Abstract
INTRODUCTION: 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. METHODS: 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/m(3)) level, above which we assumed excess TB notifications. Sensitivity analyses included counterfactuals based on the 5th (31 µg/m(3)) and 25th percentiles (38 µg/m(3)), 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. RESULTS: 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/m(3) 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%). CONCLUSION: 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.