Socioeconomic indicators like the poverty rate, population change, unemployment rate, and education levels vary across the nation. ERS has compiled the latest data on these measures into a mapping and data display/download application that allows users to identify and compare States and counties on these indicators.

Cardiovascular Disease Prevalence in Travis County (2014-2018)

Assessing Risk Factors in an Urban Community

By City of Austin [source]

About this dataset

This dataset provides invaluable insight into the prevalence of cardiovascular disease in Travis County, Texas between 2014 and 2018. By utilizing data from the Behavioral Risk Factor Surveillance System (BRFSS), this dataset offers a comprehensive look at the health of the adult population in Travis County. Are your heart health concerns growing or declining? This dataset has the answer. Through its detailed analysis, you can quickly identify any changes in cardiovascular disease over time as well as understand how disability and other factors such as age may be connected to heart-related diagnosis rates. Investigate how diabetes, lifestyle habits and other factors are affecting residents of Travis County with this insightful strategic measure!

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How to use the dataset

This dataset provides valuable insight into the prevalence of cardiovascular disease among adults in Travis County from 2014 to 2018. The data includes a Date_Time variable, which is the date and time of the survey, as well as a Year variable and Percent variable detailing prevalence within that year. This data can be used for further research into cardiovascular health outcomes in Travis County over time.

The first step in using this dataset is understanding its contents. This data contains information on each year’s percent of residents with cardiovascular disease and was collected during annual surveys by Behavioral Risk Factor Surveillance System (BRFSS). With this information, users can compare yearly changes in cardiovascular health across different cohorts. They can also use it to identify particular areas with higher or lower prevalence of cardiovascular disease throughout Travis County.

Now that you understand what’s included and what it describes, you can start exploring deeper insights within your analysis. Try examining demographic factors such as age group or sex to uncover potential trends underlying the increase or decrease in overall percentage over time . Additionally, look for other data sources relevant to your research topic and explore how prevalence differs across different factors within Travis County like specific counties or cities within it or types of geographies like rural versus urban settings . By overlaying additional datasets such as these , you will learn more about any correlations between them and this BRFSS-surveyed measure overtime .

Finally remember that any findings related to this dataset should always be interpreted carefully given their scale relative to our broader population . Yet by digging deep into the changes taking place , we are able to answer important questions about howCV risk factors might vary from county-to-county across Texas while also providing insight on where public health funding should be directed towards next !

Research Ideas

• Evaluating the correlation between cardiovascular disease prevalence and socio-economic factors such as income, education, and occupation in Travis County over time.
• Building an interactive data visualization tool to help healthcare practitioners easily understand the current trends in cardiovascular disease prevalence for adults in Travis County.
• Developing a predictive model to forecast the future prevalence of cardiovascular disease for adults in Travis County over time given relevant socio-economic factors

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: strategic-measure-percentage-of-residents-with-cardiovascular-disease-1.csv | Column name | Description | |:--------------|:---------------------------------------------------------------------------| | Date_Time | Date and time of the survey. (DateTime) | | Year | Year of the survey. (Integer) | | Percent | Percentage of adults in Travis County with cardiovascular disease. (Float) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit City of Austin.

The American Community Survey (ACS) 5 Year 2016-2020 socioeconomic estimate data is a subset of information derived from the following census tables:B08013 - Aggregate Travel Time To Work Of Workers By Sex;B08303 - Travel Time To Work;B17019 - Poverty Status In The Past 12 Months Of Families By Household Type By Tenure;B17021 - Poverty Status Of Individuals In The Past 12 Months By Living Arrangement;B19001 - Household Income In The Past 12 Months;B19013 - Median Household Income In The Past 12 Months;B19025 - Aggregate Household Income In The Past 12 Months;B19113 - Median Family Income In The Past 12 Months;B19202 - Median Non-family Household Income In The Past 12 Months;B23001 - Sex By Age By Employment Status For The Population 16 Years And Over;B25014 - Tenure By Occupants Per Room;B25026 - Total Population in Occupied Housing Units by Tenure by year Householder Moved into Unit;B25106 - Tenure By Housing Costs As A Percentage Of Household Income In The Past 12 Months;C24010 - Sex By Occupation For The Civilian Employed Population 16 Years And Over;B20004 - Median Earnings In the Past 12 Months (In 2015 Inflation-Adjusted Dollars) by Sex by Educational Attainment for the Population 25 Years and Over;B23006 - Educational Attainment by Employment Status for the Population 25 to 64 Years, and;B24021 - Occupation By Median Earnings In The Past 12 Months (In 2015 Inflation-Adjusted Dollars) For The Full-Time, Year-Round Civilian Employed Population 16 Years And Over.

To learn more about the American Community Survey (ACS), and associated datasets visit: https://www.census.gov/programs-surveys/acs, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_ACS 5-Year Socioeconomic Estimate Data by CountyDate of Coverage: 2016-2020

blockgroupvulnerability OPPORTUNITY The US Centers for Disease Control (CDC) publishes a set of percentiles that compare US geographies by vulnerability across household, socioeconomic, racial/ethnic and housing themes. These Social Vulnerability Indexes (SVI) were originally intended to to help public health officials and emergency response planners identify communities that will need support around an event. They are generally valuable for any public interest that wants to relate themselves to needy communities by geography. The SVI publication and its basis variables are provided at the Census tract level of geographic detail. The Census' American Community Survey is available down the to the block group level, however. Recasting the SVI methods at this lower level of geography allows it to be tied to thousands of other demographic variables available. Because the SVI relies on ACS variables only available at the tract level, a projection model needs to applied to approximate its results using blockgroup level ACS variables. The blockgroupvulnerability dataset casts a prediction for the CDCs logic for a new contribution to the Open Environments blockgroup series available on Harvard's dataverse platform. DATA The CDC's annual SVI publication starts with 23 simple derivations using 50 ACS Census variables. Next the SVI process ranks census geographies to calculate a rank for each, where Percentile Rank = (Rank-1) / (N-1). The SVI themes are then calculated at the tract level as a percentile rank of a sum of the percentile ranks of the first level ACS derived variables. Finally, the overall ranking is taken as the sum of the theme percentile rankings. The SVI data publication is keyed by geography (7 cols) where ultimately the Census Tract FIPS code is 2 State + 3 County + 4 Tract + 2 Tract Decimals eg, 56043000301 is 56 Wyoming, 043 Washakie County, Tract 3.01 republishes Census demographics called 'adjunct variables' including area, population, households and housing units from the ACS daytime population taken from LandScan 2020 estimates derives 23 SVI variables from 50 ACS 5 Year variables with each having an estimate (E_), estimate precentage (EP_), margin of error (M_), margin percentage (MP_) and flag variable (F_) for those greater than 90% or less than 10% provides the final 4 themes and a composite SVI percentile annually vars = ['ST', 'STATE', 'ST_ABBR', 'STCNTY', 'COUNTY', 'FIPS', 'LOCATION'] +\ ['SNGPNT','LIMENG','DISABL','AGE65','AGE17','NOVEH','MUNIT','MOBILE','GROUPQ','CROWD','UNINSUR','UNEMP','POV150','NOHSDP','HBURD','TWOMORE','OTHERRACE','NHPI','MINRTY','HISP','ASIAN','AIAN','AFAM','NOINT'] +\ ['TOTAL','THEME1','THEME2','THEME3','THEME4'] + \ ['AREA_SQMI', 'TOTPOP', 'DAYPOP', 'HU', 'HH'] knowns = vars + \ # Estimates, the result of calc against ACS vars [('E_'+v) for v in vars] + \ # Flag 0,1 whether this geog is in 90 percentile rank (its vulnerable) [('F_'+v) for v in vars] +\ # Margine of error for ACS calcs [('M_'+v) for v in vars] + \ # Margine of error for ACS calcs, as percentage [('MP_'+v) for v in vars] +\ # Estimates of ACS calcs, as percentage [('EP_'+v) for v in vars] + \ # Estimated percentile ranks [('EPL_'+v) for v in vars] + \ # Sum across var percentile ranks [('SPL_'+v) for v in vars]+ \ # Percentile rank of the sum of percentile ranks [('RPL_'+v) for v in vars] [c for c in svitract.columns if c not in knowns] The SVI themes range over [0,1] but the CDC uses -999 as an NA value; this is set for ~800 or 1% of tracts which have no total poulation. The themes are numbered: Socioeconomic Status – RPL_THEME1 Household Characteristics – RPL_THEME2 Racial & Ethnic Minority Status – RPL_THEME3 Housing Type & Transportation – RPL_THEME4 The themes with their variables and ACS sources are as follows: Unlike Census data, the CDC ranks Puerto Rico and Tribal tracts separately from the US otherwise. Theme SVI Variable ACS Table ACS Variables Socioeconomic E_UNINSUR S2701 S2701_C04_001E Socioeconomic E_UNEMP DP03 DP03_0005E Socioeconomic E_POV150 S1701 S1701_C01_040E Socioeconomic E_NOHSDP B06009 B06009_002E Socioeconomic E_HBURD S2503 S2503_C01_028E + S2503_C01_032E + S2503_C01_036E + S2503_C01_040E Household E_SNGPNT B11012 B11012_010E + B11012_015E Household E_LIMENG B16005 B16005_007E + B16005_008E + B16005_012E + B16005_013E + B16005_017E + B16005_018E + B16005_022E + B16005_023E + B16005_029E + B16005_030E + B16005_034E + B16005_035E + B16005_039E + B16005_040E + B16005_044E + B16005_045E Household E_DISABL DP02 DP02_0072E Household E_AGE65 S0101 S0101_C01_030E Household E_AGE17 B09001 B09001_001E Racial & Ethnic E_TWOMORE DP05 DP05_0083E Racial & Ethnic E_OTHERRACE DP05 DP05_0082E Racial & Ethnic E_NHPI DP05 DP05_0081E Racial & Ethnic E_MINRTY DP05 DP05_0071E + DP05_0078E + DP05_0079E + DP05_0080E + DP05_0081E + DP05_0082E + ... Visit https://dataone.org/datasets/sha256%3A3edd5defce2f25c7501953ca3e77c4f15a8c71251352373a328794f961755c1c for complete metadata about this dataset.

Stroke Mortality Rates in the US (Age-Standardized) 2012-2014

State/Territory and County Data

By US Open Data Portal, data.gov [source]

About this dataset

This dataset contains primary stroke mortality data from 2012 to 2014 among US adults aged 35+ across all states/territories and counties. Data is age-standardized and county rates are spatially smoothed to provide a better and more accurate view of the prevalence of mortality due to stroke. The data evaluation can be further divided by gender, race/ethnicity, stratification category 1, stratification 1, stratification category 2, or stratification 2. All data is sourced from the National Vital Statistics System (NVSS) ensuring it's accuracy and reliability. For even more information regarding heart disease related deaths as well as methodology employed in mapping such occurrences visit the Interactive Atlas of Heart Disease and Stroke. Looking deeper into these numbers may reveal hidden trends that could lead us closer towards reducing stroke related mortality in adults across our nation!

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How to use the dataset

The U.S. Stroke Mortality Rates (Age-Standardized) 2012-2014 dataset provides stroke mortality rates for adults aged 35 and over living in the United States from 2012 to 2014. This dataset is an ideal resource for examining the impact of stroke at a local or national level.

This guide will provide an introduction to understanding and using this data correctly, as well as highlighting some potential areas of investigation it may be used for:

• Understanding the Context: The first step towards understanding this data is to take a close look at its features and categories. These include year, location, geography level, data source, class, topic, value type/unit/ footnote symbol and stratification category/stratification which allow you to view data through multiple ways (e.g., by age group or by race).

  You can also filter your results with these attributes including specific years or different locations in order explore particular conditions within a certain area or year range (e.g., how many stroke related deaths occurred among blacks in California between 2012 – 2014?). It’s important to note that all county age-standardized rates are spatially smoothed — meaning each county rate is adjusted taking into account nearby counties — so the results you get might reflect wider regional trends more than actual localized patterns associated with individual counties.)

• Accessing & Previewing Data: Once you have familiarised yourself with the library concept behind this dataset it’s time access it's contents directly! To download your desired subset inside Kaggle platform just open up csv file titled 'csv- 1'. Alternatively ,you can use other open source tools such as Exasol Analytic Database technology (available on built-in 'notebook' feature) if you want work on even larger datasets with more processing power come into play ! Inside visualization tab users will be able view chart graphs( pie charts histograms etc ) from their query results .And once completed feel free export their respective visuals SVG PNG PDF formats too .

• Finding Answers: With all these processes complete ,you now should have plenty of datasets ready go in advance - great start but what does story tell us ? Well break things down compare different groups slices look at correlations trends deviations across various demographic filters questions about causal effects become much easier answer ! Leave creative freedom your side let those numbers feel ! So try pose some interesting interesting hypothesis determine how above factors could change across different states spend hours going through wealth

Research Ideas

• Utilizing location-specific stroke mortality data to pinpoint areas that need targeted public health interventions and outreach.
• Analyzing the correlation between age-standardized stroke mortality rates and demographic data, such as gender, race/ethnicity or socioeconomic status.
• Creating strategies focused on reducing stroke mortality in high risk demographic groups based on findings from the datasets geographical and sociological analysis tools

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: csv-1.csv | Column name | Description ...

OverviewThis feature layer visualizes the 2022 overall SVI for U.S. counties and tractsSocial Vulnerability Index (SVI) indicates the relative vulnerability of every U.S. county and tract16 social factors grouped into four major themesIndex value calculated for each county for the 16 social factors, four major themes, and the overall rankWhat is CDC/ATSDR Social Vulnerability Index?ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) has created the Social Vulnerability Index (SVI) to help emergency response planners and public health officials identify and map the communities that will most likely need support before, during, and after a hazardous event.SVI uses U.S. Census data to determine the social vulnerability of every county and tract. CDC SVI ranks each county and tract on 16 social factors, including poverty, lack of vehicle access, and crowded housing, and groups them into four related themes:Socioeconomic StatusHousehold CharacteristicsRacial & Ethnic Minority StatusHousing Type & TransportationVariablesFor a detailed description of variable uses, please refer to the full SVI 2022 documentation.RankingsWe ranked counties and tracts for the entire United States against one another. This feature layer can be used for mapping and analysis of relative vulnerability of counties in multiple states, or across the U.S. as a whole. Rankings are based on percentiles. Percentile ranking values range from 0 to 1, with higher values indicating greater vulnerability. For each county and tract, we generated its percentile rank among all counties and tracts for 1) the sixteen individual variables, 2) the four themes, and 3) its overall position.Overall Rankings:We totaled the sums for each theme, ordered the counties, and then calculated overall percentile rankings. Please note: taking the sum of the sums for each theme is the same as summing individual variable rankings.The overall tract summary ranking variable is RPL_THEMES.Theme rankings:For each of the four themes, we summed the percentiles for the variables comprising each theme. We ordered the summed percentiles for each theme to determine theme-specific percentile rankings. The four summary theme ranking variables are:Socioeconomic Status - RPL_THEME1Household Characteristics - RPL_THEME2Racial & Ethnic Minority Status - RPL_THEME3Housing Type & Transportation - RPL_THEME4FlagsCounties and tracts in the top 10%, i.e., at the 90th percentile of values, are given a value of 1 to indicate high vulnerability. Counties and tracts below the 90th percentile are given a value of 0. For a theme, the flag value is the number of flags for variables comprising the theme. We calculated the overall flag value for each county as the total number of all variable flags.SVI Informational VideosIntroduction to CDC Social Vulnerability Index (SVI)More Questions?CDC SVI 2022 Full DocumentationSVI Home PageContact the SVI Coordinator

The "Cook County Digital Equity Score by Zip Code" dataset provides comprehensive data on the digital equity score and supporting demographic information (ACS 2021 5-Year Estimates) for various zip codes in Cook County, IL. The dataset includes the Digital Equity Score, which measures the extent to which socioeconomic conditions hinder or enable access to broadband internet in each Zip Code. This score ranges from 0 to 100, where 0 indicates zip codes facing significant barriers to broadband access, while 100 represents zip codes with favorable conditions for broadband availability. Additionally, the dataset offers detailed demographic information, such as median household income, housing cost burdened households, household internet status, device availability, and minority status. These demographic variables shed light on the factors influencing digital equity and provide valuable insights into the relationship between socioeconomic conditions and broadband access within specific zip codes in Cook County.

The American Community Survey (ACS) 5 Year 2009-2013 socioeconomic information is a subset of information available for download from the U.S. Census. Tables used in the development of this dataset include:B08013 - Aggregate Travel Time To Work Of Workers By Sex;B08303 - Travel Time To Work;B17019 - Poverty Status In The Past 12 Months Of Families By Household Type By TenureB17021 - Poverty Status Of Individuals In The Past 12 Months By Living ArrangementB19001 - Household Income In The Past 12 MonthsB19013 - Median Household Income In The Past 12 MonthsB19025 - Aggregate Household Income In The Past 12 MonthsB19113 - Median Family Income In The Past 12 MonthsB19202 - Median Nonfamily Household Income In The Past 12 MonthsB23001 - Sex By Age By Employment Status For The Population 16 Years And OverB25014 - Tenure By Occupants Per RoomB25026 - Total Population in Occupied Housing Units by Tenure by year Householder Moved into UnitB25106 - Tenure By Housing Costs As A Percentage Of Household Income In The Past 12 MonthsC24010 - Sex By Occupation For The Civilian Employed Population 16 Years And OverB20004 - Median Earnings In the Past 12 Months (In 2009 Inflation-Adjusted Dollars) by Sex by Educational Attainment for the Population 25 Years and OverB23006 - Educational Attainment by Employment Status for the Population 25 to 64 Years, andB24021 - Occupation By Median Earnings In The Past 12 Months (In 2012 Inflation-Adjusted Dollars) For The Full-Time, Year-Round Civilian Employed Population 16 Years And Over.

To download additional socioeconomic information, visit: https://www.census.gov/programs-surveys/acs.Data Dictionary available for download by clicking on the following link: Data Dictionary – 2009-2013 ACS 5-Year Socioeconomic Estimate Data by Tract.

Data Current as of: 03//2017