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Wang F, Yuan Z, Qin S, Qin F, Zhang J, Mo C, Kang Y, Huang S, Qin F, Jiang J, Liu A, Liang H, Ye L. The effects of meteorological factors and air pollutants on the incidence of tuberculosis in people living with HIV/AIDS in subtropical Guangxi, China. BMC Public Health 2024; 24:1333. [PMID: 38760740 PMCID: PMC11100081 DOI: 10.1186/s12889-024-18475-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Accepted: 03/28/2024] [Indexed: 05/19/2024] Open
Abstract
BACKGROUND Previous studies have shown the association between tuberculosis (TB) and meteorological factors/air pollutants. However, little information is available for people living with HIV/AIDS (PLWHA), who are highly susceptible to TB. METHOD Data regarding TB cases in PLWHA from 2014 to2020 were collected from the HIV antiviral therapy cohort in Guangxi, China. Meteorological and air pollutants data for the same period were obtained from the China Meteorological Science Data Sharing Service Network and Department of Ecology and Environment of Guangxi. A distribution lag non-linear model (DLNM) was used to evaluate the effects of meteorological factors and air pollutant exposure on the risk of TB in PLWHA. RESULTS A total of 2087 new or re-active TB cases were collected, which had a significant seasonal and periodic distribution. Compared with the median values, the maximum cumulative relative risk (RR) for TB in PLWHA was 0.663 (95% confidence interval [CI]: 0.507-0.866, lag 4 weeks) for a 5-unit increase in temperature, and 1.478 (95% CI: 1.116-1.957, lag 4 weeks) for a 2-unit increase in precipitation. However, neither wind speed nor PM10 had a significant cumulative lag effect. Extreme analysis demonstrated that the hot effect (RR = 0.638, 95%CI: 0.425-0.958, lag 4 weeks), the rainy effect (RR = 0.285, 95%CI: 0.135-0.599, lag 4 weeks), and the rainless effect (RR = 0.552, 95%CI: 0.322-0.947, lag 4 weeks) reduced the risk of TB. Furthermore, in the CD4(+) T cells < 200 cells/µL subgroup, temperature, precipitation, and PM10 had a significant hysteretic effect on TB incidence, while temperature and precipitation had a significant cumulative lag effect. However, these effects were not observed in the CD4(+) T cells ≥ 200 cells/µL subgroup. CONCLUSION For PLWHA in subtropical Guangxi, temperature and precipitation had a significant cumulative effect on TB incidence among PLWHA, while air pollutants had little effect. Moreover, the influence of meteorological factors on the incidence of TB also depends on the immune status of PLWHA.
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Affiliation(s)
- Fengyi Wang
- Guangxi Key Laboratory of AIDS Prevention and Treatment, School of Public Health, Guangxi Medical University, Nanning, Guangxi, China
- Joint Laboratory for Emerging Infectious Diseases in China (Guangxi)-ASEAN, Life Sciences Institute, Guangxi Medical University, Nanning, Guangxi, China
| | - Zongxiang Yuan
- Guangxi Key Laboratory of AIDS Prevention and Treatment, School of Public Health, Guangxi Medical University, Nanning, Guangxi, China
- Joint Laboratory for Emerging Infectious Diseases in China (Guangxi)-ASEAN, Life Sciences Institute, Guangxi Medical University, Nanning, Guangxi, China
| | - Shanfang Qin
- Chest Hospital of Guangxi Zhuang Autonomous Region, Liuzhou, China
| | - Fengxiang Qin
- Guangxi Key Laboratory of AIDS Prevention and Treatment, School of Public Health, Guangxi Medical University, Nanning, Guangxi, China
- Joint Laboratory for Emerging Infectious Diseases in China (Guangxi)-ASEAN, Life Sciences Institute, Guangxi Medical University, Nanning, Guangxi, China
| | - Junhan Zhang
- Guangxi Key Laboratory of AIDS Prevention and Treatment, School of Public Health, Guangxi Medical University, Nanning, Guangxi, China
- Joint Laboratory for Emerging Infectious Diseases in China (Guangxi)-ASEAN, Life Sciences Institute, Guangxi Medical University, Nanning, Guangxi, China
| | - Chuye Mo
- Guangxi Key Laboratory of AIDS Prevention and Treatment, School of Public Health, Guangxi Medical University, Nanning, Guangxi, China
- Joint Laboratory for Emerging Infectious Diseases in China (Guangxi)-ASEAN, Life Sciences Institute, Guangxi Medical University, Nanning, Guangxi, China
| | - Yiwen Kang
- Guangxi Key Laboratory of AIDS Prevention and Treatment, School of Public Health, Guangxi Medical University, Nanning, Guangxi, China
- Joint Laboratory for Emerging Infectious Diseases in China (Guangxi)-ASEAN, Life Sciences Institute, Guangxi Medical University, Nanning, Guangxi, China
| | - Shihui Huang
- Guangxi Key Laboratory of AIDS Prevention and Treatment, School of Public Health, Guangxi Medical University, Nanning, Guangxi, China
- Joint Laboratory for Emerging Infectious Diseases in China (Guangxi)-ASEAN, Life Sciences Institute, Guangxi Medical University, Nanning, Guangxi, China
| | - Fang Qin
- Guangxi Key Laboratory of AIDS Prevention and Treatment, School of Public Health, Guangxi Medical University, Nanning, Guangxi, China
- Joint Laboratory for Emerging Infectious Diseases in China (Guangxi)-ASEAN, Life Sciences Institute, Guangxi Medical University, Nanning, Guangxi, China
| | - Junjun Jiang
- Guangxi Key Laboratory of AIDS Prevention and Treatment, School of Public Health, Guangxi Medical University, Nanning, Guangxi, China.
- Joint Laboratory for Emerging Infectious Diseases in China (Guangxi)-ASEAN, Life Sciences Institute, Guangxi Medical University, Nanning, Guangxi, China.
| | - Aimei Liu
- Chest Hospital of Guangxi Zhuang Autonomous Region, Liuzhou, China.
| | - Hao Liang
- Guangxi Key Laboratory of AIDS Prevention and Treatment, School of Public Health, Guangxi Medical University, Nanning, Guangxi, China.
- Joint Laboratory for Emerging Infectious Diseases in China (Guangxi)-ASEAN, Life Sciences Institute, Guangxi Medical University, Nanning, Guangxi, China.
| | - Li Ye
- Guangxi Key Laboratory of AIDS Prevention and Treatment, School of Public Health, Guangxi Medical University, Nanning, Guangxi, China.
- Joint Laboratory for Emerging Infectious Diseases in China (Guangxi)-ASEAN, Life Sciences Institute, Guangxi Medical University, Nanning, Guangxi, China.
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Liang D, Song Z, Liang X, Qin H, Huang L, Ye J, Lan R, Luo D, Zhao Y, Lin M. Whole Genomic Analysis Revealed High Genetic Diversity and Drug-Resistant Characteristics of Mycobacterium tuberculosis in Guangxi, China. Infect Drug Resist 2023; 16:5021-5031. [PMID: 37554542 PMCID: PMC10405913 DOI: 10.2147/idr.s410828] [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: 03/27/2023] [Accepted: 06/21/2023] [Indexed: 08/10/2023] Open
Abstract
Background Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), is a major public health issue in China. Nevertheless, the prevalence and drug resistance characteristics of isolates vary in different regions and provinces. In this study, we investigated the population structure, transmission dynamics and drug-resistant profiles of Mtb in Guangxi, located on the border of China. Methods From February 2016 to April 2017, 462 clinical M. tuberculosis isolates were selected from 5 locations in Guangxi. Drug-susceptibility testing was performed using 6 common anti-tuberculosis drugs. The genotypic drug resistance and transmission dynamics were analyzed by the whole genome sequence. Results Our data showed that the Mtb in Guangxi has high genetic diversity including Lineage 1 to Lineage 4, and mostly belong to Lineage 2 and Lineage 4. Novelty, 9.6% of Lineage 2 isolates were proto-Beijing genotype (L2.1), which is rare in China. About 12.6% of isolates were phylogenetically clustered and formed into 28 transmission clusters. We observed that the isolates with the high resistant rate of isoniazid (INH, 21.2%), followed by rifampicin (RIF, 13.2%), and 6.7%, 12.1%, 6.7% and 1.9% isolates were resistant to ethambutol (EMB), streptomycin (SM), ofloxacin (OFL) and kanamycin (KAN), respectively. Among these, 6.5% and 3.3% of isolates belong to MDR-TB and Pre-XDR, respectively, with a high drug-resistant burden. Genetic analysis identified the most frequently encountered mutations of INH, RIF, EMB, SM, OFL and KAN were katG_Ser315Thr (62.2%), rpoB_Ser450Leu (42.6%), embB_Met306Vol (45.2%), rpsL_Lys43Arg (53.6%), gyrA_Asp94Gly (29.0%) and rrs_A1401G (66.7%), respectively. Additionally, we discovered that isolates from border cities are more likely to be drug-resistant than isolates from non-border cities. Conclusion Our findings provide a deep analysis of the genomic population characteristics and drug-resistant of M. tuberculosis in Guangxi, which could contribute to developing effective TB prevention and control strategies.
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Affiliation(s)
- Dabin Liang
- Guangxi Zhuang Autonomous Region Center for Disease Control and Prevention, Nanning, Guangxi, People’s Republic of China
- Guangxi Key Laboratory of Major Infectious Disease Prevention and Control and Biosafety Emergency Response, Nanning, Guangxi, People’s Republic of China
| | - Zexuan Song
- National Tuberculosis Reference Laboratory, Chinese Center for Disease Control and Prevention, Beijing, People’s Republic of China
| | - Xiaoyan Liang
- Guangxi Zhuang Autonomous Region Center for Disease Control and Prevention, Nanning, Guangxi, People’s Republic of China
- Guangxi Key Laboratory of Major Infectious Disease Prevention and Control and Biosafety Emergency Response, Nanning, Guangxi, People’s Republic of China
| | - Huifang Qin
- Guangxi Zhuang Autonomous Region Center for Disease Control and Prevention, Nanning, Guangxi, People’s Republic of China
- Guangxi Key Laboratory of Major Infectious Disease Prevention and Control and Biosafety Emergency Response, Nanning, Guangxi, People’s Republic of China
| | - Liwen Huang
- Guangxi Zhuang Autonomous Region Center for Disease Control and Prevention, Nanning, Guangxi, People’s Republic of China
- Guangxi Key Laboratory of Major Infectious Disease Prevention and Control and Biosafety Emergency Response, Nanning, Guangxi, People’s Republic of China
| | - Jing Ye
- Guangxi Zhuang Autonomous Region Center for Disease Control and Prevention, Nanning, Guangxi, People’s Republic of China
- Guangxi Key Laboratory of Major Infectious Disease Prevention and Control and Biosafety Emergency Response, Nanning, Guangxi, People’s Republic of China
| | - Rushu Lan
- Department of Clinical Laboratory, Jiangbin Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, People’s Republic of China
| | - Dan Luo
- School of Public Health and Management, Guangxi University of Chinese Medicine, Nanning, Guangxi, People’s Republic of China
| | - Yanlin Zhao
- National Tuberculosis Reference Laboratory, Chinese Center for Disease Control and Prevention, Beijing, People’s Republic of China
| | - Mei Lin
- Guangxi Zhuang Autonomous Region Center for Disease Control and Prevention, Nanning, Guangxi, People’s Republic of China
- Guangxi Key Laboratory of Major Infectious Disease Prevention and Control and Biosafety Emergency Response, Nanning, Guangxi, People’s Republic of China
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Wu T, He H, Wei S, Zhu P, Feng Q, Tang Z. How to establishing an indicators framework for evaluating the performances in primary TB control institutions under the new TB control model? Based on a Delphi study conducted in Guangxi, China. BMC Public Health 2022; 22:2431. [PMID: 36575512 PMCID: PMC9792919 DOI: 10.1186/s12889-022-14865-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 12/13/2022] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND In China, the new TB control model of trinity form had been implemented in all parts, and the comprehensively evaluation to the performances in primary TB control institutions were closely related to the working capacity and quality of TB service, but there was still no an unified evaluation indicators framework in practice and few relevant studies. The purpose of this study was to establish an indicators framework for comprehensively evaluating the performances in primary TB control institutions under the new TB control model of trinity form in Guangxi, China. METHODS The Delphi method was used to establish an indicators framework for comprehensively evaluating the performances in primary TB control institutions under the new TB control model of trinity form, and the analytic hierarchy process(AHP) was used to determine the weights of all levels of indicators, from September 2021 to December 2021 in Guangxi, China. RESULTS A total of 14 experts who had at least 10 years working experience and engaged in TB prevention and control and public health management from health committee, CDC, TB designated hospitals and university of Guangxi were consulted in two rounds. The average age of the experts were (43.3 ± 7.549) years old, and the effective recovery rate of the questionnaire was 100.0%. The average value of authority coefficient of experts (Cr) in the two rounds of consultation was above 0.800. The Kendall's harmony coefficient (W) of experts' opinions on the first-level indicators, the second-level indicators and the third-level indicators were 0.786, 0.201 and 0.169, respectively, which were statistically significant (P < 0.05). Finally, an indicators framework was established, which included 2 first-level indicators, 10 second-level indicators and 37 third-level indicators. The results of analytic hierarchy process (AHP) showed that the consistency test of all levels of indicators were CI < 0.10, which indicating that the weight of each indicator was acceptable. CONCLUSION The indicators framework established in this study was in line with the reality, had reasonable weights, and could provide a scientific evaluation tool for comprehensively evaluating the performances in primary TB control institutions under the new TB control model of trinity form in Guangxi, China.
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Affiliation(s)
- Tengyan Wu
- grid.256607.00000 0004 1798 2653Department of Health Service Management, School of Information and Management, Guangxi Medical University, Nanning, China
| | - Huimin He
- grid.256607.00000 0004 1798 2653Department of Health Service Management, School of Information and Management, Guangxi Medical University, Nanning, China
| | - Suosu Wei
- grid.410652.40000 0004 6003 7358Editorial Board of Chinese Journal of New Clinical Medicine, The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Pinghua Zhu
- grid.256607.00000 0004 1798 2653Department of Health Service Management, School of Information and Management, Guangxi Medical University, Nanning, China
| | - Qiming Feng
- grid.256607.00000 0004 1798 2653Department of Health Service Management, School of Information and Management, Guangxi Medical University, Nanning, China
| | - Zhong Tang
- grid.256607.00000 0004 1798 2653Department of Health Service Management, School of Information and Management, Guangxi Medical University, Nanning, China
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