Spatio-Temporal Differences in Health Effect of Ambient PM_2.5Pollution on Acute Respiratory Infection Between Children and Adults

Abstract

Fine particulate matter (PM 2.5) has been manifested to be one of the major health-threatening airborne pollutants in the urban environment, as it is composed of inhalable particles, which may have considerable adverse health effects on the human respiratory system. However, there is limited evidence on the difference in these effects among various population groups in China. This paper aimed to perform a comparative analysis on the health effect of PM 2.5 on hospital admissions of acute respiratory infections (ARI) for both children and adults. Total 39 604 hospital admission records were collected from 98 hospitals and then matched with air pollution data from 19 monitoring stations from January 1 to December 31 in 2014. A spatial correlation test was used to estimate the spatial dependency and a time series analysis designed with a distributed lag non-linear model was further involved to evaluate the associations between PM 2.5 pollution and ARI admissions. Significant effect distinctions were detected between children and adults, mainly revealing in acute lower respiratory infections (ALRI). For ALRI, the spatial correlation coefficient between population exposure and hospital admissions was 0.69 for children and 0.34 for adults. Meanwhile, for children, significant associations between PM 2.5 and ALRI admissions were found at quite low concentrations (slightly above zero) and lasted for six days, with each 10-µg/m 3 increase in PM 2.5 corresponded to a 4.3% (95%CI: 1.2%, 7.2%) increase in the number of admissions. While for adults, no significant association emerged until the concentration of PM 2.5 exceeded a threshold value (100 µg/m 3 for the lag of two days) and lasted no more than three days. Our results suggested that short-term exposures to PM 2.5 were associated with increased risk of ALRI admissions for children and adults in various ways, and emphasized the needs for specific preventive measures for different age groups. INDEX TERMS Acute respiratory infection, children, particulate matter, hospital admission, lag effect. The associate editor coordinating the review of this manuscript and approving it for publication was Vijay Mago.

Big Geospatial Data or Geospatial Big Data? A Systematic Narrative Review on the Use of Spatial Data Infrastructures for Big Geospatial Sensing Data in Public Health

“…Geography (general, remote sensing, geoscience) [12][13][14][15][16][17][18][19][20][21] 10 Public health [22][23][24][25][26][27][28][29][30][31] 10 Environment (physical, built environment) [32][33][34][35][36][37][38][39] 8 Science (computer, engineering, multidisciplinary) [40][41][42][43] 4…”

“…China [13][14][15][23][24][25][26][27][28][29][30][33][34][35][36][37] 16 India [41] 1 Indonesia [31] 1…”

“…Environments Livability [15], green space [13,14], night lights [19], noise exposure [39,40], land use [16], park visits [14], water points [12], indoor/outdoor air pollutants [17,20,23,24,29,33,36,39], energy expenditure [39], NDVI [38], mountain green cover [32], low-elevation coastal zones [28], anthropogenic heat emissions [34], socioeconomic factors [29] 23…”

A New Deep Learning Algorithm for Detecting the Lag Effect of Fine Particles on Hospital Emergency Visits for Respiratory Diseases

“…DLNM is a model developed from the generalized additive model (GAM) to evaluate the effect when a time lag exists. In recent years, DLNM has been widely used to quantify health effects associated with air pollution in the field of epidemiology [26][27][28]. In this paper, DLNM is performed to estimate the exposure-response relationship between PM 2.5 pollution and ERV of respiratory disease.…”

Case-Crossover Spatial Lag Grid Differences Between Aerosol Optical Depth-PM2.5 and Respiratory-Cardiovascular Emergency Department Visits and Inpatient Hospitalizations in Baltimore, Maryland, USA: Identification of Homogeneous Spatial Areas

“…Numerous publications have emphasized the importance of describing the temporal and spatial attributes of PM2.5 concentration levels measured by ambient air monitors [26,33,37,49,71,[85][86][87][88] and by AOD-PM2.5 in areas with and without air monitors [3, 15, 20, 22, 48, 52, 57-59, 61, 63, 67, 71, 78, 80, 81, 84, 89-93]. Our interdisciplinary research team, with members from the Battelle Memorial Institute, the U.S. Centers for Disease Control and Prevention (CDC), funded Environmental Public Health Tracking (EPHT) programs at the Maryland Department of Health and the New York State Department of Health, and the EPA initially developed the baseline PMB and subsequently assembled the four experimental AOD-PM2.5 concentration level fused surfaces, by statistically combining PM2.5 monitor readings with National Aeronautics and Space Administration (NASA) AOD data from the MODerate Resolution Imaging Spectroradiometer (MODIS) instrument on the Aqua and Terra satellites and CMAQ PM2.5 model estimates [94][95][96] for the New York City [32] and Baltimore [3,84] study areas.…”