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 data shows a count of unintentional overdose deaths by year within the city limits of Austin. The data is reported from the Office of Vital Statistics. View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/HE-B-4b-Number-of-unintentional-overdose-deaths/vmwr-d85g/

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 measure refers to reducing the number of deaths due to unintentional overdose in the city of Austin.

,Decrease the rate of unintentional poisoning deaths involving prescription drugs from 13.3 per 100,000 in 2011 to 11.3 per 100,000 by 2017.,

A listing of each accidental death associated with drug overdose in Connecticut from 2012 to 2023. A "Y" value under the different substance columns indicates that particular substance was detected. Data are derived from an investigation by the Office of the Chief Medical Examiner which includes the toxicity report, death certificate, as well as a scene investigation. The “Morphine (Not Heroin)” values are related to the differences between how Morphine and Heroin are metabolized and therefor detected in the toxicity results. Heroin metabolizes to 6-MAM which then metabolizes to morphine. 6-MAM is unique to heroin, and has a short half-life (as does heroin itself). Thus, in some heroin deaths, the toxicity results will not indicate whether the morphine is from heroin or prescription morphine. In these cases the Medical Examiner may be able to determine the cause based on the scene investigation (such as finding heroin needles). If they find prescription morphine at the scene it is certified as “Morphine (not heroin).” Therefor, the Cause of Death may indicate Morphine, but the Heroin or Morphine (Not Heroin) may not be indicated. “Any Opioid” – If the Medical Examiner cannot conclude whether it’s RX Morphine or heroin based morphine in the toxicity results, that column may be checked

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. 2. CDC. CDC Wonder: Underlying cause of death 1999–2016. Available from: http://wonder.cdc.gov/wonder/help/ucd.html. 3. 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. 4. 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. 5. 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. 6. 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. 7. National Center for Health Statistics. County geog

Injury from poisoning exists under several Injury Intents: unintentional (accidental), intentional self-harm, assault, undetermined and adverse effect and underdosing. Only injuries in the first four categories are reported here combined. The data show rates per 100,000 people in order to standardize between areas with different population levels. Except for age specific rates, we use age-adjusted rates because they take into account where one age group dominates a population and thus are more representative. We use diagnosis by hospital records for non-fatal injury and cause of death from death certificates for fatal injury information.

National provisional drug overdose deaths by month and 2013 NCHS Urban–Rural Classification Scheme for Counties. 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). Deaths are based on the county of residence in the United States. Death counts provided are for “12-month ending periods,” defined as the number of deaths occurring in the 12-month period ending in the month indicated. Estimates for 2020 are based on provisional data. Estimates for 2018 and 2019 are based on final data. For more information on NCHS urban-rural classification, see: https://www.cdc.gov/nchs/data/series/sr_02/sr02_166.pdf