Dataset Information

Increased Outdoor PM_2.5 Concentration Is Associated with Moderate/Severe Anemia in Children Aged 6-59 Months in Lima, Peru.

ABSTRACT: Anemia affects 1.62 billion people worldwide. Although iron deficiency is the main cause of anemia, several other factors may explain its high prevalence. In this study, we sought to analyze the association between outdoor particulate matter PM_2.5 levels with anemia prevalence in children aged 6-59 months residing in Lima, Peru (n?=?139,368), one of the cities with the worst air pollution in Latin America. The study period was from 2012 to 2016. Anemia was defined according to the World Health Organization (Hb?2.5 values were estimated by a mathematical model that combined data observed from monitors, with satellite and meteorological data. PM_2.5 was analyzed by quintiles. Multiple linear and logistic regressions were used to estimate the associations between hemoglobin concentration (beta) and anemia (odds ratio) with PM_2.5, after adjusting by covariates. Prevalence of anemia was 39.6% (95% confidence interval (CI): 39.3-39.9). Mild anemia was observed in 30.8% of children and moderate/severe in 8.84% of children. Anemic children compared with nonanemic children are mainly males, have low body weight, higher rate of stunting, and live in an environment with high PM_2.5 concentration. A slight decrease in hemoglobin (4Q B: -0.03, 95% CI: -0.05 to -0.02; 5Q B: -0.04, 95% CI: -0.06 to -0.01) and an increase in the probability of moderate/severe anemia (4Q OR: 1.18, 95% CI: 1.10-1.27; 5Q OR: 1.18, 95% CI: 1.08-1.29) were observed with increased exposure to PM_2.5. We conclude that outdoor PM_2.5 levels were significantly associated with decreased hemoglobin values and an increase in prevalence of moderate/severe anemia in children under 5 years old.

SUBMITTER: Morales-Ancajima VC

PROVIDER: S-EPMC6681625 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Increased Outdoor PM2.5 Concentration Is Associated with Moderate/Severe Anemia in Children Aged 6-59 Months in Lima, Peru.

Morales-Ancajima Valeria C VC Tapia Vilma V Vu Bryan N BN Liu Yang Y Alarcón-Yaquetto Dulce E DE Gonzales Gustavo F GF

Journal of environmental and public health 20190724

Anemia affects 1.62 billion people worldwide. Although iron deficiency is the main cause of anemia, several other factors may explain its high prevalence. In this study, we sought to analyze the association between outdoor particulate matter PM2.5 levels with anemia prevalence in children aged 6-59 months residing in Lima, Peru (n = 139,368), one of the cities with the worst air pollution in Latin America. The study period was from 2012 to 2016. Anemia was defined according to ...[more]

PMID: 31428166

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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.

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:IntroductionDespite the London Underground (LU) handling on average 2.8 million passenger journeys per day, the characteristics and potential health effects of the elevated concentrations of metal-rich PM2.5 found in this subway system are not well understood.MethodsSpatial monitoring campaigns were carried out to characterise the health-relevant chemical and physical properties of PM2.5 across the LU network, including diurnal and day-to-day variability and spatial distribution (above ground, depth below ground and subway line). Population-weighted station PM2.5 rankings were produced to understand the relative importance of concentrations at different stations and on different lines.ResultsThe PM2.5 mass in the LU (mean 88??g?m-3, median 28??g?m-3) was greater than at ambient background locations (mean 19??g?m-3, median 14??g?m-3) and roadside environments in central London (mean 22??g?m-3, median 14??g?m-3). Concentrations varied between lines and locations, with the deepest and shallowest submerged lines being the District (median 4??g?m-3) and Victoria (median 361??g?m-3 but up to 885??g?m-3). Broadly in agreement with other subway systems around the world, sampled LU PM2.5 comprised 47% iron oxide, 7% elemental carbon, 11% organic carbon, and 14% metallic and mineral oxides. Although a relationship between line depth and air quality inside the tube trains was evident, there were clear influences relating to the distance from cleaner outside air and the exchange with cabin air when the doors open. The passenger population-weighted exposure analysis demonstrated a method to identify stations that should be prioritised for remediation to improve air quality.ConclusionPM2.5 concentrations in the LU are many times higher than in other London transport Environments. Failure to include this environment in epidemiological studies of the relationship between PM2.5 and health in London is therefore likely to lead to a large exposure misclassification error. Given the significant contribution of underground PM2.5 to daily exposure, and the differences in composition compared to urban PM2.5, there is a clear need for well-designed studies to better understand the health effects of underground exposure.

Project description:This study investigates thoroughly whether acute exposure to outdoor PM2.5 concentration, P, modifies the rate of change in the daily number of COVID-19 infections (R) across 18 high infection provincial capitals in China, including Wuhan. A best-fit multiple linear regression model was constructed to model the relationship between P and R, from 1 January to 20 March 2020, after accounting for meteorology, net move-in mobility (NM), time trend (T), co-morbidity (CM), and the time-lag effects. Regression analysis shows that P (β = 0.4309, p < 0.001) is the most significant determinant of R. In addition, T (β = -0.3870, p < 0.001), absolute humidity (AH) (β = 0.2476, p = 0.002), P × AH (β = -0.2237, p < 0.001), and NM (β = 0.1383, p = 0.003) are more significant determinants of R, as compared to GDP per capita (β = 0.1115, p = 0.015) and CM (Asthma) (β = 0.1273, p = 0.005). A matching technique was adopted to demonstrate a possible causal relationship between P and R across 18 provincial capital cities. A 10 µg/m3 increase in P gives a 1.5% increase in R (p < 0.001). Interaction analysis also reveals that P × AH and R are negatively correlated (β = -0.2237, p < 0.001). Given that P exacerbates R, we recommend the installation of air purifiers and improved air ventilation to reduce the effect of P on R. Given the increasing observation that COVID-19 is airborne, measures that reduce P, plus mandatory masking that reduces the risks of COVID-19 associated with viral-particulate transmission, are strongly recommended. Our study is distinguished by the focus on the rate of change instead of the individual cases of COVID-19 when modelling the statistical relationship between R and P in China; causal instead of correlation analysis via the matching analysis, while taking into account the key confounders, and the individual plus the interaction effects of P and AH on R.

Dataset Information

Increased Outdoor PM_2.5 Concentration Is Associated with Moderate/Severe Anemia in Children Aged 6-59 Months in Lima, Peru.

Publications

Increased Outdoor PM<sub>2.5</sub> Concentration Is Associated with Moderate/Severe Anemia in Children Aged 6-59 Months in Lima, Peru.

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