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Preterm birth (before 37 weeks of gestation) is a leading cause of infant morbidity and mortality, affecting 13% of births in the United States in 2005.1 An emerging body of evidence suggests that ambient levels of air pollution may play a role in the incidence of preterm birth.2–4 However, the gestational window of susceptibility has not been consistent across studies, with associations most commonly reported for exposures during early pregnancy (the first month or first trimester)5–9 or in late pregnancy (the third trimester, the last 6 weeks, the last month, the last week).5,7,8,10–14 Previous studies have also been inconsistent regarding the specific pollutants associated with preterm birth, although most studies suggest associations with ambient measures of particulate matter (PM).5–8,11–14 Sulfur dioxide (SO2), as well as traffic related pollutants such as nitrogen dioxide (NO2) and carbon monoxide (CO), have also been associated with preterm birth in several studies.5,8–16 Although the pathophysiology of preterm birth remains poorly understood, evidence suggests a role for inflammatory pathways as well as implantation errors in early pregnancy.17 Both of these pathways offer plausible mechanisms by which air pollution could increase the risk of preterm birth. Air pollution levels in the weeks following conception could disrupt implantation and placentation and increase the risk of preterm birth through suboptimal placental function. In late pregnancy, high levels of air pollution could activate either an acute or sustained inflammatory response leading to the initiation of early labor. To investigate the relationship between ambient air pollution during gestation and the incidence of preterm birth, we conducted a time-series analysis in the central five counties of metropolitan Atlanta during 1994–2004. In addition to the US Environmental Protection Agency (EPA) criteria pollutants (ozone [O3], SO2, NO2, CO, PM10, PM2.5), we investigated speciated particle measurements that are rarely available on a daily basis and have not been previously assessed in relation to preterm birth. We focused on three gestational windows of exposure based on findings from previous air pollution studies, as well as current hypotheses about biological mechanisms leading to preterm birth: the first month of gestation, the final week of gestation and the final six weeks of gestation.

Landscape features may serve as either barriers or gateways to the spread of certain infectious diseases, and understanding the way geographic structure impacts disease spread could lead to improved containment strategies. Here, we focus on the spacetime diffusion of a raccoon rabies outbreak across several states in the Eastern United States. While focusing on pattern, we move toward closer links between pattern and process by considering statistical estimation of local pattern features to gain insight on landscape influences on the underlying process. Specifically, we quantify the impact that landscape features, such as mountains and rivers, have on the speed of infectious disease diffusion. This work combines statistical modeling with operations in a geographic information system (GIS) to link observed patterns of disease diffusion with local landscape values. We explore three analytic approaches. First, we use spatial prediction (kriging) to provide a descriptive pattern of the spread of the virus. Second, we use Bayesian areal wombling to detect barriers for infectious disease transmission and examine spatial coincidence with potential features. Finally, we input landscape variables into a hierarchical Bayesian model with spatially varying coefficients to obtain model-based estimates of their local impacts on transmission time in counties.

Urinary contaminant concentrations are commonly adjusted by creatinine to account for the variability in urinary output. This approach may not be optimal among children due to developmental growth of muscle mass and the associated increase in creatinine formation. An alternative approach is to measure the specific gravity of the urine sample, which reflects the solute concentration of the urine. We compare the appropriateness of urinary creatinine and urinary-specific gravity as factors for correcting morning and evening spot urine samples collected from 23 children (3-11 years) for a total of 41 days in four different seasons. Two linear mixed-effects models were fit using age, sex, season, and sample collection time (morning/evening) as predictors with specific gravity and creatinine as dependent variables. Specific gravity was significantly associated with the sample collection time (P

We used sentinel animal enclosures to measure the rate of infestation by the Chagas disease vector, Triatoma infestans, in an urban community of Arequipa, Peru, and to evaluate the effect of deltamethrin-impregnated netting on that rate. Impregnated netting decreased the rate of infestation of sentinel enclosures (rate ratio, 0.23; 95% confidence interval, 0.13-0.38; P < 0.001), controlling for the density of surrounding vector populations and the distance of these to the sentinel enclosures. Most migrant insects were early-stage nymphs, which are less likely to carry the parasitic agent of Chagas disease, Trypanosoma cruzi. Spread of the vector in the city therefore likely precedes spread of the parasite. Netting was particularly effective against adult insects and late-stage nymphs; taking into account population structure, netting decreased the reproductive value of migrant populations from 443.6 to 40.5. Impregnated netting can slow the spread of T. infestans and is a potentially valuable tool in the control of Chagas disease.

The three papers included in this special issue represent a set of presentations in an invited session on disease ecology at the 2005 Spring Meeting of the Eastern North American Region of the International Biometric Society. The papers each address statistical estimation and inference for particular components of different disease processes and, taken together, illustrate the breadth of statistical issues arising in the study of the ecology and public health impact of disease. As an introduction, we provide a very brief overview of the area of "disease ecology", a variety of synonyms addressing different aspects of disease ecology, and present a schematic structure illustrating general components of the underlying disease process, data collection issues, and different disciplinary perspectives ranging from microbiology to public health surveillance.

Past studies consistently indicate measurable local associations between alcohol distribution and the incidence of violence. These results, coupled with measurements of spatial correlation, reveal the importance of spatial analysis in the study of the interaction of alcohol and violence. While studies increasingly incorporate spatial correlation among model residuals to improve precision and reduce bias, to date, most analyses assume associations that are constant and independent of location, an assumption coming under increasing scrutiny in the quantitative geography literature. In this paper, we review and contrast two approaches for the estimation of and inference for spatially heterogeneous effects (i.e., associative factors whose impacts on the outcome of interest vary throughout geographic space). Specifically, we provide an in-depth comparison of “geographically weighted regression” models (allowing covariate effects to vary in space but only allowing relatively ad hoc inference) with “variable coefficient” models (allowing varying effects via spatial random fields and providing model-based estimation and inference, but requiring more advanced computational techniques). We compare the approaches with respect to underlying conceptual structures, computational implementation, and inferential output. We apply both approaches to violent crime, illegal drug arrest, and alcohol distribution data from Houston, Texas and compare results in light of the differing methodological structures of the two approaches.

We present an exploratory analysis of reported county-specific incidence of Lyme disease in the northeastern United States for the years 1990–2000. We briefly review the disease ecology of Lyme disease and the use of risk maps to describe local incidence as estimates of local risk of disease. We place the relevant elements of local environmental and ecological variables, local disease incidence, and (importantly) local disease reporting in a conceptual context to frame our analysis. We then apply hierarchical linear models of increasing complexity to summarize observed patterns in reported incidence, borrowing information across counties to improve local precision. We find areas of increasing incidence in the central northeastern Atlantic coast counties, increasing incidence branching to the north and west, and an area of fairly stable and slightly decreasing reported incidence in western New York.

Background: Metrics that capture temporal contrasts in multipollutant exposures are needed for long-term health studies of air pollution Objective: Create multipollutant ambient air quality classif...

Background: Short-term exposure to ambient fine particulate matter (PM2.5) concentrations has been associated with increased mortality and morbidity. Determining which sources of PM2.5 are most toxic can help guide targeted reduction of PM2.5. However, conducting multicity epidemiologic studies of sources is difficult because source-specific PM2.5 is not directly measured, and source chemical compositions can vary between cities. Objectives: We determined how the chemical composition of primary ambient PM2.5 sources varies across cities. We estimated associations between source-specific PM2.5 and respiratory disease emergency department (ED) visits and examined between-city heterogeneity in estimated associations. Methods: We used source apportionment to estimate daily concentrations of primary source-specific PM2.5 for four U.S. cities. For sources with similar chemical compositions between cities, we applied Poisson time-series regression models to estimate associations between source-specific PM2.5 and respiratory disease ED visits. Results: We found that PM2.5 from biomass burning, diesel vehicle, gasoline vehicle, and dust sources was similar in chemical composition between cities, but PM2.5 from coal combustion and metal sources varied across cities. We found some evidence of positive associations of respiratory disease ED visits with biomass burning PM2.5; associations with diesel and gasoline PM2.5 were frequently imprecise or consistent with the null. We found little evidence of associations with dust PM2.5. Conclusions: We introduced an approach for comparing the chemical compositions of PM2.5 sources across cities and conducted one of the first multicity studies of source-specific PM2.5 and ED visits. Across four U.S. cities, among the primary PM2.5 sources assessed, biomass burning PM2.5 was most strongly associated with respiratory health. Citation: Krall JR, Mulholland JA, Russell AG, Balachandran S, Winquist A, Tolbert PE, Waller LA, Sarnat SE. 2017. Associations between source-specific fine particulate matter and emergency department visits for respiratory disease in four U.S. cities. Environ Health Perspect 125:97–103; http://dx.doi.org/10.1289/EHP271

In reproductive epidemiology, there is a growing interest to examine associations between air pollution exposure during pregnancy and the risk of preterm birth (PTB). One important research objective is to identify critical periods of exposure and estimate the associated effects at different stages of pregnancy. However, population studies have reported inconsistent findings. This may be due to limitations from the standard analytic approach of treating PTB as a binary outcome without considering time-varying exposures together over the course of pregnancy. To address this research gap, we present a Bayesian hierarchical model for conducting a comprehensive examination of gestational air pollution exposure by estimating the joint effects of weekly exposures during different vulnerable periods. Our model also treats PTB as a time-to-event outcome to address the challenge of different exposure lengths among ongoing pregnancies. The proposed model is applied to a dataset of geocoded birth records in the Atlanta metropolitan area between 1999–2005 to examine the risk of PTB associated with gestational exposure to ambient fine particulate matter \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\lt 2.5\,{\rm \mu}$\end{document}m in aerodynamic diameter (PM\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$_{2.5}$\end{document}). We find positive associations between PM\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$_{2.5}$\end{document} exposure during early and mid-pregnancy, and evidence that associations are stronger for PTBs occurring around week 30.

Increased frequency, coverage or sensitivity of HIV testing among MSM is unlikely reduce HIV incidence unless men diagnosed through enhanced testing programs also achieve viral suppression by engaging in effective HIV care at higher rates than currently observed., Background. In the United States, public health recommendations for men who have sex with men (MSM) include testing for human immunodeficiency virus (HIV) at least annually. We model the impact of different possible HIV testing policies on HIV incidence in a simulated population parameterized to represent US MSM. Methods. We used exponential random graph models to explore, among MSM, the short-term impact on baseline (under current HIV testing practices and care linkage) HIV incidence of the following: (1) increasing frequency of testing; (2) increasing the proportion who ever test; (3) increasing test sensitivity; (4) increasing the proportion of the diagnosed population achieving viral suppression; and combinations of 1–4. We simulated each scenario 20 times and calculated the median and interquartile range of 3-year cumulative incidence of HIV infection. Results. The only intervention that reduced HIV incidence on its own was increasing the proportion of the diagnosed population achieving viral suppression; increasing frequency of testing, the proportion that ever test or test sensitivity did not appreciably reduce estimated incidence. However, in an optimal scenario in which viral suppression improved to 100%, HIV incidence could be reduced by an additional 17% compared with baseline by increasing testing frequency to every 90 days and test sensitivity to 22 days postinfection. Conclusions. Increased frequency, coverage, or sensitivity of HIV testing among MSM is unlikely to result in reduced HIV incidence unless men diagnosed through enhanced testing programs are also engaged in effective HIV care resulting in viral suppression at higher rates than currently observed.

Simple interventions may facilitate vector control and prevent periurban transmission of Chagas disease., In Arequipa, Peru, vectorborne transmission of Chagas disease by Triatoma infestans has become an urban problem. We conducted an entomologic survey in a periurban community of Arequipa to identify risk factors for triatomine infestation and determinants of vector population densities. Of 374 households surveyed, triatomines were collected from 194 (52%), and Trypanosoma cruzi–carrying triatomines were collected from 72 (19.3%). Guinea pig pens were more likely than other animal enclosures to be infested and harbored 2.38× as many triatomines. Stacked brick and adobe enclosures were more likely to have triatomines, while wire mesh enclosures were protected against infestation. In human dwellings, only fully stuccoed rooms were protected against infestation. Spatially, households with triatomines were scattered, while households with T. cruzi–infected triatomines were clustered. Keeping small animals in wire mesh cages could facilitate control of T. infestans in this densely populated urban environment.

Data from the U.S. Environmental Protection Agency Air Quality System, the Southeastern Aerosol Research and Characterization database, and the Assessment of Spatial Aerosol Composition in Atlanta database for 1999 through 2002 have been used to characterize error associated with instrument precision and spatial variability on the assessment of the temporal variation of ambient air pollution in Atlanta, GA. These data are being used in time series epidemiologic studies in which associations of acute respiratory and cardiovascular health outcomes and daily ambient air pollutant levels are assessed. Modified semivariograms are used to quantify the effects of instrument precision and spatial variability on the assessment of daily metrics of ambient gaseous pollutants (SO2, CO, NOx, and O3) and fine particulate matter ([PM2.5] PM2.5 mass, sulfate, nitrate, ammonium, elemental carbon [EC], and organic carbon [OC]). Variation because of instrument imprecision represented 7-40% of the temporal variation in the daily pollutant measures and was largest for the PM2.5 EC and OC. Spatial variability was greatest for primary pollutants (SO2, CO, NOx, and EC). Population-weighted variation in daily ambient air pollutant levels because of both instrument imprecision and spatial variability ranged from 20% of the temporal variation for O3 to 70% of the temporal variation for SO2 and EC. Wind

Heterogeneous population densities complicate comparisons of statistical power between hypothesis tests evaluating spatial clusters or clustering of disease. Specifically, the location of a cluster within a heterogeneously distributed population at risk impacts power properties, complicating comparisons of tests, and allowing one to map spatial variations in statistical power for different tests. Such maps provide insight into the overall power of a particular test, and also indicate areas within the study area where tests are more or less likely to detect the same local increase in relative risk. While such maps are largely driven by local sample size, we also find differences due to features of the statistics themselves. We illustrate these concepts using two tests: Tango's index of clustering and the spatial scan statistic. Furthermore, assessments of the accuracy of the 'most likely cluster' involve not only statistical power, but also spatial accuracy in identifying the location of a true underlying cluster. We illustrate these concepts via induction of artificial clusters within the observed incidence of severe cardiac birth defects in Santa Clara County, CA in 1981.

Discussion of "Spatial accessibility of pediatric primary healthcare: Measurement and inference" by Mallory Nobles, Nicoleta Serban and Julie Swann [arXiv:1501.03626]., Comment: Published in at http://dx.doi.org/10.1214/14-AOAS728C the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

The ability to detect anomalies such as spatial clustering in data sets plays an important role in spatial data analysis, leading to interest in test statistics identifying patterns exhibiting significant levels of clustering. Toward this end, Tango (1995) proposed a statistic (and its associated distribution under a null hypothesis of no clustering) assessing overall patterns of spatial clustering in a set of observed regional counts. Rogerson (1999) observed that Tango's index may be decomposed into the summation of two distinct statistics, the first mirroring standard tests of goodness-of-fit (GOF), and the second an index of spatial association (SA) similar to Moran's I. In this article, we investigate the effectiveness of Rogerson's expression of Tango's statistic in separating GOF from SA in data sets containing clusters. We simulate data under the null hypothesis of no clustering as well as two alternative hypotheses. The first alternative hypothesis induces a poor fit from the null hypothesis while maintaining independent observations and the second alternative hypothesis induces spatial dependence while maintaining fit. Using Rogerson's decomposition and leukemia incidence data from upstate New York, we show graphically that one is unable to statistically distinguish poor fit from autocorrelation.

Spatial heterogeneity and long-distance translocation (LDT) play important roles in the spatio-temporal dynamics and management of emerging infectious diseases and invasive species. We assessed the influence of LDT events on the invasive spread of raccoon rabies through Connecticut. We identified several putative LDT events, and developed a network-model to evaluate whether they became new foci for epidemic spread. LDT was fairly common, but many of the LDTs were isolated events that did not spread. Two putative LDT events did appear to become nascent foci that affected the epidemic in surrounding townships. In evaluating the role of LDT, we simultaneously revisited the problem of spatial heterogeneity. The spread of raccoon rabies is associated with forest cover—rabies moves up to three-times slower through the most heavily forested townships compared with those with less forestation. Forestation also modified the effect of rivers. In the best overall model, rabies did not cross the river separating townships that were heavily forested, and the spread slowed substantially between townships that were lightly forested. Our results suggest that spatial heterogeneity can be used to enhance the effects of rabies control by focusing vaccine bait distribution along rivers in lightly forested areas. LDT events are a concern, but this analysis suggests that at a local scale they can be isolated and managed.

Infectious disease emergence is under the simultaneous influence of both genetic and ecological factors. Yet, we lack a general framework for linking ecological dynamics of infectious disease with underlying molecular and evolutionary change. As a model, we illustrate the linkage between ecological and evolutionary dynamics in rabies virus during its epidemic expansion into eastern and southern Ontario. We characterized the phylogeographic relationships among 83 isolates of fox rabies virus variant using nucleotide sequences from the glycoprotein-encoding glycoprotein gene. The fox rabies virus variant descended as an irregular wave with two arms invading from northern Ontario into southern Ontario over the 1980s and 1990s. Correlations between genetic and geographic distance suggest an isolation by distance population structure for the virus. The divergence among viral lineages since the most recent common ancestor correlates with position along the advancing wave front with more divergent lineages near the origin of the epidemic. Based on divergence from the most recent common ancestor, the regional population can be partitioned into two subpopulations, each corresponding to an arm of the advancing wave. Subpopulation A (southern Ontario) showed reduced isolation by distance relative to subpopulation B (eastern Ontario). The temporal dynamics of subpopulation A suggests that the subregional viral population may have undergone several smaller waves that reduced isolation by distance. The use of integrated approaches, such as the geographical analysis of sequence variants, coupled with information on spatial dynamics will become indispensable aids in understanding patterns of disease emergence.

Analytic methods commonly used in epidemiology do not account for spatial correlation between observations. In regression analyses, this omission can bias parameter estimates and yield incorrect standard error estimates. We present a Bayesian hierarchical model (BHM) approach that accounts for spatial correlation, and illustrate its strengths and weaknesses by applying this modeling approach to data on Wuchereria bancrofti infection in Haiti.A program to eliminate lymphatic filariasis in Haiti assessed prevalence of W. bancrofti infection in 57 schools across Leogane Commune. We analyzed the spatial pattern in the prevalence data using semi-variograms and correlograms. We then modeled the data using (1) standard logistic regression (GLM); (2) non-Bayesian logistic generalized linear mixed models (GLMMs) with school-specific nonspatial random effects; (3) BHMs with school-specific nonspatial random effects; and (4) BHMs with spatial random effects.An exponential semi-variogram with an effective range of 2.15 km best fit the data. GLMM and nonspatial BHM point estimates were comparable and also were generally similar with the marginal GLM point estimates. In contrast, compared with the nonspatial mixed model results, spatial BHM point estimates were markedly attenuated.The clear spatial pattern evident in the Haitian W. bancrofti prevalence data and the observation that point estimates and standard errors differed depending on the modeling approach indicate that it is important to account for residual spatial correlation in analyses of W. bancrofti infection data. Bayesian hierarchical models provide a flexible, readily implementable approach to modeling spatially correlated data. However, our results also illustrate that spatial smoothing must be applied with care.

We conducted a school-based assessment of the geographic distribution of Wuchereria bancrofti infection in Leogane Commune, Haiti, using the immunochromatographic test. In multivariate analyses performed using generalized linear mixed models, children attending schools in the foothills and plains were 3.95 (95% confidence interval [CI] = 1.28-12.23) and 23.56 (95% CI = 8.99-61.79) times as likely to be infected, respectively, as children attending mountain schools. Infection prevalence decreased with increasing altitude, but some local foci of infection were detected at higher altitudes. Higher school tuition, a marker of socioeconomic status (SES), was not associated with decreased infection prevalence. Our results indicate that although the force of infection in Leogane Commune is greatest below 70 meters above sea level, higher altitude communities are not exempt from infection. Lymphatic filariasis (LF) elimination programs should consider extending infection mapping activities to presumed non-LF altitudes. In addition, higher SES does not confer protection against W. bancrofti infection.

Directly transmitted infectious diseases spread through wildlife populations as travelling waves away from the sites of original introduction. These waves often become distorted through their interaction with environmental and population heterogeneities and by long-distance translocation of infected individuals. Accurate a priori predictions of travelling waves of infection depend upon understanding and quantifying these distorting factors. We assess the effects of anisotropies arising from the orientation of rivers in relation to the direction of disease-front propagation and the damming effect of mountains on disease movement in natural populations. The model successfully predicts the local and large-scale prevaccination spread of raccoon rabies through New York State, based on a previous spatially heterogeneous model of raccoon-rabies invasion across the state of Connecticut. Use of this model provides a rare example of a priori prediction of an epidemic invasion over a naturally heterogeneous landscape. Model predictions matched to data can also be used to evaluate the most likely points of disease introduction. These results have general implications for predicting future pathogen invasions and evaluating potential containment strategies.

The approach described in this article is premised on the idea that drug and alcohol use-related problems are heterogeneously distributed with respect to population and geography, and therefore, are essentially local problems. More specifically, it is argued that viewing a local community as an interacting set of systems that support or buffer the occurrence of specific substance misuse outcomes, opens up to research two important prospects. The first of these involves creating adequate systems models that can capture the primary community structures and relationships that support public health problems such as alcohol and drug misuse and related outcomes. The second entails rationally testing control strategies that have the potential to moderate or reduce these problems. Understanding and controlling complex dynamic systems models nowadays pervades all scientific disciplines, and it is to research in areas such as biology, ecology, engineering, computer sciences, and mathematics that researchers in the field of addictions must turn to in order to better study the complexity that confronts them as they try to understand and prevent problems resulting from alcohol and drug use and misuse. Here we set out what such a systems-based understanding of alcohol- and drug use-related problems will require and discuss its implications for public policy and prevention programming.

More than Manuscripts: Reproducibility, Rigor, and Research Productivity in the Big Data Era Lance A. Waller and Gary W. Miller Rollins Professor and Chair, Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322 and Editor-in-Chief, Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322

One often develops stochastic ecologic simulation models based on local interactions between individuals or groups and bases systemic conclusions on trends summarized over multiple data sets generated from the model. In many cases, such models generate data sets (“realizations”) each violating the usual assumptions associated with traditional statistical tests of goodness-of-fit, most notably that of independent observations. Monte Carlo hypothesis tests applied to multiple realizations from such models provide appropriate goodness-of-fit tests regardless of within-model peculiarities. The Monte Carlo tests address the question “Do the observed data appear consistent with the model?” in contrast to the usual question “Does the model appear consistent with the observed data?”. In addition, such tests can make use of the same data sets used to draw systemic inference (i.e. the tests require no additional simulation runs). We illustrate the concept using Pearson’s chi-square statistic with correlated data. We also consider the behavior of a similar statistic and of “modeling efficiency” in assessing the fit of a simulation model for the spatial spread of raccoon rabies in Connecticut. © 2003 Elsevier Science B.V. All rights reserved.

Multiple 24-h average outdoor, indoor and personal PM 2.5 measurements were made in a population of healthy non-smoking adults from the Minneapolis-St. Paul metropolitan area between April and November 1999. Personal ( P ) PM 2.5 concentrations were higher than indoor ( I ) concentrations, which were higher than outdoor (O) concentrations. For 28 adults with a median of 9 (range 5–11) measurements per person, the distribution of longitudinal (i.e., within-person) correlation coefficients between P and I was moderate (median r =0.45). The distribution of longitudinal correlation coefficients between I and O concentrations showed that these variables were less strongly associated (median r =0.25; 28 residences; measurement median n =10 per residence, range 7–13), and the distribution of P and O correlation coefficients (median r =0.02; 29 subjects; measurement median n =11 per subject, range 7–15) showed little statistical relation between these two variables for a majority of participants. A sensitivity analysis indicated that correlations did not increase if days with exposure to environmental tobacco smoke or occupational exposures were excluded. On average these adults spent 91% of their time indoors, and the mean of the average PM 2.5 “personal cloud” was 15.3 μg/m 3 . Participants who had the largest personal cloud values tended to work outside the home and spent more time outdoors than subjects who did not work outside the home. In this population of healthy non-smoking adults, personal exposure to PM 2.5 does not correlate strongly with outdoor central site PM 2.5 concentrations.

Outdoor, indoor and personal PM 2.5 measurements were made in a population of nonsmoking adults from three communities in the Minneapolis–St. Paul metropolitan area between April and November 1999. Thirty-two healthy adult subjects (23 females, 9 males; mean age 42±10, range: 24–64 yr) were monitored for 2–15 days during the spring, summer, and fall monitoring seasons. Twenty-four hour average gravimetric PM 2.5 samples were collected using a federal reference monitor (Anderson RAAS2.5-300) located at outdoor (O) central sites in the Battle Creek (BCK), East St. Paul (ESP) and Phillips (PHI) communities. Concurrent 24-h average indoor (I) and personal (P), and a limited number of outdoor-at-home (O@H) samples were collected using inertial impactors (PEM™ Model 200, MSP, Inc). The O (geometric mean {GM}=8.6; n =271; range: 1.0–41 μg/m 3 ) were lower than I concentrations (GM=10.7; n =294; range 1.3–131 μg/m 3 ), which were lower than P concentrations (GM=19.0; n =332; range 2.2–298 μg/m 3 ). Correlation coefficients between O concentrations in the three communities were high and measured GM O levels in BCK were significantly lower than ESP, most likely because of local sources, but GM concentrations in PHI were not significantly different from BCK or ESP. On days with paired samples ( n =29), O concentrations were significantly lower (mean difference 2.9 μg/m 3 ; p =0.026) than O@H measurements (GM=11.3; range: 3.5–33.8 μg/m 3 ), likely due to local sources in communities. Observed I and P concentrations were more variable, probably because of residential central air conditioning and hours of household ventilation for I and P, and occupational and environmental tobacco smoke exposures outside the residence for P. Across all individuals and days the median PM 2.5 “personal cloud” was 5.7 μg/m 3 , but the mean of the average for each participant was 15.7 μg/m 3 , with very low values in participants who did not work outside the home and much higher values in subjects with active lifestyles. Across all households and individuals the correlation between P and O concentrations was not significant, but the overall I–O correlation (0.27) and P–I correlation (0.51) were significant ( p 2.5 concentrations across the three communities compared to the variability associated with I and P samples, and the measured O levels were relatively low compared to other large metropolitan areas in the United States.

The quantitative analysis of pathogen transmission within its specific spatial context should improve our ability to predict and control the epizootic spread of that disease. We compared two methods for calibrating the effect of local, spatially distributed environmental heterogeneities on disease spread. Using the time-of-first-appearance of raccoon rabies across the 169 townships in Connecticut, we estimated local spatial variation in township-to-township transmission rate using Trend Surface Analysis (TSA) and then compared these estimates with those based on an earlier probabilistic simulation using the same data. Both the probabilistic simulation and the TSA reveal significant reduction in transmission when local spatial domains are separated by rivers. The probabilistic simulation suggested that township-to-township transmission was reduced sevenfold for townships separated by a river. The global effect of this sevenfold reduction is to increase the time-to-first-appearance in the eastern townships of Connecticut by approximately 29.7% (spread was from west to east). TSA revealed a similar effect of rivers with an overall reduction in rate of local propagation due to rivers of approximately 22%. The 7.7% difference in these two estimates reveals slightly different aspects of the spatial dynamics of this epizootic. Together, these two methods can be used to construct an overall picture of the combined effects of local spatial variation in township-to-township transmission on patterns of local rate of propagation at scales larger than the immediate nearest neighboring townships.

Often as an epidemic spreads, the leading front is irregular, reflecting spatial variation in local transmission rates. We developed a methodology for quantifying spatial variation in rates of disease spread across heterogeneous landscapes. Based on data for epidemic raccoon rabies in Connecticut, we developed a stochastic spatial model of rabies spread through the state's 169 townships. We quantified spatial variation in transmission rates associated with human demography and key habitat features. We found that large rivers act as semipermeable barriers, leading to a 7-fold reduction in the local rates of propagation. By combining the spatial distribution of major rivers with long-distance dispersal we were able to account for the observed irregular pattern of disease spread across the state without recourse to direct assessment of host-pathogen populations.

Background: In the United States, human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) continues to have a heavy impact on men who have sex with men (MSM). Among MSM, black men under the age of 30 are at the most risk for being diagnosed with HIV. The US National HIV/AIDS strategy recommends intensifying efforts in communities that are most heavily impacted; to do so requires new methods for identifying and targeting prevention resources to young MSM, especially young MSM of color. Objective: We piloted a methodology for using the geolocation features of social and sexual networking applications as a novel approach to calculating the local population density of sex-seeking MSM and to use self-reported age and race from profile postings to highlight areas with a high density of minority and young minority MSM in Atlanta, Georgia. Methods: We collected data from a geographically systematic sample of points in Atlanta. We used a sexual network mobile phone app and collected application profile data, including age, race, and distance from each point, for either the 50 closest users or for all users within a 2-mile radius of sampled points. From these data, we developed estimates of the spatial density of application users in the entire city, stratified by race. We then compared the ratios and differences between the spatial densities of black and white users and developed an indicator of areas with the highest density of users of each race. Results: We collected data from 2666 profiles at 79 sampled points covering 883 square miles; overlapping circles of data included the entire 132.4 square miles in Atlanta. Of the 2666 men whose profiles were observed, 1563 (58.63%) were white, 810 (30.38%) were black, 146 (5.48%) were another race, and 147 (5.51%) did not report a race in their profile. The mean age was 31.5 years, with 591 (22.17%) between the ages of 18-25, and 496 (18.60%) between the ages of 26-30. The mean spatial density of observed profiles was 33 per square mile, but the distribution of profiles observed across the 79 sampled points was highly skewed (median 17, range 1-208). Ratio, difference, and distribution outlier measures all provided similar information, highlighting areas with higher densities of minority and young minority MSM. Conclusions: Using a limited number of sampled points, we developed a geospatial density map of MSM using a social-networking sex-seeking app. This approach provides a simple method to describe the density of specific MSM subpopulations (users of a particular app) for future HIV behavioral surveillance and allow targeting of prevention resources such as HIV testing to populations and areas of highest need. [J Med Internet Res 2014;16(11):e249]

In reproductive epidemiology, there is a growing interest to examine associations between air pollution exposures during pregnancy and the risk of preterm birth (PTB). One important research object...

We briefly review the historical, legal, and regulatory background of environmental justice assessments, paying particular attention to the role of statistical techniques in such assessments. We stress the difference between “disparate impact” and intentional discrimination and the role of statistical comparisons as defined in the associated regulatory statutes. We contrast two general approaches, the first comparing demographic proportions within exposure categories and the second comparing exposure (or exposure surrogate) distributions between demographic categories. Keywords: legal statistics; regulatory statistics; demographics; disparity; exposure assessment; statistics

After reviewing characteristics of spatially correlated counts, rates, and proportions that compromise traditional methods of inference, we briefly survey general approaches for the analysis of such spatially referenced categorical data. The survey focuses on auto-models, generalized linear models, and generalized linear mixed models, emphasizing structural and conceptual aspects rather than computational details. Keywords: auto-models; auto-Gaussian; auto-logistic; auto-Poisson; pseudo-likelihood; quasi-likelihood; generalized linear mixed models; random effects; spatial autocorrelation

Background Development of exposure metrics that capture features of the multipollutant environment are needed to investigate health effects of pollutant mixtures. This is a complex problem that requires development of new methodologies. Objective Present a self-organizing map (SOM) framework for creating ambient air quality classifications that group days with similar multipollutant profiles. Methods Eight years of day-level data from Atlanta, GA, for ten ambient air pollutants collected at a central monitor location were classified using SOM into a set of day types based on their day-level multipollutant profiles. We present strategies for using SOM to develop a multipollutant metric of air quality and compare results with more traditional techniques. Results Our analysis found that 16 types of days reasonably describe the day-level multipollutant combinations that appear most frequently in our data. Multipollutant day types ranged from conditions when all pollutants measured low to days exhibiting relatively high concentrations for either primary or secondary pollutants or both. The temporal nature of class assignments indicated substantial heterogeneity in day type frequency distributions (~1%-14%), relatively short-term durations (

Lyme disease has been the subject of many studies due to increasing incidence rates year after year and the severe complications that can arise in later stages of the disease. Negative binomial models have been used to model Lyme disease in the past with some success. However, there has been little focus on the reliability and consistency of these models when they are used to study Lyme disease at multiple spatial scales. This study seeks to explore how sensitive/consistent negative binomial models are when they are used to study Lyme disease at different spatial scales (at the regional and sub-regional levels). The study area includes the thirteen states in the Northeastern United States with the highest Lyme disease incidence during the 2002–2006 period. Lyme disease incidence at county level for the period of 2002–2006 was linked with several previously identified key landscape and climatic variables in a negative binomial regression model for the Northeastern region and two smaller sub-regions (the New England sub-region and the Mid-Atlantic sub-region). This study found that negative binomial models, indeed, were sensitive/inconsistent when used at different spatial scales. We discuss various plausible explanations for such behavior of negative binomial models. Further investigation of the inconsistency and sensitivity of negative binomial models when used at different spatial scales is important for not only future Lyme disease studies and Lyme disease risk assessment/management but any study that requires use of this model type in a spatial context.

A Systematic Review of Effect Modification in Trials of Behavioral Interventions to Increase Fruit and Vegetable Consumption Although the health benefits of fruit and vegetable consumption (FVC) has been well documented, the evidence supporting the effectiveness of behavioral interventions to increase FVC has not been as consistent. This review aimed to identify individual and environmental level factors that may explain systematic differences in treatment effects in randomized controlled trials(RCTs) of behavioral interventions to increase FVC through effect modification (EM) and discusses the utility of examining EM in the context of a RCT.

Congratulations to Heisey and colleagues (2010) for an analysis of complex ecological data within a process-oriented framework and an equally thoughtful discussion of the ongoing challenges of linking process and pattern. Their work joins a growing literature linking modern statistical methods for describing patterns in data with expanded sets of ecologically-motivated mathematical models of population dynamics. My comments build on the authors’ framework and discussion to describe a continuum that (I feel) encompasses a conceptual path between process and pattern and illustrates how hierarchical models provide a convenient area within which to explore this continuum.

An efficient surveillance system is a crucial factor in identifying, monitoring and tackling outbreaks of infectious diseases. Scarcity of data and limited amounts of economic resources require a targeted effort from public health authorities. In this paper, we propose a mathematical method to identify areas where surveillance is critical and low reporting rates might leave epidemics undetected. Our approach combines the use of reference-based susceptible–exposed–infectious models and observed reporting data; We propose two different specifications, for constant and time-varying surveillance, respectively. Our case study is centred around the spread of the raccoon rabies epidemic in the state of New York, using data collected between 1990 and 2007. Both methods offer a feasible solution to analyse and identify areas of intervention.

A reported "cluster" of excess childhood leukemia cases and possible environmental causes in Woburn, Massachusetts, formed a key motivation for the events described in the popular book and motion picture A Civil Action. Although statistical methods to assess spatial clustering existed prior to the events in Woburn, increasing interest in environmental risk factors and recent developments in geographical information systems and data availability prompt increased attention to such methods and their application to public health data. In this article, we review statistical and epidemiological concepts involved in the analysis of disease clusters. We discuss data issues, outline some methodological approaches, and illustrate ideas using data regarding leukemia incidence in upstate New York for the years 1978-1982.

Statistical tests to detect clustering of a rare disease investigate whether an observed spatial pattern of cases appears to be due to chance alone. Heterogeneous population density and the geographic structure of the data under consideration complicate the ability to make comparisons of different tests. Further, interpretation of test results depends on the nature of the test used and what feature of the data it is designed to detect. With these issues in mind, we compare three recent tests for assessing general clustering among cases where the population is distributed heterogeneously across the study area, namely those of Besag and Newell, Turnbull et al. and Tango. We compare these methods using 1981 incidence data for severe cardiac birth defects from Santa Clara County, California. Copyright © 2000 John Wiley & Sons, Ltd.

Work-related violence is a major public health problem; however, there is a serious deficiency in the knowledge of risk factors for this problem. The purpose of this case-control study was to identify risk factors for work-related assault injuries among nurses. We used unconditional logistic regression to model the dependence of work-related assault injuries on each exposure of interest and the respective confounders. We found a decreased rate for the presence of security personnel (RR = 0.40; 95% CI = 0.19-0.82). We found increased rates for the following factors: the perception that administrators considered assault to be part of the job (RR = 8.14; 95% CI = 3.76-17.60); having received assault prevention training in the current workplace (RR = 4.64; 95% CI = 2.33-9.23); a high (>5) vs. low (

Using geographic information systems (GIS) and routinely collected data, we explored whether childhood residence near busy roads was associated with asthma in a low-income population in San Diego County, California. We examined the locations of residences of 5,996 children [less than/equal to] 14 years of age who were diagnosed with asthma in 1993 and compared them to a random control series of nonrespiratory diagnoses (n = 2,284). Locations of the children's residences were linked to traffic count data at streets within 550 ft. We also examined the number of medical care visits in 1993 for children with asthma to determine if the number of visits was related to traffic flow. Analysis of the distribution of cases and controls by quintiles and by the 90th, 95th, and 99th percentiles of traffic flow at the highest traffic street, nearest street, and total of all streets within a 550-ft buffer region did not show any significantly elevated odds ratios. However, among cases, those residing near high traffic flows (measured at the nearest street) were more likely than those residing near lower traffic flows to have two or more medical care visits for asthma than to have only one visit for asthma during the year. The results of this exploratory study suggest that higher traffic flows may be related to an increase in repeated medical visits for asthmatic children. Repeated exposure to particulate matter and other air pollutants from traffic exhaust may aggravate asthmatic symptoms in individuals already diagnosed with asthma. Images Figure 1

We enrolled 543 elderly participants of a managed care organization in a cross-sectional study to test whether the association between self-rated physical health and clinically defined illness differs for persons who are not depressed compared with persons with minor or serious depression. Depression was measured with the Diagnostic Interview Schedule (DIS). Clinically defined illness was measured with the Chronic Disease Score (CDS), a pharmacy-based measure. Additional variables included age, sex, and self-reported pain and physical function. Self-rated physical health was associated with both minor and serious depression, independent of clinically defined illness; minor depression was no longer significant when self-reported pain and physical function were added to the model. A significant negative correlation between self-rated physical health and clinically defined illness was observed for minor and no depression, but no correlation was seen for serious depression. These results confirm the association between depression and self-rated physical health and emphasize that, for persons with serious depression, self-rated health provides a less accurate picture of clinically defined illness at both ends of the spectrum. Also, a diagnosis of minor depression should not forestall investigation of inconsistencies between patient report and clinical evidence.

Recent regulatory action requires the assessment of environmental justice (equitable protection from the burdens of environmental hazards across sociodemographic subpopulations) in the siting of hazardous waste sites, and prioritization of environmental remediation efforts. Assessments of environmental justice require linking exposure, demographic, and health data. The geographic nature of the data makes the use of geographic information systems attractive for environmental justice assessments. Typical geographic assessments compare the composition of 'exposed' populations, while typical statistical assessments focus on differences in health outcomes between population subgroups, possibly adjusted for exposure. We outline an alternate approach based on summarized differences between exposure distributions within each population subgroup. We illustrate how such summaries provide a tool for site evaluation (e.g., defining exposure inequities resulting from locating a new potential hazard at any of a number of possible sites). In addition, we describe summaries, based on dose-response relationships, to describe risk differences imposed by the observed exposure differences. Reported toxic emissions from Allegheny County, Pennsylvania illustrate the approach.

This paper considers estimation of cancer incidence rates for local areas. The raw estimates usually are based on small sample sizes, and hence are usually unreliable. A hierarchical Bayes generalized linear model approach is taken which connects the local areas, thereby enabling one to ‘borrow strength’. Random effects with pairwise difference priors model the spatial structure in the data. The methods are applied to cancer incidence estimation for census tracts in a certain region of the state of New York.

BACKGROUND Depression is under-diagnosed and under-treated in the primary care sector. The purpose of this study was to determine the association between self-reported indications of depression by community-dwelling elderly enrollees in a managed care organization and clinical detection of depression by primary care clinicians. METHODS This was a 2-year cohort study of elderly people (n = 3410) who responded to the Geriatric Depression Scale (GDS) at the midpoint of the study period. A broad measure of clinical detection was used consisting of one or more of three indicators: diagnosis of depression, visit to a mental health specialist, or antidepressant medication treatment. RESULTS Approximately half of the community-based elderly people with self-reported indications of depression (GDS > or = 11) did not have documentation of clinical detection of depression by health providers. Physician recognition of depression tended to increase with the severity of enrollees' self-reported feelings of depression. Men 65-74 years old and those > or = 85 years old were at highest risk for under-detection of depression by primary care providers. CONCLUSIONS Clinical detection of depression of elderly people living in the community continues to be a problem. The implications of failure to recognize the possibility of depression among elderly White men suggest a serious public health problem.

Maps of regional morbidity and mortality rates are useful tools in determining spatial patterns of disease. Combined with sociodemographic census information, they also permit assessment of environmental justice; that is, whether certain subgroups suffer disproportionately from certain diseases or other adverse effects of harmful environmental exposures. Bayes and empirical Bayes methods have proven useful in smoothing crude maps of disease risk, eliminating the instability of estimates in low-population areas while maintaining geographic resolution. In this article we extend existing hierarchical spatial models to account for temporal effects and spatio-temporal interactions. Fitting the resulting highly parameterized models requires careful implementation of Markov chain Monte Carlo (MCMC) methods, as well as novel techniques for model evaluation and selection. We illustrate our approach using a dataset of county-specific lung cancer rates in the state of Ohio during the period 1968–1988.