Monitoring vs. modeled exposure data in time-series studies of ambient air pollution and acute health outcomes

BACKGROUND

Population-based short-term air pollution health studies often have limited spatiotemporally representative exposure data, leading to concerns of exposure measurement error.

OBJECTIVE

To compare the use of monitoring and modeled exposure metrics in time-series analyses of air pollution and cardiorespiratory emergency department (ED) visits.

METHODS

We obtained daily counts of ED visits for Atlanta, GA during 2009-2013. We leveraged daily ZIP code level concentration estimates for eight pollutants from nine exposure metrics. Metrics included central monitor (CM), monitor-based (inverse distance weighting, kriging), model-based [community multiscale air quality (CMAQ), land use regression (LUR)], and satellite-based measures. We used Poisson models to estimate air pollution health associations using the different exposure metrics. The approach involved: (1) assessing CM-based associations, (2) determining if non-CM metrics can reproduce CM-based associations, and (3) identifying potential value added of incorporating full spatiotemporal information provided by non-CM metrics.

RESULTS

Using CM exposures, we observed associations between cardiovascular ED visits and carbon monoxide, nitrogen dioxide, fine particulate matter, elemental and organic carbon, and between respiratory ED visits and ozone. Non-CM metrics were largely able to reproduce CM-based associations, although some unexpected results using CMAQ- and LUR-based metrics reduced confidence in these data for some spatiotemporally-variable pollutants. Associations with nitrogen dioxide and sulfur dioxide were only detected, or were stronger, when using metrics that incorporate all available monitoring data (i.e., inverse distance weighting and kriging).

SIGNIFICANCE

The use of routinely-collected ambient monitoring data for exposure assignment in time-series studies of large metropolitan areas is a sound approach, particularly when data from multiple monitors are available. More sophisticated approaches derived from CMAQ, LUR, or satellites may add value when monitoring data are inadequate and if paired with thorough data characterization. These results are useful for interpretation of existing literature and for improving exposure assessment in future studies.

IMPACT STATEMENT

This study compared and interpreted the use of monitoring and modeled exposure metrics in a daily time-series analysis of air pollution and cardiorespiratory emergency department visits. The results suggest that the use of routinely-collected ambient monitoring data in population-based short-term air pollution and health studies is a sound approach for exposure assignment in large metropolitan regions. CMAQ-, LUR-, and satellite-based metrics may allow for health effects estimation when monitoring data are sparse, if paired with thorough data characterization. These results are useful for interpretation of existing health effects literature and for improving exposure assessment in future air pollution epidemiology studies.

Confounding by Conception Seasonality in Studies of Temperature and Preterm Birth: A Simulation Study

BACKGROUND

Seasonal patterns of conception may confound acute associations between birth outcomes and seasonally varying exposures. We aim to evaluate four epidemiologic designs (time-stratified case-crossover, time-series, pair-matched case-control, and time-to-event) commonly used to study acute associations between ambient temperature and preterm births.

METHODS

We conducted simulations assuming no effect of temperature on preterm birth. We generated pseudo-birth data from the observed seasonal patterns of birth in the United States and analyzed them in relation to observed temperatures using design-specific seasonality adjustments.

RESULTS

Using the case-crossover approach (time-stratified by calendar month), we observed a bias (among 1,000 replicates) = 0.016 (Monte-Carlo standard error 95% CI: 0.015-0.018) in the regression coefficient for every 10°C increase in mean temperature in the warm season (May-September). Unbiased estimates obtained using the time-series approach required accounting for both the pregnancies-at-risk and their weighted probability of birth. Notably, adding the daily weighted probability of birth from the time-series models to the case-crossover models corrected the bias in the case-crossover approach. In the pair-matched case-control design, where the exposure period was matched on gestational window, we observed no bias. The time-to-event approach was also unbiased but was more computationally intensive than others.

CONCLUSIONS

Most designs can be implemented in a way that yields estimates unbiased by conception seasonality. The time-stratified case-crossover design exhibited a small positive bias, which could contribute to, but not fully explain, previously reported associations.

Joint Effects of Indoor Air Pollution and Maternal Psychosocial Factors During Pregnancy on Trajectories of Early Childhood Psychopathology

Background

Prenatal indoor air pollution and maternal psychosocial factors have been associated with adverse psychopathology. We used environmental exposure mixture methodology to investigate joint effects of both exposure classes on child behavior trajectories.

Methods

For 360 children from the South African Drakenstein Child Health Study, we created trajectories of Child Behavior Checklist scores (24, 42, 60 months) using latent class linear mixed effects models. Indoor air pollutants and psychosocial factors were measured during pregnancy (2 nd trimester). After adjusting for confounding, single-exposure effects (per natural log-1 unit increase) were assessed using polytomous logistic regression models; joint effects using self-organizing maps (SOM), and principal component (PC) analysis.

Results

High externalizing trajectory was associated with increased particulate matter (PM 10 ) exposure (OR [95%-CI]: 1.25 [1.01,1.55]) and SOM exposure profile most associated with smoking (2.67 [1.14,6.27]). Medium internalizing trajectory was associated with increased emotional intimate partner violence (2.66 [1.17,5.57]), increasing trajectory with increased benzene (1.24 [1.02,1.51]) and toluene (1.21 [1.02,1.44]) and the PC most correlated with benzene and toluene (1.25 [1.02, 1.54]).

Conclusions

Prenatal exposure to environmental pollutants and psychosocial factors was associated with internalizing and externalizing child behavior trajectories. Understanding joint effects of adverse exposure mixtures will facilitate targeted interventions to prevent childhood psychopathology.

Long-term exposure to traffic-related air pollution and non-accidental mortality: A systematic review and meta-analysis

BACKGROUND

The health effects of traffic-related air pollution (TRAP) continue to be of important public health interest across the globe. Following its 2010 review, the Health Effects Institute appointed a new expert Panel to systematically evaluate the epidemiological evidence regarding the associations between long-term exposure to TRAP and selected health outcomes. This paper describes the main findings of the systematic review on non-accidental mortality.

METHODS

The Panel used a systematic approach to conduct the review. An extensive search was conducted of literature published between 1980 and 2019. A new exposure framework was developed to determine whether a study was sufficiently specific to TRAP, which included studies beyond the near-roadway environment. We performed random-effects meta-analysis when at least three estimates were available of an association between a specific exposure and outcome. We evaluated confidence in the evidence using a modified Office of Health Assessment and Translation (OHAT) approach, supplemented with a broader narrative synthesis.

RESULTS

Thirty-six cohort studies were included. Virtually all studies adjusted for a large number of individual and area-level covariates-including smoking, body mass index, and individual and area-level socioeconomic status-and were judged at a low or moderate risk for bias. Most studies were conducted in North America and Europe, and a few were based in Asia and Australia. The meta-analytic summary estimates for nitrogen dioxide, elemental carbon and fine particulate matter-pollutants with more than 10 studies-were 1.04 (95% CI 1.01, 1.06), 1.02 (1.00, 1.04) and 1.03 (1.01, 1.05) per 10, 1 and 5 µg/m³, respectively. Effect estimates are interpreted as the relative risk of mortality when the exposure differs with the selected increment. The confidence in the evidence for these pollutants was judged as high, because of upgrades for monotonic exposure-response and consistency across populations. The consistent findings across geographical regions, exposure assessment methods and confounder adjustment resulted in a high confidence rating using a narrative approach as well.

CONCLUSIONS

The overall confidence in the evidence for a positive association between long-term exposure to TRAP and non-accidental mortality was high.

Acute Effects of Ambient Air Pollution on Asthma Emergency Department Visits in Ten U.S. States

BACKGROUND

Previous studies of short-term ambient air pollution exposure and asthma morbidity in the United States have been limited to a small number of cities and/or pollutants and with limited consideration of effects across ages.

OBJECTIVES

To estimate acute age group-specific effects of fine and coarse particulate matter (PM), major PM components, and gaseous pollutants on emergency department (ED) visits for asthma during 2005-2014 across the United States.

METHODS

We acquired ED visit and air quality data in regions surrounding 53 speciation sites in 10 states. We used quasi-Poisson log-linear time-series models with unconstrained distributed exposure lags to estimate site-specific acute effects of air pollution on asthma ED visits overall and by age group (1-4, 5-17, 18-49, 50-64, and 65+ y), controlling for meteorology, time trends, and influenza activity. We then used a Bayesian hierarchical model to estimate pooled associations from site-specific associations.

RESULTS

Our analysis included 3.19 million asthma ED visits. We observed positive associations for multiday cumulative exposure to all air pollutants examined [e.g., 8-d exposure to PM2.5: rate ratio of 1.016 with 95% credible interval (CI) of (1.008, 1.025) per 6.3-μg/m3 increase, PM10-2.5: 1.014 (95% CI: 1.007, 1.020) per 9.6-μg/m3 increase, organic carbon: 1.016 (95% CI: 1.009, 1.024) per 2.8-μg/m3 increase, and ozone: 1.008 (95% CI: 0.995, 1.022) per 0.02-ppm increase]. PM2.5 and ozone showed stronger effects at shorter lags, whereas associations of traffic-related pollutants (e.g., elemental carbon and oxides of nitrogen) were generally stronger at longer lags. Most pollutants had more pronounced effects on children (<18 y old) than adults; PM2.5 had strong effects on both children and the elderly (>64 y old); and ozone had stronger effects on adults than children.

CONCLUSIONS

We reported positive associations between short-term air pollution exposure and increased rates of asthma ED visits. We found that air pollution exposure posed a higher risk for children and older populations. https://doi.org/10.1289/EHP11661.

Short-term associations between warm-season ambient temperature and emergency department visits for Alzheimer's disease and related dementia in five US states

BACKGROUND

Ambient temperatures are projected to increase in the future due to climate change. Alzheimer's disease (AD) and Alzheimer's disease-related dementia (ADRD) affect millions of individuals and represent substantial health burdens in the US. High temperature may be a risk factor for AD/ADRD outcomes with several recent studies reporting associations between temperature and AD mortality. However, the link between heat and AD morbidity is poorly understood.

METHODS

We examined short-term associations between warm-season daily ambient temperature and AD/ADRD emergency department (ED) visits for individuals aged 45 years or above during the warm season (May to October) for up to 14 years (2005-2018) in five US states: California, Missouri, North Carolina, New Jersey, and New York. Daily ZIP code-level maximum, average and minimum temperature exposures were derived from 1 km gridded Daymet products. Associations are assessed using a time-stratified case-crossover design using conditional logistic regression.

RESULTS

We found consistent positive short-term effects of ambient temperature among 3.4 million AD/ADRD ED visits across five states. An increase of the 3-day cumulative temperature exposure of daily average temperature from the 50th to the 95th percentile was associated with a pooled odds ratio of 1.042 (95% CI: 1.034, 1.051) for AD/ADRD ED visits. We observed evidence of the association being stronger for patients 65-74 years of age and for ED visits that led to hospital admissions. Temperature associations were also stronger among AD/ADRD ED visits compared to ED visits for other reasons, particularly among patients aged 65-74 years.

CONCLUSION

People with AD/ADRD may represent a vulnerable population affected by short-term exposure to high temperature. Our results support the development of targeted strategies to reduce heat-related AD/ADRD morbidity in the context of global warming.

Prenatal exposure to ambient air pollution is associated with neurodevelopmental outcomes at 2 years of age

BACKGROUND

Higher prenatal ambient air pollution exposure has been associated with impaired neurodevelopment in preschoolers and school-aged children. The purpose of this study was to explore the relationships between prenatal ambient air pollution exposure and neurodevelopment during infancy.

METHODS

This study examined 161 Latino mother-infant pairs from the Southern California Mother's Milk Study. Exposure assessments included prenatal nitrogen dioxide (NO₂) and particulate matter smaller than 2.5 and 10 microns in diameter (PM_2.5 and PM₁₀, respectively). The pregnancy period was also examined as three windows, early, mid, and late, which describe the first, middle, and last three months of pregnancy. Infant neurodevelopmental outcomes at 2 years of age were measured using the Bayley-III Scales of Infant and Toddler Development. Multivariable linear models and distributed lag linear models (DLM) were used to examine relationships between prenatal exposures and neurodevelopmental scores, adjusting for socioeconomic status, breastfeeding frequency, time of delivery, pre-pregnancy body mass index, and infant birthweight and sex.

RESULTS

Higher prenatal exposure to PM₁₀ and PM_2.5 was negatively associated with composite cognitive score (β = -2.01 [-3.89, -0.13] and β = -1.97 [-3.83, -0.10], respectively). In addition, higher average prenatal exposure to PM₁₀ was negatively associated with composite motor (β = -2.35 [-3.95, -0.74]), scaled motor (β = -0.77 [-1.30, -0.24]), gross motor (β = -0.37 [-0.70, -0.04]), fine motor (β = -0.40 [-0.71, -0.09]), composite language (β = -1.87 [-3.52, -0.22]), scaled language (β = -0.61 [-1.18, -0.05]) and expressive communication scaled scores (β = -0.36 [-0.66, -0.05]). DLMs showed that higher prenatal air pollution exposure during mid and late pregnancy was inversely associated with motor, cognitive, and communication language scores.

CONCLUSIONS

Higher exposure to air pollutants during pregnancy, particularly in the mid and late prenatal periods, was inversely associated with scaled and composite motor, cognitive, and language scores at 2 years. These results indicate that prenatal ambient air pollution may negatively impact neurodevelopment in early life.

Association of distance between hospitals and volume of shared admissions

Background

To assess whether decreasing distance between hospitals was associated with the number of shared patients (patients with an admission to one hospital and a readmission to another).

Methods

Data were from the Healthcare Cost and Utilization Project’s State Inpatient Databases (Florida, Georgia, Maryland, Utah [2017], New York, Vermont [2016]) and the American Hospital Association Annual Survey (2016 & 2017). This was a cross-sectional analysis of patients who had an index admission and subsequent readmission at different hospitals within the same year. We used unadjusted and adjusted linear regression to evaluate the association between the number of shared patients and the distance between admission-readmission hospital pairs.

Results

There were 691 hospitals in the sample (247 in Florida, 151 in Georgia, 50 in Maryland, 172 in New York, 58 in Utah, and 13 in Vermont), accounting for a total of 596,772 admission-readmission pairs. 32.6% of the admission-readmission pairs were shared between two hospitals. On average, a one-mile decrease in distance between two hospitals was associated with of 3.05 (95% CI, 3.02, 3.07) more shared admissions. However, variability between states was wide, with Utah having 0.37 (95% CI 0.35, 0.39) more shared admissions between hospitals per one-mile shorter distance, and Maryland having 4.98 (95% CI 4.87, 5.08) more.

Conclusions

We found that proximity between hospitals is associated with higher volumes of shared admissions.

Long-term air pollution exposure and incident stroke in American older adults: A national cohort study

Aims

Stroke is a leading cause of death and disability for Americans, and growing evidence suggests that air pollution may play an important role. To facilitate pollution control efforts, the National Academy of Sciences and the World Health Organization have prioritized determining which air pollutants are most toxic. However, evidence is limited for the simultaneous effects of multiple air pollutants on stroke.

Methods and results

We constructed a nationwide population-based cohort study, using the Medicare Chronic Conditions Warehouse (2000-2017) and high-resolution air pollution data, to investigate the impact of long-term exposure to ambient PM2.5, NO2, and ground-level O3 on incident stroke. Hazard ratios (HR) for stroke incidence were estimated using single-, bi-, and tri-pollutant Cox proportional hazards models. We identified ~2.2 million incident stroke cases among 17,443,900 fee-for-service Medicare beneficiaries. Per interquartile range (IQR) increase in the annual average PM2.5 (3.7 μg/m3), NO2 (12.4 ppb), and warm-season O3 (6.5 ppb) one-year prior to diagnosis, the HRs were 1.022 (95% CI: 1.017-1.028), 1.060 (95% CI: 1.054-1.065), and 1.021 (95% CI: 1.017-1.024), respectively, from the tri-pollutant model. There was strong evidence of linearity in concentration-response relationships for all three air pollutants in single-pollutant models. This linear relationship remained robust for NO2 and O3 in tri-pollutant models while the effect of PM2.5 attenuated at the lower end of concentrations.

Conclusion

Using a large nationwide cohort, our study suggests that long-term exposure to PM2.5, NO2, and O3 may independently increase the risk of stroke among the US elderly, among which traffic-related air pollution plays a particularly crucial role.

Liquefied Petroleum Gas or Biomass for Cooking and Effects on Birth Weight

BACKGROUND

Exposure during pregnancy to household air pollution caused by the burning of solid biomass fuel is associated with adverse health outcomes, including low birth weight. Whether the replacement of a biomass cookstove with a liquefied petroleum gas (LPG) cookstove would result in an increase in birth weight is unclear.

METHODS

We performed a randomized, controlled trial involving pregnant women (18 to <35 years of age and at 9 to <20 weeks' gestation as confirmed on ultrasonography) in Guatemala, India, Peru, and Rwanda. The women were assigned in a 1:1 ratio to use a free LPG cookstove and fuel (intervention group) or to continue using a biomass cookstove (control group). Birth weight, one of four prespecified primary outcomes, was the primary outcome for this report; data for the other three outcomes are not yet available. Birth weight was measured within 24 hours after birth. In addition, 24-hour personal exposures to fine particulate matter (particles with a diameter of ≤2.5 μm [PM2.5]), black carbon, and carbon monoxide were measured at baseline and twice during pregnancy.

RESULTS

A total of 3200 women underwent randomization; 1593 were assigned to the intervention group, and 1607 to the control group. Uptake of the intervention was nearly complete, with traditional biomass cookstoves being used at a median rate of less than 1 day per month. After randomization, the median 24-hour personal exposure to fine particulate matter was 23.9 μg per cubic meter in the intervention group and 70.7 μg per cubic meter in the control group. Among 3061 live births, a valid birth weight was available for 94.9% of the infants born to women in the intervention group and for 92.7% of infants born to those in the control group. The mean (±SD) birth weight was 2921±474.3 g in the intervention group and 2898±467.9 g in the control group, for an adjusted mean difference of 19.6 g (95% confidence interval, -10.1 to 49.2).

CONCLUSIONS

The birth weight of infants did not differ significantly between those born to women who used LPG cookstoves and those born to women who used biomass cookstoves. (Funded by the National Institutes of Health and the Bill and Melinda Gates Foundation; HAPIN ClinicalTrials.gov number, NCT02944682.).

Outpatient clinic attendance and outcomes among patients hospitalized with diabetic foot ulcers

BACKGROUND

There are limited data on post-hospital discharge clinic attendance rates and outcomes among patients with diabetic foot ulcers (DFUs).

METHODS

Retrospective study of patients hospitalized with a DFU from 2016 to 2019 in a large public hospital. We measured rates and predictors of clinic attendance with providers involved with DFU care within 30 days of hospital discharge ("30-day post-discharge clinic attendance"). Log-binomial regression was used to estimate risk ratios (RR) and 95 % confidence intervals (CI).

RESULTS

Among 888 patients, 60.0 % were between 45 and 64 years old, 80.5 % were Black, and 24.1 % were uninsured. Overall, 478 (53.8 %) attended ≥1 30-day post-discharge clinic appointment. Initial hospital outcomes were associated with clinic attendance. For example, the RR of 30-day post-discharge clinic attendance was 1.39 (95%CI 1.19-1.61) among patients who underwent a major amputation compared to patients with DFUs without osteomyelitis and did not undergo an amputation during the initial hospitalization. Among 390 patients with known 12-month outcome, 71 (18.2 %) had a major amputation or died ≤12 months of hospital discharge.

CONCLUSION

We found a low post-discharge clinic attendance and high post-discharge amputation and death rates among patients hospitalized with DFUs. Interventions to increase access to outpatient DFU care are needed and could prevent amputations.

Effects of adding household water filters to Rwanda's Community-Based Environmental Health Promotion Programme: a cluster-randomized controlled trial in Rwamagana district

Unsafe drinking water remains a major cause of mortality and morbidity. While Rwanda's Community-Based Environmental Health Promotion Programme (CBEHPP) promotes boiling and safe storage, previous research found these efforts to be ineffective in reducing fecal contamination of drinking water. We conducted a cluster randomized control led trial to determine if adding a household water filter with safe storage to the CBEHPP would improve drinking water quality and reduce child diarrhea. We enrolled 1,199 households with a pregnant person or child under 5 across 60 randomly selected villages in Rwamagana district. CBEHPP implementers distributed and promoted water purifiers to a random half of villages. We conducted two unannounced follow-up visits over 13-16 months after the intervention delivery. The intervention reduced the proportions of households with detectable E. coli in drinking water samples (primary outcome) by 20% (PR 0.80, 95% CI 0.74-0.87, p < 0.001) and with moderate and higher fecal contamination (≥10 CFU/100 mL) by 35% (PR 0.65, 95% CI 0.57-0.74, p < 0.001). The proportion of children under 5 experiencing diarrhea in the last week was reduced by 49% (aPR 0.51, 95%CI 0.35-0.73, p < 0.001). Our findings identify an effective intervention for improving water quality and child health that can be added to the CBEHPP.

Exposure Contrasts of Pregnant Women during the Household Air Pollution Intervention Network Randomized Controlled Trial

BACKGROUND

Exposure to PM 2.5 arising from solid fuel combustion is estimated to result in ∼ 2.3 million premature deaths and 91 million lost disability-adjusted life years annually. Interventions attempting to mitigate this burden have had limited success in reducing exposures to levels thought to provide substantive health benefits.

OBJECTIVES

This paper reports exposure reductions achieved by a liquified petroleum gas (LPG) stove and fuel intervention for pregnant mothers in the Household Air Pollution Intervention Network (HAPIN) randomized controlled trial.

METHODS

The HAPIN trial included 3,195 households primarily using biomass for cooking in Guatemala, India, Peru, and Rwanda. Twenty-four-hour exposures to PM 2.5 , carbon monoxide (CO), and black carbon (BC) were measured for pregnant women once before randomization into control (n = 1,605 ) and LPG (n = 1,590 ) arms and twice thereafter (aligned with trimester). Changes in exposure were estimated by directly comparing exposures between intervention and control arms and by using linear mixed-effect models to estimate the impact of the intervention on exposure levels.

RESULTS

Median postrandomization exposures of particulate matter (PM) with aerodynamic diameter ≤ 2.5 μ m (PM 2.5 ) in the intervention arm were lower by 66% at the first (71.5 vs. 24.1   μ g / m 3 ), and second follow-up visits (69.5 vs. 23.7   μ g / m 3 ) compared to controls. BC exposures were lower in the intervention arm by 72% (9.7 vs. 2.7   μ g / m 3 ) and 70% (9.6 vs. 2.8   μ g / m 3 ) at the first and second follow-up visits, respectively, and carbon monoxide exposure was 82% lower at both visits (1.1 vs. 0.2  ppm ) in comparison with controls. Exposure reductions were consistent over time and were similar across research locations.

DISCUSSION

Postintervention PM 2.5 exposures in the intervention arm were at the lower end of what has been reported for LPG and other clean fuel interventions, with 69% of PM 2.5 samples falling below the World Health Organization Annual Interim Target 1 of 35   μ g / m 3 . This study indicates that an LPG intervention can reduce PM 2.5 exposures to levels at or below WHO targets. https://doi.org/10.1289/EHP10295.

CRITICAL WINDOW VARIABLE SELECTION FOR MIXTURES: ESTIMATING THE IMPACT OF MULTIPLE AIR POLLUTANTS ON STILLBIRTH

Understanding the role of time-varying pollution mixtures on human health is critical as people are simultaneously exposed to multiple pollutants during their lives. For vulnerable subpopulations who have well-defined exposure periods (e.g., pregnant women), questions regarding critical windows of exposure to these mixtures are important for mitigating harm. We extend critical window variable selection (CWVS) to the multipollutant setting by introducing CWVS for mixtures (CWVSmix), a hierarchical Bayesian method that combines smoothed variable selection and temporally correlated weight parameters to: (i) identify critical windows of exposure to mixtures of time-varying pollutants, (ii) estimate the time-varying relative importance of each individual pollutant and their first order interactions within the mixture, and (iii) quantify the impact of the mixtures on health. Through simulation we show that CWVSmix offers the best balance of performance in each of these categories in comparison to competing methods. Using these approaches, we investigate the impact of exposure to multiple ambient air pollutants on the risk of stillbirth in New Jersey, 2005-2014. We find consistent elevated risk in gestational weeks 2, 16-17, and 20 for non-Hispanic Black mothers, with pollution mixtures dominated by ammonium (weeks 2, 17, 20), nitrate (weeks 2, 17), nitrogen oxides (weeks 2, 16), PM2.5 (week 2), and sulfate (week 20). The method is available in the R package CWVSmix.

Optimizing laboratory-based surveillance networks for monitoring multi-genotype or multi-serotype infections

With the aid of laboratory typing techniques, infectious disease surveillance networks have the opportunity to obtain powerful information on the emergence, circulation, and evolution of multiple genotypes, serotypes or other subtypes of pathogens, informing understanding of transmission dynamics and strategies for prevention and control. The volume of typing performed on clinical isolates is typically limited by its ability to inform clinical care, cost and logistical constraints, especially in comparison with the capacity to monitor clinical reports of disease occurrence, which remains the most widespread form of public health surveillance. Viewing clinical disease reports as arising from a latent mixture of pathogen subtypes, laboratory typing of a subset of clinical cases can provide inference on the proportion of clinical cases attributable to each subtype (i.e., the mixture components). Optimizing protocols for the selection of isolates for typing by weighting specific subpopulations, locations, time periods, or case characteristics (e.g., disease severity), may improve inference of the frequency and distribution of pathogen subtypes within and between populations. Here, we apply the Disease Surveillance Informatics Optimization and Simulation (DIOS) framework to simulate and optimize hand foot and mouth disease (HFMD) surveillance in a high-burden region of western China. We identify laboratory surveillance designs that significantly outperform the existing network: the optimal network reduced mean absolute error in estimated serotype-specific incidence rates by 14.1%; similarly, the optimal network for monitoring severe cases reduced mean absolute error in serotype-specific incidence rates by 13.3%. In both cases, the optimal network designs achieved improved inference without increasing subtyping effort. We demonstrate how the DIOS framework can be used to optimize surveillance networks by augmenting clinical diagnostic data with limited laboratory typing resources, while adapting to specific, local surveillance objectives and constraints.

Identifying joint impacts of sun radiation, temperature, humidity, and rain duration on triggering mental disorders using a high-resolution weather monitoring system

BACKGROUND

Mental disorders (MDs) are behavioral or mental patterns that cause significant distress or impairment of personal functioning. Previously, temperature has been linked to MDs, but most studies suffered from exposure misclassification due to limited monitoring sites. We aimed to assess whether multiple meteorological factors could jointly trigger MD-related emergency department (ED) visits in warm season, using a highly dense weather monitoring system.

METHODS

We conducted a time-stratified, case-crossover study. MDs-related ED visits (primary diagnosis) from May-October 2017-2018 were obtained from New York State (NYS) discharge database. We obtained solar radiation (SR), relative humidity (RH), temperature, heat index (HI), and rainfall from Mesonet, a real-time monitoring system spaced about 17 miles (126 stations) across NYS. We used conditional logistic regression to assess the weather-MD associations.

RESULTS

For each interquartile range (IQR) increase, both SR (excess risk (ER): 4.9%, 95% CI: 3.2-6.7%) and RH (ER: 4.0%, 95% CI: 2.6-5.4%) showed the largest risk for MD-related ED visits at lag 0-9 days. While temperature presented a short-term risk (highest ER at lag 0-2 days: 3.7%, 95% CI: 2.5-4.9%), HI increased risk over a two-week period (ER range: 3.7-4.5%), and rainfall hours showed an inverse association with MDs (ER: -0.5%, 95% CI: 0.9-(-0.1)%). Additionally, we observed stronger association of SR, RH, temperature, and HI in September and October. Combination of high SR, RH, and temperature displayed the largest increase in MDs (ER: 7.49%, 95% CI: 3.95-11.15%). The weather-MD association was stronger for psychoactive substance usage, mood disorders, adult behavior disorders, males, Hispanics, African Americans, individuals aged 46-65, or Medicare patients.

CONCLUSIONS

Hot and humid weather, especially the joint effect of high sun radiation, temperature and relative humidity showed the highest risk of MD diseases. We found stronger weather-MD associations in summer transitional months, males, and minority groups. These findings also need further confirmation.

Postnatal exposure to ambient air pollutants is associated with the composition of the infant gut microbiota at 6-months of age

Epidemiological studies in adults have shown that exposure to ambient air pollution (AAP) is associated with the composition of the adult gut microbiome, but these relationships have not been examined in infancy. We aimed to determine if 6-month postnatal AAP exposure was associated with the infant gut microbiota at 6 months of age in a cohort of Latino mother-infant dyads from the Southern California Mother's Milk Study (n = 103). We estimated particulate matter (PM_2.5 and PM₁₀) and nitrogen dioxide (NO₂) exposure from birth to 6-months based on residential address histories. We characterized the infant gut microbiota using 16S rRNA amplicon sequencing at 6-months of age. At 6-months, the gut microbiota was dominated by the phyla Bacteroidetes, Firmicutes, Proteobacteria, and Actinobacteria. Our results show that, after adjusting for important confounders, postnatal AAP exposure was associated with the composition of the gut microbiota. As an example, PM₁₀ exposure was positively associated with Dialister, Dorea, Acinetobacter, and Campylobacter while PM_2.5 was positively associated with Actinomyces. Further, exposure to PM₁₀ and PM_2.5 was inversely associated with Alistipes and NO₂ exposure was positively associated with Actinomyces, Enterococcus, Clostridium, and Eubacterium. Several of these taxa have previously been linked with systemic inflammation, including the genera Dialister and Dorea. This study provides the first evidence of significant associations between exposure to AAP and the composition of the infant gut microbiota, which may have important implications for future infant health and development.

Application of Bayesian spatial-temporal models for estimating unrecognized COVID-19 deaths in the United States

In the United States, COVID-19 has become a leading cause of death since 2020. However, the number of COVID-19 deaths reported from death certificates is likely to represent an underestimate of the total deaths related to SARS-CoV-2 infections. Estimating those deaths not captured through death certificates is important to understanding the full burden of COVID-19 on mortality. In this work, we explored enhancements to an existing approach by employing Bayesian hierarchical models to estimate unrecognized deaths attributed to COVID-19 using weekly state-level COVID-19 viral surveillance and mortality data in the United States from March 2020 to April 2021. We demonstrated our model using those aged ≥ 85 years who died. First, we used a spatial-temporal binomial regression model to estimate the percent of positive SARS-CoV-2 test results. A spatial-temporal negative-binomial model was then used to estimate unrecognized COVID-19 deaths by exploiting the spatial-temporal association between SARS-CoV-2 percent positive and all-cause mortality counts using an excess mortality approach. Computationally efficient Bayesian inference was accomplished via the Polya-Gamma representation of the binomial and negative-binomial models. Among those aged ≥ 85 years, we estimated 58,200 (95% CI: 51,300, 64,900) unrecognized COVID-19 deaths, which accounts for 26% (95% CI: 24%, 29%) of total COVID-19 deaths in this age group. Our modeling results suggest that COVID-19 mortality and the proportion of unrecognized deaths among deaths attributed to COVID-19 vary by time and across states.

Acute association between heatwaves and stillbirth in six US states

BACKGROUND

Heatwaves are becoming more frequent and may acutely increase the risk of stillbirth, a rare and severe pregnancy outcome.

OBJECTIVES

Examine the association between multiple heatwave metrics and stillbirth in six U.S. states.

METHODS

Data were collected from fetal death and birth records in California (1996-2017), Florida (1991-2017), Georgia (1994-2017), Kansas (1991-2017), New Jersey (1991-2015), and Oregon (1991-2017). Cases were matched to controls 1:4 based on maternal race/ethnicity, maternal education, and county, and exposure windows were aligned (gestational week prior to stillbirth). County-level temperature data were obtained from Daymet and linked to cases and controls by residential county and the exposure window. Five heatwave metrics (1 categorical, 3 dichotomous, 1 continuous) were created using different combinations of the duration and intensity of hot days (mean daily temperature exceeding the county-specific 97.5^th percentile) during the exposure window, as well as a continuous measure of mean temperature during the exposure window modeled using natural splines to allow for nonlinear associations. State-specific odds ratios (ORs) and 95% confidence intervals (CI) were estimated using conditional logistic regression models. State-specific results were pooled using a fixed-effects meta-analysis.

RESULTS

In our data set of 140,428 stillbirths (553,928 live birth controls), three of the five heatwave metrics examined were not associated with stillbirth. However, four consecutive hot days during the previous week was associated with a 3% increase in stillbirth risk (CI: 1.01, 1.06), and a 1 °C average increase over the threshold was associated with a 10% increase in stillbirth risk (CI: 1.04, 1.17). In continuous temperature analyses, there was a slight increased risk of stillbirth associated with extremely hot temperatures (≥ 35 °C).

DISCUSSION

Most heat wave definitions examined were not associated with acute changes in stillbirth risk; however, the most extreme heatwave durations and temperatures were associated with a modest increase in stillbirth risk.

Long-term exposure to traffic-related air pollution and selected health outcomes: A systematic review and meta-analysis

The health effects of traffic-related air pollution (TRAP) continue to be of important public health interest. Following its well-cited 2010 critical review, the Health Effects Institute (HEI) appointed a new expert Panel to systematically evaluate the epidemiological evidence regarding the associations between long-term exposure to TRAP and selected adverse health outcomes. Health outcomes were selected based on evidence of causality for general air pollution (broader than TRAP) cited in authoritative reviews, relevance for public health and policy, and resources available. The Panel used a systematic approach to search the literature, select studies for inclusion in the review, assess study quality, summarize results, and reach conclusions about the confidence in the evidence. An extensive search was conducted of literature published between January 1980 and July 2019 on selected health outcomes. A new exposure framework was developed to determine whether a study was sufficiently specific to TRAP. In total, 353 studies were included in the review. Respiratory effects in children (118 studies) and birth outcomes (86 studies) were the most commonly studied outcomes. Fewer studies investigated cardiometabolic effects (57 studies), respiratory effects in adults (50 studies), and mortality (48 studies). The findings from the systematic review, meta-analyses, and evaluation of the quality of the studies and potential biases provided an overall high or moderate-to-high level of confidence in an association between long-term exposure to TRAP and the adverse health outcomes all-cause, circulatory, ischemic heart disease and lung cancer mortality, asthma onsetin chilldren and adults, and acute lower respiratory infections in children. The evidence was considered moderate, low or very low for the other selected outcomes. In light of the large number of people exposed to TRAP - both in and beyond the near-road environment - the Panel concluded that the overall high or moderate-to-high confidence in the evidence for an association between long-term exposure to TRAP and several adverse health outcomes indicates that exposures to TRAP remain an important public health concern and deserve greater attention from the public and from policymakers.

Evaluation of the Use of Saliva Metabolome as a Surrogate of Blood Metabolome in Assessing Internal Exposures to Traffic-Related Air Pollution

In the omics era, saliva, a filtrate of blood, may serve as an alternative, noninvasive biospecimen to blood, although its use for specific metabolomic applications has not been fully evaluated. We demonstrated that the saliva metabolome may provide sensitive measures of traffic-related air pollution (TRAP) and associated biological responses via high-resolution, longitudinal metabolomics profiling. We collected 167 pairs of saliva and plasma samples from a cohort of 53 college student participants and measured corresponding indoor and outdoor concentrations of six air pollutants for the dormitories where the students lived. Grand correlation between common metabolic features in saliva and plasma was moderate to high, indicating a relatively consistent association between saliva and blood metabolites across subjects. Although saliva was less associated with TRAP compared to plasma, 25 biological pathways associated with TRAP were detected via saliva and accounted for 69% of those detected via plasma. Given the slightly higher feature reproducibility found in saliva, these findings provide some indication that the saliva metabolome offers a sensitive and practical alternative to blood for characterizing individual biological responses to environmental exposures.

Developing air pollution concentration fields for health studies using multiple methods: Cross-comparison and evaluation

Past air pollution epidemiological studies have used a wide range of methods to develop concentration fields for health analyses. The fields developed differ considerably among these methods. The reasons for these differences and comparisons of their strengths, as well as the limitations for estimating exposures, remains under-investigated. Here, we applied nine methods to develop fields of eight pollutants (carbon monoxide (CO), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃), fine particulate matter (PM_2.5), and three speciated PM_2.5 constituents including elemental carbon (EC), organic carbon (OC), and sulfate (SO₄)) for the metropolitan Atlanta region for five years. The nine methods are Central Monitor (CM), Site Average (SA), Inverse Distance Weighting (IDW), Kriging (KRIG), Land Use Regression (LUR), satellite Aerosol Optical Depth (AOD), CMAQ model, CMAQ with kriging adjustment (CMAQ-KRIG), and CMAQ based data fusion (CMAQ-DF). Additionally, we applied an increasingly popular method, Random Forest (RF), and compared its results for NO₂ and PM_2.5 with other methods. For statistical evaluation, we focused our discussion on the temporal coefficient of determination, although other metrics are also calculated. Raw output from the CMAQ model contains modeling biases and errors, which are partially mitigated by fusing observational data in the CMAQ-KRIG and CMAQ-DF methods. Based on analyses that simulated model responses to more limited input data, the RF model is more robust and outperforms LUR for PM_2.5. These results suggest RF may have potential in air pollution health studies, especially when limited measurement data are available. The RF method has several important weaknesses, including a relatively poor performance for NO₂, diagnostic challenges, and computational intensiveness. The results of this study will help to improve our understanding of the strengths and weaknesses of different methods for estimating air pollutant exposures in epidemiological studies.

Using land use variable information and a random forest approach to correct spatial mean bias in fused CMAQ fields for particulate and gas species

Accurate spatiotemporal air pollution fields are essential for health impact and epidemiologic studies. There are an increasing number of studies that have combined observational data with spatiotemporally complete air pollution simulations. Land-use, speciated gaseous and particulate pollutant concentrations and chemical transport modeling are fused using a random forest approach to construct daily air quality fields for 12 pollutants (CO, NOx, NO2, SO2, O3, PM2.5, PM10, and PM2.5 constituents: SO42-, NO3-, NH4+, EC and OC) between 2005 and 2014 for the continental United States with little spatial or temporal bias. R2 ranged from 0.45 to 0.96, depending upon pollutant. Additional analysis found that temporal R2 ranged from 0.84 to 0.99 and spatial R2 values ranged from 0.76 to 0.96 across species. Four-fold cross-validation was performed to assess the model's predictive power, and ranged from 0.40 for PM10 to 0.94 for SO4 with other pollutants falling within this range. Largest improvements were found for PM10 which had substantial bias in the CMAQ fields that varied east-to-west; smallest improvements were for SO4 which was already well simulated. The random forest model results to correct the simulation biases, while largely consistent year-to-year, did show slight variation due in part to changes in the distribution of monitors and changes in CMAQ simulation inputs.

Using Innovative Machine Learning Methods to Screen and Identify Predictors of Congenital Heart Diseases

Objective: Congenital heart diseases (CHDs) are associated with an extremely heavy global disease burden as the most common category of birth defects. Genetic and environmental factors have been identified as risk factors of CHDs previously. However, high volume clinical indicators have never been considered when predicting CHDs. This study aimed to predict the occurrence of CHDs by considering thousands of variables from self-reported questionnaires and routinely collected clinical laboratory data using machine learning algorithms.

Methods: We conducted a birth cohort study at one of the largest cardiac centers in China from 2011 to 2017. All fetuses were screened for CHDs using ultrasound and cases were confirmed by at least two pediatric cardiologists using echocardiogram. A total of 1,127 potential predictors were included to predict CHDs. We used the Explainable Boosting Machine (EBM) for prediction and evaluated the model performance using area under the Receive Operating Characteristics (ROC) curves (AUC). The top predictors were selected according to their contributions and predictive values. Thresholds were calculated for the most significant predictors.

Results: Overall, 5,390 mother-child pairs were recruited. Our prediction model achieved an AUC of 76% (69-83%) from out-of-sample predictions. Among the top 35 predictors of CHDs we identified, 34 were from clinical laboratory tests and only one was from the questionnaire (abortion history). Total accuracy, sensitivity, and specificity were 0.65, 0.74, and 0.65, respectively. Maternal serum uric acid (UA), glucose, and coagulation levels were the most consistent and significant predictors of CHDs. According to the thresholds of the predictors identified in our study, which did not reach the current clinical diagnosis criteria, elevated UA (>4.38 mg/dl), shortened activated partial thromboplastin time (<33.33 s), and elevated glucose levels were the most important predictors and were associated with ranges of 1.17-1.54 relative risks of CHDs. We have developed an online predictive tool for CHDs based on our findings that may help screening and prevention of CHDs.

Conclusions: Maternal UA, glucose, and coagulation levels were the most consistent and significant predictors of CHDs. Thresholds below the current clinical definition of “abnormal” for these predictors could be used to help develop CHD screening and prevention strategies.