A Bayesian Maximum Entropy approach to address the change of support problem in the spatial analysis of childhood asthma prevalence across North Carolina

The spatial analysis of data observed at different spatial observation scales leads to the change of support problem (COSP). A solution to the COSP widely used in linear spatial statistics consists in explicitly modeling the spatial autocorrelation of the variable observed at different spatial scales. We present a novel approach that takes advantage of the nonlinear Bayesian Maximum Entropy (BME) extension of linear spatial statistics to address the COSP directly without relying on the classical linear approach. Our procedure consists in modeling data observed over large areas as soft data for the process at the local scale. We demonstrate the application of our approach to obtain spatially detailed maps of childhood asthma prevalence across North Carolina (NC). Because of the high prevalence of childhood asthma in NC, the small number problem is not an issue, so we can focus our attention solely to the COSP of integrating prevalence data observed at the county level together with data observed at a targeted local scale equivalent to the scale of school districts. Our spatially detailed maps can be used for different applications ranging from exploratory and hypothesis-generating analyses to targeting intervention and exposure mitigation efforts

Impact of temporal upscaling and chemical transport model horizontal resolution on reducing ozone exposure misclassification

We have developed a Bayesian Maximum Entropy (BME) framework that integrates observations from a surface monitoring network and predictions from a Chemical Transport Model (CTM) to create improved exposure estimates that can be resolved into any spatial and temporal resolution. The flexibility of the framework allows for input of data in any choice of time scales and CTM predictions of any spatial resolution with varying associated degrees of estimation error and cost in terms of implementation and computation. This study quantifies the impact on exposure estimation error due to these choices by first comparing estimations errors when BME relied on ozone concentration data either as an hourly average, the daily maximum 8-h average (DM8A), or the daily 24-h average (D24A). Our analysis found that the use of DM8A and D24A data, although less computationally intensive, reduced estimation error more when compared to the use of hourly data. This was primarily due to the poorer CTM model performance in the hourly average predicted ozone. Our second analysis compared spatial variability and estimation errors when BME relied on CTM predictions with a grid cell resolution of 12 × 12 km2 versus a coarser resolution of 36 × 36 km2. Our analysis found that integrating the finer grid resolution CTM predictions not only reduced estimation error, but also increased the spatial variability in daily ozone estimates by 5 times. This improvement was due to the improved spatial gradients and model performance found in the finer resolved CTM simulation. The integration of observational and model predictions that is permitted in a BME framework continues to be a powerful approach for improving exposure estimates of ambient air pollution. The results of this analysis demonstrate the importance of also understanding model performance variability and its implications on exposure error

Identifying sources of antibiotic resistance genes in the environment using the microbial Find, Inform, and Test framework

Introduction: Antimicrobial resistance (AMR) is an increasing public health concern for humans, animals, and the environment. However, the contributions of spatially distributed sources of AMR in the environment are not well defined. Methods: To identify the sources of environmental AMR, the novel microbial Find, Inform, and Test (FIT) model was applied to a panel of five antibiotic resistance-associated genes (ARGs), namely, erm(B), tet(W), qnrA, sul1, and intI1, quantified from riverbed sediment and surface water from a mixed-use region. Results: A one standard deviation increase in the modeled contributions of elevated AMR from bovine sources or land-applied waste sources [land application of biosolids, sludge, and industrial wastewater (i.e., food processing) and domestic (i.e., municipal and septage)] was associated with 34–80% and 33–77% increases in the relative abundances of the ARGs in riverbed sediment and surface water, respectively. Sources influenced environmental AMR at overland distances of up to 13 km. Discussion: Our study corroborates previous evidence of offsite migration of microbial pollution from bovine sources and newly suggests offsite migration from land-applied waste. With FIT, we estimated the distance-based influence range overland and downstream around sources to model the impact these sources may have on AMR at unsampled sites. This modeling supports targeted monitoring of AMR from sources for future exposure and risk mitigation efforts

Estimating Associations Between Annual Concentrations of Particulate Matter and Mortality in the United States, Using Data Linkage and Bayesian Maximum Entropy

Background: Exposure to fine particulate matter (PM2.5) is an established risk factor for human mortality. However, previous US studies have been limited to select cities or regions or to population subsets (e.g., older adults). Methods: Here, we demonstrate how to use the novel geostatistical method Bayesian maximum entropy to obtain estimates of PM2.5 concentrations in all contiguous US counties, 2000–2016. We then demonstrate how one could use these estimates in a traditional epidemiologic analysis examining the association between PM2.5 and rates of all-cause, cardiovascular, respiratory, and (as a negative control outcome) accidental mortality. Results: We estimated that, for a 1 log(μg/m3) increase in PM2.5 concentration, the conditional all-cause mortality incidence rate ratio (IRR) was 1.029 (95% confidence interval [CI]: 1.006, 1.053). This implies that the rate of all-cause mortality at 10 µg/m3 would be 1.020 times the rate at 5 µg/m3. IRRs were larger for cardiovascular mortality than for all-cause mortality in all gender and race–ethnicity groups. We observed larger IRRs for all-cause, nonaccidental, and respiratory mortality in Black non-Hispanic Americans than White non-Hispanic Americans. However, our negative control analysis indicated the possibility for unmeasured confounding. Conclusion: We used a novel method that allowed us to estimate PM2.5 concentrations in all contiguous US counties and obtained estimates of the association between PM2.5 and mortality comparable to previous studies. Our analysis provides one example of how Bayesian maximum entropy could be used in epidemiologic analyses; future work could explore other ways to use this approach to inform important public health questions

Using animations of risk functions to visualize trends in US all-cause and cause-specific mortality, 1968-2016

Objectives. To use dynamic visualizations of mortality risk functions over both calendar year and age as a way to estimate and visualize patterns in US life spans. Methods. We built 49 synthetic cohorts, 1 per year 1968 to 2016, using National Center for Health Statistics (NCHS) mortality and population data. Within each cohort, we estimated age-specific probabilities of dying from any cause (all-cause analysis) or from a particular cause (cause-specific analysis). We then used Kaplan–Meier (all-cause) or Aalen–Johansen (cause-specific) estimators to obtain risk functions. We illustrated risk functions using time-lapse animations. Results. Median age at death increased from 75 years in 1970 to 83 years in 2015. Risk by age 100 years of cardiovascular mortality decreased (from a risk of 55% in 1970 to 32% in 2015), whereas risk attributable to other (i.e., nonrespiratory and noncardiovascular) causes increased in compensation. Conclusions. Our findings were consistent with the trends published in the NCHS 2015 mortality report, and our dynamic animations added an efficient, interpretable tool for visualizing US mortality trends over age and calendar time

Estimating Wildfire Smoke Concentrations during the October 2017 California Fires through BME Space/Time Data Fusion of Observed, Modeled, and Satellite-Derived PM2.5

Exposure to wildfire smoke causes adverse health outcomes, suggesting the importance of accurately estimating smoke concentrations. Geostatistical methods can combine observed, modeled, and satellite-derived concentrations to produce accurate estimates. Here, we estimate daily average ground-level PM2.5 concentrations at a 1 km resolution during the October 2017 California wildfires, using the Constant Air Quality Model Performance (CAMP) and Bayesian Maximum Entropy (BME) methods to bias-correct and fuse three concentration datasets: permanent and temporary monitoring stations, a chemical transport model (CTM), and satellite-derived estimates. Four BME space/time kriging and data fusion methods were evaluated. All BME methods produce more accurate estimates than the standalone CTM and satellite products. Adding temporary station data increases the R2 by 36%. The data fusion of observations with the CAMP-corrected CTM and satellite-derived concentrations provides the best estimate (R2 = 0.713) in fire-impacted regions, emphasizing the importance of combining multiple datasets. We estimate that approximately 65,000 people were exposed to very unhealthy air (daily average PM2.5 ≥ 150.5 μg/m3)

Particulate air pollutants, APOE alleles and their contributions to cognitive impairment in older women and to amyloidogenesis in experimental models

Exposure to particulate matter (PM) in the ambient air and its interactions with APOE alleles may contribute to the acceleration of brain aging and the pathogenesis of Alzheimer's disease (AD). Neurodegenerative effects of particulate air pollutants were examined in a US-wide cohort of older women from the Women's Health Initiative Memory Study (WHIMS) and in experimental mouse models. Residing in places with fine PM exceeding EPA standards increased the risks for global cognitive decline and all-cause dementia respectively by 81 and 92%, with stronger adverse effects in APOE ɛ4/4 carriers. Female EFAD transgenic mice (5xFAD+/−/human APOE ɛ3 or ɛ4+/+) with 225 h exposure to urban nanosized PM (nPM) over 15 weeks showed increased cerebral β-amyloid by thioflavin S for fibrillary amyloid and by immunocytochemistry for Aβ deposits, both exacerbated by APOE ɛ4. Moreover, nPM exposure increased Aβ oligomers, caused selective atrophy of hippocampal CA1 neurites, and decreased the glutamate GluR1 subunit. Wildtype C57BL/6 female mice also showed nPM-induced CA1 atrophy and GluR1 decrease. In vitro nPM exposure of neuroblastoma cells (N2a-APP/swe) increased the pro-amyloidogenic processing of the amyloid precursor protein (APP). We suggest that airborne PM exposure promotes pathological brain aging in older women, with potentially a greater impact in ɛ4 carriers. The underlying mechanisms may involve increased cerebral Aβ production and selective changes in hippocampal CA1 neurons and glutamate receptor subunits

Adherence to a MIND-Like Dietary Pattern, Long-Term Exposure to Fine Particulate Matter Air Pollution, and MRI-Based Measures of Brain Volume: The Women’s Health Initiative Memory Study-MRI

BACKGROUND: Previous studies suggest that certain dietary patterns and constituents may be beneficial to brain health. Airborne exposures to fine particulate matter [particulate matter with aerodynamic diameter ≤2.5 μm (PM2.5)] are neurotoxic, but the combined effects of dietary patterns and PM2.5 have not been investigated. OBJECTIVES: We examined whether previously reported association between PM2.5 exposure and lower white matter volume (WMV) differed between women whose usual diet during the last 3 months before baseline was more or less consistent with a Mediterranean-DASH Intervention for Neurodegenerative Delay (MIND)-like diet, a dietary pattern that may slow neurodegenerative changes. METHODS: This study included 1,302 U.S. women who were 65-79 y old and free of dementia in the period 1996-1998 (baseline). In the period 2005-2006, structural brain magnetic resonance imaging (MRI) scans were performed to estimate normal-appearing brain volumes (excluding areas with evidence of small vessel ischemic disease). Baseline MIND diet scores were derived from a food frequency questionnaire. Three-year average PM2.5 exposure prior to MRI was estimated using geocoded participant addresses and a spatiotemporal model. RESULTS: Average total and temporal lobe WMVs were 0:74 cm3 [95% confidence interval (CI): 0.001, 1.48) and 0:19 cm3 (95% CI: 0.002, 0.37) higher, respectively, with each 0.5-point increase in the MIND score and were 4:16 cm3 (95% CI: -6:99, -1:33) and 1:46 cm3 (95% CI: -2:16, -0:76) lower, respectively, with each interquartile range (IQR) (IQR = 3:22 μg/m3) increase in PM2.5. The inverse association between PM2.5 per IQR and WMV was stronger (p-interaction <0:001) among women with MIND scores below the median (for total WMV, -12:47 cm3; 95% CI: −17:17, −7:78), but absent in women with scores above the median (0:16 cm3; 95% CI: −3:41, 3.72), with similar patterns for WMV in the frontal, parietal, and temporal lobes. For total cerebral and hippocampus brain volumes or WMV in the corpus callosum, the associations with PM2.5 were not significantly different for women with high MIND scores and women with low MIND scores

B vitamin intakes modify the association between particulate air pollutants and incidence of all-cause dementia: Findings from the Women's Health Initiative Memory Study

Introduction: Particulate air pollutants may induce neurotoxicity by increasing homocysteine levels, which can be lowered by high B vitamin intakes. Therefore, we examined whether intakes of three B vitamins (folate, B12, and B6) modified the association between PM2.5 exposure and incidence of all-cause dementia. Methods: This study included 7183 women aged 65 to 80 years at baseline. B vitamin intakes from diet and supplements were estimated by food frequency questionnaires at baseline. The 3-year average PM2.5 exposure was estimated using a spatiotemporal model. Results: During a mean follow-up of 9 years, 342 participants developed all-cause dementia. We found that residing in locations with PM2.5 exposure above the regulatory standard (12 μg/m3) was associated with a higher risk of dementia only among participants with lower intakes of these B vitamins. Discussion: This is the first study suggesting that the putative neurotoxicity of PM2.5 exposure may be attenuated by high B vitamin intakes

Erythrocyte omega-3 index, ambient fine particle exposure, and brain aging

OBJECTIVE: To examine whether long-chain omega-3 polyunsaturated fatty acid (LCn3PUFA) levels modify the potential neurotoxic effects of particle matter with diameters <2.5 µm (PM2.5) exposure on normal-appearing brain volumes among dementia-free elderly women. METHODS: A total of 1,315 women (age 65-80 years) free of dementia were enrolled in an observational study between 1996 and 1999 and underwent structural brain MRI in 2005 to 2006. According to prospectively collected and geocoded participant addresses, we used a spatiotemporal model to estimate the 3-year average PM2.5 exposure before the MRI. We examined the joint associations of baseline LCn3PUFAs in red blood cells (RBCs) and PM2.5 exposure with brain volumes in generalized linear models. RESULTS: After adjustment for potential confounders, participants with higher levels of RBC LCn3PUFA had significantly greater volumes of white matter and hippocampus. For each interquartile increment (2.02%) in omega-3 index, the average volume was 5.03 cm3 (p < 0.01) greater in the white matter and 0.08 cm3 (p = 0.03) greater in the hippocampus. The associations with RBC docosahexaenoic acid and eicosapentaenoic acid levels were similar. Higher LCn3PUFA attenuated the inverse associations between PM2.5 exposure and white matter volumes in the total brain and multimodal association areas (frontal, parietal, and temporal; all p for interaction <0.05), while the associations with other brain regions were not modified. Consistent results were found for dietary intakes of LCn3PUFAs and nonfried fish. CONCLUSIONS: Findings from this prospective cohort study among elderly women suggest that the benefits of LCn3PUFAs on brain aging may include the protection against potential adverse effects of air pollution on white matter volumes