1
|
Zhang J, Zhong M, Huang J, Deng W, Li P, Yao Z, Ye X, Zhong X. Spatiotemporal patterns and socioeconomic determinants of pulmonary tuberculosis in Dongguan city, China, during 2011-2020: an ecological study. BMJ Open 2024; 14:e085733. [PMID: 39260857 PMCID: PMC11409261 DOI: 10.1136/bmjopen-2024-085733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/13/2024] Open
Abstract
OBJECTIVE Pulmonary tuberculosis (PTB) is a critical challenge worldwide, particularly in China. This study aimed to explore the spatiotemporal transmission patterns and socioeconomic factors of PTB in Dongguan city, China. METHODS/DESIGN An ecological study based on the reported new PTB cases between 2011 and 2020 was conducted in Dongguan city, China. The spatiotemporal analysis methods were used to explore the long-term trend, spatiotemporal transmission pattern and socioeconomic factors of PTB. MAIN OUTCOME MEASURES The number of new PTB cases. PARTICIPANTS We collected 35 756 new PTB cases, including 23 572 males and 12 184 females. RESULTS The seasonal-trend decomposition indicated a significant downward trend for PTB with a significant peak in 2017 and 2018, and local spatial autocorrelation showed more and more high-high clusters in the central and north-central towns with high incidence. The multivariate spatial time series analysis revealed that the endemic component had a leading role in driving PTB transmission, with a high total effect value being 189.40 (95% CI: 171.65-207.15). A Bayesian spatiotemporal model revealed that PTB incidence is positively associated with the agricultural population ratio (relative risk (RR) =1.074), gender ratio (RR=1.104) and the number of beds in medical institutions (RR=1.028). CONCLUSIONS These findings revealed potential spatiotemporal variability and spatial aggregation of PTB, so targeted preventive strategies should be made in different towns based on spatiotemporal transmission patterns and risk factors.
Collapse
Affiliation(s)
- Jingfeng Zhang
- School of Public Health, Guangdong Pharmaceutical University, Guangzhou, Guangdong, China
| | - Minghao Zhong
- Department of Prevention and Health Care, The Sixth People's Hospital of Dongguan City, Dongguan, China
| | - Jiayin Huang
- School of Public Health, Guangdong Pharmaceutical University, Guangzhou, Guangdong, China
| | - Wenjun Deng
- School of Public Health, Guangdong Pharmaceutical University, Guangzhou, Guangdong, China
| | - Pingyuan Li
- School of Public Health, Guangdong Pharmaceutical University, Guangzhou, Guangdong, China
| | - ZhenJiang Yao
- School of Public Health, Guangdong Pharmaceutical University, Guangzhou, Guangdong, China
| | - Xiaohua Ye
- School of Public Health, Guangdong Pharmaceutical University, Guangzhou, Guangdong, China
| | - Xinguang Zhong
- Department of Prevention and Health Care, The Sixth People's Hospital of Dongguan City, Dongguan, China
| |
Collapse
|
2
|
Li W, Wang J, Huang W, Yan Y, Liu Y, Zhao Q, Chen M, Yang L, Guo Y, Ma W. The association between humidex and tuberculosis: a two-stage modelling nationwide study in China. BMC Public Health 2024; 24:1289. [PMID: 38734652 PMCID: PMC11088084 DOI: 10.1186/s12889-024-18772-8] [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: 02/21/2024] [Accepted: 05/03/2024] [Indexed: 05/13/2024] Open
Abstract
BACKGROUND Under a changing climate, the joint effects of temperature and relative humidity on tuberculosis (TB) are poorly understood. To address this research gap, we conducted a time-series study to explore the joint effects of temperature and relative humidity on TB incidence in China, considering potential modifiers. METHODS Weekly data on TB cases and meteorological factors in 22 cities across mainland China between 2011 and 2020 were collected. The proxy indicator for the combined exposure levels of temperature and relative humidity, Humidex, was calculated. First, a quasi-Poisson regression with the distributed lag non-linear model (DLNM) was constructed to examine the city-specific associations between humidex and TB incidence. Second, a multivariate meta-regression model was used to pool the city-specific effect estimates, and to explore the potential effect modifiers. RESULTS A total of 849,676 TB cases occurred in the 22 cities between 2011 and 2020. Overall, a conspicuous J-shaped relationship between humidex and TB incidence was discerned. Specifically, a decrease in humidex was positively correlated with an increased risk of TB incidence, with a maximum relative risk (RR) of 1.40 (95% CI: 1.11-1.76). The elevated RR of TB incidence associated with low humidex (5th humidex) appeared on week 3 and could persist until week 13, with a peak at approximately week 5 (RR: 1.03, 95% CI: 1.01-1.05). The effects of low humidex on TB incidence vary by Natural Growth Rate (NGR) levels. CONCLUSION A J-shaped exposure-response association existed between humidex and TB incidence in China. Humidex may act as a better predictor to forecast TB incidence compared to temperature and relative humidity alone, especially in regions with higher NGRs.
Collapse
Affiliation(s)
- Wen Li
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Shandong University Climate Change and Health Center, Jinan, Shandong, China
| | - Jia Wang
- National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Wenzhong Huang
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Yu Yan
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Shandong University Climate Change and Health Center, Jinan, Shandong, China
| | - Yanming Liu
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Qi Zhao
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Shandong University Climate Change and Health Center, Jinan, Shandong, China
| | - Mingting Chen
- National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
| | - Liping Yang
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China.
| | - Yuming Guo
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Wei Ma
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China.
- Shandong University Climate Change and Health Center, Jinan, Shandong, China.
| |
Collapse
|
3
|
Nie Y, Yang Z, Lu Y, Bahani M, Zheng Y, Tian M, Zhang L. Interaction between air pollutants and meteorological factors on pulmonary tuberculosis in northwest China: A case study of eight districts in Urumqi. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2024; 68:691-700. [PMID: 38182774 DOI: 10.1007/s00484-023-02615-z] [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: 03/25/2023] [Revised: 12/27/2023] [Accepted: 12/27/2023] [Indexed: 01/07/2024]
Abstract
Meteorological factors and air pollutants are associated with the spread of pulmonary tuberculosis (PTB), but few studies have examined the effects of their interactions on PTB. Therefore, this study investigated the impact of meteorological factors and air pollutants and their interactions on the risk of PTB in Urumqi, a city with a high prevalence of PTB and a high level of air pollution. The number of new PTB cases in eight districts of Urumqi from 2014 to 2019 was collected, along with data on meteorological factors and air pollutants for the same period. A generalized additive model was applied to explore the effects of meteorological factors and air pollutants and their interactions on the risk of PTB incidence. Segmented linear regression was used to estimate the nonlinear characteristics of the impact of meteorological factors on PTB. During 2014-2019, a total of 14,402 new cases of PTB were reported in eight districts, with March to May being the months of high PTB incidence. The exposure-response curves for temperature (Temp), relative humidity (RH), wind speed (WS), air pressure (AP), and diurnal temperature difference (DTR) were generally inverted "U" shaped, with the corresponding threshold values of - 5.411 °C, 52.118%, 3.513 m/s, 1021.625 hPa, and 8.161 °C, respectively. The effects of air pollutants on PTB were linear and lagged. All air pollutants were positively associated with PTB, except for O3, which was not associated with PTB, and the ER values for the effects on PTB were as follows: 0.931 (0.255, 1.612) for PM2.5, 1.028 (0.301, 1.760) for PM10, 5.061 (0.387, 9.952) for SO2, 2.830 (0.512, 5.200) for NO2, and 5.789 (1.508, 10.251) for CO. Meteorological factors and air pollutants have an interactive effect on PTB. The risk of PTB incidence was higher when in high Temp-high air pollutant, high RH-high air pollutant, high WS-high air pollutant, lowAP-high air pollutant, and high DTR-high air pollutant. In conclusion, both meteorological and pollutant factors had an influence on PTB, and the influence on PTB may have an interaction.
Collapse
Affiliation(s)
- Yanwu Nie
- School of Public Health, Xinjiang Medical University, Urumqi, China
| | - Zhen Yang
- School of Public Health, Xinjiang Medical University, Urumqi, China
| | - Yaoqin Lu
- Urumqi Center for Disease Control and Prevention, Urumqi, China
| | - Mailiman Bahani
- School of Public Health, Xinjiang Medical University, Urumqi, China
| | - Yanling Zheng
- College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, China
| | - Maozai Tian
- College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, China
| | - Liping Zhang
- College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, China.
| |
Collapse
|
4
|
Zhang L, Li Y, Ma N, Zhao Y, Zhao Y. Heterogeneity of influenza infection at precise scale in Yinchuan, Northwest China, 2012-2022: evidence from Joinpoint regression and spatiotemporal analysis. Sci Rep 2024; 14:3079. [PMID: 38321190 PMCID: PMC10847441 DOI: 10.1038/s41598-024-53767-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 02/05/2024] [Indexed: 02/08/2024] Open
Abstract
Identifying high-risk regions and turning points of influenza with a precise spatiotemporal scale may provide effective prevention strategies. In this study, epidemiological characteristics and spatiotemporal clustering analysis at the township level were performed. A descriptive study and a Joinpoint regression analysis were used to explore the epidemiological characteristics and the time trend of influenza. Spatiotemporal autocorrelation and clustering analyses were carried out to explore the spatiotemporal distribution characteristics and aggregation. Furthermore, the hotspot regions were analyzed by spatiotemporal scan analysis. A total of 4025 influenza cases were reported in Yinchuan showing an overall increasing trend. The tendency of influenza in Yinchuan consisted of three stages: increased from 2012 to the first peak in 2019 (32.62/100,000) with a slight decrease in 2016; during 2019 and 2020, the trend was downwards; then it increased sharply again and reached another peak in 2022. The Joinpoint regression analysis found that there were three turning points from January 2012 to December 2022, namely January 2020, April 2020, and February 2022. The children under ten displayed an upward trend and were statistically significant. The trend surface analysis indicated that there was a shifting trend from northern to central and southern. A significant positive spatial auto-correlation was observed at the township level and four high-incidence clusters of influenza were detected. These results suggested that children under 10 years old deserve more attention and the spatiotemporal distribution of high-risk regions of influenza in Yinchuan varies every year at the township level. Thus, more monitoring and resource allocation should be prone to the four high-incidence clusters, which may benefit the public health authorities to carry out the vaccination and health promotion timely.
Collapse
Affiliation(s)
- Lu Zhang
- School of Public Health, Ningxia Medical University, Yinchuan, 750004, Ningxia, China
- Ningxia Key Laboratory of Environmental Factors and Chronic Disease Control, Yinchuan, 750004, Ningxia, China
| | - Yan Li
- Yinchuan Center for Diseases Prevention and Control, Yinchuan, 750004, Ningxia, China
| | - Ning Ma
- School of Public Health, Ningxia Medical University, Yinchuan, 750004, Ningxia, China
- Ningxia Key Laboratory of Environmental Factors and Chronic Disease Control, Yinchuan, 750004, Ningxia, China
| | - Yi Zhao
- School of Public Health, Ningxia Medical University, Yinchuan, 750004, Ningxia, China
- Ningxia Key Laboratory of Environmental Factors and Chronic Disease Control, Yinchuan, 750004, Ningxia, China
| | - Yu Zhao
- School of Public Health, Ningxia Medical University, Yinchuan, 750004, Ningxia, China.
- Ningxia Key Laboratory of Environmental Factors and Chronic Disease Control, Yinchuan, 750004, Ningxia, China.
| |
Collapse
|
5
|
Gao J, Zhang Y, Wang X, Sun Q, Yin J. Active screening for tuberculosis among high-risk populations in high-burden areas in Zhejiang province, China. Public Health 2024; 226:138-143. [PMID: 38056401 DOI: 10.1016/j.puhe.2023.10.051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 10/13/2023] [Accepted: 10/31/2023] [Indexed: 12/08/2023]
Abstract
OBJECTIVES Tuberculosis (TB) is a major global public health concern. Although the incidence of TB in China is declining, the country continues to face many challenges regarding TB control. This study aimed to develop an active case finding (ACF) strategy for high-risk populations in areas with high TB burden and evaluate the effectiveness of the ACF strategy for early TB detection in patients to reduce TB transmission. STUDY DESIGN This was a descriptive study. METHODS From May to October 2019, active TB screening was conducted in Zhejiang Province, China. Overall, 24 high-burden townships were chosen as study sites. Residents aged ≥65 years, suffering from diabetes, diagnosed with HIV/AIDS, or with a history of TB were mobilized for screening. Chest radiography was performed for all participants in the community. Sputum specimens were collected for sputum smear tests and cultures at county-level TB-designed hospitals. A professional medical team performed the final diagnoses. RESULTS Overall, 130,643 residents were included, accounting for 8.85% of the total population in the selected areas. After screening, 89 confirmed cases and 419 suspected cases were identified. The detection rates for suspected and confirmed cases were 320.72/100,000 and 68.12/100,000, respectively. Individuals with a history of TB accounted for a large proportion of detected cases, and the detection rate was higher among males than in females. This study identified 10.5% of reported cases in the selected areas in 2019. In Zhejiang province, compared with the previous year, the rates of TB notification in 2019 and 2020 declined by 7.0% and 7.4%, respectively, compared with the previous year. However, the TB notification rate in 2019 was almost the same as that in 2018 (a decline of 2.5%) but sharply declined in 2020 (14.4%) in the screened areas. CONCLUSIONS Our findings suggest that the ACF strategy may have helped to maintain the downward trends in TB notification rates by detecting patients with TB and suspected cases in the short term.
Collapse
Affiliation(s)
- J Gao
- Center for Health Management and Policy Research, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China; NHC Key Lab of Health Economics and Policy Research (Shandong University), Jinan, 250012, China.
| | - Y Zhang
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, China.
| | - X Wang
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, China.
| | - Q Sun
- Center for Health Management and Policy Research, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China; NHC Key Lab of Health Economics and Policy Research (Shandong University), Jinan, 250012, China.
| | - J Yin
- Center for Health Management and Policy Research, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China; NHC Key Lab of Health Economics and Policy Research (Shandong University), Jinan, 250012, China.
| |
Collapse
|
6
|
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.
Collapse
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
| |
Collapse
|
7
|
Chen S, Wang X, Li D, Zhao J, Zhang J, Zhang Y, Zhang X, Kan X. Association Between Exposure to Ozone (O 3) and the Short-Term Effect on Tuberculosis Outpatient Visits: A Time-Series Study in 16 Cities of Anhui Province, China. J Multidiscip Healthc 2023; 16:2045-2055. [PMID: 37496636 PMCID: PMC10366443 DOI: 10.2147/jmdh.s412394] [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: 03/30/2023] [Accepted: 06/19/2023] [Indexed: 07/28/2023] Open
Abstract
Introduction Evidence has shown that air pollutant exposure plays a vital role in the progression of tuberculosis (TB). The aim of this research was to assess the short-term effects of ozone (O3) exposure and TB outpatient visits in 16 prefecture-level cities of Anhui, China, 2015-2020. Methods Distributed lag nonlinear model (DLNM), Poisson generalized linear regression model and random effects model were applied in this study. The effects of different age and gender on TB were investigated by stratified analysis, and then we performed sensitivity analyses to verify the stability of the results. Results A total of 186,623 active TB cases were registered from January 1, 2015 to December 31,2020 in Anhui. The average concentration of ozone is 92.77 ± 42.95 μg/m3. The maximum lag-specific and cumulative relative risk (RR) of TB outpatient visits was 1.0240 (95% CI: 1.0170-1.0310, lag 28 days) for each 10 µg/m³ increase in O3 in the single-pollutant model. Estimation for 16 prefecture-level cities indicated that the strong association between O3 and the risk of TB outpatient visits was in tongling (RR = 1.0555, 95% CI: 1.0089-1.1042), Suzhou (RR = 1.0475, 95% CI: 1.0268-1.0687), wuhu (RR = 1.0358, 95% CI: 1.0023-1.0704). Stratified analysis showed that the health effects of ozone exposure remained significant in male and older adults, and there was no significant association between exposure to ozone in children and adolescents and the risk of tuberculosis. Discussion We found that ozone exposure increases the risk of TB infection in outpatient patients, with males and the elderly being more susceptible, and it is necessary for government departments to develop targeted publicity and prevention measures in response to the local air quality conditions.
Collapse
Affiliation(s)
- Shuangshuang Chen
- Department of Tuberculosis Prevent and Control, Center for Disease Control and Prevention of Hefei, Hefei, Anhui, 230051, People’s Republic of China
- Department of Scientific Research and Education, Anhui Chest Hospital, Hefei, Anhui, 230022, People’s Republic of China
- Department of Scientific Research and Education, Anhui Provincial Tuberculosis Institute, Hefei, Anhui, 230022, People’s Republic of China
| | - Xinqiang Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, People’s Republic of China
| | - Danhui Li
- Department of Hospital Infection and Management, Anhui Chest Hospital, Hefei, Anhui, 230022, People’s Republic of China
| | - Jiawen Zhao
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, People’s Republic of China
| | - Jingjing Zhang
- Department of Scientific Research and Education, Anhui Provincial Tuberculosis Institute, Hefei, Anhui, 230022, People’s Republic of China
| | - Yongzhong Zhang
- Department of Tuberculosis Prevent and Control, Anhui Provincial Tuberculosis Institute, Hefei, Anhui, 230022, People’s Republic of China
| | - Xiujun Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, People’s Republic of China
| | - Xiaohong Kan
- Department of Scientific Research and Education, Anhui Chest Hospital, Hefei, Anhui, 230022, People’s Republic of China
- Department of Scientific Research and Education, Anhui Provincial Tuberculosis Institute, Hefei, Anhui, 230022, People’s Republic of China
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, People’s Republic of China
| |
Collapse
|
8
|
Wu Z, Fu G, Wen Q, Wang Z, Shi LE, Qiu B, Wang J. Spatiotemporally Comparative Analysis of HIV, Pulmonary Tuberculosis, HIV-Pulmonary Tuberculosis Coinfection in Jiangsu Province, China. Infect Drug Resist 2023; 16:4039-4052. [PMID: 37383602 PMCID: PMC10296641 DOI: 10.2147/idr.s412870] [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: 04/20/2023] [Accepted: 06/15/2023] [Indexed: 06/30/2023] Open
Abstract
Purpose Pulmonary tuberculosis (PTB) is a severe chronic communicable disease that causes a heavy disease burden in China. Human Immunodeficiency Virus (HIV) and PTB coinfection dramatically increases the risk of death. This study analyzes the spatiotemporal dynamics of HIV, PTB and HIV-PTB coinfection in Jiangsu Province, China, and explores the impact of socioeconomic determinants. Patients and Methods The data on all notified HIV, PTB and HIV-PTB coinfection cases were extracted from Jiangsu Provincial Center for Disease Control and Prevention. We applied the seasonal index to identify high-risk periods of the disease. Time trend, spatial autocorrelation and SaTScan were used to analyze temporal trends, hotspots and spatiotemporal clusters of diseases. The Bayesian space-time model was conducted to examine the socioeconomic determinants. Results The case notification rate (CNR) of PTB decreased from 2011 to 2019 in Jiangsu Province, but the CNR of HIV and HIV-PTB coinfection had an upward trend. The seasonal index of PTB was the highest in March, and its hotspots were mainly distributed in the central and northern parts, such as Xuzhou, Suqian, Lianyungang and Taizhou. HIV had the highest seasonal index in July and HIV-PTB coinfection had the highest seasonal index in June, with their hotspots mainly distributed in southern Jiangsu, involving Nanjing, Suzhou, Wuxi and Changzhou. The Bayesian space-time interaction model showed that socioeconomic factor and population density were negatively correlated with the CNR of PTB, and positively associated with the CNR of HIV and HIV-PTB coinfection. Conclusion The spatial heterogeneity and spatiotemporal clusters of PTB, HIV and HIV-PTB coinfection are exhibited obviously in Jiangsu. More comprehensive interventions should be applied to target TB in the northern part. While in southern Jiangsu, where the economic level is well-developed and the population density is high, we should strengthen the prevention and control of HIV and HIV-PTB coinfection.
Collapse
Affiliation(s)
- Zhuchao Wu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, People’s Republic of China
| | - Gengfeng Fu
- Department of STI and HIV Control and Prevention, Center for Disease Control and Prevention of Jiangsu Province, Nanjing, 210009, People’s Republic of China
| | - Qin Wen
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, People’s Republic of China
| | - Zheyue Wang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, People’s Republic of China
| | - Lin-en Shi
- Department of STI and HIV Control and Prevention, Center for Disease Control and Prevention of Jiangsu Province, Nanjing, 210009, People’s Republic of China
| | - Beibei Qiu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, People’s Republic of China
| | - Jianming Wang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, People’s Republic of China
- Department of Epidemiology, Gusu School, Nanjing Medical University, Nanjing, 211166, People’s Republic of China
- Changzhou Medical Center, Nanjing Medical University, Nanjing, 211166, People’s Republic of China
| |
Collapse
|
9
|
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.
Collapse
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.
| |
Collapse
|
10
|
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.
Collapse
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.
| |
Collapse
|
11
|
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.
Collapse
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.
| |
Collapse
|
12
|
Investigating Spatial Patterns of Pulmonary Tuberculosis and Main Related Factors in Bandar Lampung, Indonesia Using Geographically Weighted Poisson Regression. Trop Med Infect Dis 2022; 7:tropicalmed7090212. [PMID: 36136622 PMCID: PMC9502094 DOI: 10.3390/tropicalmed7090212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 08/15/2022] [Accepted: 08/22/2022] [Indexed: 11/16/2022] Open
Abstract
Tuberculosis (TB) is a highly infectious disease, representing one of the major causes of death worldwide. Sustainable Development Goal 3.3 implies a serious decrease in the incidence of TB cases. Hence, this study applied a spatial analysis approach to investigate patterns of pulmonary TB cases and its drivers in Bandar Lampung (Indonesia). Our study examined seven variables: the growth rate of pulmonary TB, population, distance to the city center, industrial area, green open space, built area, and slum area using geographically weighted Poisson regression (GWPR). The GWPR model demonstrated excellent results with an R2 and adjusted R2 of 0.96 and 0.94, respectively. In this case, the growth rate of pulmonary TB and population were statistically significant variables. Spatial pattern analysis of sub-districts revealed that those of Panjang and Kedaton were driven by high pulmonary TB growth rate and population, whereas that of Sukabumi was driven by the accumulation of high levels of industrial area, built area, and slums. For these reasons, we suggest that local policymakers implement a variety of infectious disease prevention and control strategies based on the spatial variation of pulmonary TB rate and its influencing factors in each sub-district.
Collapse
|
13
|
Chen L, Wang X, Jia X, Lan Y, Yi H, Wang X, Xu P. Investigation of 3-year inpatient TB cases in Zunyi, China: Increased TB burden but improved bacteriological diagnosis. Front Public Health 2022; 10:941183. [PMID: 35983359 PMCID: PMC9381004 DOI: 10.3389/fpubh.2022.941183] [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: 05/11/2022] [Accepted: 07/14/2022] [Indexed: 11/13/2022] Open
Abstract
Background As one of the top three high tuberculosis (TB) burden countries, China is a country where the overall TB incidence continues to decline. However, due to its large population and area, the increased TB burden exists in regional areas. Methods This retrospective study analyzed local inpatient pulmonary TB cases in the Affiliated Hospital of Zunyi Medical University (AHZMU) from January 2016 to December 2018 in a high TB incidence and economically-less-developed area of China. Four methods, acid-fast bacilli stain, culture, Xpert and LAMP, were used to detect Mycobacterium tuberculosis (M.tb), while proportional method and Xpert were used to identify rifampicin-resistant TB (RR-TB). Case number, treatment history, M.tb confirmed TB and rifampicin resistant proportion were analyzed to investigate the local TB epidemic. Results Total 3,910 local inpatient cases with pulmonary TB were admitted to AHZMU during this study period. The annual numbers of total TB cases increased 26.4% (from 1,173 to 1,483), while new cases increased 29.6% (from 936 to 1,213) and RR-TB cases increased 2.7 times (from 31 to 84). Meanwhile, the percentage of previously treated cases declined from 20.2 to 18.2% and the M.tb confirmed TB proportion increased from 34.7 to 49.7%. Conclusion The elevated M.tb confirmed TB proportion and the declined percentage of previously treated cases indicated the improved TB diagnosis and treatment of AHZMU. However, the increasing number of total TB cases, new and RR-TB cases showed an upward trend and increased TB burden in a relatively underdeveloped area of China.
Collapse
Affiliation(s)
- Ling Chen
- Tuberculosis Division of Respiratory and Critical Care Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, China
- *Correspondence: Ling Chen
| | - Xiaodan Wang
- Tuberculosis Division of Respiratory and Critical Care Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Xudong Jia
- School of Basic Medicine, Zunyi Medical University, Zunyi, China
| | - Yuanbo Lan
- Tuberculosis Division of Respiratory and Critical Care Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Haibo Yi
- School of Basic Medicine, Zunyi Medical University, Zunyi, China
| | - Xiaomin Wang
- School of Basic Medicine, Zunyi Medical University, Zunyi, China
- Xiaomin Wang
| | - Peng Xu
- School of Basic Medicine, Zunyi Medical University, Zunyi, China
- Peng Xu
| |
Collapse
|
14
|
Li H, Ge M, Zhang M. Spatio-temporal distribution of tuberculosis and the effects of environmental factors in China. BMC Infect Dis 2022; 22:565. [PMID: 35733132 PMCID: PMC9215012 DOI: 10.1186/s12879-022-07539-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 06/15/2022] [Indexed: 11/10/2022] Open
Abstract
Background Although the World Health Organization reports that the incidence of tuberculosis in China is decreasing every year, the burden of tuberculosis in China is still very heavy. Understanding the spatial and temporal distribution pattern of tuberculosis in China and its influencing environmental factors will provide effective reference for the prevention and treatment of tuberculosis. Methods Data of TB incidence from 2010 to 2017 were collected. Time series and global spatial autocorrelation were used to analyze the temporal and spatial distribution pattern of tuberculosis incidence in China, Geodetector and Geographically Weighted Regression model were used to analyze the environmental factors affecting the TB incidence. Results In addition to 2007 and 2008, the TB incidence decreased in general. TB has a strong spatial aggregation. Cities in Northwest China have been showing a trend of high-value aggregation. In recent years, the center of gravity of high-value aggregation area in South China has moved further south. Temperature, humidity, precipitation, PM10, PM2.5, O3, NO2 and SO2 have impacts on TB incidence, and in different regions, the environmental factors show regional differences. Conclusions Residents should pay more attention to the risk of developing TB caused by climate change and air pollutant exposure. Increased efforts should be placed on areas with high-value clustering in future public resource configurations.
Collapse
Affiliation(s)
- Hao Li
- Institute of Healthy Geography, School of Geography and Tourism, Shaanxi Normal University, Xi'an, 710119, China.,College of Resources and Environmental Science, Ningxia University, Yinchuan, 750021, China
| | - Miao Ge
- Institute of Healthy Geography, School of Geography and Tourism, Shaanxi Normal University, Xi'an, 710119, China.
| | - Mingxin Zhang
- College of Resources and Environmental Science, Ningxia University, Yinchuan, 750021, China
| |
Collapse
|
15
|
Yun W, Huijuan C, Long L, Xiaolong L, Aihua Z. Time trend prediction and spatial-temporal analysis of multidrug-resistant tuberculosis in Guizhou Province, China, during 2014-2020. BMC Infect Dis 2022; 22:525. [PMID: 35672746 PMCID: PMC9171477 DOI: 10.1186/s12879-022-07499-9] [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: 10/18/2021] [Accepted: 05/20/2022] [Indexed: 11/10/2022] Open
Abstract
Background Guizhou is located in the southwest of China with high multidrug-resistant tuberculosis (MDR-TB) epidemic. To fight this disease, Guizhou provincial authorities have made efforts to establish MDR-TB service system and perform the strategies for active case finding since 2014. The expanded case finding starting from 2019 and COVID-19 pandemic may affect the cases distribution. Thus, this study aims to analyze MDR-TB epidemic status from 2014 to 2020 for the first time in Guizhou in order to guide control strategies. Methods Data of notified MDR-TB cases were extracted from the National TB Surveillance System correspond to population information for each county of Guizhou from 2014 to 2020. The percentage change was calculated to quantify the change of cases from 2014 to 2020. Time trend and seasonality of case series were analyzed by a seasonal autoregressive integrated moving average (SARIMA) model. Spatial–temporal distribution at county-level was explored by spatial autocorrelation analysis and spatial–temporal scan statistic. Results Guizhou has 9 prefectures and 88 counties. In this study, 1,666 notified MDR-TB cases were included from 2014–2020. The number of cases increased yearly. Between 2014 and 2019, the percentage increase ranged from 6.7 to 21.0%. From 2019 to 2020, the percentage increase was 62.1%. The seasonal trend illustrated that most cases were observed during the autumn with the trough in February. Only in 2020, a peak admission was observed in June. This may be caused by COVID-19 pandemic restrictions being lifted until May 2020. The spatial–temporal heterogeneity revealed that over the years, most MDR-TB cases stably aggregated over four prefectures in the northwest, covering Bijie, Guiyang, Liupanshui and Zunyi. Three prefectures (Anshun, Tongren and Qiandongnan) only exhibited case clusters in 2020. Conclusion This study identified the upward trend with seasonality and spatial−temporal clusters of MDR-TB cases in Guizhou from 2014 to 2020. The fast rising of cases and different distribution from the past in 2020 were affected by the expanded case finding from 2019 and COVID-19. The results suggest that control efforts should target at high-risk periods and areas by prioritizing resources allocation to increase cases detection capacity and better access to treatment.
Collapse
Affiliation(s)
- Wang Yun
- Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, School of Public Health, Guizhou Medical University, Guiyang, Guizhou, China
| | - Chen Huijuan
- Department of Tuberculosis Prevention and Control, Guizhou Center for Disease Prevention and Control, Guiyang, Guizhou, China.
| | - Liao Long
- School of Medicine and Health Management, Guizhou Medical University, Guiyang, Guizhou, China
| | - Lu Xiaolong
- School of Medicine and Health Management, Guizhou Medical University, Guiyang, Guizhou, China
| | - Zhang Aihua
- Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, School of Public Health, Guizhou Medical University, Guiyang, Guizhou, China
| |
Collapse
|
16
|
Study on the Associations between Meteorological Factors and the Incidence of Pulmonary Tuberculosis in Xinjiang, China. ATMOSPHERE 2022. [DOI: 10.3390/atmos13040533] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Pulmonary tuberculosis (PTB) has been a major threat to global public health. The association between meteorological factors and the incidence of PTB has been widely investigated by the generalized additive model, auto-regressive integrated moving average model and the distributed lag model, etc. However, these models could not address a non-linear or lag correlation between them. In this paper, a penalized distributed lag non-linear model, as a generalized and improved one, was applied to explore the influence of meteorological factors (such as air temperature, relative humidity and wind speed) on the PTB incidence in Xinjiang from 2004 to 2019. Moreover, we firstly use a comprehensive index (apparent temperature, AT) to access the impact of multiple meteorological factors on the incidence of PTB. It was found that the relationships between air temperature, relative humidity, wind speed, AT and PTB incidence were nonlinear (showed “wave-type “, “invested U-type”, “U-type” and “wave-type”, respectively). When air temperature at the lowest value (−16.1 °C) could increase the risk of PTB incidence with the highest relative risk (RR = 1.63, 95% CI: 1.21–2.20). An assessment of relative humidity demonstrated an increased risk of PTB incidence between 44.5% and 71.8% with the largest relative risk (RR = 1.49, 95% CI: 1.32–1.67) occurring at 59.2%. Both high and low wind speeds increased the risk of PTB incidence, especially at the lowest wind speed 1.4 m/s (RR = 2.20, 95% CI: 1.95–2.51). In particular, the lag effects of low and high AT on PTB incidence were nonlinear. The lag effects of extreme cold AT (−18.5 °C, 1st percentile) on PTB incidence reached a relative risk peak (RR = 2.18, 95% CI: 2.06–2.31) at lag 1 month. Overall, it was indicated that the environment with low air temperature, suitable relative humidity and wind speed is more conducive to the transmission of PTB, and low AT is associated significantly with increased risk of PTB in Xinjiang.
Collapse
|
17
|
Chen Y, Zhou Q, Yang X, Shi P, Shen Q, Zhang Z, Chen Z, Pu C, Xu L, Hu Z, Ma A, Gong Z, Xu T, Wang P, Wang H, Hao C, Li C, Hao M. Influence of Public Health Services on the Goal of Ending Tuberculosis: Evidence From Panel Data in China. Front Public Health 2022; 10:826800. [PMID: 35309188 PMCID: PMC8931334 DOI: 10.3389/fpubh.2022.826800] [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: 12/01/2021] [Accepted: 02/11/2022] [Indexed: 11/23/2022] Open
Abstract
Background The World Health Organization has proposed an initiative to “end tuberculosis (TB).” Unfortunately, TB continues to endanger the health of people worldwide. We investigated the impact of public health services (PHS) in China on TB incidence. In this way, we provided policy ideas for preventing the TB epidemic. Methods We used the “New Public Management Theory” to develop two indicators to quantify policy documents: multisector participation (MP) and the Assessable Public Health Service Coverage Rate (ASCR). The panel data from 31 provinces in Chinese mainland were collected from 2005 to 2019 based on 1,129 policy documents and the China Statistical Yearbook. A fixed-effect model was used to determine the impact of MP and the ASCR on TB incidence. Results From 2005 to 2019, the average MP increased from 89.25 to 97.70%, and the average ASCR increased from 53.97 to 78.40% in Chinese mainland. However, the development of ASCR between regions was not balanced, and the average level in the western region was lower than that in the eastern coastal provinces. With an increase in MP and the ASCR, the TB incidence had been decreasing gradually in recent years. The panel analysis results showed that MP (β = −0.76, p < 0.05). and ASCR (β = −0.40, p < 0.01) had a negative effect on TB incidence, respectively. Even if the control variables were added, the negative effects of MP (β = −0.86, p < 0.05) and ASCR (β = −0.35, p < 0.01) were still statistically significant. Conclusions Promoting the participation of multiple departments, as well as emphasizing the quality of PHS delivery, are important ways to alleviate the TB epidemic. The settings of evaluation indices for PHS provision should be strengthened in the future.
Collapse
Affiliation(s)
- Yang Chen
- Research Institute of Health Development Strategies, Fudan University, Shanghai, China
- Collaborative Innovation Center of Social Risks Governance in Health, Fudan University, Shanghai, China
- Department of Health Policy and Management, School of Public Health, Fudan University, Shanghai, China
| | - Qingyu Zhou
- Research Institute of Health Development Strategies, Fudan University, Shanghai, China
- Collaborative Innovation Center of Social Risks Governance in Health, Fudan University, Shanghai, China
- Department of Health Policy and Management, School of Public Health, Fudan University, Shanghai, China
| | - Xinmei Yang
- Research Institute of Health Development Strategies, Fudan University, Shanghai, China
- Collaborative Innovation Center of Social Risks Governance in Health, Fudan University, Shanghai, China
- Department of Health Policy and Management, School of Public Health, Fudan University, Shanghai, China
| | - Peiwu Shi
- Collaborative Innovation Center of Social Risks Governance in Health, Fudan University, Shanghai, China
- Zhejiang Academy of Medical Sciences, Hangzhou, China
| | - Qunhong Shen
- Collaborative Innovation Center of Social Risks Governance in Health, Fudan University, Shanghai, China
- School of Public Policy and Management, Tsinghua University, Beijing, China
| | - Zhaoyang Zhang
- Collaborative Innovation Center of Social Risks Governance in Health, Fudan University, Shanghai, China
- Project Supervision Center of National Health Commission of the People's Republic of China, Beijing, China
| | - Zheng Chen
- Collaborative Innovation Center of Social Risks Governance in Health, Fudan University, Shanghai, China
- Department of Grassroots Public Health Management Group, Public Health Management Branch of Chinese Preventive Medicine Association, Shanghai, China
| | - Chuan Pu
- Collaborative Innovation Center of Social Risks Governance in Health, Fudan University, Shanghai, China
- School of Public Health and Management, Chongqing Medical University, Chongqing, China
| | - Lingzhong Xu
- Collaborative Innovation Center of Social Risks Governance in Health, Fudan University, Shanghai, China
- School of Public Health, Shandong University, Jinan, China
| | - Zhi Hu
- Collaborative Innovation Center of Social Risks Governance in Health, Fudan University, Shanghai, China
- School of Health Service Management, Anhui Medical University, Hefei, China
| | - Anning Ma
- Collaborative Innovation Center of Social Risks Governance in Health, Fudan University, Shanghai, China
- School of Management, Weifang Medical University, Weifang, China
| | - Zhaohui Gong
- Collaborative Innovation Center of Social Risks Governance in Health, Fudan University, Shanghai, China
- Committee on Medicine and Health of Central Committee of China Zhi Gong Party, Beijing, China
| | - Tianqiang Xu
- Collaborative Innovation Center of Social Risks Governance in Health, Fudan University, Shanghai, China
- Institute of Inspection and Supervision, Shanghai Municipal Health Commission, Shanghai, China
| | - Panshi Wang
- Collaborative Innovation Center of Social Risks Governance in Health, Fudan University, Shanghai, China
- Shanghai Municipal Health Commission, Shanghai, China
| | - Hua Wang
- Collaborative Innovation Center of Social Risks Governance in Health, Fudan University, Shanghai, China
- Jiangsu Preventive Medicine Association, Nanjing, China
| | - Chao Hao
- Collaborative Innovation Center of Social Risks Governance in Health, Fudan University, Shanghai, China
- Changzhou Center for Disease Control and Prevention, Changzhou, China
| | - Chengyue Li
- Research Institute of Health Development Strategies, Fudan University, Shanghai, China
- Collaborative Innovation Center of Social Risks Governance in Health, Fudan University, Shanghai, China
- Department of Health Policy and Management, School of Public Health, Fudan University, Shanghai, China
- *Correspondence: Chengyue Li
| | - Mo Hao
- Research Institute of Health Development Strategies, Fudan University, Shanghai, China
- Collaborative Innovation Center of Social Risks Governance in Health, Fudan University, Shanghai, China
- Department of Health Policy and Management, School of Public Health, Fudan University, Shanghai, China
- Mo Hao
| |
Collapse
|
18
|
Hu M, Feng Y, Li T, Zhao Y, Wang J, Xu C, Chen W. The unbalanced risk of pulmonary tuberculosis in China at subnational scale: A spatio-temporal analysis (Preprint). JMIR Public Health Surveill 2022; 8:e36242. [PMID: 35776442 PMCID: PMC9288096 DOI: 10.2196/36242] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 05/06/2022] [Accepted: 05/10/2022] [Indexed: 12/05/2022] Open
Abstract
Background China has one of the highest tuberculosis (TB) burdens in the world. However, the unbalanced spatial and temporal trends of TB risk at a fine level remain unclear. Objective We aimed to investigate the unbalanced risks of pulmonary tuberculosis (PTB) at different levels and how they evolved from both temporal and spatial aspects using PTB notification data from 2851 counties over a decade in China. Methods County-level notified PTB case data were collected from 2009 to 2018 in mainland China. A Bayesian hierarchical model was constructed to analyze the unbalanced spatiotemporal patterns of PTB notification rates during this period at subnational scales. The Gini coefficient was calculated to assess the inequality of the relative risk (RR) of PTB across counties. Results From 2009 to 2018, the number of notified PTB cases in mainland China decreased from 946,086 to 747,700. The average number of PTB cases in counties was 301 (SD 26) and the overall average notification rate was 60 (SD 6) per 100,000 people. There were obvious regional differences in the RRs for PTB (Gini coefficient 0.32, 95% CI 0.31-0.33). Xinjiang had the highest PTB notification rate, with a multiyear average of 155/100,000 (RR 2.3, 95% CI 1.6-2.8; P<.001), followed by Guizhou (117/100,000; RR 1.8, 95% CI 1.3-1.9; P<.001) and Tibet (108/100,000; RR 1.7, 95% CI 1.3-2.1; P<.001). The RR for PTB showed a steady downward trend. Gansu (local trend [LT] 0.95, 95% CI 0.93-0.96; P<.001) and Shanxi (LT 0.94, 95% CI 0.92-0.96; P<.001) experienced the fastest declines. However, the RRs for PTB in the western region (such as counties in Xinjiang, Guizhou, and Tibet) were significantly higher than those in the eastern and central regions (P<.001), and the decline rate of the RR for PTB was lower than the overall level (P<.001). Conclusions PTB risk showed significant regional inequality among counties in China, and western China presented a high plateau of disease burden. Improvements in economic and medical service levels are required to boost PTB case detection and eventually reduce PTB risk in the whole country.
Collapse
Affiliation(s)
- Maogui Hu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Beijing, China
| | - Yuqing Feng
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Beijing, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Tao Li
- National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yanlin Zhao
- National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jinfeng Wang
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Beijing, China
| | - Chengdong Xu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Beijing, China
| | - Wei Chen
- National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| |
Collapse
|
19
|
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.
Collapse
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.
| |
Collapse
|
20
|
Mohidem NA, Osman M, Hashim Z, Muharam FM, Mohd Elias S, Shaharudin R. Association of sociodemographic and environmental factors with spatial distribution of tuberculosis cases in Gombak, Selangor, Malaysia. PLoS One 2021; 16:e0252146. [PMID: 34138899 PMCID: PMC8211220 DOI: 10.1371/journal.pone.0252146] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 05/11/2021] [Indexed: 11/25/2022] Open
Abstract
Tuberculosis (TB) cases have increased drastically over the last two decades and it remains as one of the deadliest infectious diseases in Malaysia. This cross-sectional study aimed to establish the spatial distribution of TB cases and its association with the sociodemographic and environmental factors in the Gombak district. The sociodemographic data of 3325 TB cases such as age, gender, race, nationality, country of origin, educational level, employment status, health care worker status, income status, residency, and smoking status from 1st January 2013 to 31st December 2017 in Gombak district were collected from the MyTB web and Tuberculosis Information System (TBIS) database at the Gombak District Health Office and Rawang Health Clinic. Environmental data consisting of air pollution such as air quality index (AQI), carbon monoxide (CO), nitrogen dioxide (NO2), sulphur dioxide (SO2), and particulate matter 10 (PM10,) were obtained from the Department of Environment Malaysia from 1st July 2012 to 31st December 2017; whereas weather data such as rainfall were obtained from the Department of Irrigation and Drainage Malaysia and relative humidity, temperature, wind speed, and atmospheric pressure were obtained from the Malaysia Meteorological Department in the same period. Global Moran's I, kernel density estimation, Getis-Ord Gi* statistics, and heat maps were applied to identify the spatial pattern of TB cases. Ordinary least squares (OLS) and geographically weighted regression (GWR) models were used to determine the spatial association of sociodemographic and environmental factors with the TB cases. Spatial autocorrelation analysis indicated that the cases was clustered (p<0.05) over the five-year period and year 2016 and 2017 while random pattern (p>0.05) was observed from year 2013 to 2015. Kernel density estimation identified the high-density regions while Getis-Ord Gi* statistics observed hotspot locations, whereby consistently located in the southwestern part of the study area. This could be attributed to the overcrowding of inmates in the Sungai Buloh prison located there. Sociodemographic factors such as gender, nationality, employment status, health care worker status, income status, residency, and smoking status as well as; environmental factors such as AQI (lag 1), CO (lag 2), NO2 (lag 2), SO2 (lag 1), PM10 (lag 5), rainfall (lag 2), relative humidity (lag 4), temperature (lag 2), wind speed (lag 4), and atmospheric pressure (lag 6) were associated with TB cases (p<0.05). The GWR model based on the environmental factors i.e. GWR2 was the best model to determine the spatial distribution of TB cases based on the highest R2 value i.e. 0.98. The maps of estimated local coefficients in GWR models confirmed that the effects of sociodemographic and environmental factors on TB cases spatially varied. This study highlighted the importance of spatial analysis to identify areas with a high TB burden based on its associated factors, which further helps in improving targeted surveillance.
Collapse
Affiliation(s)
- Nur Adibah Mohidem
- Department of Environmental and Occupational Health, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
| | - Malina Osman
- Department of Medical Microbiology, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
| | - Zailina Hashim
- Department of Environmental and Occupational Health, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
| | - Farrah Melissa Muharam
- Department of Agriculture Technology, Faculty of Agriculture, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
| | - Saliza Mohd Elias
- Department of Environmental and Occupational Health, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
| | - Rafiza Shaharudin
- Institute for Medical Research, National Institutes of Health, Shah Alam, Selangor, Malaysia
| |
Collapse
|
21
|
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.
Collapse
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.
| |
Collapse
|
22
|
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: 13] [Impact Index Per Article: 4.3] [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.
Collapse
|
23
|
Wang L, Xu C, Wang J, Qiao J, Yan M, Zhu Q. Spatiotemporal heterogeneity and its determinants of COVID-19 transmission in typical labor export provinces of China. BMC Infect Dis 2021; 21:242. [PMID: 33673819 PMCID: PMC7935008 DOI: 10.1186/s12879-021-05926-x] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 02/19/2021] [Indexed: 12/29/2022] Open
Abstract
Background Previous studies have indicated that the risk of infectious disease spread is greatest in locations where a population has massive and convenient access to the epicenter of an outbreak. However, the spatiotemporal variations and risk determinants of COVID-19 in typical labor export regions of China remain unclear. Understanding the geographical distribution of the disease and the socio-economic factors affecting its transmission is critical for disease prevention and control. Methods A total of 2152 COVID-19 cases were reported from January 21 to February 24, 2020 across the 34 cities in Henan and Anhui. A Bayesian spatiotemporal hierarchy model was used to detect the spatiotemporal variations of the risk posed by COVID-19, and the GeoDetector q statistic was used to evaluate the determinant power of the potential influence factors. Results The risk posed by COVID-19 showed geographical spatiotemporal heterogeneity. Temporally, there was an outbreak period and control period. Spatially, there were high-risk regions and low-risk regions. The high-risk regions were mainly in the southwest areas adjacent to Hubei and cities that served as economic and traffic hubs, while the low-risk regions were mainly in western Henan and eastern Anhui, far away from the epicenter. The accessibility, local economic conditions, and medical infrastructure of Wuhan in Hubei province all played an important role in the spatiotemporal heterogeneity of COVID-19 transmission. The results indicated that the q statistics of the per capita GDP and the proportion of primary industry GDP were 0.47 and 0.47, respectively. The q statistic of the population flow from Wuhan was 0.33. In particular, the results showed that the q statistics for the interaction effects between population density and urbanization, population flow from Wuhan, per capita GDP, and the number of doctors were all greater than 0.8. Conclusions COVID-19 showed significant spatiotemporal heterogeneity in the labor export regions of China. The high-risk regions were mainly located in areas adjacent to the epicenter as well as in big cities that served as traffic hubs. Population access to the epicenter, as well as local economic and medical conditions, played an important role in the interactive effects of the disease transmission. Supplementary Information The online version contains supplementary material available at 10.1186/s12879-021-05926-x.
Collapse
Affiliation(s)
- Li Wang
- College of Environment and Planning, Henan University, KaiFeng, 475001, China.,Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, KaiFeng, 475001, China
| | - Chengdong Xu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resource Research, Chinese Academy of Sciences, Beijing, 100101, China
| | - Jinfeng Wang
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resource Research, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Jiajun Qiao
- College of Environment and Planning, Henan University, KaiFeng, 475001, China. .,Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, KaiFeng, 475001, China.
| | - Mingtao Yan
- College of Environment and Planning, Henan University, KaiFeng, 475001, China.,Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, KaiFeng, 475001, China
| | - Qiankun Zhu
- College of Environment and Planning, Henan University, KaiFeng, 475001, China.,Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, KaiFeng, 475001, China
| |
Collapse
|
24
|
Temperature and humidity associated with increases in tuberculosis notifications: a time-series study in Hong Kong. Epidemiol Infect 2020; 149:e8. [PMID: 33436107 PMCID: PMC8057503 DOI: 10.1017/s0950268820003040] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Previous studies have revealed associations of meteorological factors with tuberculosis (TB) cases. However, few studies have examined their lag effects on TB cases. This study was aimed to analyse nonlinear lag effects of meteorological factors on the number of TB notifications in Hong Kong. Using a 22-year consecutive surveillance data in Hong Kong, we examined the association of monthly average temperature and relative humidity with temporal dynamics of the monthly number of TB notifications using a distributed lag nonlinear models combined with a Poisson regression. The relative risks (RRs) of TB notifications were >1.15 as monthly average temperatures were between 16.3 and 17.3 °C at lagged 13–15 months, reaching the peak risk of 1.18 (95% confidence interval (CI) 1.02–1.35) when it was 16.8 °C at lagged 14 months. The RRs of TB notifications were >1.05 as relative humidities of 60.0–63.6% at lagged 9–11 months expanded to 68.0–71.0% at lagged 12–17 months, reaching the highest risk of 1.06 (95% CI 1.01–1.11) when it was 69.0% at lagged 13 months. The nonlinear and delayed effects of average temperature and relative humidity on TB epidemic were identified, which may provide a practical reference for improving the TB warning system.
Collapse
|
25
|
Martini F, Régis Leite M, Gonçalves Rosa S, Pregardier Klann I, Wayne Nogueira C. Strength exercise suppresses STZ-induced spatial memory impairment and modulates BDNF/ERK-CAMKII/CREB signalling pathway in the hippocampus of mice. Cell Biochem Funct 2020; 38:213-221. [PMID: 31978253 DOI: 10.1002/cbf.3470] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Revised: 09/11/2019] [Accepted: 10/28/2019] [Indexed: 12/22/2022]
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that has generated scientific interest because of its prevalence in the population. Studies indicate that physical exercise promotes neuroplasticity and improves cognitive function in animal models and in human beings. The aim of the present study was to investigate the effects of strength exercise on the hippocampal protein contents and memory performance in mice subjected to a model of sporadic AD induced by streptozotocin (STZ). Swiss mice received two injections of STZ (3 mg/kg, intracerebroventricular). After 21 days, they began physical training using a ladde. Mice performed this protocol for 4 weeks. After the last exercise training session, mice performed the Morris Water Maze test. The samples of hippocampus were excised and used to determine protein contents of brain-derived neurotrophic factor (BDNF), extracellular signal-regulated kinase-Ca2+ (ERK), calmodulin-dependent protein kinase (CAMKII) and cAMP-response element-binding protein (CREB) signalling pathway. Strength exercise was effective against the decrease in the time spent and distance travelled in the target quadrant by STZ-injected mice. Strength exercise was also effective against the reduction of mature BDNF, tropomyosin receptor kinase B and neuronal nuclear antigen (NeuN) hippocampal protein levels in STZ mice. The decrease in the hippocampal ratio of pERK/ERK, pCAMKII/CAMKII and pCREB/CREB induced by STZ was reversed by strength exercise. Strength exercise decreased Bax/Bcl2 ratio in the hippocampus of STZ-injected mice. The present study demonstrates that strength exercise modulated the hippocampal BDNF/ERK-CAMKII/CREB signalling pathway and suppressed STZ-induced spatial memory impairment in mice.
Collapse
Affiliation(s)
- Franciele Martini
- Laboratório de Síntese, Reatividade e Avaliação Farmacológica e Toxicológica de Organocalcogênios, Departamento de Bioquímica e Biologia Molecular, Centro de Ciências Naturais e Exatas, Universidade Federal de Santa Maria, Santa Maria, Rio Grande do Sul, Brazil
| | - Marlon Régis Leite
- Laboratório de Síntese, Reatividade e Avaliação Farmacológica e Toxicológica de Organocalcogênios, Departamento de Bioquímica e Biologia Molecular, Centro de Ciências Naturais e Exatas, Universidade Federal de Santa Maria, Santa Maria, Rio Grande do Sul, Brazil
| | - Suzan Gonçalves Rosa
- Laboratório de Síntese, Reatividade e Avaliação Farmacológica e Toxicológica de Organocalcogênios, Departamento de Bioquímica e Biologia Molecular, Centro de Ciências Naturais e Exatas, Universidade Federal de Santa Maria, Santa Maria, Rio Grande do Sul, Brazil
| | - Isabella Pregardier Klann
- Laboratório de Síntese, Reatividade e Avaliação Farmacológica e Toxicológica de Organocalcogênios, Departamento de Bioquímica e Biologia Molecular, Centro de Ciências Naturais e Exatas, Universidade Federal de Santa Maria, Santa Maria, Rio Grande do Sul, Brazil
| | - Cristina Wayne Nogueira
- Laboratório de Síntese, Reatividade e Avaliação Farmacológica e Toxicológica de Organocalcogênios, Departamento de Bioquímica e Biologia Molecular, Centro de Ciências Naturais e Exatas, Universidade Federal de Santa Maria, Santa Maria, Rio Grande do Sul, Brazil
| |
Collapse
|