4 datasets found
  1. f

    Rates and predictors of recurrent work disability due to common mental...

    • plos.figshare.com
    txt
    Updated Jun 3, 2023
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    Fraser W. Gaspar; Catherine S. Zaidel; Carolyn S. Dewa (2023). Rates and predictors of recurrent work disability due to common mental health disorders in the United States [Dataset]. http://doi.org/10.1371/journal.pone.0205170
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    txtAvailable download formats
    Dataset updated
    Jun 3, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Fraser W. Gaspar; Catherine S. Zaidel; Carolyn S. Dewa
    License

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

    Area covered
    United States
    Description

    ContextDespite the high prevalence of work disability due to common mental disorders (CMD), no information exists on the rates and predictors of recurrence in a United States population.ObjectiveTo estimate recurrent work disability statistics and evaluate factors associated with recurrence due to CMDs including adjustment, anxiety, bipolar, and depressive disorders.MethodsRecurrent work disability statistics were calculated using a nationwide database of disability claims. For the CMDs, univariate and multiple variable analyses were used to examine demographic factors and comorbidities associated with the time to recurrence.ResultsOf the CMDs, cases with bipolar (n = 3,017) and depressive disorders (n = 20,058) had the highest recurrence densities, 98.7 and 70.9 per 1000 person-years, respectively. These rates were more than three times higher than recurrence rates for other chronic disorders (e.g., diabetes, asthma; n = 105,558) and non-chronic disorders (e.g., injury, acute illnesses; n = 153,786). Individuals with CMD were also more likely to have a subsequent disability distinct from their mental health condition. Risk factors for recurrent CMD disability included being younger, being an hourly employee, living in a geographic area with more college graduates, having more previous psychiatric visits, having a previous work leave, and the type of work industry.ConclusionsResults indicate that CMD patients may benefit from additional care and disability management both during and after their work absence to help prevent subsequent CMD and non-CMD related leaves.

  2. PLACES: Local Data for Better Health, County Data 2024 release

    • data.cdc.gov
    • data.virginia.gov
    • +2more
    Updated Sep 25, 2020
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    Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Population Health (2020). PLACES: Local Data for Better Health, County Data 2024 release [Dataset]. https://data.cdc.gov/500-Cities-Places/PLACES-Local-Data-for-Better-Health-County-Data-20/swc5-untb
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    xml, csv, application/rdfxml, application/rssxml, tsv, kmz, kml, application/geo+jsonAvailable download formats
    Dataset updated
    Sep 25, 2020
    Dataset provided by
    Centers for Disease Control and Preventionhttp://www.cdc.gov/
    Authors
    Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Population Health
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Description

    This dataset contains model-based county estimates. PLACES covers the entire United States—50 states and the District of Columbia—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at four geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. This dataset includes estimates for 40 measures: 12 for health outcomes, 7 for preventive services use, 4 for chronic disease-related health risk behaviors, 7 for disabilities, 3 for health status, and 7 for health-related social needs. These estimates can be used to identify emerging health problems and to help develop and carry out effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2022 or 2021 data, Census Bureau 2022 county population estimate data, and American Community Survey 2018–2022 estimates. The 2024 release uses 2022 BRFSS data for 36 measures and 2021 BRFSS data for 4 measures (high blood pressure, high cholesterol, cholesterol screening, and taking medicine for high blood pressure control among those with high blood pressure) that the survey collects data on every other year. More information about the methodology can be found at www.cdc.gov/places.

  3. PLACES: Local Data for Better Health, ZCTA Data 2024 release

    • data.cdc.gov
    • healthdata.gov
    • +2more
    Updated Oct 19, 2020
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    Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Population Health (2020). PLACES: Local Data for Better Health, ZCTA Data 2024 release [Dataset]. https://data.cdc.gov/500-Cities-Places/PLACES-Local-Data-for-Better-Health-ZCTA-Data-2024/qnzd-25i4
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    csv, xml, tsv, application/rdfxml, application/geo+json, application/rssxml, kml, kmzAvailable download formats
    Dataset updated
    Oct 19, 2020
    Dataset provided by
    Centers for Disease Control and Preventionhttp://www.cdc.gov/
    Authors
    Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Population Health
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Description

    This dataset contains model-based ZIP Code Tabulation Area (ZCTA) level estimates. PLACES covers the entire United States—50 states and the District of Columbia—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at four geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. The dataset includes estimates for 40 measures: 12 for health outcomes, 7 for preventive services use, 4 for chronic disease-related health risk behaviors, 7 for disabilities, 3 for health status, and 7 for health-related scocial needs. These estimates can be used to identify emerging health problems and to help develop and carry out effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2022 or 2021 data, Census Bureau 2020 population data, and American Community Survey 2018–2022 estimates. The 2024 release uses 2022 BRFSS data for 36 measures and 2021 BRFSS data for 4 measures (high blood pressure, high cholesterol, cholesterol screening, and taking medicine for high blood pressure control among those with high blood pressure) that the survey collects data on every other year. More information about the methodology can be found at www.cdc.gov/places.

  4. PLACES: Local Data for Better Health, Census Tract Data 2024 release

    • data.cdc.gov
    • healthdata.gov
    • +1more
    Updated Oct 19, 2020
    + more versions
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    Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Population Health (2020). PLACES: Local Data for Better Health, Census Tract Data 2024 release [Dataset]. https://data.cdc.gov/500-Cities-Places/PLACES-Local-Data-for-Better-Health-Census-Tract-D/cwsq-ngmh
    Explore at:
    csv, xml, application/rssxml, application/rdfxml, tsv, kmz, application/geo+json, kmlAvailable download formats
    Dataset updated
    Oct 19, 2020
    Dataset provided by
    Centers for Disease Control and Preventionhttp://www.cdc.gov/
    Authors
    Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Population Health
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Description

    This dataset contains model-based census tract estimates. PLACES covers the entire United States—50 states and the District of Columbia—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at four geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. The dataset includes estimates for 40 measures: 12 for health outcomes, 7 for preventive services use, 4 for chronic disease-related health risk behaviors, 7 for disabilities, 3 for health status, and 7 for health-related social needs. These estimates can be used to identify emerging health problems and to help develop and carry out effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2022 or 2021 data, Census Bureau 2020 population data, and American Community Survey 2018–2022 estimates. The 2024 release uses 2022 BRFSS data for 36 measures and 2021 BRFSS data for 4 measures (high blood pressure, high cholesterol, cholesterol screening, and taking medicine for high blood pressure control among those with high blood pressure) that the survey collects data on every other year. More information about the methodology can be found at www.cdc.gov/places.

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Fraser W. Gaspar; Catherine S. Zaidel; Carolyn S. Dewa (2023). Rates and predictors of recurrent work disability due to common mental health disorders in the United States [Dataset]. http://doi.org/10.1371/journal.pone.0205170

Rates and predictors of recurrent work disability due to common mental health disorders in the United States

Explore at:
12 scholarly articles cite this dataset (View in Google Scholar)
txtAvailable download formats
Dataset updated
Jun 3, 2023
Dataset provided by
PLOS ONE
Authors
Fraser W. Gaspar; Catherine S. Zaidel; Carolyn S. Dewa
License

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

Area covered
United States
Description

ContextDespite the high prevalence of work disability due to common mental disorders (CMD), no information exists on the rates and predictors of recurrence in a United States population.ObjectiveTo estimate recurrent work disability statistics and evaluate factors associated with recurrence due to CMDs including adjustment, anxiety, bipolar, and depressive disorders.MethodsRecurrent work disability statistics were calculated using a nationwide database of disability claims. For the CMDs, univariate and multiple variable analyses were used to examine demographic factors and comorbidities associated with the time to recurrence.ResultsOf the CMDs, cases with bipolar (n = 3,017) and depressive disorders (n = 20,058) had the highest recurrence densities, 98.7 and 70.9 per 1000 person-years, respectively. These rates were more than three times higher than recurrence rates for other chronic disorders (e.g., diabetes, asthma; n = 105,558) and non-chronic disorders (e.g., injury, acute illnesses; n = 153,786). Individuals with CMD were also more likely to have a subsequent disability distinct from their mental health condition. Risk factors for recurrent CMD disability included being younger, being an hourly employee, living in a geographic area with more college graduates, having more previous psychiatric visits, having a previous work leave, and the type of work industry.ConclusionsResults indicate that CMD patients may benefit from additional care and disability management both during and after their work absence to help prevent subsequent CMD and non-CMD related leaves.

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