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Deng LL, Zhao F, Li ZW, Zhang WW, He GX, Ren X. Epidemiological characteristics of tuberculosis incidence and its macro-influence factors in Chinese mainland during 2014-2021. Infect Dis Poverty 2024; 13:34. [PMID: 38773558 PMCID: PMC11107005 DOI: 10.1186/s40249-024-01203-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Accepted: 05/07/2024] [Indexed: 05/24/2024] Open
Abstract
BACKGROUND Tuberculosis (TB) remains a pressing public health issue, posing a significant threat to individuals' well-being and lives. This study delves into the TB incidence in Chinese mainland during 2014-2021, aiming to gain deeper insights into their epidemiological characteristics and explore macro-level factors to enhance control and prevention. METHODS TB incidence data in Chinese mainland from 2014 to 2021 were sourced from the National Notifiable Disease Reporting System (NNDRS). A two-stage distributed lag nonlinear model (DLNM) was constructed to evaluate the lag and non-linearity of daily average temperature (℃, Atemp), average relative humidity (%, ARH), average wind speed (m/s, AWS), sunshine duration (h, SD) and precipitation (mm, PRE) on the TB incidence. A spatial panel data model was used to assess the impact of demographic, medical and health resource, and economic factors on TB incidence. RESULTS A total of 6,587,439 TB cases were reported in Chinese mainland during 2014-2021, with an average annual incidence rate of 59.17/100,000. The TB incidence decreased from 67.05/100,000 in 2014 to 46.40/100,000 in 2021, notably declining from 2018 to 2021 (APC = -8.87%, 95% CI: -11.97, -6.85%). TB incidence rates were higher among males, farmers, and individuals aged 65 years and older. Spatiotemporal analysis revealed a significant cluster in Xinjiang, Qinghai, and Xizang from March 2017 to June 2019 (RR = 3.94, P < 0.001). From 2014 to 2021, the proportion of etiologically confirmed cases increased from 31.31% to 56.98%, and the time interval from TB onset to diagnosis shortened from 26 days (IQR: 10-56 days) to 19 days (IQR: 7-44 days). Specific meteorological conditions, including low temperature (< 16.69℃), high relative humidity (> 71.73%), low sunshine duration (< 6.18 h) increased the risk of TB incidence, while extreme low wind speed (< 2.79 m/s) decreased the risk. The spatial Durbin model showed positive associations between TB incidence rates and sex ratio (β = 1.98), number of beds in medical and health institutions per 10,000 population (β = 0.90), and total health expenses (β = 0.55). There were negative associations between TB incidence rates and population (β = -1.14), population density (β = -0.19), urbanization rate (β = -0.62), number of medical and health institutions (β = -0.23), and number of health technicians per 10,000 population (β = -0.70). CONCLUSIONS Significant progress has been made in TB control and prevention in China, but challenges persist among some populations and areas. Varied relationships were observed between TB incidence and factors from meteorological, demographic, medical and health resource, and economic aspects. These findings underscore the importance of ongoing efforts to strengthen TB control and implement digital/intelligent surveillance for early risk detection and comprehensive interventions.
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Affiliation(s)
- Le-le Deng
- National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China
| | - Fei Zhao
- Department of Pharmacy, Beijing Hospital; National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Science; Beijing Key Laboratory of Drug Clinical Risk and Personalized Medication Evaluation, Beijing, 100730, China
| | - Zhuo-Wei Li
- National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China
| | - Wei-Wei Zhang
- Miyun District Center for Disease Control and Prevention, Beijing, 101500, China
| | - Guang-Xue He
- National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China
- School Of Public Health, Binzhon Medical University, Yantai, 264000, China
| | - Xiang Ren
- Division of Infectious Disease, Key Laboratory of Surveillance and Early-Warning On Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
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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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Xia L, Wei W, Zhou ZL, Zhang WQ, Luan RS. The environmental and socioeconomic effects of tuberculosis patients in the southwest of China: a population-based study. Public Health 2024; 227:131-140. [PMID: 38219290 DOI: 10.1016/j.puhe.2023.10.043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 09/14/2023] [Accepted: 10/26/2023] [Indexed: 01/16/2024]
Abstract
OBJECTIVE The objective of this study was to assess the incidence of tuberculosis (TB) and find the risk factors of TB patients with a high burden of TB in socioeconomic level, the high level of TB incidence and the great changes of economic and social factors, explore the possible factors, construct scientific and robust prediction model, and analyse whether the task of stopping TB can be accomplished by the expected global deadline. STUDY DESIGN This was an ecological study. METHODS Descriptive analysis, spatial and space-time scan, correlation analysis, and regression analysis were carried out, based on cases of TB in Sichuan Province and ecological data from 2006 to 2017, to explore the characters of TB and ecological factors, using the transfer function-noise model to forecast the trend of TB until 2035. RESULTS Factors affecting the incidence of TB, increasing per capita green area, reporting status of TB among Tibetans and Yi minorities, comprehensive treatment management, total cost of TB per capita for urban residents, proportion of males with high school education, 20 to 20 h of 24-h accumulated precipitation, reducing HIV at the same time as AIDS deaths, the increase in the proportion of males in junior high school education, and the increase in the number of registered TB cases can reduce the incidence of TB. CONCLUSIONS There was concentration mainly on enhanced control of the environment and society measures, helpful in guiding government planning to control TB. Reinforcement is required to reduce the TB of population aged 15-24 and aged 25-64 in socioeconomic level by 2035.
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Affiliation(s)
- L Xia
- Center for Disease Control and Prevention of Sichuan Province, China
| | - W Wei
- West China School of Public Health and West China Fourth Hospital, Sichuan University, China; Leshan Hospital, China
| | - Z L Zhou
- West China School of Public Health and West China Fourth Hospital, Sichuan University, China
| | - W Q Zhang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, China
| | - R S Luan
- West China School of Public Health and West China Fourth Hospital, Sichuan University, China.
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Song Z, He W, Cao X, Ma A, He P, Zhao B, Wang S, Liu C, Zhao Y. The Recent Transmission and Associated Risk Factor of Mycobacterium tuberculosis in Golmud City, China. Infect Drug Resist 2024; 17:417-425. [PMID: 38318210 PMCID: PMC10840525 DOI: 10.2147/idr.s437026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 12/05/2023] [Indexed: 02/07/2024] Open
Abstract
Background Tuberculosis (TB) remains a severe public health problem globally, and it is essential to comprehend the transmission pattern to control tuberculosis. Herein, we evaluated the drug-resistant characteristics, recent transmission, and associated risk factors of TB in Golmud, Qinghai, China. Methods In this study, we performed a population-based study of patients diagnosed with TB in Golmud from 2013 to 2018. Drug-susceptibility testing and whole-genome sequencing were performed on 133 Mycobacterium tuberculosis strains. The genomic clustering rate was calculated to evaluate the level of recent transmission. Risk factors were identified by logistic regression analysis. Results Our results showed that 46.97% (62/132) of strains were phylogenetically clustered and formed into 23 transmission clusters, suggesting a high recent transmission of TB in the area. 12.78% (17/133) strains were multidrug-resistant/rifampicin tuberculosis (MDR/RR-TB), with a high drug-resistant burden. Based on drug resistance gene analysis, we found 23 strains belonging to genotype MDR/RR-TB, where some strains may have borderline mutations. Among these strains, 65.2% (15/23) were found within putative transmission clusters. Additionally, risk factor analysis showed that recent transmission of TB happened more in patients with Tibetan nationality or older age. Conclusion Overall our study indicates that the recent transmissions of MTB strains, especially genotypic MDR/RR strains, drive the tuberculosis epidemic in Golmud, which could contribute to developing effective TB prevention and control strategies.
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Affiliation(s)
- Zexuan Song
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Changping, Beijing, 102206, People’s Republic of China
| | - Wencong He
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Changping, Beijing, 102206, People’s Republic of China
| | - Xiaolong Cao
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Changping, Beijing, 102206, People’s Republic of China
| | - Aijing Ma
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Changping, Beijing, 102206, People’s Republic of China
| | - Ping He
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Changping, Beijing, 102206, People’s Republic of China
| | - Bing Zhao
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Changping, Beijing, 102206, People’s Republic of China
| | - Shengfen Wang
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Changping, Beijing, 102206, People’s Republic of China
| | - Chunfa Liu
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Changping, Beijing, 102206, People’s Republic of China
- Animal Science and Technology College, Beijing University of Agriculture, Huilongguan, Changping, Beijing, 102206, People’s Republic of China
| | - Yanlin Zhao
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Changping, Beijing, 102206, People’s Republic of China
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Chang M, Emam M, Chen X, Lu D, Zhang L, Zheng Y. An investigation of the effects of meteorological factors on the incidence of tuberculosis. Sci Rep 2024; 14:2088. [PMID: 38267494 PMCID: PMC10808229 DOI: 10.1038/s41598-024-52278-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 01/16/2024] [Indexed: 01/26/2024] Open
Abstract
To explore the influence of meteorological factors on the incidence of tuberculosis (TB) in Yingjisha County, Kashgar Region, Xinjiang, and to provide a scientific basis for the prevention and control of TB. The Spearman correlation analysis and distribution lag nonlinear model analysis were conducted on the number of daily reported cases of TB from 2016 to 2023 to study the association effect of various meteorological factors and the daily incidence number of TB in Yingjisha County. A total of 13,288 TB cases were reported from January 2016 to June 2023, and June to October is the peak period of annual TB incidence. Spearman correlation analysis revealed that average daily temperature (AT) and average daily wind speed (WS) were positively correlated with TB incidence (rAT = 0.110, rWS = 0.090); and average daily relative humidity (RH) and TB incidence was negatively correlated (rRH = - 0.093). When AT was - 15 °C, the RR reached a maximum of 2.20 (95% CI: 0.77-6.29) at a lag of 21 days. When RH was 92%, the RR reached a maximum of 1.05 (95% CI: 0.92-1.19) at a lag of 6 days. When WS was 5.2 m/s, the RR reached a maximum of 1.30 (95% CI: 0.78-2.16) at a lag of 16 days. There is a non-linearity and a certain lag between meteorological factors and the occurrence and prevalence of TB in the population, which is mainly manifested in the fact that the risk of incidence of TB decreases with the increase of the daily AT, has a hazardous effect within a certain range of humidity as the average daily RH rises, and gradually increases with the increase of the average daily WS. Local residents are advised to pay attention to climate change so as to take appropriate preventive measures, especially women and middle and old age group should pay close attention to climate change and add more clothes in time, minimise travelling in hazy weather and windy and sandy weather, maintain good nutrition, adequate sleep and moderate exercise in daily life to enhance their immunity, wash hands frequently and ventilate the air, and try to avoid staying in humid and confined spaces in order to reduce the risk of latent TB patients developing the disease.
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Affiliation(s)
- Minli Chang
- College of Public Health, Xinjiang Medical University, Ürümqi, 830017, People's Republic of China
| | - Mawlanjan Emam
- Center for Disease Control and Prevention, Kashgar, People's Republic of China
| | - Xiaodie Chen
- College of Public Health, Xinjiang Medical University, Ürümqi, 830017, People's Republic of China
| | - Dongmei Lu
- Center of Pulmonary and Critical Care Medicine, People's Hospital of Xinjiang Uygur Autonomous Region, Ürümqi, People's Republic of China
| | - Liping Zhang
- College of Medical Engineering and Technology, Xinjiang Medical University, Ürümqi, 830017, People's Republic of China
| | - Yanling Zheng
- College of Medical Engineering and Technology, Xinjiang Medical University, Ürümqi, 830017, People's Republic of China.
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Luo D, Wang L, Zhang M, Martinez L, Chen S, Zhang Y, Wang W, Wu Q, Wu Y, Liu K, Xie B, Chen B. Spatial spillover effect of environmental factors on the tuberculosis occurrence among the elderly: a surveillance analysis for nearly a dozen years in eastern China. BMC Public Health 2024; 24:209. [PMID: 38233763 PMCID: PMC10795419 DOI: 10.1186/s12889-024-17644-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 01/02/2024] [Indexed: 01/19/2024] Open
Abstract
BACKGROUND In many areas of China, over 30% of tuberculosis cases occur among the elderly. We aimed to investigate the spatial distribution and environmental factors that predicted the occurence of tuberculosis in this group. METHODS Data were collected on notified pulmonary tuberculosis (PTB) cases aged ≥ 65 years in Zhejiang Province from 2010 to 2021. We performed spatial autocorrelation and spatial-temporal scan statistics to determine the clusters of epidemics. Spatial Durbin Model (SDM) analysis was used to identify significant environmental factors and their spatial spillover effects. RESULTS 77,405 cases of PTB among the elderly were notified, showing a decreasing trend in the notification rate. Spatial-temporal analysis showed clustering of epidemics in the western area of Zhejiang Province. The results of the SDM indicated that a one-unit increase in PM2.5 led to a 0.396% increase in the local notification rate. The annual mean temperature and precipitation had direct effects and spatial spillover effects on the rate, while complexity of the shape of the greenspace (SHAPE_AM) and SO2 had negative spatial spillover effects. CONCLUSION Targeted interventions among the elderly in Western Zhejiang may be more efficient than broad, province-wide interventions. Low annual mean temperature and high annual mean precipitation in local and neighboring areas tend to have higher PTB onset among the elderly.
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Affiliation(s)
- Dan Luo
- Department of Public Health, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Luyu Wang
- School of Urban Design, Wuhan University, Hubei, Wuhan, China
| | - Mengdie Zhang
- Zhejiang University School of Public Health, Hangzhou, Zhejiang, China
| | - Leonardo Martinez
- Department of Epidemiology, School of Public Health, Boston University, Boston, MA, USA
| | - Songhua Chen
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Yu Zhang
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Wei Wang
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Qian Wu
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Yonghao Wu
- Department of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China, 310058
| | - Kui Liu
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China.
- National Centre for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
| | - Bo Xie
- School of Urban Design, Wuhan University, Hubei, Wuhan, China.
| | - Bin Chen
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China.
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Wang Q, Li YL, Yin YL, Hu B, Yu CC, Wang ZD, Li YH, Xu CJ, Wang YB. Association of air pollutants and meteorological factors with tuberculosis: a national multicenter ecological study in China. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2023; 67:1629-1641. [PMID: 37535117 DOI: 10.1007/s00484-023-02524-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 05/17/2023] [Accepted: 07/14/2023] [Indexed: 08/04/2023]
Abstract
The impact of weather variability and air pollutants on tuberculosis (TB) has been a research hotspot. Previous studies have mostly been limited to a certain area or with a small sample size of cases, and multi-scale systematic studies are lacking. In this study, 14,816,329 TB cases were collected from 31 provinces in China between 2004 and 2018 to estimate the association between TB risk and meteorological factors and air pollutants using a two-stage time-series analysis. The impact and lagged time of meteorological factors and air pollutants on TB risk varied greatly in different provinces and regions. Overall cumulative exposure-response summary associations across 31 provinces suggested that high monthly mean relative humidity (RH) (66.8-82.4%, percentile56-100 (P56-100)), rainfall (316.5-331.1 mm, P96-100), PM2.5 exposure concentration (93.3-145.0 μg/m3, P58-100), and low monthly mean wind speed (1.6-2.1 m/s, P0-38) increased the risk of TB incidence, with a relative risk (RR) of 1.10 (95% CI: 1.04-1.16), 1.10 (95% CI: 1.03-1.16), 2.08 (95% CI: 1.18-3.65), and 2.06 (95% CI: 1.27-3.33), and attributable risk percent (AR%) of 9%, 9%, 52%, and 51%, respectively. Conversely, high monthly average wind speed (2.3-2.9 m/s, P54-100) and mean temperature (20.2-25.3 °C, P79-96), and low monthly average rainfall (2.4-25.2 mm, P0-7) and concentration of SO2 (8.1-21.2 μg/m3, P0-16) exposure decreased the risk of TB incidence, with an overall cumulative RR of 0.92 (95% CI: 0.87-0.98), 0.74 (95% CI: 0.59-0.94), 0.87 (95% CI: 0.79-0.95), and 0.72 (95% CI: 0.56-0.93), respectively. Our study provided insights into future planning of public health interventions for TB.
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Affiliation(s)
- Qian Wang
- School of Public Health, Xinxiang Medical University, Henan Province, Xinxiang, 453003, China
| | - Yan-Lin Li
- School of Public Health, Xinxiang Medical University, Henan Province, Xinxiang, 453003, China
| | - Ya-Ling Yin
- Sino-UK Joint Laboratory of Brain Function and Injury of Henan Province, Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Xinxiang Medical University, Henan Province, Xinxiang, 453003, China
| | - Bin Hu
- School of Public Health, Xinxiang Medical University, Henan Province, Xinxiang, 453003, China
| | - Chong-Chong Yu
- School of Public Health, Xinxiang Medical University, Henan Province, Xinxiang, 453003, China
| | - Zhen-de Wang
- School of Public Health, Weifang Medical University, Shandong Province, Weifang, 261053, China
- National Center for Tuberculosis Control and Prevention, China Center for Disease Control and Prevention, Beijing, 102206, China
| | - Yu-Hong Li
- National Center for Tuberculosis Control and Prevention, China Center for Disease Control and Prevention, Beijing, 102206, China
| | - Chun-Jie Xu
- Institute of Medical Biotechnology, Chinese Academy of Medical Sciences & Peking Union Medical Sciences, Beijing, 100730, China.
| | - Yong-Bin Wang
- School of Public Health, Xinxiang Medical University, Henan Province, Xinxiang, 453003, China.
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Wardani DWSR, Pramesona BA, Septiana T, Soemarwoto RAS. Risk factors for delayed sputum conversion: A qualitative case study from the person-in-charge of TB program's perspectives. J Public Health Res 2023; 12:22799036231208355. [PMID: 37901194 PMCID: PMC10605690 DOI: 10.1177/22799036231208355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 09/25/2023] [Indexed: 10/31/2023] Open
Abstract
Background One of the indicators to determine the success of TB treatment is the conversion of sputum from smear positive to negative. However, several factors can lead to this failure of sputum conversion. Objectives To investigate the risk factors for delayed sputum conversion from the person-in-charge (PIC) of the TB program's perspective. Design and methods This qualitative case study was conducted on September 7th, 2022. Thirty-one PICs of the TB program from 31 public health centers (Puskesmas) in Bandar Lampung, Indonesia, were recruited purposively. All participants were grouped into three FGDs. Developed semi-structured interview questions were used for data collection. Thematic analysis was used to synthesize and cross-reference emerging topics. Results Three themes emerged in our study: (1) individual factors with the sub-themes of medication adherence, education, initial laboratory examination, comorbid disease, nutrition, and lifestyle; (2) environmental factors with the sub-themes of types of support, sources of support, environmental conditions and stigma; and (3) health service factors with the sub-theme of access to health service facilities. Conclusions Problems related to TB management are not only the individual's responsibility but need to strengthen support from the environment and health services.
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Affiliation(s)
| | - Bayu Anggileo Pramesona
- Department of Public Health, Faculty of Medicine, Universitas Lampung, Bandar Lampung, Indonesia
| | - Trisya Septiana
- Department of Informatics Engineering, Faculty of Engineering, Universitas Lampung, Bandar Lampung, Indonesia
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Ma Z, Fan H. Influential factors of tuberculosis in mainland China based on MGWR model. PLoS One 2023; 18:e0290978. [PMID: 37651412 PMCID: PMC10470953 DOI: 10.1371/journal.pone.0290978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Accepted: 08/20/2023] [Indexed: 09/02/2023] Open
Abstract
Tuberculosis (TB), as a respiratory infectious disease, has damaged public health globally for decades, and mainland China has always been an area with high incidence of TB. Since the outbreak of COVID-19, it has seriously occupied medical resources and affected medical treatment of TB patients. Therefore, the authenticity and reliability of TB data during this period have also been questioned by many researchers. In response to this situation, this paper excludes the data from 2019 to the present, and collects the data of TB incidence in mainland China and the data of 11 influencing factors from 2014 to 2018. Using spatial autocorrelation methods and multiscale geographically weighted regression (MGWR) model to study the temporal and spatial distribution of TB incidence in mainland China and the influence of selected influencing factors on TB incidence. The experimental results show that the distribution of TB patients in mainland China shows spatial aggregation and spatial heterogeneity during this period. And the R2 and the adjusted R2 of MGWR model are 0.932 and 0.910, which are significantly better than OLS model (0.466, 0.429) and GWR model (0.836, 0.797). The fitting accuracy indicators MAE, MSE and MAPE of MGWR model reached 5.802075, 110.865107 and 0.088215 respectively, which also show that the overall fitting effect is significantly better than OLS model (19.987574, 869.181549, 0.314281) and GWR model (10.508819, 267.176741, 0.169292). Therefore, this model is based on real and reliable TB data, which provides decision-making references for the prevention and control of TB in mainland China and other countries.
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Affiliation(s)
- Zhipeng Ma
- State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
| | - Hong Fan
- State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
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Chen ZY, Deng XY, Zou Y, He Y, Chen SJ, Wang QT, Xing DG, Zhang Y. A Spatio-temporal Bayesian model to estimate risk and influencing factors related to tuberculosis in Chongqing, China, 2014-2020. Arch Public Health 2023; 81:42. [PMID: 36945028 PMCID: PMC10031926 DOI: 10.1186/s13690-023-01044-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 02/16/2023] [Indexed: 03/23/2023] Open
Abstract
BACKGROUND Tuberculosis (TB) is a serious infectious disease that is one of the leading causes of death worldwide. This study aimed to investigate the spatial and temporal distribution patterns and potential influencing factors of TB incidence risk, and to provide a scientific basis for the prevention and control of TB. METHODS We collected reported cases of TB in 38 districts and counties in Chongqing from 2014 to 2020 and data on environment, population characteristics and economic factors during the same period. By constructing a Bayesian spatio-temporal model, we explored the spatio-temporal distribution pattern of TB incidence risk and potential influencing factors, identified key areas and key populations affected by TB, compared the spatio-temporal distribution characteristics of TB in populations with different characteristics, and explored the differences in the influence of various social and environmental factors. RESULTS The high-risk areas for TB incidence in Chongqing from 2014 to 2020 were mainly concentrated in southeastern and northeastern regions of Chongqing, and the overall relative risk (RR) of TB showed a decreasing trend during the study period, while RR of TB in main urban area and southeast of Chongqing showed an increasing trend. The RR of TB was relatively high in the main urban area for the female population and the population aged 0-29 years, and the RR of TB for the population aged 30-44 years in the main urban area and the population aged 60 years or older in southeast of Chongqing had an increasing trend, respectively. For each 1 μg/m3 increase in SO2 and 1% increase in the number of low-income per 1000 non-agricultural households (LINA per 1000 persons), the RR of TB increased by 0.35% (95% CI: 0.08-0.61%) and 0.07% (95% CI: 0.05-0.10%), respectively. And LINA per 1000 persons had the greatest impact on the female population and the over 60 years old age group. Although each 1% increase in urbanization rate (UR) was associated with 0.15% (95% CI: 0.11-0.17%) reduction in the RR of TB in the whole population, the RR increased by 0.18% (95% CI: 0.16-0.21%) in the female population and 0.37% (95% CI: 0.34-0.45%) in the 0-29 age group. CONCLUSION This study showed that high-risk areas for TB were concentrated in the southeastern and northeastern regions of Chongqing, and that the elderly population was a key population for TB incidence. There were spatial and temporal differences in the incidence of TB in populations with different characteristics, and various socio-environmental factors had different effects on different populations. Local governments should focus on areas and populations at high risk of TB and develop targeted prevention interventions based on the characteristics of different populations.
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Affiliation(s)
- Zhi-Yi Chen
- School of Public Health, Chongqing Medical University, Chongqing, 400016, China
- Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing, 400016, China
- Innovation Center for Social Risk Governance in Health, Chongqing Medical University, Chongqing, 400016, China
- Research Center for Public Health Security, Chongqing Medical University, Chongqing, 400016, China
| | - Xin-Yi Deng
- School of Public Health, Chongqing Medical University, Chongqing, 400016, China
- Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing, 400016, China
- Innovation Center for Social Risk Governance in Health, Chongqing Medical University, Chongqing, 400016, China
- Research Center for Public Health Security, Chongqing Medical University, Chongqing, 400016, China
| | - Yang Zou
- School of Public Health, Chongqing Medical University, Chongqing, 400016, China
- Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing, 400016, China
- Innovation Center for Social Risk Governance in Health, Chongqing Medical University, Chongqing, 400016, China
- Research Center for Public Health Security, Chongqing Medical University, Chongqing, 400016, China
| | - Ying He
- School of Public Health, Chongqing Medical University, Chongqing, 400016, China
- Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing, 400016, China
- Innovation Center for Social Risk Governance in Health, Chongqing Medical University, Chongqing, 400016, China
- Research Center for Public Health Security, Chongqing Medical University, Chongqing, 400016, China
| | - Sai-Juan Chen
- School of Public Health, Chongqing Medical University, Chongqing, 400016, China
- Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing, 400016, China
- Innovation Center for Social Risk Governance in Health, Chongqing Medical University, Chongqing, 400016, China
- Research Center for Public Health Security, Chongqing Medical University, Chongqing, 400016, China
| | - Qiu-Ting Wang
- School of Public Health, Chongqing Medical University, Chongqing, 400016, China
- Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing, 400016, China
- Innovation Center for Social Risk Governance in Health, Chongqing Medical University, Chongqing, 400016, China
- Research Center for Public Health Security, Chongqing Medical University, Chongqing, 400016, China
| | - Dian-Guo Xing
- Office of Health Emergency, Chongqing Municipal Health Commission, No.6, Qilong Road, Yubei District, Chongqing, 401147, China.
| | - Yan Zhang
- School of Public Health, Chongqing Medical University, Chongqing, 400016, China.
- Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing, 400016, China.
- Innovation Center for Social Risk Governance in Health, Chongqing Medical University, Chongqing, 400016, China.
- Research Center for Public Health Security, Chongqing Medical University, Chongqing, 400016, China.
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Zheng Y, Emam M, Lu D, Tian M, Wang K, Peng X. Analysis of the effect of temperature on tuberculosis incidence by distributed lag non-linear model in Kashgar city, China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:11530-11541. [PMID: 36094714 PMCID: PMC9466343 DOI: 10.1007/s11356-022-22849-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 08/29/2022] [Indexed: 06/15/2023]
Abstract
The aim of this study was to explore the effect of temperature on tuberculosis (TB) incidence using the distributed lag non-linear model (DLNM) from 2017 to 2021 in Kashgar city, the region with higher TB incidence than national levels, and assist public health prevention and control measures. From January 2017 to December 2021, a total of 8730 cases of TB were reported, with the higher incidence of male than that of female. When temperature was below 1 °C, it was significantly correlated with TB incidence compared to the median observed temperature (15 °C) at lag 7, 14, and 21, and lower temperatures showed larger RR (relative risk) values. High temperature produced a protective effect on TB transmission, and higher temperature from 16 to 31 °C has lower RR. In discussion stratified by gender, the maximum RRs were achieved for both male group and female group at - 15 °C with lag 21, reporting 4.28 and 2.02, respectively. At high temperature (higher than 20 °C), the RR value of developing TB for female group was significantly larger than 1. In discussion stratified by age, the maximum RRs were achieved for all age groups (≤ 35, 36-64, ≥ 65) at - 15 °C with lag 21, reporting 3.20, 2.07, and 3.45, respectively. When the temperature was higher than 20 °C, the RR of the 36-64-year-old group and the ≥ 65-year-old group was significantly larger than 1 at lag 21, while significantly smaller than 1 for cumulative RR at lag 21, reporting 0.11, 95% confidence interval (CI) (0.01, 0.83) and 0.06, 95% CI (0.01, 0.44), respectively. In conclusion, low temperature, especially in extreme level, acts as a high-risk factor inducing TB transmission in Kashgar city. Males exhibit a significantly higher RR of developing TB at low temperature than female, as well as the elderly group in contrast to the young or middle-aged groups. High temperature has a protective effect on TB transmission in the total population, but female and middle-aged and elderly groups are also required to be alert to the delayed RR induced by it.
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Affiliation(s)
- Yanling Zheng
- College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830017, China.
| | - Mawlanjan Emam
- Center for Disease Control and Prevention, Kashgar, China
| | - Dongmei Lu
- Center of Respiratory and Critical Care Medicine of the People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, China
| | - Maozai Tian
- College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830017, China
| | - Kai Wang
- College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830017, China
| | - Xiaowang Peng
- Center for Disease Control and Prevention, Kashgar, China.
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Liang W, Hu A, Hu P, Zhu J, Wang Y. Estimating the tuberculosis incidence using a SARIMAX-NNARX hybrid model by integrating meteorological factors in Qinghai Province, China. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2023; 67:55-65. [PMID: 36271168 DOI: 10.1007/s00484-022-02385-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 09/30/2022] [Accepted: 10/06/2022] [Indexed: 06/16/2023]
Abstract
Tuberculosis (TB) is recognized as being a major public health concern owing to its increase in Qinghai, China. In this study, we aimed to estimate the long-term effects of meteorological variables on TB incidence and construct an advanced hybrid model with seasonal autoregressive integrated moving average (SARIMA) and a neural network nonlinear autoregression (SARIMAX-NNARX) by integrating meteorological factors and evaluating the model fitting and prediction effect. During 2005-2017, TB experienced an upward trend with obvious periodic and seasonal characteristics, peaking in spring and winter. The results showed that TB incidence was positively correlated with average relative humidity (ARH) with a 2-month lag (β = 1.889, p = 0.003), but negatively correlated with average atmospheric pressure (AAP) with a 1-month lag (β = - 1.633, p = 0.012), average temperature (AT) with a 2-month lag (β = - 0.093, p = 0.027), and average wind speed (AWS) with a 0-month lag (β = - 13.221, p = 0.033), respectively. The SARIMA (3,1,0)(1,1,1)12, SARIMAX(3,1,0)(1,1,1)12, and SARIMAX(3,1,0)(1,1,1)12-NNARX(15,3) were considered preferred models based on the evaluation criteria. Of them, the SARIMAX-NNARX technique had smaller error values than the SARIMA and SARIMAX models in both fitting and forecasting aspects. The sensitivity analysis also revealed the robustness of the mixture forecasting model. Therefore, the SARIMAX-NNARX model by integrating meteorological variables can be used as an accurate method for forecasting the epidemic trends which would be great importance for TB prevention and control in the coming periods in Qinghai.
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Affiliation(s)
- Wenjuan Liang
- Department of Epidemiology, International School of Public Health and One Health, Hainan Medical University, Haikou, Hainan Province, 571199, People's Republic of China
- Department of Epidemiology and Health Statistics, School of Public Health, The Third Affiliated Hospital, Xinxiang Medical University, No. 601 Jinsui Road, Hongqi District, Xinxiang, Henan Province, 453003, People's Republic of China
| | - Ailing Hu
- Department of Epidemiology and Health Statistics, School of Public Health, The Third Affiliated Hospital, Xinxiang Medical University, No. 601 Jinsui Road, Hongqi District, Xinxiang, Henan Province, 453003, People's Republic of China
| | - Pan Hu
- Department of Epidemiology and Health Statistics, School of Public Health, The Third Affiliated Hospital, Xinxiang Medical University, No. 601 Jinsui Road, Hongqi District, Xinxiang, Henan Province, 453003, People's Republic of China
| | - Jinqin Zhu
- Department of Epidemiology and Health Statistics, School of Public Health, The Third Affiliated Hospital, Xinxiang Medical University, No. 601 Jinsui Road, Hongqi District, Xinxiang, Henan Province, 453003, People's Republic of China
| | - Yongbin Wang
- Department of Epidemiology and Health Statistics, School of Public Health, The Third Affiliated Hospital, Xinxiang Medical University, No. 601 Jinsui Road, Hongqi District, Xinxiang, Henan Province, 453003, People's Republic of China.
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13
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Huang K, Hu CY, Yang XY, Zhang Y, Wang XQ, Zhang KD, Li YQ, Wang J, Yu WJ, Cheng X, Cao JY, Zhang T, Kan XH, Zhang XJ. Contributions of ambient temperature and relative humidity to the risk of tuberculosis admissions: A multicity study in Central China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 838:156272. [PMID: 35644395 DOI: 10.1016/j.scitotenv.2022.156272] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 05/08/2022] [Accepted: 05/23/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND As a communicable disease and major public health issue, many studies have quantified the associations between tuberculosis (TB) and meteorological factors with inconsistent results. The purpose of this multicenter study was to characterize the associations between ambient temperature, humidity and the risk of TB hospitalizations and to investigate potential heterogeneity. METHOD Data on daily hospitalizations for TB, meteorological factors and ambient air pollutants for 16 cities in Anhui Province were collected from 2015 to 2020. A distributed lag nonlinear model (DLNM) was performed to obtain the estimates of meteorological-TB relationships by cities. Then, we used the multivariate meta-regression model to pool the city-specific estimates with air pollution, demographic indicators, medical resource and latitude as potential modifiers to explore the sources of heterogeneity. Finally, we divided the whole province into three regions to validate the meteorological-TB relationships by regions. RESULTS The overall pooled temperature-TB association presented an approximate S-shaped curve, with relative risk (RR) peaking at 5 °C (RR = 1.536, 95% CI: 1.303-1.811) compared to the reference temperature (27 °C). Lag-response curve suggested that low temperature exposure increased the risk of TB hospitalizations at lag 0 and 1 day (lag0 day: RR = 1.136, 95% CI: 1.048-1.231, lag1 day: RR = 1.052, 95% CI: 1.023-1.082). However, the overall exposure-response curve between relative humidity and TB showed almost horizontal line with reference relative humidity to 78%. The residual heterogeneity ranged from 27.1% to 36.9%, with air pollution, latitude and medical resource explained the largest proportion. CONCLUSION We found that low temperature exposure is associated with an acute increased risk of TB hospitalizations in Anhui Province. The association between temperature and TB admission varies depending on air pollution, latitude, and medical resources. Since the effect of short-term exposure to humidity is not significant, further studies are supposed to focus on the long-term effect of humidity.
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Affiliation(s)
- Kai Huang
- Department of Hospital Infection Prevention and Control, The Second Affiliated Hospital of Anhui Medical University, 678 Furong Road, Hefei 230601, China; Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, China
| | - Cheng-Yang Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, China
| | - Xi-Yao Yang
- Department of Hospital Infection Prevention and Control, The Second Affiliated Hospital of Anhui Medical University, 678 Furong Road, Hefei 230601, China
| | - Yunquan Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College, Wuhan University of Science and Technology, Wuhan 430065, China
| | - Xin-Qiang Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, China
| | - Kang-Di Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, China
| | - Ying-Qing Li
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, China
| | - Jie Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, China
| | - Wen-Jie Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, China
| | - Xin Cheng
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, China
| | - Ji-Yu Cao
- Department of Occupational Health and Environmental Health, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, Anhui, China
| | - Tao Zhang
- Anhui Chest Hospital, 397 Jixi Road, Hefei 230022, China
| | - Xiao-Hong Kan
- Anhui Chest Hospital, 397 Jixi Road, Hefei 230022, China; Anhui Medical University Clinical College of Chest, 397 Jixi Road, Hefei 230022, China.
| | - Xiu-Jun Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, China.
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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: 5] [Impact Index Per Article: 2.5] [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.
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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
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Ma K, Lin Y, Zhang X, Fang F, Zhang Y, Li J, Yao Y, Ge L, Tan H, Wang F. Spatiotemporal Distribution and Evolution of Digestive Tract Cancer Cases in Lujiang County, China since 2012. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19127451. [PMID: 35742697 PMCID: PMC9223376 DOI: 10.3390/ijerph19127451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 06/13/2022] [Accepted: 06/15/2022] [Indexed: 11/16/2022]
Abstract
This study aims to analyze the spatiotemporal distribution and evolution of digestive tract cancer (DTC) in Lujiang County, China by using the geographic information system technology. Results of this study are expected to provide a scientific basis for effective prevention and control of DTC. The data on DTC cases in Lujiang County, China, were downloaded from the Data Center of the Center for Disease Control and Prevention in Hefei, Anhui Province, China, while the demographic data were sourced from the demographic department in China. Systematic statistical analyses, including the spatial empirical Bayes smoothing, spatial autocorrelation, hotspot statistics, and Kulldorff's retrospective space-time scan, were used to identify the spatial and spatiotemporal clusters of DTC. GM(1,1) and standard deviation ellipses were then applied to predict the future evolution of the spatial pattern of the DTC cases in Lujiang County. The results showed that DTC in Lujiang County had obvious spatiotemporal clustering. The spatial distribution of DTC cases increases gradually from east to west in the county in a stepwise pattern. The peak of DTC cases occurred in 2012-2013, and the high-case spatial clusters were located mainly in the northwest of Lujiang County. At the 99% confidence interval, two spatiotemporal clusters were identified. From 2012 to 2017, the cases of DTC in Lujiang County gradually shifted to the high-incidence area in the northwest, and the spatial distribution range experienced a process of "dispersion-clustering". The cases of DTC in Lujiang County will continue to move to the northwest from 2018 to 2025, and the predicted spatial clustering tends to be more obvious.
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Affiliation(s)
- Kang Ma
- Key Laboratory of Earth Surface Processes and Response in the Yangtze-Huaihe River Basin, School of Geography and Tourism, Anhui Normal University, Wuhu 241002, China; (K.M.); (Y.L.); (Y.Y.); (L.G.); (H.T.); (F.W.)
| | - Yuesheng Lin
- Key Laboratory of Earth Surface Processes and Response in the Yangtze-Huaihe River Basin, School of Geography and Tourism, Anhui Normal University, Wuhu 241002, China; (K.M.); (Y.L.); (Y.Y.); (L.G.); (H.T.); (F.W.)
| | - Xiaopeng Zhang
- Hefei Center for Disease Control and Prevention, Hefei 230022, China; (X.Z.); (J.L.)
| | - Fengman Fang
- Key Laboratory of Earth Surface Processes and Response in the Yangtze-Huaihe River Basin, School of Geography and Tourism, Anhui Normal University, Wuhu 241002, China; (K.M.); (Y.L.); (Y.Y.); (L.G.); (H.T.); (F.W.)
- Correspondence: ; Tel.: +86-(0553)-5910687
| | - Yong Zhang
- Department of Geological Sciences, University of Alabama, Tuscaloosa, AL 35487, USA;
| | - Jiajia Li
- Hefei Center for Disease Control and Prevention, Hefei 230022, China; (X.Z.); (J.L.)
| | - Youru Yao
- Key Laboratory of Earth Surface Processes and Response in the Yangtze-Huaihe River Basin, School of Geography and Tourism, Anhui Normal University, Wuhu 241002, China; (K.M.); (Y.L.); (Y.Y.); (L.G.); (H.T.); (F.W.)
| | - Lei Ge
- Key Laboratory of Earth Surface Processes and Response in the Yangtze-Huaihe River Basin, School of Geography and Tourism, Anhui Normal University, Wuhu 241002, China; (K.M.); (Y.L.); (Y.Y.); (L.G.); (H.T.); (F.W.)
| | - Huarong Tan
- Key Laboratory of Earth Surface Processes and Response in the Yangtze-Huaihe River Basin, School of Geography and Tourism, Anhui Normal University, Wuhu 241002, China; (K.M.); (Y.L.); (Y.Y.); (L.G.); (H.T.); (F.W.)
| | - Fei Wang
- Key Laboratory of Earth Surface Processes and Response in the Yangtze-Huaihe River Basin, School of Geography and Tourism, Anhui Normal University, Wuhu 241002, China; (K.M.); (Y.L.); (Y.Y.); (L.G.); (H.T.); (F.W.)
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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.
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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
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Spatial Characteristics and Driving Factors of Public–Private Partnership Projects Implemented in China. BUILDINGS 2022. [DOI: 10.3390/buildings12060768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
In recent years, public–private partnership (PPP), as an alternative strategy in the delivery of infrastructure services, has developed rapidly in China. However, the implementation of PPP projects differs significantly between provinces and municipalities. Using the implementation data of national PPP demonstration projects, this study employs spatial autoregression and a spatial Durbin model (SDM) to explore the spatial characteristics and driving factors of PPP projects across China. The results indicate that the PPP project implementation rate in China shows significant spatial clustering, which indicates a spatial spillover effect in the eastern, central, and western regions of the country. The fixed asset investment and infrastructure status exert a significant positive effect on the PPP project implementation rate in a certain region and aid in the implementation of PPP projects in the neighboring regions. Urbanization potentiality, the financial self-sufficiency rate, and regional openness do not have an impact on the local PPP project implementation rate, but they do inhibit the increase in this rate in the adjacent regions. Fiscal capacity, government credibility, and the level of social development do not affect the local PPP project implementation rate, but they do promote the implementation of the PPP projects of these neighboring regions. Local authorities should make additional efforts to build an inter-regional development environment for PPP and promote the implementation of PPP projects.
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Association between climate variables and pulmonary tuberculosis incidence in Brunei Darussalam. Sci Rep 2022; 12:8775. [PMID: 35610355 PMCID: PMC9130123 DOI: 10.1038/s41598-022-12796-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 05/13/2022] [Indexed: 12/20/2022] Open
Abstract
We investigated the association between climate variables and pulmonary tuberculosis (PTB) incidence in Brunei-Muara district, Brunei Darussalam. Weekly PTB case counts and climate variables from January 2001 to December 2018 were analysed using distributed lag non-linear model framework. After adjusting for long-term trend and seasonality, we observed positive but delayed relationship between PTB incidence and minimum temperature, with significant adjusted relative risk (adj.RR) at 25.1 °C (95th percentile) when compared to the median, from lag 30 onwards (adj.RR = 1.17 [95% Confidence Interval (95% CI): 1.01, 1.36]), suggesting effect of minimum temperature on PTB incidence after 30 weeks. Similar results were observed from a sub-analysis on smear-positive PTB case counts from lag 29 onwards (adj.RR = 1.21 [95% CI: 1.01, 1.45]), along with positive and delayed association with total rainfall at 160.7 mm (95th percentile) when compared to the median, from lag 42 onwards (adj.RR = 1.23 [95% CI: 1.01, 1.49]). Our findings reveal evidence of delayed effects of climate on PTB incidence in Brunei, but with varying degrees of magnitude, direction and timing. Though explainable by environmental and social factors, further studies on the relative contribution of recent (through primary human-to-human transmission) and remote (through reactivation of latent TB) TB infection in equatorial settings is warranted.
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Lu TL, Zhang JM, Li SR, Chen CW. Spatial-temporal Distribution and Influencing Factors of Helicobacter pylori Infection in Chinese Mainland, 2001-2020: A Systematic Review and Meta-Analysis. J Clin Gastroenterol 2022; 56:e273-e282. [PMID: 35324486 DOI: 10.1097/mcg.0000000000001691] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
BACKGROUND The spatial-temporal distribution of Helicobacter pylori infection in China is poorly understood. We aimed to study the spatial-temporal distribution of H. pylori infection in Chinese mainland and to explore its influencing factors. MATERIALS AND METHODS We searched the relevant literature from 2001 to 2021 and applied meta-analysis to obtain the pooled prevalence estimates of all studies and subgroups. Then, we used the pooled prevalence as the dependent variable for the following analysis, including time series analysis, statistical mapping, spatial autocorrelation analysis, and influencing factor analysis based on generalized additive model and panel data model. RESULTS A total of 726 articles and 3,407,392 people were included. The pooled prevalence was 43.7% (95% confidence interval: 42.7%-44.8%). The prevalence decreased in the past 20 years, with high in the eastern and western regions and low in the central region. Qinghai Tibet Plateau and Guizhou Plateau were the high incidence areas of this disease. The intake of vegetable oil, aquatic products, meat, milk, per capita gross domestic product, and annual average humidity were significantly correlated with H. pylori. CONCLUSIONS The prevalence of H. pylori is decreasing in Chinese mainland, but still high in underdeveloped areas. Appropriate strategies for the prevention need greater attention.
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Affiliation(s)
- Tai-Liang Lu
- Department of Gastrointestinal Surgery, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha
| | - Jia-Min Zhang
- Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Shao-Rong Li
- Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Chao-Wu Chen
- Department of Gastrointestinal Surgery, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha
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Health-Based Geographic Information Systems for Mapping and Risk Modeling of Infectious Diseases and COVID-19 to Support Spatial Decision-Making. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1368:167-188. [DOI: 10.1007/978-981-16-8969-7_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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21
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Niu Z, Qi Y, Zhao P, Li Y, Tao Y, Peng L, Qiao M. Short-term effects of ambient air pollution and meteorological factors on tuberculosis in semi-arid area, northwest China: a case study in Lanzhou. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:69190-69199. [PMID: 34291414 DOI: 10.1007/s11356-021-15445-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 07/10/2021] [Indexed: 05/21/2023]
Abstract
To investigate the short-term effects of ambient air pollution and meteorological factors on daily tuberculosis (TB), semi-parametric generalized additive model was used to assess the impacts of ambient air pollutants and meteorological factors on daily TB case from 2005 to 2010 in Chengguan District, Lanzhou, China. Then a non-stratification parametric model and a stratification parametric model were applied to study the interactive effect of air pollutants and meteorological factors on daily TB. The results show that sulfur dioxide (SO2), nitrogen dioxide (NO2), and particulate matter with aerodynamic diameter less than 10μm (PM10) were positively correlated with daily TB case; the excess risk (ER) and 95% confidence interval (CI) were 1.79% (0.40%, 3.20%), 3.86% (1.81%, 5.96%), and 0.32% (0.02%, 0.62%), respectively. Daily TB case was positively correlated with maximum temperature, minimum temperature, average temperature, vapor pressure, and relative humidity, but negatively correlated with atmospheric pressure, wind speed, and sunshine duration. The association with average temperature was the strongest, whose ER and 95% CI were 4.43% (3.15%, 5.72%). In addition, there were significant interaction effects between air pollutants and meteorological factors on daily TB case.
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Affiliation(s)
- Zhaocheng Niu
- Gansu Key Laboratory for Environmental Pollution Prediction and Control, College of Earth and Environmental Sciences, Lanzhou University, 222 South Tianshui Road, Chengguan District, Lanzhou, 730000, Gansu Province, People's Republic of China
- Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, 222 South Tianshui Road, Chengguan District, Lanzhou, 730000, Gansu Province, People's Republic of China
| | - Yuejun Qi
- Lanzhou Municipal Health Service Center, Lanzhou, 730030, China
| | - Puqiu Zhao
- Gansu Key Laboratory for Environmental Pollution Prediction and Control, College of Earth and Environmental Sciences, Lanzhou University, 222 South Tianshui Road, Chengguan District, Lanzhou, 730000, Gansu Province, People's Republic of China
- Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, 222 South Tianshui Road, Chengguan District, Lanzhou, 730000, Gansu Province, People's Republic of China
| | - Yidu Li
- Gansu Key Laboratory for Environmental Pollution Prediction and Control, College of Earth and Environmental Sciences, Lanzhou University, 222 South Tianshui Road, Chengguan District, Lanzhou, 730000, Gansu Province, People's Republic of China
- Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, 222 South Tianshui Road, Chengguan District, Lanzhou, 730000, Gansu Province, People's Republic of China
| | - Yan Tao
- Gansu Key Laboratory for Environmental Pollution Prediction and Control, College of Earth and Environmental Sciences, Lanzhou University, 222 South Tianshui Road, Chengguan District, Lanzhou, 730000, Gansu Province, People's Republic of China.
- Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, 222 South Tianshui Road, Chengguan District, Lanzhou, 730000, Gansu Province, People's Republic of China.
| | - Lu Peng
- Gansu Key Laboratory for Environmental Pollution Prediction and Control, College of Earth and Environmental Sciences, Lanzhou University, 222 South Tianshui Road, Chengguan District, Lanzhou, 730000, Gansu Province, People's Republic of China
- Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, 222 South Tianshui Road, Chengguan District, Lanzhou, 730000, Gansu Province, People's Republic of China
| | - Mingli Qiao
- Gansu Key Laboratory for Environmental Pollution Prediction and Control, College of Earth and Environmental Sciences, Lanzhou University, 222 South Tianshui Road, Chengguan District, Lanzhou, 730000, Gansu Province, People's Republic of China
- Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, 222 South Tianshui Road, Chengguan District, Lanzhou, 730000, Gansu Province, People's Republic of China
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Spatio-temporal variation in tuberculosis incidence and risk factors for the disease in a region of unbalanced socio-economic development. BMC Public Health 2021; 21:1817. [PMID: 34627189 PMCID: PMC8501584 DOI: 10.1186/s12889-021-11833-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Accepted: 09/22/2021] [Indexed: 01/14/2023] Open
Abstract
BACKGROUND Previous research pointed to a close relationship between the incidence of tuberculosis (TB) in aging populations and socio-economic conditions, however there has been lack of studies focused on a region of unbalanced socio-economic development. The aim of this paper is to explore the spatio-temporal variation in TB incidence and examine risk determinants of the disease among aging populations in a typical region. METHODS Data on TB-registered cases between 2009 and 2014, in addition to social-economic factors, were collected for each district/county in Beijing, Tianjin and Hebei, a region characterized by an aging population and disparities in social-economic development. A Bayesian space-time hierarchy model (BSTHM) was used to reveal spatio-temporal variation in the incidence of TB among the elderly in this region between 2009 to 2014. GeoDetector was applied to measure the determinant power (q statistic) of risk factors for TB among the elderly. RESULTS The incidence of TB among the elderly exhibited geographical spatial heterogeneity, with a higher incidence in underdeveloped rural areas compared with that in urban areas. Hotspots of TB incidence risk among the elderly were mostly located in north-eastern and southern areas in the study region, far from metropolitan areas. Areas with low risk were distributed mainly in the Beijing-Tianjin metropolitan areas. Social-economic factors had a non-linear influence on elderly TB incidence, with the dominant factors among rural populations being income (q = 0.20) and medical conditions (q = 0.17). These factors had a non-linear interactive effect on the incidence of TB among the elderly, with medical conditions and the level of economic development having the strongest effect (q = 0.54). CONCLUSIONS The findings explain spatio-temporal variation in TB incidence and risk determinants of elderly TB in the presence of disparities in social-economic development. High-risk zones were located mainly in rural areas, far from metropolitan centres. Medical conditions and the economic development level were significantly associated with elderly TB incidence, and these factors had a non-linear interactive effect on elderly TB incidence. The findings can help to optimize the allocation of health resources and to control TB transmission in the aging population in this region.
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Maharjan B, Gopali RS, Zhang Y. A scoping review on climate change and tuberculosis. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2021; 65:1579-1595. [PMID: 33728507 DOI: 10.1007/s00484-021-02117-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 03/04/2021] [Accepted: 03/09/2021] [Indexed: 06/12/2023]
Abstract
Climate change is a global public health challenge. The changes in climatic factors affect the pattern and burden of tuberculosis, which is a worldwide public health problem affecting low and middle-income countries. However, the evidence related to the impact of climate change on tuberculosis is few and far between. This study is a scoping review following a five-stage version of Arksey and O'Malley's method. We searched the literature using the keywords and their combination in Google scholar, and PubMed. Climate change affects tuberculosis through diverse pathways: changes in climatic factors like temperature, humidity, and precipitation influence host response through alterations in vitamin D distribution, ultraviolet radiation, malnutrition, and other risk factors. The rise in extreme climatic events induces population displacement resulting in a greater number of vulnerable and risk populations of tuberculosis. It creates a conducive environment of tuberculosis transmission and development of active tuberculosis and disrupts tuberculosis diagnosis and treatment services. Therefore, it stands to reasons that climate change affects tuberculosis, particularly in highly vulnerable countries and areas. However, further studies and novel methodologies are required to address such a complex relationship and better understand the occurrence of tuberculosis attributable to climate change.
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Affiliation(s)
- Bijay Maharjan
- Japan-Nepal Health and Tuberculosis Research Association, Kathmandu, Nepal.
| | - Ram Sharan Gopali
- Japan-Nepal Health and Tuberculosis Research Association, Kathmandu, Nepal
| | - Ying Zhang
- School of Public Health, University of Sydney, Sydney, Australia
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Liu L, Wang L, Qi C, Zhu Y, Li C, Jia Y, She K, Liu T, Zhang Y, Cui F, Li X. Epidemiological characteristics and spatiotemporal analysis of hand-foot-mouth diseases from 2010 to 2019 in Zibo city, Shandong, China. BMC Public Health 2021; 21:1640. [PMID: 34496828 PMCID: PMC8424956 DOI: 10.1186/s12889-021-11665-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 08/25/2021] [Indexed: 01/02/2023] Open
Abstract
Background Hand-foot-mouth disease (HFMD) is a global public health issues, especially in China. It has threat the health of children under 5 years old. The early recognition of high-risk districts and understanding of epidemic characteristics can facilitate health sectors to prevent the occurrence of HFMD effectively. Methods Descriptive analysis was used to summarize epidemic characteristics, and the spatial autocorrelation analysis and space-time scan analysis were utilized to explore distribution pattern of HFMD and identify hot spots with statistical significance. The result was presented in ArcMap. Results A total of 52,095 HFMD cases were collected in Zibo city from 1 Jan 2010 to 31 Dec 2019. The annual average incidence was 129.72/100,000. The distribution of HFMD was a unimodal trend, with peak from April to September. The most susceptible age group was children under 5 years old (92.46%), and the male-to-female ratio is 1.60: 1. The main clusters were identified in Zhangdian District from 12 April 2010 to 18 September 2012. Spatial autocorrelation analysis showed that the global spatial correlation in Zibo were no statistical significance, except in 2012, 2014, 2015, 2016 and 2018. Cold spots were gathered in Boshan county and Linzi district, while hot spots only in Zhangdian District in 2018, but other years were no significance. Conclusion Hot spots mainly concentrated in the central and surrounding city of Zibo city. We suggest that imminent public health planning and resource allocation should be focused within those areas.
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Affiliation(s)
- Lili Liu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Ling Wang
- Institute for Infectious Disease Control and Prevention, Zibo Center for Disease Control and Prevention, Zibo, 255026, Shandong, China
| | - Chang Qi
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Yuchen Zhu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Chunyu Li
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Yan Jia
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Kaili She
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Tingxuan Liu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Yan Zhang
- Institute for Infectious Disease Control and Prevention, Zibo Center for Disease Control and Prevention, Zibo, 255026, Shandong, China
| | - Feng Cui
- Zibo Center for Disease Control and Prevention, Zibo, 255026, Shandong, China.
| | - Xiujun Li
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China.
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Rao HX, Li DM, Zhao XY, Yu J. Spatiotemporal clustering and meteorological factors affected scarlet fever incidence in mainland China from 2004 to 2017. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 777:146145. [PMID: 33684741 DOI: 10.1016/j.scitotenv.2021.146145] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 02/21/2021] [Accepted: 02/21/2021] [Indexed: 06/12/2023]
Abstract
OBJECTIVE To analyze the spatiotemporal dynamic distribution and detect the related meteorological factors of scarlet fever from an ecological perspective, which could provide scientific information for effective prevention and control of this disease. METHODS The data on scarlet fever cases in mainland China were downloaded from the Data Center of the China Public Health Science, while monthly meteorological data were extracted from the official website of the National Bureau of Statistics. Global Moran's I, local Getis-Ord Gi⁎ hotspot statistics, and Kulldorff's retrospective space-time scan statistical analysis were used to detect the spatial and spatiotemporal clusters of scarlet fever across all settings. A spatial panel data model was conducted to estimate the impact of meteorological factors on scarlet fever incidence. RESULTS Scarlet fever in China had obvious spatial, temporal, and spatiotemporal clustering, high-incidence spatial clusters were located mainly in the north and northeast of China. Nine spatiotemporal clusters were identified. A spatial lag fixed effects panel data model was the best fit for regression analysis. After adjusting for spatial individual effects and spatial autocorrelation (ρ = 0.5623), scarlet fever incidence was positively associated with a one-month lag of average temperature, precipitation, and total sunshine hours (all P-values < 0.05). Each 10 °C, 2 cm, and 10 h increase in temperature, precipitation, and sunshine hours, respectively, was associated with a 6.41% increment and 1.04% and 1.41% decrement in scarlet fever incidence, respectively. CONCLUSION The incidence of scarlet fever in China showed an upward trend in recent years. It had obvious spatiotemporal clustering, with the high-risk areas mainly concentrated in the north and northeast of China. Areas with high temperature and with low precipitation and sunshine hours tended to have a higher scarlet fever incidence, and we should pay more attention to prevention and control in these places.
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Affiliation(s)
- Hua-Xiang Rao
- Department of Public Health and Preventive Medicine, Changzhi Medical College, Changzhi 046000, China.
| | - Dong-Mei Li
- State Key Laboratory for Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
| | - Xiao-Yin Zhao
- Department of Public Health and Preventive Medicine, Changzhi Medical College, Changzhi 046000, China.
| | - Juan Yu
- Department of Basic Medical Sciences, Changzhi Medical College, Changzhi 046000, China.
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Li J, Li Y, Ye M, Yao S, Yu C, Wang L, Wu W, Wang Y. Forecasting the Tuberculosis Incidence Using a Novel Ensemble Empirical Mode Decomposition-Based Data-Driven Hybrid Model in Tibet, China. Infect Drug Resist 2021; 14:1941-1955. [PMID: 34079304 PMCID: PMC8164697 DOI: 10.2147/idr.s299704] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Accepted: 04/14/2021] [Indexed: 12/13/2022] Open
Abstract
Objective The purpose of this study is to develop a novel data-driven hybrid model by fusing ensemble empirical mode decomposition (EEMD), seasonal autoregressive integrated moving average (SARIMA), with nonlinear autoregressive artificial neural network (NARNN), called EEMD-ARIMA-NARNN model, to assess and forecast the epidemic patterns of TB in Tibet. Methods The TB incidence from January 2006 to December 2017 was obtained, and then the time series was partitioned into training subsamples (from January 2006 to December 2016) and testing subsamples (from January to December 2017). Among them, the training set was used to develop the EEMD-SARIMA-NARNN combined model, whereas the testing set was used to validate the forecasting performance of the model. Whilst the forecasting accuracy level of this novel method was compared with the basic SARIMA model, basic NARNN model, error-trend-seasonal (ETS) model, and traditional SARIMA-NARNN mixture model. Results By comparing the accuracy level of the forecasting measurements including root-mean-square error, mean absolute deviation, mean error rate, mean absolute percentage error, and root-mean-square percentage error, it was shown that the EEMD-SARIMA-NARNN combined method produced lower error rates than the others. The descriptive statistics suggested that TB was a seasonal disease, peaking in late winter and early spring and a trough in autumn and early winter, and the TB epidemic indicated a drastic increase by a factor of 1.7 from 2006 to 2017 in Tibet, with average annual percentage change of 5.8 (95% confidence intervals: 3.5–8.1). Conclusion This novel data-driven hybrid method can better consider both linear and nonlinear components in the TB incidence than the others used in this study, which is of great help to estimate and forecast the future epidemic trends of TB in Tibet. Besides, under present trends, strict precautionary measures are required to reduce the spread of TB in Tibet.
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Affiliation(s)
- Jizhen Li
- Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, People's Republic of China
| | - Yuhong Li
- National Center for Tuberculosis Control and Prevention, China Center for Disease Control and Prevention, Beijing, People's Republic of China
| | - Ming Ye
- Preventive Medicine Clinic, Xinxiang Center for Disease Control and Prevention, Xinxiang, Henan Province, People's Republic of China
| | - Sanqiao Yao
- Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, People's Republic of China
| | - Chongchong Yu
- Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, People's Republic of China
| | - Lei Wang
- Center for Musculoskeletal Surgery, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Weidong Wu
- Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, People's Republic of China
| | - Yongbin Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, People's Republic of China
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Chen D, Lu H, Zhang S, Yin J, Liu X, Zhang Y, Dai B, Li X, Ding G. The association between extreme temperature and pulmonary tuberculosis in Shandong Province, China, 2005-2016: a mixed method evaluation. BMC Infect Dis 2021; 21:402. [PMID: 33933024 PMCID: PMC8088045 DOI: 10.1186/s12879-021-06116-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 04/20/2021] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND The effects of extreme temperature on infectious diseases are complex and far-reaching. There are few studies to access the relationship of pulmonary tuberculosis (PTB) with extreme temperature. The study aimed to identify whether there was association between extreme temperature and the reported morbidity of PTB in Shandong Province, China, from 2005 to 2016. METHODS A generalized additive model (GAM) was firstly conducted to evaluate the relationship between daily reported incidence rate of PTB and extreme temperature events in the prefecture-level cities. Then, the effect estimates were pooled using meta-analysis at the provincial level. The fixed-effect model or random-effect model was selected based on the result of heterogeneity test. RESULTS Among the 446,016 PTB reported cases, the majority of reported cases occurred in spring. The higher reported incidence rate areas were located in Liaocheng, Taian, Linyi and Heze. Extreme low temperature had an impact on the reported incidence of PTB in only one prefecture-level city, i.e., Binzhou (RR = 0.903, 95% CI: 0.817-0.999). While, extreme high temperature was found to have a positive effect on reported morbidity of PTB in Binzhou (RR = 0.924, 95% CI: 0.856-0.997) and Weihai (RR = 0.910, 95% CI: 0.843-0.982). Meta-analysis showed that extreme high temperature was associated with a decreased risk of PTB (RR = 0.982, 95% CI: 0.966-0.998). However, extreme low temperature was no relationship with the reported incidence of PTB. CONCLUSION Our findings are suggested that extreme high temperature has significantly decreased the risk of PTB at the provincial levels. The findings have implications for developing strategies to response to climate change.
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Affiliation(s)
- Dongzhen Chen
- Department of Epidemiology, School of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, No.619 Changcheng Road, Taian, 271016, Shandong Province, China
| | - Hua Lu
- Taian Centers for Diseases Prevention Control, Taian, 271000, Shandong Province, China
| | - Shengyang Zhang
- Shandong Center for Disease Control and Prevention, Jinan, 250014, Shandong Province, China
| | - Jia Yin
- Department of Epidemiology, School of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, No.619 Changcheng Road, Taian, 271016, Shandong Province, China
| | - Xuena Liu
- Department of Epidemiology, School of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, No.619 Changcheng Road, Taian, 271016, Shandong Province, China
| | - Yixin Zhang
- Shandong Center for Disease Control and Prevention, Jinan, 250014, Shandong Province, China
| | - Bingqin Dai
- Shandong Center for Disease Control and Prevention, Jinan, 250014, Shandong Province, China
| | - Xiaomei Li
- Department of Epidemiology, School of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, No.619 Changcheng Road, Taian, 271016, Shandong Province, China.
| | - Guoyong Ding
- Department of Epidemiology, School of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, No.619 Changcheng Road, Taian, 271016, Shandong Province, China.
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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.
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He WC, Ju K, Gao YM, Zhang P, Zhang YX, Jiang Y, Liao WB. Spatial inequality, characteristics of internal migration, and pulmonary tuberculosis in China, 2011-2017: a spatial analysis. Infect Dis Poverty 2020; 9:159. [PMID: 33213525 PMCID: PMC7678065 DOI: 10.1186/s40249-020-00778-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Accepted: 11/11/2020] [Indexed: 12/26/2022] Open
Abstract
Background Human migration facilitate the spread of tuberculosis (TB). Migrants face an increased risk of TB infection. In this study, we aim to explore the spatial inequity of sputum smear-positive pulmonary TB (SS + PTB) in China; and the spatial heterogeneity between SS + PTB and internal migration. Methods Notified SS + PTB cases in 31 provinces in mainland China were obtained from the national web-based PTB surveillance system database. Internal migrant data were extracted from the report on China’s migrant population development. Spatial autocorrelations were explored using the global Moran’s statistic and local indicators of spatial association. The spatial variation in temporal trends was performed using Kulldorff’s scan statistic. Fixed effect and spatial autoregressive models were used to explore the spatial inequity between SS + PTB and internal migration. Results A total of 2 380 233 SS + PTB cases were reported in China between 2011 and 2017, of which, 1 716 382 (72.11%) were male and 663 851 (27.89%) were female. Over 70% of internal migrants were from rural households and had lower income and less education. The spatial variation in temporal trend results showed that there was an 9.9% average annual decrease in the notification rate of SS + PTB from 2011 to 2017; and spatial clustering of SS + PTB cases was mainly located in western and southern China. The spatial autocorrelation results revealed spatial clustering of internal migration each year (2011–2017), and the clusters were stable within most provinces. Internal emigration, urban-to-rural migration and GDP per capita were significantly associated with SS + PTB, further, internal emigration could explain more variation in SS + PTB in the eastern region in mainland. However, internal immigration and rural-to-urban migration were not significantly associated with SS + PTB across China. Conclusions Our study found the spatial inequity between SS + PTB and internal migration. Internal emigration, urban-to-rural migration and GDP per capita were statistically associated with SS + PTB; the negative association was identified between internal emigration, urban-to-rural migration and SS + PTB. Further, we found those migrants with lower income and less education, and most of them were from rural households. These findings can help stakeholders to implement effective PTB control strategies for areas at high risk of PTB and those with high rates of internal migration.
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Affiliation(s)
- Wen-Chong He
- Research Management Office, West China Second University Hospital, Sichuan University, Chengdu, China.,Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China
| | - Ke Ju
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia. .,West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.
| | - Ya-Min Gao
- Department of Health, Northwest Minzu University, Lanzhou, China
| | - Pei Zhang
- School of Public Health, The University of Hong Kong, Hong Kong, Hong Kong Special Administrative Region, China
| | - Yin-Xia Zhang
- Department of Health, Northwest Minzu University, Lanzhou, China
| | - Ye Jiang
- School of Geography and Environmental Engineering, Lanzhou City University, Lanzhou, China
| | - Wei-Bin Liao
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.
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Zhang X, Rao H, Wu Y, Huang Y, Dai H. Comparison of spatiotemporal characteristics of the COVID-19 and SARS outbreaks in mainland China. BMC Infect Dis 2020; 20:805. [PMID: 33126857 PMCID: PMC7598229 DOI: 10.1186/s12879-020-05537-y] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 10/22/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Both coronavirus disease 2019 (COVID-19) and severe acute respiratory syndrome (SARS) are caused by coronaviruses and have infected people in China and worldwide. We aimed to investigate whether COVID-19 and SARS exhibited similar spatial and temporal features at provincial level in mainland China. METHODS The number of people infected by COVID-19 and SARS were extracted from daily briefings on newly confirmed cases during the epidemics, as of Mar. 4, 2020 and Aug. 3, 2003, respectively. We depicted spatiotemporal patterns of the COVID-19 and SARS epidemics using spatial statistics such as Moran's I and the local indicators of spatial association (LISA). RESULTS Compared to SARS, COVID-19 had a higher overall incidence. We identified 3 clusters (predominantly located in south-central China; the highest RR = 135.08, 95% CI: 128.36-142.08) for COVID-19 and 4 clusters (mainly in Northern China; the highest RR = 423.51, 95% CI: 240.96-722.32) for SARS. Fewer secondary clusters were identified after the "Wuhan lockdown". The LISA cluster map detected a significantly high-low (Hubei) and low-high spatial clustering (Anhui, Hunan, and Jiangxi, in Central China) for COVID-19. Two significant high-high (Beijing and Tianjin) and low-high (Hebei) clusters were detected for SARS. CONCLUSIONS COVID-19 and SARS outbreaks exhibited distinct spatiotemporal clustering patterns at the provincial levels in mainland China, which may be attributable to changes in social and demographic factors, local government containment strategies or differences in transmission mechanisms.
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Affiliation(s)
- Xi Zhang
- Clinical Research Unit, Xin Hua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Huaxiang Rao
- Department of Public Health and Preventive Medicine, Changzhi Medical College, Changzhi, Shanxi, People's Republic of China
| | - Yuwan Wu
- Department of Pediatrics, Xin Hua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Yubei Huang
- Department of Epidemiology and Biostatistics, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, HuanHuXi Rd., HeXi Dist, Tianjin, 300060, People's Republic of China
| | - Hongji Dai
- Department of Epidemiology and Biostatistics, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, HuanHuXi Rd., HeXi Dist, Tianjin, 300060, People's Republic of China.
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Huang K, Yang XJ, Hu CY, Ding K, Jiang W, Hua XG, Liu J, Cao JY, Sun CY, Zhang T, Kan XH, Zhang XJ. Short-term effect of ambient temperature change on the risk of tuberculosis admissions: Assessments of two exposure metrics. ENVIRONMENTAL RESEARCH 2020; 189:109900. [PMID: 32980000 DOI: 10.1016/j.envres.2020.109900] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Revised: 06/20/2020] [Accepted: 06/30/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND Although the effects of seasonal variations and ambient temperature on the incidence of tuberculosis (TB) have been well documented, it is still unknown whether ambient temperature change is an independent risk factor for TB. The aim of this study was to assess the association between ambient temperature change and the risk of TB admissions. METHOD A distributed lag non-linear model (DLNM) combined with Poisson generalized linear regression model was performed to assess the association between ambient temperature change and the risk of TB admissions from 2014 to 2018 in Hefei, China. Two temperature change metrics including temperature change between neighboring days (TCN) and diurnal temperature range (DTR) were used to assess the effects of temperature change exposure. Subgroup analyses were performed by gender, age and season. Besides, the attributable risk was calculated to evaluated the public health significance. RESULTS The overall exposure-response curves suggested that there were statistically significant associations between two temperature change metrics and the risk of TB admissions. The maximum lag-specific relative risk (RR) of TB admissions was 1.088 (95%CI: 1.012-1.171, lag 4 day) for exposing to large temperature drop (TCN= -4 °C) in winter. Besides, the overall cumulative risk of TB admissions increased continuously and peaked at a lag of 7 days (RR=1.350, 95%CI: 1.120-1.628). Subgroup analysis suggested that exposure to large temperature drop had an adverse effect on TB admissions among males, females and adults. Similarly, large level of DTR exposure (DTR=15 °C) in spring also increased the risk of TB admissions on lag 0 day (RR=1.039, 95%CI: 1.016-1.063), and the cumulative RRs peaked at a lag of 1 days (RR=1.029, 95%CI: 1.012-1.047). We also found that females and elderly people were more vulnerable to the large level of DTR exposure. Additionally, the assessment of attributable risk suggested that taking target measures for the upcoming large temperature drop (b-AF = 4.17%, 95% eCI: 1.24%, 7.22%, b-AN = 1195) may achieve great public health benefits for TB prevention. CONCLUSION This study suggests that ambient temperature change is associated with the risk of TB admissions. Besides, TCN may be a better predictor for the TB prevention and public health.
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Affiliation(s)
- Kai Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, China
| | - Xiao-Jing Yang
- 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
| | - Kun Ding
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, China
| | - Wen Jiang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, China
| | - Xiao-Guo Hua
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, China
| | - Jie Liu
- Department of Occupational Health and Environmental Health, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, 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
| | - Chen-Yu Sun
- AMITA Health Saint Joseph Hospital Chicago, 2900 N. Lake Shore Drive, Chicago, 60657, Illinois, USA
| | - Tao Zhang
- Anhui Chest Hospital, 397 Jixi Road, Hefei, 230022, China
| | - Xiao-Hong Kan
- Anhui Chest Hospital, 397 Jixi Road, Hefei, 230022, China; Anhui Medical University Clinical College of Chest, 397 Jixi Road, Hefei, 230022, China.
| | - Xiu-Jun Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, China.
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Gelaw YA, Williams G, Assefa Y, Asressie M, Soares Magalhães RJ. Sociodemographic profiling of tuberculosis hotspots in Ethiopia, 2014-2017. Trans R Soc Trop Med Hyg 2020; 113:379-391. [PMID: 30989204 DOI: 10.1093/trstmh/trz017] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Revised: 02/07/2019] [Accepted: 03/04/2019] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Tuberculosis (TB) notification rates vary across regions in Ethiopia and districts within the Amhara Region. The Amhara Region is one of the main TB hotspot regions in the country. In this study we identified the spatial distribution of TB and characterized the sociodemographic factors of spatial clusters in the Amhara Region. METHODS An ecological spatial analysis of TB notifications from 2014 to 2017 was conducted to quantify the presence and location of spatial clusters of TB notifications at the district level within the Amhara Region. Global Moran's I statistics and local indicators of spatial association were used to explore the spatial clustering of TB notifications. Notifications from hotspots and low-risk districts were compared to identify significant sociodemographic factors using analysis of variance and Classification and Regression Tree analysis. The geographic information system and 'sp' packages of R software were used for spatial analysis. RESULTS From 2014 to 2017 the average notification rate of all forms of TB in the Amhara Region was 107/100 000 population (range 18-614 per 100 000 population). District-level TB notification rates were positively spatially autocorrelated, with Moran's I value ranging from 0.207 to 0.276 (p=0.01). Hotspot TB clusters were found in the northwest and central part of the region. The proportion of migrants (F(3,124)=23.21, p<0.001, d=1.4) was found to be the most important factor associated with hotspot TB clustering. CONCLUSIONS TB notification rates in the Amhara Region of Ethiopia over the past 4 y were significantly clustered. Distinguishing high-risk areas from low-risk areas and characterizing the proportion of migrants and other risk factors is important for targeted TB prevention and control in the region.
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Affiliation(s)
- Yalemzewod Assefa Gelaw
- School of Public Health, Faculty of Medicine, University of Queensland, Herston, Brisbane, Queensland, Australia.,Institute of Public Health, College of Medicine and Health Science, University of Gondar, Gondar, Ethiopia
| | - Gail Williams
- School of Public Health, Faculty of Medicine, University of Queensland, Herston, Brisbane, Queensland, Australia
| | - Yibeltal Assefa
- School of Public Health, Faculty of Medicine, University of Queensland, Herston, Brisbane, Queensland, Australia
| | | | - Ricardo J Soares Magalhães
- Spatial Epidemiology Laboratory, School of Veterinary Science, Faculty of Science, University of Queensland, Gatton, Queensland, Australia.,Children's Health and Environment Program, Child Health Research Centre, Faculty of Medicine, University of Queensland, Brisbane, Queensland, Australia
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Liang P, Zhao Y, Zhao J, Pan D, Guo Z. The spatiotemporal distribution of human brucellosis in mainland China from 2007-2016. BMC Infect Dis 2020; 20:249. [PMID: 32216760 PMCID: PMC7099799 DOI: 10.1186/s12879-020-4946-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Accepted: 03/04/2020] [Indexed: 11/10/2022] Open
Abstract
Background Despite the considerable efforts made to address the issue of brucellosis worldwide, its prevalence in dairy products continues to be difficult to estimate and represents a key public health issue around the world today. The aim of the present study was to better understand the epidemiology of this disease in mainland China. We set out to investigate the yearly spatial distribution and possible hotspots of the disease. Methods Human brucellosis data from mainland China between 2007 and 2016 were collected from the China Information System for Disease Control and Prevention. A geographic information system ArcGIS10.3 (ESRI, Redlands) was used to identify potential changes in the spatial and temporal distribution of human brucellosis in mainland China during the study period. These distributions were evaluated using three-dimensional trend analysis and spatial autocorrelation analyse. A gravity-center was used to analyse the migration track of human brucellosis. Results A total of 399,578 cases of human brucellosis were reported during the 10-year study period. The monthly incidence of brucellosis in China demonstrates clear seasonality. Spring and summer are the peak seasons, while May is the peak month for brucellosis. Three-dimensional trend analysis suggests that brucellosis is on the rise from south to north, and that the epidemic situation in northern China is more severe. Between 2007 and 2016, the overall migration distance of the brucellosis incidence gravity-center was 906.43 km, and the direction was southwest. However, the overall gravity center of brucellosis was still in the northern part of China. In the global autocorrelation analysis, brucellosis in China demonstrated a non-random distribution between 2013 and 2014, with spatial autocorrelation (Z > 1.96, P < 0.05) and a clustering trend, while no clustering trend was found from 2007 to 2012 or from 2015 to 2016. In the local autocorrelation analysis, a Low-Low cluster phenomenon was found in the south of China in 2013 and 2014. Conclusion Human brucellosis remains a widespread challenge, particularly in northern China. The hotspots highlight potential high-risk areas which may require special plans and resources for monitoring and controlling the disease.
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Affiliation(s)
- Peifeng Liang
- Department of medical record and statistics, People's Hospital of Ningxia Hui Autonomous Region, Yinchuan, 750004, China.,Department of Epidemiology and Biostatistics, School of Public Health and management, Ningxia Medical University, Yinchuan, 750001, China
| | - Yuan Zhao
- Department of Epidemiology and Biostatistics, School of Public Health and management, Ningxia Medical University, Yinchuan, 750001, China
| | - Jianhua Zhao
- Ningxia Center for Diseases Prevention and Control, Yinchuan, 750001, China
| | - Dongfeng Pan
- Department of Emergency, People's Hospital of Ningxia Hui Autonomous Region, Yinchuan, 750004, China
| | - Zhongqin Guo
- Department of Epidemiology and Biostatistics, School of Public Health and management, Ningxia Medical University, Yinchuan, 750001, China.
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Wang Y, Xu C, Li Y, Wu W, Gui L, Ren J, Yao S. An Advanced Data-Driven Hybrid Model of SARIMA-NNNAR for Tuberculosis Incidence Time Series Forecasting in Qinghai Province, China. Infect Drug Resist 2020; 13:867-880. [PMID: 32273731 PMCID: PMC7102880 DOI: 10.2147/idr.s232854] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2019] [Accepted: 02/22/2020] [Indexed: 12/18/2022] Open
Abstract
Purpose Qinghai province has invariably been under an ongoing threat of tuberculosis (TB), which has not only been an obstacle to local development but also hampers the prevention and control process for ending the TB epidemic. Forecasting for future epidemics will serve as the base for early detection and planning resource requirements. Here, we aim to develop an advanced detection technique driven by the recent TB incidence series, by fusing a seasonal autoregressive integrated moving average (SARIMA) with a neural network nonlinear autoregression (NNNAR). Methods We collected the TB incidence data between January 2004 and December 2016. Subsequently, the subsamples from January 2004 to December 2015 were employed to measure the efficiency of the single SARIMA, NNNAR, and hybrid SARIMA-NNNAR approaches, whereas the hold-out subsamples were used to test their predictive performances. We finally selected the best-performing technique by considering minimum metrics including the mean absolute error, root-mean-squared error, mean absolute percentage error and mean error rate . Results During 2004–2016, the reported TB cases totaled 71,080 resulting in the morbidity of 97.624 per 100,000 persons annually in Qinghai province and showed notable peak activities in late winter and early spring. Moreover, the TB incidence rate was surging by 5% per year. According to the above-mentioned criteria, the best-fitting basic and hybrid techniques consisted of SARIMA(2,0,2)(1,1,0)12, NNNAR(7,1,4)12 and SARIMA(2,0,2)(1,1,0)12-NNNAR(3,1,7)12, respectively. Amongst them, the hybrid technique showed superiority in both mimic and predictive parts, with the lowest values of the measured metrics in both the parts. The sensitivity analysis indicated the same results. Conclusion The best-mimicking SARIMA-NNNAR hybrid model outperforms the best-simulating basic SARIMA and NNNAR models, and has a potential application in forecasting and assessing the TB epidemic trends in Qinghai. Furthermore, faced with the major challenge of the ongoing upsurge in TB incidence in Qinghai, there is an urgent need for formulating specific preventive and control measures.
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Affiliation(s)
- Yongbin Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan, People's Republic of China
| | - Chunjie Xu
- Department of Occupational and Environmental Health, School of Public Health, Capital Medical University, Beijing, People's Republic of China
| | - Yuchun Li
- Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan, People's Republic of China
| | - Weidong Wu
- Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan, People's Republic of China
| | - Lihui Gui
- Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan, People's Republic of China
| | - Jingchao Ren
- Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan, People's Republic of China
| | - Sanqiao Yao
- Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan, People's Republic of China
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The association between internal migration and pulmonary tuberculosis in China, 2005-2015: a spatial analysis. Infect Dis Poverty 2020; 9:5. [PMID: 32063228 PMCID: PMC7025414 DOI: 10.1186/s40249-020-0621-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Accepted: 01/07/2020] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Internal migration places individuals at high risk of contracting tuberculosis (TB). However, there is a scarcity of national-level spatial analyses regarding the association between TB and internal migration in China. In our research, we aimed to explore the spatial variation in cases of sputum smear-positive pulmonary TB (SS + PTB) in China; and the associations between SS + PTB, internal migration, socioeconomic factors, and demographic factors in the country between 2005 and 2015. METHODS Reported cases of SS + PTB were obtained from the national PTB surveillance system database; cases were obtained at the provincial level. Internal migration data were extracted from the national population sampling survey and the census. Spatial autocorrelations were explored using the global Moran's statistic and local indicators of spatial association. The spatial temporal analysis was performed using Kulldorff's scan statistic. Fixed effects regression was used to explore the association between SS + PTB and internal migration. RESULTS A total of 4 708 563 SS + PTB cases were reported in China between 2005 and 2015, of which 3 376 011 (71.7%) were male and 1 332 552 (28.3%) were female. There was a trend towards decreasing rates of SS + PTB notifications between 2005 and 2015. The result of global spatial autocorrelation indicated that there were significant spatial correlations between SS + PTB rate and internal migration each year (2005-2015). Spatial clustering of SS + PTB cases was mainly located in central and southern China and overlapped with the clusters of emigration. The proportions of emigrants and immigrants were significantly associated with SS + PTB. Per capita GDP and education level were negatively associated with SS + PTB. The internal migration flow maps indicated that migrants preferred neighboring provinces, with most migrating for work or business. CONCLUSIONS This study found a significant spatial autocorrelation between SS + PTB and internal migration. Both emigration and immigration were statistically associated with SS + PTB, and the association with emigration was stronger than that for immigration. Further, we found that SS + PTB clusters overlapped with emigration clusters, and the internal migration flow maps suggested that migrants from SS + PTB clusters may influence the TB epidemic characteristics of neighboring provinces. These findings can help stakeholders to implement effective PTB control strategies for areas at high risk of PTB and those with high rates of internal migrants.
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Spatiotemporal Distribution of Tuberculosis during Urbanization in the New Urban Area of Nanchang City, China, 2010-2018. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:ijerph16224395. [PMID: 31717896 PMCID: PMC6888413 DOI: 10.3390/ijerph16224395] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 11/02/2019] [Accepted: 11/05/2019] [Indexed: 12/01/2022]
Abstract
Background: Urbanization will play a key role in ending the tuberculosis (TB) epidemic by 2030, but understanding the relationship between urbanization and the health threats posed by TB is incomplete. Therefore, this study aimed to explore the spatiotemporal distribution of TB at the township level during urbanization in the new urban area of Nanchang. Methods: Seasonal-trend decomposition of time series analysis was used to explore the seasonal distribution and trend analysis. Global and local spatial autocorrelation statistics, and space–time scan statistics were performed to detect the spatiotemporal clusters of TB cases in the new urban area of Nanchang from 2010 to 2018. Results: A total of 3245 TB cases were reported in the study area from 2010 to 2018. Of all the TB cases, 68% occurred in individuals older than 40 years old, 73.2% were male cases, and 56.6% were farmers. The primary seasonal peak was in late spring (April), and a smaller peak was in early autumn (September). The results of local indicators of spatial association showed that Jiaoqiao town and Changleng town might be “High–High” clusters. The most likely spatiotemporal cluster was located in the southwest of the study area in 2010, which included five towns, and then shifted to the northeast gradually. Across 2010 to 2018, nine spatiotemporal clusters were identified. The most likely cluster was located at the northeast of the study area. The center of this area was in Nanji town with a circle radius of 43.74 kilometers. Conclusions: The spatial clusters of TB incidence shifted to the rural region and the fringe of the new urban area of Nanchang. Targeted management strategies for urban migrants in the process of urbanization should be strengthened.
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Wang Q, Guo L, Wang J, Zhang L, Zhu W, Yuan Y, Li J. Spatial distribution of tuberculosis and its socioeconomic influencing factors in mainland China 2013-2016. Trop Med Int Health 2019; 24:1104-1113. [PMID: 31314953 DOI: 10.1111/tmi.13289] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
BACKGROUND To assess the spatial epidemic characteristics of TB and identify the key areas for disease prevention and control. OBJECTIVE To explore the spatial distribution and socioeconomic influencing factors of TB in mainland China from 2013 to 2016. METHODS Spatial autocorrelation was used to explore the spatial distribution characteristics of TB at the quantitative level. Ordinary least squares (OLS) and geographically weighted regression (GWR) models were conducted to explore the association between factors and TB incidence from both global and local perspectives. RESULTS There was a significant positive spatial autocorrelation of TB at the provincial level (P < 0.05): hot spots were mainly located in the west of Xinjiang and Tibet, and cold spots in the eastern coastal areas. Four latent factors on the socioeconomic dimension, involving the proportion of illiterate people aged 15 and over, per capita disposable income in rural areas, the number of health technicians per 1000 population and the urban population density, were associated with TB incidence. The GWR model showed that the effect of the same factor on TB incidence varied with geographical location. CONCLUSIONS Spatial clustering of TB incidence in mainland China still exists. The differences of socioeconomic factors in different locations can be confirmed by GWR model. Targeted preventive and control measures or policies will be conducive in effectively reducing the incidence of TB, especially in hot spots.
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Affiliation(s)
- Qi Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Lanzhou University, Lanzhou, China
| | - Liang Guo
- The Second School of Clinical Medicine, Lanzhou University, Lanzhou, China
| | - Jing Wang
- Nursing Department of the First Hospital of Lanzhou University, Lanzhou, China
| | - Leijie Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Lanzhou University, Lanzhou, China
| | - Wanqi Zhu
- Department of Epidemiology and Biostatistics, School of Public Health, Lanzhou University, Lanzhou, China
| | - Yan Yuan
- Department of Epidemiology and Biostatistics, School of Public Health, Lanzhou University, Lanzhou, China
| | - Juansheng Li
- Department of Epidemiology and Biostatistics, School of Public Health, Lanzhou University, Lanzhou, China
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The spatio-temporal analysis of the incidence of tuberculosis and the associated factors in mainland China, 2009-2015. INFECTION GENETICS AND EVOLUTION 2019; 75:103949. [PMID: 31279820 DOI: 10.1016/j.meegid.2019.103949] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Revised: 06/21/2019] [Accepted: 07/01/2019] [Indexed: 12/30/2022]
Abstract
BACKGROUND Tuberculosis is still one of the most infectious diseases in China. This study aimed to explore the spatio-temporal distribution of TB and the associated factors in mainland China from 2009 to 2015. METHODS A Bayesian spatio-temporal model was utilized to analyse the correlation of socio-economic, healthcare, demographic and meteorological factors with the population level number of TB. RESULTS The Bayesian spatio-temporal analysis showed that for the population level number of TB, the estimated parameters of the ratio of males to females, the number of beds in medical institutions, the population density, the proportion of the population that is rural, the amount of precipitation, the largest wind speed and the sunshine duration were 0.556, 0.197, 0.199, 29.03,0.1958, 0.0854 and 0.2117, respectively, demonstrating positive associations. However, health personnel, per capita annual gross domestic product, minimum temperature and humidity indicated negative associations, and the corresponding parameters were -0.050, -0.095, -0.0022 and -0.0070, respectively. CONCLUSIONS Socio-economic, number of health personnel, demographic and meteorological factors could affect the case notification number of TB to different degrees and in different directions.
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Chan EYY, Ho JY, Hung HHY, Liu S, Lam HCY. Health impact of climate change in cities of middle-income countries: the case of China. Br Med Bull 2019; 130:5-24. [PMID: 31070715 PMCID: PMC6587073 DOI: 10.1093/bmb/ldz011] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Revised: 01/31/2019] [Accepted: 04/23/2019] [Indexed: 12/13/2022]
Abstract
BACKGROUND This review examines the human health impact of climate change in China. Through reviewing available research findings under four major climate change phenomena, namely extreme temperature, altered rainfall pattern, rise of sea level and extreme weather events, relevant implications for other middle-income population with similar contexts will be synthesized. SOURCES OF DATA Sources of data included bilingual peer-reviewed articles published between 2000 and 2018 in PubMed, Google Scholar and China Academic Journals Full-text Database. AREAS OF AGREEMENT The impact of temperature on mortality outcomes was the most extensively studied, with the strongest cause-specific mortality risks between temperature and cardiovascular and respiratory mortality. The geographical focuses of the studies indicated variations in health risks and impacts of different climate change phenomena across the country. AREAS OF CONTROVERSY While rainfall-related studies predominantly focus on its impact on infectious and vector-borne diseases, consistent associations were not often found. GROWING POINTS Mental health outcomes of climate change had been gaining increasing attention, particularly in the context of extreme weather events. The number of projection studies on the long-term impact had been growing. AREAS TIMELY FOR DEVELOPING RESEARCH The lack of studies on the health implications of rising sea levels and on comorbidity and injury outcomes warrants immediate attention. Evidence is needed to understand health impacts on vulnerable populations living in growing urbanized cities and urban enclaves, in particular migrant workers. Location-specific climate-health outcome thresholds (such as temperature-mortality threshold) will be needed to support evidence-based clinical management plans and health impact mitigation strategies to protect vulnerable communities.
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Affiliation(s)
- Emily Y Y Chan
- Collaborating Centre for Oxford University and CUHK for Disaster and Medical Humanitarian Response (CCOUC), Division of Global Health and Humanitarian Medicine, The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
- Division of Global Health and Humanitarian Medicine, The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- François-Xavier Bagnoud Center for Health & Human Rights, Harvard University, Boston, MA, USA
| | - Janice Y Ho
- Division of Global Health and Humanitarian Medicine, The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
| | - Heidi H Y Hung
- Division of Global Health and Humanitarian Medicine, The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
| | - Sida Liu
- Division of Global Health and Humanitarian Medicine, The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
| | - Holly C Y Lam
- Collaborating Centre for Oxford University and CUHK for Disaster and Medical Humanitarian Response (CCOUC), Division of Global Health and Humanitarian Medicine, The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
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Gehlen M, Nicola MRC, Costa ERD, Cabral VK, de Quadros ELL, Chaves CO, Lahm RA, Nicolella ADR, Rossetti MLR, Silva DR. Geospatial intelligence and health analitycs: Its application and utility in a city with high tuberculosis incidence in Brazil. J Infect Public Health 2019; 12:681-689. [PMID: 30956159 DOI: 10.1016/j.jiph.2019.03.012] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Revised: 12/26/2018] [Accepted: 03/17/2019] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Geospatial Intelligence and Health Analysis have been used to identify tuberculosis (TB) hotspots and to better understand their relationship to social and economic factors. The purpose of this study was to use geospatial intelligence to assess the distribution of TB and its correlations with Human Development Index (HDI) in a city with high TB incidence in Brazil. METHODS We conducted an ecological study, using National System of Information on Noticeable Disease (SINAN) to identify TB cases. Geocoding was performed using QGIS 2.0 software and Google Maps API 3.0. We applied geospatial intelligence to detect where in the city clustering of TB cases occurred, and assessed the association of an area's HDI (each one of the components - longevity, education, and income) with TB spatial distribution. RESULTS During the study period (2011-2013), there were 737 TB cases. TB cases showed heterogeneity across the 29 neighborhoods. The neighborhoods with HDI-income lower than the mean had higher TB incidence (p = 0.036). CONCLUSIONS We found several hotspots of TB across the 29 neighborhoods, and an inverse association between HDI-income and TB incidence. These findings provide useful information and may help to guide TB control programs.
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Affiliation(s)
- Mirela Gehlen
- Programa de Pós-Graduação em Ciências Pneumológicas, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil
| | - Maria R C Nicola
- Programa de Pós-Graduação em Biologia Molecular e Celular Aplicada a Saúde (Biosaude), Universidade Luterana do Brasil (ULBRA), Canoas, RS, Brazil
| | - Elis R D Costa
- Centro de Desenvolvimento Científico e Tecnológico, Secretaria Estadual da Saúde do Rio Grande do Sul (CDCT/SES), Porto Alegre, RS, Brazil
| | - Vagner K Cabral
- Programa de Pós-Graduação em Ciências Pneumológicas, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil
| | | | - Caroline O Chaves
- Pontifícia Universidade Católica do Rio Grande do Sul, Faculdade de Física, Brazil
| | - Regis A Lahm
- Pontifícia Universidade Católica do Rio Grande do Sul, Faculdade de Física, Brazil
| | - Alberto D R Nicolella
- Centro de Informação Toxicológica do Rio Grande do Sul, Fundação Estadual de Produção e Pesquisa em Saúde, Porto Alegre, RS, Brazil
| | - Maria L R Rossetti
- Programa de Pós-Graduação em Biologia Molecular e Celular Aplicada a Saúde (Biosaude), Universidade Luterana do Brasil (ULBRA), Canoas, RS, Brazil; Centro de Desenvolvimento Científico e Tecnológico, Secretaria Estadual da Saúde do Rio Grande do Sul (CDCT/SES), Porto Alegre, RS, Brazil
| | - Denise R Silva
- Programa de Pós-Graduação em Ciências Pneumológicas, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil; Faculdade de Medicina, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil.
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Drivers of Seasonal Variation in Tuberculosis Incidence: Insights from a Systematic Review and Mathematical Model. Epidemiology 2019; 29:857-866. [PMID: 29870427 DOI: 10.1097/ede.0000000000000877] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND Seasonality in tuberculosis incidence has been widely observed across countries and populations; however, its drivers are poorly understood. We conducted a systematic review of studies reporting seasonal patterns in tuberculosis to identify demographic and ecologic factors associated with timing and magnitude of seasonal variation. METHODS We identified studies reporting seasonal variation in tuberculosis incidence through PubMed and EMBASE and extracted incidence data and population metadata. We described key factors relating to seasonality and, when data permitted, quantified seasonal variation and its association with metadata. We developed a dynamic tuberculosis natural history and transmission model incorporating seasonal differences in disease progression and/or transmission rates to examine magnitude of variation required to produce observed seasonality in incidence. RESULTS Fifty-seven studies met inclusion criteria. In the majority of studies (n=49), tuberculosis incidence peaked in spring or summer and reached a trough in late fall or winter. A standardized seasonal amplitude was calculated for 34 of the studies, resulting in a mean of 17.1% (range: 2.7-85.5%) after weighting by sample size. Across multiple studies, stronger seasonality was associated with younger patients, extrapulmonary disease, and latitudes farther from the Equator. The mathematical model was generally able to reproduce observed levels of seasonal case variation; however, substantial variation in transmission or disease progression risk was required to replicate several extreme values. CONCLUSIONS We observed seasonal variation in tuberculosis, with consistent peaks occurring in spring, across countries with varying tuberculosis burden. Future research is needed to explore and quantify potential gains from strategically conducting mass screening interventions in the spring.
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Gelaw YA, Yu W, Magalhães RJS, Assefa Y, Williams G. Effect of Temperature and Altitude Difference on Tuberculosis Notification: A Systematic Review. J Glob Infect Dis 2019; 11:63-68. [PMID: 31198309 PMCID: PMC6555232 DOI: 10.4103/jgid.jgid_95_18] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Abstract
Background: Ecological factors are important indicators for tuberculosis (TB) notification. However, consolidation of evidence on the effect of altitude and temperature on TB notification rate has not yet been done. The aim of this review is to illustrate the effect of altitude and temperature on TB notification rate. Methods: Electronic searches were undertaken from PubMed, EMBASE, and Scopus databases. Hand searches of bibliographies of retrieved papers provided additional references. A review was performed using the Meta-analysis Of Observational Studies in Epidemiology guideline. Results: Nine articles from various geographic regions were included in the study. Five out of nine studies showed the effect of altitude and four articles identified temperature effects. Results showed that TB notification rates were lower at higher altitude and higher at a higher temperature. Conclusion: This review provides qualitative evidence that TB notification rates increase with temperature and decrease with altitude. The findings of this review will encourage policymakers and program managers to consider seasonality and altitude differences in the design and implementation of TB prevention and control strategies.
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Affiliation(s)
- Yalemzewod Assefa Gelaw
- Epidemiology and Biostatistics Division, School of Public Health, Faculty of Medicine, The University of Queensland, Brisbane, Australia.,Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Science, University of Gondar, Gondar, Ethiopia
| | - Weiwei Yu
- Epidemiology and Biostatistics Division, School of Public Health, Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - Ricardo J Soares Magalhães
- UQ Spatial Epidemiology Laboratory, Faculty of Science, School of Veterinary Science, The University of Queensland, Gatton, Australia.,Children's Health and Environment Program, Child Health Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
| | - Yibeltal Assefa
- Health Systems and Policy Division, School of Public Health, Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - Gail Williams
- Epidemiology and Biostatistics Division, School of Public Health, Faculty of Medicine, The University of Queensland, Brisbane, Australia
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Wang T, Wang X, Tie P, Bai Y, Zheng Y, Yan C, Chai Z, Chen J, Rao H, Zeng L, Chen L, Qiu L. Spatio-temporal cluster and distribution of human brucellosis in Shanxi Province of China between 2011 and 2016. Sci Rep 2018; 8:16977. [PMID: 30451894 PMCID: PMC6242928 DOI: 10.1038/s41598-018-34975-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Accepted: 10/29/2018] [Indexed: 12/20/2022] Open
Abstract
In recent years, the incidence of human brucellosis (HB) in the Shanxi province has ranked to be the top five among the 31 China provinces. HB data in Shanxi province between 2011 and 2016 were collected from the Centers for Disease Control and Prevention. Spatial and temporal distribution of HB was evaluated using spatial autocorrelation analysis and space-time scan analysis. The global Moran's I index ranged from 0.37 to 0.50 between 2011 and 2016 (all P < 0.05), and the "high-high" clusters of HB were located at the northern Shanxi, while the "low-low" clusters in the central and southeastern Shanxi. The high-incidence time interval was between March and July with a 2-fold higher risk of HB compared to the other months in the same year. One most likely cluster and three secondary clusters were identified. The radius of the most likely cluster region was 158.03 km containing 10,051 HB cases. Compared to the remaining regions, people dwelling in the most likely region were reported 4.50-fold ascended risk of incident HB. HB cases during the high-risk time interval of each year were more likely to be younger, to be males or to be farmers or herdsman than that during the low-risk time interval. The HB incidence had a significantly high correlation with the number of the cattle or sheep especially in the northern Shanxi. HB in Shanxi showed unique spatio-temporal clustering. Public health concern for HB in Shanxi should give priority to the northern region especially between the late spring and early summer.
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Affiliation(s)
- Ting Wang
- Shanxi Center for Disease Control and Prevention, Taiyuan, 030012, China
| | - Xiang Wang
- China Railway Taiyuan Group Center for Disease Control and Prevention, Disease Control Division, Taiyuan, 030000, China.,Shanxi Medical University, School of Public Health, Taiyuan, 030001, China
| | - Ping Tie
- Shanxi Center for Disease Control and Prevention, Taiyuan, 030012, China
| | - Yongfei Bai
- Shanxi Center for Disease Control and Prevention, Taiyuan, 030012, China
| | - Yuhua Zheng
- Shanxi Center for Disease Control and Prevention, Taiyuan, 030012, China
| | - Changfu Yan
- Shanxi Center for Disease Control and Prevention, Taiyuan, 030012, China
| | - Zhikai Chai
- Shanxi Center for Disease Control and Prevention, Taiyuan, 030012, China
| | - Jing Chen
- Shanxi Center for Disease Control and Prevention, Taiyuan, 030012, China
| | - Huaxiang Rao
- Qinghai Center for Disease Control and Prevention, Institute for Communicable Disease Control and Prevention, Xining, 810007, China
| | - Lingjia Zeng
- China Center for Disease Control and Prevention, Beijing, 102206, China
| | - Limin Chen
- Shanxi Center for Disease Control and Prevention, Taiyuan, 030012, China.
| | - Lixia Qiu
- Shanxi Medical University, School of Public Health, Taiyuan, 030001, China.
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Liu MY, Li QH, Zhang YJ, Ma Y, Liu Y, Feng W, Hou CB, Amsalu E, Li X, Wang W, Li WM, Guo XH. Spatial and temporal clustering analysis of tuberculosis in the mainland of China at the prefecture level, 2005-2015. Infect Dis Poverty 2018; 7:106. [PMID: 30340513 PMCID: PMC6195697 DOI: 10.1186/s40249-018-0490-8] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2018] [Accepted: 10/04/2018] [Indexed: 12/25/2022] Open
Abstract
Background Tuberculosis (TB) is still one of the most serious infectious diseases in the mainland of China. So it was urgent for the formulation of more effective measures to prevent and control it. Methods The data of reported TB cases in 340 prefectures from the mainland of China were extracted from the China Information System for Disease Control and Prevention (CISDCP) during January 2005 to December 2015. The Kulldorff’s retrospective space-time scan statistics was used to identify the temporal, spatial and spatio-temporal clusters of reported TB in the mainland of China by using the discrete Poisson probability model. Spatio-temporal clusters of sputum smear-positive (SS+) reported TB and sputum smear-negative (SS-) reported TB were also detected at the prefecture level. Results A total of 10 200 528 reported TB cases were collected from 2005 to 2015 in 340 prefectures, including 5 283 983 SS- TB cases and 4 631 734 SS + TB cases with specific sputum smear results, 284 811 cases without sputum smear test. Significantly TB clustering patterns in spatial, temporal and spatio-temporal were observed in this research. Results of the Kulldorff’s scan found twelve significant space-time clusters of reported TB. The most likely spatio-temporal cluster (RR = 3.27, P < 0.001) was mainly located in Xinjiang Uygur Autonomous Region of western China, covering five prefectures and clustering in the time frame from September 2012 to November 2015. The spatio-temporal clustering results of SS+ TB and SS- TB also showed the most likely clusters distributed in the western China. However, the clustering time of SS+ TB was concentrated before 2010 while SS- TB was mainly concentrated after 2010. Conclusions This study identified the time and region of TB, SS+ TB and SS- TB clustered easily in 340 prefectures in the mainland of China, which is helpful in prioritizing resource assignment in high-risk periods and high-risk areas, and to formulate powerful strategy to prevention and control TB. Electronic supplementary material The online version of this article (10.1186/s40249-018-0490-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Meng-Yang Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, 100069, China
| | - Qi-Huan Li
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, 100069, China
| | - Ying-Jie Zhang
- Chinese Center for Disease Control and Prevention, Beijing, 102206, China
| | - Yuan Ma
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, 100069, China
| | - Yue Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, 100069, China
| | - Wei Feng
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, 100069, China
| | - Cheng-Bei Hou
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, 100069, China
| | - Endawoke Amsalu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, 100069, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, 100069, China
| | - Xia Li
- Department of Mathematics and Statistics, La Trobe University, Melbourne, 3086, Australia
| | - Wei Wang
- School of Medical Sciences and Health, Edith Cowan University, WA6027, Perth, Australia
| | - Wei-Min Li
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, 100069, China. .,National Tuberculosis Clinical Laboratory of China, Beijing Chest Hospital, Capital Medical University, Beijing, 101149, China. .,Beijing Tuberculosis and Thoracic Tumour Research Institute, Beijing, 101149, China.
| | - Xiu-Hua Guo
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, 100069, China. .,Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, 100069, China.
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Shaweno D, Karmakar M, Alene KA, Ragonnet R, Clements AC, Trauer JM, Denholm JT, McBryde ES. Methods used in the spatial analysis of tuberculosis epidemiology: a systematic review. BMC Med 2018; 16:193. [PMID: 30333043 PMCID: PMC6193308 DOI: 10.1186/s12916-018-1178-4] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Accepted: 09/20/2018] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Tuberculosis (TB) transmission often occurs within a household or community, leading to heterogeneous spatial patterns. However, apparent spatial clustering of TB could reflect ongoing transmission or co-location of risk factors and can vary considerably depending on the type of data available, the analysis methods employed and the dynamics of the underlying population. Thus, we aimed to review methodological approaches used in the spatial analysis of TB burden. METHODS We conducted a systematic literature search of spatial studies of TB published in English using Medline, Embase, PsycInfo, Scopus and Web of Science databases with no date restriction from inception to 15 February 2017. The protocol for this systematic review was prospectively registered with PROSPERO ( CRD42016036655 ). RESULTS We identified 168 eligible studies with spatial methods used to describe the spatial distribution (n = 154), spatial clusters (n = 73), predictors of spatial patterns (n = 64), the role of congregate settings (n = 3) and the household (n = 2) on TB transmission. Molecular techniques combined with geospatial methods were used by 25 studies to compare the role of transmission to reactivation as a driver of TB spatial distribution, finding that geospatial hotspots are not necessarily areas of recent transmission. Almost all studies used notification data for spatial analysis (161 of 168), although none accounted for undetected cases. The most common data visualisation technique was notification rate mapping, and the use of smoothing techniques was uncommon. Spatial clusters were identified using a range of methods, with the most commonly employed being Kulldorff's spatial scan statistic followed by local Moran's I and Getis and Ord's local Gi(d) tests. In the 11 papers that compared two such methods using a single dataset, the clustering patterns identified were often inconsistent. Classical regression models that did not account for spatial dependence were commonly used to predict spatial TB risk. In all included studies, TB showed a heterogeneous spatial pattern at each geographic resolution level examined. CONCLUSIONS A range of spatial analysis methodologies has been employed in divergent contexts, with all studies demonstrating significant heterogeneity in spatial TB distribution. Future studies are needed to define the optimal method for each context and should account for unreported cases when using notification data where possible. Future studies combining genotypic and geospatial techniques with epidemiologically linked cases have the potential to provide further insights and improve TB control.
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Affiliation(s)
- Debebe Shaweno
- Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia.
- Victorian Tuberculosis Program at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia.
| | - Malancha Karmakar
- Victorian Tuberculosis Program at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
- Department of Microbiology and Immunology, University of Melbourne, Melbourne, Victoria, Australia
| | - Kefyalew Addis Alene
- Research School of Population Health, College of Health and Medicine, The Australian National University, Canberra, Australia
- Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
| | - Romain Ragonnet
- Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia
- Burnet Institute, Melbourne, Australia
| | | | - James M Trauer
- Victorian Tuberculosis Program at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Justin T Denholm
- Victorian Tuberculosis Program at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
- Department of Microbiology and Immunology, University of Melbourne, Melbourne, Victoria, Australia
| | - Emma S McBryde
- Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia
- Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, Queensland, Australia
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46
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Olaleye A, Beke A. Determinants of Survival of Patients with Tuberculosis in Developing Countries. Tuberculosis (Edinb) 2018. [DOI: 10.5772/intechopen.75890] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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47
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Huang L, Abe EM, Li XX, Bergquist R, Xu L, Xue JB, Ruan Y, Cao CL, Li SZ. Space-time clustering and associated risk factors of pulmonary tuberculosis in southwest China. Infect Dis Poverty 2018; 7:91. [PMID: 30115099 PMCID: PMC6097331 DOI: 10.1186/s40249-018-0470-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2017] [Accepted: 07/30/2018] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND Pulmonary tuberculosis (PTB,both smear positive and smear negative) is an airborne infectious disease of major public health concern in China and other parts of the world where PTB endemicity is reported. This study aims at identifying PTB spatio-temporal clusters and associated risk factors in Zhaotong prefecture-level city, located in southwest China, where the PTB notification rate was higher than the average rate in the entire country. METHODS Space-time scan statistics were carried out using PTB registered data in the nationwide TB online registration system from 2011 to 2015, to identify spatial clusters. PTB patients diagnosed between October 2015 and February 2016 were selected and a structured questionnaire was administered to collect a set of variables that includes socio-economic status, behavioural characteristics, local environmental and biological characteristics. Based on the discovery of detailed town-level spatio-temporal PTB clusters, we divided selected subjects into two groups including the cases that resides within and outside identified clusters. Then, logistic regression analysis was applied comparing the results of variables between the two groups. RESULTS A total of 1508 subjects consented and participated in the survey. Clusters for PTB cases were identified in 38 towns distributed over south-western Zhaotong. Logistic regression analysis showed that history of chronic bronchitis (OR = 3.683, 95% CI: 2.180-6.223), living in an urban area (OR = 5.876, 95% CI: 2.381-14.502) and using coal as the main fuel (OR = 9.356, 95% CI: 5.620-15.576) were independently associated with clustering. While, not smoking (OR = 0.340, 95% CI: 0.137-0.843) is the protection factor of spatial clustering. CONCLUSIONS We found PTB specially clustered in south-western Zhaotong. The strong associated factors influencing the PTB spatial cluster including: the history of chronic bronchitis, living in the urban area, smoking and the use of coal as the main fuel for cooking and heating. Therefore, efforts should be made to curtail these associated factors.
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Affiliation(s)
- Li Huang
- Yunnan provincial Center for Disease Control and Prevention, Kunming, China
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Ruijing Er road 207, Shanghai, 200025 China
- National Research Center for Tropical Disease, Shanghai, China
- Key Laboratory of Parasite and Vector Biology, Ministry of Health, Shanghai, China
- WHO Collaborating Center for Tropical Diseases, Shanghai, China
| | - Eniola Michael Abe
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Ruijing Er road 207, Shanghai, 200025 China
- National Research Center for Tropical Disease, Shanghai, China
- Key Laboratory of Parasite and Vector Biology, Ministry of Health, Shanghai, China
- WHO Collaborating Center for Tropical Diseases, Shanghai, China
| | - Xin-Xu Li
- Center for Drug Evaluation, China Food and Drug Administration, Beijing, China
| | | | - Lin Xu
- Yunnan provincial Center for Disease Control and Prevention, Kunming, China
| | - Jing-Bo Xue
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Ruijing Er road 207, Shanghai, 200025 China
- National Research Center for Tropical Disease, Shanghai, China
- Key Laboratory of Parasite and Vector Biology, Ministry of Health, Shanghai, China
- WHO Collaborating Center for Tropical Diseases, Shanghai, China
| | - Yao Ruan
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Ruijing Er road 207, Shanghai, 200025 China
- National Research Center for Tropical Disease, Shanghai, China
- Key Laboratory of Parasite and Vector Biology, Ministry of Health, Shanghai, China
- WHO Collaborating Center for Tropical Diseases, Shanghai, China
| | - Chun-Li Cao
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Ruijing Er road 207, Shanghai, 200025 China
- National Research Center for Tropical Disease, Shanghai, China
- Key Laboratory of Parasite and Vector Biology, Ministry of Health, Shanghai, China
- WHO Collaborating Center for Tropical Diseases, Shanghai, China
| | - Shi-Zhu Li
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Ruijing Er road 207, Shanghai, 200025 China
- National Research Center for Tropical Disease, Shanghai, China
- Key Laboratory of Parasite and Vector Biology, Ministry of Health, Shanghai, China
- WHO Collaborating Center for Tropical Diseases, Shanghai, China
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The influence of meteorological factors on tuberculosis incidence in Southwest China from 2006 to 2015. Sci Rep 2018; 8:10053. [PMID: 29968800 PMCID: PMC6030127 DOI: 10.1038/s41598-018-28426-6] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2017] [Accepted: 06/22/2018] [Indexed: 11/08/2022] Open
Abstract
The influence of meteorological determinants on tuberculosis (TB) incidence remains severely under-discussed, especially through the perspective of time series analysis. In the current study, we used a distributed lag nonlinear model (DLNM) to analyze a 10-year series of consecutive surveillance data. We found that, after effectively controlling for autocorrelation, the changes in meteorological factors related to temperature, humidity, wind and sunshine were significantly associated with subsequent fluctuations in TB incidence: average temperature was inversely associated with TB incidence at a lag period of 2 months; total precipitation and minimum relative humidity were also inversely associated with TB incidence at lag periods of 3 and 4 months, respectively; average wind velocity and total sunshine hours exhibited an instant rather than lagged influence on TB incidence. Our study results suggest that preceding meteorological factors may have a noticeable effect on future TB incidence; informed prevention and preparedness measures for TB can therefore be constructed on the basis of meteorological variations.
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Sadeq M, Bourkadi JE. Spatiotemporal distribution and predictors of tuberculosis incidence in Morocco. Infect Dis Poverty 2018; 7:43. [PMID: 29875019 PMCID: PMC5991459 DOI: 10.1186/s40249-018-0429-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Accepted: 04/19/2018] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Tuberculosis (TB) is a major health problem in Morocco. This study aims at examining trends in TB in Morocco and identifying TB spatial clusters and TB-associated predictors. METHOD Country-level surveillance data was exploited. Kendall's correlation test was used to examine trends and an exploratory spatial data analysis was conducted to assess the global and local patterns of spatial autocorrelation in TB rates (Moran's I and local indicator of spatial association [LISA]) at the prefecture/province level. Covariates including living in a prefecture versus living in a province, annual rainfall, annual mean temperature, population density, and AIDS incidence were controlled. An ordinary least squares regression was thus performed and both spatial dependence and heteroscedasticity were assessed. RESULTS A decrease in TB incidence rate was seen between 1995 and 2014 (Kendall's tau b = - 0.72; P < 0.0001). However, while the period between 2005 and 2014 (10 last years) was considered, TB rate remained stable and as high as 84 per 100 000 population per year (95% CI: 83.7-84.3). The highest incidence rates were seen in Tanger-Assilah, Fez, Tetouen-M'diq Fnidaq, Inezgane Ait Melleoul, and Casablanca. From 2005 to 2014, while TB incidence rate was stable in Fez (P = 0.500), Tetouen-M'diq Fnidaq (P = 0.300), Casablanca (P = 0.500), Mohammadia (P = 0.146), Al Hoceima (P = 0.364), and Guelmim (P = 0.242), an increase in TB incidence rate was seen in Tanger-Assilah (Kendall's tau = 0.49; P = 0.023) and a decrease in Salé (Kendall's tau b = - 0,54; P = 0.014) and Inezgane-Ait Melloul (Kendall's tau b = - 0,67; P = 0.0023). TB is strongly clustered in space (P-values of Moran's I < 0.01). Two distinct spatial regimes that affect TB spatial clustering were identified (east and west). In the east, both annual rainfall (P = 0.003) and AIDS (P = 0.0002) exert a statistically significant effect on TB rate. In the west, only the living area (prefecture versus province) was associated with TB rate (P = 0.048). CONCLUSIONS New information on TB incidence and TB-related predictors was provided to decision-making and to further pertinent research. Association between annual rainfall and TB may be of interest to be explored elsewhere.
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Affiliation(s)
- Mina Sadeq
- Environmental Epidemiology Unit, National Institute of Hygiene. Ministry of Health, Rabat, Morocco
| | - Jamal Eddine Bourkadi
- University Hospital Center. Moulay Youssef Hospital, Rabat, Morocco
- Faculty of Medicine and Pharmacy, University Mohammed V, Rabat, Morocco
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50
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Zhu B, Fu Y, Liu J, Mao Y. Spatial distribution of 12 class B notifiable infectious diseases in China: A retrospective study. PLoS One 2018; 13:e0195568. [PMID: 29621351 PMCID: PMC5886686 DOI: 10.1371/journal.pone.0195568] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2017] [Accepted: 03/26/2018] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND China is the largest developing country with a relatively developed public health system. To further prevent and eliminate the spread of infectious diseases, China has listed 39 notifiable infectious diseases characterized by wide prevalence or great harm, and classified them into classes A, B, and C, with severity decreasing across classes. Class A diseases have been almost eradicated in China, thus making class B diseases a priority in infectious disease prevention and control. In this retrospective study, we analyze the spatial distribution patterns of 12 class B notifiable infectious diseases that remain active all over China. METHODS Global and local Moran's I and corresponding graphic tools are adopted to explore and visualize the global and local spatial distribution of the incidence of the selected epidemics, respectively. Inter-correlations of clustering patterns of each pair of diseases and a cumulative summary of the high/low cluster frequency of the provincial units are also provided by means of figures and maps. RESULTS Of the 12 most commonly notifiable class B infectious diseases, viral hepatitis and tuberculosis show high incidence rates and account for more than half of the reported cases. Almost all the diseases, except pertussis, exhibit positive spatial autocorrelation at the provincial level. All diseases feature varying spatial concentrations. Nevertheless, associations exist between spatial distribution patterns, with some provincial units displaying the same type of cluster features for two or more infectious diseases. Overall, high-low (unit with high incidence surrounded by units with high incidence, the same below) and high-high spatial cluster areas tend to be prevalent in the provincial units located in western and southwest China, whereas low-low and low-high spatial cluster areas abound in provincial units in north and east China. CONCLUSION Despite the various distribution patterns of 12 class B notifiable infectious diseases, certain similarities between their spatial distributions are present. Substantial evidence is available to support disease-specific, location-specific, and disease-combined interventions. Regarding provinces that show high-high/high-low patterns of multiple diseases, comprehensive interventions targeting different diseases should be established. As to the adjacent provincial units revealing similar patterns, coordinated actions need to be taken across borders.
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Affiliation(s)
- Bin Zhu
- School of Public Policy and Administration, Xi’an Jiaotong University, Xi’an, Shaanxi, China
- Department of Public Policy, City University of Hong Kong, Hong Kong, China
| | - Yang Fu
- Department of Public Policy, City University of Hong Kong, Hong Kong, China
| | - Jinlin Liu
- School of Public Policy and Administration, Xi’an Jiaotong University, Xi’an, Shaanxi, China
| | - Ying Mao
- School of Public Policy and Administration, Xi’an Jiaotong University, Xi’an, Shaanxi, China
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