Particulate air pollution and survival in a COPD cohort

<p>Abstract</p> <p>Background</p> <p>Several studies have shown cross-sectional associations between long term exposure to particulate air pollution and survival in general population or convenience cohorts. Less is known about susceptibility, or year to year changes in exposure. We investigated whether particles were associated with survival in a cohort of persons with COPD in 34 US cities, eliminating the usual cross-sectional exposure and treating PM₁₀as a within city time varying exposure.</p> <p>Methods</p> <p>Using hospital discharge data, we constructed a cohort of persons discharged alive with chronic obstructive pulmonary disease using Medicare data between 1985 and 1999. 12-month averages of PM₁₀were merged to the individual annual follow up in each city. We applied Cox's proportional hazard regression model in each city, with adjustment for individual risk factors.</p> <p>Results</p> <p>We found significant associations in the survival analyses for single year and multiple lag exposures, with a hazard ratio for mortality for an increase of 10 μg/m³PM₁₀over the previous 4 years of 1.22 (95% CI: 1.17–1.27).</p> <p>Conclusion</p> <p>Persons discharged alive for COPD have substantial mortality risks associated with exposure to particles. The risk is evident for exposure in the previous year, and higher in a 4 year distributed lag model. These risks are significantly greater than seen in time series analyses.</p

Effect of personal exposure to black carbon on changes in allergic asthma gene methylation measured 5 days later in urban children: importance of allergic sensitization

Background Asthma gene DNA methylation may underlie the effects of air pollution on airway inflammation. However, the temporality and individual susceptibility to environmental epigenetic regulation of asthma has not been fully elucidated. Our objective was to determine the timeline of black carbon (BC) exposure, measured by personal sampling, on DNA methylation of allergic asthma genes 5 days later to capture usual weather variations and differences related to changes in behavior and activities. We also sought to determine how methylation may vary by seroatopy and cockroach sensitization and by elevated fractional exhaled nitric oxide (FeNO). Methods Personal BC levels were measured during two 24-h periods over a 6-day sampling period in 163 New York City children (age 9–14 years), repeated 6 months later. During home visits, buccal cells were collected as noninvasive surrogates for lower airway epithelial cells and FeNO measured as an indicator of airway inflammation. CpG promoter loci of allergic asthma genes (e.g., interleukin 4 (IL4), interferon gamma (IFNγ), inducible nitric oxide synthase (NOS2A)), arginase 2 (ARG2)) were pyrosequenced at the start and end of each sampling period. Results Higher levels of BC were associated with lower methylation of IL4 promoter CpG−48 5 days later. The magnitude of association between BC exposure and demethylation of IL4 CpG−48 and NOS2A CpG+5099 measured 5 days later appeared to be greater among seroatopic children, especially those sensitized to cockroach allergens (RR [95% CI] 0.55 [0.37–0.82] and 0.67 [0.45–0.98] for IL4 CpG−48 and NOS2A CpG+5099, respectively), compared to non-sensitized children (RR [95% CI] 0.87 [0.65–1.17] and 0.95 [0.69–1.33] for IL4 CpG−48 and NOS2A CpG+5099, respectively); however, the difference was not statistically different. In multivariable linear regression models, lower DNA methylation of IL4 CpG−48 and NOS2A CpG+5099 were associated with increased FeNO. Conclusions Our results suggest that exposure to BC may exert asthma proinflammatory gene demethylation 5 days later that in turn may link to airway inflammation. Our results further suggest that seroatopic children, especially those sensitized to cockroach allergens, may be more susceptible to the effect of acute BC exposure on epigenetic changes

A review of spatial causal inference methods for environmental and epidemiological applications

The scientific rigor and computational methods of causal inference have had great impacts on many disciplines, but have only recently begun to take hold in spatial applications. Spatial casual inference poses analytic challenges due to complex correlation structures and interference between the treatment at one location and the outcomes at others. In this paper, we review the current literature on spatial causal inference and identify areas of future work. We first discuss methods that exploit spatial structure to account for unmeasured confounding variables. We then discuss causal analysis in the presence of spatial interference including several common assumptions used to reduce the complexity of the interference patterns under consideration. These methods are extended to the spatiotemporal case where we compare and contrast the potential outcomes framework with Granger causality, and to geostatistical analyses involving spatial random fields of treatments and responses. The methods are introduced in the context of observational environmental and epidemiological studies, and are compared using both a simulation study and analysis of the effect of ambient air pollution on COVID-19 mortality rate. Code to implement many of the methods using the popular Bayesian software OpenBUGS is provided

Computational modelling of salamander retinal ganglion cells using machine learning approaches

Artificial vision using computational models that can mimic biological vision is an area of ongoing research. One of the main themes within this research is the study of the retina and in particular, retinal ganglion cells which are responsible for encoding the visual stimuli. A common approach to modelling the internal processes of retinal ganglion cells is the use of a linear - non-linear cascade model, which models the cell's response using a linear filter followed by a static non-linearity. However, the resulting model is generally restrictive as it is often a poor estimator of the neuron's response. In this paper we present an alternative to the linear - non-linear model by modelling retinal ganglion cells using a number of machine learning techniques which have a proven track record for learning complex non-linearities in many different domains. A comparison of the model predicted spike rate shows that the machine learning models perform better than the standard linear - non-linear approach in the case of temporal white noise stimuli