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Uninsured Population Census Data CY 2009-2014 Human Services
data.pa.gov | Last Updated 2022-10-18T14:19:11.000ZThis data is pulled from the U.S. Census website. This data is for years Calendar Years 2009-2014. Product: SAHIE File Layout Overview Small Area Health Insurance Estimates Program - SAHIE Filenames: SAHIE Text and SAHIE CSV files 2009 – 2014 Source: Small Area Health Insurance Estimates Program, U.S. Census Bureau. Internet Release Date: May 2016 Description: Model‐based Small Area Health Insurance Estimates (SAHIE) for Counties and States File Layout and Definitions The Small Area Health Insurance Estimates (SAHIE) program was created to develop model-based estimates of health insurance coverage for counties and states. This program builds on the work of the Small Area Income and Poverty Estimates (SAIPE) program. SAHIE is only source of single-year health insurance coverage estimates for all U.S. counties. For 2008-2014, SAHIE publishes STATE and COUNTY estimates of population with and without health insurance coverage, along with measures of uncertainty, for the full cross-classification of: •5 age categories: 0-64, 18-64, 21-64, 40-64, and 50-64 •3 sex categories: both sexes, male, and female •6 income categories: all incomes, as well as income-to-poverty ratio (IPR) categories 0-138%, 0-200%, 0-250%, 0-400%, and 138-400% of the poverty threshold •4 races/ethnicities (for states only): all races/ethnicities, White not Hispanic, Black not Hispanic, and Hispanic (any race). In addition, estimates for age category 0-18 by the income categories listed above are published. Each year’s estimates are adjusted so that, before rounding, the county estimates sum to their respective state totals and for key demographics the state estimates sum to the national ACS numbers insured and uninsured. This program is partially funded by the Centers for Disease Control and Prevention's (CDC), National Breast and Cervical Cancer Early Detection ProgramLink to a non-federal Web site (NBCCEDP). The CDC have a congressional mandate to provide screening services for breast and cervical cancer to low-income, uninsured, and underserved women through the NBCCEDP. Most state NBCCEDP programs define low-income as 200 or 250 percent of the poverty threshold. Also included are IPR categories relevant to the Affordable Care Act (ACA). In 2014, the ACA will help families gain access to health care by allowing Medicaid to cover families with incomes less than or equal to 138 percent of the poverty line. Families with incomes above the level needed to qualify for Medicaid, but less than or equal to 400 percent of the poverty line can receive tax credits that will help them pay for health coverage in the new health insurance exchanges. We welcome your feedback as we continue to research and improve our estimation methods. The SAHIE program's age model methodology and estimates have undergone internal U.S. Census Bureau review as well as external review. See the SAHIE Methodological Review page for more details and a summary of the comments and our response. The SAHIE program models health insurance coverage by combining survey data from several sources, including: •The American Community Survey (ACS) •Demographic population estimates •Aggregated federal tax returns •Participation records for the Supplemental Nutrition Assistance Program (SNAP), formerly known as the Food Stamp program •County Business Patterns •Medicaid •Children's Health Insurance Program (CHIP) participation records •Census 2010 Margin of error (MOE). Some ACS products provide an MOE instead of confidence intervals. An MOE is the difference between an estimate and its upper or lower confidence bounds. Confidence bounds can be created by adding the margin of error to the estimate (for the upper bound) and subtracting the margin of error from the estimate (for the lower bound). All published ACS margins of error are based on a 90-percent confidence level.
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Uninsured Population Census Data 5-year estimates for release years 2017-Current County Human Services and Insurance
data.pa.gov | Last Updated 2022-02-21T19:25:39.000ZThe American Community Survey (ACS) helps local officials, community leaders, and businesses understand the changes taking place in their communities. It is the premier source for detailed population and housing information about our nation. This dataset provides estimates by county for Health Insurance Coverage and is summarized from summary table S2701: SELECTED CHARACTERISTICS OF HEALTH INSURANCE COVERAGE IN THE UNITED STATES. The 5-year estimates are used to provide detail on every county in Pennsylvania and includes breakouts by Age, Gender, Race, Ethnicity, Household Income, and the Ratio of Income to Poverty. An blank cell within the dataset indicates that either no sample observations or too few sample observations were available to compute the statistic for that area. Margin of error (MOE). Some ACS products provide an MOE instead of confidence intervals. An MOE is the difference between an estimate and its upper or lower confidence bounds. Confidence bounds can be created by adding the margin of error to the estimate (for the upper bound) and subtracting the margin of error from the estimate (for the lower bound). All published ACS margins of error are based on a 90-percent confidence level. While an ACS 1-year estimate includes information collected over a 12-month period, an ACS 5-year estimate includes data collected over a 60-month period. In the case of ACS 1-year estimates, the period is the calendar year (e.g., the 2015 ACS covers the period from January 2015 through December 2015). In the case of ACS multiyear estimates, the period is 5 calendar years (e.g., the 2011–2015 ACS estimates cover the period from January 2011 through December 2015). Therefore, ACS estimates based on data collected from 2011–2015 should not be labeled “2013,” even though that is the midpoint of the 5-year period. Multiyear estimates should be labeled to indicate clearly the full period of time (e.g., “The child poverty rate in 2011–2015 was X percent.”). They do not describe any specific day, month, or year within that time period.
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Campaign Finance Disclosure Expense Data Current State
data.pa.gov | Last Updated 2019-04-16T21:25:15.000ZThis file contains information about expenditures made by candidates, lobbyists or committees for the purpose of influencing elections. It includes the identification number of the filer and information about the election (s) and filing cycle (s) during which expenditures were made, as well as general information about the payees. The data is also available and searchable on www.campaignfinanceonline.pa.gov.
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Uninsured Population Census Data 1-year estimates 2017-Current Statewide Human Services and Insurance
data.pa.gov | Last Updated 2022-02-21T19:25:46.000ZThe American Community Survey (ACS) helps local officials, community leaders, and businesses understand the changes taking place in their communities. It is the premier source for detailed population and housing information about our nation. This dataset provides estimates for Health Insurance Coverage in Pennsylvania and is summarized from summary table S2701: SELECTED CHARACTERISTICS OF HEALTH INSURANCE COVERAGE IN THE UNITED STATES. A blank cell within the dataset indicates that either no sample observations or too few sample observations were available to compute the statistic for that area. Margin of error (MOE). Some ACS products provide an MOE instead of confidence intervals. An MOE is the difference between an estimate and its upper or lower confidence bounds. Confidence bounds can be created by adding the margin of error to the estimate (for the upper bound) and subtracting the margin of error from the estimate (for the lower bound). All published ACS margins of error are based on a 90-percent confidence level. While an ACS 1-year estimate includes information collected over a 12-month period, an ACS 5-year estimate includes data collected over a 60-month period. In the case of ACS 1-year estimates, the period is the calendar year (e.g., the 2015 ACS covers the period from January 2015 through December 2015).
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Dangerous Dogs 1996-Current County Agriculture
data.pa.gov | Last Updated 2020-02-27T14:35:08.000ZHistorical results of Dangerous Dogs in Pennsylvania. A dangerous dog is one that has: (1) Inflicted severe injury on a human being without provocation on public or private property. (2) Killed or inflicted severe injury on a domestic animal, dog or cat without provocation while off the owner’s property. (3) Attacked a human being without provocation. (4) Been used in the commission of a crime. And the dog has either or both of the following: (1) A history of attacking human beings and/or domestic animals, dogs or cats without provocation. (2) A propensity to attack human beings and/or domestic animals, dogs or cats without provocation. *A propensity to attack may be proven by a single incident. Severe injury is defined as, [3 P.S. § 459-102] “Any physical injury that results in broken bones or disfiguring lacerations requiring multiple sutures or cosmetic surgery.” More information can be found here - https://www.agriculture.pa.gov/Animals/DogLaw/Dangerous%20Dogs/Pages/default.aspx More information on Chapter 27 Regulations - https://www.agriculture.pa.gov/Animals/DogLaw/Dangerous%20Dogs/Documents/Chapter%2027%20Dangerous%20Dogs.pdf PDF's for Chapter 27 and Pennsylvania Dog Laws are attached to the metadata
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View using General Election Mail Ballot Request by County, Applicant Party Designation with Counts
data.pa.gov | Last Updated 2022-02-21T16:54:23.000ZThis view is created from the 2020 General Election Mail Ballot Requests dataset which describes the current state of mail ballot requests. It’s a snapshot in time of the current volume of ballot requests across the Commonwealth. This view is created to assist with analyzing the original dataset of over 3M rows. This view is an aggregated count of rows by County and by Party Designation. Original Dataset is here - https://data.pa.gov/Government-Efficiency-Citizen-Engagement/2020-General-Election-Mail-Ballot-Requests-Departm/mcba-yywm
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State Corrections Population June 2015-Current Timeline
data.pa.gov | Last Updated 2020-01-22T15:25:07.000ZOne of the governor’s goals related to public safety is the Department of Corrections will reduce its state correction population by 5% by 2020. DOC overall total population directly drives the Department’s budget. The baseline for the goal is the total population on June 30, 2015. On June 30, 2015, the Pennsylvania Department of Corrections overall population was 50,366. This dataset contains the total number of state corrections population in the Department’s custody at the end of each month, including those in prison, in contracted county jails, in community phases of the State Intermediate Punishment (SIP) program, in Parole Violator Centers (PVCs), and on temporary transfer to other jurisdictions. DOC publishes a Monthly Population Report to the DOC Website (www.cor.pa.gov). The information published to the website includes the data set and breakdown of populations in each institution.
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Lobbying Disclosure Data 2017 State
data.pa.gov | Last Updated 2022-09-23T13:54:37.000ZThis dataset contains summary information on lobbying expenses incurred during the 2017 calendar year. The data is also available and searchable on https://www.palobbyingservices.pa.gov.
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Drug and Alcohol Treatment Get Help Now Intake Hotline November 2016 - Current Statewide Drug and Alcohol Programs
data.pa.gov | Last Updated 2023-09-19T14:35:29.000ZThis dataset reports statewide and county numbers of calls and intakes by individuals seeking treatment from hotline staff since the inception of Pennsylvania’s Get Help Now Hotline, text line, and chat line in November 2016. When a field is blank the information is not available; these data were not collected at the time of the phone call.
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2021 General Election Mail Ballot Requests Department of State
data.pa.gov | Last Updated 2022-12-06T20:07:02.000Zhe dataset describes a current state of mail ballot requests for the 2021 General Election. It’s a snapshot in time of the current volume of ballot requests across the Commonwealth. The reason some birth dates will display as 1/1/1800 is due to confidentiality reasons of the registered voters. Usually this is for victims of domestic violence. <B> The following are considered UOCAVA: </B> <B> Application Type</B> <B> CRI</B> - Civilian - Remote/Isolated <B> CVO</B> - Civilian Overseas <B> F</B> - Federal (Unregistered) <B> M</B> - Military <B> MRI</B> - Military - Remote/Isolated <B> V</B> - Veteran <B> BV</B> - Bedridden Veteran <B> BVRI</B> - Bedridden Veteran - Remote/Isolated We may not have all types in the file for every election.