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A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601).

Deadline for manuscript submissions: closed (30 November 2013)

Special Issue Editor

Guest Editor
Prof. Dr. Peter Congdon

Department of Geography and Life Sciences Institute, Queen Mary University of London, Mile End Road, London, E1 4NS, UK
Website | E-Mail
Phone: 44 207 882 8200
Interests: spatial epidemiology; Bayesian modeling of health outcomes; small area disease prevalence estimation; area mortality; geographic and socioeconomic inequalities in chronic disease and mental health; suicidology

Special Issue Information

Dear Colleagues,

The development of spatial epidemiology has been assisted by conceptual advances (e.g. ecological approaches to health encompassing both individual and contextual influences), and by new methods (e.g. clustering methods, multilevel models, Bayesian approaches). There remains much scope to establish in what way places affect health outcomes, and how important contextual effects are. For example, the operationalisation and measurement of relevant spatial variables, often latent constructs rather than directly observed, is often problematic and can affect levels of explanation of health outcomes attributed to contextual variables. Other issues are the changing impacts on health of area variables according to the area scale adopted, how to represent unmeasured spatially correlated influences on health outcomes, and how to measure spatial clustering in spatial health relativities or area risk factors. This special issue has a broad focus on recent advances in spatial epidemiology, and both theoretical and empirical submissions are welcome.

Prof. Dr. Peter Congdon
Guest Editor

Submission

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed Open Access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs).


Keywords

  • spatial clustering
  • spatial autocorrelation
  • contextual influences on health, area effects
  • relevance of scale, modifiable areal unit problem
  • spatial regression
  • multilevel models
  • air pollution and health
  • measuring contextual influences/exposures
  • spatial latent variable models

Published Papers (14 papers)

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Research

Open AccessArticle Spatial and Temporal Variations of Satellite-Derived Multi-Year Particulate Data of Saudi Arabia: An Exploratory Analysis
Int. J. Environ. Res. Public Health 2014, 11(11), 11152-11166; doi:10.3390/ijerph111111152
Received: 9 July 2014 / Revised: 3 September 2014 / Accepted: 12 September 2014 / Published: 27 October 2014
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Abstract
The effects of concentrations of fine particulate matter on urban populations have been gaining attention because fine particulate matter exposes the urban populace to health risks such as respiratory and cardiovascular diseases. Satellite-derived data, using aerosol optical depth (AOD), have been adopted to
[...] Read more.
The effects of concentrations of fine particulate matter on urban populations have been gaining attention because fine particulate matter exposes the urban populace to health risks such as respiratory and cardiovascular diseases. Satellite-derived data, using aerosol optical depth (AOD), have been adopted to improve the monitoring of fine particulate matter. One of such data sources is the global multi-year PM2.5 data (2001–2010) released by the Center for International Earth Science Information Network (CIESIN). This paper explores the satellite-derived PM2.5 data of Saudi Arabia to highlight the trend of PM2.5 concentrations. It also examines the changes in PM2.5 concentrations in some urbanized areas of Saudi Arabia. Concentrations in major cities like Riyadh, Dammam, Jeddah, Makkah, Madinah and the industrial cities of Yanbu and Jubail are analyzed using cluster analysis. The health risks due to exposure of the populace are highlighted by using the World Health Organization (WHO) standard and targets. The results show a trend of increasing concentrations of PM2.5 in urban areas. Significant clusters of high values are found in the eastern and south-western part of the country. There is a need to explore this topic using images with higher spatial resolution and validate the data with ground observations to improve the analysis. Full article
(This article belongs to the Special Issue Spatial Epidemiology)
Open AccessArticle Multilevel Analysis of Air Pollution and Early Childhood Neurobehavioral Development
Int. J. Environ. Res. Public Health 2014, 11(7), 6827-6841; doi:10.3390/ijerph110706827
Received: 15 August 2013 / Revised: 19 June 2014 / Accepted: 23 June 2014 / Published: 2 July 2014
Cited by 5 | PDF Full-text (234 KB) | HTML Full-text | XML Full-text
Abstract
To investigate the association between the ambient air pollution levels during the prenatal and postnatal stages and early childhood neurobehavioral development, our study recruited 533 mother-infant pairs from 11 towns in Taiwan. All study subjects were asked to complete childhood neurobehavioral development scales
[...] Read more.
To investigate the association between the ambient air pollution levels during the prenatal and postnatal stages and early childhood neurobehavioral development, our study recruited 533 mother-infant pairs from 11 towns in Taiwan. All study subjects were asked to complete childhood neurobehavioral development scales and questionnaires at 6 and 18 months. Air pollution, including particulate matter ≤10 μm (PM10), carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and hydrocarbons, was measured at air quality monitoring stations in the towns where the subjects lived. Multilevel analyses were applied to assess the association between air pollution and childhood neurobehavioral development during pregnancy and when the children were 0 to 6 months, 7 to 12 months, and 13 to 18 months old. At 18 months, poor subclinical neurodevelopment in early childhood is associated with the average SO2 exposure of prenatal, during all trimesters of pregnancy and at postnatal ages up to 12 months (first trimester β = −0.083, se = 0.030; second and third trimester β = −0.114, se = 0.045; from birth to 12 months of age β = −0.091, se = 0.034). Furthermore, adverse gross motor below average scores at six months of age were associated with increased average non-methane hydrocarbon, (NMHC) levels during the second and third trimesters (β = −8.742, se = 3.512). Low-level SO2 exposure prenatally and up to twelve months postnatal could cause adverse neurobehavioral effects at 18 months of age. Maternal NMHC exposure during the 2nd and 3rd trimesters of pregnancy would be also associated with poor gross motor development in their children at 6 months of age. Full article
(This article belongs to the Special Issue Spatial Epidemiology)
Open AccessArticle Spatial Analysis of HIV Positive Injection Drug Users in San Francisco, 1987 to 2005
Int. J. Environ. Res. Public Health 2014, 11(4), 3937-3955; doi:10.3390/ijerph110403937
Received: 1 December 2013 / Revised: 21 March 2014 / Accepted: 24 March 2014 / Published: 9 April 2014
Cited by 4 | PDF Full-text (547 KB) | HTML Full-text | XML Full-text
Abstract
Spatial analyses of HIV/AIDS related outcomes are growing in popularity as a tool to understand geographic changes in the epidemic and inform the effectiveness of community-based prevention and treatment programs. The Urban Health Study was a serial, cross-sectional epidemiological study of injection drug
[...] Read more.
Spatial analyses of HIV/AIDS related outcomes are growing in popularity as a tool to understand geographic changes in the epidemic and inform the effectiveness of community-based prevention and treatment programs. The Urban Health Study was a serial, cross-sectional epidemiological study of injection drug users (IDUs) in San Francisco between 1987 and 2005 (N = 29,914). HIV testing was conducted for every participant. Participant residence was geocoded to the level of the United States Census tract for every observation in dataset. Local indicator of spatial autocorrelation (LISA) tests were used to identify univariate and bivariate Census tract clusters of HIV positive IDUs in two time periods. We further compared three tract level characteristics (% poverty, % African Americans, and % unemployment) across areas of clustered and non-clustered tracts. We identified significant spatial clustering of high numbers of HIV positive IDUs in the early period (1987–1995) and late period (1996–2005). We found significant bivariate clusters of Census tracts where HIV positive IDUs and tract level poverty were above average compared to the surrounding areas. Our data suggest that poverty, rather than race, was an important neighborhood characteristic associated with the spatial distribution of HIV in SF and its spatial diffusion over time. Full article
(This article belongs to the Special Issue Spatial Epidemiology)
Open AccessArticle Spatial Environmental Modeling of Autoantibody Outcomes among an African American Population
Int. J. Environ. Res. Public Health 2014, 11(3), 2764-2779; doi:10.3390/ijerph110302764
Received: 19 December 2013 / Revised: 26 February 2014 / Accepted: 27 February 2014 / Published: 7 March 2014
Cited by 1 | PDF Full-text (474 KB) | HTML Full-text | XML Full-text
Abstract
In this study of autoimmunity among a population of Gullah African Americans in South Carolina, the links between environmental exposures and autoimmunity (presence of antinuclear antibodies (ANA)) have been assessed. The study population included patients with systemic lupus erythematosus (n = 10), their
[...] Read more.
In this study of autoimmunity among a population of Gullah African Americans in South Carolina, the links between environmental exposures and autoimmunity (presence of antinuclear antibodies (ANA)) have been assessed. The study population included patients with systemic lupus erythematosus (n = 10), their first degree relatives (n = 61), and unrelated controls (n = 9) where 47.5% (n = 38) were ANA positive. This paper presents the methodology used to model ANA status as a function of individual environmental influences, both self-reported and measured, while controlling for known autoimmunity risk factors. We have examined variable dimension reduction and selection methods in our approach. Following the dimension reduction and selection methods, we fit logistic spatial Bayesian models to explore the relationship between our outcome of interest and environmental exposures adjusting for personal variables. Our analysis also includes a validation “strip” where we have interpolated information from a specific geographic area for a subset of the study population that lives in that vicinity. Our results demonstrate that residential proximity to exposure site is important in this form of analysis. The use of a validation strip network demonstrated that even with small sample numbers some significant exposure-outcome relationships can be detected. Full article
(This article belongs to the Special Issue Spatial Epidemiology)
Open AccessArticle Differences in Age-Standardized Mortality Rates for Avoidable Deaths Based on Urbanization Levels in Taiwan, 1971–2008
Int. J. Environ. Res. Public Health 2014, 11(2), 1776-1793; doi:10.3390/ijerph110201776
Received: 26 November 2013 / Revised: 10 January 2014 / Accepted: 17 January 2014 / Published: 5 February 2014
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Abstract
The World is undergoing rapid urbanization, with 70% of the World population expected to live in urban areas by 2050. Nevertheless, nationally representative analysis of the health differences in the leading causes of avoidable mortality disaggregated by urbanization level is lacking. We undertake
[...] Read more.
The World is undergoing rapid urbanization, with 70% of the World population expected to live in urban areas by 2050. Nevertheless, nationally representative analysis of the health differences in the leading causes of avoidable mortality disaggregated by urbanization level is lacking. We undertake a study of temporal trends in mortality rates for deaths considered avoidable by the Concerted Action of the European Community on Avoidable Mortality for four different levels of urbanization in Taiwan between 1971 and 2008. We find that for virtually all causes of death, age-standardized mortality rates (ASMRs) were lower in more urbanized than less urbanized areas, either throughout the study period, or by the end of the period despite higher rates in urbanized areas initially. Only breast cancer had consistently higher AMSRs in more urbanized areas throughout the 38-year period. Further, only breast cancer, lung cancer, and ischemic heart disease witnessed an increase in ASMRs in one or more urbanization categories. More urbanized areas in Taiwan appear to enjoy better indicators of health outcomes in terms of mortality rates than less urbanized areas. Access to and the availability of rich healthcare resources in urban areas may have contributed to this positive result. Full article
(This article belongs to the Special Issue Spatial Epidemiology)
Open AccessArticle A Population-Based Case-Control Study of Drinking-Water Nitrate and Congenital Anomalies Using Geographic Information Systems (GIS) to Develop Individual-Level Exposure Estimates
Int. J. Environ. Res. Public Health 2014, 11(2), 1803-1823; doi:10.3390/ijerph110201803
Received: 1 December 2013 / Revised: 24 January 2014 / Accepted: 26 January 2014 / Published: 5 February 2014
Cited by 2 | PDF Full-text (722 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Animal studies and epidemiological evidence suggest an association between prenatal exposure to drinking water with elevated nitrate (NO3-N) concentrations and incidence of congenital anomalies. This study used Geographic Information Systems (GIS) to derive individual-level prenatal drinking-water nitrate exposure estimates from measured
[...] Read more.
Animal studies and epidemiological evidence suggest an association between prenatal exposure to drinking water with elevated nitrate (NO3-N) concentrations and incidence of congenital anomalies. This study used Geographic Information Systems (GIS) to derive individual-level prenatal drinking-water nitrate exposure estimates from measured nitrate concentrations from 140 temporally monitored private wells and 6 municipal water supplies. Cases of major congenital anomalies in Kings County, Nova Scotia, Canada, between 1988 and 2006 were selected from province-wide population-based perinatal surveillance databases and matched to controls from the same databases. Unconditional multivariable logistic regression was performed to test for an association between drinking-water nitrate exposure and congenital anomalies after adjusting for clinically relevant risk factors. Employing all nitrate data there was a trend toward increased risk of congenital anomalies for increased nitrate exposure levels though this was not statistically significant. After stratification of the data by conception before or after folic acid supplementation, an increased risk of congenital anomalies for nitrate exposure of 1.5–5.56 mg/L (2.44; 1.05–5.66) and a trend toward increased risk for >5.56 mg/L (2.25; 0.92–5.52) was found. Though the study is likely underpowered, these results suggest that drinking-water nitrate exposure may contribute to increased risk of congenital anomalies at levels below the current Canadian maximum allowable concentration. Full article
(This article belongs to the Special Issue Spatial Epidemiology)
Open AccessArticle Exploring Neighborhood Influences on Small-Area Variations in Intimate Partner Violence Risk: A Bayesian Random-Effects Modeling Approach
Int. J. Environ. Res. Public Health 2014, 11(1), 866-882; doi:10.3390/ijerph110100866
Received: 28 November 2013 / Revised: 31 December 2013 / Accepted: 2 January 2014 / Published: 9 January 2014
Cited by 10 | PDF Full-text (328 KB) | HTML Full-text | XML Full-text
Abstract
This paper uses spatial data of cases of intimate partner violence against women (IPVAW) to examine neighborhood-level influences on small-area variations in IPVAW risk in a police district of the city of Valencia (Spain). To analyze area variations in IPVAW risk and its
[...] Read more.
This paper uses spatial data of cases of intimate partner violence against women (IPVAW) to examine neighborhood-level influences on small-area variations in IPVAW risk in a police district of the city of Valencia (Spain). To analyze area variations in IPVAW risk and its association with neighborhood-level explanatory variables we use a Bayesian spatial random-effects modeling approach, as well as disease mapping methods to represent risk probabilities in each area. Analyses show that IPVAW cases are more likely in areas of high immigrant concentration, high public disorder and crime, and high physical disorder. Results also show a spatial component indicating remaining variability attributable to spatially structured random effects. Bayesian spatial modeling offers a new perspective to identify IPVAW high and low risk areas, and provides a new avenue for the design of better-informed prevention and intervention strategies. Full article
(This article belongs to the Special Issue Spatial Epidemiology)
Open AccessArticle Disease Mapping and Regression with Count Data in the Presence of Overdispersion and Spatial Autocorrelation: A Bayesian Model Averaging Approach
Int. J. Environ. Res. Public Health 2014, 11(1), 883-902; doi:10.3390/ijerph110100883
Received: 7 December 2013 / Revised: 4 January 2014 / Accepted: 6 January 2014 / Published: 9 January 2014
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Abstract
This paper applies the generalised linear model for modelling geographical variation to esophageal cancer incidence data in the Caspian region of Iran. The data have a complex and hierarchical structure that makes them suitable for hierarchical analysis using Bayesian techniques, but with care
[...] Read more.
This paper applies the generalised linear model for modelling geographical variation to esophageal cancer incidence data in the Caspian region of Iran. The data have a complex and hierarchical structure that makes them suitable for hierarchical analysis using Bayesian techniques, but with care required to deal with problems arising from counts of events observed in small geographical areas when overdispersion and residual spatial autocorrelation are present. These considerations lead to nine regression models derived from using three probability distributions for count data: Poisson, generalised Poisson and negative binomial, and three different autocorrelation structures. We employ the framework of Bayesian variable selection and a Gibbs sampling based technique to identify significant cancer risk factors. The framework deals with situations where the number of possible models based on different combinations of candidate explanatory variables is large enough such that calculation of posterior probabilities for all models is difficult or infeasible. The evidence from applying the modelling methodology suggests that modelling strategies based on the use of generalised Poisson and negative binomial with spatial autocorrelation work well and provide a robust basis for inference. Full article
(This article belongs to the Special Issue Spatial Epidemiology)
Figures

Open AccessArticle Analysis of the Spatial Variation of Hospitalization Admissions for Hypertension Disease in Shenzhen, China
Int. J. Environ. Res. Public Health 2014, 11(1), 713-733; doi:10.3390/ijerph110100713
Received: 14 October 2013 / Revised: 16 December 2013 / Accepted: 18 December 2013 / Published: 3 January 2014
Cited by 7 | PDF Full-text (1017 KB) | HTML Full-text | XML Full-text
Abstract
In China, awareness about hypertension, the treatment rate and the control rate are low compared to developed countries, even though China’s aging population has grown, especially in those areas with a high degree of urbanization. However, limited epidemiological studies have attempted to describe
[...] Read more.
In China, awareness about hypertension, the treatment rate and the control rate are low compared to developed countries, even though China’s aging population has grown, especially in those areas with a high degree of urbanization. However, limited epidemiological studies have attempted to describe the spatial variation of the geo-referenced data on hypertension disease over an urban area of China. In this study, we applied hierarchical Bayesian models to explore the spatial heterogeneity of the relative risk for hypertension admissions throughout Shenzhen in 2011. The final model specification includes an intercept and spatial components (structured and unstructured). Although the road density could be used as a covariate in modeling, it is an indirect factor on the relative risk. In addition, spatial scan statistics and spatial analysis were utilized to identify the spatial pattern and to map the clusters. The results showed that the relative risk for hospital admission for hypertension has high-value clusters in the south and southeastern Shenzhen. This study aimed to identify some specific regions with high relative risk, and this information is useful for the health administrators. Further research should address more-detailed data collection and an explanation of the spatial patterns. Full article
(This article belongs to the Special Issue Spatial Epidemiology)
Open AccessArticle Residential Mobility and Breast Cancer in Marin County, California, USA
Int. J. Environ. Res. Public Health 2014, 11(1), 271-295; doi:10.3390/ijerph110100271
Received: 23 October 2013 / Revised: 4 December 2013 / Accepted: 6 December 2013 / Published: 23 December 2013
Cited by 2 | PDF Full-text (2185 KB) | HTML Full-text | XML Full-text
Abstract
Marin County (California, USA) has among the highest incidences of breast cancer in the U.S. A previously conducted case-control study found eight significant risk factors in participants enrolled from 1997–1999. These included being premenopausal, never using birth control pills, lower highest lifetime body
[...] Read more.
Marin County (California, USA) has among the highest incidences of breast cancer in the U.S. A previously conducted case-control study found eight significant risk factors in participants enrolled from 1997–1999. These included being premenopausal, never using birth control pills, lower highest lifetime body mass index, having four or more mammograms from 1990–1994, beginning drinking alcohol after age 21, drinking an average two or more alcoholic drinks per day, being in the highest quartile of pack-years of cigarette smoking, and being raised in an organized religion. Previously conducted surveys provided residential histories; while  statistic accounted for participants’ residential mobility, and assessed clustering of breast cancer cases relative to controls based on the known risk factors. These identified specific cases, places, and times of excess breast cancer risk. Analysis found significant global clustering of cases localized to specific residential histories and times. Much of the observed clustering occurred among participants who immigrated to Marin County. However, persistent case-clustering of greater than fifteen years duration was also detected. Significant case-clustering among long-term residents may indicate geographically localized risk factors not accounted for in the study design, as well as uncertainty and incompleteness in the acquired addresses. Other plausible explanations include environmental risk factors and cases tending to settle in specific areas. A biologically plausible exposure or risk factor has yet to be identified. Full article
(This article belongs to the Special Issue Spatial Epidemiology)
Figures

Open AccessArticle Spatial Autocorrelation of Cancer Incidence in Saudi Arabia
Int. J. Environ. Res. Public Health 2013, 10(12), 7207-7228; doi:10.3390/ijerph10127207
Received: 6 October 2013 / Revised: 27 November 2013 / Accepted: 28 November 2013 / Published: 16 December 2013
Cited by 8 | PDF Full-text (1192 KB) | HTML Full-text | XML Full-text
Abstract
Little is known about the geographic distribution of common cancers in Saudi Arabia. We explored the spatial incidence patterns of common cancers in Saudi Arabia using spatial autocorrelation analyses, employing the global Moran’s I and Anselin’s local Moran’s I statistics to detect nonrandom
[...] Read more.
Little is known about the geographic distribution of common cancers in Saudi Arabia. We explored the spatial incidence patterns of common cancers in Saudi Arabia using spatial autocorrelation analyses, employing the global Moran’s I and Anselin’s local Moran’s I statistics to detect nonrandom incidence patterns. Global ordinary least squares (OLS) regression and local geographically-weighted regression (GWR) were applied to examine the spatial correlation of cancer incidences at the city level. Population-based records of cancers diagnosed between 1998 and 2004 were used. Male lung cancer and female breast cancer exhibited positive statistically significant global Moran’s I index values, indicating a tendency toward clustering. The Anselin’s local Moran’s I analyses revealed small significant clusters of lung cancer, prostate cancer and Hodgkin’s disease among males in the Eastern region and significant clusters of thyroid cancers in females in the Eastern and Riyadh regions. Additionally, both regression methods found significant associations among various cancers. For example, OLS and GWR revealed significant spatial associations among NHL, leukemia and Hodgkin’s disease (r² = 0.49–0.67 using OLS and r² = 0.52–0.68 using GWR) and between breast and prostate cancer (r² = 0.53 OLS and 0.57 GWR) in Saudi Arabian cities. These findings may help to generate etiologic hypotheses of cancer causation and identify spatial anomalies in cancer incidence in Saudi Arabia. Our findings should stimulate further research on the possible causes underlying these clusters and associations. Full article
(This article belongs to the Special Issue Spatial Epidemiology)
Open AccessArticle NO2 and Cancer Incidence in Saudi Arabia
Int. J. Environ. Res. Public Health 2013, 10(11), 5844-5862; doi:10.3390/ijerph10115844
Received: 19 August 2013 / Revised: 18 October 2013 / Accepted: 23 October 2013 / Published: 4 November 2013
Cited by 9 | PDF Full-text (940 KB) | HTML Full-text | XML Full-text
Abstract
Air pollution exposure has been shown to be associated with an increased risk of specific cancers. This study investigated whether the number and incidence of the most common cancers in Saudi Arabia were associated with urban air pollution exposure, specifically NO2.
[...] Read more.
Air pollution exposure has been shown to be associated with an increased risk of specific cancers. This study investigated whether the number and incidence of the most common cancers in Saudi Arabia were associated with urban air pollution exposure, specifically NO2. Overall, high model goodness of fit (GOF) was observed in the Eastern, Riyadh and Makkah regions. The significant coefficients of determination (r2) were higher at the regional level (r2 = 0.32–0.71), weaker at the governorate level (r2 = 0.03–0.43), and declined slightly at the city level (r2 = 0.17–0.33), suggesting that an increased aggregated spatial level increased the explained variability and the model GOF. However, the low GOF at the lowest spatial level suggests that additional variation remains unexplained. At different spatial levels, associations between NO2 concentration and the most common cancers were marginally improved in geographically weighted regression (GWR) analysis, which explained both global and local heterogeneity and variations in cancer incidence. High coefficients of determination were observed between NO2 concentration and lung and breast cancer incidences, followed by prostate, bladder, cervical and ovarian cancers, confirming results from other studies. These results could be improved using individual explanatory variables such as environmental, demographic, behavioral, socio-economic, and genetic risk factors. Full article
(This article belongs to the Special Issue Spatial Epidemiology)
Open AccessArticle Spatially Interpolated Disease Prevalence Estimation Using Collateral Indicators of Morbidity and Ecological Risk
Int. J. Environ. Res. Public Health 2013, 10(10), 5011-5025; doi:10.3390/ijerph10105011
Received: 25 August 2013 / Revised: 1 October 2013 / Accepted: 8 October 2013 / Published: 14 October 2013
Cited by 1 | PDF Full-text (1539 KB) | HTML Full-text | XML Full-text
Abstract
This paper considers estimation of disease prevalence for small areas (neighbourhoods) when the available observations on prevalence are for an alternative partition of a region, such as service areas. Interpolation to neighbourhoods uses a kernel method extended to take account of two types
[...] Read more.
This paper considers estimation of disease prevalence for small areas (neighbourhoods) when the available observations on prevalence are for an alternative partition of a region, such as service areas. Interpolation to neighbourhoods uses a kernel method extended to take account of two types of collateral information. The first is morbidity and service use data, such as hospital admissions, observed for neighbourhoods. Variations in morbidity and service use are expected to reflect prevalence. The second type of collateral information is ecological risk factors (e.g., pollution indices) that are expected to explain variability in prevalence in service areas, but are typically observed only for neighbourhoods. An application involves estimating neighbourhood asthma prevalence in a London health region involving 562 neighbourhoods and 189 service (primary care) areas. Full article
(This article belongs to the Special Issue Spatial Epidemiology)
Open AccessArticle Mapping Disease at an Approximated Individual Level Using Aggregate Data: A Case Study of Mapping New Hampshire Birth Defects
Int. J. Environ. Res. Public Health 2013, 10(9), 4161-4174; doi:10.3390/ijerph10094161
Received: 10 July 2013 / Revised: 23 August 2013 / Accepted: 27 August 2013 / Published: 6 September 2013
Cited by 3 | PDF Full-text (226 KB) | HTML Full-text | XML Full-text
Abstract
Background: Limited by data availability, most disease maps in the literature are for relatively large and subjectively-defined areal units, which are subject to problems associated with polygon maps. High resolution maps based on objective spatial units are needed to more precisely detect associations
[...] Read more.
Background: Limited by data availability, most disease maps in the literature are for relatively large and subjectively-defined areal units, which are subject to problems associated with polygon maps. High resolution maps based on objective spatial units are needed to more precisely detect associations between disease and environmental factors. Method: We propose to use a Restricted and Controlled Monte Carlo (RCMC) process to disaggregate polygon-level location data to achieve mapping aggregate data at an approximated individual level. RCMC assigns a random point location to a polygon-level location, in which the randomization is restricted by the polygon and controlled by the background (e.g., population at risk). RCMC allows analytical processes designed for individual data to be applied, and generates high-resolution raster maps. Results: We applied RCMC to the town-level birth defect data for New Hampshire and generated raster maps at the resolution of 100 m. Besides the map of significance of birth defect risk represented by p-value, the output also includes a map of spatial uncertainty and a map of hot spots. Conclusions: RCMC is an effective method to disaggregate aggregate data. An RCMC-based disease mapping maximizes the use of available spatial information, and explicitly estimates the spatial uncertainty resulting from aggregation. Full article
(This article belongs to the Special Issue Spatial Epidemiology)

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Type of Paper: Article
Title:
Geographic clustering of breast cancer among long-term residents of Marin County, California accounting for residential mobility, risk factors and covariate
Author: Geoffrey Jacquez
Affiliation:
Department of Geography, State University of New York at Buffalo, Buffalo, NY 14260, USA; E-mail: jacquez@biomedware.com
Abstract: Background: Marin County, California, has among the highest incidence of breast cancer in the United States. A case-control study (Wrensch, Chew et al. 2003) found 8 significant risk factors and covariates among 285 cases and 286 controls enrolled between 1997-99. Significant risk factors were being premenopausal, never to have used birth control pills, a lower highest lifetime body mass index, four or more mammograms in 1990-94, beginning drinking after the age of 21, on average drinking two or more drinks per day, the highest quartile of pack-years of cigarette smoking and having been raised in an organized religion. Cases and controls did not significantly differ with regard to having a first-degree relative with breast cancer, a history of benign breast biopsy, previous radiation treatment, age at menarche, parity, use of hormone replacement therapy, age of first living in Marin County, or total years lived in Marin County.
Methods:
We used Q-statistics accounting for the significant risk factors reported by Wrensch et al. and residential histories of the study participants obtained from Zero Breast Cancer.  These methods are sensitive to clustering of breast cancer cases after the known risk factors are accounted for, and may be used to identify specific cases, places and times of geographically localized clustering.  Persistent local clusters may be the signature of a risk factor (e.g. environmental) that was not accounted for in the parent case control study design.
Results:
We found the study population to be comprised of “movers” and “stayers”.  Statistical analyses found significant global clustering of cases that was localized to specific residential histories and times.  A substantial portion of the observed clustering occurred among the movers, who immigrated to Marin County from New York near Long Island, the upper central Midwest and other parts of California.  However, persistent case-clustering of greater than 15 years duration was found near Greenbrae, San Rafael and Novato.
Conclusions:
The finding of significant clustering of breast cancer cases among long-term residents may indicate the role of geographically localized risk factors not accounted for in the parent case-control study design.  Plausible hypotheses include environmental risk factors and differential migration such that cases tend to settle in specific areas. While the burden of statistical evidence indicates the persistent, local clusters identified in this study are statistically unusual, a biologically plausible exposure or risk factor has yet to be identified.

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