Association of air pollutants and meteorological factors with tuberculosis: a national multicenter ecological study in China

Analysis of epidemiological trends and risk factors in high-risk areas for pulmonary tuberculosis: an observational longitudinal study in Xinjiang, China

OBJECTIVE

To explore the spatial and temporal variations in the long-term risk of developing tuberculosis (TB) and the factors influencing it in order to contribute to the goal of eradicating TB.

DESIGN

Observational longitudinal study.

SETTING

Xinjiang, China, 2005-2019.

PRIMARY AND SECONDARY OUTCOME MEASURES

Comparison of TB incidence across age, period, cohort and space using socioeconomic (including gross domestic product per capita, population density, public budget revenue and total retail sales of consumer goods), public health (including the number of hospital beds, health technicians and basic medical insurance for urban residents) and environmental variables (PM2.5, mean air temperature, mean wind speed, mean relative humidity and precipitation). The relative importance of these variables to pulmonary TB (PTB) is revealed by the Q-value (0, 1), with larger values indicating that the spatial heterogeneity of the explanatory variables to PTB is more pronounced.

PARTICIPANTS

All clinically diagnosed and confirmed cases in Xinjiang, China, were collected. The descriptive analysis included confirmed cases from 2005 to 2019, while cases from various districts and counties between 2011 and 2019 were subjected to further analysis.

RESULTS

From 2005 to 2019, a total of 642 332 cases of PTB were reported in Xinjiang, with an average annual incidence rate of 172/100 000. The age risk of PTB presented a bimodal distribution, namely 20-24 years and the elderly (>60 years). The high prevalence of PTB was distributed in the southern part of Xinjiang. Among the influencing factors that had a greater effect on the incidence of PTB, the lower GDP per capita (Q-value=0.65) had a largest effect on PTB in Xinjiang compared with others factors (higher PM2.5: Q-value=0.56, lower health personnel: Q-value=0.49, higher average temperature: Q-value=0.47 and higher urban residents' health insurance: Q-value=0.46). The main influencing factors were heterogeneous in different regions. Furthermore, the interactions among these factors enhanced the explanatory power regarding the incidence of the disease.

CONCLUSIONS

Identifying the high-risk groups, regions, influencing factors and interactions of PTB in Xinjiang, China, will expand the epidemiological knowledge of PTB in high-risk areas and potentially aid in designing targeted interventions.

Predicting Tuberculosis Risk in Cattle, Buffaloes, Sheep, and Goats in China Based on Air Pollutants and Meteorological Factors

Tuberculosis is a zoonotic chronic respiratory infectious disease caused by the Mycobacterium tuberculosis complex. The outbreak and epidemic of tuberculosis can seriously threaten human and veterinary health. To investigate the effects of environmental factors on tuberculosis in domestic ruminants, we collected data regarding the prevalence of tuberculosis in cattle, buffaloes, sheep, and goats in China (1956-2024) from publicly published literature and available databases. We identified the key risk factors among six major air pollutants and 19 bioclimatic variables; simulated the risk distribution of tuberculosis in cattle, buffaloes, sheep, and goats in China using the maximum entropy ecological niche model; and evaluated the effects of environmental factors. The area under the curve of the model was 0.873 (95% confidence interval, 0.851-0.895). The risk factors that most significantly influenced the prevalence of tuberculosis were the nitrogen dioxide (NO2) level, mean temperature of the coldest quarter, cattle distribution density, sheep distribution density, ozone (O3) level, and precipitation of the driest month. The predicted map of tuberculosis risk in cattle, buffaloes, sheep, and goats indicated that the high-risk regions were mainly distributed in South, North, East, and Northwest China. Improved surveillance is needed in these high-risk areas, and early preventive measures must be implemented based on the risk factors identified to reduce the future prevalence of tuberculosis in cattle, buffaloes, sheep, and goats.

Exploring the short-term effects of extreme temperatures on tuberculosis incidence in Shantou, China: a Coastal City perspective

OBJECTIVE

Coastal cities, due to their proximity to coastlines and unique climatic conditions, face growing challenges from extreme temperature events associated with climate change. Research on the impact of extreme temperatures on tuberculosis (TB) in these cities is limited, and findings from different regions lack consensus. This study focuses on Shantou, a coastal city in China, to investigate the influence of extreme temperatures on TB within this distinctive geographical context.

METHODS

Distributed Lag Non-Linear Models (DLNM) were employed to evaluate the effect of extreme temperatures on TB incidence risk in Shantou, a coastal city in China, spanning from 2014 to 2021. Daily TB case data were provided by the Shantou Tuberculosis Prevention and Control Institute. Daily meteorological information was sourced from the Reliable Prognosis website, while daily air pollutant data were obtained from the China Air Quality Online Monitoring and Analysis Platform.

RESULTS

The study revealed a significant association between extreme temperatures and TB incidence, with the impact peaking at a lag of 27 days after exposure. Notably, extreme cold temperatures led to a temporary decrease in TB incidence with a lag of 1-2 days. Subgroup analysis indicated that males had a notably higher risk of TB under extreme temperature conditions compared to females. Additionally, individuals aged 65 years and above showed a significant cumulative effect in such conditions.

CONCLUSIONS

This research enhances our comprehension of the effects of extreme temperatures on TB in coastal cities and carries substantial public health implications for TB prevention in China.

Air pollution and upper respiratory diseases: an examination among medically insured populations in Wuhan, China

Multiple evidence has supported that air pollution exposure has detrimental effects on the cardiovascular and respiratory systems. However, most investigations focus on the general population, with limited research conducted on medically insured populations. To address this gap, the current research was designed to examine the acute effects of inhalable particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), ground-level ozone (O3), and sulfur dioxide (SO2) on the incidence of upper respiratory tract infections (URTI), utilizing medical insurance data in Wuhan, China. Data on URTI were collected from the China Medical Insurance Basic Database for Wuhan covering the period from 2014 to 2018, while air pollutant data was gathered from ten national monitoring stations situated in Wuhan city. Statistical analysis was performed using generalized additive models for quasi-Poisson distribution with a log link function. The analysis indicated that except for ozone, higher exposure to four other pollutants (NO2, SO2, PM2.5, and PM10) were significantly linked to an elevated risk of URTI, particularly during the previous 0-3 days and previous 0-4 days. Additionally, NO2 and SO2 were found to be positively linked with laryngitis. Furthermore, the effects of air pollutants on the risk of URTI were more pronounced during cold seasons than hot seasons. Notably, females and the employed population were more susceptible to infection than males and non-employed individuals. Our findings gave solid proof of the link between ambient air pollution exposure and the risk of URTI in medically insured populations.

Exploring the impact of socioeconomic and natural factors on pulmonary tuberculosis incidence in China (2013-2019) using explainable machine learning: A nationwide study

Pulmonary tuberculosis (PTB) stands as a significant and prevalent infectious disease in China. Integrating 13 natural and socioeconomic factors, we conduct nine machine learning (ML) models alongside the Tree-Structured Parzen Estimator to predict the monthly PTB incidence rate from 2013 to 2019 in mainland China. With explainable ML techniques, our research highlights that population size, per capita GDP, and PM10 concentration emerge as the primary determinants influencing the PTB incidence rate. We delineate both the independent and interactive impacts of these factors on the PTB incidence rate. Furthermore, crucial thresholds associated with factors influencing the PTB incidence rate are identified. Taking factors that have a positive effect on reducing the incidence rate of PTB as an example, the thresholds at which the effects of factors PM2.5, PM10, O3, and RH on the incidence rate change from increase to decrease are 105.5 µg/m3, 75.5 µg/m3, 90.8 µg/m3, and 72.3 % respectively. Our work will contribute valuable insights for public health interventions.