5 datasets found
  1. f

    Trend in no health insurance coverage and Medicaid coverage by marital...

    • plos.figshare.com
    xls
    Updated Jun 20, 2023
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    Jim P. Stimpson; Jessie Kemmick Pintor; Fernando A. Wilson (2023). Trend in no health insurance coverage and Medicaid coverage by marital status, sex, and state Medicaid expansion status, American Community Survey 2010–16, N = 3,874,432 Medicaid eligible respondents. [Dataset]. http://doi.org/10.1371/journal.pone.0223556.t002
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    xlsAvailable download formats
    Dataset updated
    Jun 20, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Jim P. Stimpson; Jessie Kemmick Pintor; Fernando A. Wilson
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Trend in no health insurance coverage and Medicaid coverage by marital status, sex, and state Medicaid expansion status, American Community Survey 2010–16, N = 3,874,432 Medicaid eligible respondents.

  2. f

    Triple differences linear probability model for no health insurance coverage...

    • plos.figshare.com
    xls
    Updated Jun 20, 2023
    + more versions
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    Jim P. Stimpson; Jessie Kemmick Pintor; Fernando A. Wilson (2023). Triple differences linear probability model for no health insurance coverage by marital status, sex, age, and state Medicaid expansion status, American Community Survey 2010–16. [Dataset]. http://doi.org/10.1371/journal.pone.0223556.t003
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 20, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Jim P. Stimpson; Jessie Kemmick Pintor; Fernando A. Wilson
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Triple differences linear probability model for no health insurance coverage by marital status, sex, age, and state Medicaid expansion status, American Community Survey 2010–16.

  3. f

    Data_Sheet_1_A Quasi-Experimental Study of Medicaid Expansion and Urban...

    • frontiersin.figshare.com
    • figshare.com
    docx
    Updated May 31, 2023
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    Cyrus Ayubcha; Pedram Pouladvand; Soussan Ayubcha (2023). Data_Sheet_1_A Quasi-Experimental Study of Medicaid Expansion and Urban Mortality in the American Northeast.docx [Dataset]. http://doi.org/10.3389/fpubh.2021.707907.s001
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    docxAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    Frontiers
    Authors
    Cyrus Ayubcha; Pedram Pouladvand; Soussan Ayubcha
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Northeastern United States
    Description

    Objectives: To investigate the association of state-level Medicaid expansion and non-elderly mortality rates from 1999 to 2018 in Northeastern urban settings.Methods: This quasi-experimental study utilized a synthetic control method to assess the association of Medicaid expansion on non-elderly urban mortality rates [1999–2018]. Counties encompassing the largest cities in the Northeastern Megalopolis (Washington D.C., Baltimore, Philadelphia, New York City, and Boston) were selected as treatment units (n = 5 cities, 3,543,302 individuals in 2018). Cities in states without Medicaid expansion were utilized as control units (n = 17 cities, 12,713,768 individuals in 2018).Results: Across all cities, there was a significant reduction in the neoplasm (Population-Adjusted Average Treatment Effect = −1.37 [95% CI −2.73, −0.42]) and all-cause (Population-Adjusted Average Treatment Effect = −2.57 [95%CI −8.46, −0.58]) mortality rate. Washington D.C. encountered the largest reductions in mortality (Average Treatment Effect on All-Cause Medical Mortality = −5.40 monthly deaths per 100,000 individuals [95% CI −12.50, −3.34], −18.84% [95% CI −43.64%, −11.67%] reduction, p = < 0.001; Average Treatment Effect on Neoplasm Mortality = −1.95 monthly deaths per 100,000 individuals [95% CI −3.04, −0.98], −21.88% [95% CI −34.10%, −10.99%] reduction, p = 0.002). Reductions in all-cause medical mortality and neoplasm mortality rates were similarly observed in other cities.Conclusion: Significant reductions in urban mortality rates were associated with Medicaid expansion. Our study suggests that Medicaid expansion saved lives in the observed urban settings.

  4. g

    Strategic Measure EOA.B.1 Number and percentage of residents living below...

    • gimi9.com
    Updated Jul 6, 2017
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    (2017). Strategic Measure EOA.B.1 Number and percentage of residents living below the poverty level | gimi9.com [Dataset]. https://gimi9.com/dataset/data-gov_strategic-measure-eoa-b-1-number-and-percentage-of-residents-living-below-the-poverty-leve/
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    Dataset updated
    Jul 6, 2017
    Description

    This is a historical measure for Strategic Direction 2023. For more data on Austin demographics please visit austintexas.gov/demographics. This measure answers the question of what number and percentage of residents are living below the federal poverty level, which means they meet certain thresholds set by a set of parameters and computation performed by the Census Bureau. Following the Office of Management and Budget's (OMB) Statistical Policy Directive 14, the Census Bureau uses a set of money income thresholds that vary by family size and composition to determine who is in poverty. If a family's total income is less than the family's threshold, then that family and every individual in it is considered in poverty. The official poverty thresholds do not vary geographically, but they are updated for inflation using the Consumer Price Index (CPI-U). The official poverty definition uses money income before taxes and does not include capital gains or noncash benefits (such as public housing, Medicaid, and food stamps). Data collected from the U.S. Census Bureau, American Communities Survey (1yr), Poverty Status in the Past 12 Months (Table S1701). American Communities Survey (ACS) is a survey with sampled statistics on the citywide level and is subject to a margin of error. ACS sample size and data quality measures can be found on the U.S. Census website in the Methodology section. View more details and insights related to this data set on the story page:https://data.austintexas.gov/stories/s/kgf9-tcgd

  5. d

    EOA.B.1 - Number and percentage of residents living below the poverty level...

    • datasets.ai
    • catalog.data.gov
    Updated Aug 8, 2024
    + more versions
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    City of Austin (2024). EOA.B.1 - Number and percentage of residents living below the poverty level (poverty rate) [Dataset]. https://datasets.ai/datasets/number-and-percentage-of-residents-living-below-the-poverty-level-poverty-rate
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    Dataset updated
    Aug 8, 2024
    Dataset authored and provided by
    City of Austin
    Description

    This measure answers the question of what number and percentage of residents are living below the federal poverty level, which means they meet certain threshold set by a set of parameters and computation performed by the Census Bureau. Following the Office of Management and Budget's (OMB) Statistical Policy Directive 14, the Census Bureau uses a set of money income thresholds that vary by family size and composition to determine who is in poverty. If a family's total income is less than the family's threshold, then that family and every individual in it is considered in poverty. The official poverty thresholds do not vary geographically, but they are updated for inflation using the Consumer Price Index (CPI-U). The official poverty definition uses money income before taxes and does not include capital gains or noncash benefits (such as public housing, Medicaid, and food stamps). Data collected from the U.S. Census Bureau, American Communities Survey (1yr), Poverty Status in the Past 12 Months (Table S1701). American Communities Survey (ACS) is a survey with sampled statistics on the citywide level and is subject to a margin of error. ACS sample size and data quality measures can be found on the U.S. Census website in the Methodology section.

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Jim P. Stimpson; Jessie Kemmick Pintor; Fernando A. Wilson (2023). Trend in no health insurance coverage and Medicaid coverage by marital status, sex, and state Medicaid expansion status, American Community Survey 2010–16, N = 3,874,432 Medicaid eligible respondents. [Dataset]. http://doi.org/10.1371/journal.pone.0223556.t002

Trend in no health insurance coverage and Medicaid coverage by marital status, sex, and state Medicaid expansion status, American Community Survey 2010–16, N = 3,874,432 Medicaid eligible respondents.

Related Article
Explore at:
xlsAvailable download formats
Dataset updated
Jun 20, 2023
Dataset provided by
PLOS ONE
Authors
Jim P. Stimpson; Jessie Kemmick Pintor; Fernando A. Wilson
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

Trend in no health insurance coverage and Medicaid coverage by marital status, sex, and state Medicaid expansion status, American Community Survey 2010–16, N = 3,874,432 Medicaid eligible respondents.

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