
- Jilin Provincial Armed Police General Hospital, Changchun City, Jilin Province, People's Republic of China.
| | - Tianmu Chen
- State Key Laboratory of Vaccines for Infectious Disease, Xiang An Biomedicine Laboratory, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, National Innovation Platform for Industry-Education Intergration in Vaccine Research, School of Public Health, Xiamen University, Xiamen City, Fujian Province, People's Republic of China.
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Zhang C, Wu Z, Huang X, Zhao Y, Sun Q, Chen Y, Guo H, Liao Q, Wu H, Chen X, Liang A, Dong W, Yu M, Chen Y, Wei W. A Profile of Drug-Resistant Mutations in Mycobacterium tuberculosis Isolates from Guangdong Province, China. Indian J Microbiol 2024; 64:1044-1056. [PMID: 39282200 PMCID: PMC11399372 DOI: 10.1007/s12088-024-01236-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 02/22/2024] [Indexed: 09/18/2024] Open
Abstract
Guangdong Province, China's largest economy, has a high incidence of tuberculosis (TB). At present, there are few reports on the distribution, transmission and drug resistance of Mycobacterium tuberculosis (Mtb) strains in this region. In this study, we performed minimum inhibitory concentration testing for 14 anti-TB drugs and whole-genome sequencing of 713 clinical Mtb isolates from 20,662 sputum culture-positive tuberculosis patients registered at 31 tuberculosis drug resistance surveillance sites covering 20 cities in Guangdong Province from 2016 to 2018. Moreover, we evaluated genome-wide associations between mutations and drug resistance, and further investigated the differences in the MICs of mutations. The epidemiology, drug-resistant phenotypes and whole genome sequencing data of 713 clinical Mtb isolates were analyzed, revealing the lineage distribution and drug-resistant gene profiles in Guangdong Province. WGS combined with quantitative MIC measurements identified several novel loci associated with resistance, of which 16 loci were found to be related to resistance to more than one drug. This study analyzed the lineage distribution, prevalence characteristics and resistance-corresponding gene profiles of Mtb isolates in Guangdong province, and provided a theoretical basis for the formulation of tuberculosis prevention and control policy in the province. Supplementary Information The online version contains supplementary material available at 10.1007/s12088-024-01236-3.
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Affiliation(s)
- Chenchen Zhang
- Center for Tuberculosis Control of Guangdong Province, Guangzhou, 510630 China
| | - Zhuhua Wu
- Center for Tuberculosis Control of Guangdong Province, Guangzhou, 510630 China
| | - Xinchun Huang
- Center for Tuberculosis Control of Guangdong Province, Guangzhou, 510630 China
| | - Yuchuan Zhao
- Center for Tuberculosis Control of Guangdong Province, Guangzhou, 510630 China
| | - Qi Sun
- Center for Tuberculosis Control of Guangdong Province, Guangzhou, 510630 China
- Present Address: Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Yanmei Chen
- Center for Tuberculosis Control of Guangdong Province, Guangzhou, 510630 China
| | - Huixin Guo
- Center for Tuberculosis Control of Guangdong Province, Guangzhou, 510630 China
| | - Qinghua Liao
- Center for Tuberculosis Control of Guangdong Province, Guangzhou, 510630 China
| | - Huizhong Wu
- Center for Tuberculosis Control of Guangdong Province, Guangzhou, 510630 China
| | - Xunxun Chen
- Center for Tuberculosis Control of Guangdong Province, Guangzhou, 510630 China
| | - Anqi Liang
- Center for Tuberculosis Control of Guangdong Province, Guangzhou, 510630 China
| | - Wenya Dong
- Department of Clinical Laboratory, Guangdong Women and Children Hospital, Guangzhou, 511443 China
| | - Meiling Yu
- Center for Tuberculosis Control of Guangdong Province, Guangzhou, 510630 China
| | - Yuhui Chen
- Center for Tuberculosis Control of Guangdong Province, Guangzhou, 510630 China
| | - Wenjing Wei
- Center for Tuberculosis Control of Guangdong Province, Guangzhou, 510630 China
- College of Basic Medicine and Public Hygiene, Jinan University, Guangzhou, 510632 China
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Zhang H, Sun R, Wu Z, Liu Y, Chen M, Huang J, Lv Y, Zhao F, Zhang Y, Li M, Jiang H, Zhan Y, Xu J, Xu Y, Yuan J, Zhao Y, Shen X, Yang C. Spatial pattern of isoniazid-resistant tuberculosis and its associated factors among a population with migrants in China: a retrospective population-based study. Front Public Health 2024; 12:1372146. [PMID: 38510351 PMCID: PMC10951094 DOI: 10.3389/fpubh.2024.1372146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 02/21/2024] [Indexed: 03/22/2024] Open
Abstract
Background Isoniazid-resistant, rifampicin-susceptible tuberculosis (Hr-TB) globally exhibits a high prevalence and serves as a potential precursor to multidrug-resistant tuberculosis (MDR-TB). Recognizing the spatial distribution of Hr-TB and identifying associated factors can provide strategic entry points for interventions aimed at early detection of Hr-TB and prevention of its progression to MDR-TB. This study aims to analyze spatial patterns and identify socioeconomic, demographic, and healthcare factors associated with Hr-TB in Shanghai at the county level. Method We conducted a retrospective study utilizing data from TB patients with available Drug Susceptible Test (DST) results in Shanghai from 2010 to 2016. Spatial autocorrelation was explored using Global Moran's I and Getis-Ord G i ∗ statistics. A Bayesian hierarchical model with spatial effects was developed using the INLA package in R software to identify potential factors associated with Hr-TB at the county level. Results A total of 8,865 TB patients with DST were included in this analysis. Among 758 Hr-TB patients, 622 (82.06%) were new cases without any previous treatment history. The drug-resistant rate of Hr-TB among new TB cases in Shanghai stood at 7.20% (622/8014), while for previously treated cases, the rate was 15.98% (136/851). Hotspot areas of Hr-TB were predominantly situated in southwestern Shanghai. Factors positively associated with Hr-TB included the percentage of older adult individuals (RR = 3.93, 95% Crl:1.93-8.03), the percentage of internal migrants (RR = 1.35, 95% Crl:1.15-1.35), and the number of healthcare institutions per 100 population (RR = 1.17, 95% Crl:1.02-1.34). Conclusion We observed a spatial heterogeneity of Hr-TB in Shanghai, with hotspots in the Songjiang and Minhang districts. Based on the results of the models, the internal migrant population and older adult individuals in Shanghai may be contributing factors to the emergence of areas with high Hr-TB notification rates. Given these insights, we advocate for targeted interventions, especially in identified high-risk hotspots and high-risk areas.
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Affiliation(s)
- Hongyin Zhang
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Ruoyao Sun
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Zheyuan Wu
- Division of TB and HIV/AIDS Prevention, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
- Shanghai Institutes of Preventive Medicine, Shanghai, China
| | - Yueting Liu
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Meiru Chen
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Jinrong Huang
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Yixiao Lv
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Fei Zhao
- Department of Pharmacy, Beijing Hospital, National Center of Gerontology, Beijing, China
- Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
- Beijing Key Laboratory of Assessment of Clinical Drugs Risk and Individual Application (Beijing Hospital), Beijing, China
| | - Yangyi Zhang
- Division of TB and HIV/AIDS Prevention, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
- Shanghai Institutes of Preventive Medicine, Shanghai, China
- Department of Epidemiology, School of Public Health and Key Laboratory of Public Health Safety, Fudan University, Shanghai, China
| | - Minjuan Li
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Hongbing Jiang
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Yiqiang Zhan
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Jimin Xu
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Yanzi Xu
- Nanshan District Center for Disease Control and Prevention, Shenzhen, Guangdong, China
| | - Jianhui Yuan
- Nanshan District Center for Disease Control and Prevention, Shenzhen, Guangdong, China
| | - Yang Zhao
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Xin Shen
- Division of TB and HIV/AIDS Prevention, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
- Shanghai Institutes of Preventive Medicine, Shanghai, China
| | - Chongguang Yang
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China
- Nanshan District Center for Disease Control and Prevention, Shenzhen, Guangdong, China
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, United States
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Zhou C, Li T, Du J, Yin D, Li X, Li S. Toward tuberculosis elimination by understanding epidemiologic characteristics and risk factors in Hainan Province, China. Infect Dis Poverty 2024; 13:20. [PMID: 38414000 PMCID: PMC10898115 DOI: 10.1186/s40249-024-01188-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 02/06/2024] [Indexed: 02/29/2024] Open
Abstract
BACKGROUND The disease burden of tuberculosis (TB) was heavy in Hainan Province, China, and the information on transmission patterns was limited with few studies. This atudy aims to further explore the epidemiological characteristics and influencing factors of TB in Hainan Province, and thereby contribute valuable scientific evidences for TB elimination in Hainan Province. METHODS The TB notification data in Hainan Province from 2013 to 2022 were collected from the Chinese National Disease Control Information System Tuberculosis Surveillance System, along with socio-economic data. The spatial-temporal and population distributions were analyzed, and spatial autocorrelation analysis was conducted to explore TB notification rate clustering. In addition, the epidemiological characteristics of the cases among in-country migrants were described, and the delay pattern in seeking medical care was investigated. Finally, a geographically and temporally weighted regression (GTWR) model was adopted to analyze the relationship between TB notification rate and socio-economic indicators. The tailored control suggestions in different regions for TB elimination was provided by understanding epidemiological characteristics and risk factors obtained by GTWR. RESULTS From 2013 to 2022, 64,042 cases of TB were notified in Hainan Province. The estimated annual percentage change of TB notification rate in Hainan Province from 2013 to 2020 was - 6.88% [95% confidence interval (CI): - 5.30%, - 3.69%], with higher rates in central and southern regions. The majority of patients were males (76.33%) and farmers (67.80%). Cases among in-country migrants primarily originated from Sichuan (369 cases), Heilongjiang (267 cases), Hunan (236 cases), Guangdong (174 cases), and Guangxi (139 cases), accounting for 53%. The majority (98.83%) of TB cases were notified through passive case finding approaches, with delay in seeking care. The GTWR analysis showed that gross domestic product per capita, the number of medical institutions and health personnel per 10,000 people were main factors affecting the high TB notification rates in some regions in Hainan Province. Different regional tailored measures such as more TB specialized hospitals were proposed based on the characteristics of each region. CONCLUSIONS The notification rate of TB in Hainan Province has been declining overall but still remained high in central and southern regions. Particular attention should be paid to the prevalence of TB among males, farmers, and out-of-province migrant populations. The notification rate was also influenced by economic development and medical conditions, indicating the need of more TB specialized hospitals, active surveillance and other tailored prevention and control measures to promote the progress of TB elimination in Hainan Province.
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Affiliation(s)
- Changqiang Zhou
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, 44# Wenhuaxi Road, Lixia District, Jinan, Shandong, 250012, People's Republic of China
| | - Tao Li
- National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, People's Republic of China
| | - Jian Du
- Clinical Center On TB Control, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, People's Republic of China
| | - Dapeng Yin
- Hainan Center for Disease Control and Prevention, Haikou, Hainan, 570203, People's Republic of China.
| | - Xiujun Li
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, 44# Wenhuaxi Road, Lixia District, Jinan, Shandong, 250012, People's Republic of China.
- Research Center for Tuberculosis Control, Shandong University, Jinan, Shandong, People's Republic of China.
| | - Shixue Li
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, 44# Wenhuaxi Road, Lixia District, Jinan, Shandong, 250012, People's Republic of China.
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Xia L, Wei W, Zhou ZL, Zhang WQ, Luan RS. The environmental and socioeconomic effects of tuberculosis patients in the southwest of China: a population-based study. Public Health 2024; 227:131-140. [PMID: 38219290 DOI: 10.1016/j.puhe.2023.10.043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 09/14/2023] [Accepted: 10/26/2023] [Indexed: 01/16/2024]
Abstract
OBJECTIVE The objective of this study was to assess the incidence of tuberculosis (TB) and find the risk factors of TB patients with a high burden of TB in socioeconomic level, the high level of TB incidence and the great changes of economic and social factors, explore the possible factors, construct scientific and robust prediction model, and analyse whether the task of stopping TB can be accomplished by the expected global deadline. STUDY DESIGN This was an ecological study. METHODS Descriptive analysis, spatial and space-time scan, correlation analysis, and regression analysis were carried out, based on cases of TB in Sichuan Province and ecological data from 2006 to 2017, to explore the characters of TB and ecological factors, using the transfer function-noise model to forecast the trend of TB until 2035. RESULTS Factors affecting the incidence of TB, increasing per capita green area, reporting status of TB among Tibetans and Yi minorities, comprehensive treatment management, total cost of TB per capita for urban residents, proportion of males with high school education, 20 to 20 h of 24-h accumulated precipitation, reducing HIV at the same time as AIDS deaths, the increase in the proportion of males in junior high school education, and the increase in the number of registered TB cases can reduce the incidence of TB. CONCLUSIONS There was concentration mainly on enhanced control of the environment and society measures, helpful in guiding government planning to control TB. Reinforcement is required to reduce the TB of population aged 15-24 and aged 25-64 in socioeconomic level by 2035.
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Affiliation(s)
- L Xia
- Center for Disease Control and Prevention of Sichuan Province, China
| | - W Wei
- West China School of Public Health and West China Fourth Hospital, Sichuan University, China; Leshan Hospital, China
| | - Z L Zhou
- West China School of Public Health and West China Fourth Hospital, Sichuan University, China
| | - W Q Zhang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, China
| | - R S Luan
- West China School of Public Health and West China Fourth Hospital, Sichuan University, China.
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Chen X, Zhou J, Yuan Q, Zhang R, Huang C, Li Y. Challenge of ending TB in China: tuberculosis control in primary healthcare sectors under integrated TB control model-a systematic review and meta-analysis. BMC Public Health 2024; 24:163. [PMID: 38212753 PMCID: PMC10785344 DOI: 10.1186/s12889-023-16292-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 07/11/2023] [Indexed: 01/13/2024] Open
Abstract
BACKGROUND China has the third-largest burden of tuberculosis (TB) cases in the world with great challenges towards ending TB. Primary health care (PHC) sectors play a critical role in TB prevention and control in communities under the Chinese integrated TB control model. However, there is a lack of comprehensive review of research evidence on TB control in PHC sectors under the integrated TB control model in China. METHODS This review was conducted following the PRISMA guidelines. Articles published from 2012 to January 2022 were searched from four international and three Chinese databases. Studies conducted inside mainland China and relevant with TB control service in PHC sectors under the integrated model were included. After study selection, data extraction, and quality assessment, the meta-analysis was performed with RevMan using a random-effect model.When I2 was more than 50%, subgroup analysis was performed to explore possible reasons for heterogeneity. We also conducted a post hoc sensitivity analysis for outcomes after meta-analysis by exclusion of studies with a high risk of bias or classified as low quality. RESULTS Forty-three studies from 16 provinces/municipalities in China were included in this review, and most studies included were of medium quality. PHC sectors in East China delivered TB control service better overall than that in West China, especially in tracing of patients and TB case management (TCM). In meta-analyses, both the pooled arrival rate of tracing and pooled TCM rate in East China were higher than those in West China. TB patients had a low degree of willingness to receive TCM provided by healthcare workers in PHC sectors nationwide, especially among migrant TB patients. There were 9 studies reporting factors related to TB control service in PHC sectors, 6 (2 in East and 4 in West China) of which indentified several characteristics of patients as associated factors. The context of PHC sectors was demonstrated to influence delivery of TB control service in PHC sectors in 5 studies (3 in East, 1 in Middle and 1 in West China). Most studies on strategies to promoting TB control services in PHC sectors were conducted in East China and some of these studies identified several online and offline interventions and strategies improving patients' treatment compliance [pooled OR (95% CI): 7.81 (3.08, 19.19] and awareness of TB [pooled OR (95% CI): 6.86 (2.16, 21.72)]. CONCLUSION It is of urgent need to improve TB control in PHC sector in China, particularly in West China. Formative and implementation research with rigorous design are necessary to develop comprehensive, context-specific, and patient-centered TB control strategies to promote ending TB in China.
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Affiliation(s)
- Xi Chen
- Department of Social Medicine and Health Service Management, College of Preventive Medicine, Army Medical University (Third Military Medical University), No. 30 Gaotanyan Road, Shapingba District, Chongqing, 400038, China
- Army Medical University (Third Military Medical University), Chongqing, China
| | - Jiani Zhou
- Department of Social Medicine and Health Service Management, College of Preventive Medicine, Army Medical University (Third Military Medical University), No. 30 Gaotanyan Road, Shapingba District, Chongqing, 400038, China
| | - Quan Yuan
- Department of Social Medicine and Health Service Management, College of Preventive Medicine, Army Medical University (Third Military Medical University), No. 30 Gaotanyan Road, Shapingba District, Chongqing, 400038, China
| | - Rui Zhang
- Department of Social Medicine and Health Service Management, College of Preventive Medicine, Army Medical University (Third Military Medical University), No. 30 Gaotanyan Road, Shapingba District, Chongqing, 400038, China
| | - Chunji Huang
- Army Medical University (Third Military Medical University), Chongqing, China.
| | - Ying Li
- Department of Social Medicine and Health Service Management, College of Preventive Medicine, Army Medical University (Third Military Medical University), No. 30 Gaotanyan Road, Shapingba District, Chongqing, 400038, China.
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Bekele D, Aragie S, Alene KA, Dejene T, Warkaye S, Mezemir M, Abdena D, Kebebew T, Botore A, Mekonen G, Gutema G, Dufera B, Gemede K, Kenate B, Gobena D, Alemu B, Hailemariam D, Muleta D, Siu GKH, Tafess K. Spatiotemporal Distribution of Tuberculosis in the Oromia Region of Ethiopia: A Hotspot Analysis. Trop Med Infect Dis 2023; 8:437. [PMID: 37755898 PMCID: PMC10536582 DOI: 10.3390/tropicalmed8090437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 08/29/2023] [Accepted: 09/05/2023] [Indexed: 09/28/2023] Open
Abstract
Tuberculosis (TB) is a major public health concern in low- and middle-income countries including Ethiopia. This study aimed to assess the spatiotemporal distribution of TB and identify TB risk factors in Ethiopia's Oromia region. Descriptive and spatiotemporal analyses were conducted. Bayesian spatiotemporal modeling was used to identify covariates that accounted for variability in TB and its spatiotemporal distribution. A total of 206,278 new pulmonary TB cases were reported in the Oromia region between 2018 and 2022, with the lowest annual TB case notification (96.93 per 100,000 population) reported in 2020 (i.e., during the COVID-19 pandemic) and the highest TB case notification (106.19 per 100,000 population) reported in 2019. Substantial spatiotemporal variations in the distribution of notified TB case notifications were observed at zonal and district levels with most of the hotspot areas detected in the northern and southern parts of the region. The spatiotemporal distribution of notified TB incidence was positively associated with different ecological variables including temperature (β = 0.142; 95% credible interval (CrI): 0.070, 0.215), wind speed (β = -0.140; 95% CrI: -0.212, -0.068), health service coverage (β = 0.426; 95% CrI: 0.347, 0.505), and population density (β = 0.491; 95% CrI: 0.390, 0.594). The findings of this study indicated that preventive measures considering socio-demographic and health system factors can be targeted to high-risk areas for effective control of TB in the Oromia region. Further studies are needed to develop effective strategies for reducing the burden of TB in hotspot areas.
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Affiliation(s)
- Dereje Bekele
- Communicable and Non-Communicable Diseases Prevention and Control Directorate, Oromia Region Health Bureau, Addis Ababa P.O. Box 24341, Ethiopia; (D.A.); (G.M.); (K.G.); (B.A.); (D.H.)
- Department of Microbial, Cellular and Molecular Biology, College of Natural and Computational Sciences, Addis Ababa University, Addis Ababa P.O. Box 1176, Ethiopia; (S.A.); (G.G.); (B.D.)
| | - Solomon Aragie
- Department of Microbial, Cellular and Molecular Biology, College of Natural and Computational Sciences, Addis Ababa University, Addis Ababa P.O. Box 1176, Ethiopia; (S.A.); (G.G.); (B.D.)
| | - Kefyalew Addis Alene
- Geospatial and Tuberculosis Team, Telethon Kids Institute, Perth, WA 6009, Australia;
- School of Public Health, Faculty of Public Health Sciences, Curtin University, Perth, WA 6102, Australia
| | - Tariku Dejene
- Center for Population Studies, College of Development Studies, Addis Ababa University, Addis Ababa P.O. Box 1176, Ethiopia;
| | - Samson Warkaye
- Ethiopian Public Health Institute, National Data Management Center for Health, Addis Ababa P.O. Box 1242, Ethiopia;
| | - Melat Mezemir
- Health Promotion and Diseases Prevention Directorate, Addis Ababa City Administration Health Bureau, Addis Ababa P.O. Box 30738, Ethiopia;
| | - Dereje Abdena
- Communicable and Non-Communicable Diseases Prevention and Control Directorate, Oromia Region Health Bureau, Addis Ababa P.O. Box 24341, Ethiopia; (D.A.); (G.M.); (K.G.); (B.A.); (D.H.)
| | - Tesfaye Kebebew
- Public Health Emergency Management, Research, and Blood Bank Service Directorate, Oromia Region Health Bureau, Addis Ababa P.O. Box 24341, Ethiopia; (T.K.); (A.B.); (B.K.); (D.G.); (D.M.)
| | - Abera Botore
- Public Health Emergency Management, Research, and Blood Bank Service Directorate, Oromia Region Health Bureau, Addis Ababa P.O. Box 24341, Ethiopia; (T.K.); (A.B.); (B.K.); (D.G.); (D.M.)
| | - Geremew Mekonen
- Communicable and Non-Communicable Diseases Prevention and Control Directorate, Oromia Region Health Bureau, Addis Ababa P.O. Box 24341, Ethiopia; (D.A.); (G.M.); (K.G.); (B.A.); (D.H.)
| | - Gadissa Gutema
- Department of Microbial, Cellular and Molecular Biology, College of Natural and Computational Sciences, Addis Ababa University, Addis Ababa P.O. Box 1176, Ethiopia; (S.A.); (G.G.); (B.D.)
- National HIV/AIDS and TB Research Directorate, Ethiopian Public Health Institute, Addis Ababa P.O. Box 1242, Ethiopia
| | - Boja Dufera
- Department of Microbial, Cellular and Molecular Biology, College of Natural and Computational Sciences, Addis Ababa University, Addis Ababa P.O. Box 1176, Ethiopia; (S.A.); (G.G.); (B.D.)
- Bacterial, Parasitic, and Zoonotic Research Directorate, Ethiopian Public Health Institute, Addis Ababa P.O. Box 1242, Ethiopia
| | - Kolato Gemede
- Communicable and Non-Communicable Diseases Prevention and Control Directorate, Oromia Region Health Bureau, Addis Ababa P.O. Box 24341, Ethiopia; (D.A.); (G.M.); (K.G.); (B.A.); (D.H.)
| | - Birhanu Kenate
- Public Health Emergency Management, Research, and Blood Bank Service Directorate, Oromia Region Health Bureau, Addis Ababa P.O. Box 24341, Ethiopia; (T.K.); (A.B.); (B.K.); (D.G.); (D.M.)
| | - Dabesa Gobena
- Public Health Emergency Management, Research, and Blood Bank Service Directorate, Oromia Region Health Bureau, Addis Ababa P.O. Box 24341, Ethiopia; (T.K.); (A.B.); (B.K.); (D.G.); (D.M.)
| | - Bizuneh Alemu
- Communicable and Non-Communicable Diseases Prevention and Control Directorate, Oromia Region Health Bureau, Addis Ababa P.O. Box 24341, Ethiopia; (D.A.); (G.M.); (K.G.); (B.A.); (D.H.)
| | - Dagnachew Hailemariam
- Communicable and Non-Communicable Diseases Prevention and Control Directorate, Oromia Region Health Bureau, Addis Ababa P.O. Box 24341, Ethiopia; (D.A.); (G.M.); (K.G.); (B.A.); (D.H.)
| | - Daba Muleta
- Public Health Emergency Management, Research, and Blood Bank Service Directorate, Oromia Region Health Bureau, Addis Ababa P.O. Box 24341, Ethiopia; (T.K.); (A.B.); (B.K.); (D.G.); (D.M.)
| | - Gilman Kit Hang Siu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong;
| | - Ketema Tafess
- Department of Applied Biology, School of Applied Natural Science, Adama Science and Technology University, Adama P.O. Box 1888, Ethiopia;
- Institute of Pharmaceutical Science, Adama Science and Technology University, Adama P.O. Box 1888, Ethiopia
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Wei X, Fu T, Chen D, Gong W, Zhang S, Long Y, Wu X, Shao Z, Liu K. Spatial-temporal patterns and influencing factors for pulmonary tuberculosis transmission in China: an analysis based on 15 years of surveillance data. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:96647-96659. [PMID: 37580473 DOI: 10.1007/s11356-023-29248-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 08/05/2023] [Indexed: 08/16/2023]
Abstract
Profiting from a series of anti-tuberculosis programs in China, the number of tuberculosis (TB) cases has diminished dramatically in the past decades. However, long-term spatial-temporal variations, regional trends of prevalence, and mechanisms of determinant factors remain unclear. Age-period-cohort analysis and Bayesian space-time hierarchy statistics were conducted to identify high-risk populations and areas in mainland China, and the geographical detector model was used to evaluate the important drivers of the disease. The prevalence of pulmonary TB has declined from 73.3/100,000 in 2004 to 55.45/100,000 in 2018. A bimodal distribution was found in age groups, and the birth cohorts before 1978 had relative higher risk. The high-risk areas were mainly distributed in western China and south-central China, and several provinces in eastern China showed a potential increasing trend, including Beijing, Shanghai, Liaoning, and Guangdong province. The index of night light (Q = 0.46), the population density (Q = 0.41), PM10 (Q = 0.38), urbanization rate (Q = 0.32), and PM 2.5 (Q = 0.31) contributed substantially to the spatial distribution of pulmonary tuberculosis. The identifications of epidemic patterns, high-risk areas and influence factors would help design targeted intervention measures to achieve milestones of the end TB strategy.
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Affiliation(s)
- Xiao Wei
- Department of Epidemiology, School of Public Health, Air Force Medical University, Xi'an, People's Republic of China
- Ministry of Education Key Lab of Hazard Assessment and control in Special Operational Environment, Air Force Medical University, Xi'an, People's Republic of China
| | - Ting Fu
- Department of Epidemiology, School of Public Health, Air Force Medical University, Xi'an, People's Republic of China
- Ministry of Education Key Lab of Hazard Assessment and control in Special Operational Environment, Air Force Medical University, Xi'an, People's Republic of China
| | - Di Chen
- RDFZ Chaoyang Experimental School, Beijing, People's Republic of China
| | - Wenping Gong
- Tuberculosis Prevention and Control Key Laboratory, Senior Department of Tuberculosis, The Eighth Medical Center of PLA General Hospital, Beijing, China
| | - Shuyuan Zhang
- Department of Epidemiology, School of Public Health, Air Force Medical University, Xi'an, People's Republic of China
| | - Yong Long
- Department of Epidemiology, School of Public Health, Air Force Medical University, Xi'an, People's Republic of China
| | - Xubin Wu
- Department of Epidemiology, School of Public Health, Air Force Medical University, Xi'an, People's Republic of China
- Ministry of Education Key Lab of Hazard Assessment and control in Special Operational Environment, Air Force Medical University, Xi'an, People's Republic of China
| | - Zhongjun Shao
- Department of Epidemiology, School of Public Health, Air Force Medical University, Xi'an, People's Republic of China
- Ministry of Education Key Lab of Hazard Assessment and control in Special Operational Environment, Air Force Medical University, Xi'an, People's Republic of China
| | - Kun Liu
- Department of Epidemiology, School of Public Health, Air Force Medical University, Xi'an, People's Republic of China.
- Ministry of Education Key Lab of Hazard Assessment and control in Special Operational Environment, Air Force Medical University, Xi'an, People's Republic of China.
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Wang L, Xu C, Wang J, Qiao J, Wu N, Li L. Spatiotemporal associations between hand, foot and mouth disease and meteorological factors over multiple climate zones. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2023; 67:1493-1504. [PMID: 37458818 DOI: 10.1007/s00484-023-02519-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 05/25/2023] [Accepted: 07/05/2023] [Indexed: 08/17/2023]
Abstract
Prior studies of hand, foot, and mouth disease (HFMD) have often observed inconsistent results regarding meteorological factors. We propose the hypothesis that these meteorological associations vary in regions because of the heterogeneity of their geographical characteristics. We have tested this hypothesis by applying a geographical detector and Bayesian space-time hierarchy model to measure stratified spatiotemporal heterogeneity and local associations between meteorological factors and HFMD risk in five climate zones in China from January 2016 to December 2017. We found a significant spatial stratified heterogeneity in HFMD risk and climate zone explained 15% of the spatial stratified heterogeneity. Meanwhile, there was a significant temporal stratified heterogeneity of 14% as determined by meteorological factors. Average temperatures and relative humidity had a significant positive effect on HFMD in all climate zones, they were the most obvious in the southern temperate zone. In northern temperate, southern temperate, northern subtropics, middle subtropics and southern subtropics climate zone, a 1 °C rise in temperature was related to an increase of 3.99%, 13.76%, 4.38%, 3.99%, and 7.74% in HFMD, and a 1% increment in relative humidity was associated with a 1.51%, 5.40%, 2.21%, 3.44%, and 4.78% increase, respectively. These findings provide strong support for our hypotheses that HFMD incidence has a significant spatiotemporal stratified heterogeneity and different climate zones have distinct influences on the disease. These findings provide strong support for our hypotheses: HFMD incidence had significant spatiotemporal stratified heterogeneity and different climate zones had distinct influences on it. The study suggested that HFMD prevention and policy should be made according to meteorological variation in each climate zone.
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Affiliation(s)
- Li Wang
- College of Geography and Environmental Science, Henan University, Kaifeng, China
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resource Research, Chinese Academy of Sciences, Beijing, China
- Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng, China
| | - Chengdong Xu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resource Research, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jinfeng Wang
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resource Research, Chinese Academy of Sciences, Beijing, China.
- University of Chinese Academy of Sciences, Beijing, China.
| | - Jiajun Qiao
- College of Geography and Environmental Science, Henan University, Kaifeng, China.
- Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng, China.
| | - Nalin Wu
- College of Geography and Environmental Science, Henan University, Kaifeng, China
- Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng, China
| | - Li Li
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
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Wang P, Li K, Xu C, Fan Z, Wang Z. Spatial analysis of overweight prevalence in China: exploring the association with air pollution. BMC Public Health 2023; 23:1595. [PMID: 37608324 PMCID: PMC10463435 DOI: 10.1186/s12889-023-16518-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Accepted: 08/13/2023] [Indexed: 08/24/2023] Open
Abstract
BACKGROUND Overweight is a known risk factor for various chronic diseases and poses a significant threat to middle-aged and elderly adults. Previous studies have reported a strong association between overweight and air pollution. However, the spatial relationship between the two remains unclear due to the confounding effects of spatial heterogeneity. METHODS We gathered height and weight data from the 2015 China Health and Retirement Long-term Survey (CHARLS), comprising 16,171 middle-aged and elderly individuals. We also collected regional air pollution data. We then analyzed the spatial pattern of overweight prevalence using Moran's I and Getis-Ord Gi* statistics. To quantify the explanatory power of distinct air pollutants for spatial differences in overweight prevalence across Southern and Northern China, as well as across different age groups, we utilized Geodetector's q-statistic. RESULTS The average prevalence of overweight among middle-aged and elderly individuals in each city was 67.27% and 57.39%, respectively. In general, the q-statistic in southern China was higher than that in northern China. In the north, the prevalence was significantly higher at 54.86% compared to the prevalence of 38.75% in the south. SO2 exhibited a relatively higher q-statistic in middle-aged individuals in both the north and south, while for the elderly in the south, NO2 was the most crucial factor (q = 0.24, p < 0.01). Moreover, fine particulate matter (PM2.5 and PM10) also demonstrated an important effect on overweight. Furthermore, we found that the pairwise interaction between various risk factors improved the explanatory power of the prevalence of overweight, with different effects for different age groups and regions. In northern China, the strongest interaction was found between NO2 and SO2 (q = 0.55) for middle-aged individuals and PM2.5 and SO2 (q = 0.27) for the elderly. Conversely, in southern China, middle-aged individuals demonstrated the strongest interaction between SO2 and PM10 (q = 0.60), while the elderly showed the highest interaction between NO2 and O3 (q = 0.42). CONCLUSION Significant spatial heterogeneity was observed in the effects of air pollution on overweight. Specifically, air pollution in southern China was found to have a greater impact on overweight than that in northern China. And, the impact of air pollution on middle-aged individuals was more pronounced than on the elderly, with distinct pollutants demonstrating significant variation in their impact. Moreover, we found that SO2 had a greater impact on overweight prevalence among middle-aged individuals, while NO2 had a greater impact on the elderly. Additionally, we identified significant statistically interactions between O3 and other pollutants.
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Affiliation(s)
- Peihan Wang
- Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, P.R. China
| | - Kexin Li
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, P.R. China
| | - Chengdong Xu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, P.R. China.
- University of Chinese Academy of Sciences, Beijing, 100049, P.R. China.
| | - Zixuan Fan
- Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, P.R. China.
- School of Health Policy and Management, Peking Union Medical College, Beijing, 100730, P.R. China.
| | - Zhenbo Wang
- Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, P.R. China
- University of Chinese Academy of Sciences, Beijing, 100049, P.R. China
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Teibo TKA, Andrade RLDP, Rosa RJ, Tavares RBV, Berra TZ, Arcêncio RA. Geo-spatial high-risk clusters of Tuberculosis in the global general population: a systematic review. BMC Public Health 2023; 23:1586. [PMID: 37598144 PMCID: PMC10439548 DOI: 10.1186/s12889-023-16493-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 08/09/2023] [Indexed: 08/21/2023] Open
Abstract
INTRODUCTION The objective of this systematic review is to identify tuberculosis (TB) high-risk among the general population globally. The review was conducted using the following steps: elaboration of the research question, search for relevant publications, selection of studies found, data extraction, analysis, and evidence synthesis. METHODS The studies included were those published in English, from original research, presented findings relevant to tuberculosis high-risk across the globe, published between 2017 and 2023, and were based on geospatial analysis of TB. Two reviewers independently selected the articles and were blinded to each other`s comments. The resultant disagreement was resolved by a third blinded reviewer. For bibliographic search, controlled and free vocabularies that address the question to be investigated were used. The searches were carried out on PubMed, LILACS, EMBASE, Scopus, and Web of Science. and Google Scholar. RESULTS A total of 79 published articles with a 40-year study period between 1982 and 2022 were evaluated. Based on the 79 studies, more than 40% of all countries that have carried out geospatial analysis of TB were from Asia, followed by South America with 23%, Africa had about 15%, and others with 2% and 1%. Various maps were used in the various studies and the most used is the thematic map (32%), rate map (26%), map of temporal tendency (20%), and others like the kernel density map (6%). The characteristics of the high-risk and the factors that affect the hotspot's location are evident through studies related to poor socioeconomic conditions constituting (39%), followed by high population density (17%), climate-related clustering (15%), high-risk spread to neighbouring cities (13%), unstable and non-random cluster (11%). CONCLUSION There exist specific high-risk for TB which are areas that are related to low socioeconomic conditions and spectacular weather conditions, these areas when well-known will be easy targets for intervention by policymakers. We recommend that more studies making use of spatial, temporal, and spatiotemporal analysis be carried out to point out territories and populations that are vulnerable to TB.
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Affiliation(s)
- Titilade Kehinde Ayandeyi Teibo
- Department of Maternal-Infant and Public Health Nursing, Ribeirão Preto College of Nursing, University of São Paulo, Ribeirão Preto, Sao Paulo, Brazil.
| | - Rubia Laine de Paula Andrade
- Department of Maternal-Infant and Public Health Nursing, Ribeirão Preto College of Nursing, University of São Paulo, Ribeirão Preto, Sao Paulo, Brazil
| | - Rander Junior Rosa
- Department of Maternal-Infant and Public Health Nursing, Ribeirão Preto College of Nursing, University of São Paulo, Ribeirão Preto, Sao Paulo, Brazil
| | - Reginaldo Bazon Vaz Tavares
- Department of Maternal-Infant and Public Health Nursing, Ribeirão Preto College of Nursing, University of São Paulo, Ribeirão Preto, Sao Paulo, Brazil
| | - Thais Zamboni Berra
- Department of Maternal-Infant and Public Health Nursing, Ribeirão Preto College of Nursing, University of São Paulo, Ribeirão Preto, Sao Paulo, Brazil
| | - Ricardo Alexandre Arcêncio
- Department of Maternal-Infant and Public Health Nursing, Ribeirão Preto College of Nursing, University of São Paulo, Ribeirão Preto, Sao Paulo, Brazil
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12
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Chen S, Wang Y, Zhan Y, Liu C, Wang Q, Feng J, Li Y, Chen H, Zeng Z. The incidence of tuberculous pleurisy in mainland China from 2005 to 2018. Front Public Health 2023; 11:1180818. [PMID: 37397728 PMCID: PMC10311513 DOI: 10.3389/fpubh.2023.1180818] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 05/12/2023] [Indexed: 07/04/2023] Open
Abstract
Background Currently, tuberculous pleurisy (TP) remains a serious problem affecting global public health, including in China. Our purpose was to comprehensively understand and identify the incidence of TP in mainland China between 2005 and 2018. Methods The data on registered TP cases from 2005 to 2018 were acquired from the National Tuberculosis Information Management System. We analyzed the demographics, epidemiology, and time-space distribution of TP patients. Then, the effects of potentially influential factors on TP incidences, such as medical expenses per capita, GDP per capita, and population density, were assessed using the Spearman correlation coefficient. Results The incidence of TP increased in mainland China from 2005 to 2018, with a mean incidence of 2.5 per 100,000 population. Interestingly, spring was the peak season for TP, with more notified cases. Tibet, Beijing, Xinjiang, and Inner Mongolia had the highest mean annual incidence. A moderate positive relationship was found between TP incidence, medical expenses per capita, and GDP per capita. Conclusions The notified incidence of TP had an elevated trend from 2005 to 2018 in mainland China. The findings of this study provide insight into the knowledge of TP epidemiology in the country, which can help optimize resource allocation to reduce the TP burden.
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Affiliation(s)
- Shuhan Chen
- Second Clinical College, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yi Wang
- Reproductive Medicine Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuan Zhan
- Department of Respiratory and Critical Care Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Changyu Liu
- Department of Thoracic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qi Wang
- Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jie Feng
- Department of Social Medicine and Health Management, School of Public Health, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yufeng Li
- Reproductive Medicine Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Huilong Chen
- Department and Institute of Infectious Diseases, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhilin Zeng
- Department and Institute of Infectious Diseases, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Lei Y, Wang J, Wang Y, Xu C. Geographical evolutionary pathway of global tuberculosis incidence trends. BMC Public Health 2023; 23:755. [PMID: 37095497 PMCID: PMC10123998 DOI: 10.1186/s12889-023-15553-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 03/28/2023] [Indexed: 04/26/2023] Open
Abstract
BACKGROUNDS Tuberculosis (TB) remains a serious public health and human development problem, especially in developing countries. Despite the effectiveness of directly observed therapy, short course programs in reducing transmission and progression of TB, poverty reduction and socioeconomic development remain crucial factors in decreasing TB incidence. However, the geographical pathway on the planet is not yet clear. OBJECTIVES This study was to reconstruct the geographical evolutionary process of TB in 173 countries and territories from 2010 to 2019 to analyze the socioeconomic determinants that impact the global TB epidemic. In addition, the TB incidence in 2030 was predicted. METHODS This study analyses TB incidence data from 173 countries and territories between 2010 and 2019. The Geotree model would be used to reconstruct the geographical evolutionary process of TB, which provides a simplified schema for geo-visualizing the trajectories of TB incidence and their socioeconomic drivers. Additionally, to estimate the future TB incidence in 2030, a multilevel model was utilized in conjunction with the hierarchical nature of the Geotree based on a stratified heterogeneity analysis. RESULTS Global TB incidence was found to be associated with the country type and development stages. Between 2010 and 2019, the average TB incidence rate in 173 countries and territories was -27.48%, with marked spatially stratified heterogeneity by country type and development stage. Low-income and lower-middle-income countries were most vulnerable to TB. Upper-middle-income countries experienced a faster decline in TB incidence than high-income countries, and TB incidence generally decreased as the development stage increased, except for the lower-middle development stage in 2019.The highest average rate of decline in TB incidence was observed in the upper-middle development stage of high-income countries, with a reduction of 45.24%. Meanwhile, 37 high-income countries in the high development stage demonstrated an average rate of change of -13.93%. Socioeconomic determinants, including gross domestic product per capita, urbanization rate, and sociodemographic index, were found to inhibit TB incidence. Based on current trends, the predicted average global TB incidence in 2030 is 91.581 per 100,000 population. CONCLUSIONS The trajectories of the global TB incidence have been reconstructed to formulate targeted public health responses. To eliminate TB, countries at similar development stage can draw on the experiences of countries at higher development stages that are tailored to their unique characteristics. By learning from successful TB control strategies, countries can take strategic steps toward eradicating TB and improving public health outcomes.
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Affiliation(s)
- Yanhui Lei
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jinfeng Wang
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Yang Wang
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Chengdong Xu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
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Chen ZY, Deng XY, Zou Y, He Y, Chen SJ, Wang QT, Xing DG, Zhang Y. A Spatio-temporal Bayesian model to estimate risk and influencing factors related to tuberculosis in Chongqing, China, 2014-2020. Arch Public Health 2023; 81:42. [PMID: 36945028 PMCID: PMC10031926 DOI: 10.1186/s13690-023-01044-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 02/16/2023] [Indexed: 03/23/2023] Open
Abstract
BACKGROUND Tuberculosis (TB) is a serious infectious disease that is one of the leading causes of death worldwide. This study aimed to investigate the spatial and temporal distribution patterns and potential influencing factors of TB incidence risk, and to provide a scientific basis for the prevention and control of TB. METHODS We collected reported cases of TB in 38 districts and counties in Chongqing from 2014 to 2020 and data on environment, population characteristics and economic factors during the same period. By constructing a Bayesian spatio-temporal model, we explored the spatio-temporal distribution pattern of TB incidence risk and potential influencing factors, identified key areas and key populations affected by TB, compared the spatio-temporal distribution characteristics of TB in populations with different characteristics, and explored the differences in the influence of various social and environmental factors. RESULTS The high-risk areas for TB incidence in Chongqing from 2014 to 2020 were mainly concentrated in southeastern and northeastern regions of Chongqing, and the overall relative risk (RR) of TB showed a decreasing trend during the study period, while RR of TB in main urban area and southeast of Chongqing showed an increasing trend. The RR of TB was relatively high in the main urban area for the female population and the population aged 0-29 years, and the RR of TB for the population aged 30-44 years in the main urban area and the population aged 60 years or older in southeast of Chongqing had an increasing trend, respectively. For each 1 μg/m3 increase in SO2 and 1% increase in the number of low-income per 1000 non-agricultural households (LINA per 1000 persons), the RR of TB increased by 0.35% (95% CI: 0.08-0.61%) and 0.07% (95% CI: 0.05-0.10%), respectively. And LINA per 1000 persons had the greatest impact on the female population and the over 60 years old age group. Although each 1% increase in urbanization rate (UR) was associated with 0.15% (95% CI: 0.11-0.17%) reduction in the RR of TB in the whole population, the RR increased by 0.18% (95% CI: 0.16-0.21%) in the female population and 0.37% (95% CI: 0.34-0.45%) in the 0-29 age group. CONCLUSION This study showed that high-risk areas for TB were concentrated in the southeastern and northeastern regions of Chongqing, and that the elderly population was a key population for TB incidence. There were spatial and temporal differences in the incidence of TB in populations with different characteristics, and various socio-environmental factors had different effects on different populations. Local governments should focus on areas and populations at high risk of TB and develop targeted prevention interventions based on the characteristics of different populations.
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Affiliation(s)
- Zhi-Yi Chen
- School of Public Health, Chongqing Medical University, Chongqing, 400016, China
- Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing, 400016, China
- Innovation Center for Social Risk Governance in Health, Chongqing Medical University, Chongqing, 400016, China
- Research Center for Public Health Security, Chongqing Medical University, Chongqing, 400016, China
| | - Xin-Yi Deng
- School of Public Health, Chongqing Medical University, Chongqing, 400016, China
- Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing, 400016, China
- Innovation Center for Social Risk Governance in Health, Chongqing Medical University, Chongqing, 400016, China
- Research Center for Public Health Security, Chongqing Medical University, Chongqing, 400016, China
| | - Yang Zou
- School of Public Health, Chongqing Medical University, Chongqing, 400016, China
- Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing, 400016, China
- Innovation Center for Social Risk Governance in Health, Chongqing Medical University, Chongqing, 400016, China
- Research Center for Public Health Security, Chongqing Medical University, Chongqing, 400016, China
| | - Ying He
- School of Public Health, Chongqing Medical University, Chongqing, 400016, China
- Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing, 400016, China
- Innovation Center for Social Risk Governance in Health, Chongqing Medical University, Chongqing, 400016, China
- Research Center for Public Health Security, Chongqing Medical University, Chongqing, 400016, China
| | - Sai-Juan Chen
- School of Public Health, Chongqing Medical University, Chongqing, 400016, China
- Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing, 400016, China
- Innovation Center for Social Risk Governance in Health, Chongqing Medical University, Chongqing, 400016, China
- Research Center for Public Health Security, Chongqing Medical University, Chongqing, 400016, China
| | - Qiu-Ting Wang
- School of Public Health, Chongqing Medical University, Chongqing, 400016, China
- Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing, 400016, China
- Innovation Center for Social Risk Governance in Health, Chongqing Medical University, Chongqing, 400016, China
- Research Center for Public Health Security, Chongqing Medical University, Chongqing, 400016, China
| | - Dian-Guo Xing
- Office of Health Emergency, Chongqing Municipal Health Commission, No.6, Qilong Road, Yubei District, Chongqing, 401147, China.
| | - Yan Zhang
- School of Public Health, Chongqing Medical University, Chongqing, 400016, China.
- Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing, 400016, China.
- Innovation Center for Social Risk Governance in Health, Chongqing Medical University, Chongqing, 400016, China.
- Research Center for Public Health Security, Chongqing Medical University, Chongqing, 400016, China.
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Teo AKJ, Rahevar K, Morishita F, Ang A, Yoshiyama T, Ohkado A, Kawatsu L, Yamada N, Uchimura K, Choi Y, Chen Z, Yi S, Yanagawa M, Oh KH, Viney K, Marais B, Kim H, Kato S, Liu Y, Ong CW, Islam T. Tuberculosis in older adults: case studies from four countries with rapidly ageing populations in the western pacific region. BMC Public Health 2023; 23:370. [PMID: 36810018 PMCID: PMC9942033 DOI: 10.1186/s12889-023-15197-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 02/02/2023] [Indexed: 02/23/2023] Open
Abstract
BACKGROUND The Western Pacific Region has one of the fastest-growing populations of older adults (≥ 65 years) globally, among whom tuberculosis (TB) poses a particular concern. This study reports country case studies from China, Japan, the Republic of Korea, and Singapore reflecting on their experiences in managing TB among older adults. FINDINGS Across all four countries, TB case notification and incidence rates were highest among older adults, but clinical and public health guidance focused on this population was limited. Individual country reports illustrated a range of practices and challenges. Passive case finding remains the norm, with limited active case finding (ACF) programs implemented in China, Japan, and the Republic of Korea. Different approaches have been trialled to assist older adults in securing an early diagnosis, as well as adhering to their TB treatment. All countries emphasised the need for person-centred approaches that include the creative application of new technology and tailored incentive programs, as well as reconceptualisation of how we provide treatment support. The use of traditional medicines was found to be culturally entrenched among older adults, with a need for careful consideration of their complementary use. TB infection testing and the provision of TB preventive treatment (TPT) were underutilised with highly variable practice. CONCLUSION Older adults require specific consideration in TB response policies, given the burgeoning aging population and their high TB risk. Policymakers, TB programs and funders must invest in and develop locally contextualised practice guidelines to inform evidence-based TB prevention and care practices for older adults.
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Affiliation(s)
- Alvin Kuo Jing Teo
- grid.4280.e0000 0001 2180 6431Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore ,grid.1013.30000 0004 1936 834XFaculty of Medicine and Health, University of Sydney, Sydney, NSW Australia ,grid.1013.30000 0004 1936 834XThe University of Sydney Institute for Infectious Diseases (Sydney ID) and the Centre of Research Excellence in Tuberculosis (TB-CRE), Sydney, NSW Australia
| | - Kalpeshsinh Rahevar
- World Health Organization, Regional Office for the Western Pacific, Manila, Philippines.
| | - Fukushi Morishita
- grid.483407.c0000 0001 1088 4864World Health Organization, Regional Office for the Western Pacific, Manila, Philippines
| | - Alicia Ang
- grid.508010.cDivision of Infectious Diseases, Department of Medicine, Woodlands Health, Singapore, Singapore
| | - Takashi Yoshiyama
- grid.419151.90000 0001 1545 6914Research Institute of Tuberculosis, Anti-Tuberculosis Association, Tokyo, Japan
| | - Akihiro Ohkado
- grid.419151.90000 0001 1545 6914Research Institute of Tuberculosis, Anti-Tuberculosis Association, Tokyo, Japan
| | - Lisa Kawatsu
- grid.419151.90000 0001 1545 6914Research Institute of Tuberculosis, Anti-Tuberculosis Association, Tokyo, Japan
| | - Norio Yamada
- grid.419151.90000 0001 1545 6914Research Institute of Tuberculosis, Anti-Tuberculosis Association, Tokyo, Japan
| | - Kazuhiro Uchimura
- grid.419151.90000 0001 1545 6914Research Institute of Tuberculosis, Anti-Tuberculosis Association, Tokyo, Japan
| | - Youngeun Choi
- Korean National Tuberculosis Association, Seoul, Republic of Korea
| | - Zi Chen
- Office of International Cooperation, Innovation Alliance on Tuberculosis Diagnosis and Treatment, Beijing, China
| | - Siyan Yi
- grid.4280.e0000 0001 2180 6431Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore ,grid.513124.00000 0005 0265 4996KHANA Center for Population Health Research, Phnom Penh, Cambodia ,grid.265117.60000 0004 0623 6962Center for Global Health Research, Public Health Program, Touro University California, Vallejo, CA USA
| | - Manami Yanagawa
- grid.483407.c0000 0001 1088 4864World Health Organization, Regional Office for the Western Pacific, Manila, Philippines
| | - Kyung Hyun Oh
- grid.483407.c0000 0001 1088 4864World Health Organization, Regional Office for the Western Pacific, Manila, Philippines
| | - Kerri Viney
- grid.3575.40000000121633745Global Tuberculosis Programme, World Health Organization, Geneva, Switzerland
| | - Ben Marais
- grid.1013.30000 0004 1936 834XFaculty of Medicine and Health, University of Sydney, Sydney, NSW Australia ,grid.1013.30000 0004 1936 834XThe University of Sydney Institute for Infectious Diseases (Sydney ID) and the Centre of Research Excellence in Tuberculosis (TB-CRE), Sydney, NSW Australia
| | - Heejin Kim
- Korean National Tuberculosis Association, Seoul, Republic of Korea
| | - Seiya Kato
- grid.419151.90000 0001 1545 6914Research Institute of Tuberculosis, Anti-Tuberculosis Association, Tokyo, Japan
| | - Yuhong Liu
- grid.24696.3f0000 0004 0369 153XBeijing Chest Hospital, Capital Medical University, Beijing, China
| | - Catherine W.M. Ong
- grid.412106.00000 0004 0621 9599Division of Infectious Diseases, Department of Medicine, National University Hospital, Singapore, Singapore ,grid.4280.e0000 0001 2180 6431Infectious Diseases Translational Research Programme, Department of Medicine, National University of Singapore, Singapore, Singapore ,grid.4280.e0000 0001 2180 6431Institute of Health Innovation and Technology (iHealthtech), National University of Singapore, Singapore, Singapore
| | - Tauhid Islam
- grid.483407.c0000 0001 1088 4864World Health Organization, Regional Office for the Western Pacific, Manila, Philippines
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Galicia P, Linares M, Miguel-Benito A, Pérez García F, Górgolas M, Ramos-Rincón JM, Cuadros J. [The postal code as a "bar code" of antimicrobial resistance]. REVISTA ESPANOLA DE QUIMIOTERAPIA : PUBLICACION OFICIAL DE LA SOCIEDAD ESPANOLA DE QUIMIOTERAPIA 2022; 35:492-497. [PMID: 35819817 PMCID: PMC9548063 DOI: 10.37201/req/021.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 04/22/2022] [Accepted: 05/31/2022] [Indexed: 06/15/2023]
Abstract
OBJECTIVE The need to integrate local resistances into clinical practice is increasingly urgent, especially in Primary Care where empirical treatment is frequent. METHODS A retrospective observational study of positive microbiological isolates of Neisseria gonorrhoeae from any location (urethral, cervical, pharyngeal, rectal or urine) was carried out in the health area of Alcalá de Henares. Sociodemographic characteristics and resistance to cephalosporins, azithromycin, penicillin and quinolones were analyzed. Each isolate was related to its postal code of origin. RESULTS We analyzed 256 microbiological samples of N.gonorrhoeae, most of them male (92.9%) with a mean age of 33 years. Half of the samples (49.8%) were resistant to ciprofloxacin. Temporal and spatial evolution of antimicrobial resistance was integrated in heat maps. CONCLUSIONS Knowing local resistances can help to prescribe more adequate empirical treatments, especially in Primary Care, avoiding inadequate antibiotics and decreasing resistance rates.
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Affiliation(s)
- P Galicia
- Hospital Universitario Príncipe de Asturias. Servicio de Microbiología Clínica. Carretera de Alcalá, s/n, 28805 Meco (Madrid). Spain.
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Ren H, Lu W, Li X, Shen H. Specific urban units identified in tuberculosis epidemic using a geographical detector in Guangzhou, China. Infect Dis Poverty 2022; 11:44. [PMID: 35428318 PMCID: PMC9012046 DOI: 10.1186/s40249-022-00967-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 04/07/2022] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND A remarkable drop in tuberculosis (TB) incidence has been achieved in China, although in 2019 it was still considered the second most communicable disease. However, TB's spatial features and risk factors in urban areas remain poorly understood. This study aims to identify the spatial differentiations and potential influencing factors of TB in highly urbanized regions on a fine scale. METHODS This study included 18 socioeconomic and environmental variables in the four central districts of Guangzhou, China. TB case data obtained from the Guangzhou Institute of Tuberculosis Control and Prevention. Before using Pearson correlation and a geographical detector (GD) to identify potential influencing factors, we conducted a global spatial autocorrelation analysis to select an appropriate spatial scales. RESULTS Owing to its strong spatial autocorrelation (Moran's I = 0.33, Z = 4.71), the 2 km × 2 km grid was selected as the spatial scale. At this level, TB incidence was closely associated with most socioeconomic variables (0.31 < r < 0.76, P < 0.01). Of five environmental factors, only the concentration of fine particulate matter displayed significant correlation (r = 0.21, P < 0.05). Similarly, in terms of q values derived from the GD, socioeconomic variables had stronger explanatory abilities (0.08 < q < 0.57) for the spatial differentiation of the 2017 incidence of TB than environmental variables (0.06 < q < 0.27). Moreover, a much larger proportion (0.16 < q < 0.89) of the spatial differentiation was interpreted by pairwise interactions, especially those (0.60 < q < 0.89) related to the 2016 incidence of TB, officially appointed medical institutions, bus stops, and road density. CONCLUSIONS The spatial heterogeneity of the 2017 incidence of TB in the study area was considerably influenced by several socioeconomic and environmental factors and their pairwise interactions on a fine scale. We suggest that more attention should be paid to the units with pairwise interacting factors in Guangzhou. Our study provides helpful clues for local authorities implementing more effective intervention measures to reduce TB incidence in China's municipal areas, which are featured by both a high degree of urbanization and a high incidence of TB.
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Affiliation(s)
- Hongyan Ren
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101 China
| | - Weili Lu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101 China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100190 China
| | - Xueqiu Li
- Guangzhou Chest Hospital, Guangzhou, 510000 China
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