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Zhao J, Qian C, Jiang Y, He W, Wu W. Drug-Resistant Characteristics, Genetic Diversity, and Transmission Dynamics of Multidrug-Resistant Mycobacterium tuberculosis in Jiangxi, China. Infect Drug Resist 2024; 17:2213-2223. [PMID: 38840971 PMCID: PMC11152055 DOI: 10.2147/idr.s460267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Accepted: 05/21/2024] [Indexed: 06/07/2024] Open
Abstract
Purpose In this study, we aimed to determine the transmission pattern of multidrug-resistant tuberculosis (MDR-TB) isolates circulating in Jiangxi Province with whole-genome sequencing (WGS). In addition, we also sought to describe mutational resistome of MDR-TB isolates. Patients and Methods A total of 115 MDR-TB isolates determined by the phenotypic proportion method of drug susceptibility testing between January 2018 and December 2022 from provincial drug surveillance (DRS) in Jiangxi were included in our analysis. The demographic data and treatment history were extracted from the National TB Registry System. WGS was used to analyze the genotypic characteristics of drug resistance and transmissions. Results About 62.6% of MDR-TB strains were isolated from cases that received previous anti-tuberculosis treatment. According to the WGS results, 96.5% were genotypic MDR-TB, and more than half of MDR-TB isolates tested were also resistant to streptomycin (59.1%), ethambutol (56.5%), and fluroquinolones (53.0%), while resistance to cycloserine and linezolid was lowest, only in two (1.7%) and one (0.9%) isolate, respectively. Ser450Leu in rpoB (57.9%), Ser315Thr in katG (74.1%), Met306Val in embB (40.0%), Lys43Arg in rpsL (75.0%), Ala90Val in gyrA (32.8%) were predominant mutant types among the rifampin-, isoniazid-, ethambutol-, streptomycin-, fluoroquinolones-resistant isolates, respectively. Lineage 2 (East Asian genotype) occurred at the highest frequency with 97 cases (84.3%), followed by lineage 4 (Euro-American genotype) with 18 cases (15.7%). Additionally, 5 clusters consisting of 10 isolates were identified in the present study, demonstrating a clustering rate of 8.7%. Conclusion MDR/Rifampicin-Resistant (RR)-TB epidemic in this region is driven by lineage 2 clade that also show higher resistance to other anti-tuberculosis drugs. Lower cluster rates compared with a relatively higher proportion of new MDR-TB cases indicate that a considerable number of MDR-TB cases remain undiagnosed.
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Affiliation(s)
- Jingnan Zhao
- Tuberculosis Control Department, Jiangxi Provincial Center for Disease Control and Prevention, Nanchang, 330029, People’s Republic of China
| | - Chengyu Qian
- Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing Chest Hospital, Capital Medical University, Beijing, 101149, People’s Republic of China
| | - Youqiao Jiang
- Tuberculosis Control Department, Jiangxi Provincial Center for Disease Control and Prevention, Nanchang, 330029, People’s Republic of China
- Young Scientific Research and Innovation Team, Jiangxi Provincial Center for Disease Control and Prevention, Nanchang, Jiangxi, 330029, People’s Republic of China
| | - Wangrui He
- Tuberculosis Control Department, Jiangxi Provincial Center for Disease Control and Prevention, Nanchang, 330029, People’s Republic of China
| | - Wenhua Wu
- Tuberculosis Control Department, Jiangxi Provincial Center for Disease Control and Prevention, Nanchang, 330029, People’s Republic of China
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2
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Díez Galán MDM, Redondo-Bravo L, Gómez-Barroso D, Herrera L, Amillategui R, Gómez-Castellá J, Herrador Z. The impact of meteorological factors on tuberculosis incidence in Spain: a spatiotemporal analysis. Epidemiol Infect 2024; 152:e58. [PMID: 38505884 PMCID: PMC11022253 DOI: 10.1017/s0950268824000499] [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: 10/03/2023] [Revised: 02/06/2024] [Accepted: 03/11/2024] [Indexed: 03/21/2024] Open
Abstract
Tuberculosis (TB) remains a global leading cause of death, necessitating an investigation into its unequal distribution. Sun exposure, linked to vitamin D (VD) synthesis, has been proposed as a protective factor. This study aimed to analyse TB rates in Spain over time and space and explore their relationship with sunlight exposure. An ecological study examined the associations between rainfall, sunshine hours, and TB incidence in Spain. Data from the National Epidemiological Surveillance Network (RENAVE in Spanish) and the Spanish Meteorological Agency (AEMET in Spanish) from 2012 to 2020 were utilized. Correlation and spatial regression analyses were conducted. Between 2012 and 2020, 43,419 non-imported TB cases were reported. A geographic pattern (north-south) and distinct seasonality (spring peaks and autumn troughs) were observed. Sunshine hours and rainfall displayed a strong negative correlation. Spatial regression and seasonal models identified a negative correlation between TB incidence and sunshine hours, with a four-month lag. A clear spatiotemporal association between TB incidence and sunshine hours emerged in Spain from 2012 to 2020. VD levels likely mediate this relationship, being influenced by sunlight exposure and TB development. Further research is warranted to elucidate the causal pathway and inform public health strategies for improved TB control.
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Affiliation(s)
| | - Lidia Redondo-Bravo
- Health Emergencies Department, Pan American Health Organization, Washington, DC, USA
| | - Diana Gómez-Barroso
- National Center of Epidemiology, Instituto de Salud Carlos III, Madrid, Spain
- CIBER in Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Laura Herrera
- CIBER in Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Department of Bacteriology, National Centre of Microbiology, Instituto de Salud Carlos III, Majadahonda, Spain
| | - Rocio Amillategui
- National Center of Epidemiology, Instituto de Salud Carlos III, Madrid, Spain
| | - Javier Gómez-Castellá
- División de control de VIH, ITS, Hepatitis virales y Tuberculosis. Ministerio de Sanidad, Madrid, Spain
| | - Zaida Herrador
- National Center of Epidemiology, Instituto de Salud Carlos III, Madrid, Spain
- CIBER in Epidemiology and Public Health (CIBERESP), Madrid, Spain
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Yu H, Yang J, Yan Y, Zhang H, Chen Q, Sun L. Factors affecting the incidence of pulmonary tuberculosis based on the GTWR model in China, 2004-2021. Epidemiol Infect 2024; 152:e65. [PMID: 38418421 PMCID: PMC11062777 DOI: 10.1017/s0950268824000335] [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: 07/27/2023] [Revised: 12/26/2023] [Accepted: 01/08/2024] [Indexed: 03/01/2024] Open
Abstract
Contra-posing panel data on the incidence of pulmonary tuberculosis (PTB) at the provincial level in China through the years of 2004-2021 and introducing a geographically and temporally weighted regression (GTWR) model were used to explore the effect of various factors on the incidence of PTB from the perspective of spatial heterogeneity. The principal component analysis (PCA) was used to extract the main information from twenty-two indexes under six macro-factors. The main influencing factors were determined by the Spearman correlation and multi-collinearity tests. After fitting different models, the GTWR model was used to analyse and obtain the distribution changes of regression coefficients. Six macro-factors and incidence of PTB were both correlated, and there was no collinearity between the variables. The fitting effect of the GTWR model was better than ordinary least-squares (OLS) and geographically weighted regression (GWR) models. The incidence of PTB in China was mainly affected by six macro-factors, namely medicine and health, transportation, environment, economy, disease, and educational quality. The influence degree showed an unbalanced trend in the spatial and temporal distribution.
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Affiliation(s)
- Hairu Yu
- Department of Social Medicine and Health Service Management, College of Public Health, Zhengzhou University, Zhengzhou, the People’s Republic of China
| | - Jiao Yang
- Department of Social Medicine and Health Service Management, College of Public Health, Zhengzhou University, Zhengzhou, the People’s Republic of China
| | - Yexin Yan
- Department of Social Medicine and Health Service Management, College of Public Health, Zhengzhou University, Zhengzhou, the People’s Republic of China
| | - Hui Zhang
- Department of Social Medicine and Health Service Management, College of Public Health, Zhengzhou University, Zhengzhou, the People’s Republic of China
| | - Qiuyuan Chen
- Department of Social Medicine and Health Service Management, College of Public Health, Zhengzhou University, Zhengzhou, the People’s Republic of China
| | - Liang Sun
- Department of Social Medicine and Health Service Management, College of Public Health, Zhengzhou University, Zhengzhou, the People’s Republic of China
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Chang M, Emam M, Chen X, Lu D, Zhang L, Zheng Y. An investigation of the effects of meteorological factors on the incidence of tuberculosis. Sci Rep 2024; 14:2088. [PMID: 38267494 PMCID: PMC10808229 DOI: 10.1038/s41598-024-52278-y] [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: 11/21/2023] [Accepted: 01/16/2024] [Indexed: 01/26/2024] Open
Abstract
To explore the influence of meteorological factors on the incidence of tuberculosis (TB) in Yingjisha County, Kashgar Region, Xinjiang, and to provide a scientific basis for the prevention and control of TB. The Spearman correlation analysis and distribution lag nonlinear model analysis were conducted on the number of daily reported cases of TB from 2016 to 2023 to study the association effect of various meteorological factors and the daily incidence number of TB in Yingjisha County. A total of 13,288 TB cases were reported from January 2016 to June 2023, and June to October is the peak period of annual TB incidence. Spearman correlation analysis revealed that average daily temperature (AT) and average daily wind speed (WS) were positively correlated with TB incidence (rAT = 0.110, rWS = 0.090); and average daily relative humidity (RH) and TB incidence was negatively correlated (rRH = - 0.093). When AT was - 15 °C, the RR reached a maximum of 2.20 (95% CI: 0.77-6.29) at a lag of 21 days. When RH was 92%, the RR reached a maximum of 1.05 (95% CI: 0.92-1.19) at a lag of 6 days. When WS was 5.2 m/s, the RR reached a maximum of 1.30 (95% CI: 0.78-2.16) at a lag of 16 days. There is a non-linearity and a certain lag between meteorological factors and the occurrence and prevalence of TB in the population, which is mainly manifested in the fact that the risk of incidence of TB decreases with the increase of the daily AT, has a hazardous effect within a certain range of humidity as the average daily RH rises, and gradually increases with the increase of the average daily WS. Local residents are advised to pay attention to climate change so as to take appropriate preventive measures, especially women and middle and old age group should pay close attention to climate change and add more clothes in time, minimise travelling in hazy weather and windy and sandy weather, maintain good nutrition, adequate sleep and moderate exercise in daily life to enhance their immunity, wash hands frequently and ventilate the air, and try to avoid staying in humid and confined spaces in order to reduce the risk of latent TB patients developing the disease.
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Affiliation(s)
- Minli Chang
- College of Public Health, Xinjiang Medical University, Ürümqi, 830017, People's Republic of China
| | - Mawlanjan Emam
- Center for Disease Control and Prevention, Kashgar, People's Republic of China
| | - Xiaodie Chen
- College of Public Health, Xinjiang Medical University, Ürümqi, 830017, People's Republic of China
| | - Dongmei Lu
- Center of Pulmonary and Critical Care Medicine, People's Hospital of Xinjiang Uygur Autonomous Region, Ürümqi, People's Republic of China
| | - Liping Zhang
- College of Medical Engineering and Technology, Xinjiang Medical University, Ürümqi, 830017, People's Republic of China
| | - Yanling Zheng
- College of Medical Engineering and Technology, Xinjiang Medical University, Ürümqi, 830017, People's Republic of China.
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Ge Y, Wang S, Shi Q, Shi J, Tian J. Geospatial analysis of the hospitalisation rate of patients with rheumatoid arthritis in Hunan: a cross-sectional Chinese study. BMJ Open 2023; 13:e075088. [PMID: 38000823 PMCID: PMC10679990 DOI: 10.1136/bmjopen-2023-075088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Accepted: 10/10/2023] [Indexed: 11/26/2023] Open
Abstract
OBJECTIVE Little is known about spatial variability of hospitalisation rate (HR) of patients with rheumatoid arthritis (RA) worldwide, especially in China. METHODS A cross-sectional study was conducted among patients with RA admitted to hospitals in Hunan Province. Global Moran's I and local indicators of spatial association were used to explore the geospatial pattern of the HR of patients with RA. Generalised estimating equation analysis and geographically weighted regression were used to identify the potential influencing factors of the HR of patients with RA. RESULTS There were a total of 11 599 admissions, and the average HR was 1.57 per 10 000 population in Hunan. We detected different cluster patterns of the HR among patients with RA by local indicators of spatial association. Age, ethnicity, average temperature, average temperature range, average rainfall, regions, gross domestic product per capita, and doctors and hospitals per 10 000 people were risk factors for the HR. However, only average temperature, gross domestic product per capita and hospitals per 10 000 people showed different regression coefficients on the HR in different counties. The increase in hospitals increased the probability of HR from east to west in Hunan with a positive coefficient, while temperature decreases increased the risk of HR from south to north negatively. Similarly, the growth of gross domestic product per capita decreased the probability of HR from southwest to northeast. CONCLUSION A non-random spatial distribution of the HR of patients with RA was demonstrated in Hunan, and average temperature, gross domestic product per capita and hospitals per 10 000 people showed different regression coefficients on the HR in different counties. Our study indicated that spatial and geostatistics may be useful approaches for further study among patients with RA.
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Affiliation(s)
- Yan Ge
- Department of Rheumatology and Immunology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
- Clinical Medical Research Center for Systemic Autoimmune Diseases in Hunan Province, Changsha, Hunan, China
| | - Shiwen Wang
- Department of Epidemiology and Medical Statistics, Xiangya School of Public Health, Central South University, Changsha, Hunan, China
| | - Qianshan Shi
- Information Statistics Center of Health Commission of Hunan Province, Changsha, Hunan, China
| | - Jingcheng Shi
- Department of Epidemiology and Medical Statistics, Xiangya School of Public Health, Central South University, Changsha, Hunan, China
| | - Jing Tian
- Department of Rheumatology and Immunology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
- Clinical Medical Research Center for Systemic Autoimmune Diseases in Hunan Province, Changsha, Hunan, China
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Wang Q, Li YL, Yin YL, Hu B, Yu CC, Wang ZD, Li YH, Xu CJ, Wang YB. Association of air pollutants and meteorological factors with tuberculosis: a national multicenter ecological study in China. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2023; 67:1629-1641. [PMID: 37535117 DOI: 10.1007/s00484-023-02524-1] [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: 03/02/2023] [Revised: 05/17/2023] [Accepted: 07/14/2023] [Indexed: 08/04/2023]
Abstract
The impact of weather variability and air pollutants on tuberculosis (TB) has been a research hotspot. Previous studies have mostly been limited to a certain area or with a small sample size of cases, and multi-scale systematic studies are lacking. In this study, 14,816,329 TB cases were collected from 31 provinces in China between 2004 and 2018 to estimate the association between TB risk and meteorological factors and air pollutants using a two-stage time-series analysis. The impact and lagged time of meteorological factors and air pollutants on TB risk varied greatly in different provinces and regions. Overall cumulative exposure-response summary associations across 31 provinces suggested that high monthly mean relative humidity (RH) (66.8-82.4%, percentile56-100 (P56-100)), rainfall (316.5-331.1 mm, P96-100), PM2.5 exposure concentration (93.3-145.0 μg/m3, P58-100), and low monthly mean wind speed (1.6-2.1 m/s, P0-38) increased the risk of TB incidence, with a relative risk (RR) of 1.10 (95% CI: 1.04-1.16), 1.10 (95% CI: 1.03-1.16), 2.08 (95% CI: 1.18-3.65), and 2.06 (95% CI: 1.27-3.33), and attributable risk percent (AR%) of 9%, 9%, 52%, and 51%, respectively. Conversely, high monthly average wind speed (2.3-2.9 m/s, P54-100) and mean temperature (20.2-25.3 °C, P79-96), and low monthly average rainfall (2.4-25.2 mm, P0-7) and concentration of SO2 (8.1-21.2 μg/m3, P0-16) exposure decreased the risk of TB incidence, with an overall cumulative RR of 0.92 (95% CI: 0.87-0.98), 0.74 (95% CI: 0.59-0.94), 0.87 (95% CI: 0.79-0.95), and 0.72 (95% CI: 0.56-0.93), respectively. Our study provided insights into future planning of public health interventions for TB.
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Affiliation(s)
- Qian Wang
- School of Public Health, Xinxiang Medical University, Henan Province, Xinxiang, 453003, China
| | - Yan-Lin Li
- School of Public Health, Xinxiang Medical University, Henan Province, Xinxiang, 453003, China
| | - Ya-Ling Yin
- Sino-UK Joint Laboratory of Brain Function and Injury of Henan Province, Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Xinxiang Medical University, Henan Province, Xinxiang, 453003, China
| | - Bin Hu
- School of Public Health, Xinxiang Medical University, Henan Province, Xinxiang, 453003, China
| | - Chong-Chong Yu
- School of Public Health, Xinxiang Medical University, Henan Province, Xinxiang, 453003, China
| | - Zhen-de Wang
- School of Public Health, Weifang Medical University, Shandong Province, Weifang, 261053, China
- National Center for Tuberculosis Control and Prevention, China Center for Disease Control and Prevention, Beijing, 102206, China
| | - Yu-Hong Li
- National Center for Tuberculosis Control and Prevention, China Center for Disease Control and Prevention, Beijing, 102206, China
| | - Chun-Jie Xu
- Institute of Medical Biotechnology, Chinese Academy of Medical Sciences & Peking Union Medical Sciences, Beijing, 100730, China.
| | - Yong-Bin Wang
- School of Public Health, Xinxiang Medical University, Henan Province, Xinxiang, 453003, China.
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Chen X, Emam M, Zhang L, Rifhat R, Zhang L, Zheng Y. Analysis of spatial characteristics and geographic weighted regression of tuberculosis prevalence in Kashgar, China. Prev Med Rep 2023; 35:102362. [PMID: 37584062 PMCID: PMC10424202 DOI: 10.1016/j.pmedr.2023.102362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 07/07/2023] [Accepted: 08/02/2023] [Indexed: 08/17/2023] Open
Abstract
Number of cases of tuberculosis (TB) was higher than that of the national level in Kashgar, China. This study aimed to analyze the spatial and temporal distribution of TB and the relationship between TB and social factors, which can provide a reference for the prevention and control of TB. We applied spatial autocorrelation analysis to study the distribution of tuberculosis in Kashgar. We used a geographically weighted regression (GWR) model to analyze the relationship between TB and social factors. A total of 100,330 cases of TB in Kashgar from 2016 to 2021 were analyzed. The number of TB cases in Kashgar was higher in the east, lower in the west, and most elevated in the center. The highest cumulative number of cases was found in Shache county. Global Moran's I ranged from -0.212 to -0.549, and local spatial autocorrelation analysis identified four clusters. According to our analysis, the incidence of tuberculosis was negatively correlated among the regions of Kashgar, and the related causes need to be analyzed in depth in future studies. Per capita gross domestic product (GDP), number of medical institutions per capita, and total population influenced the incidence of tuberculosis in Kashgar. Based on our findings, we suggest some effective measures to reduce the risk of TB infection, such as improving the living standard, developing the regional economy, and distributing health resources rationally.
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Affiliation(s)
- Xiaodie Chen
- College of Public Health, Xinjiang Medical University, Urumqi 830017, China
| | - Mawlanjan Emam
- Center for Disease Control and Prevention, Kashgar 844000,China
| | - Li Zhang
- First Affiliated Hospital of Xinjiang Medical University, Urumqi 830011, Xinjiang, China
| | - Ramziya Rifhat
- College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830017, China
| | - Liping Zhang
- College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830017, China
| | - Yanling Zheng
- College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830017, 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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Nazari Ashani M, Alesheikh AA, Neisani Samani Z, Lotfata A, Bayat S, Alipour S, Hoseini B. Socioeconomic and environmental determinants of foot and mouth disease incidence: an ecological, cross-sectional study across Iran using spatial modeling. Sci Rep 2023; 13:13526. [PMID: 37598281 PMCID: PMC10439931 DOI: 10.1038/s41598-023-40865-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Accepted: 08/17/2023] [Indexed: 08/21/2023] Open
Abstract
Foot-and-mouth disease (FMD) is a highly contagious animal disease caused by a ribonucleic acid (RNA) virus, with significant economic costs and uneven distribution across Asia, Africa, and South America. While spatial analysis and modeling of FMD are still in their early stages, this research aimed to identify socio-environmental determinants of FMD incidence in Iran at the provincial level by studying 135 outbreaks reported between March 21, 2017, and March 21, 2018. We obtained 46 potential socio-environmental determinants and selected four variables, including percentage of population, precipitation in January, percentage of sheep, and percentage of goats, to be used in spatial regression models to estimate variation in spatial heterogeneity. In our analysis, we employed global models, namely ordinary least squares (OLS), spatial error model (SEM), and spatial lag model (SLM), as well as local models, including geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR). The MGWR model yielded the highest adjusted [Formula: see text] of 90%, outperforming the other local and global models. Using local models to map the effects of environmental determinants (such as the percentage of sheep and precipitation) on the spatial variability of FMD incidence provides decision-makers with helpful information for targeted interventions. Our findings advocate for multiscale and multidisciplinary policies to reduce FMD incidence.
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Affiliation(s)
- Mahdi Nazari Ashani
- Department of Geospatial Information Systems, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Ali Asghar Alesheikh
- Department of Geospatial Information Systems, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Zeinab Neisani Samani
- Department of Geospatial Information Systems, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Aynaz Lotfata
- Department of Pathology, Microbiology, and Immunology, School of Veterinary Medicine, University of California, Davis, USA
| | - Sayeh Bayat
- Department of Biomedical Engineering, University of Calgary, Calgary, AB, Canada
- Department of Geomatics Engineering, University of Calgary, Calgary, AB, Canada
| | - Siamak Alipour
- Department of Geospatial Information Systems, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Benyamin Hoseini
- Pharmaceutical Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran.
- Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
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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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Wang J, Liu X, Jing Z, Yang J. Spatial and temporal clustering analysis of pulmonary tuberculosis and its associated risk factors in southwest China. GEOSPATIAL HEALTH 2023; 18. [PMID: 37246542 DOI: 10.4081/gh.2023.1169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 01/30/2023] [Indexed: 05/30/2023]
Abstract
Pulmonary tuberculosis (PTB) remains a serious public health problem, especially in areas of developing countries. This study aimed to explore the spatial-temporal clusters and associated risk factors of PTB in south-western China. Space-time scan statistics were used to explore the spatial and temporal distribution characteristics of PTB. We collected data on PTB, population, geographic information and possible influencing factors (average temperature, average rainfall, average altitude, planting area of crops and population density) from 11 towns in Mengzi, a prefecture-level city in China, between 1 January 2015 and 31 December 2019. A total of 901 reported PTB cases were collected in the study area and a spatial lag model was conducted to analyse the association between these variables and the PTB incidence. Kulldorff's scan results identified two significant space-time clusters, with the most likely cluster (RR = 2.24, p < 0.001) mainly located in northeastern Mengzi involving five towns in the time frame June 2017 - November 2019. A secondary cluster (RR = 2.09, p < 0.05) was located in southern Mengzi, covering two towns and persisting from July 2017 to December 2019. The results of the spatial lag model showed that average rainfall was associated with PTB incidence. Precautions and protective measures should be strengthened in high-risk areas to avoid spread of the disease.
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Affiliation(s)
- Jianjiao Wang
- Institution of Health Statistics and Epidemiology, School of Public Health, Lanzhou University, Gansu.
| | - Xiaoning Liu
- Institution of Health Statistics and Epidemiology, School of Public Health, Lanzhou University, Gansu.
| | - Zhengchao Jing
- Mengzi Center for Disease Control and Prevention, Yunnan.
| | - Jiawai Yang
- Mengzi Center for Disease Control and Prevention, Yunnan.
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Tang N, Yuan M, Chen Z, Ma J, Sun R, Yang Y, He Q, Guo X, Hu S, Zhou J. Machine Learning Prediction Model of Tuberculosis Incidence Based on Meteorological Factors and Air Pollutants. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:3910. [PMID: 36900920 PMCID: PMC10002212 DOI: 10.3390/ijerph20053910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 02/15/2023] [Accepted: 02/21/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Tuberculosis (TB) is a public health problem worldwide, and the influence of meteorological and air pollutants on the incidence of tuberculosis have been attracting interest from researchers. It is of great importance to use machine learning to build a prediction model of tuberculosis incidence influenced by meteorological and air pollutants for timely and applicable measures of both prevention and control. METHODS The data of daily TB notifications, meteorological factors and air pollutants in Changde City, Hunan Province ranging from 2010 to 2021 were collected. Spearman rank correlation analysis was conducted to analyze the correlation between the daily TB notifications and the meteorological factors or air pollutants. Based on the correlation analysis results, machine learning methods, including support vector regression, random forest regression and a BP neural network model, were utilized to construct the incidence prediction model of tuberculosis. RMSE, MAE and MAPE were performed to evaluate the constructed model for selecting the best prediction model. RESULTS (1) From the year 2010 to 2021, the overall incidence of tuberculosis in Changde City showed a downward trend. (2) The daily TB notifications was positively correlated with average temperature (r = 0.231), maximum temperature (r = 0.194), minimum temperature (r = 0.165), sunshine duration (r = 0.329), PM2.5 (r = 0.097), PM10 (r = 0.215) and O3 (r = 0.084) (p < 0.05). However, there was a significant negative correlation between the daily TB notifications and mean air pressure (r = -0.119), precipitation (r = -0.063), relative humidity (r = -0.084), CO (r = -0.038) and SO2 (r = -0.034) (p < 0.05). (3) The random forest regression model had the best fitting effect, while the BP neural network model exhibited the best prediction. (4) The validation set of the BP neural network model, including average daily temperature, sunshine hours and PM10, showed the lowest root mean square error, mean absolute error and mean absolute percentage error, followed by support vector regression. CONCLUSIONS The prediction trend of the BP neural network model, including average daily temperature, sunshine hours and PM10, successfully mimics the actual incidence, and the peak incidence highly coincides with the actual aggregation time, with a high accuracy and a minimum error. Taken together, these data suggest that the BP neural network model can predict the incidence trend of tuberculosis in Changde City.
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Affiliation(s)
- Na Tang
- The Key Laboratory of Model Animals and Stem Cell Biology in Hunan Province, School of Medicine, Hunan Normal University, Changsha 410013, China
| | - Maoxiang Yuan
- Changde Center for Disease Control and Prevention, Changde 415000, China
| | - Zhijun Chen
- The Key Laboratory of Model Animals and Stem Cell Biology in Hunan Province, School of Medicine, Hunan Normal University, Changsha 410013, China
| | - Jian Ma
- The Key Laboratory of Model Animals and Stem Cell Biology in Hunan Province, School of Medicine, Hunan Normal University, Changsha 410013, China
| | - Rui Sun
- The Key Laboratory of Model Animals and Stem Cell Biology in Hunan Province, School of Medicine, Hunan Normal University, Changsha 410013, China
| | - Yide Yang
- The Key Laboratory of Model Animals and Stem Cell Biology in Hunan Province, School of Medicine, Hunan Normal University, Changsha 410013, China
| | - Quanyuan He
- The Key Laboratory of Model Animals and Stem Cell Biology in Hunan Province, School of Medicine, Hunan Normal University, Changsha 410013, China
| | - Xiaowei Guo
- The Key Laboratory of Model Animals and Stem Cell Biology in Hunan Province, School of Medicine, Hunan Normal University, Changsha 410013, China
| | - Shixiong Hu
- Hunan Provincial Center for Disease Control and Prevention, Changsha 410005, China
| | - Junhua Zhou
- The Key Laboratory of Model Animals and Stem Cell Biology in Hunan Province, School of Medicine, Hunan Normal University, Changsha 410013, China
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Spatial-temporal analysis of pulmonary tuberculosis in Hubei Province, China, 2011-2021. PLoS One 2023; 18:e0281479. [PMID: 36749779 PMCID: PMC9904469 DOI: 10.1371/journal.pone.0281479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 01/24/2023] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Pulmonary tuberculosis (PTB) is an infectious disease of major public health problem, China is one of the PTB high burden counties in the word. Hubei is one of the provinces having the highest notification rate of tuberculosis in China. This study analyzed the temporal and spatial distribution characteristics of PTB in Hubei province for targeted intervention on TB epidemics. METHODS The data on PTB cases were extracted from the National Tuberculosis Information Management System correspond to population in 103 counties of Hubei Province from 2011 to 2021. The effect of PTB control was measured by variation trend of bacteriologically confirmed PTB notification rate and total PTB notification rate. Time series, spatial autonomic correlation and spatial-temporal scanning methods were used to identify the temporal trends and spatial patterns at county level of Hubei. RESULTS A total of 436,955 cases were included in this study. The total PTB notification rate decreased significantly from 81.66 per 100,000 population in 2011 to 52.25 per 100,000 population in 2021. The peak of PTB notification occurred in late spring and early summer annually. This disease was spatially clustering with Global Moran's I values ranged from 0.34 to 0.63 (P< 0.01). Local spatial autocorrelation analysis indicated that the hot spots are mainly distributed in the southwest and southeast of Hubei Province. Using the SaTScan 10.0.2 software, results from the staged spatial-temporal analysis identified sixteen clusters. CONCLUSIONS This study identified seasonal patterns and spatial-temporal clusters of PTB cases in Hubei province. High-risk areas in southwestern Hubei still exist, and need to focus on and take targeted control and prevention measures.
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Liang W, Hu A, Hu P, Zhu J, Wang Y. Estimating the tuberculosis incidence using a SARIMAX-NNARX hybrid model by integrating meteorological factors in Qinghai Province, China. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2023; 67:55-65. [PMID: 36271168 DOI: 10.1007/s00484-022-02385-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 09/30/2022] [Accepted: 10/06/2022] [Indexed: 06/16/2023]
Abstract
Tuberculosis (TB) is recognized as being a major public health concern owing to its increase in Qinghai, China. In this study, we aimed to estimate the long-term effects of meteorological variables on TB incidence and construct an advanced hybrid model with seasonal autoregressive integrated moving average (SARIMA) and a neural network nonlinear autoregression (SARIMAX-NNARX) by integrating meteorological factors and evaluating the model fitting and prediction effect. During 2005-2017, TB experienced an upward trend with obvious periodic and seasonal characteristics, peaking in spring and winter. The results showed that TB incidence was positively correlated with average relative humidity (ARH) with a 2-month lag (β = 1.889, p = 0.003), but negatively correlated with average atmospheric pressure (AAP) with a 1-month lag (β = - 1.633, p = 0.012), average temperature (AT) with a 2-month lag (β = - 0.093, p = 0.027), and average wind speed (AWS) with a 0-month lag (β = - 13.221, p = 0.033), respectively. The SARIMA (3,1,0)(1,1,1)12, SARIMAX(3,1,0)(1,1,1)12, and SARIMAX(3,1,0)(1,1,1)12-NNARX(15,3) were considered preferred models based on the evaluation criteria. Of them, the SARIMAX-NNARX technique had smaller error values than the SARIMA and SARIMAX models in both fitting and forecasting aspects. The sensitivity analysis also revealed the robustness of the mixture forecasting model. Therefore, the SARIMAX-NNARX model by integrating meteorological variables can be used as an accurate method for forecasting the epidemic trends which would be great importance for TB prevention and control in the coming periods in Qinghai.
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Affiliation(s)
- Wenjuan Liang
- Department of Epidemiology, International School of Public Health and One Health, Hainan Medical University, Haikou, Hainan Province, 571199, People's Republic of China
- Department of Epidemiology and Health Statistics, School of Public Health, The Third Affiliated Hospital, Xinxiang Medical University, No. 601 Jinsui Road, Hongqi District, Xinxiang, Henan Province, 453003, People's Republic of China
| | - Ailing Hu
- Department of Epidemiology and Health Statistics, School of Public Health, The Third Affiliated Hospital, Xinxiang Medical University, No. 601 Jinsui Road, Hongqi District, Xinxiang, Henan Province, 453003, People's Republic of China
| | - Pan Hu
- Department of Epidemiology and Health Statistics, School of Public Health, The Third Affiliated Hospital, Xinxiang Medical University, No. 601 Jinsui Road, Hongqi District, Xinxiang, Henan Province, 453003, People's Republic of China
| | - Jinqin Zhu
- Department of Epidemiology and Health Statistics, School of Public Health, The Third Affiliated Hospital, Xinxiang Medical University, No. 601 Jinsui Road, Hongqi District, Xinxiang, Henan Province, 453003, People's Republic of China
| | - Yongbin Wang
- Department of Epidemiology and Health Statistics, School of Public Health, The Third Affiliated Hospital, Xinxiang Medical University, No. 601 Jinsui Road, Hongqi District, Xinxiang, Henan Province, 453003, People's Republic of China.
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15
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Khaliq A, Ashraf U, Chaudhry MN, Shahid S, Sajid MA, Javed M. Spatial distribution and computational modeling for mapping of tuberculosis in Pakistan. J Public Health (Oxf) 2022:6842873. [DOI: 10.1093/pubmed/fdac125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 03/21/2022] [Accepted: 10/06/2022] [Indexed: 11/25/2022] Open
Abstract
Abstract
Background
Tuberculosis (TB) like many other infectious diseases has a strong relationship with climatic parameters.
Methods
The present study has been carried out on the newly diagnosed sputum smear-positive pulmonary TB cases reported to National TB Control Program across Pakistan from 2007 to 2020. In this study, spatial and temporal distribution of the disease was observed through detailed district wise mapping and clustered regions were also identified. Potential risk factors associated with this disease depending upon population and climatic variables, i.e. temperature and precipitation were also identified.
Results
Nationwide, the incidence rate of TB was observed to be rising from 7.03% to 11.91% in the years 2007–2018, which then started to decline. However, a declining trend was observed after 2018–2020. The most populous provinces, Punjab and Sindh, have reported maximum number of cases and showed a temporal association as the climatic temperature of these two provinces is higher with comparison to other provinces. Machine learning algorithms Maxent, Support Vector Machine (SVM), Environmental Distance (ED) and Climate Space Model (CSM) predict high risk of the disease with14.02%, 24.75%, 34.81% and 43.89% area, respectively.
Conclusion
SVM has a higher significant probability of prediction in the diseased area with a 1.86 partial receiver-operating characteristics (ROC) value as compared with other models.
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Affiliation(s)
- Aasia Khaliq
- Department of Life Sciences, Lahore University of Management Sciences (LUMS) , Lahore , Pakistan
| | - Uzma Ashraf
- Department of Environmental Sciences and Policy, Lahore School of Economics (LSE) , Lahore , Pakistan
| | - Muhammad N Chaudhry
- Department of Environmental Sciences and Policy, Lahore School of Economics (LSE) , Lahore , Pakistan
| | - Saher Shahid
- School of Biological Sciences (SBS), University of the Punjab , Lahore , Pakistan
| | - Muhammad A Sajid
- Foundation Department, Majan University College , Muscat 113 , Oman
| | - Maryam Javed
- Department of Environmental Sciences and Policy, Lahore School of Economics (LSE) , Lahore , Pakistan
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Spatial Co-Clustering of Tuberculosis and HIV in Ethiopia. Diseases 2022; 10:diseases10040106. [PMID: 36412600 PMCID: PMC9680430 DOI: 10.3390/diseases10040106] [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: 08/08/2022] [Revised: 11/05/2022] [Accepted: 11/08/2022] [Indexed: 11/19/2022] Open
Abstract
Background: Tuberculosis (TB) and HIV are epidemiologically associated, and their co-dynamics suggest that the two diseases are directly related at the population level and within the host. However, there is no or little information on the joint spatial patterns of the two diseases in Ethiopia. The main objective of the current study was to assess the spatial co-clustering of TB and HIV cases simultaneously in Ethiopia at the district level. Methods: District-level aggregated data collected from the national Health Management Information System (HMIS) for the years 2015 to 2018 on the number of TB cases enrolled in directly observed therapy, short course (DOTS) who were tested for HIV and the number of HIV patients enrolled in HIV care who were screened for TB during their last visit to health care facilities were used in this study. The univariate and bivariate global and local Moran’s I indices were applied to assess the spatial clustering of TB and HIV separately and jointly. Results: The results of this study show that the two diseases were significantly (p-value <0.001) spatially autocorrelated at the district level with minimum and maximum global Moran’s I values of 0.407 and 0.432 for TB, 0.102 and 0.247 for HIV, and 0.152 and 0.251 for joint TB/HIV. The district-level TB/HIV spatial co-clustering patterns in Ethiopia in most cases overlapped with the hot spots of TB and HIV. The TB/HIV hot-spot clusters may appear due to the observed high TB and HIV prevalence rates in the hot-spot districts. Our results also show that there were low-low TB/HIV co-clusters or cold spots in most of the Afar and Somali regions, which consistently appeared for the period 2015−2018. This may be due to very low notifications of both diseases in the regions. Conclusions: This study expanded knowledge about TB and HIV co-clustering in Ethiopia at the district level. The findings provide information to health policymakers in the country to plan geographically targeted and integrated interventions to jointly control TB and HIV.
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Huang K, Hu CY, Yang XY, Zhang Y, Wang XQ, Zhang KD, Li YQ, Wang J, Yu WJ, Cheng X, Cao JY, Zhang T, Kan XH, Zhang XJ. Contributions of ambient temperature and relative humidity to the risk of tuberculosis admissions: A multicity study in Central China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 838:156272. [PMID: 35644395 DOI: 10.1016/j.scitotenv.2022.156272] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 05/08/2022] [Accepted: 05/23/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND As a communicable disease and major public health issue, many studies have quantified the associations between tuberculosis (TB) and meteorological factors with inconsistent results. The purpose of this multicenter study was to characterize the associations between ambient temperature, humidity and the risk of TB hospitalizations and to investigate potential heterogeneity. METHOD Data on daily hospitalizations for TB, meteorological factors and ambient air pollutants for 16 cities in Anhui Province were collected from 2015 to 2020. A distributed lag nonlinear model (DLNM) was performed to obtain the estimates of meteorological-TB relationships by cities. Then, we used the multivariate meta-regression model to pool the city-specific estimates with air pollution, demographic indicators, medical resource and latitude as potential modifiers to explore the sources of heterogeneity. Finally, we divided the whole province into three regions to validate the meteorological-TB relationships by regions. RESULTS The overall pooled temperature-TB association presented an approximate S-shaped curve, with relative risk (RR) peaking at 5 °C (RR = 1.536, 95% CI: 1.303-1.811) compared to the reference temperature (27 °C). Lag-response curve suggested that low temperature exposure increased the risk of TB hospitalizations at lag 0 and 1 day (lag0 day: RR = 1.136, 95% CI: 1.048-1.231, lag1 day: RR = 1.052, 95% CI: 1.023-1.082). However, the overall exposure-response curve between relative humidity and TB showed almost horizontal line with reference relative humidity to 78%. The residual heterogeneity ranged from 27.1% to 36.9%, with air pollution, latitude and medical resource explained the largest proportion. CONCLUSION We found that low temperature exposure is associated with an acute increased risk of TB hospitalizations in Anhui Province. The association between temperature and TB admission varies depending on air pollution, latitude, and medical resources. Since the effect of short-term exposure to humidity is not significant, further studies are supposed to focus on the long-term effect of humidity.
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Affiliation(s)
- Kai Huang
- Department of Hospital Infection Prevention and Control, The Second Affiliated Hospital of Anhui Medical University, 678 Furong Road, Hefei 230601, China; Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, China
| | - Cheng-Yang Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, China
| | - Xi-Yao Yang
- Department of Hospital Infection Prevention and Control, The Second Affiliated Hospital of Anhui Medical University, 678 Furong Road, Hefei 230601, China
| | - Yunquan Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College, Wuhan University of Science and Technology, Wuhan 430065, China
| | - Xin-Qiang Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, China
| | - Kang-Di Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, China
| | - Ying-Qing Li
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, China
| | - Jie Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, China
| | - Wen-Jie Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, China
| | - Xin Cheng
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, China
| | - Ji-Yu Cao
- Department of Occupational Health and Environmental Health, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, Anhui, China
| | - Tao Zhang
- Anhui Chest Hospital, 397 Jixi Road, Hefei 230022, China
| | - Xiao-Hong Kan
- Anhui Chest Hospital, 397 Jixi Road, Hefei 230022, China; Anhui Medical University Clinical College of Chest, 397 Jixi Road, Hefei 230022, China.
| | - Xiu-Jun Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, China.
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Zheng J, Shen G, Hu S, Han X, Zhu S, Liu J, He R, Zhang N, Hsieh CW, Xue H, Zhang B, Shen Y, Mao Y, Zhu B. Small-scale spatiotemporal epidemiology of notifiable infectious diseases in China: a systematic review. BMC Infect Dis 2022; 22:723. [PMID: 36064333 PMCID: PMC9442567 DOI: 10.1186/s12879-022-07669-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 08/03/2022] [Indexed: 11/20/2022] Open
Abstract
Background The prevalence of infectious diseases remains one of the major challenges faced by the Chinese health sector. Policymakers have a tremendous interest in investigating the spatiotemporal epidemiology of infectious diseases. We aimed to review the small-scale (city level, county level, or below) spatiotemporal epidemiology of notifiable infectious diseases in China through a systematic review, thus summarizing the evidence to facilitate more effective prevention and control of the diseases. Methods We searched four English language databases (PubMed, EMBASE, Cochrane Library, and Web of Science) and three Chinese databases (CNKI, WanFang, and SinoMed), for studies published between January 1, 2004 (the year in which China’s Internet-based disease reporting system was established) and December 31, 2021. Eligible works were small-scale spatial or spatiotemporal studies focusing on at least one notifiable infectious disease, with the entire territory of mainland China as the study area. Two independent reviewers completed the review process based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Results A total of 18,195 articles were identified, with 71 eligible for inclusion, focusing on 22 diseases. Thirty-one studies (43.66%) were analyzed using city-level data, 34 (47.89%) were analyzed using county-level data, and six (8.45%) used community or individual data. Approximately four-fifths (80.28%) of the studies visualized incidence using rate maps. Of these, 76.06% employed various spatial clustering methods to explore the spatial variations in the burden, with Moran’s I statistic being the most common. Of the studies, 40.85% explored risk factors, in which the geographically weighted regression model was the most commonly used method. Climate, socioeconomic factors, and population density were the three most considered factors. Conclusions Small-scale spatiotemporal epidemiology has been applied in studies on notifiable infectious diseases in China, involving spatiotemporal distribution and risk factors. Health authorities should improve prevention strategies and clarify the direction of future work in the field of infectious disease research in China. Supplementary Information The online version contains supplementary material available at 10.1186/s12879-022-07669-9.
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Affiliation(s)
- Junyao Zheng
- China Institute for Urban Governance, Shanghai Jiao Tong University, Shanghai, China.,School of International and Public Affairs, Shanghai Jiao Tong University, Shanghai, China
| | - Guoquan Shen
- School of Public Administration and Policy, Renmin University of China, Beijing, China
| | - Siqi Hu
- School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, China
| | - Xinxin Han
- School of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, China
| | - Siyu Zhu
- School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, China
| | - Jinlin Liu
- School of Public Policy and Administration, Northwestern Polytechnical University, Xi'an, China
| | - Rongxin He
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Ning Zhang
- School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, China.,MRC Centre for Global Infectious Disease Analysis and the Abdul Latif Jameel Institute for Disease and Emergency Analytics, School of Public Health, Imperial College, London, UK
| | - Chih-Wei Hsieh
- Department of Public Policy, City University of Hong Kong, Hong Kong, China
| | - Hao Xue
- Freeman Spogli Institute for International Studies, Stanford University, Stanford, CA, USA
| | - Bo Zhang
- Department of Earth System Science, Tsinghua University, Beijing, China
| | - Yue Shen
- Laboratory for Urban Future, School of Urban Planning and Design, Peking University Shenzhen Graduate School, Shenzhen, China
| | - Ying Mao
- School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, China
| | - Bin Zhu
- School of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, China.
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19
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Qin T, Hao Y, Wu Y, Chen X, Zhang S, Wang M, Xiong W, He J. Association between averaged meteorological factors and tuberculosis risk: A systematic review and meta-analysis. ENVIRONMENTAL RESEARCH 2022; 212:113279. [PMID: 35561834 DOI: 10.1016/j.envres.2022.113279] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 04/07/2022] [Accepted: 04/07/2022] [Indexed: 06/15/2023]
Abstract
Inconsistencies were discovered in the findings regarding the effects of meteorological factors on tuberculosis (TB). This study conducted a systematic review of published studies on the relationship between TB and meteorological factors and used a meta-analysis to investigate the pooled effects in order to provide evidence for future research and policymakers. The literature search was completed by August 3rd, 2021, using three databases: PubMed, Web of Science and Embase. Relative risks (RRs) in included studies were extracted and all effect estimates were combined together using meta-analysis. Subgroup analyses were carried out based on the resolution of exposure time, regional climate, and national income level. A total of eight studies were included after screening for inclusion and exclusion criteria. Our results show that TB risk was positively correlated with precipitation (RR = 1.32, 95% CI: 1.14, 1.51), while temperature (RR = 1.15, 95% CI: 1.00, 1.32), humidity (RR = 1.05, 95% CI: 0.99, 1.10), air pressure (RR = 0.89, 95% CI: 0.69, 1.14) and sunshine duration (RR = 0.95, 95% CI: 0.80, 1.13) all had no statistically significant correlation. Subgroup analysis shows that quarterly measure resolution, low and middle Human Development Index (HDI) level and subtropical climate increase TB risk not only in precipitation, but also in temperature and humidity. Moreover, less heterogeneity was observed in "high and extremely high" HDI areas and subtropical areas than that in other subgroups (I2 = 0%). Precipitation, a subtropical climate, and a low HDI level are all positive influence factors to tuberculosis. Therefore, residents and public health managers should take precautionary measures ahead of time, especially in extreme weather conditions.
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Affiliation(s)
- Tianyu Qin
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Yu Hao
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - You Wu
- Key Laboratory of Health Cultivation of the Ministry of Education, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Xinli Chen
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Shuwen Zhang
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Mengqi Wang
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Weifeng Xiong
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Juan He
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China.
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20
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Feng Q, Hu X, Zhao J, Huang J, Liu L. Female genital tuberculosis presented with primary infertility and persistent CA-125 elevation: A case report. Ann Med Surg (Lond) 2022; 78:103683. [PMID: 35600189 PMCID: PMC9118513 DOI: 10.1016/j.amsu.2022.103683] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 04/19/2022] [Accepted: 04/25/2022] [Indexed: 11/20/2022] Open
Abstract
Introduction and importance: Tuberculosis is one of the leading causes of mortality in many developing countries. Female genital tuberculosis, a relatively uncommon form of tuberculosis, is critically challenging to diagnose due to its insidious and non-typical presentations. Case presentation Herein, we present with an asymptomatic infertile woman with persistently high CA-125 passed undiagnosed until laparoscopy unveiled the diagnosis of TB. Clinical discussion In this study, we highlight the diagnostic complexities in female genital tuberculosis and demonstrate the value of the CA-125 increase in prompting the suspicion of tuberculosis in the appropriate clinical context. Conclusion Given the considerably large number of individuals with tuberculosis in China, we strongly recommend routine tuberculosis screening in women seeking infertility care in China. Female genital tuberculosis is difficult to diagnose early due to its rarity and insidious symptoms. Persistent elevation of CA-125 may indicate the presence of tuberculosis in rare occasions. Performing tuberculosis screening routinely for infertile womenin tuberculosis prevailing regions is recommended.
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Affiliation(s)
- Qian Feng
- Department of Obstetrics and Gynaecology, Monash University, Melbourne, 3800, Australia
| | - Xiaoxia Hu
- Guangxi Academy of Medical Sciences & Department of Gynaecology, The People's Hospital of Guangxi Zhuang Autonomous Region, 530000, Nanning, China
| | - Jing Zhao
- Graduate College of Youjiang Medical University for Nationalities, Baise, 533000, China
| | - Jingyao Huang
- Graduate College of Youjiang Medical University for Nationalities, Baise, 533000, China
| | - Liling Liu
- Guangxi Academy of Medical Sciences & Department of Reproductive Medicine and Genetics Center, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, 530000, China
- Corresponding author. Guangxi Academy of Medical Sciences & Department of Reproductive Medicine and Genetics Center, The People's Hospital of Guangxi Zhuang Autonomous Region, No.6, Taoyuan Road, Qingxiu District, Nanning, 530000, China.
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21
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Liu M, Li Z, Liu M, Zhu Y, Liu Y, Kuetche MWN, Wang J, Wang X, Liu X, Li X, Wang W, Guo X, Tao L. Association between temperature and COVID-19 transmission in 153 countries. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:16017-16027. [PMID: 34637125 PMCID: PMC8507510 DOI: 10.1007/s11356-021-16666-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 09/18/2021] [Indexed: 04/15/2023]
Abstract
The WHO characterized coronavirus disease 2019 (COVID-19) as a global pandemic. The influence of temperature on COVID-19 remains unclear. The objective of this study was to investigate the correlation between temperature and daily newly confirmed COVID-19 cases by different climate regions and temperature levels worldwide. Daily data on average temperature (AT), maximum temperature (MAXT), minimum temperature (MINT), and new COVID-19 cases were collected from 153 countries and 31 provinces of mainland China. We used the spline function method to preliminarily explore the relationship between R0 and temperature. The generalized additive model (GAM) was used to analyze the association between temperature and daily new cases of COVID-19, and a random effects meta-analysis was conducted to calculate the pooled results in different regions in the second stage. Our findings revealed that temperature was positively related to daily new cases at low temperature but negatively related to daily new cases at high temperature. When the temperature was below the smoothing plot peak, in the temperate zone or at a low temperature level (e.g., <25th percentiles), the RRs were 1.09 (95% CI: 1.04, 1.15), 1.10 (95% CI: 1.05, 1.15), and 1.14 (95% CI: 1.06, 1.23) associated with a 1°C increase in AT, respectively. Whereas temperature was above the smoothing plot peak, in a tropical zone or at a high temperature level (e.g., >75th percentiles), the RRs were 0.79 (95% CI: 0.68, 0.93), 0.60 (95% CI: 0.43, 0.83), and 0.48 (95% CI: 0.28, 0.81) associated with a 1°C increase in AT, respectively. The results were confirmed to be similar regarding MINT, MAXT, and sensitivity analysis. These findings provide preliminary evidence for the prevention and control of COVID-19 in different regions and temperature levels.
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Affiliation(s)
- Mengyang Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, 10 Xi-Tou-Tiao, You-An-Men Street, Fengtai District, Beijing, 100069, People's Republic of China
| | - Zhiwei Li
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, 10 Xi-Tou-Tiao, You-An-Men Street, Fengtai District, Beijing, 100069, People's Republic of China
| | - Mengmeng Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, 10 Xi-Tou-Tiao, You-An-Men Street, Fengtai District, Beijing, 100069, People's Republic of China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, People's Republic of China
| | - Yingxuan Zhu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, 10 Xi-Tou-Tiao, You-An-Men Street, Fengtai District, Beijing, 100069, People's Republic of China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, People's Republic of China
| | - Yue Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, 10 Xi-Tou-Tiao, You-An-Men Street, Fengtai District, Beijing, 100069, People's Republic of China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, People's Republic of China
| | | | - Jianpeng Wang
- College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, Xinjiang, Uygur Autonomous Region, People's Republic of China
| | - Xiaonan Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, 10 Xi-Tou-Tiao, You-An-Men Street, Fengtai District, Beijing, 100069, People's Republic of China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, People's Republic of China
| | - Xiangtong Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, 10 Xi-Tou-Tiao, You-An-Men Street, Fengtai District, Beijing, 100069, People's Republic of China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, People's Republic of China
| | - Xia Li
- Department of Mathematics and Statistics, La Trobe University, Melbourne, 3086, Australia
| | - Wei Wang
- School of Medical Sciences and Health, Edith Cowan University, Perth, WA6027, Australia
| | - Xiuhua Guo
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, 10 Xi-Tou-Tiao, You-An-Men Street, Fengtai District, Beijing, 100069, People's Republic of China.
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, People's Republic of China.
| | - Lixin Tao
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, 10 Xi-Tou-Tiao, You-An-Men Street, Fengtai District, Beijing, 100069, People's Republic of China.
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, People's Republic of China.
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22
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Gilmour B, Xu Z, Bai L, Alene KA, Clements ACA. The impact of ethnic minority status on tuberculosis diagnosis and treatment delays in Hunan Province, China. BMC Infect Dis 2022; 22:90. [PMID: 35081919 PMCID: PMC8790941 DOI: 10.1186/s12879-022-07072-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 01/05/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Tuberculosis (TB) continues to be a major public health challenge in China. Understanding TB management delays within the context of China's unique ethnic diversity may be of value in tackling the disease. This study sought to evaluate the impact of ethnic minority status on TB diagnosis and treatment delays. METHODS This retrospective cohort study was conducted on patients diagnosed with TB in Hunan Province, China between 2013 and 2018. Diagnosis delay was defined as the time interval between the onset of symptoms and the date of diagnosis. Treatment delay was defined as the time interval between diagnosis and treatment commencement. Univariable and multivariable logistic regression models were used to identify factors associated with TB diagnosis and treatment delay, including ethnic minority status. Adjusted odds ratios (AOR) with 95% confidence intervals (CI) were calculated to assess the strength of association between the dependant and independent variables. RESULTS A total of 318,792 TB patients were included in the study with a mean age of 51.7 years (SD 17.7). The majority of patients were male (72.6%) and Han ethnicity (90.6%). The odds of experiencing diagnosis delay (> 21 days) were significantly higher for Tujia (AOR: 1.46, 95% CI: 1.41, 1.51), Miao (AOR: 1.31, 95% CI: 1.26, 1.37), Dong (AOR: 1.97, 95% CI: 1.85, 2.11), Yao (AOR: 1.27, 95% CI: 1.17, 1.37), and Bai (AOR: 1.45, 95% CI: 1.22, 1.74) ethnic minorities compared to the Han majority. The odds of experiencing treatment delay (> 15 days) were significantly lower for five of the seven ethnic minority groups relative to the Han majority: Tujia (AOR 0.92, 95% CI 0.88, 0.96), Miao (AOR 0.74, 95% CI 0.70, 0.79), Dong (AOR 0.87, 95% CI 0.81, 0.95), Yao (AOR 0.20, 95% CI 0.17, 0.24) and 'other' (ethnic minorities that individually represented < 0.1% of the patient population) (AOR 0.70, 955 CI 0.51, 0.97). CONCLUSIONS This study shows ethnic minority status to be a significant risk factor in diagnosis delay, but for it to reduce the odds of treatment delay. Further research is required to determine the underlying causes of diagnosis delay within ethnic minority populations.
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Affiliation(s)
- Beth Gilmour
- Faculty of Health Sciences, Curtin University, Western Australia, Kent St, Bentley, WA, 6102, Australia.
| | - Zuhui Xu
- Xiangya School of Public Health, Central South University, Changsha, China.,TB Control Institute of Hunan Province, Changsha, China
| | - Liqiong Bai
- TB Control Institute of Hunan Province, Changsha, China
| | - Kefyalew Addis Alene
- Faculty of Health Sciences, Curtin University, Western Australia, Kent St, Bentley, WA, 6102, Australia.,Telethon Kids Institute, Nedlands, WA, Australia
| | - Archie C A Clements
- Faculty of Health Sciences, Curtin University, Western Australia, Kent St, Bentley, WA, 6102, Australia.,Telethon Kids Institute, Nedlands, WA, Australia
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23
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Gilmour B, Xu Z, Bai L, Addis Alene K, Clements ACA. Risk factors associated with unsuccessful tuberculosis treatment outcomes in Hunan Province, China. Trop Med Int Health 2022; 27:290-299. [PMID: 35014123 PMCID: PMC9305245 DOI: 10.1111/tmi.13720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
OBJECTIVES Globally, China has the third highest number of tuberculosis (TB) cases despite high rates (85.6%) of effective treatment coverage. Identifying risk factors associated with unsuccessful treatment outcomes is an important component of maximizing the efficacy of TB control programs. METHODS Retrospective cohort study to evaluate the outcomes of 306,860 drug-susceptible TB patients who underwent treatment in Hunan Province, China between 2013 and 2018. Univariable and multivariable logistic regression models were used to identify factors associated with unsuccessful TB treatment outcomes. RESULTS A successful treatment outcome was recorded for 98.6% of patients, defined as the sum of patients who were cured (36.2%) and completed treatment (62.4%). An unsuccessful treatment outcome was recorded for 1.8% of patients, defined as the sum of treatment failure (1.1%), deaths (0.5%) and lost to follow up (0.2%). The odds of an unsuccessful treatment outcome showed an increasing trend in more recent years of registration (2018 Adjusted Odds Ratio (AOR): 1.43; 95% Confidence Interval (CI) 1.31, 1.57 relative to 2013). Other significant risk factors were male sex (AOR: 1.17; 95% CI 1.10, 1.25); increasing age (AOR:1.02 per year increase; 95% CI 1.02,1.02); being severely ill (AOR: 1.50; 95% CI 1.33, 1.70); having a history of TB treatment (AOR: 2.93; 95% CI 2.69, 3.20); not being under systematic management (AOR: 16.10 (14.49, 17.88) and treatment regimens that differed from full course management. CONCLUSIONS The increasing likelihood of an unsuccessful treatment outcome over time necessitates the need for further research.
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Affiliation(s)
- Beth Gilmour
- Faculty of Health Sciences, Curtin University, Bentley, Australia
| | - Zuhui Xu
- Xiangya School of Public Health, Central South University, Changsha, China.,TB Control Institute of Hunan Province, Changsha, China
| | - Liqiong Bai
- TB Control Institute of Hunan Province, Changsha, China
| | - Kefyalew Addis Alene
- Faculty of Health Sciences, Curtin University, Bentley, Australia.,Telethon Kids Institute, Nedlands, Australia
| | - Archie C A Clements
- Faculty of Health Sciences, Curtin University, Bentley, Australia.,Telethon Kids Institute, Nedlands, Australia
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24
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Li Z, Liu Q, Zhan M, Tao B, Wang J, Lu W. Meteorological factors contribute to the risk of pulmonary tuberculosis: A multicenter study in eastern China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 793:148621. [PMID: 34328976 DOI: 10.1016/j.scitotenv.2021.148621] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 06/16/2021] [Accepted: 06/19/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Most studies on associations between meteorological factors and tuberculosis (TB) were conducted in a single city, used different lag times, or merely explored the qualitative associations between meteorological factors and TB. Thus, we performed a multicenter study to quantitatively evaluate the effects of meteorological factors on the risk of pulmonary tuberculosis (PTB). METHODS We collected data on newly diagnosed PTB cases in 13 study sites in Jiangsu Province between January 1, 2014, and December 31, 2019. Data on meteorological factors, air pollutants, and socioeconomic factors at these sites during the same period were also collected. We applied the generalized additive mixed model to estimate the associations between meteorological factors and PTB. RESULTS There were 20,472 newly diagnosed PTB cases reported in the 13 study sites between 2014 and 2019. The median (interquartile range) weekly average temperature, weekly average wind speed, and weekly average relative humidity of these sites were 17.3 °C (8.0-24.1), 2.2 m/s (1.8-2.7), and 75.1% (67.1-82.0), respectively. In the single-meteorological-factor models, for a unit increase in weekly average temperature, weekly average wind speed, and weekly average relative humidity, the risk of PTB decreased by 0.9% [lag 0-13 weeks, 95% confidence interval (CI): -1.5, -0.4], increased by 56.2% (lag 0-16 weeks, 95% CI: 32.6, 84.0) when average wind speed was <3 m/s, and decreased by 28.1% (lag 0-14 weeks, 95% CI: -39.2, -14.9) when average relative humidity was ≥72%, respectively. Moreover, the associations remained significant in the multi-meteorological-factor models. CONCLUSIONS Average temperature and average relative humidity (≥72%) are negatively associated with the risk of PTB. In contrast, average wind speed (<3 m/s) is positively related to the risk of PTB, suggesting that an environment with low temperature, relatively high wind speed, and low relative humidity is conducive to the transmission of PTB.
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Affiliation(s)
- Zhongqi Li
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Qiao Liu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Department of Chronic Communicable Disease, Center for Disease Control and Prevention of Jiangsu Province, Nanjing 210009, China
| | - Mengyao Zhan
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Bilin Tao
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Jianming Wang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China.
| | - Wei Lu
- Department of Chronic Communicable Disease, Center for Disease Control and Prevention of Jiangsu Province, Nanjing 210009, China.
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25
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Maharjan B, Gopali RS, Zhang Y. A scoping review on climate change and tuberculosis. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2021; 65:1579-1595. [PMID: 33728507 DOI: 10.1007/s00484-021-02117-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 03/04/2021] [Accepted: 03/09/2021] [Indexed: 06/12/2023]
Abstract
Climate change is a global public health challenge. The changes in climatic factors affect the pattern and burden of tuberculosis, which is a worldwide public health problem affecting low and middle-income countries. However, the evidence related to the impact of climate change on tuberculosis is few and far between. This study is a scoping review following a five-stage version of Arksey and O'Malley's method. We searched the literature using the keywords and their combination in Google scholar, and PubMed. Climate change affects tuberculosis through diverse pathways: changes in climatic factors like temperature, humidity, and precipitation influence host response through alterations in vitamin D distribution, ultraviolet radiation, malnutrition, and other risk factors. The rise in extreme climatic events induces population displacement resulting in a greater number of vulnerable and risk populations of tuberculosis. It creates a conducive environment of tuberculosis transmission and development of active tuberculosis and disrupts tuberculosis diagnosis and treatment services. Therefore, it stands to reasons that climate change affects tuberculosis, particularly in highly vulnerable countries and areas. However, further studies and novel methodologies are required to address such a complex relationship and better understand the occurrence of tuberculosis attributable to climate change.
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Affiliation(s)
- Bijay Maharjan
- Japan-Nepal Health and Tuberculosis Research Association, Kathmandu, Nepal.
| | - Ram Sharan Gopali
- Japan-Nepal Health and Tuberculosis Research Association, Kathmandu, Nepal
| | - Ying Zhang
- School of Public Health, University of Sydney, Sydney, Australia
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26
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The spatial clustering analysis of COVID-19 and its associated factors in mainland China at the prefecture level. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 777:145992. [PMCID: PMC7896114 DOI: 10.1016/j.scitotenv.2021.145992] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 02/15/2021] [Accepted: 02/15/2021] [Indexed: 05/25/2023]
Abstract
Coronavirus disease 2019 (COVID-19) has become a worldwide public health threat. Many associated factors including population movement, meteorological parameters, air quality and socioeconomic conditions can affect COVID-19 transmission. However, no study has combined these various factors in a comprehensive analysis. We collected data on COVID-19 cases and the factors of interest in 340 prefectures of mainland China from 1 December 2019 to 30 April 2020. Moran's I statistic, Getis-Ord Gi⁎ statistic and Kulldorff's space-time scan statistics were used to identify spatial clusters of COVID-19, and the geographically weighted regression (GWR) model was applied to investigate the effects of the associated factors on COVID-19 incidence. A total of 67,449 laboratory-confirmed cases were reported during the study period. Wuhan city as well as its surrounding areas were the cluster areas, and January 25 to February 21, 2020, was the clustering time of COVID-19. The population outflow from Wuhan played a significant role in COVID-19 transmission, with the local coefficients varying from 14.87 to 15.02 in the 340 prefectures. Among the meteorological parameters, relative humidity and precipitation were positively associated with COVID-19 incidence, while the average wind speed showed a negative correlation, but the relationship of average temperature with COVID-19 incidence inconsistent between northern and southern China. NO2 was positively associated, and O3 was negatively associated, with COVID-19 incidence. Environment with high levels of inbound migration or travel, poor ventilation, high humidity or heavy rainfall, low temperature, and high air pollution may be favorable for the growth, reproduction and spread of SARS-CoV-2. Therefore, applying appropriate lockdown measures and travel restrictions, strengthening the ventilation of living and working environments, controlling air pollution and making sufficient preparations for a possible second wave in the relatively cold autumn and winter months may be helpful for the control and prevention of COVID-19.
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27
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Liang S, Chen J, Raymond Rui J, Guo J. The boomerang effect of tuberculosis knowledge and self-efficacy on Chinese parents' intention to seek timely treatment and adhere to doctors' regimens. PATIENT EDUCATION AND COUNSELING 2021; 104:1487-1493. [PMID: 33172736 DOI: 10.1016/j.pec.2020.10.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Revised: 10/12/2020] [Accepted: 10/23/2020] [Indexed: 06/11/2023]
Abstract
OBJECTIVE The present study tested the level of tuberculosis (TB) knowledge of Chinese parents whose children attended kindergarten, primary school, and middle school, and examined whether their TB knowledge and self-efficacy in TB management exhibited a curvilinear relationship with their intention to seek timely TB treatment and adhere to doctors' regimens if their children are infected with TB. METHODS An online cross-sectional survey (N = 1129) was conducted. TB knowledge was assessed based on the manual provided by the Chinese Center for Disease Control and Prevention (CDC). Hierarchical polynomial regression was conducted to test the proposed curvilinear relationships. RESULTS Chinese parents lacked knowledge about risks of TB and how to prevent TB. TB knowledge and self-efficacy in TB management motivated Chinese parents to seek timely TB treatment and adhere to doctors' regimens, but too much knowledge and self-efficacy predicted both intentions negatively. CONCLUSION Excessive levels of self-efficacy in self-management and health knowledge could backfire. PRACTICE IMPLICATION Practitioners should elevate Chinese parents' perceptions of severity of TB and susceptibility to TB. Additionally, health education should not be limited to providing medical facts but offer guidance on how to access professional medical resources. Self-efficacy in self-management should not be elevated.
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Affiliation(s)
- Sizhe Liang
- Shajing Health Inspection Institute, Shenzhen, China
| | - Juan Chen
- South China University of Technology, Guangzhou, China
| | - Jian Raymond Rui
- South China University of Technology, 382 Waihuan East Rd, Panyu District, Guangzhou, 510006 China.
| | - Jieqiong Guo
- South China University of Technology, Guangzhou, China
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Chen D, Lu H, Zhang S, Yin J, Liu X, Zhang Y, Dai B, Li X, Ding G. The association between extreme temperature and pulmonary tuberculosis in Shandong Province, China, 2005-2016: a mixed method evaluation. BMC Infect Dis 2021; 21:402. [PMID: 33933024 PMCID: PMC8088045 DOI: 10.1186/s12879-021-06116-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 04/20/2021] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND The effects of extreme temperature on infectious diseases are complex and far-reaching. There are few studies to access the relationship of pulmonary tuberculosis (PTB) with extreme temperature. The study aimed to identify whether there was association between extreme temperature and the reported morbidity of PTB in Shandong Province, China, from 2005 to 2016. METHODS A generalized additive model (GAM) was firstly conducted to evaluate the relationship between daily reported incidence rate of PTB and extreme temperature events in the prefecture-level cities. Then, the effect estimates were pooled using meta-analysis at the provincial level. The fixed-effect model or random-effect model was selected based on the result of heterogeneity test. RESULTS Among the 446,016 PTB reported cases, the majority of reported cases occurred in spring. The higher reported incidence rate areas were located in Liaocheng, Taian, Linyi and Heze. Extreme low temperature had an impact on the reported incidence of PTB in only one prefecture-level city, i.e., Binzhou (RR = 0.903, 95% CI: 0.817-0.999). While, extreme high temperature was found to have a positive effect on reported morbidity of PTB in Binzhou (RR = 0.924, 95% CI: 0.856-0.997) and Weihai (RR = 0.910, 95% CI: 0.843-0.982). Meta-analysis showed that extreme high temperature was associated with a decreased risk of PTB (RR = 0.982, 95% CI: 0.966-0.998). However, extreme low temperature was no relationship with the reported incidence of PTB. CONCLUSION Our findings are suggested that extreme high temperature has significantly decreased the risk of PTB at the provincial levels. The findings have implications for developing strategies to response to climate change.
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Affiliation(s)
- Dongzhen Chen
- Department of Epidemiology, School of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, No.619 Changcheng Road, Taian, 271016, Shandong Province, China
| | - Hua Lu
- Taian Centers for Diseases Prevention Control, Taian, 271000, Shandong Province, China
| | - Shengyang Zhang
- Shandong Center for Disease Control and Prevention, Jinan, 250014, Shandong Province, China
| | - Jia Yin
- Department of Epidemiology, School of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, No.619 Changcheng Road, Taian, 271016, Shandong Province, China
| | - Xuena Liu
- Department of Epidemiology, School of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, No.619 Changcheng Road, Taian, 271016, Shandong Province, China
| | - Yixin Zhang
- Shandong Center for Disease Control and Prevention, Jinan, 250014, Shandong Province, China
| | - Bingqin Dai
- Shandong Center for Disease Control and Prevention, Jinan, 250014, Shandong Province, China
| | - Xiaomei Li
- Department of Epidemiology, School of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, No.619 Changcheng Road, Taian, 271016, Shandong Province, China.
| | - Guoyong Ding
- Department of Epidemiology, School of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, No.619 Changcheng Road, Taian, 271016, Shandong Province, China.
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Wang W, Guo W, Cai J, Guo W, Liu R, Liu X, Ma N, Zhang X, Zhang S. Epidemiological characteristics of tuberculosis and effects of meteorological factors and air pollutants on tuberculosis in Shijiazhuang, China: A distribution lag non-linear analysis. ENVIRONMENTAL RESEARCH 2021; 195:110310. [PMID: 33098820 DOI: 10.1016/j.envres.2020.110310] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 08/28/2020] [Accepted: 10/05/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND Tuberculosis (TB) is a serious public health problem in China. There is evidence to prove that meteorological factors and exposure to air pollutants have a certain impact on TB. But the evidence of this relationship is insufficient, and the conclusions are inconsistent. METHODS Descriptive epidemiological methods were used to describe the distribution characteristics of TB in Shijiazhuang in the past five years. Through the generalized linear regression model (GLM) and the generalized additive model (GAM), the risk factors that affect the incidence of TB are screened. A combination of GLM and distribution lag nonlinear model (DLNM) was used to evaluate the lag effect of environmental factors on the TB. Results were tested for robustness by sensitivity analysis. RESULTS The incidence of TB in Shijiazhuang showed a downward trend year by year, with seasonality and periodicity. Every 10 μg/m3 of PM10 changes, the RR distribution is bimodal. The first peak of RR occurs on the second day of lag (RR = 1.00166, 95% CI: 1.00023, 1.00390); the second risk period starts from 13th day of lag and peaks on15th day (RR = 1.00209, 95% CI: 1.00076, 1.00341), both of which are statistically significant. The cumulative effect of increasing 10 μg/m3 showed a similar bimodal distribution. Time zones where the RR makes sense are days 4-6 and 13-20. RR peaked on the 18th day (RR = 1.02239, 95% CI: 1.00623, 1.03882). The RR has a linear relationship with the concentration. Under the same concentration, the RR peaks within 15-20 days. CONCLUSION TB in Shijiazhuang City showed a downward trend year by year, with obvious seasonal fluctuations. The air pollutant PM10 increases the risk of TB. The development of TB has a short-term lag and cumulative lag effects. We should focus on protecting susceptible people from TB in spring and autumn, and strengthen the monitoring and emission management of PM10 in the atmosphere.
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Affiliation(s)
- Wenjuan Wang
- Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, Shijiazhuang, China
| | - Weiheng Guo
- Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, Shijiazhuang, China
| | - Jianning Cai
- Department of Epidemic Control and Prevention, Center for Disease Prevention and Control of Shijiazhuang City, Shijiazhuang, China
| | - Wei Guo
- Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, Shijiazhuang, China
| | - Ran Liu
- Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, Shijiazhuang, China
| | - Xuehui Liu
- Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, Shijiazhuang, China
| | - Ning Ma
- Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, Shijiazhuang, China
| | - Xiaolin Zhang
- Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Hebei Province Key Laboratory of Environment and Human Health, Shijiazhuang, China.
| | - Shiyong Zhang
- Department of Epidemic Control and Prevention, Center for Disease Prevention and Control of Shijiazhuang City, Shijiazhuang, China.
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Influential factors and spatial-temporal distribution of tuberculosis in mainland China. Sci Rep 2021; 11:6274. [PMID: 33737676 PMCID: PMC7973528 DOI: 10.1038/s41598-021-85781-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 03/04/2021] [Indexed: 11/17/2022] Open
Abstract
Tuberculosis (TB) is an infectious disease that threatens human safety. Mainland China is an area with a high incidence of tuberculosis, and the task of tuberculosis prevention and treatment is arduous. This paper aims to study the impact of seven influencing factors and spatial–temporal distribution of the relative risk (RR) of tuberculosis in mainland China using the spatial–temporal distribution model and INLA algorithm. The relative risks and confidence intervals (CI) corresponding to average relative humidity, monthly average precipitation, monthly average sunshine duration and monthly per capita GDP were 1.018 (95% CI 1.001–1.034), 1.014 (95% CI 1.006–1.023), 1.026 (95% CI 1.014–1.039) and 1.025 (95% CI 1.011–1.040). The relative risk for average temperature and pressure were 0.956 (95% CI 0.942–0.969) and 0.767 (95% CI 0.664–0.875). Spatially, the two provinces with the highest relative risks are Xinjiang and Guizhou, and the remaining provinces with higher relative risks were mostly concentrated in the Northwest and South China regions. Temporally, the relative risk decreased year by year from 2013 to 2015. It was higher from February to May each year and was most significant in March. It decreased from June to December. Average relative humidity, monthly average precipitation, monthly average sunshine duration and monthly per capita GDP had positive effects on the relative risk of tuberculosis. The average temperature and pressure had negative effects. The average wind speed had no significant effect. Mainland China should adapt measures to local conditions and develop tuberculosis prevention and control strategies based on the characteristics of different regions and time.
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Xia T, Chen J, Rui J, Li J, Guo Y. What affected Chinese parents' decisions about tuberculosis (TB) treatment: Implications based on a cross-sectional survey. PLoS One 2021; 16:e0245691. [PMID: 33493231 PMCID: PMC7833143 DOI: 10.1371/journal.pone.0245691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 01/05/2021] [Indexed: 11/25/2022] Open
Abstract
OBJECTIVE Although progress has been made in tuberculosis (TB) treatment, China still remains one of the high-burden TB countries. One important reason that has not received sufficient scholarly attention is that Chinese individuals tend to underestimate the threat of TB. This contributed to the high rate of delay in seeking TB treatment and noncompliance with doctors' regimen. Hence, this research examined how TB knowledge affected Chinese parents' risk perceptions and their efficacy appraisal in TB treatment, and how their risk perception and efficacy appraisal affected their intentions to seek timely TB treatment for their children and adhere to doctors' regimen. METHODS We conducted an online cross-sectional survey with 1129 parents of children attending kindergarten, primary school, and middle school in Shajing, a region with high TB incidence in China. Perceived severity of TB threat to self and to others, perceived susceptibility, response efficacy, and self-efficacy were measured, in addition to TB knowledge and intentions to seek timely TB treatment and adhere to doctors' regimens. RESULTS Ordinal least squares regression demonstrated that TB knowledge was positively associated with perceived severity of TB threat to self, perceived severity of TB threat to others, perceived susceptibility, response efficacy, and self-efficacy, but it did not affect their medical decisions. In addition, binary logistic regression revealed that response efficacy and self-efficacy predicted both intentions positively, and perceived severity of TB threat to self only enhanced Chinese individuals' intention to follow doctors' regimens. CONCLUSION Health education aimed at knowledge improvement may be effective in changing one's perceptions of the given health threat but may not be effective to change their behavior. Thus, practitioners need to focus on changing Chinese parents' perceptions of TB rather than simply improving their knowledge. Specifically, it is necessary to lower their efficacy in self-management and enhance their perceived infectiousness of TB.
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Affiliation(s)
- Tingsong Xia
- Bao’an Center for Disease Control and Prevention, Shenzhen, Guangdong Province, China
| | - Juan Chen
- South China University of Technology, Shenzhen, China
| | - Jian Rui
- South China University of Technology, Shenzhen, China
| | - Jinxu Li
- South China University of Technology, Shenzhen, China
| | - Yuli Guo
- South China University of Technology, Shenzhen, China
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Asemahagn MA, Alene GD, Yimer SA. Spatial-temporal clustering of notified pulmonary tuberculosis and its predictors in East Gojjam Zone, Northwest Ethiopia. PLoS One 2021; 16:e0245378. [PMID: 33449953 PMCID: PMC7810325 DOI: 10.1371/journal.pone.0245378] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 12/29/2020] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Tuberculosis (TB) remains a key health menace in Ethiopia and its districts. This study aimed to assess the spatial-temporal clustering of notified pulmonary TB (PTB) cases in East Gojjam Zone, Northwest Ethiopia. METHODS A retrospective study was conducted among all PTB cases reported from 2013-2019. Case notification rates (CNRs) of PTB cases at Kebele (the lowest administrative unit), woreda, and zone levels were estimated. The PTB clustering was done using global Moran's I statistics on Arc GIS 10.6. We used Kulldorff SaTScan 9.6 with a discrete Poisson model to identify statistically significant spatial-temporal clustering of PTB cases at Kebele level. Similarly, a negative binomial regression analysis was used to identify factors associated with the incidence of PTB cases at kebele level. RESULTS A total of 5340 (52%) smear-positive and 4928 (48%) smear-negative PTB cases were analyzed. The overall mean CNR of PTB cases at zone, woreda and Kebele levels were 58(47-69), 82(56-204), and 69(36-347) per 100,000 population, respectively. The purely spatial cluster analysis identified eight most likely clusters (one for overall and one per year for seven reporting years) and 47 secondary clusters. Similarly, the space-time scan analysis identified one most likely and seven secondary clusters. The purely temporal analysis also detected one most likely cluster from 2013-2015. Rural residence, distance from the nearest health facility, and poor TB service readiness were factors (p-value <0.05) to PTB incidence at kebele level. CONCLUSION The distribution of PTB cases was clustered. The PTB CNR was low and showed a decreasing trend during the reporting periods. Rural residence, distance from the health facilities, and poor facility readiness were factors of PTB incidence. Improving accessibility and readiness of health facilities mainly to rural and hotspot areas is vital to increase case detection and reduce TB transmission.
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Affiliation(s)
- Mulusew Andualem Asemahagn
- School of Public Health, College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar, Ethiopia
| | - Getu Degu Alene
- School of Public Health, College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar, Ethiopia
| | - Solomon Abebe Yimer
- Faculty of Medicine, Department of Microbiology, Unit for Genome Dynamics, University of Oslo, Oslo, Norway
- Coalition for Epidemic Preparedness Innovations (CEPI), Oslo, Norway
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Li ZQ, Pan HQ, Liu Q, Song H, Wang JM. Comparing the performance of time series models with or without meteorological factors in predicting incident pulmonary tuberculosis in eastern China. Infect Dis Poverty 2020; 9:151. [PMID: 33148337 PMCID: PMC7641658 DOI: 10.1186/s40249-020-00771-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2020] [Accepted: 10/21/2020] [Indexed: 12/13/2022] Open
Abstract
Background Many studies have compared the performance of time series models in predicting pulmonary tuberculosis (PTB), but few have considered the role of meteorological factors in their prediction models. This study aims to explore whether incorporating meteorological factors can improve the performance of time series models in predicting PTB. Methods We collected the monthly reported number of PTB cases and records of six meteorological factors in three cities of China from 2005 to 2018. Based on this data, we constructed three time series models, including an autoregressive integrated moving average (ARIMA) model, the ARIMA with exogenous variables (ARIMAX) model, and a recurrent neural network (RNN) model. The ARIMAX and RNN models incorporated meteorological factors, while the ARIMA model did not. The mean absolute percentage error (MAPE) and root mean square error (RMSE) were used to evaluate the performance of the models in predicting PTB cases in 2018. Results Both the cross-correlation analysis and Spearman rank correlation test showed that PTB cases reported in the study areas were related to meteorological factors. The predictive performance of both the ARIMA and RNN models was improved after incorporating meteorological factors. The MAPEs of the ARIMA, ARIMAX, and RNN models were 12.54%, 11.96%, and 12.36% in Xuzhou, 15.57%, 11.16%, and 14.09% in Nantong, and 9.70%, 9.66%, and 12.50% in Wuxi, respectively. The RMSEs of the three models were 36.194, 33.956, and 34.785 in Xuzhou, 34.073, 25.884, and 31.828 in Nantong, and 19.545, 19.026, and 26.019 in Wuxi, respectively. Conclusions Our study revealed a possible link between PTB and meteorological factors. Taking meteorological factors into consideration increased the accuracy of time series models in predicting PTB, and the ARIMAX model was superior to the ARIMA and RNN models in study settings.
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Affiliation(s)
- Zhong-Qi Li
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Ave., Nanjing, 211166, China
| | - Hong-Qiu Pan
- Department of Tuberculosis, The Third Hospital of Zhenjiang City, Zhenjiang, 212005, China
| | - Qiao Liu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Ave., Nanjing, 211166, China
| | - Huan Song
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Ave., Nanjing, 211166, China
| | - Jian-Ming Wang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Ave., Nanjing, 211166, China.
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Yang R, Liu M, Jiang H, Zhang Y, Yin J, Li Q, Li Q, Liu Y, Wang X, Xu H, Yang Y, Li W, Guo X. The epidemiology of pulmonary tuberculosis in children in Mainland China, 2009-2015. Arch Dis Child 2020; 105:319-325. [PMID: 31771943 DOI: 10.1136/archdischild-2019-317635] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2019] [Accepted: 10/25/2019] [Indexed: 11/04/2022]
Abstract
OBJECTIVE To describe the incidence and epidemiological characteristics of child pulmonary tuberculosis (PTB) notified to the Chinese Center for Disease Control and Prevention (CDC) Tuberculosis Information Management System (TBIMS) from 2009 to 2015. STUDY DESIGN A database of child PTB for 31 provinces in mainland China over 7 years was retrospectively analysed. The spatiotemporal distribution of child PTB was described. RESULTS In total, 35,710 child PTB cases were notified to the TBIMS, representing only 0.60% of all 5,991,593 PTB case notifications. The average annual notified incidence of child PTB was 2.44/ per 100,000 children (95% CI: 1.77-3.10) and decreased by 52.53% in all age groups during the study period. Tibet had the highest incidence (15.95/ per 100,000 children), followed by Guizhou and Xinjiang. However, the case numbers were the most in Guizhou. The 0-1 year and 12-14 years of age groups exhibited the most cases. The positive rates of sputum smears, bacteriology and chest X-rays abnormality were 21.5%, 21.7% and 98.1%, respectively, which were lower than those 15 years of age and older (all p-values<0.0001). CONCLUSION The notified incidence of child PTB in mainland China decreased substantially over 7 years. Future prevention and control of PTB in children should focus on the 0-1 and 12-14 years of age groups, and Tibet and Guizhou provinces. However, the notified incidence is still low, relative to adults, suggesting substantial under-reporting. Thus, more effective care seeking, identification and registration of children with TB are crucial.
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Affiliation(s)
- Ruling Yang
- Infection Department of the Children's Hospital, National Clinical Research Center for Child Health and Disorders, The Children's Hospital of Chongqing Medical University, Chongqing, China.,Chongqing Key Laboratory of Child Infection and Immunity, The Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Mengyang Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, China
| | - Hui Jiang
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, China.,National Tuberculosis Clinical Lab of China, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing Chest Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory in Drug Resistance Tuberculosis Research, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China
| | - Yingjie Zhang
- Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jinfeng Yin
- School of Statistics, Renmin University of China, Beijing, China
| | - Qihuan Li
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, China
| | - Qing Li
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, China.,National Tuberculosis Clinical Lab of China, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing Chest Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory in Drug Resistance Tuberculosis Research, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China
| | - Yue Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, China
| | - Xiaonan Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, China
| | - Hongmei Xu
- Infection Department of the Children's Hospital, National Clinical Research Center for Child Health and Disorders, The Children's Hospital of Chongqing Medical University, Chongqing, China.,Chongqing Key Laboratory of Child Infection and Immunity, The Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Yang Yang
- Department of Biostatistics, University of Florida, Gainesville, Florida, Armenia
| | - Weimin Li
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, China .,National Tuberculosis Clinical Lab of China, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing Chest Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory in Drug Resistance Tuberculosis Research, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China
| | - Xiuhua Guo
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China .,Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, China
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Wang Q, Guo L, Wang J, Zhang L, Zhu W, Yuan Y, Li J. Spatial distribution of tuberculosis and its socioeconomic influencing factors in mainland China 2013-2016. Trop Med Int Health 2019; 24:1104-1113. [PMID: 31314953 DOI: 10.1111/tmi.13289] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
BACKGROUND To assess the spatial epidemic characteristics of TB and identify the key areas for disease prevention and control. OBJECTIVE To explore the spatial distribution and socioeconomic influencing factors of TB in mainland China from 2013 to 2016. METHODS Spatial autocorrelation was used to explore the spatial distribution characteristics of TB at the quantitative level. Ordinary least squares (OLS) and geographically weighted regression (GWR) models were conducted to explore the association between factors and TB incidence from both global and local perspectives. RESULTS There was a significant positive spatial autocorrelation of TB at the provincial level (P < 0.05): hot spots were mainly located in the west of Xinjiang and Tibet, and cold spots in the eastern coastal areas. Four latent factors on the socioeconomic dimension, involving the proportion of illiterate people aged 15 and over, per capita disposable income in rural areas, the number of health technicians per 1000 population and the urban population density, were associated with TB incidence. The GWR model showed that the effect of the same factor on TB incidence varied with geographical location. CONCLUSIONS Spatial clustering of TB incidence in mainland China still exists. The differences of socioeconomic factors in different locations can be confirmed by GWR model. Targeted preventive and control measures or policies will be conducive in effectively reducing the incidence of TB, especially in hot spots.
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Affiliation(s)
- Qi Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Lanzhou University, Lanzhou, China
| | - Liang Guo
- The Second School of Clinical Medicine, Lanzhou University, Lanzhou, China
| | - Jing Wang
- Nursing Department of the First Hospital of Lanzhou University, Lanzhou, China
| | - Leijie Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Lanzhou University, Lanzhou, China
| | - Wanqi Zhu
- Department of Epidemiology and Biostatistics, School of Public Health, Lanzhou University, Lanzhou, China
| | - Yan Yuan
- Department of Epidemiology and Biostatistics, School of Public Health, Lanzhou University, Lanzhou, China
| | - Juansheng Li
- Department of Epidemiology and Biostatistics, School of Public Health, Lanzhou University, Lanzhou, China
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