Effects and Interaction of Meteorological Factors on Pulmonary Tuberculosis in Urumqi, China, 2013–2019

Abstract

BackgroundMost 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.MethodsNewly 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.ResultsA 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.ConclusionTemperature, 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.

Epidemiological characteristics of tuberculosis incidence and its macro-influence factors in Chinese mainland during 2014–2021

“…Although only limited evidence is currently available regarding the association between meteorological factors and TB incidence at the nationwide in China, the findings of the present study are basically consistent with existing reports that Atemp, ARH, AWS, PRE and SD were associated with TB incidence [ 4 , 10 , 11 , 14 , 33 , 34 ]. A study conducted in 16 cities in Anhui province reported that low temperature increased the risk of TB hospitalizations [ 34 ].…”

“…Previous study showed temperature and relative humidity were found as the vital factors for the formation of droplet diameter influencing the containing pathogens [ 35 ]. Clearly, Mycobacterium tuberculosis ( Mtb ), the pathogen of TB, is more likely to survive in the environment with low temperature and high humidity [ 10 ]. The short sunshine duration could reduce the UV rays, increasing the degree of low temperature and high relatively humidity, which contribute to Mtb survive in the environment.…”

“…We found extreme low wind speed decrease the risk of TB incidence. But a previous study reported the low wind speed is a risk factor of TB, the high wind speed is a protective factor of TB [ 10 ]. The wind speed is a biphasic factor for TB incidence, which is difficult to clarify the impact on TB incidence.…”

“…Various macro-level factors, such as climate change, population migration, and urbanization, significantly influence infectious disease patterns [ 9 ]. Recent researches have investigated the relationship between these macroscopic factors and TB incidence, using diverse statistical analyses [ 4 , 5 , 10 , 11 ]. The logistic and line regression models were usually used for exploring the linear relationship, based on the individual data.…”

“…The logistic and line regression models were usually used for exploring the linear relationship, based on the individual data. However, studies have proved the relationship between meteorological factors and TB incidence was nonlinear and lagged [ 10 – 12 ]. Based on the data in our study, the distributed lag nonlinear model (DLNM) can effectively capture the non-linear and lagged relationship between meteorological factors and TB incidence, revealing exposure-lag-response effects [ 10 – 12 ].…”