- API data.pa.gov | Last Updated 2019-02-15T14:38:30.000Z
OverdoseFreePA OverdoseFreePA is made possible by the Pennsylvania Commission on Crime and Delinquency, and is directed and managed by the Pennsylvania Overdose Reduction Technical Assistance Center (TAC), University of Pittsburgh School of Pharmacy. The website is a result of collaboration with county and state partners across the Commonwealth of Pennsylvania. Our partnerships include: Pennsylvania District Attorneys Association Pennsylvania Medical Society Pennsylvania Pharmacist Association Pennsylvania Psychiatric Society The Hospital and Healthsystem Association of Pennsylvania Pennsylvania Dental Association Drug Enforcement Administration 360 Strategy There are a growing number of Pennsylvania counties involved in ramping up overdose prevention, treatment, and recovery activities to address the opioid overdose epidemic. The counties involved are collaborating to develop resources that can be used by all Pennsylvanians to increase community awareness and knowledge of overdose and overdose prevention strategies as well as to support initiatives aimed at decreasing drug overdoses and deaths within the participating counties. As a centralized resource and technical assistance hub, OverdoseFreePA is a central repository for these efforts to facilitate increased treatment and prevention efforts in these communities. Pennsylvania Opioid Overdose Reduction Technical Assistance Center (TAC) Pennsylvania, and the nation at large, is in the midst of opioid overdose epidemic. The TAC’s vision is to lead Pennsylvania communities to zero overdoses.The TAC hopes to achieve this vision by providing concierge technical assistance in the form of data driven recommendations and customized strategic planning to counties working to eliminate overdoses. The TAC strives to lead the field in identifying and sharing strategies to eliminate overdose through the central repository of OverdoseFreePA. Based out of the Program Evaluation and Research Unit (PERU) at the University of Pittsburgh’s School of Pharmacy, the TAC assists counties and communities in assessing needs, building capacity to address the needs, developing and implementing data driven plans with high quality outcomes, and sustaining initiatives to eliminate overdoses, both fatal and non-fatal, throughout Pennsylvania. More information here -http://www.overdosefreepa.pitt.edu/who-we-are/
Consumer Guide to Private Health Insurance Coverage for Mental Health and Substance Use Disorder Insurancedata.pa.gov | Last Updated 2019-02-15T14:41:56.000Z
Treatment for mental health and substance use disorders, also known as drug and alcohol issues, is essential to the health and wellbeing of Pennsylvanians. And, insurance coverage for mental health and substance use disorder benefits is critical to ensuring consumers can access and afford these services. In many cases, the laws and regulations governing insurance companies require certain services to be covered in certain ways. In order to ensure Pennsylvanians understand what benefits they are guaranteed access to, the Pennsylvania Insurance Department has put together this consumer guide to health insurance coverage for mental health and substance use disorder treatment in the Commonwealth.
- API data.pa.gov | Last Updated 2020-01-27T15:59:19.000Z
These 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>
- API data.pa.gov | Last Updated 2017-09-28T18:49:43.000Z
The 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.
- API data.pa.gov | Last Updated 2017-07-19T15:39:08.000Z
Data provides 2013-2014 undergraduate student enrollment in engineering, biological and biomedical science, mathematics and physical sciences at Pennsylvania’s publicly supported institutions. Data is collected in the Spring and reported out in the Fall
- API data.pa.gov | Last Updated 2019-04-16T21:25:15.000Z
This 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.
- API data.pa.gov | Last Updated 2019-07-19T15:11:53.000Z
This is a connection to the Pennsylvania Spatial Data Access (PASDA) Pennsylvania's official public access geospatial information clearinghouse. PASDA was developed in 1996 by the Pennsylvania State University and has served as the clearinghouse for Pennsylvania for over twenty years. PASDA is a cooperative project of the Governor's Office of Administration, Office for Information Technology, and Penn State Institutes of Energy and the Environment of the Pennsylvania State University. Funding and support is provided by the Pennsylvania Office for Information Technology. Penn State contributions include system administration support and infrastructure from the Institute for CyberScience, and the College of Earth and Mineral Sciences. PASDA was developed as a service to the citizens of the Commonwealth of Pennsylvania. The purpose of PASDA is to serve as the Commonwealth's comprehensive and coordinated open geospatial data portal that provides free public access to geospatial data and information by, for, and about the Commonwealth of Pennsylvania. PASDA is Pennsylvania's node on the National Spatial Data Infrastructure,Geospatial One-Stop, and is integrated with the National States Geographic Information Council GIS Inventory.
- API data.pa.gov | Last Updated 2018-03-28T18:41:40.000Z
A listing of completion counts by Institution name, School year and the type of Degrees.
- API data.pa.gov | Last Updated 2019-04-01T15:15:07.000Z
This 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.
Uninsured Population Census Data 5-year estimates for release years 2017-Current County Human Services and Insurancedata.pa.gov | Last Updated 2020-06-25T15:02:07.000Z
The 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.