Data on drug overdose death rates, by drug type and selected population characteristics. Please refer to the PDF or Excel version of this table in the HUS 2019 Data Finder (https://www.cdc.gov/nchs/hus/contents2019.htm) for critical information about measures, definitions, and changes over time. SOURCE: NCHS, National Vital Statistics System, numerator data from annual public-use Mortality Files; denominator data from U.S. Census Bureau national population estimates; and Murphy SL, Xu JQ, Kochanek KD, Arias E, Tejada-Vera B. Deaths: Final data for 2018. National Vital Statistics Reports; vol 69 no 13. Hyattsville, MD: National Center for Health Statistics.2021. Available from: https://www.cdc.gov/nchs/products/nvsr.htm. For more information on the National Vital Statistics System, see the corresponding Appendix entry at https://www.cdc.gov/nchs/data/hus/hus19-appendix-508.pdf.

Analysis of ‘💉 Opioid Overdose Deaths’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/opioid-overdose-deathse on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

Opioid addiction and death rates in the U.S. and abroad have reached "epidemic" levels. The CDC's data reflects the incredible spike in overdoses caused by drugs containing opioids.

The United States is experiencing an epidemic of drug overdose (poisoning) deaths. Since 2000, the rate of deaths from drug overdoses has increased 137%, including a 200% increase in the rate of overdose deaths involving opioids (opioid pain relievers and heroin). Source: CDC

In-the-News:

• STAT: 26 overdoses in just hours: Inside a community on the front lines of the opioid epidemic
• NPR: Organ Donations Spike In The Wake Of The Opioid Epidemic, Deadly Opioid Overwhelms First Responders And Crime Labs in Ohio
• Scientific American: Wave of Overdoses with Little-Known Drug Raises Alarm Amid Opioid Crisis
• Washington Post: A 7-year-old told her bus driver she couldn’t wake her parents. Police found them dead at home.
• Wall Street Journal: For Small-Town Cops, Opioid Scourge Hits Close to Home
• Food & Drug Administration: FDA launches competition to spur innovative technologies to help reduce opioid overdose deaths

This data was compiled using the CDC's WONDER database. Opioid overdose deaths are defined as: deaths in which the underlying cause was drug overdose, and the ICD-10 code used was any of the following: T40.0 (Opium), T40.1 (Heroin), T40.2 (Other opioids), T40.3 (Methadone), T40.4 (Other synthetic narcotics), T40.6 (Other and unspecified narcotics).

Age-adjusted rate of drug overdose deaths and drug overdose deaths involving opioids
http://i.imgur.com/ObpzUKq.gif" alt="Opioid Death Rate" style="">
Source: CDC

What are opioids?
Opioids are substances that act on opioid receptors to produce morphine-like effects. Opioids are most often used medically to relieve pain. Opioids include opiates, an older term that refers to such drugs derived from opium, including morphine itself. Other opioids are semi-synthetic and synthetic drugs such as hydrocodone, oxycodone and fentanyl; antagonist drugs such as naloxone and endogenous peptides such as the endorphins.[4] The terms opiate and narcotic are sometimes encountered as synonyms for opioid. Source: Wikipedia

contributors-wanted See comment in Discussion

Footnotes

• The crude rate is per 100,000.
• Certain totals are hidden due to suppression constraints. More Information: http://wonder.cdc.gov/wonder/help/faq.html#Privacy.
• The population figures are briged-race estimates. The exceptions being years 2000 and 2010, in which Census counts are used.
• v1.1: Added Opioid Prescriptions Dispensed by US Retailers in that year (millions).

Citation: Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999-2014 on CDC WONDER Online Database, released 2015. Data are from the Multiple Cause of Death Files, 1999-2014, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/mcd-icd10.html on Oct 19, 2016 2:06:38 PM.

Citation for Opioid Prescription Data: IMS Health, Vector One: National, years 1991-1996, Data Extracted 2011. IMS Health, National Prescription Audit, years 1997-2013, Data Extracted 2014. Accessed at NIDA article linked (Figure 1) on Oct 23, 2016.

Data Use Restrictions:
The Public Health Service Act (42 U.S.C. 242m(d)) provides that the data collected by the National Center for Health Statistics (NCHS) may be used only for the purpose for which they were obtained; any effort to determine the identity of any reported cases, or to use the information for any purpose other than for health statistical reporting and analysis, is against the law. Therefore users will:
Use these data for health statistical reporting and analysis only.
For sub-national geography, do not present or publish death counts of 9 or fewer or death rates based on counts of nine or fewer (in figures, graphs, maps, tables, etc.).
Make no attempt to learn the identity of any person or establishment included in these data.
Make no disclosure or other use of the identity of any person or establishment discovered inadvertently and advise the NCHS Confidentiality Officer of any such discovery.
Eve Powell-Griner, Confidentiality Officer
National Center for Health Statistics
3311 Toledo Road, Rm 7116
Hyattsville, MD 20782
Telephone 301-458-4257 Fax 301-458-4021

This dataset was created by Health and contains around 800 samples along with Crude Rate, Crude Rate Lower 95% Confidence Interval, technical information and other features such as: - Year - Deaths - and more.

How to use this dataset

• Analyze Crude Rate Upper 95% Confidence Interval in relation to Prescriptions Dispensed By Us Retailers In That Year (millions)
• Study the influence of State on Crude Rate
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Health

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

The National Hospital Care Survey (NHCS) collects data on patient care in hospital-based settings to describe patterns of health care delivery and utilization in the United States. Settings currently include inpatient and emergency departments (ED). From this collection, the NHCS contributes data that may inform emerging national health threats such as the current opioid public health emergency. The 2022 - 2024 NHCS are not yet fully operational so it is important to note that the data presented here are preliminary and not nationally representative. The data are from 24 hospitals submitting inpatient and 23 hospitals submitting ED Uniform Bill (UB)-04 administrative claims from October 1, 2022–September 30, 2024. Even though the data are not nationally representative, they can provide insight into the use of opioids and other overdose drugs. The NHCS data is submitted from various types of hospitals (e.g., general/acute, children’s, etc.) and can show results from a variety of indicators related to drug use, such as overall drug use, comorbidities, and drug and polydrug overdose. NHCS data can also be used to report on patient conditions within the hospital over time.

This data contains provisional counts for drug overdose deaths based on a current flow of mortality data in the National Vital Statistics System. Counts for the most recent final annual data are provided for comparison. National provisional counts include deaths occurring within the 50 states and the District of Columbia as of the date specified and may not include all deaths that occurred during a given time period. Provisional counts are often incomplete and causes of death may be pending investigation (see Technical notes) resulting in an underestimate relative to final counts. To address this, methods were developed to adjust provisional counts for reporting delays by generating a set of predicted provisional counts (see Technical notes). Starting in June 2018, this monthly data release will include both reported and predicted provisional counts.

The provisional data include: (a) the reported and predicted provisional counts of deaths due to drug overdose occurring nationally and in each jurisdiction; (b) the percentage changes in provisional drug overdose deaths for the current 12 month-ending period compared with the 12-month period ending in the same month of the previous year, by jurisdiction; and (c) the reported and predicted provisional counts of drug overdose deaths involving specific drugs or drug classes occurring nationally and in selected jurisdictions. The reported and predicted provisional counts represent the numbers of deaths due to drug overdose occurring in the 12-month periods ending in the month indicated. These counts include all seasons of the year and are insensitive to variations by seasonality. Deaths are reported by the jurisdiction in which the death occurred.

Several data quality metrics, including the percent completeness in overall death reporting, percentage of deaths with cause of death pending further investigation, and the percentage of drug overdose deaths with specific drugs or drug classes reported are included to aid in interpretation of provisional data as these measures are related to the accuracy of provisional counts (see Technical notes). Reporting of the specific drugs and drug classes involved in drug overdose deaths varies by jurisdiction, and comparisons of death rates involving specific drugs across selected jurisdictions should not be made (see Technical notes). Provisional data will be updated on a monthly basis as additional records are received.

Technical notes

Nature and sources of data

Provisional drug overdose death counts are based on death records received and processed by the National Center for Health Statistics (NCHS) as of a specified cutoff date. The cutoff date is generally the first Sunday of each month. National provisional estimates include deaths occurring within the 50 states and the District of Columbia. NCHS receives the death records from state vital registration offices through the Vital Statistics Cooperative Program (VSCP).

The timeliness of provisional mortality surveillance data in the National Vital Statistics System (NVSS) database varies by cause of death. The lag time (i.e., the time between when the death occurred and when the data are available for analysis) is longer for drug overdose deaths compared with other causes of death (1). Thus, provisional estimates of drug overdose deaths are reported 6 months after the date of death.

Provisional death counts presented in this data visualization are for “12-month ending periods,” defined as the number of deaths occurring in the 12-month period ending in the month indicated. For example, the 12-month ending period in June 2017 would include deaths occurring from July 1, 2016, through June 30, 2017. The 12-month ending period counts include all seasons of the year and are insensitive to reporting variations by seasonality. Counts for the 12-month period ending in the same month of the previous year are shown for comparison. These provisional counts of drug overdose deaths and related data quality metrics are provided for public health surveillance and monitoring of emerging trends. Provisional drug overdose death data are often incomplete, and the degree of completeness varies by jurisdiction and 12-month ending period. Consequently, the numbers of drug overdose deaths are underestimated based on provisional data relative to final data and are subject to random variation. Methods to adjust provisional counts have been developed to provide predicted provisional counts of drug overdose deaths, accounting for delayed reporting (see Percentage of records pending investigation and Adjustments for delayed reporting).

Provisional data are based on available records that meet certain data quality criteria at the time of analysis and may not include all deaths that occurred during a given time period. Therefore, they should not be considered comparable with final data and are subject to change.

Cause-of-death classification and definition of drug deaths
Mortality statistics are compiled in accordance with World Health Organization (WHO) regulations specifying that WHO member nations classify and code causes of death with the current revision of the International Statistical Classification of Diseases and Related Health Problems (ICD). ICD provides the basic guidance used in virtually all countries to code and classify causes of death. It provides not only disease, injury, and poisoning categories but also the rules used to select the single underlying cause of death for tabulation from the several diagnoses that may be reported on a single death certificate, as well as definitions, tabulation lists, the format of the death certificate, and regulations on use of the classification. Causes of death for data presented in this report were coded according to ICD guidelines described in annual issues of Part 2a of the NCHS Instruction Manual (2).

Drug overdose deaths are identified using underlying cause-of-death codes from the Tenth Revision of ICD (ICD–10): X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), and Y10–Y14 (undetermined). Drug overdose deaths involving selected drug categories are identified by specific multiple cause-of-death codes. Drug categories presented include: heroin (T40.1); natural opioid analgesics, including morphine and codeine, and semisynthetic opioids, including drugs such as oxycodone, hydrocodone, hydromorphone, and oxymorphone (T40.2); methadone, a synthetic opioid (T40.3); synthetic opioid analgesics other than methadone, including drugs such as fentanyl and tramadol (T40.4); cocaine (T40.5); and psychostimulants with abuse potential, which includes methamphetamine (T43.6). Opioid overdose deaths are identified by the presence of any of the following MCOD codes: opium (T40.0); heroin (T40.1); natural opioid analgesics (T40.2); methadone (T40.3); synthetic opioid analgesics other than methadone (T40.4); or other and unspecified narcotics (T40.6). This latter category includes drug overdose deaths where ‘opioid’ is reported without more specific information to assign a more specific ICD–10 code (T40.0–T40.4) (3,4). Among deaths with an underlying cause of drug overdose, the percentage with at least one drug or drug class specified is defined as that with at least one ICD–10 multiple cause-of-death code in the range T36–T50.8.

Drug overdose deaths may involve multiple drugs; therefore, a single death might be included in more than one category when describing the number of drug overdose deaths involving specific drugs. For example, a death that involved both heroin and fentanyl would be included in both the number of drug overdose deaths involving heroin and the number of drug overdose deaths involving synthetic opioids other than methadone.

Selection of specific states and other jurisdictions to report
Provisional counts are presented by the jurisdiction in which the death occurred (i.e., the reporting jurisdiction). Data quality and timeliness for drug overdose deaths vary by reporting jurisdiction. Provisional counts are presented for reporting jurisdictions based on measures of data quality: the percentage of records where the manner of death is listed as “pending investigation,” the overall completeness of the data, and the percentage of drug overdose death records with specific drugs or drug classes recorded. These criteria are defined below.

Percentage of records pending investigation

Drug overdose deaths often require lengthy investigations, and death certificates may be initially filed with a manner of death “pending investigation” and/or with a preliminary or unknown cause of death. When the percentage of records reported as “pending investigation” is high for a given jurisdiction, the number of drug overdose deaths is likely to be underestimated. For jurisdictions reporting fewer than 1% of records as “pending investigation”, the provisional number of drug overdose deaths occurring in the fourth quarter of 2015 was approximately 5% lower than the final count of drug overdose deaths occurring in that same time period. For jurisdictions reporting greater than 1% of records as “pending investigation” the provisional counts of drug overdose deaths may underestimate the final count of drug overdose deaths by as much as 30%. Thus, jurisdictions are included in Table 2 if 1% or fewer of their records in NVSS are reported as “pending investigation,” following a 6-month lag for the 12-month ending periods included in the dashboard. Values for records pending investigation are updated with each monthly release and reflect the most current data available.

Percent completeness

NCHS receives monthly counts of the estimated number of deaths from each jurisdictional vital registration offices (referred to as “control counts”). This number represents the best estimate of how many

Annual number of deaths registered related to drug poisoning, by local authority, England and Wales.

Introduction: Social safety net programs (e.g., Medicaid and government assistance) may facilitate drug use disorder (DUD) treatment receipt. We explored the association of social safety net programs with drug treatment and medication for opioid use disorder (MOUD)receipt among women with DUD and opioid use disorder (OUD), respectively. Methods: We used data from women ages 18-64 who met criteria for past-year DSM-5 DUD (n=2,784) and OUD (n=458) in the 2022 public-use National Survey for Drug Use and Health. We estimated the odds of past-year DUD treatment among women with DUD and past-year MOUD treatment among women with OUD by government assistance and/or Medicaid receipt in primary analyses, followed by secondary categorizations of exposure (any government assistance; number of programs received), using separate logistic regressions, controlling for sociodemographics. Results: In primary analyses, women with DUD receiving both Medicaid and government assistan..., , # Government assistance and Medicaid: the relationship with drug treatment and medication for opioid use disorder among women in the United States

Dataset DOI: 10.5061/dryad.np5hqc05n

Description of the data and file structure

Uploaded files are a supplement to Government assistance and Medicaid: the relationship with drug treatment and medication for opioid use disorder among women in the United States

'Rmd' files refer to R Markdown documents which knit to HTML output.

'R' files can be used in the R Linux interface or in R Studio or a similar code editor.Â

'Rds' files are data files that we use for our intermediate or 'working' dataset.

'Rdata' file is data provided by SAMHSA for the full dataset. Â

Individual File Descriptions

• NSDUH21_22_working.rds - This is the working dataset for the paper. We pared down the variables in the public use NSDUH dataset from over 2000 variables down to 44. There are 56069 observations. If users want t...,

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).

U.S. Opiate Prescriptions

Accidental death by fatal drug overdose is a rising trend in the United States. What can you do to help?

This dataset contains summaries of prescription records for 250 common opioid and non-opioid drugs written by 25,000 unique licensed medical professionals in 2014 in the United States for citizens covered under Class D Medicare as well as some metadata about the doctors themselves. This is a small subset of data that was sourced from cms.gov. The full dataset contains almost 24 million prescription instances in long format. I have cleaned and compiled this data here in a format with 1 row per prescriber and limited the approximately 1 million total unique prescribers down to 25,000 to keep it manageable. If you are interested in more data, you can get the script I used to assemble the dataset here and run it yourself. The main data is in prescriber-info.csv. There is also opioids.csv that contains the names of all opioid drugs included in the data and overdoses.csv that contains information on opioid related drug overdose fatalities.

The increase in overdose fatalities is a well-known problem, and the search for possible solutions is an ongoing effort. My primary interest in this dataset is detecting sources of significant quantities of opiate prescriptions. However, there is plenty of other studies to perform, and I am interested to see what other Kagglers will come up with, or if they can improve the model I have already built.

The data consists of the following characteristics for each prescriber

• NPI – unique National Provider Identifier number
• Gender - (M/F)
• State - U.S. State by abbreviation
• Credentials - set of initials indicative of medical degree
• Specialty - description of type of medicinal practice
• A long list of drugs with numeric values indicating the total number of prescriptions written for the year by that individual
• Opioid.Prescriber - a boolean label indicating whether or not that individual prescribed opiate drugs more than 10 times in the year

This dataset contains frequencies, rates, and proportions that describe drug toxicity deaths in Nova Scotia over time and space and by certain demographic and contextual characteristics. See usage considerations for further details on these data.

This study investigated demographic trends over time in the use of prescription opioids versus heroin among addicted individuals. ICD9 codes associated with hospitalizations for overdoses from either prescription opioids (POD) or heroin (HOD) were harvested from the Nationwide Inpatient Sample (NIS) for the years 1993 through 2009, inclusive. Population data were taken from U.S. Census statistics. Demographic specific rates of POD and HOD hospital admissions were analyzed to determine if fluctuations in the dynamics of one form of opiate, such as supply-based reduction, are correlated with changes in the rates of overdoses of the other. Dataset includes statistical and demographic data.

Data from surveillance reports provide information on opioid- and stimulant-related harms (deaths, hospitalizations, emergency department visits, and responses by emergency medical services) in Canada. The Public Health Agency of Canada (PHAC) works closely with the provinces and territories to collect and share accurate information about the overdose crisis in order to provide a national picture of the public health impact of opioids and other drugs in Canada and to help guide efforts to reduce substance-related harms.

record abstracts Several limitations to the data exist and should be noted: The number and client mix of TEDS records depends, to some extent, on external factors, including the availability of public funds. In states with higher funding levels, a larger percentage of the substance-abusing population may be admitted to treatment, including the less severely impaired and the less economically disadvantaged.; The primary, secondary, and tertiary substances of abuse reported to TEDS are those substances that led to the treatment episode, and not necessarily a complete enumeration of all drugs used at the time of admission. ; The way an admission is defined may vary from state to state such that the absolute number of admissions is not a valid measure for comparing states. ; States continually review the quality of their data processing. As systematic errors are identified, revisions may be enacted in historical TEDS data files. While this process improves the dataset over time, reported historical statistics may change slightly from year to year. ; States vary in the extent to which coercion plays a role in referral to treatment. This variation derives from criminal justice practices and differing concentrations of abuser subpopulations. ; Public funding constraints may direct states to selectively target special populations, for example, pregnant women or adolescents. ; TEDS consists of treatment admissions, and therefore may include multiple admissions for the same client. Thus, any statistics derived from the data will represent admissions, not clients. It is possible for clients to have multiple initial admissions within a state and even within providers that have multiple treatment sites within the state. TEDS provides a national snapshot of what is seen at admission to treatment, but is currently not designed to follow individual clients through a sequence of treatment episodes. ; TEDS distinguishes between "transfer admissions" and "initial admissions." Transfer admissions include clients transferred for distinct services within an episode of treatment. Only initial admissions are included in the public-use file. ; Some states have no Opioid Treatment Programs (OTPs) that provide medication-assisted therapy using methadone and/or buprenorphine. ; In 2012, a new variable was added that reports the number of times, if any, that a client was arrested in the 30 days preceding his or her admission into treatment. The variable is not on files prior to 2008. In 2012, changes were made to the full TEDS series. The changes consisted of the following: The recoding scheme of the variable DENTLF (Detailed Not in Labor Force Category) was changed. The cases for "Inmate of Institution" have been separated from "Other" and are now a standalone category. ; The recoding scheme of the variable DETCRIM (Detailed Criminal Justice Referral) was changed. The cases for "Prison" have been separated from "Probation/Parole" and are now a standalone category. The same was done for the cases for "Diversionary Program" which were previously combined with "Other". But the cases for "Other Recognized Legal Entity" previously combined with "State/Federal Court, Other Court" have now been combined with the "Other" category. ; In 2011, a change was made to the full TEDS series. All records for which the age is missing are now excluded from the dataset. In 2010, changes were made to the full TEDS series. The changes consisted of the following: Clients 11 years old and younger are excluded from the dataset. ; Puerto Rico now has its own category for Census Region and Division. Clients in Puerto Rico were formerly classified into the South Census Region and South Atlantic Census Division.; The state FIPS (STFIPS) variable is retained and a second state variable was dropped to reduce redundancy.; Value labels and question text are better aligned with the TEDS State Instruction Manual for Admissions Data.; The variable RACE is no longer recoded. Codes for "Asian" (code 13) and "Native Hawaiian or Pacific Islander" (code 23) are now retained. Previously these codes were combined into the single code "Asian or Pacific Islander" (code 3). Each state may report any of the three codes. Therefore, all three codes remain in the data, unchanged from the way they are collected by the states.; The categories and codes in this public-use file differ somewhat from those used by SAMHSA and those found in the TEDS Crosswalks and in other reports. This is a result of the recoding that was performed to protect client privacy in creating the public-use file. To further protect respondent and provider privacy, all Behavioral Health Services Information System (BHSIS) unique identification numbers have been removed from the public-use data. Therefore, no linkages are possible between the TEDS and the National Survey of Substance Abuse Treatment Services (N-SSATS) public-use files. The data are collected from the states by Synectics for Management De...

Life expectancy at birth, at the health region level, is decomposed by drug overdose deaths. Changes in mortality rates for a given cause of death change over time and contribute to the overall change in life expectancy.

Nicotine use among U.S. youth is cause for concern, as previous studies have shown that nicotine use in adolescence increases the risk of developing substance use disorders later in life. This exploratory study aimed to understand patterns of nicotine use and perceptions of various nicotine products among adolescents and young adults (AYA) receiving medication treatment for opioid use disorder (MOUD). We administered an adapted version of the National Youth Tobacco Survey via REDCap to AYA (n=32) receiving outpatient care in the Medication-Assisted Treatment of Addiction at Nationwide Children’s Hospital in Columbus, Ohio, U.S.A. Thirty (97%) participants had tried a combustible cigarette and 27 (90%) had tried an electronic cigarette. By age 13, nineteen (61%) participants had tried combustible cigarettes and eight (25%) had tried opioids. Twenty-two (71%) participants reported smoking combustible cigarettes every day for the past 30 days, and 15 (48%) reported smoking more than 10 cigarettes per day on average. Only ten (32%) participants reported e-cigarette use in the last 30 days. Participants universally agreed that tobacco products are dangerous, and twenty (67%) current tobacco users reported that they planned to quit in the next year. Nicotine use patterns among AYA receiving MOUD differ from that previously shown in the general population, primarily by high prevalence of nicotine use in early adolescence and high current combustible cigarette use. Interventions such as universal screening for nicotine use before age 13 and tailored smoking cessation programs for AYA with OUD may help optimize care for these individuals. Methods We administered an adapted version of the National Youth Tobacco Survey via REDCap to adolescents and young adults (n=32) receiving medication treatment for opioid use disorder. This dataset includes deidentified survey responses. Survey responses that may directly or indirectly identify participants (i.e age, race, gender, occupation, marital status) have been removed from the public dataset.

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.

BackgroundWhile the rise in opioid analgesic prescribing and overdose deaths was multifactorial, financial relationships between opioid drug manufacturers and physicians may be one important factor.MethodsUsing national data from 2013 to 2015, we conducted a retrospective cohort study linking the Open Payments database and Medicare Part D drug utilization data. We created two cohorts of physicians, those receiving opioid-related payments in 2014 and 2015, but not in 2013, and those receiving opioid-related payments in 2015 but not in 2013 and 2014. Our main outcome measures were expenditures on filled prescriptions, daily doses filled, and expenditures per daily dose. For each cohort, we created a comparison group that did not receive an opioid-related payment in any year and was matched on state, specialty, and baseline opioid expenditures. We used a difference-in-differences analysis with linear generalized estimating equations regression models.ResultsWe identified 6,322 physicians who received opioid-related payments in 2014 and 2015, but not in 2013; they received a mean total of $251. Relative to comparison group physicians, they had a significantly larger increase in mean opioid expenditures ($6,171; 95% CI: 4,997 to 7,346), daily doses dispensed (1,574; 95%CI: 1,330 to 1,818) and mean expenditures per daily dose ($0.38; 95% CI: 0.29 to 0.47). We identified 8,669 physicians who received opioid-related payments in 2015, but not in 2013 or 2014; they received a mean total of $40. Relative to comparison physicians, they also had a larger increase in mean opioid expenditures ($1,031; 95% CI: 603 to 1,460), daily doses dispensed (557; 95% CI: 417 to 697), and expenditures per daily dose ($0.06; 95% CI: 0.002 to 0.13).ConclusionsOur findings add to the growing public policy concern that payments from opioid drug manufacturers can influence physician prescribing. Interventions are needed to reduce such promotional activities or to mitigate their influence.

Predictive Opioid Misuse Analytics Market Outlook

According to our latest research, the global Predictive Opioid Misuse Analytics market size reached USD 1.32 billion in 2024, driven by the escalating opioid crisis and the urgent need for advanced data-driven solutions in healthcare. The market is anticipated to expand at a robust CAGR of 19.7% from 2025 to 2033, projecting a value of USD 6.37 billion by 2033. This remarkable growth is fueled by the rising adoption of artificial intelligence (AI) and machine learning (ML) technologies in healthcare analytics, which are enabling more accurate identification, prevention, and management of opioid misuse across diverse care settings.

One of the primary growth factors for the Predictive Opioid Misuse Analytics market is the intensifying focus on public health and regulatory compliance. Governments and healthcare organizations worldwide are under immense pressure to curb the opioid epidemic, which has resulted in significant morbidity, mortality, and economic burden. Predictive analytics tools are being increasingly integrated into electronic health records (EHRs) and healthcare information systems to proactively identify patients at risk of opioid misuse or overdose. This allows clinicians and payers to intervene early, optimize prescribing practices, and implement targeted prevention programs, thereby reducing adverse outcomes and healthcare costs.

Another crucial driver of market expansion is the technological advancement in big data analytics and interoperability. With the proliferation of digital health records, prescription drug monitoring programs (PDMPs), and real-time data exchanges, predictive opioid misuse analytics platforms can now aggregate and analyze vast datasets from multiple sources. This facilitates the development of sophisticated risk scoring algorithms and decision support tools that enhance the accuracy and timeliness of opioid misuse detection. Additionally, the integration of natural language processing (NLP) and AI-powered analytics is enabling the extraction of valuable insights from unstructured clinical notes, further strengthening the predictive capabilities of these solutions.

The growing collaboration between healthcare providers, payers, and technology vendors is also propelling the adoption of predictive analytics in opioid misuse management. Value-based care initiatives and reimbursement models increasingly incentivize the use of data-driven approaches to improve patient outcomes and reduce avoidable hospitalizations related to opioid misuse. Furthermore, public-private partnerships and government-funded research projects are accelerating innovation and facilitating the deployment of advanced analytics tools across hospitals, clinics, research institutes, and public health agencies. These collaborative efforts are expected to sustain the market’s high growth trajectory throughout the forecast period.

Regionally, North America leads the Predictive Opioid Misuse Analytics market due to the high prevalence of opioid misuse, robust healthcare infrastructure, and proactive regulatory environment. The United States, in particular, accounts for the largest share, supported by substantial investments in healthcare IT and analytics, as well as stringent government mandates for opioid prescribing and monitoring. Europe is witnessing steady growth, driven by rising awareness and policy initiatives, while the Asia Pacific region is emerging as a lucrative market owing to increasing healthcare digitization and growing incidence of substance abuse. Latin America and the Middle East & Africa are expected to register moderate growth, primarily due to improving healthcare access and evolving regulatory frameworks.

Component Analysis

The Component segment of the Predictive Opioid Misuse Analytics market is bifurcated into Software and Services, each playing a pivotal role in enabling healthcare organizations to combat opioid misuse. Software solutions form the backbone of this market, encompassing predict

Accuracy:Monthly data are from the "Emergency department visits for opioid overdose" report published by the Ministry of Health based on data from the National Ambulatory Care Reporting System (NACRS).An opioid overdose is defined as an unscheduled ED visits where opioid poisoning was recorded as the main or other problem (ICD10-CA T40.0-T40.4, T40.6). Unconfirmed diagnoses are excluded.ED visits may underestimate the full extent of drug overdoses in the community as not all individuals who experience overdose seek care.Data for the last 3 months are preliminary and will change over time. Update Frequency: Monthly

Attributes:Month – Year and month of ED visit.Number of confirmed opioid overdose ED visits in Ottawa hospitals – number of confirmed opioid overdose ED visits in Ottawa hospitals. Contact: Ottawa Public Health Epidemiology Team

Accuracy:Data are from the "Suspect drug-related and confirmed opioid toxicity deaths in Ottawa Public Health" custom report provided by the OCCO.Confirmed opioid toxicity deaths are deaths for which a coroner or forensic pathologist determined the cause of death to be drug toxicity with opioid involvement. These may include drug toxicity deaths involving multiple substances. Conclusions on cause of death may take several months to become available.Deaths have been assigned to public health unit based on six-digit postal code of the residence of the decedent. If residence postal code was unavailable, the postal code of the incident location was used. If postal code of the incident location was unavailable, the postal code of the death location was used. This methodology aligns with how deaths are assigned in PHO's Interactive Opioid Tool.The attribution of deaths to an ONS neighbourhood reflects the location of incident or death, based on postal code, and not necessarily the neighbourhood where the decedent resided. Postal codes that straddle more than one ONS neighbourhood are allocated to neighbourhoods based on the area of overlap between the straddled neighbourhoods. If there is no postal code information for the location of incident, the location of incident/death is not attributed to an ONS neighbourhood. Update Frequency: Yearly

Attributes:ONS neighbourhood ID – the identification number for the Ottawa Neighbourhood Study (ONS) neighbourhood.ONS neighbourhood name – the name of the ONS neighbourhood.Cumulative number of confirmed opioid toxicity deaths – the cumulative number of confirmed opioid toxicity deaths of Ottawa residents based on the location of the incident.Yearly average of confirmed opioid toxicity deaths – the cumulative number of confirmed opioid toxicity deaths of Ottawa residents divided by the number of years of data.Average yearly rate (per 100,000 population) of confirmed opioid toxicity deaths – the average of the yearly rate (per 100,000 population) of confirmed opioid toxicity deaths of Ottawa residents. To calculate each neighbourhood’s annual rate, the total number of drug overdose deaths by ONS neighbourhood is divided by the neighbourhood population estimate for that year and then multiplied by 100,000. This gives us an annual neighbourhood rate per 100,000 population for each year of data and neighbourhood. For each neighbourhood, the annual rates are summed to create a cumulative neighbourhood rate, which is then divided by the number of years to create the average yearly rate for each neighbourhood.Years – the range of years during which the deaths occurred. Contact: Ottawa Public Health Epidemiology Team

Achievement of viral load suppression among people living with HIV is one of the most important goals for effective HIV epidemic response. In Ukraine, people who inject drugs (PWID) experience the largest HIV burden. At the same time, this group disproportionally missed out in HIV treatment services. We performed a secondary data analysis of the national-wide cross-sectional bio-behavioral surveillance survey among PWID to assess the population-level prevalence of detectable HIV viremia and identify key characteristics that explain the outcome. Overall, 11.4% of PWID or 52.6% of HIV-positive PWID had a viral load level that exceeded the 1,000 copies/mL threshold. In the group of HIV-positive PWID, the detectable viremia was attributed to younger age, monthly income greater than minimum wage, lower education level, and non-usage of antiretroviral therapy (ART) and opioid agonistic therapy. Compared with HIV-negative PWID, the HIV-positive group with detectable viremia was more likely to be female, represented the middle age group (35–49 years old), had low education and monthly income levels, used opioid drugs, practiced risky injection behavior, and had previous incarceration history. Implementing the HIV case identification and ART linkage interventions focused on the most vulnerable PWID sub-groups might help closing the gaps in ART service coverage and increasing the proportion of HIV-positive PWID with viral load suppression.