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COVID-19 Federal Pharmacy Partners Long Term Care Facility Vaccine Clinics Current Health
data.pa.gov | Last Updated 2024-05-08T15:07:19.000ZThe long-term care facility clinic data shows the facilities that have clinics scheduled for a certain week. These clinics will be held by either CVS or Walgreens through their work to vaccinate within the Federal Pharmacy Partnership. The federal pharmacy partners dataset represents the clinics that CVS and Walgreens are holding for a given week at long-term care facilities that are part of the federal pharmacy mission. These are nursing homes, assisted living facilities, and other long-term care facilities receiving vaccinations. <br> For the Pfizer vaccination the clinics are 3-weeks apart. For the Moderna vaccination the clinics are 4-weeks apart.<br> This dataset will be updated Wednesday’s at 12:00pm.
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Post Secondary Enrollment Un-duplicated SY 2002-2003 - Current Education
data.pa.gov | Last Updated 2023-06-14T18:41:31.000ZThe purpose of the 12-Month Enrollment component of IPEDS is to collect unduplicated enrollment counts of all students enrolled for credit and instructional activity data in postsecondary institutions for an entire 12-month period. Data are collected by level of student and by race/ethnicity and gender. Instructional activity is collected as total credit and/or contact hours attempted at the undergraduate, graduate, and doctor's professional levels. Using the instructional activity data reported, a full-time equivalent (FTE) student enrollment at the undergraduate and graduate level is estimated.
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College and Career Readiness Indicators Current, Annual, Education
data.pa.gov | Last Updated 2023-05-17T19:22:00.000ZThese data describe the percentages of students that attained specific education milestones and objectives. The indictor-level definitions can be found here: <a href="https://futurereadypa.org/Home/Glossary">Future Ready Glossary</a> The Industry Based measured are for the 2020-2021 school year, and all other measures are for the 2018-2019 school year,
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Case Closing without a New Offense CY 2018 By County as Reported by the Juvenile Court Judges' Commission
data.pa.gov | Last Updated 2022-10-17T20:21:58.000ZThe juvenile justice system has a responsibility to protect the community from known juvenile offenders. Juveniles who do not commit a new offense while under court supervision have a higher probability of remaining crime free. This data represents the number and percentage of juveniles who successfully completed supervision without a new offense resulting in a Consent Decree, Adjudication of Delinquency, ARD, Nolo Contendere, or finding of guilt in a criminal procedure. <br/> CY - Calendar Year This dataset is contained within the Juvenile Court Judges' Commission’s 2018 Statewide Juvenile Justice Outcome Measure report: this report reflects outcomes of juvenile offenders whose cases were closed during the report period and who have received a period of supervision from a county juvenile probation department. These reported outcomes are associated with community protection, accountability, and competency development; three core goals of Pennsylvania's juvenile justice system. Since 2009, county juvenile probation departments have supervised and closed 126,006 cases. The proportion of cases closed successfully (without a new offense) during this time is 84.1%. For the year 2018, the proportion of cases closed successfully was 85.4%.
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Post Secondary Completions Total Awards/Degrees SY 2015-16 - Current Education
data.pa.gov | Last Updated 2023-06-14T18:42:27.000ZA listing of completion counts by Institution name, School year and the type of Degrees.
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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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Educational Program Report 2017 - Current Education
data.pa.gov | Last Updated 2023-06-14T18:35:35.000ZA report completed for the type of credentialed awards at each Administrative Unit by type of Major or Program area of study including the type of instruction.
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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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Industry Certifications Offered By Career and Technical Education Programs SY 2016-2022 - Current Education
data.pa.gov | Last Updated 2022-07-24T16:33:38.000ZThis dataset contains the Industry Certifications offered by each secondary Career and Technical Education (CTE) program for 2016 school year to the current period. For additional information on Career and Technical Education visit <a href="https://www.education.pa.gov/K-12/Career%20and%20Technical%20Education/Pages/default.aspx">Career and Technical Education</a>.
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COVID-19 Aggregate Cases NO FURTHER UPDATES
data.pa.gov | Last Updated 2024-05-08T14:25:45.000ZWeekly updates have finished with the June 28th update. Some information may be found here: https://covid.cdc.gov/covid-data-tracker/#maps_new-admissions-rate-state <br> This dataset contains aggregate COVID-19 case counts and rates by date of first report for all counties in Pennsylvania and for the state as a whole. Counts include both confirmed and probable cases as defined by the Council of State and Territorial Epidemiologists (CSTE). At present, a person is counted as a case only once. Note that case counts by date of report are influenced by a variety of factors, including but not limited to testing availability, test ordering patterns (such as day of week patterns), labs reporting backlogged test results, and mass screenings in nursing homes, workplaces, schools, etc. Case reports received without a patient address are assigned to the county of the ordering provider or facility based on provider zip code. Cases reported with a residential address that does not match to a known postal address per the commonwealth geocoding service are assigned to a county based on the zip code of residence. Many zip codes cross county boundaries so there is some degree of misclassification of county. All counts may change on a daily basis due to reassignment of jurisdiction, removal of duplicate case reports, correction of errors, and other daily data cleaning activities. Downloaded data represents the best information available as of the previous day. <br> Data will be updated between 11:30 am to 1:30pm each Wednesday.