The population density of Madison County, ID was 81 in 2016.

Population Density

Population Density is computed by dividing the total population by Land Area Per Square Mile.

Above charts are based on data from the U.S. Census American Community Survey | ODN Dataset | API - Notes:

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Geographic and Population Datasets Involving Madison County, ID

  • API

    Durham County Population Density (2012)

    brigades.opendatanetwork.com | Last Updated 2015-02-21T02:47:33.000Z

    2012 - The population per square mile of the selected blockgroup. Data Dictionary Attached Data from American Community Survey (ACS) for 2010-2014.

  • API

    Jail Population Data

    data.sonomacounty.ca.gov | Last Updated 2018-07-17T08:20:23.000Z

    Historical population data captured daily. Two figures are shown those in custody and those in outside custody but are still under the responsibility of Sonoma County Sheriff. Examples of outside custody include home confinement, state prison, hospital stays, weekend custody, and supervised by other agencies.

  • API

    Population by County - Dataset

    opendata.ramseycounty.us | Last Updated 2017-08-10T19:16:01.000Z

    Dataset showing the population counts from the Census, estimates base, and annual estimates of the resident population to July 2016.

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    B-6, Comparison By County

    data.ftb.ca.gov | Last Updated 2018-06-11T19:22:34.000Z

    This dataset contains personal income tax statistics for taxpayers by the county of residence based on tax returns. Population data comes from the Department of Finance Exhibits. <br/><br/>For more information by taxable year, see <a href='https://www.ftb.ca.gov/aboutftb/plans_reports.shtml'>https://www.ftb.ca.gov/aboutftb/plans_reports.shtml</a>, Annual Report, Table B-6.

  • API

    Population Health Measures: Age-Adjusted Mortality Rates

    data.montgomerycountymd.gov | Last Updated 2015-06-17T16:53:22.000Z

    Age-adjustment mortality rates are rates of deaths that are computed using a statistical method to create a metric based on the true death rate so that it can be compared over time for a single population (i.e. comparing 2006-2008 to 2010-2012), as well as enable comparisons across different populations with possibly different age distributions in their populations (i.e. comparing Hispanic residents to Asian residents). Age adjustment methods applied to Montgomery County rates are consistent with US Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS) as well as Maryland Department of Health and Mental Hygiene’s Vital Statistics Administration (DHMH VSA). PHS Planning and Epidemiology receives an annual data file of Montgomery County resident deaths registered with Maryland Department of Health and Mental Hygiene’s Vital Statistics Administration (DHMH VSA). Using SAS analytic software, MCDHHS standardizes, aggregates, and calculates age-adjusted rates for each of the leading causes of death category consistent with state and national methods and by subgroups based on age, gender, race, and ethnicity combinations. Data are released in compliance with Data Use Agreements between DHMH VSA and MCDHHS. This dataset will be updated Annually.

  • API

    Prevention Agenda 2013-2018 Tracking Indicators: County Trend Data

    health.data.ny.gov | Last Updated 2017-02-08T19:27:05.000Z

    There are two datasets related to the County Level Prevention Agenda Tracking Indicators posted on this site. Each dataset consists of county level data for 68 health tracking indicators and sub-indicators for the Prevention Agenda 2013-2018: New York State’s Health Improvement Plan.

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    Prevention Agenda 2013-2018 Tracking Indicators: County Most Recent Data

    health.data.ny.gov | Last Updated 2017-01-24T19:05:46.000Z

    There are two datasets related to the County Level Prevention Agenda Tracking Indicators posted on this site. Each dataset consists of county level data for 68 health tracking indicators and sub-indicators for the Prevention Agenda 2013-2018: New York State’s Health Improvement Plan. A health tracking indicator is a metric through which progress on a certain area of health improvement can be assessed. The indicators are organized by the Priority Area of the Prevention Agenda as well as the Focus Area under each Priority Area. Each dataset includes tracking indicators for the five Priority Areas of the Prevention Agenda 2013-2018. The most recent year dataset includes the most recent county level data for all indicators. The trend dataset includes the most recent county level data and historical data, where available. Each dataset also includes the Prevention Agenda 2018 state targets for the indicators. Sub-indicators are included in these datasets to measure health disparities among socioeconomic groups.

  • API

    All Payer Patient Safety Indicators (PSI) Area Measures by Patient County: Beginning 2009

    health.data.ny.gov | Last Updated 2018-03-22T18:34:01.000Z

    The datasets contain hospital discharges counts (numerators, denominators, volume counts), observed, expected and risk-adjusted rates with corresponding 95% confidence intervals for Patient Safety Indicators generated using methodology developed by Agency for Healthcare Research and Quality (AHRQ). The PSIs are a set of indicators providing information on potential in hospital complications and adverse events following surgeries, procedures, and childbirth. The PSIs were developed by AHRQ after a comprehensive literature review, analysis of ICD-9-CM codes, review by a clinician panel, implementation of risk adjustment, and empirical analyses. All PSI measures were calculated using Statewide Planning and Research Cooperative System (SPARCS) inpatient data beginning 2009. US Census data files provided by AHRQ were used to derive denominators for county level (area level) PSI measures. The mortality, volume and utilization measures PSIs are presented by hospital as rates or counts. Area-level measures are presented by county as rates.

  • API

    All Payer Inpatient Quality Indicators (IQI) Area Measures by Patient County (SPARCS): Beginning 2009

    health.data.ny.gov | Last Updated 2018-02-16T16:02:45.000Z

    The datasets contain hospital discharges counts (numerators, denominators, volume counts), observed, expected and risk-adjusted rates with corresponding 95% confidence intervals for IQIs generated using methodology developed by Agency for Healthcare Research and Quality (AHRQ). The IQIs are a set of measures that provide a perspective on hospital quality of care using hospital administrative data. These indicators reflect quality of care inside hospitals and include inpatient mortality for certain procedures and medical conditions; utilization of procedures for which there are questions of overuse, underuse, and misuse; and volume of procedures for which there is some evidence that a higher volume of procedures is associated with lower mortality. All the IQI measures were calculated using Statewide Planning and Research Cooperative System (SPARCS) inpatient data beginning 2009. US Census data files provided by AHRQ were used to derive denominators for county level (area level) IQI measures. The mortality, volume and utilization measures IQIs are presented by hospital as rates or counts. Area-level utilization measures are presented by county as rates.

  • API

    Iowa Medicaid Payments & Recipients by Month and County

    data.iowa.gov | Last Updated 2018-06-27T22:01:27.000Z

    This dataset contains aggregate Medicaid payments, and counts for eligible recipients and recipients served by month and county in Iowa, starting with month ending 1/31/2011. Eligibility groups are a category of people who meet certain common eligibility requirements. Some Medicaid eligibility groups cover additional services, such as nursing facility care and care received in the home. Others have higher income and resource limits, charge a premium, only pay the Medicare premium or cover only expenses also paid by Medicare, or require the recipient to pay a specific dollar amount of their medical expenses. Eligible Medicaid recipients may be considered medically needy if their medical costs are so high that they use up most of their income. Those considered medically needy are responsible for paying some of their medical expenses. This is called meeting a spend down. Then Medicaid would start to pay for the rest. Think of the spend down like a deductible that people pay as part of a private insurance plan.