Kristen M. Rappazzo

Construction of an environmental quality index for public health research

BackgroundA more comprehensive estimate of environmental quality would improve our understanding of the relationship between environmental conditions and human health. An environmental quality index (EQI) for all counties in the U.S. was developed.MethodsThe EQI was developed in four parts: domain identification; data source acquisition; variable construction; and data reduction. Five environmental domains (air, water, land, built and sociodemographic) were recognized. Within each domain, data sources were identified; each was temporally (years 2000–2005) and geographically (county) restricted. Variables were constructed for each domain and assessed for missingness, collinearity, and normality. Domain-specific data reduction was accomplished using principal components analysis (PCA), resulting in domain-specific indices. Domain-specific indices were then combined into an overall EQI using PCA. In each PCA procedure, the first principal component was retained. Both domain-specific indices and overall EQI were stratified by four rural–urban continuum codes (RUCC). Higher values for each index were set to correspond to areas with poorer environmental quality.ResultsConcentrations of included variables differed across rural–urban strata, as did within-domain variable loadings, and domain index loadings for the EQI. In general, higher values of the air and sociodemographic indices were found in the more metropolitan areas and the most thinly populated areas have the lowest values of each of the domain indices. The less-urbanized counties (RUCC 3) demonstrated the greatest heterogeneity and range of EQI scores (−4.76, 3.57) while the thinly populated strata (RUCC 4) contained counties with the most positive scores (EQI score ranges from −5.86, 2.52).ConclusionThe EQI holds promise for improving our characterization of the overall environment for public health. The EQI describes the non-residential ambient county-level conditions to which residents are exposed and domain-specific EQI loadings indicate which of the environmental domains account for the largest portion of the variability in the EQI environment. The EQI was constructed for all counties in the United States, incorporating a variety of data to provide a broad picture of environmental conditions. We undertook a reproducible approach that primarily utilized publically-available data sources.

Exposure to Perfluorinated Alkyl Substances and Health Outcomes in Children: A Systematic Review of the Epidemiologic Literature

Perfluoroalkyl substances (PFAS), chemicals used to make products stain and stick resistant, have been linked to health effects in adults and adverse birth outcomes. A growing body of literature also addresses health effects in children exposed to PFAS. This review summarizes the epidemiologic evidence for relationships between prenatal and/or childhood exposure to PFAS and health outcomes in children as well as to provide a risk of bias analysis of the literature. A systematic review was performed by searching PubMed for studies on PFAS and child health outcomes. We identified 64 studies for inclusion and performed risk of bias analysis on those studies. We determined that risk of bias across studies was low to moderate. Six categories of health outcomes emerged. These were: immunity/infection/asthma, cardio-metabolic, neurodevelopmental/attention, thyroid, renal, and puberty onset. While there are a limited number of studies for any one particular health outcome, there is evidence for positive associations between PFAS and dyslipidemia, immunity (including vaccine response and asthma), renal function, and age at menarche. One finding of note is that while PFASs are mixtures of multiple compounds few studies examine them as such, therefore the role of these compounds as complex mixtures remains largely unknown.

The Associations between Environmental Quality and Mortality in the Contiguous United States, 2000-2005.

Background: Assessing cumulative effects of the multiple environmental factors influencing mortality remains a challenging task. Objectives: This study aimed to examine the associations between cumulative environmental quality and all-cause and leading cause-specific (heart disease, cancer, and stroke) mortality rates. Methods: We used the overall Environmental Quality Index (EQI) and its five domain indices (air, water, land, built, and sociodemographic) to represent environmental exposure. Associations between the EQI and mortality rates (CDC WONDER) for counties in the contiguous United States (n = 3,109) were investigated using multiple linear regression models and random intercept and random slope hierarchical models. Urbanicity, climate, and a combination of the two were used to explore the spatial patterns in the associations. Results: We found 1 standard deviation increase in the overall EQI (worse environment) was associated with a mean 3.22% (95% CI: 2.80%, 3.64%) increase in all-cause mortality, a 0.54% (95% CI: –0.17%, 1.25%) increase in heart disease mortality, a 2.71% (95% CI: 2.21%, 3.22%) increase in cancer mortality, and a 2.25% (95% CI: 1.11%, 3.39%) increase in stroke mortality. Among the environmental domains, the associations ranged from –1.27% (95% CI: –1.70%, –0.84%) to 3.37% (95% CI: 2.90%, 3.84%) for all-cause mortality, –2.62% (95% CI: –3.52%, –1.73%) to 4.50% (95% CI: 3.73%, 5.27%) for heart disease mortality, –0.88% (95% CI: –2.12%, 0.36%) to 3.72% (95% CI: 2.38%, 5.06%) for stroke mortality, and –0.68% (95% CI: –1.19%, –0.18%) to 3.01% (95% CI: 2.46%, 3.56%) for cancer mortality. Air had the largest associations with all-cause, heart disease, and cancer mortality, whereas the sociodemographic index had the largest association with stroke mortality. Across the urbanicity gradient, no consistent trend was found. Across climate regions, the associations ranged from 2.29% (95% CI: 1.87%, 2.72%) to 5.30% (95% CI: 4.30%, 6.30%) for overall EQI, and larger associations were generally found in dry areas for both overall EQI and domain indices. Conclusions: These results suggest that poor environmental quality, particularly poor air quality, was associated with increased mortality and that associations vary by urbanicity and climate region. Citation: Jian Y, Messer LC, Jagai JS, Rappazzo KM, Gray CL, Grabich SC, Lobdell DT. 2017. Associations between environmental quality and mortality in the contiguous United States, 2000–2005. Environ Health Perspect 125:355–362; http://dx.doi.org/10.1289/EHP119

County‐level cumulative environmental quality associated with cancer incidence

Individual environmental exposures are associated with cancer development; however, environmental exposures occur simultaneously. The Environmental Quality Index (EQI) is a county‐level measure of cumulative environmental exposures that occur in 5 domains.

The Effect of Housing Compliance Status on Children's Blood Lead Levels

In a secondary analysis of data from the Childhood Lead Poisoning Prevention Program of Philadelphia (July 1, 1999 through September 1, 2004), the authors evaluated the effect of housing compliance status and time to achieve compliance on changes in childrens blood lead levels. Blood lead level changes were not significantly different between children living in compliant housing and those living in noncompliant housing for periods of 1.5 to 2 years, 2 to 3 years, or more than 3 years (-11.01 μg/dL, -9.72 μg/dL, -12.5 μg/dL, -11.57 μg/dL, and -14.31 μg/dL, and -14.61 μL, respectively). In a stratified analysis of children younger than 2 years, the authors also found no association. Neither a houses lead hazard control status nor the time it took to achieve compliance affected long-term changes in childrens lead levels. Current compliance programs may be helpful for primary prevention but did not impact change in blood lead for exposed children.

Comparison of gestational dating methods and implications for exposure–outcome associations: an example with PM2.5 and preterm birth

Objectives Estimating gestational age is usually based on date of last menstrual period (LMP) or clinical estimation (CE); both approaches introduce potential bias. Differences in methods of estimation may lead to misclassification and inconsistencies in risk estimates, particularly if exposure assignment is also gestation-dependent. This paper examines a ‘what-if’ scenario in which alternative methods are used and attempts to elucidate how method choice affects observed results. Methods We constructed two 20-week gestational age cohorts of pregnancies between 2000 and 2005 (New Jersey, Pennsylvania, Ohio, USA) using live birth certificates: one defined preterm birth (PTB) status using CE and one using LMP. Within these, we estimated risk for 4 categories of preterm birth (PTBs per 106 pregnancies) and risk differences (RD (95% CIs)) associated with exposure to particulate matter (PM2.5). Results More births were classified preterm using LMP (16%) compared with CE (8%). RD divergences increased between cohorts as exposure period approached delivery. Among births between 28 and 31 weeks, week 7 PM2.5 exposure conveyed RDs of 44 (21 to 67) for CE and 50 (18 to 82) for LMP populations, while week 24 exposure conveyed RDs of 33 (11 to 56) and −20 (−50 to 10), respectively. Conclusions Different results from analyses restricted to births with both CE and LMP are most likely due to differences in dating methods rather than selection issues. Results are sensitive to choice of gestational age estimation, though degree of sensitivity can vary by exposure timing. When both outcome and exposure depend on estimate of gestational age, awareness of nuances in the method used for estimation is critical.

The association between physical inactivity and obesity is modified by five domains of environmental quality in U.S. adults: A cross-sectional study

Physical inactivity is a primary contributor to the obesity epidemic, but may be promoted or hindered by environmental factors. To examine how cumulative environmental quality may modify the inactivity-obesity relationship, we conducted a cross-sectional study by linking county-level Behavioral Risk Factor Surveillance System data with the Environmental Quality Index (EQI), a composite measure of five environmental domains (air, water, land, built, sociodemographic) across all U.S. counties. We estimated the county-level association (N = 3,137 counties) between 2009 age-adjusted leisure-time physical inactivity (LTPIA) and 2010 age-adjusted obesity from BRFSS across EQI tertiles using multi-level linear regression, with a random intercept for state, adjusted for percent minority and rural-urban status. We modelled overall and sex-specific estimates, reporting prevalence differences (PD) and 95% confidence intervals (CI). In the overall population, the PD increased from best (PD = 0.341 (95% CI: 0.287, 0.396)) to worst (PD = 0.645 (95% CI: 0.599, 0.690)) EQI tertile. We observed similar trends in males from best (PD = 0.244 (95% CI: 0.194, 0.294)) to worst (PD = 0.601 (95% CI: 0.556, 0.647)) quality environments, and in females from best (PD = 0.446 (95% CI: 0.385, 0.507)) to worst (PD = 0.655 (95% CI: 0.607, 0.703)). We found that poor environmental quality exacerbates the LTPIA-obesity relationship. Efforts to improve obesity through LTPIA may benefit from considering this relationship.

Human exposure factors as potential determinants of the heterogeneity in city-specific associations between PM 2.5 and mortality

Multi-city population-based epidemiological studies of short-term fine particulate matter (PM2.5) exposures and mortality have observed heterogeneity in risk estimates between cities. Factors affecting exposures, such as pollutant infiltration, which are not captured by central-site monitoring data, can differ between communities potentially explaining some of this heterogeneity. This analysis evaluates exposure factors as potential determinants of the heterogeneity in 312 core-based statistical areas (CBSA)-specific associations between PM2.5 and mortality using inverse variance weighted linear regression. Exposure factor variables were created based on data on housing characteristics, commuting patterns, heating fuel usage, and climatic factors from national surveys. When survey data were not available, air conditioning (AC) prevalence was predicted utilizing machine learning techniques. Across all CBSAs, there was a 0.95% (Interquartile range (IQR) of 2.25) increase in non-accidental mortality per 10 µg/m3 increase in PM2.5 and significant heterogeneity between CBSAs. CBSAs with larger homes, more heating degree days, a higher percentage of home heating with oil had significantly (p < 0.05) higher health effect estimates, while cities with more gas heating had significantly lower health effect estimates. While univariate models did not explain much of heterogeneity in health effect estimates (R2 < 1%), multivariate models began to explain some of the observed heterogeneity (R2 = 13%).

A cross-disciplinary evaluation of evidence for multipollutant effects on cardiovascular disease

Background: The current single‐pollutant approach to regulating ambient air pollutants is effective at protecting public health, but efficiencies may be gained by addressing issues in a multipollutant context since multiple pollutants often have common sources and individuals are exposed to more than one pollutant at a time. Objective: We performed a cross‐disciplinary review of the effects of multipollutant exposures on cardiovascular effects. Methods: A broad literature search for references including at least two criteria air pollutants (particulate matter [PM], ozone [O3], oxides of nitrogen, sulfur oxides, carbon monoxide) was conducted. References were culled based on scientific discipline then searched for terms related to cardiovascular disease. Most multipollutant epidemiologic and experimental (i.e., controlled human exposure, animal toxicology) studies examined PM and O3 together. Discussion: Epidemiologic and experimental studies provide some evidence for O3 concentration modifying the effect of PM, although PM did not modify O3 risk estimates. Experimental studies of combined exposure to PM and O3 provided evidence for additivity, synergism, and/or antagonism depending on the specific health endpoint. Evidence for other pollutant pairs was more limited. Conclusions: Overall, the evidence for multipollutant effects was often heterogeneous, and the limited number of studies inhibited making a conclusion about the nature of the relationship between pollutant combinations and cardiovascular disease. HIGHLIGHTSEfficiencies may be gained using a multipollutant context to regulate air pollutants.We perform an evaluation of multipollutant effects on cardiovascular disease CVD.effect of combined exposure than either PM or O3 exposure alone.Heterogeneous results were observed across disciplines for other two‐pollutant combinations: PM and CO, NO2 and PM10, O3 and NO2, CO and O3.

Associations between environmental quality and adult asthma prevalence in medical claims data

ABSTRACT As of 2014, approximately 7.4% of U.S. adults had current asthma. The etiology of asthma is complex, involving genetics, behavior, and environmental factors. To explore the association between cumulative environmental quality and asthma prevalence in U.S. adults, we linked the U.S. Environmental Protection Agencys Environmental Quality Index (EQI) to the MarketScan® Commercial Claims and Encounters Database. The EQI is a summary measure of five environmental domains (air, water, land, built, sociodemographic). We defined asthma as having at least 2 claims during the study period, 2003–2013. We used a Bayesian approach with non‐informative priors, implementing mixed‐effects regression modeling with a Poisson link function. Fixed effects variables were EQI, sex, race, and age. Random effects were counties. We modeled quintiles of the EQI comparing higher quintiles (worse quality) to lowest quintile (best quality) to estimate prevalence ratios (PR) and credible intervals (CIs). We estimated associations using the cumulative EQI and domain‐specific EQIs; we assessed U.S. overall (non‐stratified) as well as stratified by rural‐urban continuum codes (RUCC) to assess rural/urban heterogeneity. Among the 71,577,118 U.S. adults with medical claims who could be geocoded to county of residence, 1,147,564 (1.6%) met the asthma definition. Worse environmental quality was associated with increased asthma prevalence using the non‐RUCC‐stratified cumulative EQI, comparing the worst to best EQI quintile (PR:1.27; 95% CI: 1.21, 1.34). Patterns varied among different EQI domains, as well as by rural/urban status. Poor environmental quality may increase asthma prevalence, but domain‐specific drivers may operate differently depending on rural/urban status. HighlightsAsthma is a complex condition affecting 7.4% of U.S. adults.Asthma may be driven by multiple exposures operating in tandem.The Environmental Quality Index measures 5 environmental domains simultaneously.Worsening overall environmental quality is associated with increasing asthma.Patterns vary by domain (air, water, land, built, sociodemograhic) and rurality.