- API data.pa.gov | Last Updated 2019-03-14T19:22:02.000Z
Countywide counts of maternal hospital stays with opioid use and countywide rates of maternal hospital stays with opioid use per 1,000 maternal stays. Maternal stays include those involving a delivery, as well as other pregnancy-related stays. Opioid use, or opioid use disorder, is a diagnosis indicating opioid dependence, abuse, or use. Some opioid drugs may be prescribed as part of medication-assisted treatment to relieve withdrawal symptoms and psychological cravings often associated with opioid use disorders. Opioid use during pregnancy can lead to Neonatal Abstinence Syndrome (NAS) for newborns. This analysis is restricted to maternal hospital stays for Pennsylvania-state residents who were hospitalized in Pennsylvania hospitals. Disclaimer: PHC4’s database contains statewide hospital discharge data submitted to PHC4 by Pennsylvania hospitals. Every reasonable effort has been made to ensure the accuracy of the information obtained from the Uniform Claims and Billing Form (UB-82/92/04) data elements. Computer collection edits and validation edits provide opportunity to correct specific errors that may have occurred prior to, during or after submission of data. The ultimate responsibility for data accuracy lies with individual providers. PHC4 agents and staff make no representation, guarantee, or warranty, expressed or implied that the data received from the hospitals are error-free, or that the use of this data will prevent differences of opinion or disputes with those who use published reports or purchased data. PHC4 will bear no responsibility or liability for the results or consequences of its use.
Workforce Innovation Opportunity Act (WIOA) Title III Performance Accountability Metrics PY 2017-2018 - Current Annual Labor and Industrydata.pa.gov | Last Updated 2020-01-14T19:22:20.000Z
A comprehensive collection of data that assesses the effectiveness of Pennsylvania in achieving positive outcomes for individuals served by the workforce development system’s Title III Wagner-Peyser (Labor Exchange) program. Data is compiled in compliance with US Department of Labor’s Employment and Training Administration guidance on Workforce Innovation and Opportunity Act (WIOA) Performance Accountability. Data is available for the state and each of the CareerLink® offices in the commonwealth.
- API data.pa.gov | Last Updated 2020-07-29T17:51:35.000Z
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 2020-07-29T18:45:44.000Z
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.
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.
- API data.pa.gov | Last Updated 2018-09-20T14:30:44.000Z
This data set provides an estimate of the number of people aged 15-34 years with newly identified confirmed chronic (or past/present) hepatitis C infection, by county and by year. The dataset is limited to persons aged 15 to 34 because hepatitis C infection is usually asymptomatic for decades after infection occurs. Cases are usually identified because they have finally become symptomatic, or they were screened. Until very recently, screening for hepatitis C was not routinely performed. This makes it very challenging to identify persons with recent infection. Limiting the age of newly identified patients to 15-34 years makes it more likely that the cases included in the dashboard were infected fairly recently. It is not meant to imply that the opioid crisis’ effect on hepatitis C transmission is limited to younger people. The process by which case counts are determined is as follows: Case reports, which include lab test results and address data, are sent to Pennsylvania’s electronic disease surveillance system (PA-NEDSS). Confirmation status is determined by public health investigators who evaluate test results against the CDC case definition for hepatitis C in place for the year in which the patient was first reported (https://wwwn.cdc.gov/nndss/conditions/hepatitis-c-chronic/). Reportable disease data, including hepatitis C, is extracted from PA-NEDSS, combined with similar data sent by the Philadelphia Department of Public Health (PDPH, which uses a separate surveillance system), and sent to CDC. Case data sent to CDC (from PA-NEDSS and PDPH combined) are used to create a statewide reportable disease dataset. This statewide file was used to generate the dashboard dataset. Note that the term that CDC has used to denote persons with hepatitis C infection that is not known to be acute has switched back and forth between “Hepatitis C, past or present” and “Hepatitis C, chronic” over the past several years. The CDC case definition for hepatitis C, chronic (or past or present) changed in 2005, 2010, 2011, 2012, and 2016. Persons reported as confirmed in one year may not have been considered confirmed in another year. For example, patients with a positive radioimmunoblot assay (RIBA) or elevated enzyme immunoassay (EIA) signal-to-cutoff level were counted as confirmed in 2012, but not counted as confirmed in 2016. Data sent to CDC’s National Notifiable Disease Surveillance System use a measure for aggregating cases by year called the MMWR year. The MMWR, or the Morbidity and Mortality Weekly Report, is an official publication by CDC and the means by which CDC has historically presented aggregated case count data. Since data in the MMWR are presented by week, the MMWR year always starts on the Sunday closest to Jan 1 and ends on the Saturday closest to Dec 31. The most recent year for which case counts are finalized is 2016. Annual case counts are finalized in May of the following year. The patient zip code, as submitted to PA-NEDSS, is used to determine the case’s county of residence at the time of initial case report. In some instances, the patient zip code is unavailable. In those circumstances, the zip code of the provider that ordered the lab test is used as a proxy for patient zip code. Users should note that the state prison system routinely screens all incoming inmates for hepatitis C. If these inmates are determined to be confirmed cases, they are assigned to the county in which they were incarcerated when their confirmed hepatitis C was first identified. Hepatitis C case counts in counties with state prisons should be interpreted cautiously in light of this enhanced screening activity.
- 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.
- 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 2019-04-16T21:25:15.000Z
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 2020-08-03T13:31:38.000Z
This dataset contains the list of tobacco products tax licenses as maintained by the Department of Revenue (DOR) that are currently active. For the purposes of this dataset, active status indicates dealers authorized to handle tobacco products subject to Pennsylvania tax. This list is intended to be refreshed monthly, removing the licenses that are cancelled or expire without renewal, and adding new licenses once they are approved. The addresses provided are supposed to be the physical location where the taxable sales happen or taxable service provided. The DOR generally does not validate the location address, so there may be misspelled items. Tobacco products licenses must be conspicuously displayed at the location issued. In order to reduce fraud, a portion of the license number was masked. If you suspect someone is selling unstamped cigarettes, or selling tobacco products without a license in Pennsylvania, use this link to Report Tax Fraud. Tips can be left anonymously, but supporting documentation is helpful. More information about the tobacco products tax: PA Code, Article III Cigarette and Beverage Taxes. PA Department of Revenue website: https://www.revenue.pa.gov. Common questions: • Why are there out of state/out of country licenses? Tobacco products tax requires licensing at all levels before shipping to PA wholesalers and retailers. Some stampers, manufacturers or wholesalers maintain product outside PA and ship to PA retailers or wholesalers. The license should be the location the product is stored. • What is “Other Tobacco Products” (OTP)? The cigarette tax law was amended in 2016 to include electronic cigarettes and related liquid (commonly referred to as vaping), roll-your-own tobacco (loose tobacco that can be used to make cigarettes), snuff, pipe tobacco, and other tobacco products used for chewing, ingesting or smoking. More information is available from Revenue’s web site. • Does this list contain every tobacco product retailer? It should. In order to sell product legally in PA, a retailer must be licensed. There could be pending licenses at the time of publication – if you have a concern, use the Report Fraud link above.