Associations between ambient air pollution and daily incidence of pediatric hand, foot and mouth disease in Ningbo, 2014–2016: a distributed lag nonlinear model

Abstract

Hand, foot and mouth disease (HFMD) has high prevalence around the world, with serious consequences for children. Due to the long survival period of HFMD virus in ambient air, air pollutants may play a critical role in HFMD epidemics. We collected data on daily cases of HFMD among children aged 0–14 years in Ningbo City between 2014 and 2016. Distributed lag nonlinear models were used to assess the effects of particulate matter (PM2.5), sulphur dioxide (SO2), nitrogen dioxide (NO2) and ozone (O3) on the daily incidence of HFMD among children, with analyses stratified by gender and age. Compared with moderate levels of air pollution, high SO2 levels had a relative risk (RR) of 2.32 (95% CI 1.42–3.79) and high NO2 levels had a RR of 2.01 (95% CI 1.22–3.31). The RR of O3 was 2.12 (95% CI 1.47–3.05) and that of PM2.5 was 0.77 (95% CI 0.64–0.92) at moderate levels of air pollution. Specifically, high levels of SO2 and NO2 had RRs of 2.39 (95% CI 1.44–3.96) and 2.02 (95% CI 1.21–3.39), respectively, among 0–4-year-old children, while high O3 had an RR of 2.31 (95% CI 1.09–4.89) among 5–14-year-old children. Our findings suggest significant associations of high SO2 and NO2 levels and moderate O3 levels in HFMD epidemics, and also indicate that air pollution causes lagged effects on HFMD epidemics. Our study provides practical and useful data for targeted prevention and control of HMFD based on environmental evidence.

Daily Mean Temperature and HFMD: Risk Assessment and Attributable Fraction Identification in Ningbo China

“…Previous studies have shed light on the effect of meteorological factors such as temperature, relative humidity and altitude on HFMD [4][5][6][7][8][9]. However, many studies have shown that the association between the incidence of HFMD and seasons or temperatures varied with different regions.…”

Time Series Analysis and Forecasting of the Hand-Foot-Mouth Disease Morbidity in China Using An Advanced Exponential Smoothing State Space TBATS Model

“…22 For the double peak pattern of HFMD, current evidence indicated that it was predominantly seen in southern China, 12 in good agreement with the data reported in Hong Kong and Taiwan of China, 47,51 and Vietnam, 11 whereas inconsistent with the data reported in most regions of northern China, 12 where a single peak was predominately seen, this concurs well with the finding from Japan and Malaysia. 12,49 The peak activity of HFMD varied by geography, which may be ascribed to the different pathogens, 12,49 meteorological factors (such as temperature, atmospheric pressure, precipitation, sunshine hours, humidity, and wind), 12,15 and socio-economic differences. 12,22 Prior published work discovered that although the leading pathogens of EV71 and CVA16 arise per year in China (which is different from that in Japan and Malaysia where the leading pathogens arise every 3 or 4 years 12,49 ), they are circulated in different peaks.…”

“…14 However, it is documented that this illness still has a significant influence on the general susceptible population, with more than two million children developed HFMD per year in China, and more crucially, the HFMD incidence appears to exhibit an increasing tendency in recent years, partly attributable to the emergence of new Coxsackievirus, climate change, and environmental degradation. 14,15 Therefore, to attenuate or contain the spreading of this illness, early warning for the temporal patterns of HFMD epidemic trends in the upcoming years using a suitable forecasting model plays an important role in developing effective preventive measures. 16 In the past, different mathematical simulation models, including autoregressive integrated moving average (ARIMA) method, 13 artificial neural networks (ANN S ), exponential smoothing (ES) method, 17 support vector machine (SVM), 18 decomposition methods, 18 and grey model 19 have been applied to forecast the epidemics of communicable diseases.…”

“…16 In the past, different mathematical simulation models, including autoregressive integrated moving average (ARIMA) method, 13 artificial neural networks (ANN S ), exponential smoothing (ES) method, 17 support vector machine (SVM), 18 decomposition methods, 18 and grey model 19 have been applied to forecast the epidemics of communicable diseases. The occurrence of communicable diseases is typically limited by different influencing factors (eg, meteorological variables, government interventions, vaccines, and air quality 15,20 ), leading to showing complex epidemiological features with multiple seasonal periods, high-frequency seasonality, non-integer seasonality, and dual-calendar effects of the time series. 21 For this reason, the above frequently used forecasting tools may be incapable of mining information in such a complex time series reasonably well.…”

Study the current scenario of hand foot mouth disease an Indian prospective

“…5 Asian countries, including India, Singapore and South Korea, have also reported cases of HFMD. [6][7][8][9] Present study was conducted with the objective to study the trend of hand foot and mouth disease in a private hospital in Uttarakhand over 5 successive years.…”