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.

This data presents 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 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. 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. 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. Provisional data presented will be updated on a monthly basis as additional records are received. For more information please visit: https://www.cdc.gov/nchs/nvss/vsrr/drug-overdose-data.htm

A. SUMMARY This dataset includes unintentional drug overdose death rates by race/ethnicity by year. This dataset is created using data from the California Electronic Death Registration System (CA-EDRS) via the Vital Records Business Intelligence System (VRBIS). Substance-related deaths are identified by reviewing the cause of death. Deaths caused by opioids, methamphetamine, and cocaine are included. Homicides and suicides are excluded. Ethnic and racial groups with fewer than 10 events are not tallied separately for privacy reasons but are included in the “all races” total.

Unintentional drug overdose death rates are calculated by dividing the total number of overdose deaths by race/ethnicity by the total population size for that demographic group and year and then multiplying by 100,000. The total population size is based on estimates from the US Census Bureau County Population Characteristics for San Francisco, 2022 Vintage by age, sex, race, and Hispanic origin.

These data differ from the data shared in the Preliminary Unintentional Drug Overdose Death by Year dataset since this dataset uses finalized counts of overdose deaths associated with cocaine, methamphetamine, and opioids only.

B. HOW THE DATASET IS CREATED This dataset is created by copying data from the Annual Substance Use Trends in San Francisco report from the San Francisco Department of Public Health Center on Substance Use and Health.

C. UPDATE PROCESS This dataset will be updated annually, typically at the end of the year.

D. HOW TO USE THIS DATASET N/A

E. RELATED DATASETS Overdose-Related 911 Responses by Emergency Medical Services Preliminary Unintentional Drug Overdose Deaths San Francisco Department of Public Health Substance Use Services

F. CHANGE LOG

• 12/16/2024 - Updated with 2023 data. Asian/Pacific Islander race/ethnicity group was changed to Asian.
• 12/16/2024 - Past year totals by race/ethnicity were revised after obtaining accurate race/ethnicity for some decedents that were previously marked as “unknown” race/ethnicity.

Annual number of deaths registered related to drug poisoning in England and Wales by sex, region and whether selected substances were mentioned anywhere on the death certificate, with or without other drugs or alcohol, and involvement in suicides.

A. SUMMARY This dataset includes data from the Office of the Chief Medical Examiner on the number of preliminary unintentional fatal drug overdoses per month.

B. HOW THE DATASET IS CREATED The Office of the Chief Medical Examiner releases a monthly report containing the previous month’s preliminary count of unintentional fatal drug overdoses. This dataset is manually updated based on that report.

The San Francisco Office of the Chief Medical Examiner (OCME) investigates any unknown cause of death for deaths that occur in San Francisco. OCME uses drug testing, death scene investigation, autopsy, medical record, and informant information to determine the cause of death. Preliminary determinations are generally based on drug testing and death scene investigations.

Preliminary deaths reported by the medical examiner consist of two categories: (a) cases that are still under investigation and involve suspected acute toxicity from opioids, cocaine, or methamphetamine; and (b) cases that have been finalized and were attributed to acute toxicity from any substance (including prescribed medication and over-the-counter medication).

C. UPDATE PROCESS This dataset is updated monthly following the release of the monthly accidental fatal drug overdose report from the Office of the Chief Medical Examiner. Department of Public Health staff manually copy data from the Office of the Chief Medical Examiner’s report to update this dataset.

D. HOW TO USE THIS DATASET This dataset is updated each month to include the most recent month’s preliminary accidental fatal drug overdose count. Counts from previous months are often also updated as it can take more than a month for the Office of the Chief Medical Examiner to finish reviewing cases.

E. RELATED DATASETS San Francisco Department of Public Health Substance Use Services Overdose-Related 911 Responses by Emergency Medical Services (EMS) Unintentional Drug Overdose Death Rate by Race/Ethnicity

This dataset provides model-based provisional estimates of the weekly numbers of drug overdose, suicide, and transportation-related deaths using “nowcasting” methods to account for the normal lag between the occurrence and reporting of these deaths. Estimates less than 10 are suppressed. These early model-based provisional estimates were generated using a multi-stage hierarchical Bayesian modeling process to generate smoothed estimates of the weekly numbers of death, accounting for reporting lags. These estimates are based on several assumptions about how the reporting lags have changed in recent months across different jurisdictions, and the resulting estimates differ from other sources of provisional mortality data. For now, these estimates should be considered highly uncertain until further evaluations can be done to determine the validity of these assumptions about timeliness. The true patterns in reporting lags will not be known until data are finalized, typically 11–12 months after the end of the calendar year. Importantly, these estimates are not a replacement for monthly provisional drug overdose death counts, or quarterly provisional mortality estimates. For more detail about the nowcasting methods and models, see:

Rossen LM, Hedegaard H, Warner M, Ahmad FB, Sutton PD. Early provisional estimates of drug overdose, suicide, and transportation-related deaths: Nowcasting methods to account for reporting lags. Vital Statistics Rapid Release; no 11. Hyattsville, MD: National Center for Health Statistics. February 2021. DOI: https://doi.org/10.15620/ cdc:101132

Source: Office of State Medical Examiners (OSME), Rhode Island Department of Health (RIDOH) Note: Data are limited to opioid-involved accidental drug overdose deaths. Counts may not add to annual totals due to missing case information. Percentages may not add to 100 due to rounding. Percentages are displayed as decimals. Prescription medications include prescription opioids such as oxycodone, hydrocodone, and benzodiazepines. Illicit drugs include substances such as heroin, illicit fentanyl, and cocaine.

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

Deaths related to drug poisoning in England and Wales by cause of death, sex, age, substances involved in the death, geography and registration delay.

This dashboard provides in-depth analysis surrounding events and characteristics of individuals who experienced non-fatal and/or fatal opioid overdoses in the District of Columbia. It includes data on ambulance transports for overdoses, fatalities, naloxone distribution, harm reduction efforts and the results of our used syringe testing. Data is aggregated at the neighborhood and ward levels. Data on fatal opioid overdoses will include deaths from 2021-2024. Data on non-fatal opioid overdoses will include incidents from 2021-2024. Note: Fatal opioid overdose data are delayed by approximately 90 days due to toxicological testing.

A. SUMMARY This dataset includes data on a variety of substance use services funded by the San Francisco Department of Public Health (SFDPH). This dataset only includes Drug MediCal-certified residential treatment, withdrawal management, and methadone treatment. Other private non-Drug Medi-Cal treatment providers may operate in the city. Withdrawal management discharges are inclusive of anyone who left withdrawal management after admission and may include someone who left before completing withdrawal management. This dataset also includes naloxone distribution from the SFDPH Behavioral Health Services Naloxone Clearinghouse and the SFDPH-funded Drug Overdose Prevention and Education program. Both programs distribute naloxone to various community-based organizations who then distribute naloxone to their program participants. Programs may also receive naloxone from other sources. Data from these other sources is not included in this dataset. Finally, this dataset includes the number of clients on medications for opioid use disorder (MOUD). The number of people who were treated with methadone at a Drug Medi-Cal certified Opioid Treatment Program (OTP) by year is populated by the San Francisco Department of Public Health (SFDPH) Behavioral Health Services Quality Management (BHSQM) program. OTPs in San Francisco are required to submit patient billing data in an electronic medical record system called Avatar. BHSQM calculates the number of people who received methadone annually based on Avatar data. Data only from Drug MediCal certified OTPs were included in this dataset. The number of people who receive buprenorphine by year is populated from the Controlled Substance Utilization Review and Evaluation System (CURES), administered by the California Department of Justice. All licensed prescribers in California are required to document controlled substance prescriptions in CURES. The Center on Substance Use and Health calculates the total number of people who received a buprenorphine prescription annually based on CURES data. Formulations of buprenorphine that are prescribed only for pain management are excluded. People may receive buprenorphine and methadone in the same year, so you cannot add the Buprenorphine Clients by Year, and Methadone Clients by Year data together to get the total number of unique people receiving medications for opioid use disorder. For more information on where to find treatment in San Francisco, visit findtreatment-sf.org.  B. HOW THE DATASET IS CREATED This dataset is created by copying the data into this dataset from the SFDPH Behavioral Health Services Quality Management Program, the California Controlled Substance Utilization Review and Evaluation System (CURES), and the Office of Overdose Prevention. C. UPDATE PROCESS Residential Substance Use Treatment, Withdrawal Management, Methadone, and Naloxone data are updated quarterly with a 45-day delay. Buprenorphine data are updated quarterly and when the state makes this data available, usually at a 5-month delay. D. HOW TO USE THIS DATASET Throughout the year this dataset may include partial year data for methadone and buprenorphine treatment. As both methadone and buprenorphine are used as long-term treatments for opioid use disorder, many people on treatment at the end of one calendar year will continue into the next. For this reason, doubling (methadone), or quadrupling (buprenorphine) partial year data will not accurately project year-end totals. E. RELATED DATASETS Overdose-Related 911 Responses by Emergency Medical Services Unintentional Overdose Death Rates by Race/Ethnicity Preliminary Unintentional Drug Overdose Deaths

Age adjusted rate of deaths from heroin overdoses among residents of Santa Clara County by total population and sex; trends if available. Source: California Department of Public Health. California Opioid Overdose Surveillance Dashboard. California Department of Public Health. https://discovery.cdph.ca.gov/CDIC/ODdash/METADATA:Notes (String): Lists table title, note and sourceYear (Numeric): Year of dataRate per 100,000 people (Numeric): Age adjusted rate of deaths from heroin overdoses among residents of Santa Clara County (rate per 100,000 people)

Prior dataset name - Estimated Accidental and Undetermined Drug Overdose Deaths CY 2012-Current County Health
View annual counts of Any Drug 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. Counts do not include suicides or homicides where someone intended to harm another person by poisoning.
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:
- 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
- X85 when it does not appear that someone intended to harm another person by poisoning
Source Office of Drug Surveillance and Misuse Prevention*
* 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.
Any Drug Overdose Death - all drug overdose deaths, regardless of type of drug involved, excluding alcohol only deaths

This dataset contains model-based county estimates for drug-poisoning mortality.

Deaths are classified using the International Classification of Diseases, Tenth Revision (ICD–10). Drug-poisoning deaths are defined as having ICD–10 underlying cause-of-death codes X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), or Y10–Y14 (undetermined intent).

Estimates are based on the National Vital Statistics System multiple cause-of-death mortality files (1). Age-adjusted death rates (deaths per 100,000 U.S. standard population for 2000) are calculated using the direct method. Populations used for computing death rates for 2011–2016 are postcensal estimates based on the 2010 U.S. census. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published.

Death rates for some states and years may be low due to a high number of unresolved pending cases or misclassification of ICD–10 codes for unintentional poisoning as R99, “Other ill-defined and unspecified causes of mortality” (2). For example, this issue is known to affect New Jersey in 2009 and West Virginia in 2005 and 2009 but also may affect other years and other states. Drug poisoning death rates may be underestimated in those instances.

Smoothed county age-adjusted death rates (deaths per 100,000 population) were obtained according to methods described elsewhere (3–5). Briefly, two-stage hierarchical models were used to generate empirical Bayes estimates of county age-adjusted death rates due to drug poisoning for each year. These annual county-level estimates “borrow strength” across counties to generate stable estimates of death rates where data are sparse due to small population size (3,5). Estimates for 1999-2015 have been updated, and may differ slightly from previously published estimates. Differences are expected to be minimal, and may result from different county boundaries used in this release (see below) and from the inclusion of an additional year of data. Previously published estimates can be found here for comparison.(6) Estimates are unavailable for Broomfield County, Colorado, and Denali County, Alaska, before 2003 (7,8). Additionally, Clifton Forge County, Virginia only appears on the mortality files prior to 2003, while Bedford City, Virginia was added to Bedford County in 2015 and no longer appears in the mortality file in 2015. These counties were therefore merged with adjacent counties where necessary to create a consistent set of geographic units across the time period. County boundaries are largely consistent with the vintage 2005-2007 bridged-race population file geographies, with the modifications noted previously (7,8).

REFERENCES 1. National Center for Health Statistics. National Vital Statistics System: Mortality data. Available from: http://www.cdc.gov/nchs/deaths.htm.

1. CDC. CDC Wonder: Underlying cause of death 1999–2016. Available from: http://wonder.cdc.gov/wonder/help/ucd.html.

2. Rossen LM, Khan D, Warner M. Trends and geographic patterns in drug-poisoning death rates in the U.S., 1999–2009. Am J Prev Med 45(6):e19–25. 2013.

3. Rossen LM, Khan D, Warner M. Hot spots in mortality from drug poisoning in the United States, 2007–2009. Health Place 26:14–20. 2014.

4. Rossen LM, Khan D, Hamilton B, Warner M. Spatiotemporal variation in selected health outcomes from the National Vital Statistics System. Presented at: 2015 National Conference on Health Statistics, August 25, 2015, Bethesda, MD. Available from: http://www.cdc.gov/nchs/ppt/nchs2015/Rossen_Tuesday_WhiteOak_BB3.pdf.

5. Rossen LM, Bastian B, Warner M, and Khan D. NCHS – Drug Poisoning Mortality by County: United States, 1999-2015. Available from: https://data.cdc.gov/NCHS/NCHS-Drug-Poisoning-Mortality-by-County-United-Sta/pbkm-d27e.

6. National Center for Health Statistics. County geog

Source: Office of State Medical Examiners (OSME), Rhode Island Department of Health (RIDOH)Note: Rates are calculated using CDC WONDER single-race population estimates for each year (Obtained September 9, 2022) . 2021 rates are applied to 2022. The rate is the number of deaths, divided by the total population for each category, multiplied by 100,000. Hispanic or Latino includes people who identify as any race. All other racial and ethnic groups include people who identify as non-Hispanic ethnicity or have unknown ethnicity. People whose race was "Unknown" or "Asian" have been excluded. Data are limited to accidental drug overdose deaths pronounced in Rhode Island among Rhode Island residents. Some data have been suppressed due to unstable rates.

To: State, territorial, tribal, and local policymakers and administrators of agencies and programs focused on child, youth, and family health and well-being Dear Colleagues, Thank you for your work to support children, youth, and families. Populations served by Administration for Children and Families (ACF)-funded programs — including victims of trafficking or violence, those who are unhoused, and young people and families involved in the child welfare system — are often at particularly high risk for substance use and overdose. A variety of efforts are underway at the federal, state, and local levels to reduce overdose deaths. These efforts focus on stopping drugs from entering communities, providing life-saving resources, and preventing drug use before it starts. Initiatives across the country are already saving lives: the overdose death rate has declined over the past year but remains too high at 32.6 per 100,000 individuals. Fentanyl, a powerful synthetic opioid, raises the risk of overdose deaths because even a tiny amount can be deadly. Young people are particularly at risk for fentanyl exposure, driven in part by widespread availability of counterfeit pills containing fentanyl that are marketed to youth through social media. While overdose deaths among teens have recently begun to decline, there were 6,696 deaths among adolescents and young adults in 2022 (the latest year with data available)[1], making unintentional drug overdose the second leading cause of death for youth ages 15—19 and the first leading cause of death among young adults ages 20-24.[2] Often these deaths happen with others nearby and can be prevented when opioid overdose reversal medications, like naloxone, are administered in time. CDC’s State Unintentional Drug Overdose Reporting System dashboard shows that in all 30 jurisdictions with available data, 64.7% of drug overdose deaths had at least one potential opportunity for intervention.[3] Naloxone rapidly reverses an overdose and should be given to any person who shows signs of an opioid overdose or when an overdose is suspected. It can be given as a nasal spray. Studies show that naloxone administration reduces death rates and does not cause harm if used on a person who is not overdosing on opioids. States have different policies and regulations regarding naloxone distribution and administration. Forty-nine states and the District of Columbia have Good Samaritan laws protecting bystanders who aid at the scene of an overdose.[4] ACF grant recipients and partners can play a critical role in reducing overdose deaths by taking the following actions: Stop Overdose Now (U.S. Centers for Disease Control and Prevention) Integrating Harm Reduction Strategies into Services and Supports for Young Adults Experiencing Homelessness (PDF) (ACF) Thank you for your dedication and partnership. If you have any questions, please contact your local public health department or state behavioral health agency. Together, we can meaningfully reduce overdose deaths in every community. /s/ Meg Sullivan Principal Deputy Assistant Secretary [1] Products - Data Briefs - Number 491 - March 2024 [2] WISQARS Leading Causes of Death Visualization Tool [3] SUDORS Dashboard: Fatal Drug Overdose Data | Overdose Prevention | CDC [4] Based on 2024 report from the Legislative Analysis and Public Policy Association (PDF). Note that the state of Kansas adopted protections as well following the publication of this report. Metadata-only record linking to the original dataset. Open original dataset below.

Attitude towards drugs. Topics: Preferred contact for information about illicit drugs and drug use in general; information sources for information about the effects and risks of drug use of illicit drugs; consumption of new psychoactive substances (‘legal highs’) that imitate the effects of illicit drugs, in the last year; purchase of new substances by a friend, from a specialised shop, from the Internet or from a drug dealer; circumstances of use (alone, with friends, during a party or an event or during normal daily activities); information sources for information about the effects and risks of the use of new substances; assessment of the risk to a person’s health using cannabis, ecstasy, alcohol, cocaine, and new substances that imitate the effects of illicit drugs, once or twice and regularly; most effective ways for public authorities to reduce drugs problems (information and prevention campaigns, treatment and rehabilitation of drug users, tough measures against drug dealers and traffickers, as well as drug users, legalize drugs, reduction of poverty and unemployment, more leisure activities for young people); demand for (continued) banning or a legal regulation of the following substances (cannabis, tobacco, ecstasy, heroin, alcohol, cocaine); appropriate way to handle new psychoactive substances (introduce regulation, ban them only if they pose a risk to health, ban them under any circumstance, do nothing); possibility to obtain selected substances within 24 hours (cannabis, alcohol, cocaine, ecstasy, tobacco, heroin, new psychoactive substances); respondent has used cannabis. Demography: age; sex; highest education level; occupation and professional position of the main wage earner in the household (only full time students); occupation and professional position of the respondent; region; type of community; own a mobile phone and fixed (landline) phone in the household; number of persons aged 15 years and older in the household (household size). Einstellung zu Drogen. Themen: Präferierte Ansprechpartner für Informationen über illegale Drogen und Drogenkonsum; Informationsquellen für Informationen zu Auswirkungen und Risiken des Drogenkonsums; Konsum ´neuer psychoaktiver Substanzen (NPS)´ (´Legal Highs´), die die Wirkung illegaler Drogen imitieren, in den letzten zwölf Monaten; Kauf der neuen synthetischen Drogen von einem Freund, in einem Spezialgeschäft, im Internet bzw. von einem Drogendealer; Konsumsituation (allein, mit Freunden, während einer Party oder Veranstaltung bzw. im Alltag); Informationsquellen für erhaltene Informationen zu Auswirkungen und Risiken des Konsums neuer synthetischer Drogen; Einschätzung des Gesundheitsrisikos jeweils beim ein- oder zweimaligen Konsum und beim regelmäßigen Konsum von Cannabis, Ecstasy, Alkohol, Kokain sowie von neuen synthetischen Drogen, die die Wirkung illegaler Drogen imitieren; effektivste staatliche Maßnahmen zur Reduzierung der Drogenproblematik (Kampagnen zur Information und Vorbeugung, Behandlung und Rehabilitation von Drogenkonsumenten, strenge Maßnahmen gegen Drogendealer und Drogenhändler bzw. gegen Drogenkonsumenten, Drogen legalisieren, Reduzierung von Armut und Arbeitslosigkeit mehr Freizeitangebote für Jugendliche); Forderung nach einem (weiteren) Verbot oder einer gesetzlichen Regelung des Konsums ausgewählter Substanzen (Cannabis, Tabak, Ecstasy, Heroin, Alkohol, Kokain); geeigneter Umgang mit legalen neuen psychoaktiven Substanzen (Regulierung einführen, Verbot nur bei Gesundheitsrisiko, generelles Verbot, nichts tun); Beschaffungsmöglichkeit ausgewählter Substanzen innerhalb von 24 Stunden (Cannabis, Alkohol, Kokain, Ecstasy, Tabak, Heroin, neue psychoaktive Substanzen); Cannabiskonsum. Demographie: Alter; Geschlecht; höchster Bildungsabschluss; Beschäftigungsstatus und berufliche Stellung des Haupteinkommensbeziehers im Haushalt (falls Befragter Schüler oder Student); Beschäftigungsstatus und berufliche Stellung des Befragten; Region; Urbanisierungsgrad des Wohnortes; Mobiltelefonbesitz; Festnetztelefon im Haushalt; Anzahl der Personen im Haushalt ab 15 Jahren (Haushaltsgröße).

Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.

Einstellung zu Drogen. Themen: Präferierte Ansprechpartner für Informationen über illegale Drogen und Drogenkonsum; Informationsquellen für Informationen zu Auswirkungen und Risiken des Drogenkonsums; Konsum ´neuer psychoaktiver Substanzen (NPS)´ (´Legal Highs´), die die Wirkung illegaler Drogen imitieren, in den letzten zwölf Monaten; Kauf der neuen synthetischen Drogen von einem Freund, in einem Spezialgeschäft, im Internet bzw. von einem Drogendealer; Konsumsituation (allein, mit Freunden, während einer Party oder Veranstaltung bzw. im Alltag); Informationsquellen für erhaltene Informationen zu Auswirkungen und Risiken des Konsums neuer synthetischer Drogen; Einschätzung des Gesundheitsrisikos jeweils beim ein- oder zweimaligen Konsum und beim regelmäßigen Konsum von Cannabis, Ecstasy, Alkohol, Kokain sowie von neuen synthetischen Drogen, die die Wirkung illegaler Drogen imitieren; effektivste staatliche Maßnahmen zur Reduzierung der Drogenproblematik (Kampagnen zur Information und Vorbeugung, Behandlung und Rehabilitation von Drogenkonsumenten, strenge Maßnahmen gegen Drogendealer und Drogenhändler bzw. gegen Drogenkonsumenten, Drogen legalisieren, Reduzierung von Armut und Arbeitslosigkeit mehr Freizeitangebote für Jugendliche); Forderung nach einem (weiteren) Verbot oder einer gesetzlichen Regelung des Konsums ausgewählter Substanzen (Cannabis, Tabak, Ecstasy, Heroin, Alkohol, Kokain); geeigneter Umgang mit legalen neuen psychoaktiven Substanzen (Regulierung einführen, Verbot nur bei Gesundheitsrisiko, generelles Verbot, nichts tun); Beschaffungsmöglichkeit ausgewählter Substanzen innerhalb von 24 Stunden (Cannabis, Alkohol, Kokain, Ecstasy, Tabak, Heroin, neue psychoaktive Substanzen); Cannabiskonsum. Demographie: Alter; Geschlecht; höchster Bildungsabschluss; Beschäftigungsstatus und berufliche Stellung des Haupteinkommensbeziehers im Haushalt (falls Befragter Schüler oder Student); Beschäftigungsstatus und berufliche Stellung des Befragten; Region; Urbanisierungsgrad des Wohnortes; Mobiltelefonbesitz; Festnetztelefon im Haushalt; Anzahl der Personen im Haushalt ab 15 Jahren (Haushaltsgröße). Attitude towards drugs. Topics: Preferred contact for information about illicit drugs and drug use in general; information sources for information about the effects and risks of drug use of illicit drugs; consumption of new psychoactive substances (‘legal highs’) that imitate the effects of illicit drugs, in the last year; purchase of new substances by a friend, from a specialised shop, from the Internet or from a drug dealer; circumstances of use (alone, with friends, during a party or an event or during normal daily activities); information sources for information about the effects and risks of the use of new substances; assessment of the risk to a person’s health using cannabis, ecstasy, alcohol, cocaine, and new substances that imitate the effects of illicit drugs, once or twice and regularly; most effective ways for public authorities to reduce drugs problems (information and prevention campaigns, treatment and rehabilitation of drug users, tough measures against drug dealers and traffickers, as well as drug users, legalize drugs, reduction of poverty and unemployment, more leisure activities for young people); demand for (continued) banning or a legal regulation of the following substances (cannabis, tobacco, ecstasy, heroin, alcohol, cocaine); appropriate way to handle new psychoactive substances (introduce regulation, ban them only if they pose a risk to health, ban them under any circumstance, do nothing); possibility to obtain selected substances within 24 hours (cannabis, alcohol, cocaine, ecstasy, tobacco, heroin, new psychoactive substances); respondent has used cannabis. Demography: age; sex; highest education level; occupation and professional position of the main wage earner in the household (only full time students); occupation and professional position of the respondent; region; type of community; own a mobile phone and fixed (landline) phone in the household; number of persons aged 15 years and older in the household (household size). Telephone interview: CATI Bevölkerung der jeweiligen Nationalitäten der 28 Mitgliedsstaaten der EU, wohnhaft in den jeweiligen Mitgliedsstaaten im Alter zwischen 15 und 24 Jahren Die Umfrage umfast die nationale Bevölkerung der Bürger (in diesen Ländern) sowie die Bevölkerung der Bürger aller Mitgliedstaaten der Europäischen Union, die Bewohner dieser Länder sind und über ausreichende Kenntnisse der Landessprachen verfügen, um den Fragebogen zu beantworten. Population of the respective nationalities of the European Union Member States, resident in each of the 28 Member States and aged between 15 and 24 years old. The survey covers the national population of citizens (in these countries) as well as the population of citizens of all the European Union Member States that are residents in these countries and have a sufficient command of the national languages to answer the questionnaire.

BackgroundDrug overdose causes approximately 183,000 deaths worldwide annually and 50,000 deaths in Canada and the United States combined. Drug-related deaths are concentrated among young people, leading to a substantial burden of disease and loss of potential life years. Understanding the epidemiology, patterns of care, and prognosis of drug-related prehospital emergencies may lead to improved outcomes.MethodsWe conducted a retrospective cohort study of out-of-hospital cardiac arrests with drug-related and presumed cardiac causes between 2007 and 2013 using the Toronto Regional RescuNet Epistry database. The primary outcome was survival to hospital discharge. We computed standardized case fatality rates, and odds ratios of survival to hospital discharge for cardiac arrests with drug-related versus presumed cardiac causes, adjusting for confounders using logistic regression.ResultsThe analysis involved 21,497 cardiac arrests, including 378 (1.8%) drug-related and 21,119 (98.2%) presumed cardiac. Compared with the presumed cardiac group, drug-related arrest patients were younger and less likely to receive bystander resuscitation, have initial shockable cardiac rhythms, or be transported to hospital. There were no significant differences in emergency medical service response times, return of spontaneous circulation, or survival to discharge. Standardized case fatality rates confirmed that these effects were not due to age or sex differences. Adjusting for known predictors of survival, drug-related cardiac arrest was associated with increased odds of survival to hospital discharge (OR1.44, 95%CI 1.15–1.81).InterpretationIn out-of-hospital cardiac arrest, patients with drug-related causes are less likely than those with presumed cardiac causes to receive bystander resuscitation or have an initial shockable rhythm, but are more likely to survive after accounting for predictors of survival. The demographics and outcomes among drug-related cardiac arrest patients offers unique opportunities for prehospital intervention.