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This data is Census Collection Districts (CD) based Socio-Economic Indexes for Areas (SEIFA) Index of Disadvantage (IRSD) - focuses on low-income earners, relatively lower educational attainment, high unemployment and variables reflecting disadvantage. This data is based on the 2006 census and follows the 2006 Australian Standard Geographical Classification (ASGC) boundaries. The Australian Bureau of Statistics (ABS) has developed indexes to allow ranking of regions/areas, providing a method of determining the level of social and economic wellbeing in that region. There are four indexes included in the SEIFA 2006 product. They relate to socio-economic aspects of geographic areas. Each index summarises a different aspect of the socio-economic conditions in an area. The indexes have been obtained by a technique called principal components analysis. This technique summarises the information from a variety of social and economic variables, calculating weights that will give the best summary for the underlying variables. For the SEIFA indexes, each index uses a different set of underlying variables. All the indexes (including the Index of Relative Socio-Economic Disadvantage) have been constructed so that relatively disadvantaged areas (e.g. areas with many low income earners) have low index values. This data is ABS data (catalogue number: 2033.0.55.001) used with permission from the Australian Bureau of Statistics.

This data is Census Collection Districts (CD) based Socio-Economic Indexes for Areas (SEIFA) Index of Advantage/Disadvantage (IRSAD) - Is a continuum of advantage to disadvantage. Low values indicate areas of disadvantage; and high values indicate areas of advantage. This data is based on the 2006 census and follows the 2006 Australian Standard Geographical Classification (ASGC) boundaries. The Australian Bureau of Statistics (ABS) has developed indexes to allow ranking of regions/areas, providing a method of determining the level of social and economic wellbeing in that region. There are four indexes included in the SEIFA 2006 product. They relate to socio-economic aspects of geographic areas. Each index summarises a different aspect of the socio-economic conditions in an area. The indexes have been obtained by a technique called principal components analysis. This technique summarises the information from a variety of social and economic variables, calculating weights that will give the best summary for the underlying variables. For the SEIFA indexes, each index uses a different set of underlying variables. All the indexes (including the Index of Relative Socio-Economic Disadvantage) have been constructed so that relatively disadvantaged areas (e.g. areas with many low income earners) have low index values. This data is ABS data (catalogue number: 2033.0.55.001) used with permission from the Australian Bureau of Statistics.

FY2024 full and partial census tracts that qualify as Low-Moderate Income Areas (LMA) where 51% or more of the population are considered as having Low-Moderate Income. The low- and moderate-income summary data (LMISD) is based on the 2016-2020 American Community Survey (ACS). As of August 1, 2024, to qualify any new low- and moderate-income area (LMA) activities, Community Development Block Grant (CDBG) grantees should use this map and data.

For more information about LMA/LMI click the following link to open in new browser tab https://www.hudexchange.info/programs/cdbg/cdbg-low-moderate-income-data/

This data is Statistical Local Areas (SLA) based Socio-Economic Indexes for Areas (SEIFA) Index of Disadvantage (IRSD) - focuses on low-income earners, relatively lower educational attainment, high …Show full descriptionThis data is Statistical Local Areas (SLA) based Socio-Economic Indexes for Areas (SEIFA) Index of Disadvantage (IRSD) - focuses on low-income earners, relatively lower educational attainment, high unemployment and variables reflecting disadvantage. This data is based on the 2006 census and follows the 2006 Australian Standard Geographical Classification (ASGC) boundaries. The Australian Bureau of Statistics (ABS) has developed indexes to allow ranking of regions/areas, providing a method of determining the level of social and economic wellbeing in that region. There are four indexes included in the SEIFA 2006 product. They relate to socio-economic aspects of geographic areas. Each index summarises a different aspect of the socio-economic conditions in an area. The indexes have been obtained by a technique called principal components analysis. This technique summarises the information from a variety of social and economic variables, calculating weights that will give the best summary for the underlying variables. For the SEIFA indexes, each index uses a different set of underlying variables. All the indexes (including the Index of Relative Socio-Economic Disadvantage) have been constructed so that relatively disadvantaged areas (e.g. areas with many low income earners) have low index values. This data is ABS data (catalogue number: 2033.0.55.001) used with permission from the Australian Bureau of Statistics. For more information on this data please visit the Australian Bureau of Statistics. Please note: AURIN has spatially enabled the original data following the 2006 ASGC. Copyright attribution: Government of the Commonwealth of Australia - Australian Bureau of Statistics, (2008): ; accessed from AURIN on 12/16/2021. Licence type: Creative Commons Attribution 4.0 International (CC BY 4.0)

This layer shows census tracts that meet the following definitions: Census tracts with median household incomes at or below 80 percent of the statewide median income or with median household incomes at or below the threshold designated as low income by the Department of Housing and Community Development’s list of state income limits adopted under Healthy and Safety Code section 50093 and/or Census tracts receiving the highest 25 percent of overall scores in CalEnviroScreen 4.0 or Census tracts lacking overall scores in CalEnviroScreen 4.0 due to data gaps, but receiving the highest 5 percent of CalEnviroScreen 4.0 cumulative population burden scores or Census tracts identified in the 2017 DAC designation as disadvantaged, regardless of their scores in CalEnviroScreen 4.0 or Lands under the control of federally recognized Tribes.

Background: The impact of socioeconomic differences on cancer survival has been investigated for several cancer types showing lower cancer survival in patients from lower socioeconomic groups. However, little is known about the relation between the strength of association and the level of adjustment and level of aggregation of the socioeconomic status measure. Here, we conduct the first systematic review and meta-analysis on the association of individual and area-based measures of socioeconomic status with lung cancer survival.Methods: In accordance with PRISMA guidelines, we searched for studies on socioeconomic differences in lung cancer survival in four electronic databases. A study was included if it reported a measure of survival in relation to education, income, occupation, or composite measures (indices). If possible, meta-analyses were conducted for studies reporting on individual and area-based socioeconomic measures.Results: We included 94 studies in the review, of which 23 measured socioeconomic status on an individual level and 71 on an area-based level. Seventeen studies were eligible to be included in the meta-analyses. The meta-analyses revealed a poorer prognosis for patients with low individual income (pooled hazard ratio: 1.13, 95 % confidence interval: 1.08–1.19, reference: high income), but not for individual education. Group comparisons for hazard ratios of area-based studies indicated a poorer prognosis for lower socioeconomic groups, irrespective of the socioeconomic measure. In most studies, reported 1-, 3-, and 5-year survival rates across socioeconomic status groups showed decreasing rates with decreasing socioeconomic status for both individual and area-based measures. We cannot confirm a consistent relationship between level of aggregation and effect size, however, comparability across studies was hampered by heterogeneous reporting of socioeconomic status and survival measures. Only eight studies considered smoking status in the analysis.Conclusions: Our findings suggest a weak positive association between individual income and lung cancer survival. Studies reporting on socioeconomic differences in lung cancer survival should consider including smoking status of the patients in their analysis and to stratify by relevant prognostic factors to further explore the reasons for socioeconomic differences. A common definition for socioeconomic status measures is desirable to further enhance comparisons between nations and across different levels of aggregation.

This data is Statistical Local Areas (SLA) based Socio-Economic Indexes for Areas (SEIFA) Index of Economic Resources (IER) - This index includes variables that are associated with economic resources. Variables include rent paid, income by family type, mortgage payments, and rental properties, based on the 2006 census. The data follows the 2006 Australian Standard Geographical Classification (ASGC) boundaries. The Australian Bureau of Statistics (ABS) has developed indexes to allow ranking of regions/areas, providing a method of determining the level of social and economic wellbeing in that region. There are four indexes included in the SEIFA 2006 product. They relate to socio-economic aspects of geographic areas. Each index summarises a different aspect of the socio-economic conditions in an area. The indexes have been obtained by a technique called principal components analysis. This technique summarises the information from a variety of social and economic variables, calculating weights that will give the best summary for the underlying variables. For the SEIFA indexes, each index uses a different set of underlying variables. All the indexes (including the Index of Relative Socio-Economic Disadvantage) have been constructed so that relatively disadvantaged areas (e.g. areas with many low income earners) have low index values. This data is ABS data (catalogue number: 2033.0.55.001) used with permission from the Australian Bureau of Statistics. For more information on this data please visit the Australian Bureau of Statistics.Please note: AURIN has spatially enabled the original data following the 2006 ASGC.

This data is Statistical Local Areas (SLA) based Socio-Economic Indexes for Areas (SEIFA) Index of Advantage/Disadvantage (IRSAD) - Is a continuum of advantage to disadvantage. Low values indicate areas of disadvantage; and high values indicate areas of advantage. This data is based on the 2006 census and follows the 2006 Australian Standard Geographical Classification (ASGC) boundaries.

The Australian Bureau of Statistics (ABS) has developed indexes to allow ranking of regions/areas, providing a method of determining the level of social and economic wellbeing in that region. There are four indexes included in the SEIFA 2006 product. They relate to socio-economic aspects of geographic areas. Each index summarises a different aspect of the socio-economic conditions in an area. The indexes have been obtained by a technique called principal components analysis. This technique summarises the information from a variety of social and economic variables, calculating weights that will give the best summary for the underlying variables. For the SEIFA indexes, each index uses a different set of underlying variables.

All the indexes (including the Index of Relative Socio-Economic Disadvantage) have been constructed so that relatively disadvantaged areas (e.g. areas with many low income earners) have low index values.

This data is ABS data (catalogue number: 2033.0.55.001) used with permission from the Australian Bureau of Statistics.

For more information on this data please visit the Australian Bureau of Statistics.Please note:

• AURIN has spatially enabled the original data following the 2006 ASGC.

Between 2019 and 2023, people living in households in the Asian and ‘Other’ ethnic groups were most likely to be in persistent low income before and after housing costs

Income determines where a person or family is on the path to opportunities like home ownership, education, job training and other components of the American Dream. If each income level is considered to be a step on the stairway to success, how many people occupy each step?In each urban area, this map checks the number of households found in the five lowest income groups that Esri demographics reports, and indicates which is the predominant (largest) low income group. The resulting patterns may surprise you. Zoom in on different regions of the country to see the actual urban boundaries.The data shown is from Esri's 2015 Demographic estimates using Census 2010 geographic boundaries. Esri offers U.S. Updated Demographic (2017/2022) Data - Population, age, income, sex, race, home value, and marital status are among the variables included in the database. Each year, Esri's Data Development team employs its proven methodologies to update more than 2,000 demographic variables for a variety of U.S. geographies.

ABSTRACT OBJECTIVE: To analyze the moderator effect of socioeconomic status in the association between the perceived environment and active commuting to school. METHODS: A total of 495 adolescents and their parents were interviewed. Perceived environment was operationalized in traffic and crime safety and assessed with the Neighborhood Environment Walkability Scale. Active commuting was self-reported by the adolescents, categorized in walking, bicycling or skating at least one time/week. Socioeconomic status was used as moderator effect, reported from adolescents' parents or guardians using Brazilian standardized socioeconomic status classification. Analyses were performed with Poisson regression on Stata 12.0. RESULTS: Prevalence of active commuting was 63%. Adolescents with low socioeconomic status who reported “it is easy to observe pedestrians and cyclists” were more likely to actively commute to school (PR = 1.18, 95%CI 1.03–1.13). Adolescents with low socioeconomic status whose parents or legal guardians reported positively to “being safe crossing the streets” had increased probability of active commuting to school (PR = 1.10, 95%CI 1.01–1.20), as well as those with high socioeconomic status with “perception of crime” were positively associated to the outcome (PR = 1.33, 95%CI 1.03–1.72). CONCLUSIONS: Socioeconomic status showed moderating effects in the association between the perceived environment and active commuting to school.

IntroductionType 2 diabetes is increasing worldwide, and the trend is also observed in Sweden. In Malmö, the third largest city in Sweden, the prevalence has doubled. Populations with lower socioeconomic status have a higher prevalence and poorer outcomes, making preventive interventions targeting these groups increasingly important.ObjectiveTo investigate the types of interventions that have been tested and reported regarding the prevention of type 2 diabetes targeting low socioeconomic populations and are applicable in a high-income country.MethodsBased on a systematic search strategy developed using the People, Concept, and Context model, the databases CINAHL, PubMed, and Web of Science were searched in January 2024 and updated in December 2024, and EMBASE was searched in May 2025. A flowchart of the screening process has been created. From the selected studies, data were extracted, charted, and the findings were compiled in a narrative form.ResultsSeventeen studies were included, 12 were conducted in the United States and five in Europe. Most used culturally adapted diabetes prevention programs, and a higher proportion of participants were women. Key features included flexibility in attendance and format, development through a community-based participatory approach, gender-specific groups, and the involvement of significant others. Increases of physical activity proved challenging within broader lifestyle interventions. Screening interventions were conducted in community and healthcare facility settings, as well as through a school-and community-based program. Challenges with enrollment and retention were commonly reported.ConclusionThere is a need for more interventions in the European context and for interventions to engage more men with strategies such as male peer coaches and community screening in locations frequented by men. Longer time frames and sustained engagement strategies are necessary to reach and retain groups with low socioeconomic status in preventive type 2 diabetes interventions.

These geospatial data resources and the linked mapping tool below reflect currently available data on three categories of potentially qualifying Low-Income communities: Census tracts that meet the CDFI's New Market Tax Credit Program's threshold for Low Income, thereby are able to apply to Category 1. Census tracts that meet the White House's Climate and Economic Justice Screening Tool's threshold for disadvantage in the 'Energy' category, thereby are able to apply for Additional Selection Criteria Geography. Counties that meet the USDA's threshold for Persistent Poverty, thereby are able to apply for Additional Selection Criteria Geography. Note that Category 2 - Indian Lands are not shown on this map. Note that Persistent Poverty is not calculated for US Territories. Note that CEJST Energy disadvantage is not calculated for US Territories besides Puerto Rico. The excel tool provides the land area percentage of each 2023 census tract meeting each of the above categories. To examine geographic eligibility for a specific address or latitude and longitude, visit the program's mapping tool. Additional information on this tax credit program can be found on the DOE Landing Page for the 48e program at https://www.energy.gov/diversity/low-income-communities-bonus-credit-program or the IRS Landing Page at https://www.irs.gov/credits-deductions/low-income-communities-bonus-credit. Maps last updated: September 1st, 2024 Next map update expected: December 7th, 2024 Disclaimer: The spatial data and mapping tool is intended for geolocation purposes. It should not be relied upon by taxpayers to determine eligibility for the Low-Income Communities Bonus Credit Program. Source Acknowledgements: The New Market Tax Credit (NMTC) Tract layer using data from the 2016-2020 ACS is from the CDFI Information Mapping System (CIMS) and is created by the U.S. Department of Treasury Community Development Financial Institutions Fund. To learn more, visit CDFI Information Mapping System (CIMS) | Community Development Financial Institutions Fund (cdfifund.gov). https://www.cdfifund.gov/mapping-system. Tracts are displayed that meet the threshold for the New Market Tax Credit Program. The 'Energy' Category Tract layer from the Climate and Economic Justice Screening Tool (CEJST) is created by the Council on Environmental Quality (CEQ) within the Executive Office of the President. To learn more, visit https://screeningtool.geoplatform.gov/en/. Tracts are displayed that meet the threshold for the 'Energy' Category of burden. I.e., census tracts that are at or above the 90th percentile for (energy burden OR PM2.5 in the air) AND are at or above the 65th percentile for low income. The Persistent Poverty County layer is created by joining the U.S. Department of Agriculture, Economic Research Service's Poverty Area Official Measures dataset, with relevant county TIGER/Line Shapefiles from the US Census Bureau. To learn more, visit https://www.ers.usda.gov/data-products/poverty-area-measures/. Counties are displayed that meet the thresholds for Persistent Poverty according to 'Official' USDA updates. i.e. areas with a poverty rate of 20.0 percent or more for 4 consecutive time periods, about 10 years apart, spanning approximately 30 years (baseline time period plus 3 evaluation time periods). Until Dec 7th, 2024 both the USDA estimates using 2007-2011 and 2017-2021 ACS 5-year data. On Dec 8th, 2024, only the USDA estimates using 2017-2021 data will be accepted for program eligibility.

This dataset contains interview transcriptions of interviews with 13 GPs on their experiences with communication with patients from different cultural backgrounds and/or low socio-economic status

This data is Census Collection Districts (CD) based Socio-Economic Indexes for Areas (SEIFA) Index of Education and Occupation (IEO) - This index includes all education and occupation variables only, based on the 2006 census. The data follows the 2006 Australian Standard Geographical Classification (ASGC) boundaries. The Australian Bureau of Statistics (ABS) has developed indexes to allow ranking of regions/areas, providing a method of determining the level of social and economic wellbeing in that region. There are four indexes included in the SEIFA 2006 product. They relate to socio-economic aspects of geographic areas. Each index summarises a different aspect of the socio-economic conditions in an area. The indexes have been obtained by a technique called principal components analysis. This technique summarises the information from a variety of social and economic variables, calculating weights that will give the best summary for the underlying variables. For the SEIFA indexes, each index uses a different set of underlying variables. All the indexes (including the Index of Relative Socio-Economic Disadvantage) have been constructed so that relatively disadvantaged areas (e.g. areas with many low income earners) have low index values. This data is ABS data (catalogue number: 2033.0.55.001) used with permission from the Australian Bureau of Statistics. For more information on this data please visit the Australian Bureau of Statistics.Please note: AURIN has spatially enabled the original data following the 2006 ASGC.

The database with the variables used in the study. The codebook of the database.

Low income (LI) equity focus areas are Census tracts that represent communities where the rate of people with low income, i.e., incomes equal to or less than 200% of the Federal Poverty Level, is greater than the regional average and the density of low income persons (per acre) is double the regional average. The original development of the equity focus areas occurred in conjunction with the 2018 Regional Transportation Plan and were informed through discussions of the transportation equity work group, regional advisory committees (TPAC, MTAC, JPACT, and MPAC), four Regional Leadership Forums, and direction from Metro Council.The equity focus areas here are based on data from the American Community Survey 2017 5-year estimates. We include census tracts outside the Metro boundary. However, only census tracts inside the Metro jurisdictional boundary were used when determining criteria to qualify a census tract as an equity focus area.Tract-level compilation and aggregation of population estimates, including sets of attributes related to sex, age, race/ethnicity, language, income, and educational attainment. Estimates are accompanied by margins of error. Aggregate estimates are accompanied by recalculated margins of error. Geometry source: 2010 Census. Attribute source: 2013-2017 ACS 5-year estimates, tables B01001, B03002, B06001, B06007, B06009, B16004, C16001, and C17002.

Comparison between the Belo Horizonte and Campinas datasets.