The population count of New Hampshire was 1,343,622 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 New Hampshire

  • API

    NYCHA Resident Data Book Summary

    data.cityofnewyork.us | Last Updated 2020-02-08T00:56:30.000Z

    Contains resident demographic data at a summary level as of January 1, 2019. The Resident Data Book is compiled to serve as an information source for queries involving resident demographic as well as a source of data for internal analysis. Statistics are compiled via HUD mandated annual income reviews involving NYCHA Staff and residents. Data is then aggregated and compiled by development. Each record pertains to a single public housing development.

  • API

    Census Data - Selected socioeconomic indicators in Chicago, 2008 – 2012

    data.cityofchicago.org | Last Updated 2014-09-12T20:56:56.000Z

    This dataset contains a selection of six socioeconomic indicators of public health significance and a “hardship index,” by Chicago community area, for the years 2008 – 2012. The indicators are the percent of occupied housing units with more than one person per room (i.e., crowded housing); the percent of households living below the federal poverty level; the percent of persons in the labor force over the age of 16 years that are unemployed; the percent of persons over the age of 25 years without a high school diploma; the percent of the population under 18 or over 64 years of age (i.e., dependency); and per capita income. Indicators for Chicago as a whole are provided in the final row of the table. See the full dataset description for more information at: https://data.cityofchicago.org/api/views/fwb8-6aw5/files/A5KBlegGR2nWI1jgP6pjJl32CTPwPbkl9KU3FxlZk-A?download=true&filename=P:\EPI\OEPHI\MATERIALS\REFERENCES\ECONOMIC_INDICATORS\Dataset_Description_socioeconomic_indicators_2012_FOR_PORTAL_ONLY.pdf

  • API

    Provisional Death Counts for Coronavirus Disease (COVID-19): Weekly State-Specific Data Updates

    data.cdc.gov | Last Updated 2020-07-08T19:37:34.000Z

    This report provides a weekly summary of deaths with coronavirus disease 2019 (COVID-19) by select geographic and demographic variables. In this release, counts of deaths are provided by the race and Hispanic origin of the decedent. Topics will be added to the release as they become available. These provisional counts are based on a current flow of mortality data in the National Vital Statistics System. National provisional counts include deaths occurring within the 50 states and the District of Columbia that have been received and coded as of the date specified. Data shown on this page may be incomplete and will likely not include all deaths that occurred during a given time period, especially for the more recent time periods. Data on this page are revised weekly and may increase or decrease as new and updated death certificate data are received from the states by NCHS. COVID-19 death counts shown here may differ from other published sources, as data currently are lagged by an average of 1–2 weeks. Weighted population distributions more accurately reflect race/ethnic distributions of the geographic locations where COVID outbreaks are occurring (see below for the methods used to calculate weighted percentages). The weighted population distributions ensure that the population estimates and percentages of COVID-19 deaths represent comparable geographic areas, in order to provide information about whether certain racial and ethnic subgroups are experiencing a disproportionate burden of COVID-19 mortality. See Table 2 below for unweighted populations. Estimated distributions of COVID-19 deaths and population size by race and Hispanic origin The percentages of COVID-19 deaths by race and Hispanic origin were calculated by dividing the number of COVID-19 deaths for each race and Hispanic origin group by the total number of COVID-19 deaths. Percentages may not sum to 100 due to rounding. The distribution of deaths involving COVID-19 by race/ethnicity should not be compared to the race/ethnicity distribution of the U.S. population because COVID-19 deaths are concentrated in certain geographic locations where the racial and ethnic population distribution differs from that of the United States overall. Additionally, COVID-19 deaths are concentrated in certain areas within states, and it is therefore not appropriate to compare the percent of COVID-19 deaths by race/ethnicity to the racial/ethnic population distribution of a given state. To make the estimated population distribution more comparable to the geographic areas where COVID-19 deaths are occurring, weighted population distributions are provided in this report. The weighted population distributions were calculated as follows. County-level population counts by race and Hispanic origin were multiplied by the corresponding total count of COVID-19 deaths by county (of residence). These weighted counts were then summed to the state (or national) level. The percentage of the population within each race and Hispanic origin group by state (or for the U.S.) was then estimated using these weighted counts. Counties with no COVID-19 deaths received a weight of zero, and thus do not contribute to the weighted population totals. Population counts for counties with large numbers of COVID-19 deaths are upweighted proportional to their numbers of COVID-19 deaths. These weighted population distributions ensure that the population estimates and percentages of COVID-19 deaths represent comparable geographic areas, in order to provide information about whether certain racial and ethnic subgroups are experiencing a disproportionate burden of COVID-19 mortality. For example, assume that 75% of the total number of COVID deaths occurred in a single county, County X, while the other 25% of COVID deaths occurred in County Y, and all other counties reported zero deaths. The weighted population counts for County X would contribute 75% of the total population counts, while the population counts for Count

  • API

    New York State Population Data: Beginning 2003

    health.data.ny.gov | Last Updated 2019-09-30T15:01:56.000Z

    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

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

    health.data.ny.gov | Last Updated 2018-03-22T19:12:25.000Z

    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 2018-03-16T12:36:59.000Z

    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

    NCHS - Birth Rates for Unmarried Women by Age, Race, and Hispanic Origin: United States

    data.cdc.gov | Last Updated 2020-06-05T17:33:34.000Z

    This dataset includes birth rates for unmarried women by age group, race, and Hispanic origin in the United States since 1970. Methods for collecting information on marital status changed over the reporting period and have been documented in: • Ventura SJ, Bachrach CA. Nonmarital childbearing in the United States, 1940–99. National vital statistics reports; vol 48 no 16. Hyattsville, Maryland: National Center for Health Statistics. 2000. Available from: http://www.cdc.gov/nchs/data/nvsr/nvsr48/nvs48_16.pdf. • National Center for Health Statistics. User guide to the 2013 natality public use file. Hyattsville, Maryland: National Center for Health Statistics. 2014. Available from: http://www.cdc.gov/nchs/data_access/VitalStatsOnline.htm. National data on births by Hispanics origin exclude data for Louisiana, New Hampshire, and Oklahoma in 1989; for New Hampshire and Oklahoma in 1990; for New Hampshire in 1991 and 1992. Information on reporting Hispanic origin is detailed in the Technical Appendix for the 1999 public-use natality data file (see (ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/DVS/natality/Nat1999doc.pdf.) All birth data by race before 1980 are based on race of the child. Starting in 1980, birth data by race are based on race of the mother. SOURCES CDC/NCHS, National Vital Statistics System, birth data (see http://www.cdc.gov/nchs/births.htm); public-use data files (see http://www.cdc.gov/nchs/data_access/Vitalstatsonline.htm); and CDC WONDER (see http://wonder.cdc.gov/). REFERENCES 1. Curtin SC, Ventura SJ, Martinez GM. Recent declines in nonmarital childbearing in the United States. NCHS data brief, no 162. Hyattsville, MD: National Center for Health Statistics. 2014. Available from: http://www.cdc.gov/nchs/data/databriefs/db162.pdf. 2. Martin JA, Hamilton BE, Osterman MJK, et al. Births: Final data for 2015. National vital statistics reports; vol 66 no 1. Hyattsville, MD: National Center for Health Statistics. 2017. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr66/nvsr66_01.pdf.

  • API

    Census Demographics 2010

    data.baltimorecity.gov | Last Updated 2017-02-06T04:44:33.000Z

    BNIA-JFI analyzed data from the Census to provide greater understandingof the socioeconomic and demographic characteristics of the residents of the City and its neighborhoods . BNIA-JFI also used this data as denominators for many of the Vital Signs indicators allowing for data to be normalized and rates to be computed. Census data analyzed by BNIA-JFI is grouped into the following categories: population, race and ethnicity; households and families; and income.

  • API

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

    health.data.ny.gov | Last Updated 2018-01-24T16:46:31.000Z

    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

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

    health.data.ny.gov | Last Updated 2018-01-24T16:43:17.000Z

    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).