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Wang Z, Guo T, Xu L, Liu J, Hou Y, Jin J, Zhang Q, Jiang T, Zhao Z, Xue Y. Regional differences of Mycobacterium tuberculosis complex infection and multidrug resistance epidemic in Luoyang. BMC Infect Dis 2024; 24:578. [PMID: 38862881 PMCID: PMC11167740 DOI: 10.1186/s12879-024-09395-w] [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: 12/09/2023] [Accepted: 05/09/2024] [Indexed: 06/13/2024] Open
Abstract
BACKGROUND Tuberculosis (TB) remains a global public health event of great concern, however epidemic data on TB covering entire areas during the special period of the COVID-19 epidemic have rarely been reported. We compared the dissemination and multidrug-resistance patterns of Mycobacterium tuberculosis complex (MTBC) in the main urban area of Luoyang City, China (including six municipal jurisdictions) and nine county and township areas under its jurisdiction, aimed to establish the epidemiology of TB in this region and to provide reference for precision anti-TB in places with similar settings. METHODS From 2020 to 2022, sputum samples were collected from 18,504 patients with confirmed, suspected and unexcluded TB in 10 designated TB medical institutions. Insertion sequence 6110 was amplified by PCR (rpoB gene detection if necessary) to confirm the presence of MTBC. PCR-positive specimens were analyzed by multicolor melting curve analysis to detect multidrug resistance. RESULTS Among the 18,504 specimens, 2675 (14.5%) were MTBC positive. The positive rate was higher in the main urban area than in the county and township areas (29.8% vs. 10.9%, p < 0.001). Male, re-treated and smear-positive groups were high-burden carriers of MTBC. Individuals aged > 60 years were the largest group infected with MTBC in the main urban area, compared with individuals aged < 61 years in the county and township areas. The detection of multidrug-resistant TB (MDR-TB) was higher in the main urban area than in the county and township areas (13.9% vs. 7.8%, p < 0.001). In all areas, MDR-TB groups were dominated by males, patients with a history of TB treatment, and patients aged < 61 years. Stratified analysis of MDR-TB epidemiology showed that MDR4 (INH þ RIF þ EMB þ SM) was predominant in the main urban area, while MDR3 (INH þ RIF þ SM) was predominant in the county and township areas. MDR-TB detection rate and epidemiology differed among the county and township areas. CONCLUSIONS For local TB control, it is necessary to plan more appropriate and accurate prevention and control strategies according to the regional distribution of MTBC infection.
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Affiliation(s)
- Zhenzhen Wang
- The First Affiliated Hospital and Clinical Medical College, Henan University of Science and Technology, Luoyang, China
- School of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, China
| | - Tengfei Guo
- The First Affiliated Hospital and Clinical Medical College, Henan University of Science and Technology, Luoyang, China
| | - Liyang Xu
- Luoyang Center for Disease Control and Prevention, Luoyang, China
- School of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, China
| | - Jinwei Liu
- The First Affiliated Hospital and Clinical Medical College, Henan University of Science and Technology, Luoyang, China
| | - Yi Hou
- Luoyang Center for Disease Control and Prevention, Luoyang, China
| | - Junrong Jin
- The First Affiliated Hospital and Clinical Medical College, Henan University of Science and Technology, Luoyang, China
| | - Qing Zhang
- The First Affiliated Hospital and Clinical Medical College, Henan University of Science and Technology, Luoyang, China
| | - Tao Jiang
- The First Affiliated Hospital and Clinical Medical College, Henan University of Science and Technology, Luoyang, China
| | - Zhanqin Zhao
- College of Animal Science and Technology, Henan University of Science and Technology, Luoyang, China.
| | - Yun Xue
- School of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, China.
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Chen K, Cheng L, Yu H, Zhou Y, Zhu L, Li Z, Li T, Martinez L, Liu Q, Wang B. Spatial-temporal distribution characteristics of pulmonary tuberculosis in eastern China from 2011 to 2021. Epidemiol Infect 2024; 152:e84. [PMID: 38745412 PMCID: PMC11149027 DOI: 10.1017/s0950268824000785] [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: 07/03/2023] [Revised: 04/23/2024] [Accepted: 05/02/2024] [Indexed: 05/16/2024] Open
Abstract
China is still among the 30 high-burden tuberculosis (TB) countries in the world. Few studies have described the spatial epidemiological characteristics of pulmonary TB (PTB) in Jiangsu Province. The registered incidence data of PTB patients in 95 counties of Jiangsu Province from 2011 to 2021 were collected from the Tuberculosis Management Information System. Three-dimensional spatial trends, spatial autocorrelation, and spatial-temporal scan analysis were conducted to explore the spatial clustering pattern of PTB. From 2011 to 2021, a total of 347,495 newly diagnosed PTB cases were registered. The registered incidence rate of PTB decreased from 49.78/100,000 in 2011 to 26.49/100,000 in 2021, exhibiting a steady downward trend (χ2 = 414.22, P < 0.001). The average annual registered incidence rate of PTB was higher in the central and northern regions. Moran's I indices of the registered incidence of PTB were all >0 (P< 0.05) except in 2016, indicating a positive spatial correlation overall. Local autocorrelation analysis showed that 'high-high' clusters were mainly distributed in northern Jiangsu, and 'low-low' clusters were mainly concentrated in southern Jiangsu. The results of this study assist in identifying settings and locations of high TB risk and inform policy-making for PTB control and prevention.
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Affiliation(s)
- Ke Chen
- Key Laboratory of Environmental Medicine Engineering of Ministry of Education, Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, China
| | - Liang Cheng
- Department of Tuberculosis, Affiliated Wuxi Fifth Hospital of Jiangnan University, Wuxi, China
| | - Hao Yu
- Department of Chronic Communicable Disease, Center for Disease Control and Prevention of Jiangsu Province, Nanjing, China
| | - Yong Zhou
- Department of Chronic Disease, Center for Disease Control and Prevention of Heilongjiang Province, Harbin, China
| | - Limei Zhu
- Department of Chronic Communicable Disease, Center for Disease Control and Prevention of Jiangsu Province, Nanjing, China
| | - Zhongqi Li
- Department of Chronic Communicable Disease, Center for Disease Control and Prevention of Jiangsu Province, Nanjing, China
| | - Tenglong Li
- Academy of Pharmacy, Xi’an Jiaotong-Liverpool University, Suzhou, China
| | - Leonardo Martinez
- Department of Epidemiology, School of Public Health, Boston University, Boston, MA, USA
| | - Qiao Liu
- Key Laboratory of Environmental Medicine Engineering of Ministry of Education, Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, China
- Department of Chronic Communicable Disease, Center for Disease Control and Prevention of Jiangsu Province, Nanjing, China
| | - Bei Wang
- Key Laboratory of Environmental Medicine Engineering of Ministry of Education, Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, China
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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. Assessing heterogeneity of patient and health system delay among TB in a population with internal migrants in China. Front Public Health 2024; 12:1354515. [PMID: 38371243 PMCID: PMC10869454 DOI: 10.3389/fpubh.2024.1354515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 01/18/2024] [Indexed: 02/20/2024] Open
Abstract
Backgrounds The diagnostic delay of tuberculosis (TB) contributes to further transmission and impedes the implementation of the End TB Strategy. Therefore, we aimed to describe the characteristics of patient delay, health system delay, and total delay among TB patients in Shanghai, identify areas at high risk for delay, and explore the potential factors of long delay at individual and spatial levels. Method The study included TB patients among migrants and residents in Shanghai between January 2010 and December 2018. Patient and health system delays exceeding 14 days and total delays exceeding 28 days were defined as long delays. Time trends of long delays were evaluated by Joinpoint regression. Multivariable logistic regression analysis was employed to analyze influencing factors of long delays. Spatial analysis of delays was conducted using ArcGIS, and the hierarchical Bayesian spatial model was utilized to explore associated spatial factors. Results Overall, 61,050 TB patients were notified during the study period. Median patient, health system, and total delays were 12 days (IQR: 3-26), 9 days (IQR: 4-18), and 27 days (IQR: 15-43), respectively. Migrants, females, older adults, symptomatic visits to TB-designated facilities, and pathogen-positive were associated with longer patient delays, while pathogen-negative, active case findings and symptomatic visits to non-TB-designated facilities were associated with long health system delays (LHD). Spatial analysis revealed Chongming Island was a hotspot for patient delay, while western areas of Shanghai, with a high proportion of internal migrants and industrial parks, were at high risk for LHD. The application of rapid molecular diagnostic methods was associated with reduced health system delays. Conclusion Despite a relatively shorter diagnostic delay of TB than in the other regions in China, there was vital social-demographic and spatial heterogeneity in the occurrence of long delays in Shanghai. While the active case finding and rapid molecular diagnosis reduced the delay, novel targeted interventions are still required to address the challenges of TB diagnosis among both migrants and residents in this urban setting.
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Affiliation(s)
- Ruoyao Sun
- School of Public Health (Shenzhen), Shenzhen Campus, Sun Yat-sen University, Shenzhen, Guangdong Province, 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
| | - Hongyin Zhang
- School of Public Health (Shenzhen), Shenzhen Campus, Sun Yat-sen University, Shenzhen, Guangdong Province, China
| | - Jinrong Huang
- School of Public Health (Shenzhen), Shenzhen Campus, Sun Yat-sen University, Shenzhen, Guangdong Province, China
| | - Yueting Liu
- School of Public Health (Shenzhen), Shenzhen Campus, Sun Yat-sen University, Shenzhen, Guangdong Province, China
| | - Meiru Chen
- School of Public Health (Shenzhen), Shenzhen Campus, Sun Yat-sen University, Shenzhen, Guangdong Province, China
| | - Yixiao Lv
- School of Public Health (Shenzhen), Shenzhen Campus, Sun Yat-sen University, Shenzhen, Guangdong Province, China
| | - Fei Zhao
- Department of Pharmacy, Beijing Hospital, National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences; 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, Sun Yat-sen University, Shenzhen, Guangdong Province, China
| | - Jiaqi Yan
- School of Public Health (Shenzhen), Shenzhen Campus, Sun Yat-sen University, Shenzhen, Guangdong Province, China
| | - Hongbing Jiang
- School of Public Health (Shenzhen), Shenzhen Campus, Sun Yat-sen University, Shenzhen, Guangdong Province, China
| | - Yiqiang Zhan
- School of Public Health (Shenzhen), Shenzhen Campus, Sun Yat-sen University, Shenzhen, Guangdong Province, China
| | - Jimin Xu
- School of Public Health (Shenzhen), Shenzhen Campus, Sun Yat-sen University, Shenzhen, Guangdong Province, China
| | - Yanzi Xu
- Nanshan District Center for Disease Control and Prevention, Shenzhen, Guangdong Province, China
| | - Jianhui Yuan
- Nanshan District Center for Disease Control and Prevention, Shenzhen, Guangdong Province, China
| | - Yang Zhao
- School of Public Health (Shenzhen), Shenzhen Campus, Sun Yat-sen University, Shenzhen, Guangdong Province, 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, Sun Yat-sen University, Shenzhen, Guangdong Province, China
- Nanshan District Center for Disease Control and Prevention, Shenzhen, Guangdong Province, China
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, United States
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Luo D, Wang L, Zhang M, Martinez L, Chen S, Zhang Y, Wang W, Wu Q, Wu Y, Liu K, Xie B, Chen B. Spatial spillover effect of environmental factors on the tuberculosis occurrence among the elderly: a surveillance analysis for nearly a dozen years in eastern China. BMC Public Health 2024; 24:209. [PMID: 38233763 PMCID: PMC10795419 DOI: 10.1186/s12889-024-17644-5] [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: 08/17/2023] [Accepted: 01/02/2024] [Indexed: 01/19/2024] Open
Abstract
BACKGROUND In many areas of China, over 30% of tuberculosis cases occur among the elderly. We aimed to investigate the spatial distribution and environmental factors that predicted the occurence of tuberculosis in this group. METHODS Data were collected on notified pulmonary tuberculosis (PTB) cases aged ≥ 65 years in Zhejiang Province from 2010 to 2021. We performed spatial autocorrelation and spatial-temporal scan statistics to determine the clusters of epidemics. Spatial Durbin Model (SDM) analysis was used to identify significant environmental factors and their spatial spillover effects. RESULTS 77,405 cases of PTB among the elderly were notified, showing a decreasing trend in the notification rate. Spatial-temporal analysis showed clustering of epidemics in the western area of Zhejiang Province. The results of the SDM indicated that a one-unit increase in PM2.5 led to a 0.396% increase in the local notification rate. The annual mean temperature and precipitation had direct effects and spatial spillover effects on the rate, while complexity of the shape of the greenspace (SHAPE_AM) and SO2 had negative spatial spillover effects. CONCLUSION Targeted interventions among the elderly in Western Zhejiang may be more efficient than broad, province-wide interventions. Low annual mean temperature and high annual mean precipitation in local and neighboring areas tend to have higher PTB onset among the elderly.
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Affiliation(s)
- Dan Luo
- Department of Public Health, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Luyu Wang
- School of Urban Design, Wuhan University, Hubei, Wuhan, China
| | - Mengdie Zhang
- Zhejiang University School of Public Health, Hangzhou, Zhejiang, China
| | - Leonardo Martinez
- Department of Epidemiology, School of Public Health, Boston University, Boston, MA, USA
| | - Songhua Chen
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Yu Zhang
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Wei Wang
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Qian Wu
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Yonghao Wu
- Department of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China, 310058
| | - Kui Liu
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China.
- National Centre for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
| | - Bo Xie
- School of Urban Design, Wuhan University, Hubei, Wuhan, China.
| | - Bin Chen
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China.
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Gao J, Zhang Y, Wang X, Sun Q, Yin J. Active screening for tuberculosis among high-risk populations in high-burden areas in Zhejiang province, China. Public Health 2024; 226:138-143. [PMID: 38056401 DOI: 10.1016/j.puhe.2023.10.051] [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: 10/13/2023] [Accepted: 10/31/2023] [Indexed: 12/08/2023]
Abstract
OBJECTIVES Tuberculosis (TB) is a major global public health concern. Although the incidence of TB in China is declining, the country continues to face many challenges regarding TB control. This study aimed to develop an active case finding (ACF) strategy for high-risk populations in areas with high TB burden and evaluate the effectiveness of the ACF strategy for early TB detection in patients to reduce TB transmission. STUDY DESIGN This was a descriptive study. METHODS From May to October 2019, active TB screening was conducted in Zhejiang Province, China. Overall, 24 high-burden townships were chosen as study sites. Residents aged ≥65 years, suffering from diabetes, diagnosed with HIV/AIDS, or with a history of TB were mobilized for screening. Chest radiography was performed for all participants in the community. Sputum specimens were collected for sputum smear tests and cultures at county-level TB-designed hospitals. A professional medical team performed the final diagnoses. RESULTS Overall, 130,643 residents were included, accounting for 8.85% of the total population in the selected areas. After screening, 89 confirmed cases and 419 suspected cases were identified. The detection rates for suspected and confirmed cases were 320.72/100,000 and 68.12/100,000, respectively. Individuals with a history of TB accounted for a large proportion of detected cases, and the detection rate was higher among males than in females. This study identified 10.5% of reported cases in the selected areas in 2019. In Zhejiang province, compared with the previous year, the rates of TB notification in 2019 and 2020 declined by 7.0% and 7.4%, respectively, compared with the previous year. However, the TB notification rate in 2019 was almost the same as that in 2018 (a decline of 2.5%) but sharply declined in 2020 (14.4%) in the screened areas. CONCLUSIONS Our findings suggest that the ACF strategy may have helped to maintain the downward trends in TB notification rates by detecting patients with TB and suspected cases in the short term.
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Affiliation(s)
- J Gao
- Center for Health Management and Policy Research, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China; NHC Key Lab of Health Economics and Policy Research (Shandong University), Jinan, 250012, China.
| | - Y Zhang
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, China.
| | - X Wang
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, China.
| | - Q Sun
- Center for Health Management and Policy Research, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China; NHC Key Lab of Health Economics and Policy Research (Shandong University), Jinan, 250012, China.
| | - J Yin
- Center for Health Management and Policy Research, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China; NHC Key Lab of Health Economics and Policy Research (Shandong University), Jinan, 250012, China.
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Mijiti P, Liu C, Hong C, Li M, Tan X, Zheng K, Li B, Ji L, Mao Q, Jiang Q, Takiff H, Fang H, Tan W, Gao Q. Implications for TB control among migrants in large cities in China: A prospective population-based genomic epidemiology study in Shenzhen. Emerg Microbes Infect 2023; 13:2287119. [PMID: 37990991 PMCID: PMC10810669 DOI: 10.1080/22221751.2023.2287119] [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: 08/29/2023] [Accepted: 11/19/2023] [Indexed: 11/23/2023]
Abstract
Internal migrants are a challenge for TB control in large Chinese cities and understanding this epidemiology is crucial for designing effective control and prevention strategies. We conducted a prospective genomic epidemiological study of culture-positive TB patients diagnosed between June 1, 2018 and May 31, 2021 in the Longhua District of Shenzhen. Treatment status was obtained from local and national TB registries and all isolates were sequenced. Genomic clusters were defined as strains differing by ≤12 SNPs. Risk factors for clustering were identified with multivariable analysis and then Bayesian models and TransPhylo were used to infer the timing of transmission within clusters. Of the 2277 culture-positive patients, 70.1% (1596/2277) were migrants: 72.1% (1043/1446) of the migrants patients developed TB within two years of arriving in Longhua; 38.8% within 6 months of arriving; and 12.3% (104/843) had TB symptoms when they arrived. Only 15.4% of Longhua strains were in genomic clusters. More than one third (33.6%) of patients were not treated in Shenzhen but were involved in nearly one third of the recent transmission events. Clustering was associated with migrants not treated in Shenzhen, males, and teachers/trainers. TB in Longhua is prinicipally due to reactivation of infections in migrants, but a proportion may have had clinical or incipient TB upon arrival in the district. Patients diagnosed but not treated in Longhua were involved in recent local TB transmission. Controlling TB in Shenzhen will require strategies to comprehensively diagnose and treat active TB in the internal migrant population.
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Affiliation(s)
- Peierdun Mijiti
- Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), School of Basic Medical Science, Shanghai Medical College, Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, People’s Republic of China
- Xinjiang Medical University, School of Public Health, Department of Epidemiology, Wulumuqi, People's Republic of China
| | - Changwei Liu
- Longhua District Center for Chronic Disease Control, Shenzhen, People’s Republic of China
| | - Chuangyue Hong
- Shenzhen Center for Chronic Disease Control, Shenzhen, People’s Republic of China
| | - Meng Li
- Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), School of Basic Medical Science, Shanghai Medical College, Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, People’s Republic of China
| | - Xiaoping Tan
- Longhua District Center for Chronic Disease Control, Shenzhen, People’s Republic of China
| | - Kaiqiao Zheng
- Longhua District Center for Chronic Disease Control, Shenzhen, People’s Republic of China
| | - Bin Li
- Longhua District Center for Chronic Disease Control, Shenzhen, People’s Republic of China
| | - Lecai Ji
- Shenzhen Center for Chronic Disease Control, Shenzhen, People’s Republic of China
| | - Qizhi Mao
- Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), School of Basic Medical Science, Shanghai Medical College, Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, People’s Republic of China
| | - Qi Jiang
- Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), School of Basic Medical Science, Shanghai Medical College, Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, People’s Republic of China
| | - Howard Takiff
- Laboratorio de Genética Molecular, CMBC, IVIC, Caracas, Venezuela
| | - Hongxia Fang
- Longhua District Center for Chronic Disease Control, Shenzhen, People’s Republic of China
| | - Weiguo Tan
- Shenzhen Center for Chronic Disease Control, Shenzhen, People’s Republic of China
| | - Qian Gao
- Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), School of Basic Medical Science, Shanghai Medical College, Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, People’s Republic of China
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Zhou Y, Luo D, Liu K, Chen B, Chen S, Pan J, Liu Z, Jiang J. Trend of the Tuberculous Pleurisy Notification Rate in Eastern China During 2017-2021: Spatiotemporal Analysis. JMIR Public Health Surveill 2023; 9:e49859. [PMID: 37902822 PMCID: PMC10644181 DOI: 10.2196/49859] [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: 06/12/2023] [Revised: 08/31/2023] [Accepted: 09/19/2023] [Indexed: 10/31/2023] Open
Abstract
BACKGROUND Tuberculous pleurisy (TP) presents a serious allergic reaction in the pleura caused by Mycobacterium tuberculosis; however, few studies have described its spatial epidemiological characteristics in eastern China. OBJECTIVE This study aimed to determine the epidemiological distribution of TP and predict its further development in Zhejiang Province. METHODS Data on all notified cases of TP in Zhejiang Province, China, from 2017 to 2021 were collected from the existing tuberculosis information management system. Analyses, including spatial autocorrelation and spatial-temporal scan analysis, were performed to identify hot spots and clusters, respectively. The prediction of TP prevalence was performed using the seasonal autoregressive integrated moving average (SARIMA), Holt-Winters exponential smoothing, and Prophet models using R (The R Foundation) and Python (Python Software Foundation). RESULTS The average notification rate of TP in Zhejiang Province was 7.06 cases per 100,000 population, peaking in the summer. The male-to-female ratio was 2.18:1. In terms of geographical distribution, clusters of cases were observed in the western part of Zhejiang Province, including parts of Hangzhou, Quzhou, Jinhua, Lishui, Wenzhou, and Taizhou city. Spatial-temporal analysis identified 1 most likely cluster and 4 secondary clusters. The Holt-Winters model outperformed the SARIMA and Prophet models in predicting the trend in TP prevalence. CONCLUSIONS The western region of Zhejiang Province had the highest risk of TP. Comprehensive interventions, such as chest x-ray screening and symptom screening, should be reinforced to improve early identification. Additionally, a more systematic assessment of the prevalence trend of TP should include more predictors.
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Affiliation(s)
- Ying Zhou
- School of Public Health, Hangzhou Normal University, Hangzhou, China
| | - Dan Luo
- School of Public Health, Hangzhou Medical College, Hangzhou, China
| | - Kui Liu
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
- National Centre for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Bin Chen
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Songhua Chen
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Junhang Pan
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Zhengwei Liu
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Jianmin Jiang
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
- Key Laboratory of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
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8
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Liu K, Ge R, Luo D, Zheng Y, Shen Z, Chen B, Feng W, Wu Q. Delay analysis of pulmonary tuberculosis in the eastern coastal county of China from 2010 to 2021: evidence from two surveillance systems. Front Public Health 2023; 11:1233637. [PMID: 37637823 PMCID: PMC10450766 DOI: 10.3389/fpubh.2023.1233637] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 07/31/2023] [Indexed: 08/29/2023] Open
Abstract
Background Tuberculosis (TB) remains a major public health challenge. However, indicators of delays in assessing effective TB prevention and control and its influencing factors have not been investigated in the eastern coastal county of China. Methods All notified pulmonary tuberculosis (PTB) cases in the Fenghua District, China were collected between 2010 and 2021 from the available TB information management system. Comparison of delays involving patient, health system, and total delays among local and migrant cases. Additionally, in correlation with available Basic Public Health Service Project system, we performed univariate and multivariate logistic regression analyses identified the influencing factors associated with patient and total delays in patients aged >60 years. Results In total, 3,442 PTB cases were notified, including 1,725 local and 1,717 migrant patients, with a male-to-female ratio of 2.13:1. Median patient and total delays of local TB patients were longer than those for migrant patients; the median health system delay did not show any significant difference. For patient delay among the older adult, female (cOR: 1.93, 95% CI: 1.07-3.48), educational level of elementary school and middle school (cOR: 0.23, 95% CI: 0.06-0.84) had a statistical difference from univariable analysis; however, patients without diabetes showed a higher delay for multiple-factor analysis (aOR: 2.12, 95% CI: 1.02-4.41). Furthermore, only the education level of elementary school and middle school presented a low total delay for both univariate (cOR: 0.22, 95% CI: 0.06-0.82) and multivariate analysis (aOR: 0.21, 95% CI: 0.05-0.83) in the older patients. Conclusion The delay of TB cases among migrants was lower than the local population in the Fenghua District, which may be related to the "healthy migrant effect". It highlights that women, illiterate people, and people without diabetes are key groups for reducing delays among older adults. Health awareness should focus on these target populations, providing accessible health services, and reducing the time from symptom onset to diagnosis.
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Affiliation(s)
- Kui Liu
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Rui Ge
- Department of Tuberculosis Control and Prevention, Jiaxing Center for Disease Control and Prevention, Jiaxing, Zhejiang, China
| | - Dan Luo
- Department of Public Health, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Yan Zheng
- Fenghua Center for Disease Control and Prevention, Ningbo, Zhejiang, China
| | - Zhenye Shen
- Fenghua Center for Disease Control and Prevention, Ningbo, Zhejiang, China
| | - Bin Chen
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Wei Feng
- Fenghua Center for Disease Control and Prevention, Ningbo, Zhejiang, China
| | - Qionghai Wu
- Taizhou Central Hospital (Taizhou University Hospital), Taizhou, Zhejiang, China
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Zhan J, Xiong G, Luo D, Peng Y, Chen X, Zeng L, Chen K. Characteristics and Treatment Outcome of Culture-Positive Tuberculosis Patients Among Rural and Urban Residents in Jiangxi, China: A Retrospective Cross-sectional Study. Asia Pac J Public Health 2023; 35:291-294. [PMID: 37162271 DOI: 10.1177/10105395231169083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Affiliation(s)
- Jiahuan Zhan
- Department of Clinical Laboratory, The First Affiliated Hospital of Nanchang University, Nanchang City, P.R. China
| | - Guangchu Xiong
- Department of Clinical Laboratory, Jiangxi Chest Hospital, Nanchang, P.R. China
| | - Dong Luo
- Department of Clinical Laboratory, The First Affiliated Hospital of Nanchang University, Nanchang City, P.R. China
| | - Yiping Peng
- Department of Clinical Laboratory, Jiangxi Chest Hospital, Nanchang, P.R. China
| | - Xiaowen Chen
- Department of Clinical Laboratory, The First Affiliated Hospital of Nanchang University, Nanchang City, P.R. China
| | - Lingbing Zeng
- Department of Clinical Laboratory, The First Affiliated Hospital of Nanchang University, Nanchang City, P.R. China
| | - Kaisen Chen
- Department of Clinical Laboratory, The First Affiliated Hospital of Nanchang University, Nanchang City, P.R. China
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10
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Xue M, Zhong J, Gao M, Pan R, Mo Y, Hu Y, Du J, Huang Z. Analysis of spatial-temporal dynamic distribution and related factors of tuberculosis in China from 2008 to 2018. Sci Rep 2023; 13:4974. [PMID: 36973322 PMCID: PMC10041483 DOI: 10.1038/s41598-023-31430-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 03/11/2023] [Indexed: 03/29/2023] Open
Abstract
Through spatial-temporal scanning statistics, the spatial-temporal dynamic distribution of pulmonary tuberculosis incidence in 31 provinces and autonomous regions of China from 2008 to 2018 is obtained, and the related factors of spatial-temporal aggregation of tuberculosis in China are analyzed to provide strong scientific basis and data support for the prevention and control of pulmonary tuberculosis. This is a retrospective study, using spatial epidemiological methods to reveal the spatial-temporal clustering distribution characteristics of China's tuberculosis epidemic from 2008 to 2018, in which cases data comes from the China Center for Disease Control and prevention. Office Excel is used for general statistical description, and the single factor correlation analysis adopts χ2 Test (or trend χ2 Inspection). Retrospective discrete Poisson distribution space time scanning statistics of SaTScan 9.6 software are used to analyze the space time dynamic distribution of tuberculosis incidence in 31 provinces, cities and autonomous regions in China from 2008 to 2018. ArcGIS 10.2 software is used to visualize the results. The global spatial autocorrelation analysis adopts Moran's I of ArcGIS Map(Monte Carlo randomization simulation times of 999) is used to analyze high-risk areas, low-risk areas and high-low risk areas. From 2008 to 2018, 10,295,212 cases of pulmonary tuberculosis were reported in China, with an average annual incidence rate of 69.29/100,000 (95% CI: (69.29 ± 9.16)/100,000). The annual GDP (gross domestic product) of each province and city showed an upward trend year by year, and the number of annual medical institutions in each province and city showed a sharp increase in 2009, and then tended to be stable; From 2008 to 2018, the national spatiotemporal scanning statistics scanned a total of 6 clusters, including 23 provinces and cities. The national high-low spatiotemporal scanning statistics of the number of pulmonary tuberculosis cases scanned a total of 2 high-risk and low-risk clusters. The high-risk cluster included 8 provinces and cities, and the low-risk cluster included 12 provinces and cities. The global autocorrelation Moran's I index of the incidence rate of pulmonary tuberculosis in all provinces and cities was greater than the expected value (E (I) = -0.0333); The correlation analysis between the average annual GDP and the number of pulmonary tuberculosis cases in each province and city from 2008 to 2018 was statistically significant. From 2008 to 2018, the spatial and temporal scanning and statistical scanning areas of tuberculosis incidence in China were mainly concentrated in the northwest and southern regions of China. There is an obvious positive spatial correlation between the annual GDP distribution of each province and city, and the aggregation degree of the development level of each province and city is increasing year by year. There is a correlation between the average annual GDP of each province and the number of tuberculosis cases in the cluster area. There is no correlation between the number of medical institutions set up in each province and city and the number of pulmonary tuberculosis cases.
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Affiliation(s)
- Mingjin Xue
- School of Public Health, Guangdong Medical University, Dongguan, Guangdong Province, China
| | - Jinlin Zhong
- School of Public Health, Guangdong Medical University, Dongguan, Guangdong Province, China
| | - Miao Gao
- School of Public Health, Guangdong Medical University, Dongguan, Guangdong Province, China
| | - Rongling Pan
- School of Public Health, Guangdong Medical University, Dongguan, Guangdong Province, China
| | - Yuqian Mo
- School of Public Health, Guangdong Medical University, Dongguan, Guangdong Province, China
| | - Yudi Hu
- School of Public Health, Guangdong Medical University, Dongguan, Guangdong Province, China
| | - Jinlin Du
- School of Public Health, Guangdong Medical University, Dongguan, Guangdong Province, China
- Pension Industry Research Institute, Guangdong Medical University, Dongguan, Guangdong Province, China
| | - Zhigang Huang
- School of Public Health, Guangdong Medical University, Dongguan, Guangdong Province, China.
- Pension Industry Research Institute, Guangdong Medical University, Dongguan, Guangdong Province, China.
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11
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Zhang M, Chen S, Luo D, Chen B, Zhang Y, Wang W, Wu Q, Liu K, Wang H, Jiang J. Spatial-temporal analysis of pulmonary tuberculosis among students in the Zhejiang Province of China from 2007-2020. Front Public Health 2023; 11:1114248. [PMID: 36844836 PMCID: PMC9947845 DOI: 10.3389/fpubh.2023.1114248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 01/23/2023] [Indexed: 02/11/2023] Open
Abstract
Background Pulmonary tuberculosis (PTB) is a serious chronic communicable disease that causes a significant disease burden in China; however, few studies have described its spatial epidemiological features in students. Methods Data of all notified PTB cases from 2007 to 2020 in the student population were collected in the Zhejiang Province, China using the available TB Management Information System. Analyses including time trend, spatial autocorrelation, and spatial-temporal analysis were performed to identify temporal trends, hotspots, and clustering, respectively. Results A total of 17,500 PTB cases were identified among students in the Zhejiang Province during the study period, accounting for 3.75% of all notified PTB cases. The health-seeking delay rate was 45.32%. There was a decreasing trend in PTB notifications throughout the period; clustering of cases was seen in the western area of Zhejiang Province. Additionally, one most likely cluster along with three secondary clusters were identified by spatial-temporal analysis. Conclusion Although was a downward trend in PTB notifications among students during the time period, an upward trend was seen in bacteriologically confirmed cases since 2017. The risk of PTB was higher among senior high school and above than of junior high school. The western area of Zhejiang Province was the highest PTB risk settings for students, and more comprehensive interventions should be strengthened such as admission screening and routine health monitoring to improve early identification of PTB.
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Affiliation(s)
- Mengdie Zhang
- Department of Social Medicine of School of Public Health and Department of Pharmacy of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Songhua Chen
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Dan Luo
- Department of Public Health, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Bin Chen
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China,Key Laboratory of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Yu Zhang
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Wei Wang
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Qian Wu
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Kui Liu
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China,Key Laboratory of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China,*Correspondence: Kui Liu ✉
| | - Hongmei Wang
- Department of Social Medicine of School of Public Health and Department of Pharmacy of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China,Hongmei Wang ✉
| | - Jianmin Jiang
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China,Key Laboratory of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China,Jianmin Jiang ✉
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12
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Liu K, Xie Z, Xie B, Chen S, Zhang Y, Wang W, Wu Q, Cai G, Chen B. Bridging the Gap in End Tuberculosis Targets in the Elderly Population in Eastern China: Observational Study From 2015 to 2020. JMIR Public Health Surveill 2022; 8:e39142. [PMID: 35904857 PMCID: PMC9377476 DOI: 10.2196/39142] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 06/06/2022] [Accepted: 06/09/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND With a progressive increase in the aging process, the challenges posed by pulmonary tuberculosis (PTB) are also increasing for the elderly population. OBJECTIVE This study aimed to identify the epidemiological distribution of PTB among the elderly, forecast the achievement of the World Health Organization's 2025 goal in this specific group, and predict further advancement of PTB in the eastern area of China. METHODS All notified active PTB cases aged ≥65 years from Zhejiang Province were screened and analyzed. The general epidemiological characteristics were depicted and presented using the ArcGIS software. Further prediction of PTB was performed using R and SPSS software programs. RESULTS Altogether 41,431 cases aged ≥65 years were identified by the surveillance system from 2015 to 2020. After excluding extrapulmonary TB cases, we identified 39,832 PTB cases, including laboratory-confirmed (23,664, 59.41%) and clinically diagnosed (16,168, 40.59%) PTB. The notified PTB incidence indicated an evident downward trend with a reduction of 30%; however, the incidence of bacteriologically positive cases was steady at approximately 60/100,000. Based on the geographical distribution, Quzhou and Jinhua Cities had a higher PTB incidence among the elderly. The delay in PTB diagnosis was identified, and a significantly prolonged treatment course was observed in the elderly. Moreover, a 50% reduction of PTB incidence by the middle of 2024 was predicted using a linear regression model. It was found that using the exponential smoothing model would be better to predict the PTB trend in the elderly than a seasonal autoregressive integrated moving average model. CONCLUSIONS More comprehensive and effective interventions such as active PTB screening combined with physical checkup and succinct health education should be implemented and strengthened in the elderly. A more systematic assessment of the PTB epidemic trend in the elderly population should be considered to incorporate more predictive factors.
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Affiliation(s)
- Kui Liu
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | | | - Bo Xie
- School of Urban Design, Wuhan University, Wuhan, China
| | - Songhua Chen
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Yu Zhang
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Wei Wang
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Qian Wu
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Gaofeng Cai
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Bin Chen
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
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