BACKGROUND/AIM : In sub-Sahara Africa, few studies have investigated the short-term association between hospital admissions and ambient air pollution. Therefore, this study explored the association between multiple air pollutants and hospital admissions in Cape Town, South Africa. METHODS : Generalized additive quasi-Poisson models were used within a distributed lag linear modelling framework to estimate the cumulative effects of PM10, NO2 , and SO2 up to a lag of 21 days. We further conducted multi-pollutant models and stratified our analysis by age group, sex, and season. RESULTS : The overall relative risk (95% confidence interval (CI)) for PM10, NO2 , and SO2 at lag 0–1 for hospital admissions due to respiratory disease (RD) were 1.9% (0.5–3.2%), 2.3% (0.6–4%), and 1.1% (−0.2–2.4%), respectively. For cardiovascular disease (CVD), these values were 2.1% (0.6–3.5%), 1% (−0.8–2.8%), and −0.3% (−1.6–1.1%), respectively, per inter-quartile range increase of 12 µg/m3 for PM10, 7.3 µg/m3 for NO2 , and 3.6 µg/m3 for SO2 . The overall cumulative risks for RD per IQR increase in PM10 and NO2 for children were 2% (0.2–3.9%) and 3.1% (0.7–5.6%), respectively. CONCLUSION : We found robust associations of daily respiratory disease hospital admissions with daily PM10 and NO2 concentrations. Associations were strongest among children and warm season for RD.DATA AVAILABILITY STATEMENT : Exposure data are available for download on the South African Air
Quality Information System (SAAQIS) https://saaqis.environment.gov.za/; (accessed on 22 April
2019) however, restrictions apply to the health outcome data.SUPPLEMENTARY MATERIAL : This document describes the air pollution data by station for each year and outlines the imputation analysis. In addition, it tabulates the estimates for age groups, sex, and season per interquartile range and 10 µg/m3.https://www.mdpi.com/journal/ijerphSchool of Health Systems and Public Health (SHSPH
