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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. 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. 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. 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. • Large temperature drop and large level of DTR are the risk factors for TB admission. • Female, adult and elderly patients are more vulnerable to large level of temperature change. • TCN may be a better predictor for the TB prevention and public health. [ABSTRACT FROM AUTHOR]

The current evidence has presented mixed results between air pollutants exposure and the progression of tuberculosis (TB). The purpose of this study was to explore the association between short-term exposure to air pollutants and the risk of TB outpatient visits in Hefei, China. Time-series analysis was used to assess the effect of short-term exposure to ambient air pollutants on the risk of TB outpatient visits. A Poisson generalized linear regression model combined with a distributed lag non-linear model (DLNM) was applied to explore the association. The effects of different gender (male, female), age (≤65 years old, >65 years old) and season (cold season, warm season) on the risk of TB were investigated by stratified analysis. Sensitivity analyses were conducted to test the robustness of our findings. A total of 22,749 active TB cases were identified from November 1, 2013 to December 31, 2018 in Hefei. The overall exposure-response curve showed that the concentration of particulate matter with aerodynamic diameter less than 2.5 μm (PM 2.5) and nitrogen dioxide (NO 2) exposure were positively correlated with the risk of TB outpatient visits, while ozone (O 3) and sulfur dioxide (SO 2) exposure were negatively correlated with the risk of TB outpatient visits. The maximum lag-specific and cumulative relative risk (RR) of TB outpatient visits were 1.057 [95%CI: 1.002–1.115, lag 3 day] and 1.559 (95%CI: 1.057–2.300, lag 13 days) for each 10 μg/m³ increase in PM 2.5 ; 1.026 (95% CI: 1.008–1.044, lag 0 day) and 1.559 (95%CI: 1.057–2.300, lag 07 days) for each 10 μg/m³ increase in NO 2 ; 0.866 (95% CI: 0.801–0.935, lag 5 day) and 0.852 (95%CI: 1.01–1.11, lag 0–14 days) for each 10 μg/m³ increase in SO 2 in the single-pollutant model. There was only a negative association between O 3 exposure and the cumulative risk of TB outpatient visits (RR = 0.960, 95%CI: 0.936–0.984, lag 07 days). Stratified analyses showed that the effects of SO 2 and O 3 exposure were different between warm and cold seasons. The effect of NO 2 exposure remained statistically significant in male, younger, and cold season subgroups. Besides, elderly people are more susceptible to PM 2.5 exposure. This study suggests that exposure to PM 2.5 , NO 2 , SO 2 , and O 3 are associated with the risk of TB outpatient visits. Seasonal variation may have a greater impact on the risk of TB outpatient visits compared with gender and age. • For the first time, we explore the association between air pollution exposure and the risk of TB outpatient visits in Hefei. • Exposure to PM 2.5 , NO 2 , SO 2 and O 3 are associated with the risk of TB outpatient visits. • Elder people are more vulnerable to PM 2.5 exposure, while younger and male individuals are more vulnerable to NO 2 exposure. • Seasonal variation may have a great modified effect on the risk of TB outpatient visits than age and gender. [ABSTRACT FROM AUTHOR]

Previous studies have shown that ambient air pollution exposure can increase the risk of type 2 diabetes mellitus (T2DM) significantly. In consideration of the common underlying pathophysiologic mechanisms, exposure to air pollution may also increase the risk of gestational diabetes mellitus (GDM), but the current evidence was inconsistent and has not well been systematically reviewed. Our goal was to perform a systematic review and meta-analysis assessing the association between air pollution exposure and GDM. An extensive literature search was conducted in selected electronic databases for related human epidemiological studies published in English language. Summary effect estimates were calculated using random-effect models for a) risk per unit increase in continuous air pollutant concentration and b) risk of high versus low exposure level in individual study if each exposure that had been examined in ≥2 studies. We evaluated the heterogeneity using Cochran's Q test and quantified it by I2 statistic. Publication bias was also evaluated through the funnel plot when sufficient number of studies are available. A total of 11 studies evaluating the association between GDM and exposure to air pollution were identified finally. The summary odds ratio (OR) for incidence of GDM following a 10 μg/m3 increase in PM 2.5 exposure during the second trimester was 1.04 (95% Confidence Interval (CI): 1.01, 1.09) and in NO x during the first trimester was 1.03 (95%CI: 1.00, 1.07) per 10 ppb increase, while for high versus low SO 2 exposure during the second trimester was 1.25 (95%CI: 1.02, 1.53). High heterogeneity among study-specific results in majority of the analyses were observed, and attributed to different exposure assessment methods, populations, study locations, and covariates adjustment. Publication bias cannot be excluded because of the inclusion of small number of studies. The present study supports the evidence that air pollution exposure increases the risk the GDM, albeit the existence of high heterogeneity. Further studies are necessary to elaborate the suggestive associations. These results are of public health significance since worsening air pollution in developing countries has been expected to increase the risk of GDM development. [ABSTRACT FROM AUTHOR]