As of 1/19/2022, this dataset is no longer being updated. For more data on COVID-19 in Connecticut, visit data.ct.gov/coronavirus. This tables shows the percent of people who have received at least one dose of COVID-19 vaccine who live in a Priority SVI Zip Code. About a third of people in CT live in a Priority SVI zip code. SVI refers to the CDC's Social Vulnerability Index - a measure that combines 15 demographic variables to identify communities most vulnerable to negative health impacts from disasters and public health crises. Measures of social vulnerability include socioeconomic status, household composition, disability, race, ethnicity, language, and transportation limitations - among others. SVI scores were calculated for each zip code in CT. The zip codes in the top 20% were designated as Priority SVI zip codes. Percentages are based on 2018 zip code population data supplied by ESRI corporation. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. The data are presented cumulatively and by week of first dose of vaccine. Percentages are reported for all providers combined and for pharmacies, FQHCs (Federally Qualified Health Centers), local public health departments / districts and hospitals. The table excludes people with a missing or out-of-state zip code and doses administered by the Federal government (including Department of Defense, Department of Correction, Department of Veteran’s Affairs, Indian Health Service) or out-of-state providers.

ObjectiveTo create county-, 5-digit ZIP code (ZIP-5)–, and 3-digit ZIP code (ZIP-3)–level datasets of the Social Vulnerability Index (SVI) and its components for 2016–2022 to validate the methodology beyond county level, explore trends in SVI over time and space, and demonstrate its usage in an enrichment exercise with health plan claims.Materials and methodsThe SVI consolidates 16 structural, economic, and demographic variables from the American Community Survey (ACS) into 4 themes: socioeconomic status, household characteristics, racial and ethnic minority status, and housing type and transportation. ACS estimates of the 16 variables for 2016–2022 were extracted for counties and ZIP code tabulation areas and for ZIP code geographies, crosswalked to ZIP-5, and aggregated to ZIP-3. Areas received a percentile ranking (range, 0–1) for SVI and each variable and composite theme, with higher values indicating greater social vulnerability.ResultsSVI estimates were produced for up to 3,143 counties, 32,243 ZIP-5s, and 886 ZIP-3s. SDoH trends across the US were largely consistent from 2016 to 2022 despite slight local changes over time. SVI varied across regions, with generally higher vulnerability in the South and lower vulnerability in the North and Northeast. When linked with health plan claims data, higher SVI (i.e., higher vulnerability) was associated with greater comorbidity burden.ConclusionSVI can be estimated at the ZIP-3 and ZIP-5 levels to provide area-level context, allowing for more routine integration of socioeconomic and health equity–related concepts into health claims and other datasets.

ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) created the Centers for Disease Control and Prevention Social Vulnerability Index (CDC SVI or simply 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. The SVI is derived from American Community Survey (ACS), 5-year data. The CDC releases updated SVI data every two years. The Arizona Department of Health Services (ADHS) calculates yearly SVI updates in between CDC release years using an R script to reproduce CDC's SVI calculations and newly released ACS 5-year data.All methods and background information for SVI can be found at: https://www.atsdr.cdc.gov/placeandhealth/svi/documentation/SVI_documentation_2018.htmlAs the R script is merely a reproduction of the CDC's calculations, any questions about the CDC's SVI methodology should be directed to svi_coordinator@cdc.govIf you find any discrepancies between the CDC's 2018 SVI values and the 2018 values generated from this R Script, please email gis@azdhs.gov.Please note that the SVI data set generated from this R script does not include 2 variables that are present in the CDC's data set. Those are AREA_SQMI (Tract area in square miles) and E_DAYPOP (Adjunct variable - Estimated daytime population, LandScan 2018). These variables do not affect the SVI calculations but may be useful for mapping the data.LAST UPDATED: October 2021UPDATE FREQUENCY: None planned

The Chicago CCVI identifies communities that have been disproportionately affected by COVID-19 and are vulnerable to barriers to COVID-19 vaccine uptake​. Vulnerability is defined as a combination of sociodemographic factors, epidemiological factors​, occupational factors​, and cumulative COVID-19 burden.

The 10 components of the index include COVID-19 specific risk factors and outcomes and social factors known to be associated with social vulnerability in the context of emergency preparedness. The CCVI is derived from ranking values of the components by Chicago Community Area, then synthesizing them into a single composite weighted score. The higher the score, the more vulnerable the geographic area.

ZIP Code CCVI is included to enable comparison with other COVID-19 data available on the Chicago Data Portal. Some elements of the CCVI are not available by ZIP Code. To create ZIP Code CCVI, the proportion of the ZIP Code population contributed by each Community Areas was determined. The apportioned populations were then weighted by the Community Area CCVI score and averaged to determine a ZIP Code CCVI score.

The COVID-19 Community Vulnerability Index (CCVI) is adapted and modified from a Surgo Ventures collaboration (https://precisionforcovid.org/ccvi) and the CDC Social Vulnerability Index​. ZIP Codes are based on ZIP Code Tabulation Areas (ZCTAs) developed by the U.S. Census Bureau. For full documentation see: https://www.chicago.gov/content/dam/city/sites/covid/reports/012521/Community_Vulnerability_Index_012521.pdf

NOTE: As of 2/16/2023, this page is not being updated. This tables shows the number and percent of people that have initiated COVID-19 vaccination, are fully vaccinated and had additional dose 1 grouped by whether they live in an SVI Priority Zip Code. People with an out-of-state zip code are excluded from this analysis. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. A person who has received at least one dose of any COVID-19 vaccine is considered to have initiated vaccination. A person is considered fully vaccinated if they have completed a primary vaccine series by receiving 2 doses of the Pfizer, Novavax or Moderna vaccines or 1 dose of the Johnson & Johnson vaccine. The fully vaccinated are a subset of the number who have received at least one dose. A person who completed a Pfizer, Moderna, Novavax or Johnson & Johnson primary series (as defined above) and then had an additional dose of COVID-19 vaccine is considered to have had additional dose 1. The additional monovalent dose may be Pfizer, Moderna, Novavax or Johnson & Johnson and may be a different type from the primary series. For people who had a primary Pfizer or Moderna series, additional dose 1 was counted starting August 18th, 2021. For people with a Johnson & Johnson primary series additional dose 1 was counted starting October 22nd, 2021. For most people, additional dose 1 is a booster. However, additional dose 1 may represent a supplement to the primary series for a people who is moderately or severely immunosuppressed. Bivalent booster administrations are not included in the additional dose 1 calculations. SVI refers to the CDC's Social Vulnerability Index - a measure that combines 15 demographic variables to identify communities most vulnerable to negative health impacts from disasters and public health crises. Measures of social vulnerability include socioeconomic status, household composition, disability, race, ethnicity, language, and transportation limitations - among others. SVI scores were calculated for each zip code in CT. The zip codes in the top 20% were designated as SVI Priority Zip Codes. Percentages are based on 2018 zip code population data supplied by ESRI corporation. The percent with at least one dose many be over-estimated and the percent fully vaccinated and with additional dose 1 may be under-estimated because of vaccine administration records for individuals that cannot be linked because of differences in how names or date of birth are reported. Connecticut COVID-19 Vaccine Program providers are required to report information on all COVID-19 vaccine doses administered to CT WiZ, the Connecticut Immunization Information System. Data on doses administered to CT residents out-of-state are being added to CT WiZ jurisdiction-by-jurisdiction. Doses administered by some Federal entities (including Department of Defense, Department of Correction, Department of Veteran’s Affairs, Indian Health Service) are not yet reported to CT WiZ.  Data reported here reflect the vaccination records currently reported to CT WiZ. Note: As part of continuous data quality improvement efforts, duplicate records were removed from the COVID-19 vaccination data during the weeks of 4/19/2021 and 4/26/2021.

The COVID-19 Vulnerability and Recovery Index uses Tract and ZIP Code-level data* to identify California communities most in need of immediate and long-term pandemic and economic relief. Specifically, the Index is comprised of three components — Risk, Severity, and Recovery Need with the last scoring the ability to recover from the health, economic, and social costs of the pandemic. Communities with higher Index scores face a higher risk of COVID-19 infection and death and a longer uphill economic recovery. Conversely, those with lower scores are less vulnerable.

The Index includes one overarching Index score as well as a score for each of the individual components. Each component includes a set of indicators we found to be associated with COVID-19 risk, severity, or recovery in our review of existing indices and independent analysis. The Risk component includes indicators related to the risk of COVID-19 infection. The Severity component includes indicators designed to measure the risk of severe illness or death from COVID-19. The Recovery Need component includes indicators that measure community needs related to economic and social recovery. The overarching Index score is designed to show level of need from Highest to Lowest with ZIP Codes in the Highest or High need categories, or top 20th or 40th percentiles of the Index, having the greatest need for support.

The Index was originally developed as a statewide tool but has been adapted to LA County for the purposes of the Board motion. To distinguish between the LA County Index and the original Statewide Index, we refer to the revised Index for LA County as the LA County ARPA Index.

*Zip Code data has been crosswalked to Census Tract using HUD methodology

Indicators within each component of the LA County ARPA Index are:Risk: Individuals without U.S. citizenship; Population Below 200% of the Federal Poverty Level (FPL); Overcrowded Housing Units; Essential Workers Severity: Asthma Hospitalizations (per 10,000); Population Below 200% FPL; Seniors 75 and over in Poverty; Uninsured Population; Heart Disease Hospitalizations (per 10,000); Diabetes Hospitalizations (per 10,000)Recovery Need: Single-Parent Households; Gun Injuries (per 10,000); Population Below 200% FPL; Essential Workers; Unemployment; Uninsured PopulationData are sourced from US Census American Communities Survey (ACS) and the OSHPD Patient Discharge Database. For ACS indicators, the tables and variables used are as follows:

Indicator

ACS Table/Years

Numerator

Denominator

Non-US Citizen

B05001, 2019-2023

b05001_006e

b05001_001e

Below 200% FPL

S1701, 2019-2023

s1701_c01_042e

s1701_c01_001e

Overcrowded Housing Units

B25014, 2019-2023

b25014_006e + b25014_007e + b25014_012e + b25014_013e

b25014_001e

Essential Workers

S2401, 2019-2023

s2401_c01_005e + s2401_c01_011e + s2401_c01_013e + s2401_c01_015e + s2401_c01_019e + s2401_c01_020e + s2401_c01_023e + s2401_c01_024e + s2401_c01_029e + s2401_c01_033e

s2401_c01_001

Seniors 75+ in Poverty

B17020, 2019-2023

b17020_008e + b17020_009e

b17020_008e + b17020_009e + b17020_016e + b17020_017e

Uninsured

S2701, 2019-2023

s2701_c05_001e

NA, rate published in source table

Single-Parent Households

S1101, 2019-2023

s1101_c03_005e + s1101_c04_005e

s1101_c01_001e

Unemployment

S2301, 2019-2023

s2301_c04_001e

NA, rate published in source table

The remaining indicators are based data requested and received by Advancement Project CA from the OSHPD Patient Discharge database. Data are based on records aggregated at the ZIP Code level:

Indicator

Years

Definition

Denominator

Asthma Hospitalizations

2017-2019

All ICD 10 codes under J45 (under Principal Diagnosis)

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

Gun Injuries

2017-2019

Principal/Other External Cause Code "Gun Injury" with a Disposition not "Died/Expired". ICD 10 Code Y38.4 and all codes under X94, W32, W33, W34, X72, X73, X74, X93, X95, Y22, Y23, Y35 [All listed codes with 7th digit "A" for initial encounter]

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

Heart Disease Hospitalizations

2017-2019

ICD 10 Code I46.2 and all ICD 10 codes under I21, I22, I24, I25, I42, I50 (under Principal Diagnosis)

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

Diabetes (Type 2) Hospitalizations

2017-2019

All ICD 10 codes under E11 (under Principal Diagnosis)

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

For more information about this dataset, please contact egis@isd.lacounty.gov.

Over the past thirty years, disaster scholars have highlighted that communities with stronger social infrastructure - including social ties that enable trust, mutual aid, and collective action - tend to respond to and recover better from crisis. However, comprehensive measurements of social capital across communities have been rare. This study adapts Kyne and Aldrich’s (2019) county-level social capital index to the census-tract level, generating social capital indices from 2011 to 2018 at the census-tract, zipcode, and county subdivision levels. To demonstrate their usefulness to disaster planners, public health experts, and local officials, we paired these with the CDC’s Social Vulnerability Index to predict the incidence of COVID-19 in case studies in Massachusetts, Wisconsin, Illinois, and New York City. We found that social capital and social vulnerability predicted as much as 95% of the variation in COVID outbreaks, highlighting their power as diagnostic and predictive tools for combating the spread of COVID.

This product has been archived in accordance with Federal Grant Compliance and is no longer actively updated. The site remains accessible for historical reference purposes.[Disclaimer: This application is a DRAFT and is still under development. Your feedback is welcome.]Data Use: The primary purpose of the Equity Impact Assessment scores is to aid in policymaking, program development, and resource allocation by pinpointing Equity Priority Areas that would benefit most from equity-focused interventions. It is designed to be utilized as part of a suite of tools, in conjunction with the expertise of various Departments, to inform and guide decision-making processes effectively.Using both ZIP Code and Census Tract geographies appropriately is important. Use ZIP Codes for a general overview of larger areas. Use Census Tracts when you require a more detailed analysis of smaller, specific areas. EIA (Equity Impact Assessment) Score Methodology

Data were analyzed for each area in the city limits, assessed against the key questions below, and assigned a risk score (5: Most Impact, 1: Least Impact).

Do Black, Hispanic, and Native American populations make up more than 70% of the community? (Table DP05: 2022)

Does the area have 15% or more people living below poverty? (Table: S1701: 2022)

Do less than 50% of the area’s households own their home? (Table: DP04: 2022)Are Are more than 12% of the area’s residents 65+ Yrs. Old? (Table: DP05: 2022)

Is the area rated “High” on the CDC’s Social Vulnerability level? (Texas 2020 SVI: RPL_Themes)

*The compound score for the factors is calculated to assess the overall impact for a community.Example: 80% Minority Population = 1, "High" Social Vulnerability Index = 1, 30% of 65+ Residents = 1, 5% Below Poverty=0, 70% Tenured Homeownership = 0; Compound Risk Score = 3Race Includes the sum of the percentage of (African American Population + Latino or Hispanic Population+ Native American)Replace (-) with null (blank values)The datasets collected to update the Equity Impact Assessment Score Methodology are from the ACS 2022 dataset and the Centers for Disease Control and Prevention/ Agency for Toxic Substances and Disease Registry/ Geospatial Research, Analysis, and Services Program. CDC/ATSDR Social Vulnerability Index 2020 Database Texas.

Census Tracts that receive a score of 0 are scored as 1.2020 CDC’s Social Vulnerability Level

Rating

RPL_Themes

1 (High)

.75-1

2 (Moderate to High)

.5-75

3 (Low to Moderate)

.25-.49

4 (Low)

0-.24

Data source: U.S. Census Bureau. American Community Survey, 2022 5-Year Estimates, Tables DP04, DP05, S1701. Retrieved from https://data.census.gov.Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry/Geospatial Research, Analysis, and Services Program. CDC/ATSDR Social Vulnerability Index 2020 Database Texas. Retrieved from https://www.atsdr.cdc.gov/placeandhealth/svi/data_documentation_download.htmlYear: 2022 ACS 5-Year Estimates, 2020 Social Vulnerability Index Provider: U.S. Census Bureau, Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry/Geospatial Research, Analysis, and Services Program

The Heat Vulnerability Index (HVI) shows neighborhoods whose residents are more at risk for dying during and immediately following extreme heat. It uses a statistical model to summarize the most important social and environmental factors that contribute to neighborhood heat risk. The factors included in the HVI are surface temperature, green space, access to home air conditioning, and the percentage of residents who are low-income or non-Latinx Black. Differences in these risk factors across neighborhoods are rooted in past and present racism. Neighborhoods are scored from 1 (lowest risk) to 5 (highest risk) by summing the following factors and assigning them into 5 groups (quintiles):

Median Household Income (American Community Survey 5 year estimate, 2016-2020) Percent vegetative cover (trees, shrubs or grass) (2017 LiDAR, NYC DOITT) Percent of population reported as Non-Hispanic Black on Census 2020 Average surface temperature Fahrenheit from ECOSSTRESS thermal imaging, August 27,2020 Percent of households reporting Air Conditioning access, Housing ad Vacancy Survey, 2017

Unpublished data product not for circulation Persistent Poverty tracts*Persistent poverty area and enduring poverty area measures with reference year 2015-2019 are research measures only. The ERS offical measures are updated every ten years. The next updates will use 1960 through 2000 Decennial Census data and 2007-2011 and 2017-2021 5-year ACS estimates. The updates will take place following the Census Bureau release of the 2017-2021 estimates (anticipated December 2022).A reliability index is calculated for each poverty rate (PctPoor) derived using poverty count estimates and published margins of error from the 5-yr ACS. If the poverty rate estimate has low reliability (=3) AND the upper (PctPoor + derived MOE) or lower (PctPoor - derived MOE) bounds of the MOE adjusted poverty rate would change the poverty status of the estimate (high = 20.0% or more; extreme = 40.0% or more) then the county/tract type is coded as "N/A". If looking at metrics named "PerPov0711" and PerPov1519" ERS says: The official measure ending in 2007-11 included data from 1980. The research measure ending in 2015-19 drops 1980 and begins instead with 1990. There were huge differences in geographic coverage of census tracts and data quality between 1980 and 1990, namely "because tract geography wasn’t assigned to all areas of the country until the 1990 Decennial Census. Last date edited 9/1/2022Variable NamesVariable Labels and ValuesNotesGeographic VariablesGEO_ID_CTCensus download GEOID when downloading county and tract data togetherSTUSABState Postal AbbreviationfipsCounty FIPS code, in numericCountyNameArea Name (county, state)TractNameArea Name (tract, county, state)TractCensus Tract numberRegionCensus region numeric code 1 = Northeast 2 = Midwest 3 = South 4 = Westsubreg3ERS subregions 1 = Northeast and Great Lakes 2 = Eastern Metropolitan Belt 3 = Eastern and Interior Uplands 4 = Corn Belt 5 = Southeastern Coast 6 = Southern Coastal Plain 7 = Great Plains 8 = Rio Grande and Southwest 9 = West, Alaska and HawaiiMetNonmet2013Metro and nonmetro county code 0 = nonmetro county 1 = metro countyBeale2013ERS Rural-urban Continuum Code 2013 (counties) 1 = counties in metro area of 1 million population or more 2 = counties in metro area of 250,000 to 1 million population 3 = counties in metro area of fewer than 250,000 population 4 = urban population of 20,000 or more, adjacent to a metro area 5 = urban population of 20,000 or more, not adjacent to a metro area 6 = urban population of 2,500 to 19,999, adjacent to a metro area 7 = urban population of 2,500 to 19,999, not adjacent to a metro area 8 = completely rural or less than 2,500, adjacent to a metro area 9 = completely rural or less than 2,500, not adjacent to a metro areaRUCA_2010Rural Urban Commuting Areas, primary code (census tracts) 1 = Metropolitan area core: primary flow within an urbanized area (UA) 2 = Metropolitan area high commuting: primary flow 30% or more to a UA 3 = Metropolitan area low commuting: primary flow 10% to 30% to a UA 4 = Micropolitan area core: primary flow within an Urban Cluster of 10,000 to 49,999 (large UC) 5 = Micropolitan high commuting: primary flow 30% or more to a large UC 6 = Micropolitan low commuting: primary flow 10% to 30% to a large UC 7 = Small town core: primary flow within an Urban Cluster of 2,500 to 9,999 (small UC) 8 = Small town high commuting: primary flow 30% or more to a small UC 9 = Small town low commuting: primary flow 10% to 30% to a small UC 10 = Rural areas: primary flow to a tract outside a UA or UC 99 = Not coded: Census tract has zero population and no rural-urban identifier informationBNA01Census tract represents block numbering areas; BNAs are small statistical subdivisions of a county for numbering and grouping blocks in nonmetropolitan counties where local committees have not established tracts. 0 = not a BNA tract 1 = BNA tractPoverty Areas MeasuresHiPov60Poverty Rate greater than or equal to 20.0% 1960 (counties only) -1 = N/A 0 = PctPoor60 < 20.0% 1 = PctPoor60 >= 20.0%HiPov70Poverty Rate greater than or equal to 20.0% 1970 -1 = N/A 0 = PctPoor70 < 20.0% 1 = PctPoor70 >= 20.0%HiPov80Poverty Rate greater than or equal to 20.0% 1980 -1 = N/A 0 = PctPoor80 < 20.0% 1 = PctPoor80 >= 20.0%HiPov90Poverty Rate greater than or equal to 20.0% 1990 -1 = N/A 0 = PctPoor90 < 20.0% 1 = PctPoor90 >= 20.0%HiPov00Poverty Rate greater than or equal to 20.0% 2000 -1 = N/A 0 = PctPoor00 < 20.0% 1 = PctPoor00 >= 20.0%HiPov0711Poverty Rate greater than or equal to 20.0% 2007-11 ACS -1 = N/A 0 = PctPoor0711 < 20.0% 1 = PctPoor0711 >= 20.0%HiPov1519Poverty Rate greater than or equal to 20.0% 2015-19 ACS -1 = N/A 0 = PctPoor1519 < 20.0% 1 = PctPoor1519 >= 20.0%ExtPov60Poverty Rate greater than or equal to 40.0% 1960 (counties only) -1 = N/A 0 = PctPoor60 < 40.0% 1 = PctPoor60 >= 40.0%ExtPov70Poverty Rate greater than or equal to 40.0% 1970 -1 = N/A 0 = PctPoor70 < 40.0% 1 = PctPoor70 >= 40.0%ExtPov80Poverty Rate greater than or equal to 40.0% 1980 -1 = N/A 0 = PctPoor80 < 40.0% 1 = PctPoor80 >= 40.0%ExtPov90Poverty Rate greater than or equal to 40.0% 1990 -1 = N/A 0 = PctPoor90 < 40.0% 1 = PctPoor90 >= 40.0%ExtPov00Poverty Rate greater than or equal to 40.0% 2000 -1 = N/A 0 = PctPoor00 < 40.0% 1 = PctPoor00 >= 40.0%ExtPov0711Poverty Rate greater than or equal to 40.0% 2007-11 ACS -1 = N/A 0 = PctPoor0711 < 40.0% 1 = PctPoor0711 >= 40.0%ExtPov1519Poverty Rate greater than or equal to 40.0% 2015-19 ACS -1 = N/A 0 = PctPoor1519 < 40.0% 1 = PctPoor1519 >= 40.0%PerPov90Official ERS Measure: Persistent Poverty 1990: poverty rate >= 20.0% in 1960, 1970, 1980, and 1990 (counties only) May not match previously published versions due to changes in geographic normalization procedures. -1 = N/A 0 = poverty rate not >= 20.0% in 1960, 1970, 1980, and 1990 1 = poverty rate >= 20.0% in 1960, 1970, 1980, and 1990PerPov00Official ERS Measure: Persistent Poverty 2000: poverty rate >= 20.0% in 1970, 1980, 1990, and 2000May not match previously published versions due to changes in geographic normalization procedures. -1 = N/A 0 = poverty rate not >= 20.0% in 1970, 1980, 1990, and 2000 1 = poverty rate >= 20.0% in 1970, 1980, 1990, and 2000PerPov0711Official ERS Measure: Persistent Poverty 2007-11: poverty rate >= 20.0% in 1980, 1990, 2000, and 2007-11May not match previously published versions due to changes in geographic normalization procedures and -1 = N/A application of reliability criteria. 0 = poverty rate not >= 20.0% in 1980, 1990, 2000, and 2007-11 1 = poverty rate >= 20.0% in 1980, 1990, 2000, and 2007-11PerPov1519Research Measure Only: Persistent Poverty 2015-19: poverty rate >= 20.0% in 1990, 2000, 2007-11, and 2015May not match previously published versions due to changes in geographic normalization procedures and -1 = N/A application of reliability criteria. 0 = poverty rate not >= 20.0% in 1990, 2000, 2007-11, and 2015-19 1 = poverty rate >= 20.0% in 1990, 2000, 2007-11, and 2015-19EndurePov0711Official ERS Measure: Enduring Poverty 2007-11: poverty rate >= 20.0% for at least 5 consecutive time periods up-to and including 2007-11 -1 = N/A 0 = Poverty Rate not >=20.0% in 1970, 1980, 1990, 2000, and 2007-11 1 = poverty rate >= 20.0% in 1970, 1980, 1990, 2000, and 2007-11 2 = poverty rate >=20.0% in 1960, 1970, 1980, 1990, 2000, and 2007-11 (counties only)EndurePov1519Research Measure Only: Enduring Poverty 2015-19: poverty rate >= 20.0% for at least 5 consecutive time periods, up-to and including 2015-19 -1 = N/A 0 = Poverty Rate not >=20.0% in 1980, 1990, 2000, 2007-11, and 2015-19 1 = poverty rate >= 20.0% in 1980, 1990, 2000, 2007-11, and 2015-19 2 = poverty rate >= 20.0% in 1970, 1980, 1990, 2000, 2007-11, and 2015-19 3 = poverty rate >=20.0% in 1960, 1970, 1980, 1990, 2000, 2007-11, and 2015-19 (counties only)Additional Notes: *In the combined data tab each variable ends with a 'C' for county and a 'T' for tractThe spreadsheet was joined to Esri's Living Atlas Social Vulnerability Tract Data (CDC) and therefore contains the following information as well: ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) has created a tool 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. The Social Vulnerability Index (SVI) uses U.S. Census data to determine the social vulnerability of every county and tract. CDC SVI ranks each county and tract on 15 social factors, including poverty, lack of vehicle access, and crowded housing, and groups them into four related themes:SocioeconomicHousing Composition and DisabilityMinority Status and LanguageHousing and TransportationThis feature layer visualizes the 2018 overall SVI for U.S. counties and tracts. Social Vulnerability Index (SVI) indicates the relative vulnerability of every U.S. county and tract.15 social factors grouped into four major themes | Index value calculated for each county for the 15 social factors, four major themes, and the overall rank

This dataset contains all the code, notebooks, datasets used in the study conducted to measure the spatial accessibility of COVID-19 healthcare resources with a particular focus on Illinois, USA. Specifically, the dataset measures spatial access for people to hospitals and ICU beds in Illinois. The spatial accessibility is measured by the use of an enhanced two-step floating catchment area (E2FCA) method (Luo & Qi, 2009), which is an outcome of interactions between demands (i.e, # of potential patients; people) and supply (i.e., # of beds or physicians). The result is a map of spatial accessibility to hospital beds. It identifies which regions need more healthcare resources, such as the number of ICU beds and ventilators. This notebook serves as a guideline of which areas need more beds in the fight against COVID-19. ## What's Inside A quick explanation of the components of the zip file * COVID-19Acc.ipynb is a notebook for calculating spatial accessibility and COVID-19Acc.html is an export of the notebook as HTML. * Data contains all of the data necessary for calculations: * Chicago_Network.graphml/Illinois_Network.graphml are GraphML files of the OSMNX street networks for Chicago and Illinois respectively. * GridFile/ has hexagonal gridfiles for Chicago and Illinois * HospitalData/ has shapefiles for the hospitals in Chicago and Illinois * IL_zip_covid19/COVIDZip.json has JSON file which contains COVID cases by zip code from IDPH * PopData/ contains population data for Chicago and Illinois by census tract and zip code. * Result/ is where we write out the results of the spatial accessibility measures * SVI/contains data about the Social Vulnerability Index (SVI) * img/ contains some images and HTML maps of the hospitals (the notebook generates the maps) * README.md is the document you're currently reading! * requirements.txt is a list of Python packages necessary to use the notebook (besides Jupyter/IPython). You can install the packages with python3 -m pip install -r requirements.txt

PurposeHospital readmissions are a pervasive problem for patients with heart failure. While Social Determinants of Health (SDoH) influence many aspects of care, the relationship between readmissions for acute heart failure (AHF) and social vulnerability is incompletely characterized. Such data are needed to develop interventions to maximize successful stabilization in the post-discharge phase.MethodsRetrospective review of administrative clinical data paired with ZIP code-level SDoH data from an integrated health system in Detroit, MI. We explored the relationship between Social Deprivation Index (SDI; greater scores indicate more deprivation) and hospital admissions for AHF within 180-days of a prior AHF admission using zero-hurdle regression (logistic model for >0 readmissions; negative binomial model for count of readmissions). Mixed-effects logistic regression, accounting for repeat visits, was used to determine if SDI was associated with AHF-admission for any given ED visit.ResultsFrom January 2022 through December 2023, with data from 2,333 unique patients (accounting for 3,281 total visits), we found that each SD increase in SDI (30.6) was associated with increased likelihood of at least one 180day-readmission (OR 1.52 [CI 1.10–2.11]). In the count model, each SD (28.3) increase in SDI was positively associated with 180day-readmissions (relative risk (RR) 1.57 [CI 1.10–1.23]). In the mixed model, after adjusting for characteristics of prior visits, SDI was not associated with AHF admission (including at Index visits).ConclusionThese results indicate that area-level social vulnerability may play a role in recovery and stabilization after a decompensation event; it may also extend the post-discharge vulnerable phase. That SDI was not associated with Index AHF admission suggests that social factors may play a different role in development of acute decompensation, as opposed to recovery from it. Development of targeted admission-reduction interventions should consider the varied influences of social vulnerability in the AHF lifecycle.

Display risk score for Dallas ZCTA, per CDC guidelines, regarding COVID-19 impactThe Office of Resilience then calculated a vulnerability score based on the following: MethodologyData was analyzed for each area in the city limits, assessed against the key questions below, and assigned a risk score (5:Highest Risk à 0: No Risk).Do Black, Hispanic and Native American populations together make up more than 70% of the community?Does the area have 15% or more of its families at or below 100% of the federal poverty level?Do less than 50% of the area’s households own the home they live in?Is the area rated “High” on the CDC’s Social Vulnerability Index, Socioeconomic Level?Are more than 12% of the area’s residents 65 or older?COVID Cases Data is pulled from below link. Data is as of 2-Feb-2021 https://covid-analytics-pccinnovation.hub.arcgis.com/

78744 Zip Code Feature Layer This feature layer represents the 78744 zip code boundary within Travis County and is used in this StoryMap to provide geographic context for Austin Public Health (APH) Community Health Worker (CHW) outreach efforts. On June 8, 2024, APH CHW Strike Teams conducted a targeted West Nile Virus (WNV) education campaign in the 78744 zip code, an area with high social vulnerability and environmental factors that may contribute to increased mosquito activity and disease transmission. This outreach aimed to:

Assess community awareness of WNV transmission and prevention strategies Distribute educational materials on mosquito control and personal protection Engage with residents to encourage proactive public health behaviors

Why the 78744 Zip Code? The 78744 zip code was identified as a priority area for WNV education due to:

Social Vulnerability Index (SVI) Considerations – Populations with higher vulnerability may have limited access to health resources or face greater risks from vector-borne diseases. Environmental Risk Factors – Standing water, dense vegetation, and urban drainage patterns that may support higher mosquito populations. Historical Public Health Needs – Previous outreach efforts have highlighted the importance of continued engagement in this area.

Feature Layer Use This feature layer helps visualize the geographic scope of the CHW outreach efforts and supports public health decision-making by aligning intervention strategies with spatial data and community needs. Future applications of this layer may include:

Mapping mosquito surveillance data and environmental risk factors Overlaying additional public health data for targeted outreach Informing response strategies for future vector-borne disease outbreaks

By incorporating geographic data into public health initiatives, Austin Public Health can ensure a more data-driven, equitable, and effective approach to disease prevention and community engagement.

Public Information Requests If you cannot locate the information or records you need online, Section 552.234 of the Texas Public Information Act allows you to submit a written request using the following methods:

Online submission: City of Austin Public Records Request | Mail: P.O. Box 689001, Austin, Texas, 78768

Odds ratio of admission at any given heart failure-related ED visit.

Contains the following information:COVID cases, case prevalence over different time spans, current COVID hotspots, and number of tests for the ABQ metro area at zip code level. Social vulnerability factors for the ABQ metro area at zip code level. COVID deaths at the small area level. The location of testing sites (updated regularly as new sites and information are found)The spread of COVID, testing, deaths, and PPE supply information by nursing homes (updated regularly)The locations of summer meal sites. This dashboard runs in this app: https://nmcdc.maps.arcgis.com/apps/MapSeries/index.html?appid=1ff0aa71c0ae427cbb5753d08ae19eabThis dashboard runs the following maps:Social Vulnerability Index, Albuquerque Metro Area, Census Tracts & Zip Codes, 2018 - https://nmcdc.maps.arcgis.com/home/item.html?id=850e8f2e7c394fb99041b94f813cb5faCOVID-19 Testing Locations - New Mexico - https://nmcdc.maps.arcgis.com/home/item.html?id=aace827af8fa4d2d9037ce5c7fb0e880COVID Deaths, NM Small Areas - CABQ - https://nmcdc.maps.arcgis.com/home/item.html?id=a56dab27204b4573a7f8d1663bc95844COVID-19 TESTING & CASES by TIME PERIODS, ZIP CODES - v1 - https://nmcdc.maps.arcgis.com/home/item.html?id=14e05ddda38d40cb9746750072d00c80Summer Meal Sites - CABQ - https://nmcdc.maps.arcgis.com/home/item.html?id=5fb8f3e689df4f03ab8be107d04fcd30Nursing Homes, COVID-19 Cases and Deaths, New Mexico and USA - https://nmcdc.maps.arcgis.com/home/item.html?id=8e74a05a32324aa3bcc07e2b1545d446

MethodologyData was analyzed for each area in the city limits, assessed against the key questions below, and assigned a risk score (5:Highest Risk à 0: No Risk).Do Black, Hispanic and Native American populations together make up more than 70% of the community?Does the area have 15% or more of its families at or below 100% of the federal poverty level?Do less than 50% of the area’s households own the home they live in?Is the area rated “High” on the CDC’s Social Vulnerability Index, Socioeconomic Level?Are more than 42% of the area’s residents 18-44 Yrs Old?This map also feeds this dashboard's First tab: https://dallasgis.maps.arcgis.com/home/item.html?id=8fadfdfd5f884a75b7a1999fffb9fe77

we utilized data from two main sources: the United States Census Bureau's American Community Survey (ACS) and the Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry (CDC/ATSDR) Social Vulnerability Index (SVI).American Community Survey (ACS):Conducted by the U.S. Census Bureau, the ACS is an ongoing survey that provides detailed demographic and socio-economic data on the population and housing characteristics of the United States.The survey collects information on various topics such as income, education, employment, health insurance coverage, and housing costs and conditions.It offers more frequent and up-to-date information compared to the decennial census, with annual estimates produced based on a rolling sample of households.The ACS data is essential for policymakers, researchers, and communities to make informed decisions and address the evolving needs of the population.CDC/ATSDR Social Vulnerability Index (SVI):Created by ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) and utilized by the CDC, the SVI is designed to identify and map communities that are most likely to need support before, during, and after hazardous events.