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The population count of York County, ME was 203,102 in 2018. The population count of Rensselaer County, NY was 159,431 in 2018.

Population

Population Change

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

1. ODN datasets and APIs are subject to change and may differ in format from the original source data in order to provide a user-friendly experience on this site.

2. To build your own apps using this data, see the ODN Dataset and API links.

3. If you use this derived data in an app, we ask that you provide a link somewhere in your applications to the Open Data Network with a citation that states: "Data for this application was provided by the Open Data Network" where "Open Data Network" links to http://opendatanetwork.com. Where an application has a region specific module, we ask that you add an additional line that states: "Data about REGIONX was provided by the Open Data Network." where REGIONX is an HREF with a name for a geographical region like "Seattle, WA" and the link points to this page URL, e.g. http://opendatanetwork.com/region/1600000US5363000/Seattle_WA

Demographics and Population Datasets Involving Rensselaer County, NY or York County, ME

  • API

    Hospital Inpatient Prevention Quality Indicators (PQI) for Adult Discharges by County (SPARCS): Beginning 2009

    health.data.ny.gov | Last Updated 2023-01-26T19:47:59.000Z

    This is one of two datasets that contain observed and expected rates for Agency for Healthcare Research and Quality Prevention Quality Indicators – Adult (AHRQ PQI) beginning in 2009. This dataset is at the county level. The Agency for Healthcare Research and Quality (AHRQ) Prevention Quality Indicators (PQIs) are a set of population based measures that can be used with hospital inpatient discharge data to identify ambulatory care sensitive conditions. These are conditions where 1) the need for hospitalization is potentially preventable with appropriate outpatient care, or 2) conditions that could be less severe if treated early and appropriately. All PQIs apply only to adult populations (over the age of 18 years). The rates were calculated using Statewide Planning and Research Cooperative System (SPARCS) inpatient data and Claritas population information. The observed rates and expected rates for each AHRQ PQI is presented by either resident county (including a statewide total) or resident zip code (including a statewide total).

  • API

    Hospital Inpatient Prevention Quality Indicators (PQI) for Adult Discharges by Zip Code (SPARCS): Beginning 2009

    health.data.ny.gov | Last Updated 2023-01-27T17:26:56.000Z

    This dataset is one of two datasets that contain observed and expected rates for Agency for Healthcare Research and Quality Prevention Quality Indicators – Adult (AHRQ PQI) beginning in 2009. The observed rates and expected rates for each AHRQ PQI is presented by either resident county (including a statewide total) or resident zip code (including a statewide total).

  • API

    Medicaid Inpatient Prevention Quality Indicators (PQI) for Adult Discharges by Patient County: Beginning 2011

    health.data.ny.gov | Last Updated 2016-12-05T21:58:39.000Z

    The datasets contain number of Medicaid PQI hospitalizations (numerator), county Medicaid population (denominator), observed rate, expected number of hospitalizations and rate, and risk-adjusted rate for Agency for Healthcare Research and Quality Prevention Quality Indicators – Adult (AHRQ PQI) for Medicaid enrollees beginning in 2011.

  • API

    All Payer Potentially Preventable Emergency Visit (PPV) Rates by Patient County (SPARCS): Beginning 2011

    health.data.ny.gov | Last Updated 2023-06-02T18:19:21.000Z

    The datasets contain Potentially Preventable Visit (PPV) observed, expected, and risk-adjusted rates for all payer beneficiaries by patient county and patient zip code beginning in 2011. The Potentially Preventable Visits (PPV), obtained from software created by 3M Health Information Systems, are emergency visits that may result from a lack of adequate access to care or ambulatory care coordination. These ambulatory sensitive conditions could be reduced or eliminated with adequate patient monitoring and follow up. The rates were calculated using Statewide Planning and Research Cooperative System (SPARCS) inpatient and outpatient data and Claritas population information. The observed, expected and risk adjusted rates for PPV are presented by either resident county (including a statewide total) or resident zip code (including a statewide total).

  • API

    Medicaid Inpatient Prevention Quality Indicators (PDI) for Pediatric Discharges by Patient County: Beginning 2011

    health.data.ny.gov | Last Updated 2016-12-16T16:16:51.000Z

    The datasets contain number of Medicaid PDI hospitalizations (numerator), county or zip Medicaid population (denominator), observed rate, expected number of hospitalizations and rate, and risk-adjusted rate for Agency for Healthcare Research and Quality Pediatric Quality Indicators – Pediatric (AHRQ PDI) for Medicaid enrollees beginning in 2011. The Agency for Healthcare Research and Quality (AHRQ) Pediatric Quality Indicators (PDIs) are a set of population based measures that can be used with hospital inpatient discharge data to identify ambulatory care sensitive conditions. These are conditions where 1) the need for hospitalization is potentially preventable with appropriate outpatient care, or 2) conditions that could be less severe if treated early and appropriately. Both the Urinary Tract Infection and Gastroenteritis PDIs include admissions for patients aged 3 months through 17 years. The asthma PDI includes admissions for patients aged 2 through 17 years. Eligible admissions for the Diabetes Short-term Complications PDI includes admissions for patients aged 6 through 17 years.

  • API

    New York State Population Data: Beginning 2003

    health.data.ny.gov | Last Updated 2024-03-07T16:13:08.000Z

    Population data file is provided as an additional reference file when interpreting vital statistics death rates. The population data is derived from the corresponding release of the NCHS annual estimates of "Bridged Race Vintage" which are consistent with the Bureau of the Census estimates from "Vintage" (released in the summer). For more information, check out: http://www.health.ny.gov/statistics/vital_statistics/. The "About" tab contains additional details concerning this dataset.

  • API

    All Payer Potentially Preventable Emergency Visit (PPV) Rates by Patient Zip Code (SPARCS): Beginning 2011

    health.data.ny.gov | Last Updated 2023-06-02T18:20:35.000Z

    The datasets contain Potentially Preventable Visit (PPV) observed, expected, and risk-adjusted rates for all payer beneficiaries by patient county and patient zip code beginning in 2011. The Potentially Preventable Visits (PPV), obtained from software created by 3M Health Information Systems, are emergency visits that may result from a lack of adequate access to care or ambulatory care coordination. These ambulatory sensitive conditions could be reduced or eliminated with adequate patient monitoring and follow up.

  • API

    Medicaid Potentially Preventable Emergency Visit (PPV) Rates by Patient County: Beginning 2011

    health.data.ny.gov | Last Updated 2016-12-16T15:57:37.000Z

    The dataset contains Potentially Preventable Visit (PPV) observed, expected, and risk-adjusted rates for Medicaid beneficiaries by patient county beginning in 2011. The Potentially Preventable Visits (PPV) obtained from software created by 3M Health Information Systems are emergency visits that may result from a lack of adequate access to care or ambulatory care coordination. These ambulatory sensitive conditions could be reduced or eliminated with adequate patient monitoring and follow up.

  • API

    Participating Lenders with SONYMA (State of New York Mortgage Agency)

    data.ny.gov | Last Updated 2019-06-10T18:02:02.000Z

    This is a current listing of participating State of New York Mortgage Agency (SONYMA) lenders, their main office addresses and phone numbers, their websites, the SONYMA regions they service and the regional contact phone number.

  • API

    NYSERDA Low- to Moderate-Income New York State Census Population Analysis Dataset: Average for 2013-2015

    data.ny.gov | Last Updated 2019-11-15T22:30:02.000Z

    How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov. The Low- to Moderate-Income (LMI) New York State (NYS) Census Population Analysis dataset is resultant from the LMI market database designed by APPRISE as part of the NYSERDA LMI Market Characterization Study (https://www.nyserda.ny.gov/lmi-tool). All data are derived from the U.S. Census Bureau’s American Community Survey (ACS) 1-year Public Use Microdata Sample (PUMS) files for 2013, 2014, and 2015. Each row in the LMI dataset is an individual record for a household that responded to the survey and each column is a variable of interest for analyzing the low- to moderate-income population. The LMI dataset includes: county/county group, households with elderly, households with children, economic development region, income groups, percent of poverty level, low- to moderate-income groups, household type, non-elderly disabled indicator, race/ethnicity, linguistic isolation, housing unit type, owner-renter status, main heating fuel type, home energy payment method, housing vintage, LMI study region, LMI population segment, mortgage indicator, time in home, head of household education level, head of household age, and household weight. The LMI NYS Census Population Analysis dataset is intended for users who want to explore the underlying data that supports the LMI Analysis Tool. The majority of those interested in LMI statistics and generating custom charts should use the interactive LMI Analysis Tool at https://www.nyserda.ny.gov/lmi-tool. This underlying LMI dataset is intended for users with experience working with survey data files and producing weighted survey estimates using statistical software packages (such as SAS, SPSS, or Stata).