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

Abstract

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, PM2.5, and CO and TB cases notified were identified. The concentrations of PM10, PM2.5, and CO corresponding to the minimum numbers of TB cases notified (the minimum TB notification concentrations, MTNCs) were 58.3 μg/m 3 , 41.7 μg/m 3 , and 0.1 mg/m 3 , 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 PM2.5 and for the 97.5th percentiles of CO, respectively. The TB notification rate attributed to concentration ranges above the 97.5th percentile of PM10, PM2.5, and CO were 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.

Association of air pollutants and hospital admissions for respiratory diseases in Lanzhou, China, 2014–2019

“…These models can only observe the average risk estimates of RD morbidity related to exposure to air pollution during a single-exposure time window. In recent years, a distributed lag nonlinear model (DLNM), which attracted much attention from scholars, has revealed an exposure–response relationship, as well as a lag-response relationship from the lag dimension, which indicated the duration of lag effects (Sofwan et al, 2021 ; Xie et al, 2019 ; Xu et al, 2021 ). To avoid the limitations of Poisson regression model, we adopted an advanced method, a distributed lag nonlinear model (DLNM) which can represent both nonlinear exposure–response dependencies and delay effects.…”

A systematic review and meta-analysis of the association between air pollutants and the incidence of tuberculosis

“…82 were left after removing duplicates, and then 27 were left after filtering by title. Finally, we accepted 9 articles as the included articles for our research after filtering for the full text and checking references [ 9 , 15 , [17] , [18] , [19] , [20] , [21] , [22] , [23] ].…”

Air Pollutant Sulfur Dioxide is a Major Risk Factor for Pulmonary Tuberculosis Hospitalizations of HIV-infected Patients: Distribution Lag Non-linear Analyses

“…In cross-basis functions, "ns" and "poly" functions were applied to t the exposure-response and lagresponse relationship (28). Given that the incubation period of tuberculosis is typically no more than two years (median, 15.38 months) (29)(30)(31), the maximum lag was set to 18 months via exploratory analysis (31). To evaluate the effect of air pollutant exposure on PTB cases, the median concentration of each pollutant was set as a reference (32), and the relative risk (RR) and cumulative RR for a 10-unit increase in the concentration of each air pollutant were calculated.…”

“…Since SO 2 and PM 2.5 respectively represented a risk factor and protective factor of PTB cases in multipollutant GLM, we further evaluated their relationships using DLNM, which have been recently applied for studying environmental impact on health outcomes (17,27,(31)(32)(33)(34). In single-pollutant DLNM, a comprehensive summary of the association between SO 2 and PTB cases over an 18-month period is shown in Fig.…”