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.

Data will be updated between 9-11 am every day.

This View is created to group Death counts by County and by Month.
This dataset contains aggregate death data at the state and county level for Pennsylvania residents. The data are displayed by county, date, death counts, averages, rates based on population. Pennsylvania statewide numbers are listed with Pennsylvania named as the county for the statewide totals. Do not add up the entire file (all rows) or counts will be duplicated.
Data will be updated midafternoon each Wednesday.

View annual counts of Accidental or Undetermined overdose deaths for 2012 forward, including provisional estimates of annual counts of overdose deaths for recent years, as noted with an asterisk and the month the data was pulled. NOTE: Finalized death records for overdose deaths are often delayed by 3-6 months. Counties labeled “no value” have data suppressed because the counts are between 1 and 9.
Dataset includes overdose deaths where the Manner of Death is Accidental or Undetermined.
County complement counts file located here - https://data.pa.gov/Opioid-Related/Estimated-Accidental-and-Undetermined-Drug-Overdos/azzc-q64m

Overdose Deaths are classified using the International Classification of Diseases, Tenth Revision (ICD–10). Accidental and Undetermined drug overdose deaths are identified using underlying cause-of-death codes X40–X44, and Y10–Y14, and include
- R99 when the Injury Description indicates an overdose death.
- X49 when literal COD is Mixed or Combined or Multiple Substance Toxicity, as these are likely drug overdoses
- X47 when substance indicated is difluoroethane, alone or in combination with other drugs
Source Pennsylvania Prescription Drug Monitoring Program *
* These data were supplied by the Bureau of Health Statistics and Registries, Harrisburg, Pennsylvania. The Bureau of Health Statistics and Registries specifically disclaims responsibility for any analyses, interpretations or conclusions.
- Estimates are broken down by type of drugs involved in the overdose
- Any Drug Overdose Death - all drug overdose deaths, regardless of type of drug involved, excluding alcohol only deaths
- Opioid Overdose Death - any overdose death involving opioids, prescription or illegal

This data set includes the estimated number of individuals in Pennsylvania with Drug Use Disorder, which is an approximation for Opioid Use Disorder prevalence. The estimates are developed by applying mortality weights derived from the CDC’s National Center for Health Statistics to statewide illicit drug use estimates from the National Survey on Drug Use and Health (NSDUH, sponsored by the Substance Abuse and Mental Health Services Administration).

This is a state aggregated filtered view from the original dataset describes as follows: quarterly data by county, including inmate admissions, average Texas Christian University Drug Screen II (TCU) score, number and percent of inmate admissions who identify opioids as top three drug of choice, inmates with a substance abuse problem, and number and percent who used opioids in the year prior to admission.
This data is available starting with the first Quarter of Calendar Year 2016.

Listing of all Markers that do not have a status of Missing.
Since 1913, the Pennsylvania Historical and Museum Commission has administered a program of historical markers to capture the memory of people, places, and events that have affected the lives of Pennsylvanians over the centuries since William Penn founded his Commonwealth. More than 2,000 bronze plaques and cast aluminum markers tell the stories of Native Americans and settlers, government and politics, athletes, entertainers, artists, struggles for freedom and equality, factories and businesses, and a multitude of other topics. This dataset includes Pennsylvania historical markers by title, dedication date, location, county, category and marker text.

This indicator includes the rate of hospitalization per 1,000 individuals estimated to have Opioid Use Disorder (OUD) for Opioid Use Disorder, Intracranial and intraspinal Abscess, Osteomyelitis, Endocarditis, Soft skin tissue infection, and Viral Hepatitis (B, C, and D) for individuals diagnosed with OUD in the same calendar year.
Analyses were completed by the University of Pittsburgh using data from the PA Health Care Cost Containment Council and in cooperation with PA DOH.
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.

Since 1913, the Pennsylvania Historical and Museum Commission has administered a program of historical markers to capture the memory of people, places, and events that have affected the lives of Pennsylvanians over the centuries since William Penn founded his Commonwealth. More than 2,000 bronze plaques and cast aluminum markers tell the stories of Native Americans and settlers, government and politics, athletes, entertainers, artists, struggles for freedom and equality, factories and businesses, and a multitude of other topics. This dataset includes Pennsylvania historical markers by title, dedication date, location, county, category and marker text.

The database was constructed from using the source “Sapphire Emar”. The raw data was condensed to remove any duplicate data that expressed the following: data rows with the same inmate name who had multiple of the same medications or multiple of the same medication type. Then the data sets were sorted by admission dates. Then grouped by quarters or years or both. After that, data was sorted by various descriptor columns: Age Range, Ethnicity, Gender and Drug type.
What is the story the data is telling? The data shows a significantly higher admission rate for Caucasians and Males. The data also shows the age range of 30-34 consistently has the highest percent admission rate. The data also shows a high percentage of inmates receive Vivitrol/Revia as opposed to Suboxone.
What to realize about this data? Although Males and Caucasians have a higher admission rate it is worth noting that this does not take into the account of how many inmates based on gender or ethnicity there are/were in the DOC at a given time. Regardless of Year the Inmate age peaks at 30-34 suggesting they are the most likely to seek treatment.

This indicator includes the number of days hospitalized for Opioid Use Disorder and the estimated lost wages due to hospitalization.
This data view is filtered by Annual date and All Ages for All genders based from the original dataset.