Cancer

Maps are well recognized as an effective means of presenting and communicating health data, such as cancer incidence and mortality rates. These data can be linked to geographic features like counties or census tracts and their associated attributes for mapping and analysis. Such visualization and analysis provide insights regarding the geographic distribution of cancer and can be important for advancing effective cancer prevention and control programs. Applying a spatial approach allows users to identify location-based patterns and trends related to risk factors, health outcomes, and population health. Geographic information science (GIScience) is the discipline that applies Geographic Information Systems (GIS) and other spatial concepts and methods in research. This review explores the current state and evolution of GIScience in cancer research by addressing fundamental topics and issues regarding spatial data and analysis that need to be considered. GIScience, along with its health-specific application in the spatial epidemiology of cancer, incorporates multiple geographic perspectives pertaining to the individual, the health care infrastructure, and the environment. Challenges addressing these perspectives and the synergies among them can be explored through GIScience methods and associated technologies as integral parts of epidemiologic research, analysis efforts, and solutions. The authors suggest GIScience is a powerful tool for cancer research, bringing additional context to cancer data analysis and potentially informing decision-making and policy, ultimately aimed at reducing the burden of cancer.CC999999/ImCDC/Intramural CDC HHS/United States2019-08-01T00:00:00Z31145834PMC66259158081vault:3254

Treatment-seeking rates in malaria endemic countries

BACKGROUND: The proportion of individuals who seek treatment for fever is an important quantity in understanding access to and use of health systems, as well as for interpreting data on disease incidence from routine surveillance systems. For many malaria endemic countries (MECs), treatment-seeking information is available from national household surveys. The aim of this paper was to assemble sub-national estimates of treatment-seeking behaviours and to predict national treatment-seeking measures for all MECs lacking household survey data. METHODS: Data on treatment seeking for fever were obtained from Demographic and Health Surveys, Malaria Indicator Surveys and Multiple Cluster Indicator Surveys for every MEC and year that data were available. National-level social, economic and health-related variables were gathered from the World Bank as putative covariates of treatment-seeking rates. A generalized additive mixed model (GAMM) was used to estimate treatment-seeking behaviours for countries where survey data were unavailable. Two separate models were developed to predict the proportion of fever cases that would seek treatment at (1) a public health facility or (2) from any kind of treatment provider. RESULTS: Treatment-seeking data were available for 74 MECs and modelled for the remaining 24. GAMMs found that the percentage of pregnant women receiving prenatal care, vaccination rates, education level, government health expenditure, and GDP growth were important predictors for both categories of treatment-seeking outcomes. Treatment-seeking rates, which varied both within and among regions, revealed that public facilities were not always the primary facility type used. CONCLUSIONS: Estimates of treatment-seeking rates show how health services are utilized and help correct reported malaria case numbers to obtain more accurate measures of disease burden. The assembled and modelled data demonstrated that while treatment-seeking rates have overall increased over time, access remains low in some malaria endemic regions and utilization of government services is in some areas limited

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

Comprehensive Strategic Plan To Reduce and Ultimately Eliminate Health Disparities

Despite progress in biomedical science over the past several decades that has increased longevity and improved quality of life for many in our Nation, a heavier burden of disease continues to be borne by some populations, particularly minorities, the poor and underserved. For example, the death rate from prostate cancer among African American men is almost twice that of white men, and stomach cancer mortality is substantially higher among Asian-Pacific Islanders, including Native Hawaiians, than other populations. Cervical cancer incidence in Hispanic women has been consistently higher at all ages than for other women, and African American women have the highest death rate from cervical cancer. Overall, men are about 50 percent more likely than women to die from cancer, and among all women, Alaskan Natives are about 30 percent more likely to die from cancer. It is these disturbing statistics coupled with the fact that reductions in cancer incidence and mortality are occurring in many, but not all, sectors of our Nation, that prompts NCI to examine major determinants of cancer health disparities (e.g., poverty, culture, and social injustice). It is the interrelationship among these factors that must be carefully weaved into the cancer research agenda in order to remedy the unequal burden of cancer

Exploring Environmental and Geographical Factors Influencing the Spread of Infectious Diseases with Interactive Maps

Publisher Copyright: © 2022 by the authors.Environmental problems due to human activities such as deforestation, urbanisation, and large scale intensive farming are some of the major factors behind the rapid spread of many infectious diseases. This in turn poses significant challenges not only in as regards providing adequate healthcare, but also in supporting healthcare workers, medical researchers, policy makers, and others involved in managing infectious diseases. These challenges include surveillance, tracking of infections, communication of public health knowledge and promotion of behavioural change. Behind these challenges lies a complex set of factors which include not only biomedical and population health determinants but also environmental, climatic, geographic, and socioeconomic variables. While there is broad agreement that these factors are best understood when considered in conjunction, aggregating and presenting diverse information sources requires effective information systems, software tools, and data visualisation. In this article, weargue that interactive maps, which couple geographical information systems and advanced information visualisation techniques, provide a suitable unifying framework for coordinating these tasks. Therefore, we examine how interactive maps can support spatial epidemiological visualisation and modelling involving distributed and dynamic data sources and incorporating temporal aspects of disease spread. Combining spatial and temporal aspects can be crucial in such applications. We discuss these issues in the context of support for disease surveillance in remote regions, utilising tools that facilitate distributed data collection and enable multidisciplinary collaboration, while also providing support for simulation and data analysis. We show that interactive maps deployed on a combination of mobile devices and large screens can provide effective means for collection, sharing, and analysis of health data.Peer reviewe

Cartografías de la COVID-19 y divisiones funcionales del territorio: un análisis de la evolución de la pandemia basada en las Zonas Básicas de Salud (ZBS) en Castilla y León (España)

In the face of the confusion and uncertainty that COVID-19 has caused over the last year, Geography has proven to be a useful aid in the interpretation of the spatial dynamics that explain the transmission of the virus. Applied cartography and GIS analysis of epidemiological data have been consolidated as essential tools for interpreting the health crisis. This paper explores the usefulness of maps for the study of the evolution of the pandemic in Castile and Leon, one of the Spanish regions with the highest levels of infection and mortality. Based on the statistical variables of sick and dead people at the scale of the Basic Health Area (BHA), a first analytical approach is carried out by means of a sequence of dynamic maps during the first wave. Afterwards, a systematic study is carried out using thematic mapping for the period of the three waves, a period between March 2020 and March 2021. The analysis unravels the differential impact of the disease between rural and urban areas and reveals the problems of the mismatch between the functional divisions of the territory (BHA, as units of health analysis) and the scale of administrative management (municipalities, as the effective scale of action).Ante el desconcierto y el desconocimiento generado en el último año por la COVID-19, la Geografía ha demostrado su utilidad para la interpretación de las dinámicas espaciales que explican la transmisión del virus. La cartografía aplicada y el análisis de datos epidemiológicos mediante SIG se han consolidado como herramientas esenciales para interpretar la crisis sanitaria. Este trabajo explora la utilidad de los mapas para el estudio de la evolución de la pandemia en Castilla y León, una de las regiones españolas con mayores niveles de contagio y mortalidad. A partir de las variables estadísticas de enfermos y fallecidos en la escala de la Zona Básica de Salud (ZBS), se efectúa una primera aproximación analítica mediante una secuencia de mapas dinámicos durante la primera ola. Posteriormente, se realiza un estudio sistemático mediante cartografía temática para las tres olas, entre marzo de 2020 y marzo de 2021. El análisis muestra el impacto diferencial de la enfermedad entre espacios rurales y núcleos urbanos y revela los problemas del desajuste entre las divisiones funcionales del territorio (ZBS, como unidades de análisis sanitario) y la escala de la gestión administrativa (municipios, como escala efectiva de actuación)

Using Spatial Visualization Software to Influence Cancer Control Policy: A Case Study of Prostate Cancer in South Carolina.

Prostate cancer in the United States shows great disparities among race and socioeconomic status. Disparities in cancer rates in South Carolina are severe. Cancer control policies are lacking in ways to identify reasons for high risk populations and cost-effective ways to do so. An innovative spatial visualization program called the GeoViz Toolkit was used to determine areas of high Prostate Cancer incidence and mortality in South Carolina (rates obtained from the South Carolina Central Cancer Registry) compared with socioeconomic variables (education, income, lack of health insurance, and living in rural areas) and race. From there, recommendations were made using the South Carolina Cancer Alliance\u27s South Carolina Comprehensive Cancer Control Plan objectives for Prostate Cancer for the top counties that were determined to have the highest need of intervention. These 11 counties include Colleton, Hampton, Allendale, Barnwell, Fairfield, Dillon, Marion, Marlboro, Williamsburg, Bamberg, and Orangeburg

Comprehensive Strategic Plan To Reduce and Ultimately Eliminate Health Disparities

Despite progress in biomedical science over the past several decades that has increased longevity and improved quality of life for many in our Nation, a heavier burden of disease continues to be borne by some populations, particularly minorities, the poor and underserved. For example, the death rate from prostate cancer among African American men is almost twice that of white men, and stomach cancer mortality is substantially higher among Asian-Pacific Islanders, including Native Hawaiians, than other populations. Cervical cancer incidence in Hispanic women has been consistently higher at all ages than for other women, and African American women have the highest death rate from cervical cancer. Overall, men are about 50 percent more likely than women to die from cancer, and among all women, Alaskan Natives are about 30 percent more likely to die from cancer. It is these disturbing statistics coupled with the fact that reductions in cancer incidence and mortality are occurring in many, but not all, sectors of our Nation, that prompts NCI to examine major determinants of cancer health disparities (e.g., poverty, culture, and social injustice). It is the interrelationship among these factors that must be carefully weaved into the cancer research agenda in order to remedy the unequal burden of cancer.http://www.ncmhd.nih.gov/strategicmock/our_programs/strategic/pubs/NCI.pd

The Spatial Distribution of Cancer Incidence in Fars Province: A GIS-Based Analysis of Cancer Registry Data

Background: Cancer is a major health problem in the developing countries. Variations of its incidence rate among geographical areas are due to various contributing factors. This study was performed to assess the spatial patterns of cancer incidence in the Fars Province, based on cancer registry data and to determine geographical clusters. Methods: In this cross sectional study, the new cases of cancer were recorded from 2001 to 2009. Crude incidence rate was estimated based on age groups and sex in the counties of the Fars Province. Age standardized incidence rates (ASR) per 100,000 was calculated in each year. Spatial autocorrelation analysis was performed in measuring the geographic patterns and clusters using geographic information system (GIS). Also, comparisons were made between ASRs in each county. Results: A total of 28,411 new cases were diagnosed with cancer during 2001 2009 in the Fars Province, 55.5% of which were men. The average age was 61.6 ± 0.5 years. The highest ASR was observed in Shiraz, which is the largest county in Fars. The Moran\u27s Index of cancer was significantly clustered in 2004, 2005, and 2006 in total, men, and women. The type of spatial clustering was high high cluster, that to indicate from north west to south east of Fars Province. Conclusions: Analysis of the spatial distribution of cancer shows significant differences from year to year and between different areas. However, a clear spatial autocorrelation is observed, which can be of great interest and importance to researchers for future epidemiological studies, and to policymakers for applying preventive measures

Exploring Neighborhood Influences on Small-Area Variations in Intimate Partner Violence Risk: A Bayesian Random-Effects Modeling Approach

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