Urban Air Pollution May Enhance COVID-19 Case-Fatality and Mortality Rates in the United States

Background

The novel human coronavirus disease 2019 (COVID-19) pandemic has claimed more than 600,000 lives worldwide, causing tremendous public health, social, and economic damages. Although the risk factors of COVID-19 are still under investigation, environmental factors, such as urban air pollution, may play an important role in increasing population susceptibility to COVID-19 pathogenesis.

Methods

We conducted a cross-sectional nationwide study using zero-inflated negative binomial models to estimate the association between long-term (2010–2016) county-level exposures to NO₂, PM_2.5, and O₃ and county-level COVID-19 case-fatality and mortality rates in the United States. We used both single- and multi-pollutant models and controlled for spatial trends and a comprehensive set of potential confounders, including state-level test positive rate, county-level health care capacity, phase of epidemic, population mobility, population density, sociodemographics, socioeconomic status, race and ethnicity, behavioral risk factors, and meteorology.

Results

From January 22, 2020, to July 17, 2020, 3,659,828 COVID-19 cases and 138,552 deaths were reported in 3,076 US counties, with an overall observed case-fatality rate of 3.8%. County-level average NO₂ concentrations were positively associated with both COVID-19 case-fatality rate and mortality rate in single-, bi-, and tri-pollutant models. When adjusted for co-pollutants, per interquartile-range (IQR) increase in NO₂ (4.6 ppb), COVID-19 case-fatality rate and mortality rate were associated with an increase of 11.3% (95% CI 4.9%–18.2%) and 16.2% (95% CI 8.7%–24.0%), respectively. We did not observe significant associations between COVID-19 case-fatality rate and long-term exposure to PM_2.5 or O₃, although per IQR increase in PM_2.5 (2.6 μg/m³) was marginally associated, with a 14.9% (95% CI 0.0%–31.9%) increase in COVID-19 mortality rate when adjusted for co-pollutants.

Discussion

Long-term exposure to NO₂, which largely arises from urban combustion sources such as traffic, may enhance susceptibility to severe COVID-19 outcomes, independent of long-term PM_2.5 and O₃ exposure. The results support targeted public health actions to protect residents from COVID-19 in heavily polluted regions with historically high NO₂ levels. Continuation of current efforts to lower traffic emissions and ambient air pollution may be an important component of reducing population-level risk of COVID-19 case fatality and mortality.

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One of the first US studies on air pollution exposures and COVID-19 death outcomes

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Urban air pollutants, especially NO₂, may enhance population susceptibility to death fromCOVID-19

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Reduction in air pollution would have avoided over 14,000 COVID-19 deaths in the US as of July 17, 2020

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Public health actions needed to protect populations from COVID-19 in areas with historically high NO₂ exposure

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Expansion of efforts to lower air pollution may reduce population-level risk of COVID-19

Faecal contamination of the environment and child health: a systematic review and individual participant data meta-analysis

BACKGROUND

Exposure to faecal contamination is believed to be associated with child diarrhoea and possibly stunting; however, few studies have explicitly measured the association between faecal contamination and health. We aimed to assess individual participant data (IPD) across multiple trials and observational studies to quantify the relationship for common faecal-oral transmission pathways.

METHODS

We did a systematic review and meta-analysis of IPD from studies identified in an electronic search of PubMed, Web of Science, and Embase on May 21, 2018. The search was done in English, but full texts published in French, Portuguese, and Spanish were also reviewed. Eligible studies quantified (1) household-level faecal indicator bacteria concentrations along common faecal-oral transmission pathways of drinking water, soil, or food, on children's hands or fomites, or fly densities in food preparation areas; and (2) individual-level diarrhoea or linear growth measures for children younger than 5 years in low-income and middle-income countries. For the diarrhoea analysis, all definitions of diarrhoea were eligible but studies were excluded if they used a recall period longer than 7 days. For the linear growth analysis (using height-for-age Z scores [HAZ]), cross-sectional studies were excluded, because of the absence of longitudinal environmental contamination data measured before the growth outcomes. We used multilevel generalised mixed-effects models to estimate the odds ratio (OR) for diarrhoea and the difference in HAZ scores for individual studies associated with a 1-log₁₀ higher measure of faecal contamination. Estimates from each study were combined under a random-effects meta-analysis framework. The study protocol was pre-registered with PROSPERO (CRD42018102114).

FINDINGS

From 72 eligible studies, we included IPD for 20 studies in the meta-analyses, totalling 54 225 diarrhoea or linear growth observations matched to faecal indicator bacteria concentrations in drinking water, and a further 35 010 observations with faecal contamination data for the other transmission pathways. Child diarrhoea was associated with 1-log₁₀ higher faecal indicator bacteria concentrations in drinking water (OR 1·09, 95% CI 1·04 to 1·13; p=0·0002, I²=34%, 95% CI 0 to 62) and on children's hands (1·11, 1·02 to 1·22; p=0·021, I²=0%, 0 to 71). Lower HAZ scores were associated with 1-log₁₀ higher median faecal indicator bacteria concentrations in drinking water (HAZ -0·04, 95% CI -0·06 to -0·01; p=0·0054; I²=19%, 95% CI 0 to 63) and on fomites (-0·06, -0·12 to 0·00; p=0·044, I²=57%, 0 to 90).

INTERPRETATION

Although summary measures from individual studies often report little or no effect of measured faecal contamination on child health, this multi-study IPD analysis indicates that household faecal indicator bacteria concentrations are associated with important adverse health outcomes in young children. Improved direct measures of exposure and enteric pathogens could help to better characterise the relationship and inform intervention design in future studies.

FUNDING

None.

Drinking Water Salinity, Urinary Macro-Mineral Excretions, and Blood Pressure in the Southwest Coastal Population of Bangladesh

Background

Sodium (Na⁺) in saline water may increase blood pressure (BP), but potassium (K⁺), calcium (Ca²⁺), and magnesium (Mg²⁺) may lower BP. We assessed the association between drinking water salinity and population BP.

Methods and Results

We pooled 6487 BP measurements from 2 cohorts in coastal Bangladesh. We used multilevel linear models to estimate BP differences across water salinity categories: fresh water (electrical conductivity, <0.7 mS/cm), mild salinity (electrical conductivity ≥0.7 and <2 mS/cm), and moderate salinity (electrical conductivity ≥2 and <10 mS/cm). We assessed whether salinity categories were associated with hypertension using multilevel multinomial logistic models. Models included participant‐, household‐, and community‐level random intercepts. Models were adjusted for age, sex, body mass index (BMI), physical activity, smoking, household wealth, alcohol consumption, sleep hours, religion, and salt consumption. We evaluated the 24‐hour urinary minerals across salinity categories, and the associations between urinary minerals and BP using multilevel linear models. Compared with fresh water drinkers, mild‐salinity water drinkers had lower mean systolic BP (−1.55 [95% CI: −3.22–0.12] mm Hg) and lower mean diastolic BP (−1.26 [95% CI: −2.21–−0.32] mm Hg) adjusted models. The adjusted odds ratio among mild‐salinity water drinkers for stage 1 hypertension was 0.60 (95% CI: 0.43–0.84) and for stage 2 hypertension was 0.56 (95% CI: 0.46–0.89). Mild‐salinity water drinkers had high urinary Ca²⁺, and Mg²⁺, and both urinary Ca²⁺ and Mg²⁺ were associated with lower BP.

Conclusions

Drinking mild‐salinity water was associated with lower BP, which can be explained by higher intake of Ca²⁺ and Mg²⁺ through saline water.

See Editorial Bispham and Nowak

Assessing longer-term effectiveness of a combined household-level piped water and sanitation intervention on child diarrhoea, acute respiratory infection, soil-transmitted helminth infection and nutritional status: a matched cohort study in rural Odisha, India

BACKGROUND

Open defecation is widespread in rural India, and few households have piped water connections. While government and other efforts have increased toilet coverage in India, and evaluations found limited immediate impacts on health, longer-term effects have not been rigorously assessed.

METHODS

We conducted a matched cohort study to assess the longer-term effectiveness of a combined household-level piped water and sanitation intervention implemented by Gram Vikas (an Indian NGO) in rural Odisha, India. Forty-five intervention villages were randomly selected from a list of those where implementation was previously completed at least 5 years before, and matched to 45 control villages. We conducted surveys and collected stool samples between June 2015 and October 2016 in households with a child <5 years of age (n = 2398). Health surveillance included diarrhoea (primary outcome), acute respiratory infection (ARI), soil-transmitted helminth infection, and anthropometry.

RESULTS

Intervention villages had higher improved toilet coverage (85% vs 18%), and increased toilet use by adults (74% vs 13%) and child faeces disposal (35% vs 6%) compared with control villages. There was no intervention association with diarrhoea [adjusted OR (aOR): 0.94, 95% confidence interval (CI): 0.74-1.20] or ARI. Compared with controls, children in intervention villages had lower helminth infection (aOR: 0.44, 95% CI: 0.18, 1.00) and improved height-for-age z scores (HAZ) (+0.17, 95% CI: 0.03-0.31).

CONCLUSIONS

This combined intervention, where household water connections were contingent on community-wide household toilet construction, was associated with improved HAZ, and reduced soil-transmitted helminth (STH) infection, though not reduced diarrhoea or ARI. Further research should explore the mechanism through which these heterogenous effects on health may occur.

Learning from local to global: An efficient distributed algorithm for modeling time-to-event data

OBJECTIVE

We developed and evaluated a privacy-preserving One-shot Distributed Algorithm to fit a multicenter Cox proportional hazards model (ODAC) without sharing patient-level information across sites.

MATERIALS AND METHODS

Using patient-level data from a single site combined with only aggregated information from other sites, we constructed a surrogate likelihood function, approximating the Cox partial likelihood function obtained using patient-level data from all sites. By maximizing the surrogate likelihood function, each site obtained a local estimate of the model parameter, and the ODAC estimator was constructed as a weighted average of all the local estimates. We evaluated the performance of ODAC with (1) a simulation study and (2) a real-world use case study using 4 datasets from the Observational Health Data Sciences and Informatics network.

RESULTS

On the one hand, our simulation study showed that ODAC provided estimates nearly the same as the estimator obtained by analyzing, in a single dataset, the combined patient-level data from all sites (ie, the pooled estimator). The relative bias was <0.1% across all scenarios. The accuracy of ODAC remained high across different sample sizes and event rates. On the other hand, the meta-analysis estimator, which was obtained by the inverse variance weighted average of the site-specific estimates, had substantial bias when the event rate is <5%, with the relative bias reaching 20% when the event rate is 1%. In the Observational Health Data Sciences and Informatics network application, the ODAC estimates have a relative bias <5% for 15 out of 16 log hazard ratios, whereas the meta-analysis estimates had substantially higher bias than ODAC.

CONCLUSIONS

ODAC is a privacy-preserving and noniterative method for implementing time-to-event analyses across multiple sites. It provides estimates on par with the pooled estimator and substantially outperforms the meta-analysis estimator when the event is uncommon, making it extremely suitable for studying rare events and diseases in a distributed manner.

Urban Air Pollution May Enhance COVID-19 Case-Fatality and Mortality Rates in the United States

BACKGROUND

The novel human coronavirus disease 2019 (COVID-19) pandemic has claimed more than 240,000 lives worldwide, causing tremendous public health, social, and economic damages. While the risk factors of COVID-19 are still under investigation, environmental factors, such as urban air pollution, may play an important role in increasing population susceptibility to COVID-19 pathogenesis.

METHODS

We conducted a cross-sectional nationwide study using zero-inflated negative binomial models to estimate the association between long-term (2010-2016) county-level exposures to NO2, PM2.5 and O3 and county-level COVID-19 case-fatality and mortality rates in the US. We used both single and multipollutant models and controlled for spatial trends and a comprehensive set of potential confounders, including state-level test positive rate, county-level healthcare capacity, phase-of-epidemic, population mobility, sociodemographic, socioeconomic status, behavior risk factors, and meteorological factors.

RESULTS

1,027,799 COVID-19 cases and 58,489 deaths were reported in 3,122 US counties from January 22, 2020 to April 29, 2020, with an overall observed case-fatality rate of 5.8%. Spatial variations were observed for both COVID-19 death outcomes and long-term ambient air pollutant levels. County-level average NO2 concentrations were positively associated with both COVID-19 case-fatality rate and mortality rate in single-, bi-, and tri-pollutant models (p-values<0.05). Per inter-quartile range (IQR) increase in NO2 (4.6 ppb), COVID-19 case-fatality rate and mortality rate were associated with an increase of 7.1% (95% CI 1.2% to 13.4%) and 11.2% (95% CI 3.4% to 19.5%), respectively. We did not observe significant associations between long-term exposures to PM2.5 or O3 and COVID-19 death outcomes (p-values>0.05), although per IQR increase in PM2.5 (3.4 ug/m3) was marginally associated with 10.8% (95% CI: -1.1% to 24.1%) increase in COVID-19 mortality rate.

DISCUSSIONS AND CONCLUSIONS

Long-term exposure to NO2, which largely arises from urban combustion sources such as traffic, may enhance susceptibility to severe COVID-19 outcomes, independent of long-term PM2.5 and O3 exposure. The results support targeted public health actions to protect residents from COVID-19 in heavily polluted regions with historically high NO2 levels. Moreover, continuation of current efforts to lower traffic emissions and ambient air pollution levels may be an important component of reducing population-level risk of COVID-19 deaths.

Design and Rationale of the HAPIN Study: A Multicountry Randomized Controlled Trial to Assess the Effect of Liquefied Petroleum Gas Stove and Continuous Fuel Distribution

BACKGROUND

Globally, nearly 3 billion people rely on solid fuels for cooking and heating, the vast majority residing in low- and middle-income countries (LMICs). The resulting household air pollution (HAP) is a leading environmental risk factor, accounting for an estimated 1.6 million premature deaths annually. Previous interventions of cleaner stoves have often failed to reduce exposure to levels that produce meaningful health improvements. There have been no multicountry field trials with liquefied petroleum gas (LPG) stoves, likely the cleanest scalable intervention.

OBJECTIVE

This paper describes the design and methods of an ongoing randomized controlled trial (RCT) of LPG stove and fuel distribution in 3,200 households in 4 LMICs (India, Guatemala, Peru, and Rwanda).

METHODS

We are enrolling 800 pregnant women at each of the 4 international research centers from households using biomass fuels. We are randomly assigning households to receive LPG stoves, an 18-month supply of free LPG, and behavioral reinforcements to the control arm. The mother is being followed along with her child until the child is 1 year old. Older adult women (40 to < 80   years of age) living in the same households are also enrolled and followed during the same period. Primary health outcomes are low birth weight, severe pneumonia incidence, stunting in the child, and high blood pressure (BP) in the older adult woman. Secondary health outcomes are also being assessed. We are assessing stove and fuel use, conducting repeated personal and kitchen exposure assessments of fine particulate matter with aerodynamic diameter ≤ 2.5 μ m (PM 2.5 ), carbon monoxide (CO), and black carbon (BC), and collecting dried blood spots (DBS) and urinary samples for biomarker analysis. Enrollment and data collection began in May 2018 and will continue through August 2021. The trial is registered with ClinicalTrials.gov (NCT02944682).

CONCLUSIONS

This study will provide evidence to inform national and global policies on scaling up LPG stove use among vulnerable populations. https://doi.org/10.1289/EHP6407.

Environmental and spatial determinants of enteric pathogen infection in rural Lao People's Democratic Republic: A cross-sectional study

TRIAL REGISTRATION

clinicaltrials.gov (NCT02342860).

A spatially varying distributed lag model with application to an air pollution and term low birth weight study

Distributed lag models have been used to identify critical pregnancy periods of exposure (i.e. critical exposure windows) to air pollution in studies of pregnancy outcomes. However, much of the previous work in this area has ignored the possibility of spatial variability in the lagged health effect parameters that may result from exposure characteristics and/or residual confounding. We develop a spatially varying Gaussian process model for critical windows called 'SpGPCW' and use it to investigate geographic variability in the association between term low birth weight and average weekly concentrations of ozone and PM_2:5 during pregnancy by using birth records from North Carolina. SpGPCW is designed to accommodate areal level spatial correlation between lagged health effect parameters and temporal smoothness in risk estimation across pregnancy. Through simulation and a real data application, we show that the consequences of ignoring spatial variability in the lagged health effect parameters include less reliable inference for the parameters and diminished ability to identify true critical window sets, and we investigate the use of existing Bayesian model comparison techniques as tools for determining the presence of spatial variability. We find that exposure to PM_2:5 is associated with elevated term low birth weight risk in selected weeks and counties and that ignoring spatial variability results in null associations during these periods. An R package (SpGPCW) has been developed to implement the new method.

Incorporating Low-Cost Sensor Measurements into High-Resolution PM2.5 Modeling at a Large Spatial Scale

Low-cost air quality sensors are promising supplements to regulatory monitors for PM_2.5 exposure assessment. However, little has been done to incorporate the low-cost sensor measurements in large-scale PM_2.5 exposure modeling. We conducted spatially varying calibration and developed a downweighting strategy to optimize the use of low-cost sensor data in PM_2.5 estimation. In California, PurpleAir low-cost sensors were paired with air quality system (AQS) regulatory stations, and calibration of the sensors was performed by geographically weighted regression. The calibrated PurpleAir measurements were then given lower weights according to their residual errors and fused with AQS measurements into a random forest model to generate 1 km daily PM_2.5 estimates. The calibration reduced PurpleAir's systematic bias to ∼0 μg/m³ and residual errors by 36%. Increased sensor bias was found to be associated with higher temperature and humidity, as well as longer operating time. The weighted prediction model outperformed the AQS-based prediction model with an improved random cross-validation (CV) R² of 0.86, an improved spatial CV R² of 0.81, and a lower prediction error. The temporal CV R² did not improve due to the temporal discontinuity of PurpleAir. The inclusion of PurpleAir data allowed the predictions to better reflect PM_2.5 spatial details and hotspots.

Effects of transforming growth factor-β1 on plasminogen activation in stem cells from the apical papilla: role of activating receptor-like kinase 5/Smad2 and mitogen-activated protein kinase kinase (MEK)/extracellular signal-regulated kinase (ERK) signalling

AIM

To study the effects of TGF-β1 on the plasminogen activation (PA) system of stem cells from the apical papilla (SCAP) and its signalling.

METHODOLOGY

SCAP cells were isolated from the apical papilla of immature permanent teeth extracted for orthodontic reasons. They were exposed to various concentration of TGF-β1 with/without pretreatment and coincubation by SB431542 (ALK/Smad2/3 inhibitor), or U0126 (MEK/ERK inhibitor). MTT assay, Western blotting and enzyme-linked immunosorbent assay (ELISA) were used to detect their effects on cell viability, and the protein expression of plasminogen activator inhibitor-1 (PAI-1), urokinase-type plasminogen activator (uPA), uPA receptor (uPAR) and their secretion. The paired Student's t-test was used for statistical analysis.

RESULTS

TGF-β1 significantly stimulated PAI-1 and soluble uPAR (suPAR) secretion of SCAP cells (P < 0.05), whereas uPA secretion was inhibited. Accordingly, TGF-β1 induced both PAI-1 and uPAR protein expression of SCAP cells. SB431542 (an ALK5/Smad2/3 inhibitor) pretreatment and coincubation prevented the TGF-β1-induced PAI-1 and uPAR of SCAP. U0126 attenuated the TGF-β1-induced expression/secretion of uPAR, but not PAI-1 in SCAP. SB431542 reversed the TGF-β1-induced decline of uPA.

CONCLUSIONS

TGF-β1 may affect the repair/regeneration activities of SCAP via differential increase or decrease of PAI-1, uPA and uPAR. These effects induced by TGF-β1 are associated with ALK5/Smad2/3 and MEK/ERK activation. Elucidation the signalling pathways and effects of TGF-β1 is useful for treatment of immature teeth with open apex by revascularization/revitalization procedures and tissue repair/regeneration.

A comparison of statistical and machine learning methods for creating national daily maps of ambient PM2.5 concentration

A typical challenge in air pollution epidemiology is to perform detailed exposure assessment for individuals for which health data are available. To address this problem, in the last few years, substantial research efforts have been placed in developing statistical methods or machine learning techniques to generate estimates of air pollution at fine spatial and temporal scales (daily, usually) with complete coverage. However, it is not clear how much the predicted exposures yielded by the various methods differ, and which method generates more reliable estimates. In this paper, we aim to address this gap by evaluating a variety of exposure modeling approaches, comparing their predictive performance. Using PM2.5 in year 2011 over the continental U.S. as a case study, we generate national maps of ambient PM2.5 concentration using: (i) ordinary least squares and inverse distance weighting; (ii) kriging; (iii) statistical downscaling models, that is, spatial statistical models that use the information contained in air quality model outputs; (iv) land use regression, that is, linear regression modeling approaches that leverage the information in Geographical Information System (GIS) covariates; and (v) machine learning methods, such as neural networks, random forests and support vector regression. We examine the various methods' predictive performance via cross-validation using Root Mean Squared Error, Mean Absolute Deviation, Pearson correlation, and Mean Spatial Pearson Correlation. Additionally, we evaluated whether factors such as, season, urbanicty, and levels of PM2.5 concentration (low, medium or high) affected the performance of the different methods. Overall, statistical methods that explicitly modeled the spatial correlation, e.g. universal kriging and the downscaler model, outperform all the other exposure assessment approaches regardless of season, urbanicity and PM2.5 concentration level. We posit that the better predictive performance of spatial statistical models over machine learning methods is due to the fact that they explicitly account for spatial dependence, thus borrowing information from neighboring observations. In light of our findings, we suggest that future exposure assessment methods for regional PM2.5 incorporate information from neighboring sites when deriving predictions at unsampled locations or attempt to account for spatial dependence.

Age-Specific Associations of Ozone and Fine Particulate Matter with Respiratory Emergency Department Visits in the United States

RATIONALE

Whereas associations between air pollution and respiratory morbidity for adults 65 years and older are well documented in the United States, the evidence for people under 65 is less extensive. To address this gap, the Centers for Disease Control and Prevention's National Environmental Public Health Tracking Program collected respiratory emergency department (ED) data from 17 states.

OBJECTIVES

To estimate age-specific acute effects of ozone and fine particulate matter (particulate matter ≤2.5 mm in aerodynamic diameter [PM_2.5]) on respiratory ED visits.

METHODS

We conducted time-series analyses in 894 counties by linking daily respiratory ED visits with estimated ozone and PM_2.5 concentrations during the week before the date of the visit. Overall effect estimates were obtained with a Bayesian hierarchical model to combine county estimates for each pollutant by age group (children, 0-18; adults, 19-64; adults ≥ 65, and all ages) and by outcome group (acute respiratory infection, asthma, chronic obstructive pulmonary disease, pneumonia, and all respiratory ED visits).

MEASUREMENTS AND MAIN RESULTS

Rate ratios (95% credible interval) per 10-μg/m³ increase in PM_2.5 and all respiratory ED visits were 1.024 (1.018-1.029) among children, 1.008 (1.004-1.012) among adults younger than 65 years, and 1.002 (0.996-1.007) among adults 65 and older. Per 20-ppb increase in ozone, rate ratios were 1.017 (1.011-1.023) among children, 1.051 (1.046-1.056) among adults younger than 65, and 1.033 (1.026-1.040) among adults 65 and older. Associations varied in magnitude by age group for each outcome group.

CONCLUSIONS

These results address a gap in the evidence used to ensure adequate public health protection under national air pollution policies.

Hydroclimatic drivers of highly seasonal leptospirosis incidence suggest prominent soil reservoir of pathogenic Leptospira spp. in rural western China

Climate exerts complex influences on leptospirosis transmission, affecting human behavior, zoonotic host population dynamics, and survival of the pathogen in the environment. Here, we describe the spatiotemporal distribution of leptospirosis incidence reported to China’s National Infectious Disease Surveillance System from 2004–2014 in an endemic region in western China, and employ distributed lag models at annual and sub-annual scales to analyze its association with hydroclimatic risk factors and explore evidence for the potential role of a soil reservoir in the transmission of Leptospira spp. More than 97% of the 2,934 reported leptospirosis cases occurred during the harvest season between August and October, and most commonly affected farmers (83%). Using a distributed lag Poisson regression framework, we characterized incidence rate ratios (IRRs) associated with interquartile range increases in precipitation of 3.45 (95% confidence interval 2.57–4.64) over 0-1-year lags, and 1.90 (1.18–3.06) over 0-15-week lags. Adjusting for soil moisture decreased IRRs for precipitation at both timescales (yearly adjusted IRR: 1.05, 0.74–1.49; weekly adjusted IRR: 1.36, 0.72–2.57), suggesting precipitation effects may be mediated through soil moisture. Increased soil moisture was positively associated with leptospirosis at both timescales, suggesting that the survival of pathogenic Leptospira spp. in moist soils may be a critical control on harvest-associated leptospirosis transmission in the study region. These results support the hypothesis that soils may serve as an environmental reservoir and may play a significant yet underrecognized role in leptospirosis transmission.

Leptospirosis is among the leading causes of morbidity from zoonotic infections worldwide, affecting populations that are exposed to contaminated water. The disease is caused by Leptospira spp. bacteria, which are transmitted to humans either through direct contact with infected animals, or indirectly through the environment. Climatic conditions can influence transmission by altering human exposure, animal host population dynamics, and environmental conditions that allow Leptospira spp. to persist in the environment (e.g., moist environments, warm temperatures). Here, we investigated the spatiotemporal distribution of leptospirosis cases in a rural setting in western China and estimated the association between hydroclimatic conditions and leptospirosis incidence. We found that incidence of leptospirosis—especially high amongst farmers—may be associated with rice harvest, and modulated by prior bacterial accumulation within the soil under moist conditions. These results corroborate previous findings that soils may be underrecognized environmental reservoirs of pathogenic Leptospira spp., and that their role in explaining leptospirosis incidence should be considered when developing prevention programs.

Associations of wildfire smoke PM2.5 exposure with cardiorespiratory events in Colorado 2011-2014

BACKGROUND

Substantial 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 (PM_2.5). However, relatively few epidemiologic studies focus on the health impacts of wildfire smoke PM_2.5 compared with the number of studies focusing on total PM_2.5 exposure.

OBJECTIVES

We estimated the associations between cardiorespiratory acute events and exposure to smoke PM_2.5 in Colorado using a novel exposure model to separate smoke PM_2.5 from background ambient PM_2.5 levels.

METHODS

We 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 PM_2.5 and non-smoke PM_2.5 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 PM_2.5 and non-smoke PM_2.5 for overall and age-stratified outcomes using 2-day averaging windows for cardiovascular disease and 3-day windows for respiratory disease.

RESULTS

Per 1 μg/m³ increase in fire smoke PM_2.5, 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 PM_2.5. Associations with non-smoke PM_2.5 were null for all outcomes. Positive age-specific associations related to smoke PM_2.5 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.

DISCUSSION

This is the first multi-year, high-resolution epidemiologic study to incorporate statistical and chemical transport modeling methods to estimate PM_2.5 exposure due to wildfires. Our results allow for a more precise assessment of the population health impact of wildfire-related PM_2.5 exposure in a changing climate.

Comparison of Urinary Sodium and Blood Pressure Relationship From the Spot Versus 24-Hour Urine Samples

Background We compared the relationship between sodium (Na) intake and blood pressure when Na intake was estimated from first- and second-morning spot urine samples using the INTERSALT (International Study on Salt and Blood Pressure) formula, versus directly measured 24-hour samples. Methods and Results We collected 24-hour urine and first- and second-morning voids of 383 participants in coastal Bangladesh for 2 visits. We measured participants' blood pressure using an Omron® HEM-907 monitor. To assess the shape of the relationship between urinary Na and blood pressure, we created restricted cubic spline plots adjusted for age, sex, body mass index, smoking and alcohol consumption, physical activities, religion, sleep hours, and household wealth. To assess multicollinearity, we reported variance inflation factors, tolerances, and Leamer's and Klein's statistics following linear regression models. The mean daily urinary Na was 122 (SD 26) mmol/d for the first; 122 (SD 27) mmol/d for the second; and 134 (SD 70) mmol/d for the 24-hour samples. The restricted cubic spline plots illustrated no association between first-morning urinary Na and systolic blood pressure until the 90th percentile distribution followed by a downward relationship; a nonlinear inverse-V-shaped relationship between second-morning urinary Na and systolic blood pressure; and a monotonic upward relationship between 24-hour urinary Na and systolic blood pressure. We found no evidence of multicollinearity for the 24-hour urinary Na model. Conclusions The urinary Na and systolic blood pressure relationship varied for 3 urinary Na measurements. Twenty-four-hour urinary Na captured more variability of Na intake compared with spot urine samples, and its regression models were not affected by multicollinearity.

A Bayesian ensemble approach to combine PM2.5 estimates from statistical models using satellite imagery and numerical model simulation

Ambient fine particulate matter less than 2.5 μm in aerodynamic diameter (PM_2.5) has been linked to various adverse health outcomes. PM_2.5 arises from both natural and anthropogenic sources, and PM_2.5 concentrations can vary over space and time. However, the sparsity of existing air quality monitors greatly restricts the spatial-temporal coverage of PM_2.5 measurements, potentially limiting the accuracy of PM_2.5-related health studies. Various methods exist to address these limitations by supplementing air quality monitoring measurements with additional data. We develop a method to combine PM_2.5 estimated from satellite-retrieved aerosol optical depth (AOD) and chemical transport model (CTM) simulations using statistical models. While most previous methods utilize AOD or CTM separately, we aim to leverage advantages offered by both data sources in terms of resolution and coverage using Bayesian ensemble averaging. Our approach differs from previous ensemble approaches in its ability to not only incorporate uncertainties in PM_2.5 estimates from individual models but also to provide uncertainties for the resulting ensemble estimates. In an application of estimating daily PM_2.5 in the Southeastern US, the ensemble approach outperforms previously developed spatial-temporal statistical models that use either AOD or bias-corrected CTM simulations in cross-validation (CV) analyses. More specifically, in spatially clustered CV experiments, the ensemble approach reduced the AOD-only and CTM-only model's root mean squared error (RMSE) by at least 13%. Similar improvements were seen in R². The enhanced prediction performance that the ensemble technique provides at fine-scale spatial resolution, as well as the availability of prediction uncertainty, can be further used in health effect analyses of air pollution exposure.

Sex differences in the interaction of short-term particulate matter exposure and psychosocial stressors on C-reactive protein in a Puerto Rican cohort

There is substantial evidence linking particulate matter air pollution with cardiovascular morbidity and mortality. However, health disparities between populations may exist due to imprecisely defined non-innate susceptibility factors. Psychosocial stressors are associated with cardiovascular disease and may increase non-innate susceptibility to air-pollution. We investigated whether the association between short-term changes in ambient particulate matter and cardiovascular health risk differed by psychosocial stressors in a Puerto Rican cohort, comparing women and men. We used data from the Boston Puerto Rican Health Study (BPRHS), a longitudinal study of cardiovascular health among adults, collected between 2004 and 2013. We used mixed effect models to estimate the association of current-day ambient particle number concentration (PNC) on C-reactive protein (CRP), a marker of systemic inflammation, and effect modification by psychosocial stressors (depression, acculturation, perceived stress, discrimination, negative life events and a composite score). Point estimates of percent difference in CRP per interquartile range change in PNC varied among women with contrasting levels of stressors: negative life events (15.7% high vs. 6.5% low), depression score (10.6% high vs. 4.6% low) and composite stress score (16.2% high vs. 7.0% low). There were minimal differences among men. For Puerto Rican adults, cardiovascular non-innate susceptibility to adverse effects of ambient particles may be greater for women under high stress. This work contributes to understanding health disparities among minority ethnic populations.

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Psychosocial stress is associated with disease and thus may enhance cardiovascular susceptibility to air pollution exposure.

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Point estimates of association between particle matter (PM) and C-reactive protein differed by stress in Puerto Ricans.

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Effects of PM on C-reactive protein were higher for Puerto Rican women under high stress compared to this with lower stress.

Application of a Fusion Method for Gas and Particle Air Pollutants between Observational Data and Chemical Transport Model Simulations Over the Contiguous United States for 2005-2014

Accurate spatiotemporal air quality data are critical for use in assessment of regulatory effectiveness and for exposure assessment in health studies. A number of data fusion methods have been developed to combine observational data and chemical transport model (CTM) results. Our approach focuses on preserving the temporal variation provided by observational data while deriving the spatial variation from the community multiscale air quality (CMAQ) simulations, a type of CTM. Here we show the results of fusing regulatory monitoring observational data with 12 km resolution CTM simulation results for 12 pollutants (CO, NOx, NO₂, SO_2, O₃, PM_2.5, PM₁₀, NO₃⁻, NH₄⁺, EC, OC, SO₄²⁻) over the contiguous United States on a daily basis for a period of ten years (2005–2014). An annual mean regression between the CTM simulations and observational data is used to estimate the average spatial fields, and spatial interpolation of observations normalized by predicted annual average is used to provide the daily variation. Results match the temporal variation well (R² values ranging from 0.84–0.98 across pollutants) and the spatial variation less well (R² values 0.42–0.94). Ten-fold cross validation shows normalized root mean square error values of 60% or less and spatiotemporal R² values of 0.4 or more for all pollutants except SO₂.

Precipitation and Salmonellosis Incidence in Georgia, USA: Interactions between Extreme Rainfall Events and Antecedent Rainfall Conditions

BACKGROUND

The southeastern United States consistently has high salmonellosis incidence, but disease drivers remain unknown. Salmonella is regularly detected in this region's natural environment, leading to numerous exposure opportunities. Rainfall patterns may impact the survival/transport of environmental Salmonella in ways that can affect disease transmission.

OBJECTIVES

This study investigated associations between short-term precipitation (extreme rainfall events) and longer-term precipitation (rainfall conditions antecedent to these extreme events) on salmonellosis counts in the state of Georgia in the United States.

METHODS

For the period 1997-2016, negative binomial models estimated associations between weekly county-level extreme rainfall events (≥90th percentile of daily rainfall) and antecedent conditions (8-week precipitation sums, categorized into tertiles) and weekly county-level salmonellosis counts.

RESULTS

In Georgia's Coastal Plain counties, extreme and antecedent rainfall were associated with significant differences in salmonellosis counts. In these counties, extreme rainfall was associated with a 5% increase in salmonellosis risk (95% CI: 1%, 10%) compared with weeks with no extreme rainfall. Antecedent dry periods were associated with a 9% risk decrease (95% CI: 5%, 12%), whereas wet periods were associated with a 5% increase (95% CI: 1%, 9%), compared with periods of moderate rainfall. In models considering the interaction between extreme and antecedent rainfall conditions, wet periods were associated with a 13% risk increase (95% CI: 6%, 19%), whereas wet periods followed by extreme events were associated with an 11% increase (95% CI: 5%, 18%). Associations were substantially magnified when analyses were restricted to cases attributed to serovars commonly isolated from wildlife/environment (e.g., Javiana). For example, wet periods followed by extreme rainfall were associated with a 34% risk increase (95% CI: 20%, 49%) in environmental serovar infection.

CONCLUSIONS

Given the associations of short-term extreme rainfall events and longer-term rainfall conditions on salmonellosis incidence, our findings suggest that avoiding contact with environmental reservoirs of Salmonella following heavy rainfall events, especially during the rainy season, may reduce the risk of salmonellosis. https://doi.org/10.1289/EHP4621.

Impact of a school-based water, sanitation, and hygiene intervention on school absence, diarrhea, respiratory infection, and soil-transmitted helminths: results from the WASH HELPS cluster-randomized trial

Background

Water, sanitation, and hygiene (WASH) in schools is promoted by development agencies as a modality to improve school attendance by reducing illness. Despite biological plausibility, the few rigorous studies that have assessed the effect of WASH in schools (WinS) interventions on pupil health and school attendance have reported mixed impacts. We evaluated the impact of the Laos Basic Education, Water, Sanitation and Hygiene Programme – a comprehensive WinS project implemented by UNICEF Lao People’s Democratic Republic (Lao PDR) in 492 primary schools nationwide between 2013 and 2017 – on pupil education and health.

Methods

From 2014-2017, we conducted a cluster-randomized trial among 100 randomly selected primary schools lacking functional WASH facilities in Saravane Province, Lao PDR. Schools were randomly assigned to either the intervention (n = 50) or comparison (n = 50) arm. Intervention schools received a school water supply, sanitation facilities, handwashing facilities, drinking water filters, and behavior change education and promotion. Comparison schools received the intervention after research activities ended. At unannounced visits every six to eight weeks, enumerators recorded pupils’ roll-call absence, enrollment, attrition, progression to the next grade, and reported illness (diarrhea, respiratory infection, conjunctivitis), and conducted structured observations to measure intervention fidelity and adherence. Stool samples were collected annually prior to de-worming and analyzed for soil-transmitted helminth (STH) infection. In addition to our primary intention-to-treat analysis, we conducted secondary analyses to quantify the role of intervention fidelity and adherence on project impacts.

Results

We found no impact of the WinS intervention on any primary (pupil absence) or secondary (enrollment, dropout, grade progression, diarrhea, respiratory infection, conjunctivitis, STH infection) impacts. Even among schools with the highest levels of fidelity and adherence, impact of the intervention on absence and health was minimal.

Conclusions

While WinS may create an important enabling environment, WinS interventions alone and as currently delivered may not be sufficient to independently impact pupil education and health. Our results are consistent with other recent evaluations of WinS projects showing limited or mixed effects of WinS.

Mass Gatherings and Diarrheal Disease Transmission Among Rural Communities in Coastal Ecuador

Mass gatherings exacerbate infectious disease risks by creating crowded, high-contact conditions and straining the capacity of local infrastructure. While mass gatherings have been extensively studied in the context of epidemic disease transmission, the role of gatherings in incidence of high-burden, endemic infections has not been previously studied. Here, we examine diarrheal incidence among 17 communities in Esmeraldas, Ecuador, in relation to recurrent gatherings characterized using ethnographic data collected during and after the epidemiologic surveillance period (2004-2007). Using distributed-lag generalized estimating equations, adjusted for seasonality, trend, and heavy rainfall events, we found significant increases in diarrhea risk in host villages, peaking 2 weeks after an event's conclusion (incidence rate ratio, 1.21; confidence interval, adjusted for false coverage rate of ≤0.05: 1.02, 1.43). Stratified analysis revealed heightened risks associated with events where crowding and travel were most likely (2-week-lag incidence rate ratio, 1.51; confidence interval, adjusted for false coverage rate of ≤0.05: 1.09, 2.10). Our findings suggest that community-scale mass gatherings might play an important role in endemic diarrheal disease transmission and could be an important focus for interventions to improve community health in low-resource settings.

Time-series analysis of satellite-derived fine particulate matter pollution and asthma morbidity in Jackson, MS

In order to examine associations between asthma morbidity and local ambient air pollution in an area with relatively low levels of pollution, we conducted a time-series analysis of asthma hospital admissions and fine particulate matter pollution (PM_2.5) in and around Jackson, MS, for the period 2003 to 2011. Daily patient-level records were obtained from the Mississippi State Department of Health (MSDH) Asthma Surveillance System. Patient geolocations were aggregated into a grid with 0.1° × 0.1° resolution within the Jackson Metropolitan Statistical Area. Daily PM_2.5 concentrations were estimated via machine-learning algorithms with remotely sensed aerosol optical depth and other associated parameters as inputs. Controlling for long-term temporal trends and meteorology, we estimated a 7.2% (95% confidence interval 1.7-13.1%) increase in daily all-age asthma emergency room admissions per 10 μg/m³ increase in the 3-day average of PM_2.5 levels (current day and two prior days). Stratified analyses reveal significant associations between asthma and 3-day average PM_2.5 for males and blacks. Our results contribute to the current epidemiologic evidence on the association between acute ambient air pollution exposure and asthma morbidity, even in an area characterized by relatively good air quality.

Associations between soil-transmitted helminthiasis and viral, bacterial, and protozoal enteroinfections: a cross-sectional study in rural Laos

Background

Humans are susceptible to over 1400 pathogens. Co-infection by multiple pathogens is common, and can result in a range of neutral, facilitative, or antagonistic interactions within the host. Soil-transmitted helminths (STH) are powerful immunomodulators, but evidence of the effect of STH infection on the direction and magnitude of concurrent enteric microparasite infections is mixed.

Methods

We collected fecal samples from 891 randomly selected children and adults in rural Laos. Samples were analyzed for 5 STH species, 6 viruses, 9 bacteria, and 5 protozoa using a quantitative reverse transcription polymerase chain reaction (qRT-PCR) assay. We utilized logistic regression, controlling for demographics and household water, sanitation, and hygiene access, to examine the effect of STH infection on concurrent viral, bacterial, and protozoal infection.

Results

We found that STH infection was associated with lower odds of concurrent viral infection [odds ratio (OR): 0.48, 95% confidence interval (CI): 0.28–0.83], but higher odds of concurrent bacterial infections (OR: 1.81, 95% CI: 1.06–3.07) and concurrent protozoal infections (OR: 1.50, 95% CI: 0.95–2.37). Trends were consistent across STH species.

Conclusions

The impact of STH on odds of concurrent microparasite co-infection may differ by microparasite taxa, whereby STH infection was negatively associated with viral infections but positively associated with bacterial and protozoal infections. Results suggest that efforts to reduce STH through preventive chemotherapy could have a spillover effect on microparasite infections, though the extent of this impact requires additional study. The associations between STH and concurrent microparasite infection may reflect a reverse effect due to the cross-sectional study design. Additional research is needed to elucidate the exact mechanism of the immunomodulatory effects of STH on concurrent enteric microparasite infection.

Electronic supplementary material

The online version of this article (10.1186/s13071-019-3471-2) contains supplementary material, which is available to authorized users.