This maps displays the composite outcomes for all 5 domains (Economic Stability, Education Access and Quality, Health Care Access and Quality, Social and Community Context, and Neighborhood / Built Environment), from the PLAN4Health - Miami Valley Health Environment Assessment project. Learn more about the PLAN4Health - Miami Valley Health Environment Assessment by following this link.

This dataset includes materials for an undergraduate learning activity focused on exploring the Social Determinants of Health (SDOH) through applied data analysis and mapping from Coastal Carolina University - Department of Nursing and Health Sciences. The download contains two files:a written assignment with step-by-step instructions, andan Excel file containing county-level health and SDOH data for South Carolina. The data were compiled from three sources (CDC PLACES, US Census Bureau's American Community Survey, Feeding America's Map the Meal Gap)Students use these materials to create maps, correlation matrices, and scatterplots in Microsoft Excel, enabling them to examine relationships between health outcomes and social factors such as poverty, education, and food access.

The Health Atlas for the City of Los Angeles 2021 presents a data-driven snapshot of health conditions and outcomes in the City of Los Angeles. It illustrates geographic variation in socio-economic conditions, demographic characteristics, the physical environment, and access to support systems and services, and provides a context for understanding how these factors contribute to the health of Angelenos.The data underscore a key issue: where Angelenos live often influences their health and well-being. Los Angeles is a city with great health disparities and the patterns of inequality are reflected in many of the indicators highlighted in the Health Atlas. The spatial characteristics of physical and social determinants of health have roots in structural racism and historic and ongoing discrimination. Historic policies such as redlining have had lasting effects in Los Angeles. The analysis is a first step in understanding the areas of the City burdened with the most adverse health-related conditions in order to improve health outcomes and environmental justice for all Angelenos.The Health Atlas contains 115 maps covering regional context, demographic and social characteristics, economic conditions, education, health conditions, land use, transportation, food systems, crime, housing, and environmental health. In addition to displaying US Census Bureau, City, County, and other data, the Health Atlas contains several indices to facilitate comparisons across the city on subjects including environmental hazards (Map 113: Pollution Burden Index), transportation quality (Map 84: Transportation Index), and economic conditions (Map 19: Hardship Index). The Health Atlas culminates in a Community Health and Equity Index (Maps 114 and 115) which combines many of the above variables into a single index to compare health conditions across the City of Los Angeles. The Community Health and Equity Index can be used to understand the areas of the city with the highest vulnerabilities and cumulative burdens as compared to other portions of the City.The Health Atlas for the City of Los Angeles was originally developed in 2013 as an early step in the process to develop a Health, Wellness, and Equity Element of the General Plan (also known as the Plan for a Healthy Los Angeles). This data set is an update of the Health Atlas, completed in 2021. The Health Element and both editions of the Health Atlas are available as PDFs on the Los Angeles City Planning website, https://planning.lacity.gov.

This maps displays the composite outcomes for all 5 domains (Economic Stability, Education Access and Quality, Health Care Access and Quality, Social and Community Context, and Neighborhood / Built Environment), from the PLAN4Health - Miami Valley Health Environment Assessment project. Learn more about the PLAN4Health - Miami Valley Health Environment Assessment by following this link.

Find Massachusetts health data by community, county, and region, including population demographics. Build custom data reports with over 100 health and social determinants of health data indicators and explore over 28,000 current and historical data layers in the map room.

This dataset contains ZCTA-level social determinants of health (SDOH) measures from the American Community Survey 5-year data for the entire United States—50 states and the District of Columbia. Data were downloaded from data.census.gov using Census API and processed by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. These measures complement existing PLACES measures, including PLACES SDOH measures (e.g., health insurance, routine check-up). These data can be used together with PLACES data to identify which health and SDOH issues overlap in a community to help inform public health planning. To access spatial data, please use the ArcGIS Online service: https://cdcarcgis.maps.arcgis.com/home/item.html?id=d51009ea78b54635be95c6ec9955ec17.

A series of story maps displaying Social Determinants of Health indicators and a combined index map.

The Health of the City report summarizes data on community health in Philadelphia through interactive charts and maps. The Health of the City table contains aggregate metrics on population statistics, social determinants of health, and health outcomes that were used to build this report.

This web mapping application runs from the web map New Mexico Food Pantries - MAP

This dataset contains place-level (incorporated and census-designated places) social determinants of health (SDOH) measures from the American Community Survey 5-year data for the entire United States—50 states and the District of Columbia. Data were downloaded from data.census.gov using Census API and processed by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. These measures complement existing PLACES measures, including PLACES SDOH measures (e.g., health insurance, routine check-up). These data can be used together with PLACES data to identify which health and SDOH issues overlap in a community to help inform public health planning. To access spatial data, please use the ArcGIS Online service: https://cdcarcgis.maps.arcgis.com/home/item.html?id=d51009ea78b54635be95c6ec9955ec17.

Publicly available geocoded social determinants of health and mobility datasets used in the analysis of "Chronic Acid Suppression and Social Determinants of COVID-19 Infection".These datasets are required for the analytical workflow shared on Github which demonstrates how the analysis in the manuscript was done using randomly generated samples to protect patient privacy.zcta_county_rel_10.txt - Population and housing density from the 2010 decennial census. Obtained from: https://www2.census.gov/geo/docs/maps-data/data/rel/zcta_county_rel_10.txtcre-2018-a11.csv - Community Resilience Estimates which is is the capacity of individuals and households to absorb, endure, and recover from the health, social, and economic impacts of a disaster such as a hurricane or pandemic. Data obtained from: https://www.census.gov/data/experimental-data-products/community-resilience-estimates.htmlzcta_tract_rel_10.txt - Relationship between ZCTA and US Census tracts (used to map census tracts to ZCTA). Data obtained from: https://www.census.gov/geographies/reference-files/time-series/geo/relationship-files.html#par_textimage_674173622mask-use-by-county.txt - Mask Use By County comes from a large number of interviews conducted online by the global data and survey firm Dynata at the request of The New York Times. The firm asked a question about mask use to obtain 250,000 survey responses between July 2 and July 14, enough data to provide estimates more detailed than the state level. Data obtained from: https://github.com/nytimes/covid-19-data/tree/master/mask-usemobility_report_US.txt - Google mobility report which charts movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential. Data obtained from: https://github.com/ActiveConclusion/COVID19_mobility/blob/master/google_reports/mobility_report_US.csvACS2015_zctaallvars.csv - Social Deprivation Index is a composite measure of area level deprivation based on seven demographic characteristics collected in the American Community Survey (https://www.census.gov/programs-surveys/acs/) and used to quantify the socio-economic variation in health outcomes. Factors are: Income, Education, Employment, Housing, Household Characteristics, Transportation, Demographics. Data obtained from: https://www.graham-center.org/rgc/maps-data-tools/sdi/social-deprivation-index.html

ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) created Centers for Disease Control and Prevention Social Vulnerability Index (CDC SVI or simply SVI, hereafter) to help public health officials and emergency response planners identify and map the communities that will most likely need support before, during, and after a hazardous event.

SVI indicates the relative vulnerability of every U.S. Census tract. Census tracts are subdivisions of counties for which the Census collects statistical data. SVI ranks the tracts on 15 social factors, including unemployment, minority status, and disability, and further groups them into four related themes. Thus, each tract receives a ranking for each Census variable and for each of the four themes, as well as an overall ranking.

In addition to tract-level rankings, SVI 2018 also has corresponding rankings at the county level. Notes below that describe “tract” methods also refer to county methods.

Additional historical data can be found here: https://www.atsdr.cdc.gov/placeandhealth/svi/data_documentation_download.html

This dataset contains census tract-level social determinants of health (SDOH) measures from the American Community Survey 5-year data for the entire United States—50 states and the District of Columbia. Data were downloaded from data.census.gov using Census API and processed by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. These measures complement existing PLACES measures, including PLACES SDOH measures (e.g., health insurance, routine check-up). These data can be used together with PLACES data to identify which health and SDOH issues overlap in a community to help inform public health planning.

To access spatial data, please use the ArcGIS Online service: https://cdcarcgis.maps.arcgis.com/home/item.html?id=d51009ea78b54635be95c6ec9955ec17.

IntroductionThe exposome concept provides a framework to better incorporate the environment into the study of health and disease and has been defined by academics to encompass all lifetime exposures including toxicants, diet, and lifestyle choices. However, initial applications of the exposome concept have been less apt at measuring social determinants of health, focusing primarily on conventional environmental exposures and lifestyle choices that do not reflect the complex lived experience of many communities. To bring community voice into the exposome concept, the HERCULES Exposome Research Center and its Stakeholder Advisory Board co-developed the Exposome Roadshow. We present and discuss the resulting community-exposome definition to inform and improve exposome research.Materials and MethodsFour communities from distinct areas across metro-Atlanta participated in separate 2-day Exposome Roadshow workshops with concept mapping. Aligned with a popular education approach in which community knowledge is used to work collectively for change, concept mapping provided a systematic method to collect and visualize community members' knowledge and create a shared understanding to take action. Community members brainstormed, sorted, and rated their responses to the prompt: “What in your environment is affecting your and your community's health?” Responses were analyzed and visually depicted by concept maps consisting of separate but interrelated clusters of ideas. Community members discussed and validated the maps, selecting a final map illustrating their community's exposome.ResultsA total of 118 community members completed concept mapping. On average communities identified 7 clusters to define their exposome. The resulting concept maps offer a community definition of the exposome. Five major themes arose across all four communities: conventional environmental concerns, built environment, social relationships, crime and safety, and individual health and behaviors.DiscussionThe resulting community-exposome definition demonstrates the importance of expanding the scope of exposures beyond traditional environmental influences to include the lived experience of individuals and communities. While newer exposome definitions align more closely with this community definition, traditional exposome methods do not routinely include these factors. To truly capture the totality of lifetime exposures and improve human health, researchers should incorporate community perspectives into exposome research.

This scoping review aims to map the extent and nature of existing scientific evidence on mental disorders in the Black population globally. Grounded in the framework of social determinants of health, the review understands race as a social construct and recognizes structural racism as a key factor shaping disparities in mental health. Studies addressing the experiences, diagnosis, treatment, or prevention of mental disorders among Black individuals of any age, gender identity, or geographical setting will be included, based on DSM or ICD classifications. Research from any country or language will be considered, incorporating diverse healthcare models and sociopolitical contexts influenced by systemic racism. This review seeks to identify knowledge gaps and inform equitable mental health strategies.The review will follow JBI methodology.

What is Social Vulnerability?Every community must prepare for and respond to hazardous events, whether a natural disaster like a tornado or a disease outbreak, or an anthropogenic event such as a harmful chemical spill. The degree to which a community exhibits certain social conditions, including high poverty, low percentage of vehicle access, or crowded households, among others, may affect that community’s ability to prevent human suffering and financial loss in the event of a disaster. These factors describe a community’s social vulnerability.What is the CDC/ATSDR Social Vulnerability Index?ATSDR’s Geospatial Research, Analysis, & Services Program (GRASP) created the Centers for Disease Control and Prevention and Agency for Toxic Substances and Disease Registry Social Vulnerability Index (hereafter, CDC/ATSDR SVI or SVI) to help public health officials and emergency response planners identify and map the communities that will most likely need support before, during, and after a hazardous event.SVI indicates the relative vulnerability of every U.S. census tract. Census tracts are subdivisions of counties for which the Census collects statistical data. SVI ranks the tracts on 16 social factors, such as unemployment, racial and ethnic minority status, and disability status. Then, SVI further groups the factors into four related themes. Thus, each tract receives a ranking for each Census variable and for each of the four themes as well as an overall ranking.Below, text that describes “tract” methods also refers to county methods.How can the SVI help communities be better prepared for hazardous events?SVI provides specific socially and spatially relevant information to help public health officials and local planners better prepare communities to respond to emergency events such as severe weather, floods, disease outbreaks, or chemical exposure.SVI can be used to:Assess community need during emergency preparedness planning.Estimate the type and quantity of needed supplies such as food, water, medicine, and bedding.Decide the number of emergency personnel required to assist people.Identify areas in need of emergency shelters.Create a plan to evacuate people, accounting for those who have special needs, such as those without vehicles, the elderly, or people who do not speak English well.Identify communities that will need continued support to recover following an emergency or natural disaster.For more detailed methodology and attribute details, please review this document.

Abstract

MEJ aims to create easy-to-use, publicly-available maps that paint a holistic picture of intersecting environmental, social, and health impacts experienced by communities across the US.

With guidance from the residents of impacted communities, MEJ combines environmental, public health, and demographic data into an indicator of vulnerability for communities in every state. MEJ’s goal is to fill an existing data gap for individual states without environmental justice mapping tools, and to provide a valuable tool for advocates, scholars, students, lawyers, and policy makers.

Methodology

The negative effects of pollution depend on a combination of vulnerability and exposure. People living in poverty, for example, are more likely to develop asthma or die due to air pollution. The method MEJ uses, following the method developed for CalEnviroScreen, reflects this in the two overall components of a census tract’s final “Cumulative EJ Impact”: population characteristics and pollution burden. The CalEnviroScreen methodology was developed through an intensive, multi-year effort to develop a science-backed, peer-reviewed tool to assess environmental justice in a holistic way, and has since been replicated by several other states.

CalEnviroScreen Methodology:

• Population characteristics are a combination of socioeconomic data (often referred to as the social determinants of health) and health data that together reflect a populations' vulnerability to pollutants. Pollution burden is a combination of direct exposure to a pollutant and environmental effects, which are adverse environmental conditions caused by pollutants, such as toxic waste sites or wastewater releases. Together, population characteristics and pollution burden help describe the disproportionate impact that environmental pollution has on different communities.

• Every indicator is ranked as a percentile from 0 to 100 and averaged with the others of the same component to form an overall score for that component. Each component score is then percentile ranked to create a component percentile. The Sensitive Populations component score, for example, is the average of a census tract’s Asthma, Low Birthweight Infants, and Heart Disease indicator percentiles, and the Sensitive Populations component percentile is the percentile rank of the Sensitive Populations score.

• The Population Characteristics score is the average of the Sensitive Populations component score and the Socioeconomic Factors component score. The Population Characteristics percentile is the percentile rank of the Population Characteristics score.

• The Pollution Burden score is the average of the Pollution Exposure component score and one half of the Environmental Effects component score (Environmental Effects may have a smaller effect on health outcomes than the indicators included the Exposures component so are weighted half as much as Exposures). The Pollution Burden percentile is the percentile rank of the Pollution Burden score.

• The Populaton Characteristics and Pollution Burden scores are then multiplied to find the final Cumulative EJ Impact score for a census tract, and then this final score is percentile-ranked to find a census tract's final Cumulative EJ Impact percentile.

• Census tracts with no population aren't given a Population Characteristics score.

• Census tracts with an indicator score of zero are assigned a percentile rank of zero. Percentile rank is then only calculated for those census tracts with a score above zero.

• Census tracts that are missing data for more than two indicators don't receive a final Cumulative EJ Impact ranking.

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Health outcomes are influenced by social and environmental determinants of health. As places where people work, live, meet and consume, high street retail environments are influential in shaping health. In recent decades, high streets have been in decline, prompting policies to revitalise retail environments and support local businesses, particularly in European and North American countries. The aim of this scoping review was to systematically map evidence on retail environment interventions, to gain a deeper understanding of the current evidence base assessing their possible health and wellbeing impacts. The objectives were to identify different types of interventions and the outcomes they address; and the mechanism through which interventions are theorised to influence health and equity. Peer-reviewed studies were identified through academic databases (MEDLINE, Embase, EconLit, Web of Science and Social Policy and Practice) using relevant search terms. Additional (grey) literature was identified using citation scanning and online searches. Studies were eligible if they evaluated interventions with a significant focus on supporting the retail environment, reported on at least one health and wellbeing outcome and were written in English. Relevant data were extracted and presented descriptively. An interpretive approach was taken to analyse theories of change. The searches identified 53 peer-reviewed studies and nine grey literature reports. Interventions were categorised as follows: area-based initiatives, business improvement districts, business incentives, and demand-side incentives. Studies predominantly evaluated impacts on social and environmental determinants of health. Some studies measured impacts on self-rated (mental) health, physical activity and food consumption and purchasing. Studies reported evidence of both improved and worsening outcomes. Theories of change were often under-specified and reductionist, lacking a clear understanding of the complex systems in which interventions take place. Future interventions could benefit from more comprehensive theories of change that meaningfully integrate economic, and health and wellbeing outcomes. This requires intersectoral collaboration.

The documentation below is in reference to this items placement in the NM Supply Chain Data Hub. The documentation is of use to understanding the source of this item, and how to reproduce it for updatesTitle: NMSU County Cooperative Extension Offices Map, as of 7/27/2017Item Type: PDFSummary: NMSU County Cooperative Extension Offices Map- Data Revised 7/27/2017 - Data Posted online 3/29/2022Notes: Prepared by: Uploaded by EMcRae_NMCDCSource: New Mexico State University 4H ProgramFeature Service: https://nmcdc.maps.arcgis.com/home/item.html?id=c62d7c0ee35444e09f76c092b24ab75dUID: 3Data Requested: NMSU County Extension - # of agents, service areaMethod of Acquisition: Data is publicly posted online for downloadDate Acquired: May 2022Priority rank as Identified in 2022 (scale of 1 being the highest priority, to 11 being the lowest priority): 7Tags: PENDING

This dataset contains county-level social determinants of health (SDOH) measures from the American Community Survey 5-year data for the entire United States—50 states and the District of Columbia. Data were downloaded from data.census.gov using Census API and processed by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. These measures complement existing PLACES measures, including PLACES SDOH measures (e.g., health insurance, routine check-up). These data can be used together with PLACES data to identify which health and SDOH issues overlap in a community to help inform public health planning.

To access spatial data, please use the ArcGIS Online service: https://cdcarcgis.maps.arcgis.com/home/item.html?id=d51009ea78b54635be95c6ec9955ec17.