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ABSTRACT: We propose two spatial point process models for the spatial structure of epidermal nerve fibers (ENFs) across human skin. The models derive from two point processes, Φb and Φe, describing the locations of the base and end points of the fibers. Each point of Φe (the end point process) is connected to a unique point in Φb (the base point process). In the first model, both Φe and Φb are Poisson processes, yielding a null model of uniform coverage of the skin by end points and general baseline results and reference values for moments of key physiologic indicators. The second model provides a mechanistic model to generate end points for each base, and we model the branching structure more directly by defining Φe as a cluster process conditioned on the realization of Φb as its parent points. In both cases, we derive distributional properties for observable quantities of direct interest to neurologists such as the number of fibers per base, and the direction and range of fibers on the skin. We contrast both models by fitting them to data from skin blister biopsy images of ENFs and provide inference regarding physiological properties of ENFs.
Mathematical biosciences 03/2013; · 1.30 Impact Factor
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ABSTRACT: Objectives. We assessed the longitudinal association between housing transitions and pregnancy outcomes in a sample of public housing residents. Methods. A cohort of 2670 Black women residing in Atlanta, Georgia, housing projects with 1 birth occurring between 1994 and 2007 was created from maternally linked longitudinal birth files and followed for subsequent births. Traditional regression and marginal structural models adjusting for time-varying confounding estimated the risk of preterm low birth weight (LBW) or small for gestational age LBW by maternal housing transition patterns. Results. Women moving from public to private housing as a result of housing project demolition were at elevated risk for preterm LBW (risk ratio = 1.74; 95% confidence interval = 1.00-3.04) compared with women not affected by project demolition. Other non-policy-related housing transition patterns were not associated with pregnancy outcomes. Conclusions. Further longitudinal study of housing transitions among public housing residents is needed to better understand the relationship between housing, neighborhoods, housing policy, and perinatal outcomes. (Am J Public Health. Published online ahead of print October 18, 2012: e1-e7. doi:10.2105/AJPH.2012.300782).
American Journal of Public Health 10/2012; · 3.93 Impact Factor
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ABSTRACT: Exploring spatial-temporal patterns of disease incidence and mortality can identify areas of significantly elevated or decreased risk, providing potential etiologic clues. Several methodological issues arise in spatial-temporal analysis of cancer, including population mobility, disease latency, and confounding, but applying modern statistical methods to case-control studies with residential histories can address these issues. As an example, we present a spatial-temporal analysis of non-Hodgkin lymphoma (NHL) risk using data from Los Angeles County, one of four centers in a population-based case-control study. Using residential histories, we fitted generalized additive models (GAMs) adjusted for known risk factors to model spatially the probability that an individual had NHL and identify areas of significantly elevated NHL risk. In previous analyses using models with single lag times, the lag time of 20 years yielded the most significant decrease in model deviance. To better assess cumulative effects of unmeasured environmental exposures over space and time, we considered models that allowed for multiple residences per subject through spatial smoothing functions of residential location at different times. We found that the model with the best goodness-of-fit included components for residential change and residential duration, although the model that included residential duration was not meaningfully better than the model that included only residential change. The estimated cumulative spatial risk surface from the model with residential change amplified the risk surface in some areas compared with the surface based on the model with a single component for the most significant time lag.
Spatial and spatio-temporal epidemiology. 06/2012; 3(2):163-71.
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ABSTRACT: Residual confounding is challenging to detect. Recently, we described a method for detecting confounding and justified it primarily for time-series studies. The method depends on an indicator with 2 key characteristics: (1) it is conditionally independent (given measured exposures and covariates) of the outcome, in the absence of confounding, misspecification, and measurement errors; and (2) like the exposure, it is associated with confounders, possibly unmeasured. We proposed using future exposure levels as the indicator to detect residual confounding. This choice seems natural for time-series studies because future exposure cannot have caused the event, yet they could be spuriously related to it. A related question addressed here is whether an analogous indicator can be used to identify residual confounding in a study based on spatial, rather than temporal, contrasts.
Using directed acyclic graphs, we show that future air pollution levels may have the characteristics appropriate for an indicator of residual confounding in spatial studies of environmental exposures. We empirically evaluate performance for spatial studies using simulations.
In simulations based on a spatial study of ambient air pollution levels and birth weight in Atlanta, and using ambient air pollution 1 year after conception as the indicator, we were able to detect residual confounding. The discriminatory ability approached 100% for some factors intentionally omitted from the model, but was very weak for others.
The simulations illustrate that an indicator based on future exposures can have excellent ability to detect residual confounding in spatial studies, although performance varied by situation.
Epidemiology (Cambridge, Mass.) 11/2011; 22(6):823-6. · 5.51 Impact Factor
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ABSTRACT: Breakthroughs in imaging of skin tissue reveal new details on the distribution of nerve fibers in the epidermis. Preliminary neurologic studies indicate qualitative differences in the spatial patterns of nerve fibers based on pathophysiologic conditions in the subjects. Of particular interest is the evolution of spatial patterns observed in the progression of diabetic neuropathy. It appears that the spatial distribution of nerve fibers becomes more 'clustered' as neuropathy advances, suggesting the possibility of diagnostic prediction based on patterns observed in skin biopsies. We consider two approaches to establish statistical inference relating to this observation. First, we view the set of locations where the nerves enter the epidermis from the dermis as a realization of a spatial point process. Secondly, we treat the set of fibers as a realization of a planar fiber process. In both cases, we use estimated second-order properties of the observed data patterns to describe the degree and scale of clustering observed in the microscope images of blister biopsies. We illustrate the methods using confocal microscopy blister images taken from the thigh of one normal (disease-free) individual and two images each taken from the thighs of subjects with mild, moderate, and severe diabetes and report measurable differences in the spatial patterns of nerve entry points/fibers associated with disease status.
Statistics in Medicine 08/2011; 30(23):2827-41. · 1.88 Impact Factor
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ABSTRACT: Two distinctly different types of measurement error are Berkson and classical. Impacts of measurement error in epidemiologic studies of ambient air pollution are expected to depend on error type. We characterize measurement error due to instrument imprecision and spatial variability as multiplicative (i.e. additive on the log scale) and model it over a range of error types to assess impacts on risk ratio estimates both on a per measurement unit basis and on a per interquartile range (IQR) basis in a time-series study in Atlanta.
Daily measures of twelve ambient air pollutants were analyzed: NO2, NOx, O3, SO2, CO, PM10 mass, PM2.5 mass, and PM2.5 components sulfate, nitrate, ammonium, elemental carbon and organic carbon. Semivariogram analysis was applied to assess spatial variability. Error due to this spatial variability was added to a reference pollutant time-series on the log scale using Monte Carlo simulations. Each of these time-series was exponentiated and introduced to a Poisson generalized linear model of cardiovascular disease emergency department visits.
Measurement error resulted in reduced statistical significance for the risk ratio estimates for all amounts (corresponding to different pollutants) and types of error. When modelled as classical-type error, risk ratios were attenuated, particularly for primary air pollutants, with average attenuation in risk ratios on a per unit of measurement basis ranging from 18% to 92% and on an IQR basis ranging from 18% to 86%. When modelled as Berkson-type error, risk ratios per unit of measurement were biased away from the null hypothesis by 2% to 31%, whereas risk ratios per IQR were attenuated (i.e. biased toward the null) by 5% to 34%. For CO modelled error amount, a range of error types were simulated and effects on risk ratio bias and significance were observed.
For multiplicative error, both the amount and type of measurement error impact health effect estimates in air pollution epidemiology. By modelling instrument imprecision and spatial variability as different error types, we estimate direction and magnitude of the effects of error over a range of error types.
Environmental Health 06/2011; 10:61. · 2.65 Impact Factor
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Lance A Waller
Ecology 12/2010; 91(12):3500-2; discussion 3503-14. · 4.85 Impact Factor
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ABSTRACT: A difficult issue in observational studies is assessment of whether important confounders are omitted or misspecified. In this study, we present a method for assessing whether residual confounding is present. Our method depends on availability of an indicator with 2 key characteristics: first, it is conditionally independent (given measured exposures and covariates) of the outcome in the absence of confounding, misspecification, and measurement errors; second, it is associated with the exposure and, like the exposure, with any unmeasured confounders.
We demonstrate the method using a time-series study of the effects of ozone on emergency department visits for asthma in Atlanta. We argue that future air pollution may have the characteristics appropriate for an indicator, in part because future ozone cannot have caused yesterday's health events. Using directed acyclic graphs and specific causal relationships, we show that one can identify residual confounding using an indicator with the stated characteristics. We use simulations to assess the discriminatory ability of future ozone as an indicator of residual confounding in the association of ozone with asthma-related emergency department visits. Parameter choices are informed by observed data for ozone, meteorologic factors, and asthma.
In simulations, we found that ozone concentrations 1 day after the emergency department visits had excellent discriminatory ability to detect residual confounding by some factors that were intentionally omitted from the model, but weaker ability for others. Although not the primary goal, the indicator can also signal other forms of modeling errors, including substantial measurement error, and does not distinguish between them.
The simulations illustrate that the indicator based on future air pollution levels can have excellent discriminatory ability for residual confounding, although performance varied by situation. Application of the method should be evaluated by considering causal relationships for the intended application, and should be accompanied by other approaches, including evaluation of a priori knowledge.
Epidemiology (Cambridge, Mass.) 11/2010; 22(1):59-67. · 5.51 Impact Factor
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ABSTRACT: Recent advances in geographic information systems software and multilevel methodology provide opportunities for more extensive characterization of "at-risk" populations in epidemiologic studies. The authors used age-restricted, geocoded data from the all-African-American Jackson Heart Study (JHS), 2000-2004, to demonstrate a novel use of the Lorenz curve and Gini coefficient to determine the representativeness of the JHS cohort to the African-American population in a geographic setting. The authors also used a spatial binomial model to assess the geographic variability in participant recruitment across the Jackson, Mississippi, Metropolitan Statistical Area. The overall Gini coefficient, an equality measure that ranges from 0 (perfect equality) to 1 (perfect inequality), was 0.37 (95% confidence interval (CI): 0.30, 0.45), indicating moderate representation. The population of sampled women (Gini coefficient = 0.34, 95% CI: 0.30, 0.39) tended to be more representative of the underlying population than did the population of sampled men (Gini coefficient = 0.49, 95% CI: 0.35, 0.61). Representative recruitment of JHS participants was observed in predominantly African-American and mixed-race census tracts and in the center of the study area, the area nearest the examination clinic. This is of critical importance as the authors continue to explore novel approaches to investigate the geographic variation in disease etiology.
American journal of epidemiology 11/2010; 173(1):110-7. · 5.59 Impact Factor
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ABSTRACT: There is substantial geographic variability in both incident and prevalent arteriovenous fistula (AVF) use among patients with ESRD. This study examined the degree to which these variations associate with poverty in the county of a patient's treatment center. We performed a cross-sectional study including 28,135 patients treated by 1127 hemodialysis centers in five ESRD networks (16 states) between June 1, 2005 and May 31, 2006. We used the 2000 U.S. Census to categorize county-level poverty and ascertained incident AVF use from the Medicare CMS 2728 form. We calculated the 30-month slope of change in AVF prevalence from monthly facility reports collected between 2003 and 2005. More than 33% of treatment centers were located in high-poverty counties. County poverty inversely associated with incident AVF use (P for trend = 0.001). In contrast, substantial increases in prevalent AVF rates from 30.9 to 38.6% (P < 0.001) among treatment centers did not associate with county poverty (P = 0.9519). In conclusion, the concentration of poverty in the county where a treatment is located associates with incident AVF use by patients with ESRD but not with subsequent improvement in AVF use among prevalent patients. These results suggest that the Medicare ESRD program may mitigate poverty effects on AVF use.
Journal of the American Society of Nephrology 10/2010; 21(10):1776-82. · 9.66 Impact Factor
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ABSTRACT: In time-series studies of ambient air pollution and health in large urban areas, measurement errors associated with instrument precision and spatial variability vary widely across pollutants. In this paper, we characterize these errors for selected air pollutants and estimate their impacts on epidemiologic results from an ongoing study of air pollution and emergency department visits in Atlanta. Error was modeled for daily measures of 12 air pollutants using collocated monitor data to characterize instrument precision and data from multiple study area monitors to estimate population-weighted spatial variance. Time-series simulations of instrument and spatial error were generated for each pollutant, added to a reference pollutant time-series, and used in a Poisson generalized linear model of air pollution and cardiovascular emergency department visits. Reductions in risk ratio due to instrument precision error were less than 6%. Error due to spatial variability resulted in average risk ratio reductions of less than 16% for secondary pollutants (O(3), PM(2.5) sulfate, nitrate and ammonium) and between 43% and 68% for primary pollutants (NO(x), NO(2), SO(2), CO, PM(2.5) elemental carbon); pollutants of mixed origin (PM(10), PM(2.5), PM(2.5) organic carbon) had intermediate impacts. Quantifying impacts of measurement error on health effect estimates improves interpretation across ambient pollutants.
Environmental Science & Technology 10/2010; 44(19):7692-8. · 4.80 Impact Factor
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ABSTRACT: We evaluated the completeness of West Nile fever (WNF) surveillance within the U.S. public health system. We surveyed laboratory and surveillance programs on policies, practices, and capacities for testing, confirmation, and reporting (collectively called ascertainment) from 2003 through 2005. We calculated syndrome ascertainment ratios by dividing WNF counts by neuroinvasive disease counts; separately, we performed multilevel modeling. Jurisdictions were more likely to ascertain at least one WNF cases per West Nile neuroinvasive disease case when ≤ 1 testing restrictions existed (odds ratio [OR] = 7.7, 95% confidence interval [CI] = 1.3-46.4), when conducting ≥ 4 activities to enhance reporting (OR = 9.3, 95% CI = 1.6-54.8), and when ≥ 5.0 staff per million residents were dedicated to arboviral surveillance (OR = 6.4, 95% CI = 1.0-40.3). Ascertainment of WNF was less likely among Blacks (OR = 0.56, 95% CI = 0.31-0.99) and Hispanics (OR = 0.69, 95% CI = 0.48-0.98) than among Whites. Ascertainment was more complete when testing and reporting were enhanced, but differentially incomplete for minorities.
The American journal of tropical medicine and hygiene 10/2010; 83(4):795-802. · 2.59 Impact Factor
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ABSTRACT: Residential isolation segregation (a measure of residential inter-racial exposure) has been associated with rates of preterm birth (<37 weeks gestation) experienced by Black women. Epidemiologic differences between very preterm (<32 weeks gestation) and moderately preterm births (32-36 weeks) raise questions about whether this association is similar across gestational ages, and through what pathways it might be mediated. Hierarchical Bayesian models were fit to answer three questions: is the isolation-prematurity association similar for very and moderately preterm birth; is this association mediated by maternal chronic disease, socioeconomic status, or metropolitan area crime and poverty rates; and how much of the geographic variation in Black-White very preterm birth disparities is explained by isolation segregation? Singleton births to Black and White women in 231 U.S. metropolitan statistical areas in 2000-2002 were analyzed and isolation segregation was calculated for each. We found that among Black women, isolation is associated with very preterm birth and moderately preterm birth. The association may be partially mediated by individual level socioeconomic characteristics and metropolitan level violent crime rates. There is no association between segregation and prematurity among White women. Isolation segregation explains 28% of the geographic variation in Black-White very preterm birth disparities. Our findings highlight the importance of isolation segregation for the high-burden outcome of very preterm birth, but unexplained excess risk for prematurity among Black women is substantial.
Social Science [?] Medicine 09/2010; 71(12):2108-16. · 2.70 Impact Factor
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ABSTRACT: The cougar (Puma concolor) is a large predatory feline found widely in the Americas that is susceptible to feline immunodeficiency virus (FIV), a fast-evolving lentivirus found in wild feline species that is analogous to simian immunodeficiency viruses in wild primates and belongs to the same family of viruses as human immunodeficiency virus. FIV infection in cougars can lead to a weakened immune system that creates opportunities for other infecting agents. FIV prevalence and lineages have been studied previously in several areas in the western United States, but typically without spatially explicit statistical techniques. To describe the distribution of FIV in a sample of cougars located in the northern Rocky Mountain region of North America, we first used kernel density ratio estimation to map the log relative risk of FIV. The risk surface showed a significant cluster of FIV in northwestern Montana. We also used Bayesian cluster models for genetic data to investigate the spatial structure of the feline immunodeficiency virus with virus genetic sequence data. A result of the models was two spatially distinct FIV lineages that aligned considerably with an interstate highway in Montana. Our results suggest that the use of spatial information and models adds novel insight when investigating an infectious animal disease. The results also suggest that the influence of landscape features likely plays an important role in the spatiotemporal spread of an infectious disease within wildlife populations.
Spatial and spatio-temporal epidemiology. 07/2010; 1(2-3):151-61.
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ABSTRACT: Many diagnostic tools and goodness-of-fit measures, such as the Akaike information criterion (AIC) and the Bayesian deviance information criterion (DIC), are available to evaluate the overall adequacy of linear regression models. In addition, visually assessing adequacy in models has become an essential part of any regression analysis. In this paper, we focus on a spatial consideration of the local DIC measure for model selection and goodness-of-fit evaluation. We use a partitioning of the DIC into the local DIC, leverage, and deviance residuals to assess local model fit and influence for both individual observations and groups of observations in a Bayesian framework. We use visualization of the local DIC and differences in local DIC between models to assist in model selection and to visualize the global and local impacts of adding covariates or model parameters. We demonstrate the utility of the local DIC in assessing model adequacy using HIV prevalence data from pregnant women in the Butare province of Rwanda during 1989-1993 using a range of linear model specifications, from global effects only to spatially varying coefficient models, and a set of covariates related to sexual behavior. Results of applying the diagnostic visualization approach include more refined model selection and greater understanding of the models as applied to the data.
Computational Statistics & Data Analysis 06/2010; 54(6):1657-1671. · 1.03 Impact Factor
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ABSTRACT: Certain outdoor air pollutants cause asthma exacerbations in children. To advance understanding of these relationships, further characterization of the dose-response and pollutant lag effects are needed, as are investigations of pollutant species beyond the commonly measured criteria pollutants.
Investigate short-term associations between ambient air pollutant concentrations and emergency department visits for pediatric asthma.
Daily counts of emergency department visits for asthma or wheeze among children aged 5 to 17 years were collected from 41 Metropolitan Atlanta hospitals during 1993-2004 (n = 91,386 visits). Ambient concentrations of gaseous pollutants and speciated particulate matter were available from stationary monitors during this time period. Rate ratios for the warm season (May to October) and cold season (November to April) were estimated using Poisson generalized linear models in the framework of a case-crossover analysis.
Both ozone and primary pollutants from traffic sources were associated with emergency department visits for asthma or wheeze; evidence for independent effects of ozone and primary pollutants from traffic sources were observed in multipollutant models. These associations tended to be of the highest magnitude for concentrations on the day of the emergency department visit and were present at relatively low ambient concentrations.
Even at relatively low ambient concentrations, ozone and primary pollutants from traffic sources independently contributed to the burden of emergency department visits for pediatric asthma.
American Journal of Respiratory and Critical Care Medicine 04/2010; 182(3):307-16. · 11.08 Impact Factor
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ABSTRACT: Racial residential segregation is hypothesized to affect population health by systematically patterning health-relevant exposures and opportunities according to individuals' race or income. Growing interest into the association between residential segregation and health disparities demands more rigorous appraisal of commonly used measures of segregation. Most current studies rely on census tracts as approximations of the local residential environment when calculating segregation indices of either neighborhoods or metropolitan areas. Because census tracts are arbitrary in size and shape, reliance on this geographic scale limits understanding of place-health associations. More flexible, explicitly spatial derivations of traditional segregation indices have been proposed but have not been compared with tract-derived measures in the context of health disparities studies common to social epidemiology, health demography, or medical geography. We compared segregation measured with tract-derived as well as GIS surface-density-derived indices. Measures were compared by region and population size, and segregation measures were linked to birth record to estimate the difference in association between segregation and very preterm birth. Separate analyses focus on metropolitan segregation and on neighborhood segregation.
Across 231 metropolitan areas, tract-derived and surface-density-derived segregation measures are highly correlated. However overall correlation obscures important differences by region and metropolitan size. In general the discrepancy between measure types is greatest for small metropolitan areas, declining with increasing population size. Discrepancies in measures are greatest in the South, and smallest in Western metropolitan areas. Choice of segregation index changed the magnitude of the measured association between segregation and very preterm birth. For example among black women, the risk ratio for very preterm birth in metropolitan areas changed from 2.12 to 1.68 for the effect of high versus low segregation when using surface-density-derived versus tract-derived segregation indices. Variation in effect size was smaller but still present in analyses of neighborhood racial composition and very preterm birth in Atlanta neighborhoods.
Census tract-derived measures of segregation are highly correlated with recently introduced spatial segregation measures, but the residual differences among measures are not uniform for all areas. Use of surface-density-derived measures provides researchers with tools to further explore the spatial relationships between segregation and health disparities.
International Journal of Health Geographics 01/2010; 9:29. · 2.62 Impact Factor
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ABSTRACT: Late referral of patients with chronic kidney disease is associated with increased morbidity and mortality, but the contribution of center-to-center and geographic variability of pre-ESRD nephrology care to mortality of patients with ESRD is unknown. We evaluated the pre-ESRD care of > 30,000 incident hemodialysis patients, 5088 (17.8%) of whom died during follow-up (median 365 d). Approximately half (51.3%) of incident patients had received at least 6 mo of pre-ESRD nephrology care, as reported by attending physicians. Pre-ESRD nephrology care was independently associated with survival (odds ratio 1.54; 95% confidence interval 1.45 to 1.64). There was substantial center-to-center variability in pre-ESRD care, which was associated with increased facility-specific death rates. As the proportion of patients who were in a treatment center and receiving pre-ESRD nephrology care increased from lowest to highest quintile, the mortality rate decreased from 19.6 to 16.1% (P = 0.0031). In addition, treatment centers in the lowest quintile of pre-ESRD care were clustered geographically. In conclusion, pre-ESRD nephrology care is highly variable among treatment centers and geographic regions. Targeting these disparities could have substantial clinical impact, because the absence of > or = 6 mo of pre-ESRD care by a nephrologist is associated with a higher risk for death.
Journal of the American Society of Nephrology 04/2009; 20(5):1078-85. · 9.66 Impact Factor
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ABSTRACT: Our research focuses on the association between exposure to an airborne pollutant and counts of emergency department visits attributed to a specific chronic illness. The motivating example for this analysis of measurement error in time series studies of air pollution and acute health outcomes was a study of emergency department visits from a 20-county Atlanta metropolitan statistical area from 1993-1999. The research presented illustrates the impact of using various surrogates for unobserved measurements of ambient concentrations at the zip code level. Simulation results indicate that the impact of measurement error on the association between pollutant exposure and a health outcome can be substantial. The proposed conditional expectation approach provided reliable estimates of the association and exhibited good confidence interval coverage for a variety of magnitudes of association. Use of a single-centrally located monitor, the arithmetic average, the nearest-neighbor monitor, and the inverse-distance weighted average surrogates resulted in biased estimates and poor coverage rates, especially for larger magnitudes of the association. A focus on obtaining reasonable exposure measurements within clearly defined subregions is important when the pollutant exposure of interest exhibits strong spatial variability.
Environmetrics 03/2009; 20(7):877-894. · 1.06 Impact Factor
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ABSTRACT: Relatively few studies have evaluated the effects of heterogeneous spatiotemporal pollutant distributions on health risk estimates in time-series analyses that use data from a central monitor to assign exposures. We present a method for examining the effects of exposure measurement error relating to spatiotemporal variability in ambient air pollutant concentrations on air pollution health risk estimates in a daily time-series analysis of emergency department visits in Atlanta, Georgia. We used Poisson generalized linear models to estimate associations between current-day pollutant concentrations and circulatory emergency department visits for the 1998-2004 time period. Data from monitoring sites located in different geographical regions of the study area and at different distances from several urban geographical subpopulations served as alternative measures of exposure. We observed associations for spatially heterogeneous pollutants (CO and NO(2)) using data from several different urban monitoring sites. These associations were not observed when using data from the most rural site, located 38 miles from the city center. In contrast, associations for spatially homogeneous pollutants (O(3) and PM(2.5)) were similar, regardless of the monitoring site location. We found that monitoring site location and the distance of a monitoring site to a population of interest did not meaningfully affect estimated associations for any pollutant when using data from urban sites located within 20 miles from the population center under study. However, for CO and NO(2), these factors were important when using data from rural sites located > or = 30 miles from the population center, most likely owing to exposure measurement error. Overall, our findings lend support to the use of pollutant data from urban central sites to assess population exposures within geographically dispersed study populations in Atlanta and similar cities.
Journal of Exposure Science and Environmental Epidemiology 03/2009; 20(2):135-46. · 2.93 Impact Factor
