Dataset Information

The influence of meteorological factors on tuberculosis incidence in Southwest China from 2006 to 2015.

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.

SUBMITTER: Xiao Y

PROVIDER: S-EPMC6030127 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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The influence of meteorological factors on tuberculosis incidence in Southwest China from 2006 to 2015.

Xiao Yuanyuan Y He Limei L Chen Ying Y Wang Qinying Q Meng Qiong Q Chang Wei W Xiong Lifen L Yu Zhen Z

Scientific reports 20180703 1

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 subseq ...[more]

PMID: 29968800

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Project description:Background: Scrub typhus was epidemic in the western Pacific Ocean area and East Asia, scrub typhus epidemic in densely populated areas in southern China. To better understand the association between meteorological variables, Southern Oscillation Index (SOI), and scrub typhus incidence in Guangzhou was benefit to the control and prevention. Methodology/Principal Findings: We collected weekly data for scrub typhus cases and meteorological variables in Guangzhou, and Southern Oscillation Index from 2006 to 2018, and used the distributed lag non-linear models to evaluate the relationships between meteorological variables, SOI and scrub typhus. The median value of each variable was set as the reference. The high-risk occupations were farmer (51.10%), house worker (17.51%), and retiree (6.29%). The non-linear relationships were observed with different lag weeks. For example, when the mean temperature was 27.7°C with1-week lag, the relative risk (RR) was highest as 1.08 (95% CI: 1.01-1.17). The risk was the highest when the relative humidity was 92.0% with 9-week lag, with the RR of 1.10 (95% CI: 1.02-1.19). For aggregate rainfall, the highest RR was 1.06 (95% CI: 1.03-1.11), when it was 83.0 mm with 4-week lag. When the SOI was 19 with 11-week lag, the highest RR was 1.06 (95% CI: 1.01-1.12). Most of the extreme effects of SOI and meteorological factors on scrub typical cases were statistically significant. Conclusion/Significance: The high-risk occupations of scrub typhus in Guangzhou were farmer, house worker, and retiree. Meteorological factors and SOI played an important role in scrub typhus occurrence in Guangzhou. Non-linear relationships were observed in almost all the variables in our study. Approximately, mean temperature, and relative humidity positively correlated to the incidence of scrub typhus, on the contrary to atmospheric pressure and weekly temperature range (WTR). Aggregate rainfall and wind velocity showed an inverse-U curve, whereas the SOI appeared the bimodal distribution. These findings can be helpful to facilitate the development of the early warning system to prevent the scrub typhus.

Project description:Background Tuberculosis (TB) poses a severe public health challenge in China and worldwide. This study evaluated the effects of age, period, and birth cohort on reported incidence trends of TB based on population and refined the characteristics of high-risk groups. Methods Aggregate data that reported pulmonary tuberculosis (PTB) cases from China Tuberculosis Management Information System (TBIMS) from 2006 to 2020 were used to analyze effect coefficients through the age–period–cohort (APC) model based on intrinsic estimator (IE) method, and converted them into relative risk (RR) to estimate trends. Results A total of 14.82 million cases of PTB were reported in China from 2006 to 2020, showing a continuous downward trend. The reporting rate increased with age by age group, with 70–74 years old being 2–3 times higher than that in 20–24 years old. APC analysis model showed that age effects were bimodal in 20–24 years old [RR = 2.29, 95% confidence interval (CI): 1.73–3.03] and 70–74 years old (RR = 1.95, 95% CI: 1.67–2.27), and lower than the overall average in the groups under 15 years old. Stratified results showed that the risk was higher for women under age 40 than men and higher for men over 40. The risk was higher in urban than in rural areas under 30 years old and slightly higher in rural than in urban between 30 and 64 years old. The risk for 15–34 years old was significantly higher in the east than in other regions. The period effects showed a decreasing trend, and the risk was higher in rural in recent years. Except for cohorts born in 1961–1965 and 2001–2005, where the RR increased, the later the cohort was born, the lower the risk. The cohort 1926–1930 in eastern had the highest risk (RR = 3.49, 95% CI: 2.44–4.98). Conclusions The reported incidence of PTB continued to decline in China from 2006 to 2020. The young (20–24 years old) and the elderly (70–74 years old) were equally at high risk. There were differences in the age, period and cohort effects on PTB incidence among gender, urban–rural and regions. Our findings better reflected the characteristics of high-risk populations, thus contributing to the development of timely and effective intervention strategies, and providing clues for etiological research. Graphical abstract Supplementary Information The online version contains supplementary material available at 10.1186/s40249-022-01009-4.

Project description:Background Most existing studies have only investigated the delayed effect of meteorological factors on pulmonary tuberculosis (PTB). However, the effect of extreme climate and the interaction between meteorological factors on PTB has been rarely investigated. Methods Newly diagonsed PTB cases and meteorological factors in Urumqi in each week between 2013 and 2019 were collected. The lag-exposure-response relationship between meteorological factors and PTB was analyzed using the distributed lag non-linear model (DLNM). The generalized additive model (GAM) was used to visualize the interaction between meteorological factors. Stratified analysis was used to explore the impact of meteorological factors on PTB in different stratification and RERI, AP and SI were used to quantitatively evaluate the interaction between meteorological factors. Results A total of 16,793 newly diagnosed PTB cases were documented in Urumqi, China from 2013 to 2019. The median (interquartile range) temperature, relative humidity, wind speed, and PTB cases were measured as 11.3°C (−5.0–20.5), 57.7% (50.7–64.2), 4.1m/s (3.4–4.7), and 47 (37–56), respectively. The effects of temperature, relative humidity and wind speed on PTB were non-linear, which were found with the “N”-shaped, “L”-shaped, “N”-shaped distribution, respectively. With the median meteorological factor as a reference, extreme low temperature was found to have a protective effect on PTB. However, extreme high temperature, extreme high relative humidity, and extreme high wind speed were found to increase the risk of PTB and peaked at 31.8°C, 83.2%, and 7.6 m/s respectively. According to the existing monitoring data, no obvious interaction between meteorological factors was found, but low temperature and low humidity (RR = 1.149, 95%CI: 1.003–1.315), low temperature and low wind speed (RR = 1.273, 95%CI: 1.146–1.415) were more likely to cause the high incidence of PTB. Conclusion Temperature, relative humidity and wind speed were found to play vital roles in PTB incidence with delayed and non-linear effects. Extreme high temperature, extreme high relative humidity, and extreme high wind speed could increase the risk of PTB. Moreover, low temperature and low humidity, low temperature and low wind speed may increase the incidence of PTB.

Dataset Information

The influence of meteorological factors on tuberculosis incidence in Southwest China from 2006 to 2015.

Publications

The influence of meteorological factors on tuberculosis incidence in Southwest China from 2006 to 2015.

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