Adipose Tissue Distribution and Survival Among Women with Nonmetastatic Breast Cancer.

ObjectivePrevious studies of breast cancer survival have not considered specific depots of adipose tissue such as subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT).MethodsThis study assessed these relationships among 3,235 women with stage II and III breast cancer diagnosed between 2005 and 2013 at Kaiser Permanente Northern California and between 2000 and 2012 at Dana Farber Cancer Institute. SAT and VAT areas (in centimeters squared) were calculated from routine computed tomography scans within 6 (median: 1.2) months of diagnosis, covariates were collected from electronic health records, and vital status was assessed by death records. Hazard ratios (HRs) and 95% CIs were estimated using Cox regression.ResultsSAT and VAT ranged from 19.0 to 891 cm2 and from 0.484 to 454 cm2 , respectively. SAT was related to increased risk of death (127-cm2 increase; HR [95% CI]: 1.13 [1.02-1.26]), but no relationship was found with VAT (78.18-cm2 increase; HR [95% CI]: 1.02 [0.91-1.14]). An association with VAT was noted among women with stage II cancer (stage II: HR: 1.17 [95% CI: 0.99-1.39]; stage III: HR: 0.90 [95% CI: 0.76-1.07]; P interaction < 0.01). Joint increases in SAT and VAT were associated with mortality above either alone (simultaneous 1-SD increase: HR 1.19 [95% CI: 1.05-1.34]).ConclusionsSAT may be an underappreciated risk factor for breast cancer-related death

Measurement error in a multi-level analysis of air pollution and health: a simulation study.

BACKGROUND: Spatio-temporal models are increasingly being used to predict exposure to ambient outdoor air pollution at high spatial resolution for inclusion in epidemiological analyses of air pollution and health. Measurement error in these predictions can nevertheless have impacts on health effect estimation. Using statistical simulation we aim to investigate the effects of such error within a multi-level model analysis of long and short-term pollutant exposure and health. METHODS: Our study was based on a theoretical sample of 1000 geographical sites within Greater London. Simulations of "true" site-specific daily mean and 5-year mean NO2 and PM10 concentrations, incorporating both temporal variation and spatial covariance, were informed by an analysis of daily measurements over the period 2009-2013 from fixed location urban background monitors in the London area. In the context of a multi-level single-pollutant Poisson regression analysis of mortality, we investigated scenarios in which we specified: the Pearson correlation between modelled and "true" data and the ratio of their variances (model versus "true") and assumed these parameters were the same spatially and temporally. RESULTS: In general, health effect estimates associated with both long and short-term exposure were biased towards the null with the level of bias increasing to over 60% as the correlation coefficient decreased from 0.9 to 0.5 and the variance ratio increased from 0.5 to 2. However, for a combination of high correlation (0.9) and small variance ratio (0.5) non-trivial bias (> 25%) away from the null was observed. Standard errors of health effect estimates, though unaffected by changes in the correlation coefficient, appeared to be attenuated for variance ratios > 1 but inflated for variance ratios < 1. CONCLUSION: While our findings suggest that in most cases modelling errors result in attenuation of the effect estimate towards the null, in some situations a non-trivial bias away from the null may occur. The magnitude and direction of bias appears to depend on the relationship between modelled and "true" data in terms of their correlation and the ratio of their variances. These factors should be taken into account when assessing the validity of modelled air pollution predictions for use in complex epidemiological models

Measurement error in environmental exposures: Statistical implications for spatial air pollution models and gene environment interaction tests

Measurement error is an important issue in studies of environmental epidemiology. We considered the effects of measurement error in environmental covariates in several important settings affecting current public health research. Throughout this dissertation, we investigate the impacts of measurement error and consider statistical methodology to fix that error

Landscape epidemiology modeling using an agent-based model and a geographic information system

A landscape epidemiology modeling framework is presented which integrates the simulation outputs from an established spatial agent-based model (ABM) of malaria with a geographic information system (GIS). For a study area in Kenya, five landscape scenarios are constructed with varying coverage levels of two mosquito-control interventions. For each scenario, maps are presented to show the average distributions of three output indices obtained from the results of 750 simulation runs. Hot spot analysis is performed to detect statistically significant hot spots and cold spots. Additional spatial analysis is conducted using ordinary kriging with circular semivariograms for all scenarios. The integration of epidemiological simulation-based results with spatial analyses techniques within a single modeling framework can be a valuable tool for conducting a variety of disease control activities such as exploring new biological insights, monitoring epidemiological landscape changes, and guiding resource allocation for further investigation

prepared for

We thank the U.S. Environmental Protection Agency (Office of Water; Nationa

Modification by hemochromatosis gene polymorphisms of the association between traffic-related air pollution and cognition in older men: a cohort study

Background: Previous studies found effect modification of associations between traffic-related air pollution and cardiovascular outcomes by polymorphisms in the hemochromatosis gene (HFE). As traffic-related air pollution may impact cognition through effects on cardiovascular health or through mechanisms which may also influence cardiovascular outcomes, we hypothesized that HFE polymorphisms would also modify a previously observed association between traffic-related air pollution exposure and cognition in older men. Methods: We considered data from 628 participants of the VA Normative Aging Study. We estimated long term exposure to black carbon (BC), a marker of traffic related air pollution, using a spatio-temporal land use regression model. We assessed cognition using the Mini-Mental State Examination (MMSE), a test of global function, and performance on a battery of other tests, covering a wide range of domains. We investigated whether variants of HFE C282Y and H63D modified the association between BC and having a low MMSE score using logistic models with generalized estimating equations and multiplicative interaction terms. Similarly, we assessed whether HFE variants modified the association between BC and performance on the cognitive battery using linear mixed models with multiplicative interaction terms. Results: Our results suggest modification of the BC-cognition association by HFE C282Y, although the test of interaction did not achieve statistical significance. In multivariable-adjusted models, participants who lacked a HFE C282Y variant (CC) exhibited an adverse association between BC and total cognition z-score (beta for a doubling in BC concentration: -0.061, 95% CI: -0.115, -0.007), while we did not observe an association in participants with at least one variant genotype (CY or YY) (beta for a doubling in BC concentration: 0.073, 95% CI: -0.081, 0.228; p-value for interaction: 0.11). The pattern of association was similar for analyses considering performance on the Mini-Mental State Examination. There was little evidence to support effect modification of the BC-cognition association by the HFE H63D genotype. Conclusions: Our data suggest that older adults who lack an HFE C282Y variant may be more susceptible to an adverse effect of traffic-related air pollution exposure on cognition. This finding and the proposed biological mechanism require confirmation

Residential Black Carbon Exposure and Circulating Markers of Systemic Inflammation in Elderly Males: The Normative Aging Study

Background: Traffic-related particles (TRPs) are associated with adverse cardiovascular events. The exact mechanisms are unclear, but systemic inflammatory responses likely play a role

Medium-Term Exposure to Traffic-Related Air Pollution and Markers of Inflammation and Endothelial Function

Bac k g r o u n d: Exposure to traffic-related air pollution (TRAP) contributes to increased cardiovascular risk. Land-use regression models can improve exposure assessment for TRAP. Objectives: We examined the association between medium-term concentrations of black carbon (BC) estimated by land-use regression and levels of soluble intercellular adhesion molecule-1 (sICAM-1) and soluble vascular cell adhesion molecule-1 (sVCAM-1), both markers of inflammatory and endothelial response. Me t h o d s: We studied 642 elderly men participating in the Veterans Administration (VA) Normative Aging Study with repeated measurements of sICAM‑1 and sVCAM‑1 during 1999–2008. Daily estimates of BC exposure at each geocoded participant address were derived using a validated spatiotemporal model and averaged to form 4-, 8-, and 12-week exposures. We used linear mixed models to estimate associations, controlling for confounders. We examined effect modification by statin use, obesity, and diabetes. Re s u l t s: We found statistically significant positive associations between BC and sICAM‑1 for averages of 4, 8, and 12 weeks. An interquartile-range increase in 8-week BC exposure (0.30 μg/m3) was associated with a 1.58 % increase in sICAM‑1 (95 % confidence interval, 0.18–3.00%). Overall association