A Framework for Widespread Replication of a Highly Spatially Resolved Childhood Lead Exposure Risk Model

Background Preventive approaches to childhood lead poisoning are critical for addressing this longstanding environmental health concern. Moreover, increasing evidence of cognitive effects of blood lead levels < 10 μg/dL highlights the need for improved exposure prevention interventions. Objectives Geographic information system–based childhood lead exposure risk models, especially if executed at highly resolved spatial scales, can help identify children most at risk of lead exposure, as well as prioritize and direct housing and health-protective intervention programs. However, developing highly resolved spatial data requires labor-and time-intensive geocoding and analytical processes. In this study we evaluated the benefit of increased effort spent geocoding in terms of improved performance of lead exposure risk models. Methods We constructed three childhood lead exposure risk models based on established methods but using different levels of geocoded data from blood lead surveillance, county tax assessors, and the 2000 U.S. Census for 18 counties in North Carolina. We used the results to predict lead exposure risk levels mapped at the individual tax parcel unit. Results The models performed well enough to identify high-risk areas for targeted intervention, even with a relatively low level of effort on geocoding. Conclusions This study demonstrates the feasibility of widespread replication of highly spatially resolved childhood lead exposure risk models. The models guide resource-constrained local health and housing departments and community-based organizations on how best to expend their efforts in preventing and mitigating lead exposure risk in their communities

Spatial autocorrelation analysis of health care hotspots in Taiwan in 2006

<p>Abstract</p> <p>Background</p> <p>Spatial analytical techniques and models are often used in epidemiology to identify spatial anomalies (hotspots) in disease regions. These analytical approaches can be used to not only identify the location of such hotspots, but also their spatial patterns.</p> <p>Methods</p> <p>In this study, we utilize spatial autocorrelation methodologies, including Global Moran's I and Local Getis-Ord statistics, to describe and map spatial clusters, and areas in which these are situated, for the 20 leading causes of death in Taiwan. In addition, we use the fit to a logistic regression model to test the characteristics of similarity and dissimilarity by gender.</p> <p>Results</p> <p>Gender is compared in efforts to formulate the common spatial risk. The mean found by local spatial autocorrelation analysis is utilized to identify spatial cluster patterns. There is naturally great interest in discovering the relationship between the leading causes of death and well-documented spatial risk factors. For example, in Taiwan, we found the geographical distribution of clusters where there is a prevalence of tuberculosis to closely correspond to the location of aboriginal townships.</p> <p>Conclusions</p> <p>Cluster mapping helps to clarify issues such as the spatial aspects of both internal and external correlations for leading health care events. This is of great aid in assessing spatial risk factors, which in turn facilitates the planning of the most advantageous types of health care policies and implementation of effective health care services.</p

Does the choice of neighbourhood supermarket access measure influence associations with individual-level fruit and vegetable consumption? A case study from Glasgow

BackgroundPrevious studies have provided mixed evidence with regards to associations between food store access and dietary outcomes. This study examines the most commonly applied measures of locational access to assess whether associations between supermarket access and fruit and vegetable consumption are affected by the choice of access measure and scale.MethodSupermarket location data from Glasgow, UK (n = 119), and fruit and vegetable intake data from the \u27Health and Well-Being\u27 Survey (n = 1041) were used to compare various measures of locational access. These exposure variables included proximity estimates (with different points-of-origin used to vary levels of aggregation) and density measures using three approaches (Euclidean and road network buffers and Kernel density estimation) at distances ranging from 0.4 km to 5 km. Further analysis was conducted to assess the impact of using smaller buffer sizes for individuals who did not own a car. Associations between these multiple access measures and fruit and vegetable consumption were estimated using linear regression models.ResultsLevels of spatial aggregation did not impact on the proximity estimates. Counts of supermarkets within Euclidean buffers were associated with fruit and vegetable consumption at 1 km, 2 km and 3 km, and for our road network buffers at 2 km, 3 km, and 4 km. Kernel density estimates provided the strongest associations and were significant at a distance of 2 km, 3 km, 4 km and 5 km. Presence of a supermarket within 0.4 km of road network distance from where people lived was positively associated with fruit consumption amongst those without a car (coef. 0.657; s.e. 0.247; p0.008).ConclusionsThe associations between locational access to supermarkets and individual-level dietary behaviour are sensitive to the method by which the food environment variable is captured. Care needs to be taken to ensure robust and conceptually appropriate measures of access are used and these should be grounded in a clear a priori reasoning

Measuring socioeconomic and geographic deprivation to healthcare: development of a Missouri Deprivation Index

This study reviews the development and use of socioeconomic deprivation indices in health-related research and the variables and methods used in the construction of some of those indices. Relevant literature is summarized and the most significant methodological contributions to the topic are further described. Those methods are then closely adapted and used to create a block group level composite index of socioeconomic deprivation for the state of Missouri. In addition to the construction of the Missouri Deprivation Index (MDI), spatial analyses of access to emergency healthcare services in Missouri are performed, access scores for each block group in Missouri are generated and estimates of the population residing in those access areas are calculated. The access component is then incorporated into the deprivation index and used to explore the extent to which access may influence or be associated with measures of deprivation. This research will contribute to the literature by providing a background and a framework for the use of socioeconomic deprivation indices as potential explanatory variables in future research.Includes bibliographical references

Measuring community resilience to disaster

"May 2014."Thesis supervisor: Dr. Timothy Matisziw.Although geographic studies of disaster vulnerability and resilience have been central to the formulation of federal emergency management policy, recent community resilience research has diverged significantly from the core foci of the discipline: the importance of place, of scale, and the complexity of human-environment interactions. Three disconcerting trends in the literature can be observed. First, there has been a heavy reliance on the tools of linear systems science to characterize and measure the human dimensions of resilience - dimensions which are increasingly examined in terms of their nonlinearity, dynamism and complexity in other scientific disciplines. Second, most of the variables typically used as proxies for community resilience are not actually indicative of community-scale processes, but rather describe individual-scale behavioral and household-scale socioeconomic characteristics. Third, the current practice of aggregating resilience indicators to large, heterogeneous geographic areas in order to communicate community-level resilience can actually mask and mischaracterize the local, place-specific variability of those indicators. This thesis presents a rethinking of geography's conceptual model of population disaster resilience and the methods used to measure it at the community level. Drawing on diverse theoretical linkages on the subject from across the social and natural sciences, and on the current perspectives and information requirements of local emergency managers, a more holistic and meaningful approach to measuring community resilience is proposed. Specifically, in recognition of a need to integrate both expert and lay local perspectives into resilience calculations, a system for assimilating such qualitative data into quantitative analysis is adapted from complexity theory. Also, in acknowledgement of the multiple levels at which resilience-building processes may operate in human systems, and the unique ways disaster resilience can manifest in different places, a new framework for balIncludes bibliographical references (pages 158-173)

Using GIS to link SEER-Medicare and California pesticide data: a population-based case-control study of pesticide exposure and hepatocellular carcinoma risk

Geographic information systems (GIS), used to analyze spatial data, represent a powerful method to study human health. This research demonstrates the usage of GIS in (1) designing a pesticide exposure metric and (2) linking population-based data sources to conduct an epidemiologic study examining the association between pesticide exposure and hepatocellular carcinoma (HCC). The first study presents a new GIS method to estimate individual-level agricultural pesticide exposure in California. Landsat remotely sensed satellite images were classified into crop fields and matched to California Pesticide Use Report (PUR) agricultural pesticide application data. Pesticide exposure was calculated using pesticide-treated crop fields intersecting a 500-meter buffer around geocoded locations. Compared to the standard GIS method of matching PUR data to infrequently updated crop land use surveys (LUS’s), our method was able to match significantly more PUR temporary crop pesticide applications to Landsat vs. LUS crops (65.4% vs. 52.4%; n=2,466; McNemar’s p<0.0001). The second study explored different ways of scaling up Public Land Survey System (PLSS) section pesticide data, the geographic level of reporting for PURs, to the ZIP Code level. We observed substantial agreement between area-weighted ZIP Code pesticide application rates and gold standard census block rates in rural areas (weighted kappa 0.63; 95% confidence interval [CI] 0.57, 0.69). Area weighting was used to estimate pesticide exposure in the third study. The third (and primary) study was a population-based case-control study examining the association between agricultural pesticide exposure and hepatocellular carcinoma in California via implementing a novel data linkage between Surveillance, Epidemiology, and End Results (SEER)-Medicare and PURs using Medicare ZIP Codes in a GIS. Among rural California residents, previous annual ZIP Code exposure to over 0.06 applied organochlorine pounds per acre significantly increased the risk of developing HCC after adjusting for liver disease and diabetes (odds ratio 1.52; 95% CI 1.02, 2.28; p=0.0415). This is the first epidemiologic study using GIS to examine pesticide exposure and HCC. The public health significance of this research is related to using epidemiologic, GIS, and biostatistical methods to form a better understanding of pesticides as a potential risk factor for HCC, which is increasing in incidence