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 ß-amyloid by thioflavin S for fibrillary amyloid and by immunocytochemistry for Aß deposits, both exacerbated by APOE ɛ4. Moreover, nPM exposure increased Aß 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 Aß production and selective changes in hippocampal CA1 neurons and glutamate receptor subunits.

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.

An application of the holistochastic human exposure methodology to naturally occurring arsenic in Bangladesh drinking water.

The occurrence of arsenic in drinking water is an issue of considerable interest. In the case of Bangladesh, arsenic concentrations have been closely monitored since the early 1990s through an extensive sampling network. The focus of the present work is methodological. In particular, we propose the application of a holistochastic framework of human exposure to study lifetime population damage due to arsenic exposure across Bangladesh. The Bayesian Maximum Entropy theory is an important component of this framework, which possesses solid theoretical foundations and offers powerful tools to assimilate a variety of knowledge bases (physical, epidemiologic, toxicokinetic, demographic, etc.) and uncertainty sources (soft data, measurement errors, etc.). The holistochastic exposure approach leads to physically meaningful and informative spatial maps of arsenic distribution in Bangladesh drinking water. Global indicators of the adverse health effects on the population are generated, and valuable insight is gained by blending information from different scientific disciplines. The numerical results indicate an increased lifetime bladder cancer probability for the Bangladesh population due to arsenic. The health effect estimates obtained and the associated uncertainty assessments are valuable tools for a broad spectrum of end-users.

Spatiotemporal analysis of environmental exposure-health effect associations.

The goal of this work is to discuss a general methodology for studying associations between environmental exposures and health effect by means of the spatiotemporal random field theory. This theory is the tool of choice for rigorously accounting for important spatiotemporal variations and uncertainties related to exposures and effect. Within the framework of the random field theory, the Bayesian maximum entropy model neatly synthesizes various sources of physical and epidemiological knowledge into spatiotemporal analysis. Therefore, unlike technical statistics, this approach relies on the blending of substantive physical knowledge with powerful mathematical techniques and a coherent rationale. Given the well-founded fact that certain health effects may be caused by environmental exposures, the significance of these exposures is assessed in terms of a criterion that is based on the joint stochastic representation of exposure and health-effect distributions in space/time. In view of this criterion, the strength and consistency of the exposure-effect association are evaluated on the basis of the health-effect predictions that the combined physico-epidemiologic analysis generates in space/time. The main features of the approach are demonstrated by a simulation example and a real case study involving mortality and cold temperatures in North Carolina. The studies demonstrated the practical usefulness of the stochastic human exposure analysis in assessing the exposure-effect association. The results reported here emphasize the links between spatiotemporal models of physical systems and population health-effect distributions, thus suggesting directions for improving the current understanding of quantitative "exposure-health effect" functions.