On the association between outdoor PM2.5 concentration and the seasonality of tuberculosis for Beijing and Hong Kong

Changes in the incidence, clinical features and outcomes of tuberculosis during COVID-19 pandemic

BACKGROUND

COVID-19 pandemic has disrupted tuberculosis (TB) services in many countries, but the impacts on sites of involvement, drug susceptibility, smear positivity and clinical outcomes, and clinical outcomes of co-infection with influenza and COVID-19 remain unclear.

METHODS

Descriptive epidemiological study using episode-based and patient unique data of tuberculosis from Hospital Authority's territory-wide electronic medical record database, comparing baseline (January 2015-December 2019) and COVID-19 period (January 2020-December 2022), followed by univariate and multivariate analyses. Effects of co-infection with influenza and COVID-19 were investigated.

RESULTS

The study included 10,473 episodes of laboratory-confirmed TB, with 6818 in baseline period and 3655 during COVID-19 period. During COVID-19 period, TB patients had a lower proportion of smear positivity (49.2 % vs 54.7 %, P < 0.001), and fewer cases of extrapulmonary TB (7.0 % vs 8.0 %, P = 0.078) and multidrug resistant TB (1.0 % vs 1.6 %, P = 0.020). Mortality was higher in TB patients with COVID-19 coinfection (OR 1.7, P = 0.003) and influenza coinfection (OR 2.6, P = 0.004). During COVID-19 period, there were higher rates of treatment delay (20.5 % vs 15.5 %, P < 0.001) and episodic death (15.1 % vs 13.3 %, P = 0.006). Factors associated with higher mortality included age ≥ 70 years (OR 7.24), treatment delay (OR 2.16), extrapulmonary TB (OR 2.13). smear positivity (OR 1.71) and Charlson comorbidity index score ≥ 3 (OR 1.37). Higher mortality was observed with co-infection by influenza (OR 1.18) and COVID-19 (OR 1.7).

CONCLUSIONS

The epidemiology and outcomes of TB were changed during COVID-19 period. Mortality was higher during COVID-19 period and with co-infection by influenza and COVID-19.

Association between ambient temperature, PM2.5 and tuberculosis in Northwest China

   Existing evidence suggested that the risk of tuberculosis (TB) infection was associated to the variations in temperature and PM2.5. A total of 9,111 cases of TB were reported in Ningxia Hui Autonomous Region, China from 2013 to 2015 on a daily basis, and 57.2% of them were male. The TB risk was more prominent for a lower temperature in males (RR of 1.724, 95% CI: 1.241, 2.394), the aged over 64 years (RR of 2.241, 95% CI: 1.554, 3.231), and the high mobility occupation subpopulation (RR of 2.758, 95% CI: 1.745, 4.359). High concentration of PM2.5 showed a short-term effect and was only associated with an increased risk in the early stages of exposure for the female, and aged 36-64 years group. There were 15.06% (1370 cases) of cases of TB may be attributable to the temperature, and 2.94% (268 cases) may be attributable to the increase of PM2.5 exposures. Low temperatures may be associated with significantly increase in the risk of TB, and high PM2.5 concentrations have a short-term association on increasing the risk of TB. Strengthening the monitoring and regular prevention and control of high risk groups will provide scientific guidance to reduce the incidence of TB.

Particulate matter deposition and its impact on tuberculosis severity: A cross-sectional study in Taipei

The objective of this study was to examine the association between the lung lobe-deposited dose of inhaled fine particulate matter (PM2.5) and chest X-ray abnormalities in different lung lobes of pulmonary tuberculosis (TB), multidrug-resistant tuberculosis (MDR-TB), and non-tuberculosis mycobacteria infections (NTM). A cross-sectional study was conducted between 2014 and 2022, comprising 1073 patients who were recruited from chest department clinic in a tertial refer hospital in Taipei City, Taiwan. Ambient 1-, 7-, and 30-day PM2.5 exposure and the deposition of PM2.5 in different lung lobes were estimated in each subject. The β coefficient for PM2.5 and deposited PM2.5 in lungs with the outcome variables (pulmonary TB, MDR-TB, and NTM infection) was derived through regression analysis and adjusted for age, gender, BMI, smoking status, and family income. We observed that a 1 μg/m3 increase in ambient PM2.5 was associated with an increase of MDR-TB infections of 0.004 times (95%CI: 0.001-0.007). A 1 μg/m3 increase in 1-day and 7-day PM2.5 deposition in left upper lobe and left lower lobe was associated with an increase in chest X-ray abnormalities of 9.19 % and 1.18 % (95%CI: 0.87-17.51 and 95%CI: 0.08-2.28), and 4.52 % and 5.20 % (95%CI: 0.66-8.38 and 95%CI: 0.51-9.89) in left lung of TB patients, respectively. A 1 μg/m3 increase in 30-day PM2.5 deposition in alveolar region was associated with an increase in percent abnormality of 2.50 % (95%CI: 0.65-4.35) in left upper lobe and 3.33 % (95%CI: 0.65-6.01) in right middle lobe, while in total lung was 0.63 % (95%CI: 0.01-1.27) in right upper lobe and 0.37 % (95%CI, 0.06-0.81) in right lung of MDR-TB patients. Inhaled PM2.5 deposition in lungs was associated with an exacerbation of the radiographic severity of pulmonary TB, particularly in pulmonary MDR-TB patients in upper and middle lobes. Particulate air pollution may potentially exacerbate the radiographic severity and treatment resistance in individuals with pulmonary TB.

Is Pollution the Primary Driver of Infectious Syndemics?

Syndemics, the adverse interaction of two or more coterminous diseases or other negative health conditions, have probably existed since human settlement, plant and animal domestication, urbanization, and the growth of social inequality beginning about 10-12,000 years ago. These dramatic changes in human social evolution significantly increased opportunities for the spread of zoonotic infectious diseases in denser human communities with increased sanitation challenges. In light of a growing body of research that indicates that anthropogenic air pollution causes numerous threats to health and is taking a far greater toll on human life and wellbeing than had been reported, this paper proposes the possibility that air pollution is now the primary driver of infectious disease syndemics. In support of this assertion, this paper reviews the growth and health impacts of air pollution, the relationship of air pollution to the development and spread of infectious diseases, and reported cases of air pollution-driven infectious disease syndemics, and presents public health recommendations for leveraging the biosocial insight of syndemic theory in responding to infectious disease.

Distributed lag effects and vulnerable groups of PM and active pulmonary TB in Qingdao, China

There has been a gap regarding current knowledge of the effect of PM on pulmonary TB, such as the exposure-time-response between them. This study aimed to explore the distributed lag effects of particulate matter (PM) on active pulmonary tuberculosis (TB) and identify the vulnerable groups. A generalized additive mixed model combined with a distributed lag non-linear model was applied to quantify the association between PM and active pulmonary TB with adjustment for potential confounders. Relative risk (RR) and cumulative RR with 95% confidence interval (CI) were calculated to quantify the exposure-time-response. A total of 16,486 cases of active pulmonary TB were notified. Results suggested that a unit 10 µg/m3 increase of daily PM2.5 concentration was positively associated with active pulmonary TB morbidity at 36-115 lag day and RR reached maximum at 66 lag day (1.0076; 95%CI, 1.0031-1.0122), and the cumulative RR was 2.1940 (95%CI, 1.2292-3.9161). For PM10, this association was significantly positive at 73-117 lag day, and RR reached maximum at 100 lag day (1.0036; 95%CI, 1.0003-1.0067), and the cumulative RR was not significant. This study provides evidence that PM significantly associate with active pulmonary TB. Vulnerability to PM2.5 was identified in male, female, 0-18 ages, 19-64 ages, workers, and students. Our findings have significant implications for developing local strategies to prevent and reduce health impact in PM polluted areas.

Association of ambient PM2.5 concentration with tuberculosis reactivation diseases-an integrated spatio-temporal analysis

Objectives

While the plausible role of ambient particulate matter (PM)2.5 exposure in tuberculosis (TB) reactivation has been inferred from in vitro experiments, epidemiologic evidence is lacking. We examined the relationship between ambient PM2.5 concentration and pulmonary TB (PTB) in an intermediate TB endemicity city dominated by reactivation diseases.

Methods

Spatio-temporal analyses were performed on TB notification data and satellite-based annual mean PM2.5 concentration in Hong Kong. A total of 52,623 PTB cases from 2005-2018 were mapped to over 400 subdistrict units. PTB standardized notification ratio by population subgroups (elderly aged ≥65, middle-aged 50-64, and young adults aged 15-49) was calculated and correlated with ambient PM2.5 concentration.

Results

Significant associations were detected between high ambient PM2.5 concentration and increased PTB among the elderly. Such associations were stable to the adjustment for socio-economic factors and other criteria pollutants. Unstable patterns of association between PM2.5 and PTB risk were observed in the middle-aged population and young adults, for which the observed associations were confounded by other criteria pollutants.

Conclusion

With elderly PTB almost exclusively attributable to reactivation, our findings suggested that increased TB reactivations have occurred in association with high ambient PM2.5 exposure, lending support to preventive measures that minimize PM2.5-related TB reactivation.

Effect of gaseous pollutant and greenness exposure on mortality during treatment of newly treated tuberculosis patients: a provincial population-based cohort study

BACKGROUND

Previous studies addressing the impact of environmental factors on TB prognosis are scarce, with only some studies examining the effect of particulate pollutants on TB mortality. Moreover, few studies have evaluated the effects of multiple gaseous pollutants and greenness exposures on newly treated TB patients on a large population scale.

METHODS

Through the Centers for Disease Control and Prevention, data were collected from January 1, 2015 to December 31, 2020 for newly treated TB patients in Anhui Province, China. Data on gaseous pollutants sulfur dioxide, nitrogen dioxide, carbon monoxide, and ozone were collected through the National Earth System Science Data Center of China. Normalized vegetation index data were obtained through NASA. The Cox proportional risk model was also applied to calculate the hazard ratios of SO2, NO2, CO, O3, and NDVI with 95% confidence intervals for mortality among newly treated TB patients.

RESULTS

Multifactorial Cox regression analysis showed that for every 0.10 μg/m3 increase in SO2, the risk of death among newly treated TB patients increased by 13.2% (HR = 1.132, 95% CI: 1.045-1.1.225), for every 10 μg/m3 increase in NO2, the risk of death among newly treated TB patients increased by 11.4%, and for each 0.1 mg/m3 increase in CO, the risk of death among newly treated TB patients increased by 5.8%. For each 0.1 increase in NDVI 250m-buffer and 500m-buffer, the risk of death among newly treated TB patients decreased by 8.5% and 6.4%, respectively. The effect of gaseous pollutants on mortality decreased progressively with elevated greenness exposure when greenness exposure was grouped from low to high.

CONCLUSION

Gaseous pollutants are a risk factor during the treatment of newly treated TB patients and greenness exposure is a protective factor. Higher greenness exposure reduces the risk of death due to exposure to gaseous pollutants.

Factors associated with diagnostic delay of pulmonary tuberculosis among children and adolescents in Quzhou, China: results from the surveillance data 2011-2021

PURPOSE

Tuberculosis is a high-burden disease and a major health concern in China, especially among children and adolescents. The purpose of this study was to assess risk factors for diagnostic delay in students with pulmonary tuberculosis in Quzhou City in eastern China.

PATIENTS AND METHODS

Cases of PTB in students and relevant information in Quzhou from 2011 to 2021 were collected using the TB Management Information System. The outcome of interest was diagnostic delay (i.e. ≥ 28 days between symptom onset and treatment initiation). Risk factors for diagnostic delay were identified using multivariable logistic regression.

RESULTS

A total of 629 students in Quzhou were diagnosed with PTB during the study period, of whom 55.5% were male. The median diagnostic delay was 18 days (Inter Quartile Range, [IQR]: 8-38) and 38.0% of the students had a diagnostic delay. Living in a rural area (adjusted odds ratio, [AOR]: 1.56, 95% confidence interval [CI:] 1.11-2.19), developing PTB symptoms in the first quarter of the year (AOR: 2.18, 95% CI: 1.40-3.40), and no sputum smear result (AOR: 8.73, 95% CI: 1.68-45.30) were significantly associated with a diagnostic delay. Discovery through health examinations (AOR: 0.33, 95% CI: 0.17-0.63) was associated with reduced risk of diagnostic delay.

CONCLUSION

Schools in rural areas should pay special attention to increasing student awareness of the symptoms of tuberculosis and provide health education on tuberculosis prevention and control to students and staff.

Human exposure risk assessment for infectious diseases due to temperature and air pollution: an overview of reviews

Air pollution and global temperature change are expected to affect infectious diseases. Air pollution usually causes inflammatory response and disrupts immune defense system, while temperature mainly exacerbates the effect of vectors on humans. Yet to date overview of systematic reviews assessing the exposure risk of air pollutants and temperature on infectious diseases is unavailable. This article aims to fill this research gap. PubMed, Embase, the Cochrane Library, Web of Science, and the Cumulative Index to Nursing and Allied Health Literature were searched. Systematic reviews and meta-analyses investigated the exposure risk of pollutants or temperature on infectious diseases were included. Two investigators screened literature, extracted data and performed the risk of bias assessments independently. A total of 23 articles met the inclusion criteria, which 3 (13%) were "low" quality and 20 (87%) were "critically low" quality. COVID-19 morbidity was associated with long-term exposure PM2.5 (RR = 1.056 per 1 [Formula: see text], 95% CI: 1.039-1.072) and NO2 (RR = 1.042 per 1 [Formula: see text], 95% CI: 1.017-1.068). In addition, for each 1 °C increase in temperature, the morbidity risk of dengue increased 13% (RR = 1.130 per 1 °C, 95% CI: 1.120-1.150), infectious diarrhea increased 8% (RR = 1.080 per 1 °C, 95% CI: 1.050-1.200), and hand, foot and mouth disease (HFMD) increased 5% (RR = 1.050 per 1 °C, 95% CI: 1.020-1.080). In conclusion, PM2.5 and NO2 increased the risk of COVID-19 and temperatures were associated with dengue, infectious diarrhoea and HFMD morbidity. Moreover, the exposure risk of temperature on COVID-19 was recommended to be further explored.

Associations of exposures to air pollution and greenness with mortality in a newly treated tuberculosis cohort

Some previous studies had linked air pollutants and greenness to the risk of death from tuberculosis (TB). Only a few studies had examined the effect of particulate matter (PM_2.5) on the mortality of TB, and few studies had assessed the impact and interaction of multiple air pollutants and greenness on the mortality of newly treated TB patients. The study included 29,519 newly treated TB patients from three cities in Anhui province. We collected meteorological data and five pollutants data from The National Meteorological Science Center and air quality monitoring stations. Greenness data were generated by remote sensing inversion of medium-resolution satellite images. We geocoded each patient based on the residential address to calculate the average exposure to air pollutants and the average greenness exposure for each patient during treatment. The Cox proportional risk regression model was used to evaluate the effects of air pollutants and greenness on mortality in newly treated tuberculosis patients. Our results found that the higher the concentration of air pollutants in the living environment of newly treated TB patients, the greater the risk of death: HR 1.135 (95% CI: 1.123-1.147) and HR 1.333 (95% CI: 1.296-1.370) per 10 μg/m³ of PM_2.5 and SO₂, respectively. Greenness reduced the mortality among newly treated TB patients: HR for NDVI exposure 0.936 (95% CI: 0.925-0.947), HR for NDVI_250m exposure 0.927 (95% CI: 0.916-0.938), and HR for NDVI_500m exposure 0.919 (95% CI: 0.908-0.931). Stratifying the cohort by median greenness exposure, HRs for air pollutants were lower in the high greenness exposure group. Mortality in newly treated TB patients is influenced by air pollutants and greenness. Higher green exposure can mitigate the effects of air pollution. Improving air quality may help reduce mortality among newly treated TB patients.

Effects of long-term ambient air pollution exposure on township-level pulmonary tuberculosis notification rates during 2005-2017 in Ningxia, China

Studies examining long-term effects of ambient air pollution exposure, measured as annual averages, on pulmonary tuberculosis (TB) incidence are scarce, particularly in endemic, rural settings. We performed a small-area study in Ningxia Hui Autonomous Region (NHAR), a high TB-burden area in rural China, using township-level (n = 358 non-overlapping townships) annual TB notification data (2005-2017). We aimed to determine if annual average concentrations of ambient air pollution (particulate matter <2·5 μm [PM_2·5], nitrogen dioxide [NO₂] ozone [O₃]) were associated with TB notification rates (as a proxy for incidence). Air pollution effects on TB notification rates at township-level were estimated as incidence rate ratios (IRR), fitted using a generalised estimating equation (GEE) adjusted for covariates (age, sex, occupation, education, ethnicity, remoteness [urban or rural], household crowding and solid fuel use). A total of 38,942 TB notifications were reported in NHAR between 2005 and 2017. The mean annual TB notification rate was 67 (standard deviation [SD]; 7) per 100,000 people. Median concentrations of PM_2·5, NO₂, and O₃ were 42 μg/m³ (interquartile range [IQR]; 38-48 μg/m³), 15 ppb (IQR; 12-16 ppb), and 56 ppb (IQR; 56-57 ppb), respectively. In single pollutant models, adjusted for covariates, an interquartile range (IQR) increase (10 μg/m³) in PM_2·5 was significantly associated with higher TB notification rates (IRR: 1∙35; 95% CI: 1·25-1·48). Comparable effects on notifications of TB were observed for increases in NO₂ exposure (IRR: 1·20 per IQR (4 ppb) increase; 95% CI: 1·08-1·31). Ground-level ozone was not associated with TB notification rate in any models. The observed effects were consistent over time, in multi-pollutant models, and appeared robust to additional adjustment for indicators of household crowding, solid fuel use and remoteness. More rigorous study designs are needed to understand if improving air quality has population-level benefits on TB disease incidence in endemic settings.

The association between air pollutants, meteorological factors and tuberculosis cases in Beijing, China: A seven-year time series study

BACKGROUND

Tuberculosis (TB) is a severe public health problem globally. Previous studies have revealed insufficient and inconsistent associations between air pollutants, meteorological factors and TB cases. Yet few studies have examined the associations between air pollutants, meteorological factors and TB cases in Beijing.

OBJECTIVE

The purpose of this study was to explore the impact of air pollutants and meteorological factors on TB in Beijing, and to provide novel insights into public health managers to formulate control strategies of TB.

METHODS

Data on the daily case of TB in Beijing during 2014-2020 were obtained from Chinese tuberculosis information management system. Concurrent data on the daily PM₁₀, PM_2.5, SO₂, NO₂, CO and O₃, were obtained from the online publication platform of the Chinese National Environmental Monitoring Center. Daily average temperature, average wind speed, relative humidity, sunshine duration and total precipitation were collected from the China Meteorological Science Data Sharing Service System. A distributed lag non-linear model was fitted to identify the non-linear exposure-response relationship and the lag effects between air pollutions, meteorological factors and TB cases in Beijing.

RESULTS

In the single-factor model, the excess risk (ER) of TB was significantly positively associated with every 10 μg/m³ increase in NO₂ in lag 1 week (ER: 1.3%; 95% confidence interval [CI]: 0.4%, 2.3%) and every 0.1 m/s increase in average wind speed in lag 5 weeks (ER: 0.3%; 95% CI: 0.1%, 0.5%), and was negatively associated with every 10 μg/m³ increase in O₃ in lag 1 week (ER: -1.2%; 95% CI: -1.8%, -0.5%), every 5 °C increase in average temperature (ER: -1.7%; 95% CI: -2.9%, -0.4%) and every 10% increase in average relative humidity (ER: -0.4%; 95% CI: -0.8%, -0.1%) in lag 10 weeks, respectively. In the multi-factor model, the lag effects between TB cases and air pollutants, meteorological factors were similar. The subgroup analysis suggests that the effects of NO₂, O₃, average wind speed and relative humidity on TB were greater in male or labor age subgroup, while the effect of CO was greater in the elderly. In addition, no significant associations were found between PM_2.5, SO_2, sunshine duration and TB cases.

CONCLUSION

Our findings provide a better understanding of air pollutants and meteorological factors driving tuberculosis occurrence in Beijing, which enhances the capacity of public health manager to target early warning and disease control policy-making.

Ambient PM2.5 exposures could increase risk of tuberculosis recurrence

BACKGROUND

The effect of ambient PM2.5 on the incidence of tuberculosis (TB) has been investigated in epidemiological studies. However, they did not separately study new and relapsed TB infection and focused on relatively short-term effects of PM2.5. In this regard, we examined the associations of long-term PM2.5 exposures with both new and relapsed TB incidences in South Korea, where the disease burden of TB is greatest among high-income countries.

METHODS

An area-level ecological study of 250 districts was conducted from 2015 to 2019. Age- and sex-standardized TB incidence ratios for each district and year were used as outcome variables, and their associations with PM2.5 concentrations for one to five-year average were examined. Negative binomial regression models incorporating spatiotemporal autocorrelation were employed using integrated nested Laplace approximations. Stratified analyses were conducted by type of TB (total, new, and relapsed cases).

RESULTS

Districts with higher PM2.5 concentrations tended to have significantly higher TB recurrence rate. The relative risks per 10 µg/m3 PM2.5 increase were 1.218 (95% credible interval 1.051-1.411), 1.260 (1.039-1.527) and 1.473 (1.015-2.137) using the two, three and five-year average PM2.5 exposures, respectively.

CONCLUSIONS

The results imply that interventions for reducing air pollution might help prevent TB recurrence.

Health and Clinical Impacts of Air Pollution and Linkages with Climate Change

Air Pollution Impacts and Climate Change LinksAs part of the NEJM Group series on climate change, Keswani and colleagues review the linkages between climate change and air pollution and suggest strategies that clinicians may use to mitigate the adverse health impacts of air pollution.

Spatio-temporal distribution of tuberculosis and the effects of environmental factors in China

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, PM_2.5, 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.

Lagged Effects of Exposure to Air Pollutants on the Risk of Pulmonary Tuberculosis in a Highly Polluted Region

BACKGROUND

Although significant correlations have been observed between air pollutants and the development of pulmonary tuberculosis (PTB) in many developed countries, data are scarce for developing and highly polluted regions.

METHOD

A combined Poisson generalized linear regression-distributed lag nonlinear model was used to determine the associations between long-term exposure (2005-2017) to air pollutants and the risk of PTB in the Beijing-Tianjin-Hebei region.

RESULTS

The monthly PTB cases exhibited a fluctuating downward trend. For each 10 μg/m3 increase in concentration, the maximum lag-specific risk and cumulative relative risk (RR) were 1.011 (95% confidence interval (CI): 1.0091.012, lag: 3 months) and 1.042 (1.036-1.048, 5 months) for PM2.5, and 1.023 (1.015-1.031, 0 months) and 1.041 (1.026-1.055, 2 months) for NO2. The risk of PTB was negatively correlated with O3 exposure, and the minimum lag-specific risk and cumulative RR were 0.991 (95% CI: 0.987-0.994, lag: 0 months) and 0.974 (0.968-0.981, 4 months), respectively. No age-dependent effects were observed.

CONCLUSIONS

Our results revealed potential associations between outdoor exposure to PM2.5, NO2, and O3 and the risk of PTB. Further research should explore the corresponding interactions and potential mechanisms.

Effects of short-term ambient particulate matter exposure on the risk of severe COVID-19

OBJECTIVES

Previous studies have suggested a relationship between outdoor air pollution and the risk of coronavirus disease 2019 (COVID-19). However, there is a lack of data related to the severity of disease, especially in China. This study aimed to explore the association between short-term exposure to outdoor particulate matter (PM) and the risk of severe COVID-19.

METHODS

We recruited patients diagnosed with COVID-19 during a recent large-scale outbreak in eastern China caused by the Delta variant. We collected data on meteorological factors and ambient air pollution during the same time period and in the same region where the cases occurred and applied a generalized additive model (GAM) to analyze the effects of short-term ambient PM exposure on the risk of severe COVID-19.

RESULTS

A total of 476 adult patients with confirmed COVID-19 were recruited, of which 42 (8.82%) had severe disease. With a unit increase in PM₁₀, the risk of severe COVID-19 increased by 81.70% (95% confidence interval [CI]: 35.45, 143.76) at a lag of 0-7 days, 86.04% (95% CI: 38.71, 149.53) at a lag of 0-14 days, 76.26% (95% CI: 33.68, 132.42) at a lag of 0-21 days, and 72.15% (95% CI: 21.02, 144.88) at a lag of 0-28 days. The associations remained significant at lags of 0-7 days, 0-14 days, and 0-28 days in the multipollutant models. With a unit increase in PM_2.5, the risk of severe COVID-19 increased by 299.08% (95% CI: 92.94, 725.46) at a lag of 0-7 days, 289.23% (95% CI: 85.62, 716.20) at a lag of 0-14 days, 234.34% (95% CI: 63.81, 582.40) at a lag of 0-21 days, and 204.04% (95% CI: 39.28, 563.71) at a lag of 0-28 days. The associations were still significant at lags of 0-7 days, 0-14 days, and 0-28 days in the multipollutant models.

CONCLUSIONS

Our results indicated that short-term exposure to outdoor PM was positively related to the risk of severe COVID-19, and that reducing air pollution may contribute to the control of COVID-19.

An influence of dew point temperature on the occurrence of Mycobacterium tuberculosis disease in Chennai, India

Climate factors such as dew point temperature, relative humidity and atmospheric temperature may be crucial for the spread of tuberculosis. This study was conducted for the first time to investigate the relationship of climatic factors with TB occurrence in an Indian setting. Daily tuberculosis notification data during 2008–2015 were generated from the National Treatment Elimination Program, and analogous daily climatic data were obtained from the Regional Meteorological Centre at Chennai city, Tamil Nadu, India. The decomposition method was adopted to split the series into deterministic and non-deterministic components, such as seasonal, non-seasonal, trend and cyclical, and non-deterministic climate factors. A generalized linear model was used to assess the relation independently. TB disease progression from latent stage infection to active was supported by higher dew point temperature and moderate temperature. It had a significant association with TB progression in the summer and monsoon seasons. The relative humidity may be favored in the winter and post-monsoon. The water tiny dew droplets may support the TB bacterium to recuperate in the environment.

Association of long-term exposure to ambient air pollution with the number of tuberculosis cases notified: a time-series study in Hong Kong

To analyze the association of long-term exposure to air pollution and its attributable risks with the number of tuberculosis (TB) cases notified, a quasi-Poisson regression model combined with a distributed lag nonlinear model (DLNM) was constructed using monthly data on air pollution and TB cases notified in Hong Kong from 1999 to 2018. Nonlinear relationships between PM₁₀, PM_2.5, and CO and TB cases notified were identified. The concentrations of PM₁₀, PM_2.5, and CO corresponding to the minimum numbers of TB cases notified (the minimum TB notification concentrations, MTNCs) were 58.3 μg/m³, 41.7 μg/m³, and 0.1 mg/m³, respectively. Compared with the MTNCs, the overall cumulative numbers of TB cases notified increased by 76.93% (95% CI: 13.08%, 176.83%), 88.81% (95% CI: 26.09%, 182.71%), and 233.43% (95% CI: 13.56%, 879.03%) for the 95th percentiles of PM₁₀ and PM_2.5 and for the 97.5th percentiles of CO, respectively. The TB notification rate attributed to concentration ranges above the 97.5th percentile of PM₁₀, PM_2.5, and CO was 3.38% (95% empirical confidence intervals [eCI]: 0.93%, 5.61%), 4.73% (95% eCI: 1.87%, 7.15%), and 3.34% (95% eCI: 0.29%, 5.83%), respectively. Long-term exposure to high concentrations of air pollution in Hong Kong may be associated with increases in the number of TB cases notified for this area.

PM2.5 mediated alterations in the in vitro human granuloma and its effect on reactivation of mycobacteria

Exposure to particulate matter pollutant PM_2.5 diminishes the immune response to mycobacterial antigens relevant to contain the infection in the granuloma, thus leading to reactivation of latent bacilli. The present study was therefore designed based on the hypothesis that exposure to PM_2.5 affects the granuloma formation and reactivation of latent mycobacterial bacilli contained in the granuloma. For the sampling of PM_2.5, based on initial standardisations, Teflon filter was selected over the quartz filter. Two different approaches were used to study the effect of PM_2.5 on the human PBMC granuloma formed by Mycobacterium bovis BCG at multiplicity of infection (MOI) 0.1. In the first approach, granuloma formed in the presence of PM_2.5 was loosely packed and ill-defined with significant downregulation of dormancy-associated mycobacterial genes, upregulation of reactivation-associated rpfB gene along with a significant increase in TNFα level without any change in the bacterial load in terms of CFUs. In the second approach, preformed human PBMC granuloma using M. bovis BCG was treated with PM_2.5 that resulted in the disruption of granuloma architecture along with downregulation of not only dormancy-associated genes but also reactivation-associated rpfB gene of mycobacterial bacilli recovered from granuloma. However, there was no significant change in the host cytokine levels. Therefore, it can be inferred that PM_2.5 can modulate the granuloma formation in vitro as well as mycobacterial gene expression in the granuloma with a possible role in the reactivation of latent bacilli.

Associations between lung-deposited dose of particulate matter and culture-positive pulmonary tuberculosis pleurisy

Epidemiological studies identified the relationship between air pollution and pulmonary tuberculosis. Effects of lung-deposited dose of particulate matter (PM) on culture-positive pulmonary tuberculosis remain unclear. This study investigates the association between lung-deposited dose of PM and pulmonary tuberculosis pleurisy. A case-control study of subjects undergoing pleural effusion drainage of pulmonary tuberculosis (case) and chronic heart failure (control) was conducted. Metals and biomarkers were quantified in the pleural effusion. The air pollution exposure was measured and PM deposition in the head, tracheobronchial, alveolar region, and total lung region was estimated by Multiple-path Particle Dosimetry (MPPD) Model. We performed multiple logistic regression to examine the associations of these factors with the risk of tuberculosis. We observed that 1-μg/m³ increase in PM₁₀ was associated with 1.226-fold increased crude odds ratio (OR) of tuberculosis (95% confidence interval (CI): 1.023-1.469, p<0.05), 1-μg/m³ increase in PM_2.5-10 was associated with 1.482-fold increased crude OR of tuberculosis (95% CI: 1.048-2.097, p < 0.05), 1-ppb increase in NO₂ was associated with 1.218-fold increased crude OR of tuberculosis (95% CI: 1.025-1.447, p < 0.05), and 1-ppb increase in O₃ was associated with 0.735-fold decreased crude OR of tuberculosis (95% CI: 0.542 0.995). We observed 1-μg/m³ increase in PM deposition in head and nasal region was associated with 1.699-fold increased crude OR of tuberculosis (95% CI: 1.065-2.711, p < 0.05), 1-μg/m³ increase in PM deposition in tracheobronchial region was associated with 1.592-fold increased crude OR of tuberculosis (95% CI: 1.095-2.313, p < 0.05), 1-μg/m³ increase in PM deposition in alveolar region was associated with 3.981-fold increased crude OR of tuberculosis (95% CI: 1.280-12.386, p < 0.05), and 1-μg/m³ increase in PM deposition in total lung was associated with 1.511-fold increased crude OR of tuberculosis (95% CI: 1.050-2.173, p < 0.05). The results indicate that particle deposition in alveolar region could cause higher risk of pulmonary tuberculosis pleurisy than deposition in other lung regions.

Prediction of tuberculosis cases based on sociodemographic and environmental factors in gombak, Selangor, Malaysia: A comparative assessment of multiple linear regression and artificial neural network models

Background

Early prediction of tuberculosis (TB) cases is very crucial for its prevention and control. This study aims to predict the number of TB cases in Gombak based on sociodemographic and environmental factors.

Methods

The sociodemographic data of 3325 TB cases from January 2013 to December 2017 in Gombak district were collected from the MyTB web and TB Information System database. Environmental data were obtained from the Department of Environment, Malaysia; Department of Irrigation and Drainage, Malaysia; and Malaysian Metrological Department from July 2012 to December 2017. Multiple linear regression (MLR) and artificial neural network (ANN) were used to develop the prediction model of TB cases. The models that used sociodemographic variables as the input datasets were referred as MLR1 and ANN1, whereas environmental variables were represented as MLR2 and ANN2 and both sociodemographic and environmental variables together were indicated as MLR3 and ANN3.

Results

The ANN was found to be superior to MLR with higher adjusted coefficient of determination (R²) values in predicting TB cases; the ranges were from 0.35 to 0.47 compared to 0.07 to 0.14, respectively. The best TB prediction model, that is, ANN3 was derived from nationality, residency, income status, CO, NO₂, SO₂, PM₁₀, rainfall, temperature, and atmospheric pressure, with the highest adjusted R² value of 0.47, errors below 6, and accuracies above 96%.

Conclusions

It is envisaged that the application of the ANN algorithm based on both sociodemographic and environmental factors may enable a more accurate modeling for predicting TB cases.

Short-term effects of ambient air pollution and meteorological factors on tuberculosis in semi-arid area, northwest China: a case study in Lanzhou

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.

Preparation, Characterization and Antimicrobial Properties of Nanosized Silver-Containing Carbon/Silica Composites from Rice Husk Waste

Rice husk, one of the main side products in the rice production, and its sustainable management represent a challenge in many countries. Herein, we describe the use of this abundant agricultural bio-waste as feedstock for the preparation of silver-containing carbon/silica nano composites with antimicrobial properties. The synthesis was performed using a fast and cheap methodology consisting of wet impregnation followed by pyrolysis, yielding C/SiO2 composite materials doped with varying amounts of silver from 28 to 0.001 wt %. The materials were fully characterized and their antimicrobial activity against ESKAPE pathogens, namely E. faecium, S. aureus, K. pneumoniae, A. baumannii, P. aeruginosa, and E. coli, and the pathogenic yeast C. albicans was investigated. Sensitivities of these strains against the prepared materials were demonstrated, even with exceptional low amounts of 0.015 m% silver. Hence, we report a straightforward method for the synthesis of antimicrobial agents from abundant sources which addresses urgent questions like bio-waste valorization and affordable alternatives to increasingly fewer effective antibiotics.

Meteorological factors contribute to the risk of pulmonary tuberculosis: A multicenter study in eastern China

BACKGROUND

Most studies on associations between meteorological factors and tuberculosis (TB) were conducted in a single city, used different lag times, or merely explored the qualitative associations between meteorological factors and TB. Thus, we performed a multicenter study to quantitatively evaluate the effects of meteorological factors on the risk of pulmonary tuberculosis (PTB).

METHODS

We collected data on newly diagnosed PTB cases in 13 study sites in Jiangsu Province between January 1, 2014, and December 31, 2019. Data on meteorological factors, air pollutants, and socioeconomic factors at these sites during the same period were also collected. We applied the generalized additive mixed model to estimate the associations between meteorological factors and PTB.

RESULTS

There were 20,472 newly diagnosed PTB cases reported in the 13 study sites between 2014 and 2019. The median (interquartile range) weekly average temperature, weekly average wind speed, and weekly average relative humidity of these sites were 17.3 °C (8.0-24.1), 2.2 m/s (1.8-2.7), and 75.1% (67.1-82.0), respectively. In the single-meteorological-factor models, for a unit increase in weekly average temperature, weekly average wind speed, and weekly average relative humidity, the risk of PTB decreased by 0.9% [lag 0-13 weeks, 95% confidence interval (CI): -1.5, -0.4], increased by 56.2% (lag 0-16 weeks, 95% CI: 32.6, 84.0) when average wind speed was <3 m/s, and decreased by 28.1% (lag 0-14 weeks, 95% CI: -39.2, -14.9) when average relative humidity was ≥72%, respectively. Moreover, the associations remained significant in the multi-meteorological-factor models.

CONCLUSIONS

Average temperature and average relative humidity (≥72%) are negatively associated with the risk of PTB. In contrast, average wind speed (<3 m/s) is positively related to the risk of PTB, suggesting that an environment with low temperature, relatively high wind speed, and low relative humidity is conducive to the transmission of PTB.

Seasonality of tuberculosis in intermediate endemicity setting dominated by reactivation diseases in Hong Kong

Summer-spring predominance of tuberculosis (TB) has been widely reported. The relative contributions of exogenous recent infection versus endogenous reactivation to such seasonality remains poorly understood. Monthly TB notifications data between 2005 and 2017 in Hong Kong involving 64,386 cases (41% aged ≥ 65; male-to-female ratio 1.74:1) were examined for the timing, amplitude, and predictability of variation of seasonality. The observed seasonal variabilities were correlated with demographics and clinical presentations, using wavelet analysis coupled with dynamic generalised linear regression models. Overall, TB notifications peaked annually in June and July. No significant annual seasonality was demonstrated for children aged ≤ 14 irrespective of gender. The strongest seasonality was detected in the elderly (≥ 65) among males, while seasonal pattern was more prominent in the middle-aged (45–64) and adults (30–44) among females. The stronger TB seasonality among older adults in Hong Kong suggested that the pattern has been contributed largely by reactivation diseases precipitated by defective immunity whereas seasonal variation of recent infection was uncommon.

Association of sociodemographic and environmental factors with spatial distribution of tuberculosis cases in Gombak, Selangor, Malaysia

Tuberculosis (TB) cases have increased drastically over the last two decades and it remains as one of the deadliest infectious diseases in Malaysia. This cross-sectional study aimed to establish the spatial distribution of TB cases and its association with the sociodemographic and environmental factors in the Gombak district. The sociodemographic data of 3325 TB cases such as age, gender, race, nationality, country of origin, educational level, employment status, health care worker status, income status, residency, and smoking status from 1st January 2013 to 31st December 2017 in Gombak district were collected from the MyTB web and Tuberculosis Information System (TBIS) database at the Gombak District Health Office and Rawang Health Clinic. Environmental data consisting of air pollution such as air quality index (AQI), carbon monoxide (CO), nitrogen dioxide (NO2), sulphur dioxide (SO2), and particulate matter 10 (PM10,) were obtained from the Department of Environment Malaysia from 1st July 2012 to 31st December 2017; whereas weather data such as rainfall were obtained from the Department of Irrigation and Drainage Malaysia and relative humidity, temperature, wind speed, and atmospheric pressure were obtained from the Malaysia Meteorological Department in the same period. Global Moran's I, kernel density estimation, Getis-Ord Gi* statistics, and heat maps were applied to identify the spatial pattern of TB cases. Ordinary least squares (OLS) and geographically weighted regression (GWR) models were used to determine the spatial association of sociodemographic and environmental factors with the TB cases. Spatial autocorrelation analysis indicated that the cases was clustered (p<0.05) over the five-year period and year 2016 and 2017 while random pattern (p>0.05) was observed from year 2013 to 2015. Kernel density estimation identified the high-density regions while Getis-Ord Gi* statistics observed hotspot locations, whereby consistently located in the southwestern part of the study area. This could be attributed to the overcrowding of inmates in the Sungai Buloh prison located there. Sociodemographic factors such as gender, nationality, employment status, health care worker status, income status, residency, and smoking status as well as; environmental factors such as AQI (lag 1), CO (lag 2), NO2 (lag 2), SO2 (lag 1), PM10 (lag 5), rainfall (lag 2), relative humidity (lag 4), temperature (lag 2), wind speed (lag 4), and atmospheric pressure (lag 6) were associated with TB cases (p<0.05). The GWR model based on the environmental factors i.e. GWR2 was the best model to determine the spatial distribution of TB cases based on the highest R2 value i.e. 0.98. The maps of estimated local coefficients in GWR models confirmed that the effects of sociodemographic and environmental factors on TB cases spatially varied. This study highlighted the importance of spatial analysis to identify areas with a high TB burden based on its associated factors, which further helps in improving targeted surveillance.

Effect of ambient air pollution on tuberculosis risks and mortality in Shandong, China: a multi-city modeling study of the short- and long-term effects of pollutants

Few studies conducted in China have assessed the effects of ambient air pollution exposure on tuberculosis (TB) risk and mortality, especially with a multicity setting. We evaluated the effect of short- and long-term ambient sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), ozone (O₃), and particulate matter≤2.5 μm in aerodynamic diameter (PM_2.5) exposures on development and mortality of active TB in 7 Chinese cities in Shandong province from January 1, 2013, to December 31, 2017. We estimated the pollution-associated risk to new infection TB, recurrent TB, and mortality in relation to 1-μg/m³ increases in air pollutants using the penalized multivariate Poisson regression models. A total of 83,555 new infective TB and 3060 recurrent TB including 997 deaths were recorded. Short- and long-term exposures to outdoor air pollutants (SO₂, NO₂, CO, O₃, and PM_2.5) were significantly associated with new infection TB, recurrent TB risk, and mortality. The dominant positive effects of SO₂, NO₂, CO, and PM_2.5 for new infection and recurrent TB risk were observed at long-term (>30 days) exposure, whereas the dominant effects of SO₂, CO, and PM_2.5 for mortality were observed at short-term (≤30 days) exposures. Of the 5 air pollutants we assessed, SO₂ and PM_2.5 exhibited more consistent and strong associations with TB-related outcomes. We estimated an increase of 1.33% (95% CI 1.29%, 1.37%) and 3.04% (95% CI 2.98%, 3.11%) in new infection TB count for each 1-μg/m³ increase of SO₂ at lag 0-180 days and PM_2.5 at lag 0-365 days, respectively. This epidemiologic study in China shows that air pollution exposure is associated with increased risk of active TB development and mortality. The control of ambient air pollution may benefit the control and decrease the mortality of TB disease.

Atmospheric PM2.5 Prediction Based on Multiple Model Adaptive Unscented Kalman Filter

The PM2.5 concentration model is the key to predict PM2.5 concentration. During the prediction of atmospheric PM2.5 concentration based on prediction model, the prediction model of PM2.5 concentration cannot be usually accurately described. For the PM2.5 concentration model in the same period, the dynamic characteristics of the model will change under the influence of many factors. Similarly, for different time periods, the corresponding models of PM2.5 concentration may be different, and the single model cannot play the corresponding ability to predict PM2.5 concentration. The single model leads to the decline of prediction accuracy. To improve the accuracy of PM2.5 concentration prediction in this solution, a multiple model adaptive unscented Kalman filter (MMAUKF) method is proposed in this paper. Firstly, the PM2.5 concentration data in three time periods of the day are taken as the research object, the nonlinear state space model frame of a support vector regression (SVR) method is established. Secondly, the frame of the SVR model in three time periods is combined with an adaptive unscented Kalman filter (AUKF) to predict PM2.5 concentration in the next hour, respectively. Then, the predicted value of three time periods is fused into the final predicted PM2.5 concentration by Bayesian weighting method. Finally, the proposed method is compared with the single support vector regression-adaptive unscented Kalman filter (SVR-AUKF), autoregressive model-Kalman (AR-Kalman), autoregressive model (AR) and back propagation neural network (BP). The prediction results show that the accuracy of PM2.5 concentration prediction is improved in whole time period.

Long-term exposure to indoor air pollution and risk of tuberculosis

Indoor air pollution (IAP) is a recognized risk factor for various diseases. This paper examines the role of indoor solid fuel exposure in the risk of mycobacterium tuberculosis (TB) in Delhi Metropolitan, India. Using a cross-sectional design, subjects were screened for a history of active TB and lifelong exposure to IAP sources, such as solid fuel burning and kerosene. The TB prevalence rate in the study area was 1117 per 100 000 population. Every year, increase in solid fuel exposure was associated with a three percent higher likelihood of a history of active TB. Subjects exposed to solid fuel and kerosene use for both heating home and cooking showed significant associations with TB. Age, household expenditure (a proxy of income), lung function, and smoking also showed significant associations with TB. Smokers and solid fuel-exposed subjects were four times more likely to have a history of active TB than non-smoker and unexposed subjects. These finding calls strategies to mitigate solid fuel exposure, such as use of clean cookstove and ventilation, to mitigate the risk of TB which aligns with the United Nations' goal of "End TB by 2030."

Exceedances and trends of particulate matter (PM2.5) in five Indian megacities

Fine particulate matter (PM_2.5) is the leading environmental risk factor that requires regular monitoring and analysis for effective air quality management. This work presents the variability, trend, and exceedance analysis of PM_2.5 measured at US Embassy and Consulate in five Indian megacities (Chennai, Kolkata, Hyderabad, Mumbai, and New Delhi) for six years (2014-2019). Among all cities, Delhi is found to be the most polluted city followed by Kolkata, Mumbai, Hyderabad, and Chennai. The trend analysis for six years for five megacities suggests a statistically significant decreasing trend ranging from 1.5 to 4.19 μg/m³ (2%-8%) per year. Distinct diurnal, seasonal, and monthly variations are observed in the five cities due to the different site locations and local meteorology. All cities show the highest and lowest concentrations in the winter and monsoon months respectively except for Chennai which observed the lowest levels in April. All the cities consistently show morning peaks (~08: 00-10:00 h) and the lowest level in late afternoon hours (~15:00-16:00 h). We found that the PM_2.5 levels in the cities exceed WHO standards and Indian NAAQS for 50% and 33% of days in a year except for Chennai. Delhi is found to have more than 200 days of exceedances in a year and experiences an average 15 number of episodes per year when the level exceeds the Indian NAAQS. The trends in the exceedance with a varying threshold (20-380 μg/m³) suggest that not only is the annual mean PM_2.5 decreasing in Delhi but also the number of exceedances is decreasing. This decrease can be attributed to the recent policies and regulations implemented in Delhi and other cities for the abatement of air pollution. However, stricter compliance of the National Clean Air Program (NCAP) policies can further accelerate the reduction of the pollution levels.

Short-term effects of outdoor air pollution on acute ischaemic stroke occurrence: a case-crossover study in Tianjin, China

OBJECTIVE

Ambient air pollution is associated with ischaemic stroke incidence. However, most of the previous studies used stroke-related hospital admission rather than stroke onset itself. This study aimed to evaluate the relationship between ambient air pollutant exposures and acute ischaemic stroke based on the timing of symptom onset.

METHODS

A time-stratified, case-crossover analysis was performed among 520 patients who had ischaemic stroke admitted to the Second Hospital of Tianjin Medical University (Tianjin, China) between 1 April 2018 and 31 March 2019 (365 days). Daily air pollutant concentrations of particulate matter with aerodynamic diameter 2.5 µm, particulate matter with aerodynamic diameter 10 µm (PM10), sulfur dioxide, nitrogen dioxide, carbon monoxide and ozone were obtained from fixed-site monitoring stations. We used conditional logistic regression to estimate OR and 95% CI corresponding to an increase in IQR of each air pollutant after adjusting for the effects of temperature and relative humidity.

RESULTS

Overall, a higher risk of ischaemic stroke was found between April and September. During this period PM10 was associated with an increased risk of ischaemic stroke (1-day lag: OR=1.49, 95% CI 1.09 to 2.02; 3-day mean: OR=1.58, 95% CI 1.09 to 2.29) among patients between 34 and 70 years old. Positive associations were also observed between PM10 (1-day lag: OR=1.51, 95% CI 1.10 to 2.07; 3-day mean: OR=1.57, 95% CI 1.08 to 2.29), ozone (1-day lag: OR=1.83, 95% CI 1.16 to 2.87; 3-day mean: OR=1.90, 95% CI 1.06 to 3.42) and ischaemic stroke occurrence among those with hyperlipidaemia.

CONCLUSION

Our results suggest that air pollution is associated with a higher risk of ischaemic stroke in younger people or people with hyperlipidemia. These findings still need to be further investigated.

Particulate Matter and COVID-19 Disease Diffusion in Emilia-Romagna (Italy). Already a Cold Case?

As we prepare to emerge from an extensive and unprecedented lockdown period, due to the COVID-19 virus infection that hit the Northern regions of Italy with the Europe’s highest death toll, it becomes clear that what has gone wrong rests upon a combination of demographic, healthcare, political, business, organizational, and climatic factors that are out of our scientific scope. Nonetheless, looking at this problem from a patient’s perspective, it is indisputable that risk factors, considered as associated with the development of the virus disease, include older age, history of smoking, hypertension and heart disease. While several studies have already shown that many of these diseases can also be favored by a protracted exposure to air pollution, there has been recently an insurgence of negative commentary against authors who have correlated the fatal consequences of COVID-19 (also) to the exposition of specific air pollutants. Well aware that understanding the real connection between the spread of this fatal virus and air pollutants would require many other investigations at a level appropriate to the scale of this phenomenon (e.g., biological, chemical, and physical), we propose the results of a study, where a series of the measures of the daily values of PM2.5, PM10, and NO2 were considered over time, while the Granger causality statistical hypothesis test was used for determining the presence of a possible correlation with the series of the new daily COVID19 infections, in the period February–April 2020, in Emilia-Romagna. Results taken both before and after the governmental lockdown decisions show a clear correlation, although strictly seen from a Granger causality perspective. Moving beyond the relevance of our results towards the real extent of such a correlation, our scientific efforts aim at reinvigorating the debate on a relevant case, that should not remain unsolved or no longer investigated.

Urban-rural disparity in the relationship between ambient air pollution and preterm birth

BACKGROUND

Some studies have reported that air pollution exposure can have adverse effects on pregnancy outcomes. However, the disparity between urban and rural areas in the risk of preterm birth (PTB) has yet to be elucidated. Considering geographic contexts as homogeneous or ignoring urban-rural differences cannot accurately reveal the disparities in the health effects of air pollution under different geographic contexts. The aims of this study were to examine the disparities in the risks of PTB in three different regions and five urban-rural types and to investigate the extent to which fine particulate matter (PM2.5) exposure during the entire pregnancy can explain the variations.

METHODS

We collected data on 429,865 singleton newborns born in 2014 in Hubei Province, China, and divided Hubei Province into three regions. Spatial correlation methods were employed to measure the associations between the rate of PTB and air pollution using average annual indexes for the entire province and regions. A series of multilevel logistic models were conducted to examine disparities in the risks of PTB with decreases in urbanity and the effects of air pollution exposure on the occurrence of preterm births.

RESULTS

The PM2.5 concentration was significantly different across the regions. The eastern region had the most wide-ranged and serious level of pollution, whereas the levels in the middle and western regions weakened. The odds of PTB and air pollution exhibited a positive spatial correlation for the entire province and in the east (BiMoran's I = 0.106 and 0.697, respectively). Significant urban-rural disparities in the risks of PTB were noted in the east and middle regions, and the mean PM2.5 exposure during the entire pregnancy was positively associated with PTB risk. However, in the west, the results showed weak differences in the risks of PTB among the five urban-rural types and an insignificant effect of PM2.5 exposure. The direction of the effect of district/county-level income on PTB varied by region.

CONCLUSIONS

This study finds that air pollution exposure and PTB have significant and positive spatial relationships in areas with a serious air pollution burden. The risks of PTB in three regions of Hubei Province follow the same W-shaped pattern as urbanity decreases and rurality increases. High levels of air pollution exposure may be an important disadvantage for urban pregnant women in this setting.

Effect of ambient air pollutants and meteorological variables on COVID-19 incidence

Objective:

To determine whether ambient air pollutants and meteorological variables are associated with daily COVID-19 incidence.

Design:

A retrospective cohort from January 25 to February 29, 2020.

Setting:

Cities of Wuhan, Xiaogan, and Huanggang, China.

Patients:

The COVID-19 cases detected each day.

Methods:

We collected daily data of COVID-19 incidence, 8 ambient air pollutants (particulate matter of ≤2.5 µm [PM_2.5], particulate matter ≤10 µm [PM₁₀], sulfur dioxide [SO₂], carbon monoxide [CO], nitrogen dioxide [NO₂], and maximum 8-h moving average concentrations for ozone [O₃-8h]) and 3 meteorological variables (temperature, relative humidity, and wind) in China’s 3 worst COVID-19–stricken cities during the study period. The multivariate Poisson regression was performed to understand their correlation.

Results:

Daily COVID-19 incidence was positively associated with PM_2.5 and humidity in all cities. Specifically, the relative risk (RR) of PM_2.5 for daily COVID-19 incidences were 1.036 (95% confidence interval [CI], 1.032–1.039) in Wuhan, 1.059 (95% CI, 1.046–1.072) in Xiaogan, and 1.144 (95% CI, 1.12–1.169) in Huanggang. The RR of humidity for daily COVID-19 incidence was consistently lower than that of PM_2.5, and this difference ranged from 0.027 to 0.111. Moreover, PM₁₀ and temperature also exhibited a notable correlation with daily COVID-19 incidence, but in a negative pattern The RR of PM₁₀ for daily COVID-19 incidence ranged from 0.915 (95% CI, 0.896–0.934) to 0.961 (95% CI, 0.95–0.972, while that of temperature ranged from 0.738 (95% CI, 0.717–0.759) to 0.969 (95% CI, 0.966–0.973).

Conclusions:

Our data show that PM_2.5 and humidity are substantially associated with an increased risk of COVID-19 and that PM₁₀ and temperature are substantially associated with a decreased risk of COVID-19.

Association between short-term exposure to ambient air pollutants and the risk of tuberculosis outpatient visits: A time-series study in Hefei, China

BACKGROUND

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.

METHODS

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.

RESULTS

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 (NO₂) exposure were positively correlated with the risk of TB outpatient visits, while ozone (O₃) and sulfur dioxide (SO₂) 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 NO₂; 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 SO₂ in the single-pollutant model. There was only a negative association between O₃ 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 SO₂ and O₃ exposure were different between warm and cold seasons. The effect of NO₂ exposure remained statistically significant in male, younger, and cold season subgroups. Besides, elderly people are more susceptible to PM_2.5 exposure.

CONCLUSION

This study suggests that exposure to PM_2.5, NO₂, SO₂, and O₃ 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.

A study on the relationship between air pollution and pulmonary tuberculosis based on the general additive model in Wulumuqi, China

OBJECTIVE

This study aimed to explore the impact of atmospheric pollutants on the incidence of tuberculosis (TB), and provide new ideas for the prevention and control of TB in the future.

METHODS

It explored the relationship between air pollutants and meteorological factors, as well as between air pollutants and heating through Spearman correlation analysis and rank sum test. Additionally, it analyzed the relationship between air pollutants and TB incidence using the general additive model. Statistical analysis results at the p<0.05 level were considered significant.

RESULTS

Three months after exposure to air pollutants (PM_2.5, SO₂, NO₂, and CO) TB incidence increased. However, TB incidence increased 9 months after exposure to PM₁₀. The single pollutant model showed when concentrations of PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃ increased by 1μg/m³ (or 1mg/m³), the number of TB cases increased by 0.09%, 0.08%, 0.58%, 0.42%, 6.9%, and 0.57%, respectively. The optimal multi-pollutant model was a two-factor model (PM₁₀+NO₂).

CONCLUSION

Air pollutants including PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃ increased the risk of TB. Few studies have been conducted in this area of research, especially regarding the mechanism. The results of this study should contribute to the understanding of TB incidence and prompt additional research.

Effects of particulate air pollution on tuberculosis development in seven major cities of Korea from 2010 to 2016: methodological considerations involving long-term exposure and time lag

OBJECTIVES

Epidemiological evidence of associations between ambient particulate matter (PM) and tuberculosis (TB) risk is accumulating. Two previous studies in Korea found associations between air pollution—especially sulfur dioxide (SO₂)—and TB. In this study, we conducted an annual time-series cross-sectional study to assess the effect of PM with an aerodynamic diameter less than 10 μm (PM₁₀) on TB risk in seven major cities of Korea from 2010 to 2016, taking into account time lag and long-term cumulative exposure.

METHODS

Age-standardized TB notification rates were derived using the Korea National TB Surveillance System. Annual average PM₁₀ concentrations were obtained from annual Korean air quality reports. We applied a generalized linear mixed model with unconstrained distributed lags of exposure to PM₁₀. We adjusted for potential confounders such as age, health behaviors, and area-level characteristics.

RESULTS

Both average annual PM₁₀ concentrations and age-standardized TB notification rates decreased over time. The association between cumulative exposure to PM₁₀ and TB incidence became stronger as a longer exposure duration was considered. An increase of one standard deviation (5.63 μg/m³) in PM₁₀ exposure for six years was associated with a 1.20 (95% confidence interval, 1.17 to 1.22) times higher TB notification rate. The marginal association of exposure duration with the TB notification rate was highest at four and five years prior to TB notification. This association remained consistent even after adjusting it for exposure to SO₂.

CONCLUSIONS

The findings of this study suggest that cumulative exposure to PM₁₀ may affect TB risk, with a potential lag effect.

Does sunlight drive seasonality of TB in Vietnam? A retrospective environmental ecological study of tuberculosis seasonality in Vietnam from 2010 to 2015

BACKGROUND

Tuberculosis (TB) is a major global health burden, with an estimated quarter of the world's population being infected. The World Health Organization (WHO) launched the "End TB Strategy" in 2014 emphasising knowing the epidemic. WHO ranks Vietnam 12th in the world of high burden countries. TB spatial and temporal patterns have been observed globally with evidence of Vitamin D playing a role in seasonality. We explored the presence of temporal and spatial clustering of TB in Vietnam and their determinants to aid public health measures.

METHODS

Data were collected by the National TB program of Vietnam from 2010 to 2015 and linked to the following datasets: socio-demographic characteristics; climatic variables; influenza-like-illness (ILI) incidence; geospatial data. The TB dataset was aggregated by province and quarter. Descriptive time series analyses using LOESS regression were completed per province to determine seasonality and trend. Harmonic regression was used to determine the amplitude of seasonality by province. A mixed-effect linear model was used with province and year as random effects and all other variables as fixed effects.

RESULTS

There were 610,676 cases of TB notified between 2010 and 2015 in Vietnam. Heat maps of TB incidence per quarter per province showed substantial temporal and geospatial variation. Time series analysis demonstrated seasonality throughout the country, with peaks in spring/summer and troughs in autumn/winter. Incidence was consistently higher in the south, the three provinces with the highest incidence per 100,000 population were Tay Ninh, An Giang and Ho Chi Minh City. However, relative seasonal amplitude was more pronounced in the north. Mixed-effect linear model confirmed that TB incidence was associated with time and latitude. Of the demographic, socio-economic and health related variables, population density, percentage of those under 15 years of age, and HIV infection prevalence per province were associated with TB incidence. Of the climate variables, absolute humidity, average temperature and sunlight were associated with TB incidence.

CONCLUSION

Preventative public health measures should be focused in the south of Viet Nam where incidence is highest. Vitamin D is unlikely to be a strong driver of seasonality but supplementation may play a role in a package of interventions.

Short-term Effect of Air Pollution on Tuberculosis Based on Kriged Data: A Time-series Analysis

Tuberculosis (TB) has a very high mortality rate worldwide. However, only a few studies have examined the associations between short-term exposure to air pollution and TB incidence. Our objectives were to estimate associations between short-term exposure to air pollutants and TB incidence in Wuhan city, China, during the 2015–2016 period. We applied a generalized additive model to access the short-term association of air pollution with TB. Daily exposure to each air pollutant in Wuhan was determined using ordinary kriging. The air pollutants included in the analysis were particulate matter (PM) with an aerodynamic diameter less than or equal to 2.5 micrometers (PM_2.5), PM with an aerodynamic diameter less than or equal to 10 micrometers (PM₁₀), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), and ground-level ozone (O₃). Daily incident cases of TB were obtained from the Hubei Provincial Center for Disease Control and Prevention (Hubei CDC). Both single- and multiple-pollutant models were used to examine the associations between air pollution and TB. Seasonal variation was assessed by splitting the all-year data into warm (May–October) and cold (November–April) seasons. In the single-pollutant model, for a 10 μg/m³ increase in PM_2.5, PM₁₀, and O₃ at lag 7, the associated TB risk increased by 17.03% (95% CI: 6.39, 28.74), 11.08% (95% CI: 6.39, 28.74), and 16.15% (95% CI: 1.88, 32.42), respectively. In the multi-pollutant model, the effect of PM_2.5 on TB remained statistically significant, while the effects of other pollutants were attenuated. The seasonal analysis showed that there was not much difference regarding the impact of air pollution on TB between the warm season and the cold season. Our study reveals that the mechanism linking air pollution and TB is still complex. Further research is warranted to explore the interaction of air pollution and TB.

Impact of Ambient Temperature and Relative Humidity on the Incidence of Hand-Foot-Mouth Disease in Wuhan, China

Background: Few studies have previously explored the relationship between hand, foot, and mouth disease (HFMD) and meteorological factors with the effect modification of air pollution, and these studies had inconsistent findings. We therefore applied a time-series analysis assessing the effects of temperature and humidity on the incidence of HFMD in Wuhan, China to deepen our understanding of the relationship between meteorological factors and the risk of HFMD. Methods: Daily HFMD cases were retrieved from Hubei Provincial Center for Disease Control and Prevention from 1 February 2013 to 31 January 2017. Daily meteorological data including 24 h average temperature, relative humidity, wind velocity, and atmospheric pressure were obtained from Hubei Meteorological Bureau. Data on Air pollution was collected from 10 national air-monitoring stations in Wuhan city. We adopted a distributed lag non-linear model (DLNM) combined with Poisson regression and time-series analysis to estimate the effects of temperature and relative humidity on the incidence HFMD. Results: We found that the association between temperature and HFMD incidence was non-linear, exhibiting an approximate "M" shape with two peaks occurring at 2.3 °C (RR = 1.760, 95% CI: 1.218-2.542) and 27.9 °C (RR = 1.945, 95% CI: 1.570-2.408), respectively. We observed an inverted "V" shape between relative humidity and HFMD. The risk of HFMD reached a maximum value at a relative humidity of 89.2% (RR = 1.553, 95% CI: 1.322-1.824). The largest delayed cumulative effects occurred at lag 6 for temperature and lag 13 for relative humidity. Conclusions: The non-linear relationship between meteorological factors and the incidence of HFMD on different lag days could be used in the early targeted warning system of infectious diseases, reducing the possible outbreaks and burdens of HFMD among sensitive populations.

Association Between Ambient Air Pollution and Elevated Risk of Tuberculosis Development

Background

Broad-scale evidence has shown the significant association between ambient air pollutants and the development of tuberculosis (TB). However, the impact of air quality on the risk of TB in Taiwan is still poorly understood.

Objective

To develop a probabilistic integrated population-level risk assessment approach for evaluating the contribution of ambient air pollution exposure to the risk of TB development among different regions of Taiwan.

Materials and methods

A Bayesian-based probabilistic risk assessment model was implemented to link exposure concentrations of various air pollutants quantified in a probabilistic manner with the population-based exposure-response models developed by using an epidemiological investigation.

Results

The increment of the risk of TB occurred in a region with a higher level of air pollution, indicating a strong relationship between ambient air pollution exposures and TB incidences. Carbon monoxide (CO) exposure showed the highest population attributable fraction (PAF), followed by nitrogen oxides (NO_X) and nitrogen dioxide (NO₂) exposures. In a region with higher ambient air pollution, it is most likely (80% risk probability) that the contributions of CO exposure to development of TB were 1.6–12.2% (range of median PAFs), whereas NO_X and NO₂ exposures contributed 1.2–9.8% to developing TB.

Conclusion

Our findings provide strong empirical support for the hypothesis and observations from the literature that poor air quality is highly likely to link aetiologically to the risk of TB. Therefore, substantial reductions in CO, NO_X, and NO₂ exposures are predicted to have health benefits to susceptible and latently infected individuals that provide complementary mitigation efforts in reducing the burden of TB. Considering that people continue to be exposed to both TB bacilli and ambient air pollutants, our approach can be applied for different countries/regions to identify which air pollutants contribute to a higher risk of TB in order to develop potential mitigation programs.

The association between short-term exposure to ambient air pollution and the incidence of mumps in Wuhan, China: A time-series study

BACKGROUND

Previous studies have estimated the association between meteorological factors and mumps outbreaks without assessing the influence of air pollution. In this research, we explored the effects of short-term exposure to air pollution on the incidence of mumps.

METHODS

Our time-series analysis was conducted using data collected in Wuhan, China from 2015 to 2017. Daily number of mumps cases was obtained from Disease Reporting System in Hubei Provincial Center for Disease Control and Prevention. Data on air pollution was obtained from 10 national air quality monitoring stations, including nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ground-level ozone (O₃), particulate matter less than or equal to 10 μm in aerodynamic diameter (PM₁₀), and particulate matter less than or equal to 2.5 μm in aerodynamic diameter (PM_2.5). Daily meteorological data including temperature and relative humidity were obtained from Hubei Meteorological Bureau. We performed a Poisson regression in generalized additive models (GAM) to explore the association between the incidence of mumps and exposure to air pollution.

RESULTS

We observed that the effects of air pollutants were statistically significant mainly in two periods, lag 0 to lag 5 and lag 20 to lag 25, with the strongest effects appearing at lag 2 and lag 23. The cumulative effects were stronger than single-day lag effects. The stratified analysis showed the effect of pollutants during the hot season was stronger than that during the cold season, especially for NO₂ and SO₂.

CONCLUSIONS

We found that exposure to NO₂ and SO₂ was significantly associated with higher risk of developing mumps. Our findings could help deepen the understanding of how air pollution exposure affects the incidence of mumps.

Temporal trends analysis of tuberculosis morbidity in mainland China from 1997 to 2025 using a new SARIMA-NARNNX hybrid model

OBJECTIVE

Tuberculosis (TB) remains a major deadly threat in mainland China. Early warning and advanced response systems play a central role in addressing such a wide-ranging threat. The purpose of this study is to establish a new hybrid model combining a seasonal autoregressive integrated moving average (SARIMA) model and a non-linear autoregressive neural network with exogenous input (NARNNX) model to understand the future epidemiological patterns of TB morbidity.

METHODS

We develop a SARIMA-NARNNX hybrid model for forecasting future levels of TB incidence based on data containing 255 observations from January 1997 to March 2018 in mainland China, and the ultimate simulating and forecasting performances were compared with the basic SARIMA, non-linear autoregressive neural network (NARNN) and error-trend-seasonal (ETS) approaches, as well as the SARIMA-generalised regression neural network (GRNN) and SARIMA-NARNN hybrid techniques.

RESULTS

In terms of the root mean square error, mean absolute error, mean error rate and mean absolute percentage error, the identified best-fitting SARIMA-NARNNX combined model with 17 hidden neurons and 4 feedback delays had smaller values in both in-sample simulating scheme and the out-of-sample forecasting scheme than the preferred single SARIMA(2,1,3)(0,1,1)₁₂ model, a NARNN with 19 hidden neurons and 6 feedback delays and ETS(M,A,A), and the best-performing SARIMA-GRNN and SARIMA-NARNN models with 32 hidden neurons and 6 feedback delays. Every year, there was an obvious high-risk season for the notified TB cases in March and April. Importantly, the epidemic levels of TB from 2006 to 2017 trended slightly downward. According to the projection results from 2018 to 2025, TB incidence will continue to drop by 3.002% annually but will remain high.

CONCLUSIONS

The new SARIMA-NARNNX combined model visibly outperforms the other methods. This hybrid model should be used for forecasting the long-term epidemic patterns of TB, and it may serve as a beneficial and effective tool for controlling this disease.

Long-term effect of exposure to ambient air pollution on the risk of active tuberculosis

OBJECTIVES

To explore the long-term effect of exposure to ambient air pollution on the risk of active tuberculosis (TB).

METHODS

We constructed a distributed-lag nonlinear model (DLNM) to evaluate the relatively long-term influence of outdoor PM_2.5, PM₁₀, SO₂ and NO₂ exposure on active TB risk in the city of Lianyungang in Jiangsu Province, China.

RESULTS

There were 7,282 TB cases reported in the study area during 2014-2017, with annual median (interquartile range) concentrations of PM_2.5, PM₁₀, SO₂ and NO₂ at 45.86 (34.57-64.14) μg/m³, 85.43 (62.86-116.14) μg/m³, 22.00 (15.71-30.86) μg/m³ and 30.00 (23.29-38.57) μg/m³, respectively. The single-pollutant model showed that for each 10 μg/m³ increase in concentration, the cumulative relative risk of TB was 1.12 (lag 0-24 weeks, 95% CI: 1.03-1.22) for PM_2.5 with reference to 35 μg/m³; 1.11 (lag 0-21 weeks, 95% CI: 1.06-1.17) for PM₁₀ with reference to 70 μg/m³; 1.37 (lag 0-20 weeks, 95% CI: 1.16-1.62) for SO₂ with reference to 60 μg/m³; and 1.29 (lag 0-22 weeks, 95% CI: 1.11-1.49) for NO₂ with reference to 40 μg/m³. In the multipollutant model considering both PM₁₀ and NO₂, the association remained significant.

CONCLUSIONS

Our results revealed a potential association between outdoor exposure to PM_2.5, PM₁₀, SO₂, and NO₂ and active TB. Considering that people from developing countries continue to be exposed to both severe outdoor air pollution and high rates of latent TB infection, the association between worsening air pollution and active TB deserves further attention.

Association of air pollution with the risk of initial outpatient visits for tuberculosis in Wuhan, China

Objectives

Previous studies suggested the association of air pollution with initial Mycobacterium tuberculosis infection and the disease development. However, few studies have been conducted on air pollution and initial tuberculosis (TB) consults using short-interval data. We investigated the weekly association between air pollution and initial TB outpatient visits.

Methods

We used a Poisson regression model combined with a distributed lag non-linear model to conduct a time-series study with weekly air pollution data and TB cases during 2014–2017 in Wuhan, China.

Results

A 10 µg/m3 increase in NO2 (nitrogen dioxide) was associated with 11.74% (95% CI: 0.70 to 23.98, lag 0–1 weeks), 21.45% (95% CI: 1.44 to 45.41, lag 0–2 weeks) and 12.8% (95% CI: 0.97 to 26.02, lag 0–1 weeks) increase in initial TB consults among all patients with TB, old patients (≥60 years old) and male ones, respectively. A 10 µg/m3 increase in SO2 (sulfur dioxide) was associated with −22.23% (95% CI: −39.23 to −0.49, lag 0–16 weeks), −28.65% (95% CI: −44.3 to −8.58, lag 0–16 weeks), −23.85 (95% CI: −41.79 to −0.37, lag 0–8 weeks) and −23.82% (95% CI: −41.31 to −1.11, lag 0–16 weeks) increase in initial TB consults among the total, young (aged 15–59 years old), old and male patients, respectively. In old patients, a 0.1 mg/m3 increase in CO (carbon monoxide) and a 10 µg/m3 increase in PM2.5 (particulate matter) were separately associated with 42.32% (95% CI: 1.16 to 100.22, lag 0–16 weeks) and 17.38% (95% CI: 0.28 to 37.38, lag 0–16 weeks) increases in TB consults.

Conclusion

Our study first highlighted the importance of weekly association between air pollution and the risk of initial TB consults, which is helpful for the arrangements of TB screening and medical assistance.

Evidence for heterogeneity in China's progress against pulmonary tuberculosis: uneven reductions in a major center of ongoing transmission, 2005-2017

Background

China contributed 8.9% of all incident cases of tuberculosis globally in 2017, and understanding the spatiotemporal distribution of pulmonary tuberculosis (PTB) in major transmission foci in the country is critical to ongoing efforts to improve population health.

Methods

We estimated annual PTB notification rates and their spatiotemporal distributions in Sichuan province, a major center of ongoing transmission, from 2005 to 2017. Time series decomposition was used to obtain trend components from the monthly incidence rate time series. Spatiotemporal cluster analyses were conducted to detect spatiotemporal clusters of PTB at the county level.

Results

From 2005 to 2017, 976,873 cases of active PTB and 388,739 cases of smear-positive PTB were reported in Sichuan Province, China. During this period, the overall reported incidence rate of active PTB decreased steadily at a rate of decrease (3.77 cases per 100,000 per year, 95% confidence interval (CI): 3.28–4.31) that was slightly faster than the national average rate of decrease (3.14 cases per 100,000 per year, 95% CI: 2.61–3.67). Although reported PTB incidence decreased significantly in most regions of the province, incidence was observed to be increasing in some counties with high HIV incidence and ethnic minority populations. Active and smear-positive PTB case reports exhibited seasonality, peaking in March and April, with apparent links to social dynamics and climatological factors.

Conclusions

While PTB incidence rates decreased strikingly in the study area over the past decade, improvements have not been equally distributed. Additional surveillance and control efforts should be guided by the seasonal-trend and spatiotemporal cluster analyses presented here, focusing on areas with increasing incidence rates, and updated to reflect the latest information from real-time reporting.

Electronic supplementary material

The online version of this article (10.1186/s12879-019-4262-2) contains supplementary material, which is available to authorized users.