Semiparametric Latent Variable Regression Models for Spatio-temporal Modeling of Mobile Source Particles in the Greater Boston Area

Traffic particle concentrations show considerable spatial variability within a metropolitan area. We consider latent variable semiparametric regression models for modeling the spatial and temporal variability of black carbon and elemental carbon concentrations in the greater Boston area. Measurements of these pollutants, which are markers of traffic particles, were obtained from several individual exposure studies conducted at specific household locations as well as 15 ambient monitoring sites in the city. The models allow for both flexible, nonlinear effects of covariates and for unexplained spatial and temporal variability in exposure. In addition, the different individual exposure studies recorded different surrogates of traffic particles, with some recording only outdoor concentrations of black or elemental carbon, some recording indoor concentrations of black carbon, and others recording both indoor and outdoor concentrations of black carbon. A joint model for outdoor and indoor exposure that specifies a spatially varying latent variable provides greater spatial coverage in the area of interest. We propose a penalised spline formation of the model that relates to generalised kringing of the latent traffic pollution variable and leads to a natural Bayesian Markov Chain Monte Carlo algorithm for model fitting. We propose methods that allow us to control the degress of freedom of the smoother in a Bayesian framework. Finally, we present results from an analysis that applies the model to data from summer and winter separatel

Family planning services provided by healthcare providers in the Bamenda Health District Cameroon

Family planning is one of the ways through which maternal deaths can be reduced. Studies have shown that up to 40% of maternal deaths could have been averted through the use of family planning services. Objective: The objective of this study was to assess the available family planning services offered to the population of the Bamenda Health District by health care providers. Method: A multistage cross-sectional, descriptive study design was used where data was collected at a point in time. The study population constituted all health workers (Nurses and midwives), randomly selected from the Bamenda Health District. Data were collected from randomly selected health facilities from the 13 health areas of the Bamenda Health Districts with the use of a semi-structured questionnaire. Data analysis was done using SPSS version 21. Result: The result showed that only 36.4% of respondents offer all the aspects of family planning. Based on the types of family planning services available, there were three aspects of family planning services they know: Contraceptive services (27.1%), pregnancy Testing and Counselling (6.4%), and Sexually Transmitted Disease services (3.6%). The most used services by clients were the provision of contraceptives (48.6%) and premarital counseling/preconception care (31.4%). Family planning services least used by clients were sexual and reproductive health education (21.4%), sex determination (27.9%), and breast/cervical cancer screening (7.1%). Success has been made in FP such as reduced unwanted pregnancy and abortion (69.3%) and greater spacing between births, reducing the risk of infant and child mortality (10%). Conclusion: all health centers and hospitals, should consider all aspects of family planning services as an integral part of maternal and child health with Primary Health Care services at all levels to ensure the provision of complete Family Planning services. This will improve the uptake of family planning services by the population

Air conditioning and source-specific particles as modifiers of the effect of PM(10) on hospital admissions for heart and lung disease.

Studies on acute effects of particulate matter (PM) air pollution show significant variability in exposure-effect relations among cities. Recent studies have shown an influence of ventilation on personal/indoor-outdoor relations and stronger associations of adverse effects with combustion-related particles. We evaluated whether differences in prevalence of air conditioning (AC) and/or the contribution of different sources to total PM(10) emissions could partly explain the observed variability in exposure-effect relations. We used regression coefficients of the relation between PM(10) and hospital admissions for chronic obstructive pulmonary disease (COPD), cardiovascular disease (CVD), and pneumonia from a recent study in 14 U.S. cities. We obtained data on the prevalence of AC from the 1993 American Housing Survey and data on PM(10) emissions by source category, vehicle miles traveled (VMT), and population density from the U.S. EPA. We analyzed data using meta-regression techniques. PM(10) regression coefficients for CVD and COPD decreased significantly with increasing percentage of homes with central AC when cities were stratified by whether their PM(10) concentrations peaked in winter or non-winter months. PM(10) coefficients for CVD increased significantly with increasing percentage of PM(10) emission from highway vehicles, highway diesels, oil combustion, metal processing, decreasing percentage of PM(10) emission from fugitive dust, and increasing population density and VMT/mile(2). In multivariate analysis, only percentage of PM(subscript)10(/subscript) from highway vehicles/diesels and oil combustion remained significant. For COPD and pneumonia, associations were less significant but the patterns of the associations were similar to that for CVD. The results suggest that air conditioning and proportion of especially traffic-related particles significantly modify the effect of PM(10) on hospital admissions, especially for CVD

Diabetes, Obesity, and Hypertension May Enhance Associations between Air Pollution and Markers of Systemic Inflammation

Airborne particulate matter (PM) may lead to increased cardiac risk through an inflammatory pathway. Therefore, we investigated associations between ambient PM and markers of systemic inflammation among repeated measures from 44 senior citizens (≥ 60 years of age) and examined susceptibility by conditions linked to chronic inflammation. Mixed models were used to identify associations between concentrations of fine PM [aerodynamic diameter ≤ 2.5 μm (PM2.5)] averaged over 1–7 days and measures of C-reactive protein (CRP), interleukin-6 (IL-6), and white blood cells (WBCs). Effect modification was investigated for diabetes, obesity, hypertension, and elevated mean inflammatory markers. We found positive associations between longer moving averages of PM2.5 and WBCs across all participants, with a 5.5% [95% confidence interval (CI), 0.10 to 11%] increase per interquartile increase (5.4 μg/m3) of PM2.5 averaged over the previous week. PM2.5 and CRP also exhibited positive associations among all individuals for averages longer than 1 day, with the largest associations for persons with diabetes, obesity, and hypertension. For example, an interquartile increase in the 5-day mean PM2.5 (6.1 μg/m3) was associated with a 14% increase in CRP (95% CI, −5.4 to 37%) for all individuals and an 81% (95% CI, 21 to 172%) increase for persons with diabetes, obesity, and hypertension. Persons with diabetes, obesity, and hypertension also exhibited positive associations between PM2.5 and IL-6. Individuals with elevated mean inflammatory markers exhibited enhanced associations with CRP, IL-6, and WBCs. We found modest positive associations between PM2.5 and indicators of systemic inflammation, with larger associations suggested for individuals with diabetes, obesity, hypertension, and elevated mean inflammatory markers

The impact of source contribution uncertainty on the effects of source-specific PM2.5 on hospital admissions: A case study in Boston, MA

Epidemiologic studies of particulate sources and adverse health do not account for the uncertainty in the source contribution estimates. Our goal was to assess the impact of uncertainty on the effect estimates of particulate sources on emergency cardiovascular (CVD) admissions. We examined the effects of PM2.5 sources, identified by positive matrix factorization (PMF) and absolute principle component analysis (APCA), on emergency CVD hospital admissions among Medicare enrollees in Boston, MA, during 2003–2010, given stronger associations for this period. We propagated uncertainty in source contributions using a block bootstrap procedure. We further estimated average across-methods source-specific effect estimates using bootstrap samples. We estimated contributions for regional, mobile, crustal, residual oil combustion, road dust, and sea salt sources. Accounting for uncertainty, same-day exposures to regional pollution were associated with an across-methods average effect of 2.00% (0.18, 3.78%) increase in the rate of CVD admissions. Weekly residual oil exposures resulted in an average 2.12% (0.19, 4.22%) increase. Same-day and 2-day exposures to mobile-related PM2.5 were also associated with increased admissions. Confidence intervals when accounting for the uncertainty were wider than otherwise. Agreement in PMF and APCA results was stronger when uncertainty was considered in health models. Accounting for uncertainty in source contributions leads to more stable effect estimates across methods and potentially to fewer spurious significant associations

Practical large-scale spatio-temporal modeling of particulate matter concentrations

The last two decades have seen intense scientific and regulatory interest in the health effects of particulate matter (PM). Influential epidemiological studies that characterize chronic exposure of individuals rely on monitoring data that are sparse in space and time, so they often assign the same exposure to participants in large geographic areas and across time. We estimate monthly PM during 1988--2002 in a large spatial domain for use in studying health effects in the Nurses' Health Study. We develop a conceptually simple spatio-temporal model that uses a rich set of covariates. The model is used to estimate concentrations of $PM_{10}$ for the full time period and $PM_{2.5}$ for a subset of the period. For the earlier part of the period, 1988--1998, few $PM_{2.5}$ monitors were operating, so we develop a simple extension to the model that represents $PM_{2.5}$ conditionally on $PM_{10}$ model predictions. In the epidemiological analysis, model predictions of $PM_{10}$ are more strongly associated with health effects than when using simpler approaches to estimate exposure. Our modeling approach supports the application in estimating both fine-scale and large-scale spatial heterogeneity and capturing space--time interaction through the use of monthly-varying spatial surfaces. At the same time, the model is computationally feasible, implementable with standard software, and readily understandable to the scientific audience. Despite simplifying assumptions, the model has good predictive performance and uncertainty characterization.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS204 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

A Novel Genetic Score Approach Using Instruments to Investigate Interactions between Pathways and Environment: Application to Air Pollution

Air pollution has been associated with increased systemic inflammation markers. We developed a new pathway analysis approach to investigate whether gene variants within relevant pathways (oxidative stress, endothelial function, and metal processing) modified the association between particulate air pollution and fibrinogen, C-reactive protein (CRP), intercellular adhesion molecule-1 (ICAM-1), and vascular cell adhesion molecule-1 (VCAM-1). Our study population consisted of 822 elderly participants of the Normative Aging Study (1999–2011). To investigate the role of biological mechanisms and to reduce the number of comparisons in the analysis, we created pathway-specific scores using gene variants related to each pathway. To select the most appropriate gene variants, we used the least absolute shrinkage and selection operator (Lasso) to relate independent outcomes representative of each pathway (8-hydroxydeoxyguanosine for oxidative stress, augmentation index for endothelial function, and patella lead for metal processing) to gene variants. A high genetic score corresponds to a higher allelic risk profile. We fit mixed-effects models to examine modification by the genetic score of the weekly air pollution association with the outcome. Among participants with higher genetic scores within the oxidative stress pathway, we observed significant associations between particle number and fibrinogen, while we did not find any association among participants with lower scores (pinteraction = 0.04). Compared to individuals with low genetic scores of metal processing gene variants, participants with higher scores had greater effects of particle number on fibrinogen (pinteraction = 0.12), CRP (pinteraction = 0.02), and ICAM-1 (pinteraction = 0.08). This two-stage penalization method is easy to implement and can be used for large-scale genetic applications

Postural Changes in Blood Pressure Associated with Interactions between Candidate Genes for Chronic Respiratory Diseases and Exposure to Particulate Matter

BACKGROUND. Fine particulate matter [aerodynamic diameter ≤ 2.5 μm (PM2.5)] has been associated with autonomic dysregulation. OBJECTIVE. We hypothesized that PM2.5 influences postural changes in systolic blood pressure (ΔSBP) and in diastolic blood pressure (ΔDBP) and that this effect is modified by genes thought to be related to chronic lung disease. METHODS. We measured blood pressure in participants every 3-5 years. ΔSBP and ΔDBP were calculated as sitting minus standing SBP and DBP. We averaged PM2.5 over 48 hr before study visits and analyzed 202 single nucleotide polymorphisms (SNPs) in 25 genes. To address multiple comparisons, data were stratified into a split sample. In the discovery cohort, the effects of SNP x PM2.5 interactions on ΔSBP and ΔDBP were analyzed using mixed models with subject-specific random intercepts. We defined positive outcomes as p < 0.1 for the interaction; we analyzed only these SNPs in the replicate cohort and confirmed them if p < 0.025 with the same sign. Confirmed associations were analyzed within the full cohort in models adjusted for anthropometric and lifestyle factors. RESULTS. Nine hundred forty-five participants were included in our analysis. One interaction with rs9568232 in PHD finger protein 11 (PHF11) was associated with greater ΔDBP. Interactions with rs1144393 in matrix metalloprotease 1 (MMP1) and rs16930692, rs7955200, and rs10771283 in inositol 1,4,5-triphosphate receptor, type 2 (ITPR2) were associated with significantly greater ΔSBP. Because SNPs associated with ΔSBP in our analysis are in genes along the renin-angiotensin pathway, we then examined medications affecting that pathway and observed significant interactions for angiotensin receptor blockers but not angiotensin-converting enzyme inhibitors with PM2.5. CONCLUSIONS. PM2.5 influences blood pressure and autonomic function. This effect is modified by genes and drugs that also act along this pathway.National Institute of Environmental Health Sciences (T32 ES07069, ES0002, ES015172-01, ES014663, P01 ES09825); United States Environmental Protection Agency (R827353, R832416); National Institutes of Health/National Institute of Aging (AG027014); United States Department of Veterans Affairs; Massachusetts Veterans Epidemiology Research and Information Cente

The effect of primary organic particles on emergency hospital admissions among the elderly in 3 US cities

Background: Fine particle (PM2.5) pollution related to combustion sources has been linked to a variety of adverse health outcomes. Although poorly understood, it is possible that organic carbon (OC) species, particularly those from combustion-related sources, may be partially responsible for the observed toxicity of PM2.5. The toxicity of the OC species may be related to their chemical structures; however, few studies have examined the association of OC species with health impacts. Methods: We categorized 58 primary organic compounds by their chemical properties into 5 groups: n-alkanes, hopanes, cyclohexanes, PAHs and isoalkanes. We examined their impacts on the rate of daily emergency hospital admissions among Medicare recipients in Atlanta, GA and Birmingham, AL (2006–2009), and Dallas, TX (2006–2007). We analyzed data in two stages; we applied a case-crossover analysis to simultaneously estimate effects of individual OC species on cause-specific hospital admissions. In the second stage we estimated the OC chemical group-specific effects, using a multivariate weighted regression. Results: Exposures to cyclohexanes of six days and longer were significantly and consistently associated with increased rate of hospital admissions for CVD (3.40%, 95%CI = (0.64, 6.24%) for 7-d exposure). Similar increases were found for hospitalizations for ischemic heart disease and myocardial infarction. For respiratory related hospital admissions, associations with OC groups were less consistent, although exposure to iso-/anteiso-alkanes was associated with increased respiratory-related hospitalizations. Conclusions: Results suggest that week-long exposures to traffic-related, primary organic species are associated with increased rate of total and cause-specific CVD emergency hospital admissions. Associations were significant for cyclohexanes, but not hopanes, suggesting that chemical properties likely play an important role in primary OC toxicity