Exposure to air pollution and scarlet fever resurgence in China: a six-year surveillance study

Scarlet fever has resurged in China starting in 2011, and the environment is one of the potential reasons. Nationwide data on 655,039 scarlet fever cases and six air pollutants were retrieved. Exposure risks were evaluated by multivariate distributed lag nonlinear models and a meta-regression model. We show that the average incidence in 2011–2018 was twice that in 2004–2010 [RR = 2.30 (4.40 vs. 1.91), 95% CI: 2.29–2.31; p < 0.001] and generally lower in the summer and winter holiday (p = 0.005). A low to moderate correlation was seen between scarlet fever and monthly NO2 (r = 0.21) and O3 (r = 0.11). A 10 μg/m3 increase of NO2 and O3 was significantly associated with scarlet fever, with a cumulative RR of 1.06 (95% CI: 1.02–1.10) and 1.04 (95% CI: 1.01–1.07), respectively, at a lag of 0 to 15 months. In conclusion, long-term exposure to ambient NO2 and O3 may be associated with an increased risk of scarlet fever incidence, but direct causality is not established.

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Notes: a. Annual incidence of scarlet fever per 100,000 people in the 31 Chinese provinces investigated. The 13 rings contain data for each year studied, with the innermost ring bearing data for 2004, and moving outwards through the years to the outermost ring bearing data for 2018. b. Choropleth maps of the average annual incidence of scarlet fever, by region, based on the annual incidence per 100,000 people in China in 2004-10, before the upsurge in the incidence of infections. c. Choropleth maps of the average annual incidence of scarlet fever, by region, based on the annual incidence per 100,000 people in China in 2011-18, after the upsurge in the incidence of infections. Description: Scarlet fever was predominantly distributed in the north, northeast, and northwest of China. All parts of China had marked increases in the incidence of scarlet fever during 2011-2018. The provinces with a latitude higher than 33.4 degrees north had a higher annual incidence than those at lower latitudes.

Supplementary Figure 4. Time series plot of the monthly mean air pollution concentrations at high and low degrees of latitude of China, 2013-2018
Notes: PM2.5=particulate matter with aerodynamic diameter less than 2.5μm. PM10=particulate matter with aerodynamic diameter less than 10μm. Notes: The predictive exposure-response relationships were modeled by two-stage methods including distributed lag non-linear model (DLNM) and multivariate meta-analysis. The reference concentrations were set at the 15th percentile of the air pollutants: 23.22 μg/m3 for NO2, 30.49 μg/m3 for PM2.5 and 58.58 μg/m3 for PM10. The prediction values were set at the 25th and 75th percentile of population density and average incidence rate. Eight provinces were detected as hotspots and classified as high-incidence areas and the remaining provinces were classified as low-incidence areas. Those eight high-incidence provinces included Inner Mongolia, Jilin, Liaoning, Beijing, Tianjin, Hebei, Heilongjiang and Shandong. Reference is set at the 15th percentile of concentration.
Supplementary Figure 10. Predicted exposure-response relationships in relative risk between monthly wind speed, precipitation, sunlight (percentiles) and scarlet fever incidence before and after 2011 a. Precipitation b. Wind Speed c. Sunlight Notes: a. Excludes three provinces including Tianjin and Beijing because of extreme outliers and Hainan because of many zero cases in months b. Excludes Hainan because of many zero cases in months c. Excludes Hainan because of many zero cases in months Reference is set at the 15th percentile.  Notes: In the stratified analyses, the mean concentration of five air pollutants in high-latitude regions was much higher than in low-latitude regions (all p<0.05). a The single poulltant models consider not only the interested air pollutants but also consider the temporal trend, quantile groups for average incidences and incidence in the previous month.

Supplementary
b The mutple variables models consider not only the interested air pollutants but also consider other air pollutants (NO2 and O3), meteorological conditions (sunlight, wind speed, relative humidity, precipitation and mean temperature), the other adjusted variables in the model are temporal trend, the indicator variable of summer and winter breaks, quantile groups for average incidences, and incidence in the previous month.