Dataset Information

Effect modification by maximum temperature of the association between PM_2.5 and short-term cardiorespiratory mortality and emergency room visits in Lima, Peru, 2010-2016.

ABSTRACT:

Background

The health effects of fine particulate matter (PM_2.5) may be worse at higher temperatures.

Objective

To investigate temperature's effect on PM_2.5-mortality/morbidity associations in Lima, Peru.

Methods

Time-series regressions relating PM_2.5 and temperature to mortality and emergency room (ER) visits during 2010-2016. Daily PM_2.5 levels (assigned to 40 Lima districts) and daily maximum temperature (Lima-wide) were estimated based on ground monitors, remote sensing, and modeling. We analyzed all-cause, cardiovascular (ICD codes I00-I99), and respiratory (ICD codes J00-J99) mortality, and cardiovascular and respiratory causes for ER visits.

Results

The average PM_2.5 concentration was 20.9 µg/m³ (IQR 17.5-23.5). The mean daily maximum temperature was 23.8 °C (IQR 20.8-26.9). PM_2.5's effect on all-cause, respiratory, and circulatory disease mortality was significantly (p < 0.05) stronger at temperatures above the maximum temperature median. The rate ratios per increase of 10 µg/m³ of PM_2.5 for all cause, respiratory, and circulatory mortality respectively were 1.03 (1.00-1.06), 1.04 (0.98-1.10), and 1.04 (0.98-1.10) at temperatures below the median, vs. 1.08 (1.04-1.12), 1.11 (1.03-1.19), and 1.14 (1.05-1.25) when temperatures were above the median. Results were analogous for ER visits for respiratory but not circulatory disease.

Significance

Results strengthen the evidence that air pollution may be more dangerous when temperatures are higher.

Impact

Our data contribute to a growing body of literature which indicates that the damaging effects of PM_2.5 may be worse at higher temperature, adding new evidence from Lima, Peru.

SUBMITTER: Steenland K

PROVIDER: S-EPMC9012782 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Effect modification by maximum temperature of the association between PM<sub>2.5</sub> and short-term cardiorespiratory mortality and emergency room visits in Lima, Peru, 2010-2016.

Steenland Kyle K Vu Bryan B Scovronick Noah N

Journal of exposure science & environmental epidemiology 20211016 4

<h4>Background</h4>The health effects of fine particulate matter (PM<sub>2.5</sub>) may be worse at higher temperatures.<h4>Objective</h4>To investigate temperature's effect on PM<sub>2.5</sub>-mortality/morbidity associations in Lima, Peru.<h4>Methods</h4>Time-series regressions relating PM<sub>2.5</sub> and temperature to mortality and emergency room (ER) visits during 2010-2016. Daily PM<sub>2.5</sub> levels (assigned to 40 Lima districts) and daily maximum temperature (Lima-wide) were estima ...[more]

PMID: 34657126

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Project description:BackgroundAsthma affects millions of people worldwide. Lima, Peru is one of the most polluted cities in the Americas but has insufficient ground PM2.5 (particulate matter that are 2.5 μm or less in diameter) measurements to conduct epidemiologic studies regarding air pollution. PM2.5 estimates from a satellite-driven model have recently been made, enabling a study between asthma and PM2.5.ObjectiveWe conducted a daily time-series analysis to determine the association between asthma emergency department (ED) visits and estimated ambient PM2.5 levels in Lima, Peru from 2010 to 2016.MethodsWe used Poisson generalized linear models to regress aggregated counts of asthma on district-level population weighted PM2.5. Indicator variables for hospitals, districts, and day of week were included to account for spatial and temporal autocorrelation while assessing same day, previous day, day before previous and average across all 3-day exposures. We also included temperature and humidity to account for meteorology and used dichotomous percent poverty and gender variables to assess effect modification.ResultsThere were 103,974 cases of asthma ED visits during the study period across 39 districts in Lima. We found a 3.7% (95% CI: 1.7%-5.8%) increase in ED visits for every interquartile range (IQR, 6.02 μg/m3) increase in PM2.5 same day exposure with no age stratification. For the 0-18 years age group, we found a 4.5% (95% CI: 2.2%-6.8%) increase in ED visits for every IQR increase in PM2.5 same day exposure. For the 19-64 years age group, we found a 6.0% (95% CI: 1.0%-11.0%) increase in ED visits for every IQR in average 3-day exposure. For the 65 years and up age group, we found a 16.0% (95% CI: 7.0%-24.0%) decrease in ED visits for every IQR increase in PM2.5 average 3-day exposure, although the number of visits in this age group was low (4,488). We found no effect modification by SES or gender.DiscussionResults from this study provide additional literature on use of satellite-driven exposure estimates in time-series analyses and evidence for the association between PM2.5 and asthma in a low- and middle-income (LMIC) country.

Project description:It is well recognized that exposure to fine particulate matter (PM2.5) affects health adversely, yet few studies from South America have documented such associations due to the sparsity of PM2.5 measurements. Lima's topography and aging vehicular fleet results in severe air pollution with limited amounts of monitors to effectively quantify PM2.5 levels for epidemiologic studies. We developed an advanced machine learning model to estimate daily PM2.5 concentrations at a 1 km2 spatial resolution in Lima, Peru from 2010 to 2016. We combined aerosol optical depth (AOD), meteorological fields from the European Centre for Medium-Range Weather Forecasts (ECMWF), parameters from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), and land use variables to fit a random forest model against ground measurements from 16 monitoring stations. Overall cross-validation R2 (and root mean square prediction error, RMSE) for the random forest model was 0.70 (5.97 μg/m3). Mean PM2.5 for ground measurements was 24.7 μg/m3 while mean estimated PM2.5 was 24.9 μg/m3 in the cross-validation dataset. The mean difference between ground and predicted measurements was -0.09 μg/m3 (Std.Dev. = 5.97 μg/m3), with 94.5% of observations falling within 2 standard deviations of the difference indicating good agreement between ground measurements and predicted estimates. Surface downwards solar radiation, temperature, relative humidity, and AOD were the most important predictors, while percent urbanization, albedo, and cloud fraction were the least important predictors. Comparison of monthly mean measurements between ground and predicted PM2.5 shows good precision and accuracy from our model. Furthermore, mean annual maps of PM2.5 show consistent lower concentrations in the coast and higher concentrations in the mountains, resulting from prevailing coastal winds blown from the Pacific Ocean in the west. Our model allows for construction of long-term historical daily PM2.5 measurements at 1 km2 spatial resolution to support future epidemiological studies.

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Project description:BackgroundExposure to particulate matter, particularly with aerodynamic diameter < 2.5 µm (PM2.5), may increase inflammation and oxidative stress in pregnant women and affect fetal growth. We examined trimester specific PM2.5 exposure levels and small for gestational age (SGA) using the statewide birth registry of Ohio from 2007 to 2010.MethodsExposure to PM2.5 in each trimester and for each gestational week was determined using data from 57 Environmental Protection Agency network monitoring stations across the state of Ohio. We restricted the data to 224,921 singleton live births, with a gestational age of 20-42 weeks, no genetic disorders or congenital abnormalities, and who had home addresses within a 10 km radius of any PM2.5 monitoring station. We estimated odds ratios of SGA using Generalized Linear Models (GLMs) and Distributed Lag Models (DLMs), and adjustment for maternal age, race, education, parity, body mass index, insurance type, tobacco use, prenatal care initiation, birth year, season of birth, and sex of the baby.ResultsMean PM2.5 levels during the entire pregnancy were 13.03 µg/m3 with a standard deviation of 1.57 µg/m3. Covariates adjusted odds ratios and 95% confidence intervals of a 10 µg/m3 increase in PM2.5 levels with a 10 km buffer radius for SGA and trimesters modeled separately were 0.94 (0.88, 1.00) for the first trimester, 0.93 (0.86, 1.00) for the second trimester, 1.07 (1.00, 1.15) for the third trimester, and 0.92 (0.81, 1.06) for the entire pregnancy. When a 5 km buffer radius was used, adjusted odds ratios and 95% confidence intervals for SGA were 0.97 (0.89, 1.05) for the first trimester, 0.96 (0.88, 1.05) for the second trimester, 1.09 (1.02, 1.17) for the third trimester, and 0.99 (0.85, 1.14) for the overall pregnancy, indicating sensitivity to buffer choice. DLMs showed gestational weeks 30-35 to be a particular window of vulnerability.ConclusionIncreasing exposure to PM2.5 during the third trimester of pregnancy was associated with a small increase in risk of SGA in this population-based study. Selection of a buffer radius significantly impacted our results in the first trimester, but not in the third trimester.

Project description:BackgroundSubstantial increases in wildfire activity have been recorded in recent decades. Wildfires influence the chemical composition and concentration of particulate matter ≤2.5 μm in aerodynamic diameter (PM2.5). However, relatively few epidemiologic studies focus on the health impacts of wildfire smoke PM2.5 compared with the number of studies focusing on total PM2.5 exposure.ObjectivesWe estimated the associations between cardiorespiratory acute events and exposure to smoke PM2.5 in Colorado using a novel exposure model to separate smoke PM2.5 from background ambient PM2.5 levels.MethodsWe obtained emergency department visits and hospitalizations for acute cardiorespiratory outcomes from Colorado for May-August 2011-2014, geocoded to a 4 km geographic grid. Combining ground measurements, chemical transport models, and remote sensing data, we estimated smoke PM2.5 and non-smoke PM2.5 on a 1 km spatial grid and aggregated to match the resolution of the health data. Time-stratified, case-crossover models were fit using conditional logistic regression to estimate associations between fire smoke PM2.5 and non-smoke PM2.5 for overall and age-stratified outcomes using 2-day averaging windows for cardiovascular disease and 3-day windows for respiratory disease.ResultsPer 1 μg/m3 increase in fire smoke PM2.5, statistically significant associations were observed for asthma (OR = 1.081 (1.058, 1.105)) and combined respiratory disease (OR = 1.021 (1.012, 1.031)). No significant relationships were evident for cardiovascular diseases and smoke PM2.5. Associations with non-smoke PM2.5 were null for all outcomes. Positive age-specific associations related to smoke PM2.5 were observed for asthma and combined respiratory disease in children, and for asthma, bronchitis, COPD, and combined respiratory disease in adults. No significant associations were found in older adults.DiscussionThis is the first multi-year, high-resolution epidemiologic study to incorporate statistical and chemical transport modeling methods to estimate PM2.5 exposure due to wildfires. Our results allow for a more precise assessment of the population health impact of wildfire-related PM2.5 exposure in a changing climate.

Dataset Information

Effect modification by maximum temperature of the association between PM_2.5 and short-term cardiorespiratory mortality and emergency room visits in Lima, Peru, 2010-2016.

Background

Objective

Methods

Results

Significance

Impact

Publications

Effect modification by maximum temperature of the association between PM<sub>2.5</sub> and short-term cardiorespiratory mortality and emergency room visits in Lima, Peru, 2010-2016.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Dataset Information

Effect modification by maximum temperature of the association between PM2.5 and short-term cardiorespiratory mortality and emergency room visits in Lima, Peru, 2010-2016.

Background

Objective

Methods

Results

Significance

Impact

Publications

Effect modification by maximum temperature of the association between PM<sub>2.5</sub> and short-term cardiorespiratory mortality and emergency room visits in Lima, Peru, 2010-2016.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Effect modification by maximum temperature of the association between PM_2.5 and short-term cardiorespiratory mortality and emergency room visits in Lima, Peru, 2010-2016.