Power and sample size analysis for longitudinal mixed models of health in populations exposed to environmental contaminants: a tutorial.

BACKGROUND: When evaluating the impact of environmental exposures on human health, study designs often include a series of repeated measurements. The goal is to determine whether populations have different trajectories of the environmental exposure over time. Power analyses for longitudinal mixed models require multiple inputs, including clinically significant differences, standard deviations, and correlations of measurements. Further, methods for power analyses of longitudinal mixed models are complex and often challenging for the non-statistician. We discuss methods for extracting clinically relevant inputs from literature, and explain how to conduct a power analysis that appropriately accounts for longitudinal repeated measures. Finally, we provide careful recommendations for describing complex power analyses in a concise and clear manner. METHODS: For longitudinal studies of health outcomes from environmental exposures, we show how to [1] conduct a power analysis that aligns with the planned mixed model data analysis, [2] gather the inputs required for the power analysis, and [3] conduct repeated measures power analysis with a highly-cited, validated, free, point-and-click, web-based, open source software platform which was developed specifically for scientists. RESULTS: As an example, we describe the power analysis for a proposed study of repeated measures of per- and polyfluoroalkyl substances (PFAS) in human blood. We show how to align data analysis and power analysis plan to account for within-participant correlation across repeated measures. We illustrate how to perform a literature review to find inputs for the power analysis. We emphasize the need to examine the sensitivity of the power values by considering standard deviations and differences in means that are smaller and larger than the speculated, literature-based values. Finally, we provide an example power calculation and a summary checklist for describing power and sample size analysis. CONCLUSIONS: This paper provides a detailed roadmap for conducting and describing power analyses for longitudinal studies of environmental exposures. It provides a template and checklist for those seeking to write power analyses for grant applications.

Source apportionment of serum PFASs in two highly exposed communities.

Per- and polyfluoroalkyl substances (PFASs) are synthetic chemicals that are ubiquitous in environmental and biological systems, including human serum. PFASs are used in many products and industrial processes and are tied to numerous health effects. Due to multiple sources and exposure pathways, methods are needed to identify PFAS sources in communities to develop targeted interventions. We assessed effectiveness of three source apportionment methods (UNMIX, positive matrix factorization [PMF], and principal component analysis - multiple linear regression [PCA-MLR]) for identifying contributors to human serum PFAS concentrations in two highly exposed populations in Colorado and North Carolina where drinking water was contaminated via upstream sources, including a Space Force base and a fluorochemical manufacturing plant. UNMIX and PMF models extracted three to four potential PFAS exposure sources in the Colorado and North Carolina cohorts while PCA-MLR classified two in each cohort. No sources were characterized in NHANES (National Health and Nutrition Examination Study). Results suggest that these three methods can successfully identify sources in highly exposed populations. Future PFAS exposure research should focus on analyzing serum for an expanded PFAS panel, identifying cohorts with other distinct point source exposures, and combining biological and environmental data to better understand source apportionment results in the context of PFAS toxicokinetic behavior.

Cross-sectional associations between serum PFASs and inflammatory biomarkers in a population exposed to AFFF-contaminated drinking water.

BACKGROUND: Per- and polyfluoroalkyl substances (PFASs) are widespread and persistent environmental contaminants. Exposure to several PFASs has been associated with altered immune function in humans, including autoimmune disease and impaired response to vaccination. However, changes to the profile of inflammatory biomarkers in adults exposed to PFASs has not been extensively described. OBJECTIVE: To estimate cross-sectional associations between serum PFASs and markers of inflammation among adults in a population exposed to aqueous film forming foam (AFFF)-contaminated drinking water. METHODS: We quantified concentrations of 48 PFASs in non-fasting serum samples from 212 non-smoking adults. In the same serum samples, we measured concentrations of ten pro- and anti-inflammatory cytokines. We restricted analysis to seven PFASs detected in >85% of participants and the following four cytokines detected in ≥30% of participants: interleukin [IL]-1ß, IL-6, IL-10, and tumor necrosis factor [TNF]-α. We fit multiple linear regression or logistic regression models, adjusted for potential confounders, to estimate associations between concentrations of each PFAS and either continuous or categorical (above vs below limit of detection) concentrations of each cytokine. We additionally applied Bayesian kernel machine regression to describe the combined effect of the PFAS mixture on each cytokine outcome. RESULTS: Certain PFAS concentrations in this sample were elevated compared to a US nationally representative sample; median levels of PFHxS, ΣPFOS and ΣPFOA in this sample were 13.8, 2.1 and 1.7 times higher, respectively, than medians observed in the U.S. SAMPLE: Higher concentrations of multiple PFASs were significantly associated with lower odds of detectable IL-1ß. Weaker associations were observed with other cytokines. In general, perfluoroalkyl carboxylic acids had inverse associations with TNF-α, whereas the perfluoroalkyl sulfonic acids showed positive associations. CONCLUSIONS: We observed preliminary evidence of altered inflammatory profiles among adults with elevated serum concentrations of PFASs due to contaminated drinking water. Modifications to inflammatory pathways may be one mechanism by which PFAS exposures produce adverse health effects in humans, but this finding requires verification in longitudinal studies as well as phenotypic anchoring to immune function outcomes.

Unsaturated PFOS and Other PFASs in Human Serum and Drinking Water from an AFFF-Impacted Community.

Understanding how exposure to aqueous film-forming foam (AFFF)-impacted drinking water translates to bioaccumulation of per- and polyfluoroalkyl substances (PFASs) is essential to assess health risks. To investigate spatial variability of PFAS exposure in communities near an AFFF source zone, blood serum was collected in 2018 from 220 adult residents of El Paso County (Colorado), as were raw water samples from several wells. C6 and C8 perfluoroalkyl sulfonates (PFSAs) were predominant in serum and water. PFASs were most elevated in the water district nearest the source zone (median ∑PFSA of 618 ng/L in water and 33 ng/mL in serum). A novel PFAS, unsaturated perfluorooctane sulfonate, was detected in >80% of water and serum samples at low concentrations (≤1.9 ng/mL in serum). Drinking water wells nearest the source zone displayed increased prevalence of perfluoroalkyl sulfonamide precursors not detected in serum. Serum-to-water ratios were the greatest for long-chain PFASs and were elevated in the least impacted water district. Additional serum samples collected from a subset of study participants in June 2019 showed that PFAS concentrations in serum declined after exposure ceased, although declines for perfluoropentane sulfonate were minimal. Our findings demonstrate that AFFF-impacted communities are exposed to complex, spatially variable mixtures of PFASs.

Sociodemographic and behavioral determinants of serum concentrations of per- and polyfluoroalkyl substances in a community highly exposed to aqueous film-forming foam contaminants in drinking water.

BACKGROUND: Per- and polyfluoroalkyl substances (PFAS) are a chemical class widely used in industrial and commercial applications because of their unique physical and chemical properties. Between 2013 and 2016 PFAS were detected in public water systems and private wells in El Paso County, Colorado. The contamination was likely due to aqueous film forming foams used at a nearby Air Force base. OBJECTIVE: To cross-sectionally describe the serum concentrations of PFAS in a highly exposed community, estimate associations with drinking water source, and explore potential demographic and behavioral predictors. METHODS: In June 2018, serum PFAS concentrations were quantified and questionnaires administered in 213 non-smoking adult (ages 19-93) participants residing in three affected water districts. Twenty PFAS were quantified and those detected in >50% of participants were analyzed: perfluorohexane sulfonate (PFHxS), perfluorooctane sulfonate (PFOS), perfluorooctanoate (PFOA), perfluorononanoate (PFNA) and perfluoroheptane sulfonate (PFHpS). Unadjusted associations were estimated between serum PFAS concentrations and several predictors, including water consumption, demographics, personal behaviors and employment. A multiple linear regression model estimated adjusted associations with smoking history. RESULTS: Study participants' median PFHxS serum concentration (14.8 ng/mL) was approximately 12 times as high as the U.S. national average. Median serum concentrations for PFOS, PFOA, PFNA and PFHpS were 9.7 ng/mL, 3.0 ng/mL, 0.4 ng/mL and 0.2 ng/mL, respectively. Determinants of PFHxS serum concentrations were water district of residence, frequency of bottled water consumption, age, race/ethnicity, and smoking history. Determinants of serum concentrations for the other four PFAS evaluated included: water district of residence, bottled water consumption, age, sex, race/ethnicity, smoking history, and firefighter or military employment. CONCLUSIONS: Determinants of serum concentrations for multiple PFAS, including PFHxS, included water district of residence and frequency of bottled water consumption. Participants' dominant PFAS exposure route was likely consumption of PFAS-contaminated water, but certain demographic and behavioral characteristics also predicted serum concentrations.

Air infiltration in low-income, urban homes and its relationship to lung function.

Previous research has found increased home ventilation, which may affect health by altering the composition of indoor air, is associated with improvement of respiratory health, but evidence linking home ventilation to objectively measured lung function is sparse. The Colorado Home Energy Efficiency and Respiratory health (CHEER) study, a cross-sectional study of low-income, urban, nonsmoking homes across the Northern Front Range of Colorado, USA, focused on elucidating this link. We used a multipoint depressurization blower door test to measure the air tightness of the homes and calculate the annual average infiltration rate (AAIR). Lung function tests were administered to eligible participants. We analyzed data from 253 participants in 187 homes with two or more acceptable spirometry tests. We used generalized estimating equations to model forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), and FEV1/FVC z-scores as a function of AAIR. AAIRs ranged from 0.10 to 1.98 air changes per hour. Mean z-scores for FEV1, FVC, and FEV1/FVC were -0.57, 0.32, and -0.43, respectively. AAIR was positively associated with increased FEV1/FVC z-scores, such that a 1-unit change in AAIR corresponded to a half of a standard deviation in lung function (ß = 0.51, CI: 0.02-0.99). These associations were strongest for healthy populations and weaker for those with asthma and asthma-like symptoms. AAIR was not associated with FEV1 or FVC. Our study is the first in the United States to link home ventilation by infiltration to objectively measured lung function in low-income, urban households.

Impact of Outdoor Air Pollution on Indoor Air Quality in Low-Income Homes during Wildfire Seasons.

Indoor and outdoor number concentrations of fine particulate matter (PM2.5), black carbon (BC), carbon monoxide (CO), and nitrogen dioxide (NO2) were monitored continuously for two to seven days in 28 low-income homes in Denver, Colorado, during the 2016 and 2017 wildfire seasons. In the absence of indoor sources, all outdoor pollutant concentrations were higher than indoors except for CO. Results showed that long-range wildfire plumes elevated median indoor PM2.5 concentrations by up to 4.6 times higher than outdoors. BC, CO, and NO2 mass concentrations were higher indoors in homes closer to roadways compared to those further away. Four of the homes with mechanical ventilation systems had 18% higher indoor/outdoor (I/O) ratios of PM2.5 and 4% higher I/O ratios of BC compared to other homes. Homes with exhaust stove hoods had PM2.5 I/O ratios 49% less than the homes with recirculating hoods and 55% less than the homes with no stove hoods installed. Homes with windows open for more than 12 hours a day during sampling had indoor BC 2.4 times higher than homes with windows closed. This study provides evidence that long-range wildfire plumes, road proximity, and occupant behavior have a combined effect on indoor air quality in low-income homes.

Social and Environmental Neighborhood Typologies and Lung Function in a Low-Income, Urban Population.

Consensus is growing on the need to investigate the joint impact of neighborhood-level social factors and environmental hazards on respiratory health. This study used latent profile analysis (LPA) to empirically identify distinct neighborhood subtypes according to a clustering of social factors and environmental hazards, and to examine whether those subtypes are associated with lung function. The study included 182 low-income participants who were enrolled in the Colorado Home Energy Efficiency and Respiratory Health (CHEER) study during the years 2015⁻2017. Distinct neighborhood typologies were identified based on analyses of 632 census tracts in the Denver-Metro and Front Range area of Colorado; neighborhood characteristics used to identify typologies included green space, traffic-related air pollution, violent and property crime, racial/ethnic composition, and socioeconomic status (SES). Generalized estimating equations were used to examine the association between neighborhood typology and lung function. We found four distinct neighborhood typologies and provide evidence that these social and environmental aspects of neighborhoods cluster along lines of advantage/disadvantage. We provide suggestive evidence of a double jeopardy situation where low-income populations living in disadvantaged neighborhoods may have decreased lung function. Using LPA with social and environmental characteristics may help to identify meaningful neighborhood subtypes and inform research on the mechanisms by which neighborhoods influence health.