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.

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 dataset contains the total counts of PA Department of Human Services (DHS) Medical Assistance (MA) individuals diagnosed with Opioid Use Disorder (OUD) or OUD Poisoning. Also included are individuals receiving MAT (Medication assisted treatment - the use of medications in combination with counseling and behavioral therapies for the treatment of substance use disorders.) NOT diagnosed in the same period. Counts are reported by case county and cover calendar years 2015 - 2018.

This dataset is 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.

The county aggregate number of individuals on Medical Assistance (MA, Medicaid) who have filled a Naloxone Prescription. This includes all individuals on Medicaid. Naloxone is an opioid antagonist. It acts on the CNS to block the effects of narcotics, especially the "high'' feeling that makes you want to use them. It will not produce any narcotic-like effects or cause mental or physical dependence. Numbers are for the Calendar Year and updated with as current as possible until the next data refresh.

This dataset contains the total counts of PA Department of Human Services (DHS) Medical Assistance (MA) individuals diagnosed with Opioid Use Disorder (OUD) or OUD Poisoning. Also included are individuals receiving MAT (Medication assisted treatment - the use of medications in combination with counseling and behavioral therapies for the treatment of substance use disorders.) NOT diagnosed in the same period. Limited to the Newly Eligible (Under the Medical Assistance Expansion Program. Find more information here: http://www.dhs.pa.gov/cs/groups/webcontent/documents/document/c_257436.pdf) segment of DHS population. Internally defined as DHS Category of Assistance = MG (Modified Adjusted Gross Income - MAGI) MG and Program Status = 91 (Newly Eligible). Counts are reported by Pennsylvania case county and covers calendar years 2015 -2018.

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.

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.

This indicator includes the hospitalization count and rate of hospitalization per 1,000 individuals estimated to have Drug Use Disorder for any of the following reasons: Opioid Use Disorder (OUD), Cellulitis, Osteomyelitis, Endocarditis, Soft skin tissue infection, or 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.

This dataset summarizes the rate of newborn/neonatal hospital stays in which there is a diagnosis of withdrawal symptoms from maternal use of drugs of addiction or diagnosis of maternal substance exposure in the first 28 days of life, relative to the total number of birth hospitalizations.
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.