Long-term exposure to ambient fine particulate matter (PM_2.5) and attributable pulmonary tuberculosis notifications in Ningxia Hui Autonomous Region, China: a health impact assessment.

Abstract

Long-term exposure to fine particulate matter (≤2.5 µm (PM_2.5)) has been associated with pulmonary tuberculosis (TB) notifications or incidence in recent publications. Studies quantifying the relative contribution of long-term PM_2.5 on TB notifications have not been documented. We sought to perform a health impact assessment to estimate the PM_2.5- attributable TB notifications during 2007-2017 in Ningxia Hui Autonomous Region (NHAR), China. PM_2.5 attributable TB notifications were estimated at township level (n=358), stratified by age group and summed across NHAR. PM_2.5-associated TB-notifications were estimated for total and anthropogenic PM_2.5 mass and expressed as population attributable fractions (PAFs). The main analysis used effect and uncertainty estimates from our previous study in NHAR, defining a counterfactual of the lowest annual PM_2.5 (30 µg/m³) level, above which we assumed excess TB notifications. Sensitivity analyses included counterfactuals based on the 5th (31 µg/m³) and 25th percentiles (38 µg/m³), and substituting effect estimates from a recent meta-analysis. We estimated the influence of PM_2.5 concentrations, population growth and baseline TB-notification rates on PM_2.5 attributable TB notifications. Over 2007-2017, annual PM_2.5 had an estimated average PAF of 31.2% (95% CI 22.4% to 38.7%) of TB notifications while the anthropogenic PAF was 12.2% (95% CI 9.2% to 14.5%). With 31 and 38 µg/m³ as counterfactuals, the PAFs were 29.2% (95% CI 20.9% to 36.3%) and 15.4% (95% CI 10.9% to 19.6%), respectively. PAF estimates under other assumptions ranged between 6.5% (95% CI 2.9% to 9.6%) and 13.7% (95% CI 6.2% to 19.9%) for total PM_2.5, and 2.6% (95% CI 1.2% to 3.8%) to 5.8% (95% CI 2.7% to 8.2%) for anthropogenic PM_2.5. Relative to 2007, overall changes in PM_2.5 attributable TB notifications were due to reduced TB-notification rates (-23.8%), followed by decreasing PM2.5 (-6.2%), and population growth (+4.9%). We have demonstrated how the potential impact of historical or hypothetical air pollution reduction scenarios on TB notifications can be estimated, using public domain, PM_2.5 and population data. The method may be transferrable to other settings where comparable TB-notification data are available.