The population count of Harford County, MD was 251,025 in 2018. The population count of Cumberland County, PA was 247,433 in 2018. The population count of Westmoreland County, PA was 354,751 in 2018.
Population
Population Change
Above charts are based on data from the U.S. Census American Community Survey | ODN Dataset | API -
Demographics and Population Datasets Involving Westmoreland County, PA or Cumberland County, PA or Harford County, MD
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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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Counts and Rates of New HIV Diagnoses Among Individuals Using Injection Drugs January 2016 - Current Monthly County & Statewide Health
data.pa.gov | Last Updated 2023-09-19T14:46:53.000ZThis indicator includes the count and rate of new HIV diagnoses among individuals using injection drugs per 100,000 individuals estimated to have Drug Use Disorder.
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Estimated Accidental and Undetermined Drug Overdose Deaths with Demographics CY 2012-Current Statewide Health
data.pa.gov | Last Updated 2023-12-19T19:58:50.000ZView 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.<br> 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.<br> - Overdose Deaths are classified using the International Classification of Diseases, Tenth Revision (ICD–10). Drug-poisoning deaths are identified using underlying cause-of-death codes X40–X44, and Y10–Y14, and include the following: <br> - R99 when the Injury Description indicates an overdose death.<br> - X49 when literal COD is Mixed or Combined or Multiple Substance Toxicity, as these are likely drug overdoses<br> - X47 when substance indicated is difluoroethane, alone or in combination with other drugs<BR> - Source Pennsylvania Prescription Drug Monitoring Program*<br> * 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.<br> - Estimates are broken down by type of drugs involved in the overdose <br> - Any Drug Overdose Death - all drug overdose deaths, regardless of type of drug involved, excluding alcohol only deaths<br> - Opioid Overdose Death - any overdose death involving opioids, prescription or illegal
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Buprenorphine Dispensation Data Quarter 3 2016 - Current Quarterly Statewide Health
data.pa.gov | Last Updated 2023-12-14T20:40:35.000ZView quarterly trends in buprenorphine dispensation data. Please note that buprenorphine data received by the PDMP is restricted to prescriptions filled by pharmacies. The PDMP does not collect information on the reason a controlled substance is prescribed, nor does it collect data from substance abuse treatment facilities or dispensing prescribers providing buprenorphine for substance abuse treatment. Buprenorphine is sometimes prescribed off-label for pain. Please see PDMP Data Technical Notes for additional details: https://www.health.pa.gov/topics/programs/PDMP/Pages/Data.aspx
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Risky Prescribing Measures Quarter 3 2016 - Current Quarterly County & Statewide Health
data.pa.gov | Last Updated 2023-12-14T20:41:20.000ZView quarterly trends in Risky Prescribing Measures, including: o Number/Rate of Individuals Seeing 5+ Prescribers and 5+ Dispensers: Number of individuals who received prescriptions from 5 or more prescribers AND 5 or more dispensers for any Schedule II-V substance in a 3-month period. This measure is also referred to as Multiple Provider Episodes. County rates are calculated based on the patient’s county of residence. o Number/Rate of Individuals Seeing 4+ Prescribers and 4+ Dispensers: Number of individuals who received prescriptions from 5 or more prescribers AND 5 or more dispensers for any Schedule II-V substance in a 3-month period. This measure is also referred to as Multiple Provider Episodes. County rates are calculated based on the patient’s county of residence. o Number/Rate of Individuals Seeing 3+ Prescribers and 3+ Dispensers: Number of individuals who received prescriptions from 5 or more prescribers AND 5 or more dispensers for any Schedule II-V substance in a 3-month period. This measure is also referred to as Multiple Provider Episodes. County rates are calculated based on the patient’s county of residence. o Number/Rate of Individuals with an Average Daily MME >50, >90 or >120: Average Daily MME is calculated as the sum of the total MME on each day in a time period based on all prescriptions an individual has filled divided by the number of days in the prescription(s). Measures include the number and rate of individuals prescribed greater than 50 MME per day, greater than 90 MME per day, or greater than 120 MME per day and is based on the patient’s county of residence. o Number/Rate of Individuals with Overlapping Opioid/Benzodiazepine Prescriptions: Number of individuals receiving overlapping opioid and benzodiazepine prescriptions during a given quarter. This measure is based on the patients’ county of residence. o Number/Rate of Individuals with > 30 Days of Overlapping Opioid/Benzodiazepine Prescriptions: Number and rate of individuals receiving overlapping opioid and benzodiazepine prescriptions for 30 days or more during a given quarter using state/county populations as denominators. This measure is based on the patients’ county of residence. Please see PDMP Data Technical Notes for additional details: https://www.health.pa.gov/topics/programs/PDMP/Pages/Data.aspx
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Rate of Dependent Children Removed from their Home Where Parental Drug Use was Factor FFY 2017 - Current Human Services
data.pa.gov | Last Updated 2022-02-21T17:56:36.000ZThis dataset summarizes the number of dependent children (less than 18 years old) removed from households due to parental drug abuse. The data indicates if the dependent children were placed in kinship care or not. The total number of children in this data set are provided by the U.S. Census Bureau’s American Community Survey (ACS), which publishes 5 year estimates of the population. The most recent year of entries in this data set may be available before the corresponding ACS population estimates for that year are published. In that case, the data set uses values from the most recently published ACS estimates and notes the year from which those estimates are pulled. These values are updated once the Census Bureau releases the most recent estimates.” *Kinship care refers to the care of children by relatives or, in some jurisdictions, close family friends (often referred to as fictive kin). Relatives are the preferred resource for children who must be removed from their birth parents because it maintains the children's connections with their families. *The Adoption and Foster Care Analysis and Reporting System (AFCARS) definition of parental drug abuse is “Principal caretaker’s compulsive use of drugs that is not of a temporary nature.”
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Rate of Hospitalizations for Opioid Overdose per 100,000 Residents by Demographics CY 2016- 2017 Statewide Health Care Cost Containment Council (PHC4)
data.pa.gov | Last Updated 2022-10-17T20:22:39.000ZRate of hospitalization for opioid overdose per 100,000 PA Residents categorized by principal diagnosis of heroin or opioid pain medication overdose by year and demographic. This analysis is restricted to Pennsylvania residents age 15 and older who were hospitalized in Pennsylvania general acute care 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.
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Emissions Inventory System (EIS) Facilities 2017 - Current County Environmental Protection
data.pa.gov | Last Updated 2021-07-23T17:51:25.000ZEPA's Emissions Inventory System (EIS) contains information about sources that emit criteria air pollutants (CAPs) and hazardous air pollutants (HAPs). This data contains the facility information for Pennsylvania counties. EPA collects information about emission sources and releases an updated version of the NEI database every three years. The data made available in the NEI are used for air dispersion modeling, regional strategy development, setting regulations, air toxins risk assessment, and tracking trends in emissions over time. The data derived in the State of Pennsylvania is published and searchable online on the www.pa.gov website. This data will be updated annually for the prior calendar year in the first Quarter of the following year.
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National Immunization Survey Adult COVID Module (NIS-ACM): COVIDVaxViews| Data | Centers for Disease Control and Prevention (cdc.gov)
data.cdc.gov | Last Updated 2024-05-23T18:49:19.000Z• National Immunization Survey Adult COVID Module (NIS-ACM): CDC is providing information on the Updated 2023-24 COVID-19 vaccine confidence to supplement vaccine administration data. These data represent trends in vaccination status and intent, and other behavioral indicators, by demographics and other characteristics. • The data start in October 2023. • The archived data can be found here:
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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.