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: The health effects of fine particulate matter (PM2.5) may be worse at higher temperatures. OBJECTIVE: To investigate temperature's effect on PM2.5-mortality/morbidity associations in Lima, Peru. METHODS: Time-series regressions relating PM2.5 and temperature to mortality and emergency room (ER) visits during 2010-2016. Daily PM2.5 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 PM2.5 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). PM2.5'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 PM2.5 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 PM2.5 may be worse at higher temperature, adding new evidence from Lima, Peru.

Association between maximum temperature and PM2.5 with pregnancy outcomes in Lima, Peru.

BACKGROUND: We have previously documented an inverse relationship between PM2.5 in Lima, Peru, and reproductive outcomes. Here, we investigate the effect of temperature on birth weight, birth weight-Z-score adjusted for gestational age, low birth weight, and preterm birth. We also explore interactions between PM2.5 and temperature. METHODS: We studied 123,034 singleton births in three public hospitals of Lima with temperature and PM2.5 during gestation between 2012 and 2016. We used linear, logistic, and Cox regression to estimate associations between temperature during gestation and birth outcomes and explored possible modification of the temperature effect by PM2.5. RESULTS: Exposure to maximum temperature in the last trimester was inversely associated with both birth weight [ß: -23.7; 95% confidence interval [CI]: -28.0, -19.5] and z-score weight-for-gestational-age (ß: -0.024; 95% CI: -0.029, -0.020) with an interquartile range of 5.32 °C. There was also an increased risk of preterm birth with higher temperature (interquartile range) in the first trimester (hazard ratio: 1.04; 95% CI: 1.001, 1.070). The effect of temperature on birthweight was primarily seen at higher PM2.5 levels. There were no statistically significant associations between temperature exposure with low birth weight. CONCLUSIONS: Exposition to maximum temperature was associated with lower birth weight and z-score weight-for-gestational-age and higher risk of preterm birth, in accordance with much of the literature. The effects on birth weight were seen only in the third trimester.

The association between asthma emergency department visits and satellite-derived PM2.5 in Lima, Peru.

BACKGROUND: Asthma 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. OBJECTIVE: We 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. METHODS: We 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. RESULTS: There 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. DISCUSSION: Results 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.

PM2.5 exposure on daily cardio-respiratory mortality in Lima, Peru, from 2010 to 2016.

BACKGROUND: There have been no studies of air pollution and mortality in Lima, Peru. We evaluate whether daily environmental PM2.5 exposure is associated to respiratory and cardiovascular mortality in Lima during 2010 to 2016. METHODS: We analyzed 86,970 deaths from respiratory and cardiovascular diseases in Lima from 2010 to 2016. Estimated daily PM2.5 was assigned based on district of residence. Poisson regression was used to estimate associations between daily district-level PM2.5 exposures and daily counts of deaths. RESULTS: An increase in 10 µg/m3 PM2.5 on the day before was significantly associated with daily cardiorespiratory mortality (RR 1.029; 95% CI: 1.01-1.05) across all ages and in the age group over 65 (RR 1.04; 95% CI: 1.005-1.09) which included 74% of all deaths. We also observed associations with circulatory deaths for all age groups (RR 1.06; 95% CI: 1.01-1.11), and those over 65 (RR 1.06; 95% CI 1.00-1.12). A borderline significant trend was seen (RR 1.05; 95% CI 0.99-1.06; p = 0.10) for respiratory deaths in persons aged over 65. Trends were driven by the highest quintile of exposure. CONCLUSIONS: PM2.5 exposure is associated with daily cardiorespiratory mortality in Lima, especially for older people. Our data suggest that the existing limits on air pollution exposure are too high.

Correction to: Association of PM2.5 concentration with health center outpatient visits for respiratory diseases of children under 5 years old in Lima, Peru.

The original version of this article [1], published on 15 January 2020, contained incorrect name of the co- author. In this Correction the affected part of the article is shown.

Association of PM2.5 concentration with health center outpatient visits for respiratory diseases of children under 5 years old in Lima, Peru.

BACKGROUND: Lima is one of the more polluted cities in Latin America. High levels of PM2.5 have been shown to increase health center outpatient visits of respiratory diseases. METHODS: Health center outpatient visits for children < 5 years for childhood respiratory disease (acute lower respiratory infections (ALRI), pneumonia and acute bronchiolitis/asthma) from 498 public clinics in Lima were available on a weekly basis from 2011 to 2015 from Peru's Ministry of Health (MINSA). The association between the average weekly concentrations of PM2.5 was evaluated in relation to the number of weekly health center outpatient visits for children. Weekly PM2.5 values were estimated using a recently developed model that combined data observed from ground monitors, with data from space satellite and meteorology. Ground monitoring data came from 10 fixed stations of the Peruvian National Service of Meteorology and Hydrology (SENAMHI) and from 6 mobile stations located in San Juan de Miraflores by Johns Hopkins University. We conducted a time-series analysis using a negative binomial model. RESULTS: We found a significant association between exposure to PM2.5 and all three types of respiratory diseases, across all age groups. For an interquartile increase in PM2.5, we found an increase of 6% for acute lower respiratory infections, an increase of 16-19% for pneumonia, and an increase of 10% for acute bronchiolitis / asthma. CONCLUSIONS: Higher emissions of environmental pollutants such as PM2,5 could be a trigger for the increase of health center outpatients visits for respiratory diseases (ALRI, pneumonia and asthma), which are themselves risk factors for mortality for children in Lima province, Peru.

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

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 the World Health Organization (Hb < 11 g/dL). PM2.5 values were estimated by a mathematical model that combined data observed from monitors, with satellite and meteorological data. PM2.5 was analyzed by quintiles. Multiple linear and logistic regressions were used to estimate the associations between hemoglobin concentration (beta) and anemia (odds ratio) with PM2.5, 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 PM2.5 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 PM2.5. We conclude that outdoor PM2.5 levels were significantly associated with decreased hemoglobin values and an increase in prevalence of moderate/severe anemia in children under 5 years old.

Developing an Advanced PM2.5 Exposure Model in Lima, Peru.

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.