Marc L. Serre

A Hybrid Approach to Estimating National Scale Spatiotemporal Variability of PM2.5 in the Contiguous United States

Airborne fine particulate matter exhibits spatiotemporal variability at multiple scales, which presents challenges to estimating exposures for health effects assessment. Here we created a model to predict ambient particulate matter less than 2.5 μm in aerodynamic diameter (PM2.5) across the contiguous United States to be applied to health effects modeling. We developed a hybrid approach combining a land use regression model (LUR) selected with a machine learning method, and Bayesian Maximum Entropy (BME) interpolation of the LUR space-time residuals. The PM2.5 data set included 104,172 monthly observations at 1464 monitoring locations with approximately 10% of locations reserved for cross-validation. LUR models were based on remote sensing estimates of PM2.5, land use and traffic indicators. Normalized cross-validated R(2) values for LUR were 0.63 and 0.11 with and without remote sensing, respectively, suggesting remote sensing is a strong predictor of ground-level concentrations. In the models including the BME interpolation of the residuals, cross-validated R(2) were 0.79 for both configurations; the model without remotely sensed data described more fine-scale variation than the model including remote sensing. Our results suggest that our modeling framework can predict ground-level concentrations of PM2.5 at multiple scales over the contiguous U.S.

Spatial analysis and mapping of sexually transmitted diseases to optimise intervention and prevention strategies

Objective: We analysed and mapped the distribution of four reportable sexually transmitted diseases, chlamydial infection/non-gonococcal urethritis (chlamydial infection), gonorrhoea, primary and secondary syphilis (syphilis), and HIV infection, for Wake County, North Carolina, to optimise an intervention. Methods: We used STD surveillance data reported to Wake County, for the year 2000 to analyse and map STD rates. STD rates were mathematically represented as a spatial random field. We analysed spatial variability by calculating and modelling covariance functions of random field theory. Covariances are useful in assessing spatial patterns of disease locally and at a distance. We combined observed STD rates and appropriate covariance models using a geostatistical method called kriging, to predict STD rates and associated prediction errors for a grid covering Wake County. Final disease estimates were interpolated using a spline with tension and mapped to generate a continuous surface of infection. Results: Lower incidence STDs exhibited larger spatial variability and smaller neighbourhoods of influence than higher incidence STDs. Each reported STD had a clustered spatial distribution with one primary core area of infection. Core areas overlapped for all four STDs. Conclusions: Spatial heterogeneity within STD suggests that STD specific prevention strategies should not be targeted uniformly across Wake County, but rather to core areas. Overlap of core areas among STDs suggests that intervention and prevention strategies can be combined to target multiple STDs effectively. Geostatistical techniques are objective, population level approaches to spatial analysis and mapping that can be used to visualise disease patterns and identify emerging outbreaks.

BME analysis of spatiotemporal particulate matter distributions in North Carolina

Abstract Spatiotemporal maps of particulate matter (PM) concentrations contribute considerably to the understanding of the underlying natural processes and the adequate assessment of the PM health effects. These maps should be derived using an approach that combines rigorous mathematical formulation with sound science. To achieve such a task, the PM10 distribution in the state of North Carolina is studied using the Bayesian maximum entropy (BME) mapping method. This method is based on a realistic representation of the spatiotemporal domain, which can integrate rigorously and efficiently various forms of physical knowledge and sources of uncertainty. BME offers a complete characterization of PM10 concentration patterns in terms of multi-point probability distributions and allows considerable flexibility regarding the choice of the appropriate concentration estimates. The PM10 maps show significant variability both spatially and temporally, a finding that may be associated with geographical characteristics, climatic changes, seasonal patterns, and random fluctuations. The inherently spatiotemporal nature of PM10 variation is demonstrated by means of theoretical considerations as well as in terms of the more accurate PM10 predictions of composite space/time analysis compared to spatial estimation. It is shown that the study of PM10 distributions in North Carolina can be improved by properly incorporating uncertain data into the mapping process, whereas more informative estimates are generated by considering soft data at the estimation points. Uncertainty maps illustrate the significance of stochastic PM10 characterization in space/time, and identify limitations associated with inadequate interpolation techniques. Stochastic PM10 analysis has important applications in the optimization of monitoring networks in space and time, environmental risk assessment, health management and administration, etc.

Fecal indicators in sand, sand contact, and risk of enteric illness among beachgoers

Background: Beach sand can harbor fecal indicator organisms and pathogens, but enteric illness risk associated with sand contact remains unclear. Methods: In 2007, visitors at 2 recreational marine beaches were asked on the day of their visit about sand contact. Ten to 12 days later, participants answered questions about health symptoms since the visit. F+ coliphage, Enterococcus, Bacteroidales, fecal Bacteroides, and Clostridium spp. in wet sand were measured using culture and molecular methods. Results: We analyzed 144 wet sand samples and completed 4999 interviews. Adjusted odds ratios (aORs) were computed, comparing those in the highest tertile of fecal indicator exposure with those who reported no sand contact. Among those digging in sand compared with those not digging in sand, a molecular measure of Enterococcus spp. (calibrator cell equivalents/g) in sand was positively associated with gastrointestinal (GI) illness (aOR = 2.0 [95% confidence interval (CI) = 1.2–3.2]) and diarrhea (2.4 [1.4–4.2]). Among those buried in sand, point estimates were greater for GI illness (3.3 [1.3–7.9]) and diarrhea (4.9 [1.8–13]). Positive associations were also observed for culture-based Enterococcus (colony-forming units/g) with GI illness (aOR digging = 1.7 [1.1–2.7]) and diarrhea (2.1 [1.3–3.4]). Associations were not found among nonswimmers with sand exposure. Conclusions: We observed a positive relationship between sand-contact activities and enteric illness as a function of concentrations of fecal microbial pollution in beach sand.

Particulate matter exposure, prenatal and postnatal windows of susceptibility, and autism spectrum disorders.

Background: Recent studies suggest that exposure to traffic-related air pollutants, including particulate matter (PM), is associated with autism spectrum disorder (autism). Methods: Children with autism were identified by records-based surveillance (n = 645 born in North Carolina in 1994, 1996, 1998, or 2000, and n = 334 born in the San Francisco Bay Area in California in 1996). They were compared with randomly sampled children born in the same counties and years identified from birth records (n = 12,434 in North Carolina and n = 2,232 in California). Exposure to PM less than 10 &mgr;m (PM10) at the birth address was assigned to each child by a geostatistical interpolation method using daily concentrations from air pollution regulatory monitors. We estimated odds ratios (ORs) and 95% confidence intervals (CIs) for a 10 &mgr;g/m3 increase in PM10 within 3-month periods from preconception through the child’s first birthday, adjusting for year, state, maternal education and age, race/ethnicity, and neighborhood-level urbanization and median household income, and including a nonparametric term for week of birth to account for seasonal trends. Results: Temporal patterns in PM10 were pronounced, leading to an inverse correlation between the first- and third-trimester concentrations (r = −0.7). Adjusted ORs were, for the first trimester, 0.86 (95% CI = 0.74–0.99), second trimester, 0.97 (0.83–1.15), and third trimester, 1.36 (1.13–1.63); and, after simultaneously including first- and third-trimester concentrations to account for the inverse correlation, were: first trimester, 1.01 (0.81–1.27) and third trimester, 1.38 (1.03–1.84). Conclusions: Our study adds to previous work in California showing a relation between traffic-related air pollution and autism, and adds similar findings in an eastern US state, with results consistent with increased susceptibility in the third-trimester.

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.

Mapping Health Data: Improved Privacy Protection With Donut Method Geomasking

A major challenge in mapping health data is protecting patient privacy while maintaining the spatial resolution necessary for spatial surveillance and outbreak identification. A new adaptive geomasking technique, referred to as the donut method, extends current methods of random displacement by ensuring a user-defined minimum level of geoprivacy. In donut method geomasking, each geocoded address is relocated in a random direction by at least a minimum distance, but less than a maximum distance. The authors compared the donut method with current methods of random perturbation and aggregation regarding measures of privacy protection and cluster detection performance by masking multiple disease field simulations under a range of parameters. Both the donut method and random perturbation performed better than aggregation in cluster detection measures. The performance of the donut method in geoprivacy measures was at least 42.7% higher and in cluster detection measures was less than 4.8% lower than that of random perturbation. Results show that the donut method provides a consistently higher level of privacy protection with a minimal decrease in cluster detection performance, especially in areas where the risk to individual geoprivacy is greatest.

Ambient air pollution and neurotoxicity on brain structure: Evidence from women's health initiative memory study

The aim of this study was to examine the putative adverse effects of ambient fine particulate matter (PM2.5: PM with aerodynamic diameters <2.5μm) on brain volumes in older women.

Fecal Contamination of Shallow Tubewells in Bangladesh Inversely Related to Arsenic

The health risks of As exposure due to the installation of millions of shallow tubewells in the Bengal Basin are known, but fecal contamination of shallow aquifers has not systematically been examined. This could be a source of concern in densely populated areas with poor sanitation because the hydraulic travel time from surface water bodies to shallow wells that are low in As was previously shown to be considerably shorter than for shallow wells that are high in As. In this study, 125 tubewells 6−36 m deep were sampled in duplicate for 18 months to quantify the presence of the fecal indicator Escherichia coli. On any given month, E. coli was detected at levels exceeding 1 most probable number per 100 mL in 19−64% of all shallow tubewells, with a higher proportion typically following periods of heavy rainfall. The frequency of E. coli detection averaged over a year was found to increase with population surrounding a well and decrease with the As content of a well, most likely because of downward transport of E. coli associated with local recharge. The health implications of higher fecal contamination of shallow tubewells, to which millions of households in Bangladesh have switched in order to reduce their exposure to As, need to be evaluated.

Comparison of geostatistical interpolation and remote sensing techniques for estimating long-term exposure to ambient PM2.5 concentrations across the continental United States.

Background: A better understanding of the adverse health effects of chronic exposure to fine particulate matter (PM2.5) requires accurate estimates of PM2.5 variation at fine spatial scales. Remote sensing has emerged as an important means of estimating PM2.5 exposures, but relatively few studies have compared remote-sensing estimates to those derived from monitor-based data. Objective: We evaluated and compared the predictive capabilities of remote sensing and geostatistical interpolation. Methods: We developed a space–time geostatistical kriging model to predict PM2.5 over the continental United States and compared resulting predictions to estimates derived from satellite retrievals. Results: The kriging estimate was more accurate for locations that were about 100 km from a monitoring station, whereas the remote sensing estimate was more accurate for locations that were > 100 km from a monitoring station. Based on this finding, we developed a hybrid map that combines the kriging and satellite-based PM2.5 estimates. Conclusions: We found that for most of the populated areas of the continental United States, geostatistical interpolation produced more accurate estimates than remote sensing. The differences between the estimates resulting from the two methods, however, were relatively small. In areas with extensive monitoring networks, the interpolation may provide more accurate estimates, but in the many areas of the world without such monitoring, remote sensing can provide useful exposure estimates that perform nearly as well.