Choropleth map legend design for visualizing community health disparities

<p>Abstract</p> <p>Background</p> <p>Disparities in health outcomes across communities are a central concern in public health and epidemiology. Health disparities research often links differences in health outcomes to other social factors like income. Choropleth maps of health outcome rates show the geographical distribution of health outcomes. This paper illustrates the use of cumulative frequency map legends for visualizing how the health events are distributed in relation to social characteristics of community populations. The approach uses two graphs in the cumulative frequency legend to highlight the difference between the raw count of the health events and the raw count of the social characteristic like low income in the geographical areas of the map. The approach is applied to mapping publicly available data on low birth weight by town in Connecticut and Lyme disease incidence by town in Connecticut in relation to income. The steps involved in creating these legends are described in detail so that health analysts can adopt this approach.</p> <p>Results</p> <p>The different health problems, low birth weight and Lyme disease, have different cumulative frequency signatures. Graphing poverty population on the cumulative frequency legends revealed that the poverty population is distributed differently with respect to the two different health problems mapped here.</p> <p>Conclusion</p> <p>Cumulative frequency legends can be useful supplements for choropleth maps. These legends can be constructed using readily available software. They contain all of the information found in standard choropleth map legends, and they can be used with any choropleth map classification scheme. Cumulative frequency legends effectively communicate the proportion of areas, the proportion of health events, and/or the proportion of the denominator population in which the health events occurred that falls within each class interval. They illuminate the context of disease through graphing associations with other variables.</p

A Geographical Analysis of HIV/AIDS Infection in Nigeria, 1991-2001

Abstract Objectives: Within the gradually accumulating literature on the Acquired Immune Deficiency Syndrome (AIDS) in Nigeria, investigations concerning the spatial dimensions of the infection are virtually non-existent. An understanding of the spatial dimensions of the epidemic is central in the development and implementation of appropriate intervention strategies. This study is a geographic analysis of HIV/AIDS infection in Nigeria from 1991 – 2001. The three objectives of this study were: 1) to examine the geographic pattern of the epidemic, 2) to examine the spatial-temporal trend and diffusion pattern of the epidemic, and 3) to explore the factors associated with the spatial (geographic) variation of the infection. Methods: Data was spruced from HIV/AIDS sentinel surveys conducted in Nigeria 1991-2001 and the 1999 Nigeria National Demographic and Health Survey. Data analyses involved descriptive cartographic analysis, spatial autocorrelation analysis, spatial-temporal analysis and comparative data analysis. Results:- Cartographic analysis revealed distinctive regional differences in the spatial pattern and intensity of HIV infection within the country. Spatial autocorrelation analyses indicated that HIV/AIDS rates were strongly autocorrelated. The epidemic\u27s epicenter was located in a narrow contiguous band bypassed by a major highway. The diffusion processes indicated a general trend of increasing spread to rural Nigeria. Conclusion: This study provided one of the first in-depth geographic analyses of the HIV infection in Nigeria in the first decade of the epidemic. More detailed and comprehensive HIV/AIDS data is required for further study of the spatial epidemiology of the infection

Optimal location of centralized biodigesters for small dairy farms: A case study from the United States

Anaerobic digestion technology is available for converting livestock waste to bio-energy, but its potential is far from fully exploited in the United States because the technology has a scale effect. Utilization of the centralized anaerobic digester (CAD) concept could make the technology economically feasible for smaller dairy farms. An interdisciplinary methodology to determine the cost minimizing location, size, and number of CAD facilities in a rural dairy region with mostly small farms is described. This study employs land suitability analysis, operations research model and Geographical Information System (GIS) tools to evaluate the environmental, social, and economic constraints in selecting appropriate sites for CADs in Windham County, Connecticut. Results indicate that overall costs are lower if the CADs are of larger size and are smaller in number

BioMed Central Methodology

which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background: Disparities in health outcomes across communities are a central concern in public health and epidemiology. Health disparities research often links differences in health outcomes to other social factors like income. Choropleth maps of health outcome rates show the geographical distribution of health outcomes. This paper illustrates the use of cumulative frequency map legends for visualizing how the health events are distributed in relation to social characteristics of community populations. The approach uses two graphs in the cumulative frequency legend to highlight the difference between the raw count of the health events and the raw count of the social characteristic like low income in the geographical areas of the map. The approach is applied to mapping publicly available data on low birth weight by town in Connecticut and Lyme disease incidence by town in Connecticut in relation to income. The steps involved in creating these legends are described in detail so that health analysts can adopt this approach. Results: The different health problems, low birth weight and Lyme disease, have different cumulative frequency signatures. Graphing poverty population on the cumulative frequency legend