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Spatial Epidemiology

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

Deadline for manuscript submissions: closed (30 November 2013) | Viewed by 113985

Special Issue Editor

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

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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

2507 KiB  
Article
Spatial and Temporal Variations of Satellite-Derived Multi-Year Particulate Data of Saudi Arabia: An Exploratory Analysis
by Yusuf A. Aina, Johannes H. Van der Merwe and Habib M. Alshuwaikhat
Int. J. Environ. Res. Public Health 2014, 11(11), 11152-11166; https://doi.org/10.3390/ijerph111111152 - 27 Oct 2014
Cited by 12 | Viewed by 6569
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)
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234 KiB  
Article
Multilevel Analysis of Air Pollution and Early Childhood Neurobehavioral Development
by Ching-Chun Lin, Shih-Kuan Yang, Kuan-Chia Lin, Wen-Chao Ho, Wu-Shiun Hsieh, Bih-Ching Shu and Pau-Chung Chen
Int. J. Environ. Res. Public Health 2014, 11(7), 6827-6841; https://doi.org/10.3390/ijerph110706827 - 02 Jul 2014
Cited by 41 | Viewed by 7279
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)
547 KiB  
Article
Spatial Analysis of HIV Positive Injection Drug Users in San Francisco, 1987 to 2005
by Alexis N. Martinez, Lee R. Mobley, Jennifer Lorvick, Scott P. Novak, Andrea M. Lopez and Alex H. Kral
Int. J. Environ. Res. Public Health 2014, 11(4), 3937-3955; https://doi.org/10.3390/ijerph110403937 - 09 Apr 2014
Cited by 16 | Viewed by 7693
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)
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474 KiB  
Article
Spatial Environmental Modeling of Autoantibody Outcomes among an African American Population
by Rachel Carroll, Andrew B. Lawson, Delia Voronca, Chawarat Rotejanaprasert, John E. Vena, Claire Marjorie Aelion and Diane L. Kamen
Int. J. Environ. Res. Public Health 2014, 11(3), 2764-2779; https://doi.org/10.3390/ijerph110302764 - 07 Mar 2014
Cited by 5 | Viewed by 7924
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)
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722 KiB  
Article
A Population-Based Case-Control Study of Drinking-Water Nitrate and Congenital Anomalies Using Geographic Information Systems (GIS) to Develop Individual-Level Exposure Estimates
by Caitlin E. Holtby, Judith R. Guernsey, Alexander C. Allen, John A. VanLeeuwen, Victoria M. Allen and Robert J. Gordon
Int. J. Environ. Res. Public Health 2014, 11(2), 1803-1823; https://doi.org/10.3390/ijerph110201803 - 05 Feb 2014
Cited by 15 | Viewed by 7313
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)
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1057 KiB  
Article
Differences in Age-Standardized Mortality Rates for Avoidable Deaths Based on Urbanization Levels in Taiwan, 1971–2008
by Brian K. Chen and Chun-Yuh Yang
Int. J. Environ. Res. Public Health 2014, 11(2), 1776-1793; https://doi.org/10.3390/ijerph110201776 - 05 Feb 2014
Cited by 12 | Viewed by 6224
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)
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1259 KiB  
Article
Disease Mapping and Regression with Count Data in the Presence of Overdispersion and Spatial Autocorrelation: A Bayesian Model Averaging Approach
by Mohammadreza Mohebbi, Rory Wolfe and Andrew Forbes
Int. J. Environ. Res. Public Health 2014, 11(1), 883-902; https://doi.org/10.3390/ijerph110100883 - 09 Jan 2014
Cited by 20 | Viewed by 8328
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)
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328 KiB  
Article
Exploring Neighborhood Influences on Small-Area Variations in Intimate Partner Violence Risk: A Bayesian Random-Effects Modeling Approach
by Enrique Gracia, Antonio López-Quílez, Miriam Marco, Silvia Lladosa and Marisol Lila
Int. J. Environ. Res. Public Health 2014, 11(1), 866-882; https://doi.org/10.3390/ijerph110100866 - 09 Jan 2014
Cited by 45 | Viewed by 10138
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)
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1017 KiB  
Article
Analysis of the Spatial Variation of Hospitalization Admissions for Hypertension Disease in Shenzhen, China
by Zhensheng Wang, Qingyun Du, Shi Liang, Ke Nie, De-nan Lin, Yan Chen and Jia-jia Li
Int. J. Environ. Res. Public Health 2014, 11(1), 713-733; https://doi.org/10.3390/ijerph110100713 - 03 Jan 2014
Cited by 20 | Viewed by 9462
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)
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2185 KiB  
Article
Residential Mobility and Breast Cancer in Marin County, California, USA
by Geoffrey M. Jacquez, Janice Barlow, Robert Rommel, Andy Kaufmann, Michael Rienti, Jr., Gillian AvRuskin and Jawaid Rasul
Int. J. Environ. Res. Public Health 2014, 11(1), 271-295; https://doi.org/10.3390/ijerph110100271 - 23 Dec 2013
Cited by 11 | Viewed by 5733
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)
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1192 KiB  
Article
Spatial Autocorrelation of Cancer Incidence in Saudi Arabia
by Khalid Al-Ahmadi and Ali Al-Zahrani
Int. J. Environ. Res. Public Health 2013, 10(12), 7207-7228; https://doi.org/10.3390/ijerph10127207 - 16 Dec 2013
Cited by 56 | Viewed by 12272
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)
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940 KiB  
Article
NO2 and Cancer Incidence in Saudi Arabia
by Khalid Al-Ahmadi and Ali Al-Zahrani
Int. J. Environ. Res. Public Health 2013, 10(11), 5844-5862; https://doi.org/10.3390/ijerph10115844 - 04 Nov 2013
Cited by 50 | Viewed by 10716
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)
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1539 KiB  
Article
Spatially Interpolated Disease Prevalence Estimation Using Collateral Indicators of Morbidity and Ecological Risk
by Peter Congdon
Int. J. Environ. Res. Public Health 2013, 10(10), 5011-5025; https://doi.org/10.3390/ijerph10105011 - 14 Oct 2013
Cited by 3 | Viewed by 5420
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)
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Article
Mapping Disease at an Approximated Individual Level Using Aggregate Data: A Case Study of Mapping New Hampshire Birth Defects
by Xun Shi, Stephanie Miller, Kevin Mwenda, Akikazu Onda, Judy Rees, Tracy Onega, Jiang Gui, Margaret Karagas, Eugene Demidenko and John Moeschler
Int. J. Environ. Res. Public Health 2013, 10(9), 4161-4174; https://doi.org/10.3390/ijerph10094161 - 06 Sep 2013
Cited by 10 | Viewed by 7460
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)
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