Fatal opioid-detected overdoses in Region 2, 2018-2023
This analysis uses accidental/undetermined opioid-detected overdose data from the Connecticut Office of the Chief Medical Examiner.
Race
The vast majority of decedents are either white, Black or Latine. There is limited information available on the race/ethnicity of other decedents, so they are listed as “other”.
2018
2019
2020
2021
2022
2023
Sex
CT OCME captures limited information about trans decedents, seemingly from hrt scripts and possibly other scene information/witness statements. Please note that this is almost certainly an undercount of the actual number of trans decedents, since OCME only captures sex by visual inspection and would require witness statements to get information about gender. Even though “MtF” and other trans designators present in the data are not related to sex, I are presenting them for the sake of not obscuring data about trans people and otherwise list this variable as “sex” since OCME seeks to capture sex, not gender. In recent years, OCME has debuted an “X” value for sex which seems to function as a catch-all designator for trans/nonbinary people.
2018
2019
2020
2021
2022
2023
Race and sex
I create these graphs by combining the information present in the “race” and “sex” variables.
2018
2019
2020
2021
2022
2023
Age
The red lines on the plots represent modes. The distribution has multiple peaks – each peak suggests that specific age groups are more likely to die than others. More information about the age distribution, including a better look at these multiple modes and peaks, will be available in an upcoming publication. I exclude modes generated from clusters of ~10 deaths of either very old or very young decedents.
2018
2019
2020
2021
2022
2023
Day of week
While there isn’t a clear trend for fatal overdoses, it is worth noting that there are more nonfatal overdoses on Friday and Saturday [analysis done with DPH’s EMS data]. I do not see any trends in day of week for 2009-2017 either [data to be published].
2018
2019
2020
2021
2022
2023
Month
I do not see trends in month of overdose for 2009-2017 either.
2018
2019
2020
2021
2022
2023
Town of OD
To see these images in better resolution, right click and “open in new tab.” It may be more useful to see the corresponding version of these graphs in your DMHAS region of interest.
2018
2019
2020
2021
2022
2023
Injury town v. residence town
I chose towns based on what regional harm reduction workers wanted and included, when I could, towns with overdose prevention infrastructure or towns with the most ODs in the past few years. I’m looking at instances where people who OD’d in a town didn’t live there [so they traveled to the town and OD’d there] – I am referring to this as “out-of-town ODs.”
2018
## [1] "There were 20 instances where a person's residence town didn't match up with their injury town, out of 114 ODs in 2018 in region 2. This is 0.175439 of all ODs in this year."## [1] "Here's a more in-depth look at out-of-town ODs in specific towns:"## Town name Out-of-town ODs Total ODs Proportion of out-of-town ODs
## 1 New Haven 5 38 0.13
## 2 North Haven 0 1 0
## 3 East Haven 0 7 0
## 4 West Haven 2 12 0.17
## 5 Orange 1 1 12019
## [1] "There were 29 instances where a person's residence town didn't match up with their injury town, out of 172 ODs in 2019 in region 2. This is 0.168605. "## [1] "Here's a more in-depth look at out-of-town ODs in specific towns:"## Town name Out-of-town ODs Total ODs Proportion of out-of-town ODs
## 1 New Haven 9 55 0.16
## 2 North Haven 0 6 0
## 3 East Haven 0 12 0
## 4 West Haven 6 24 0.25
## 5 Orange 0 2 02020
## [1] "There were 29 instances where a person's residence town didn't match up with their injury town, out of 208 ODs in 2020 in region 2. This is 0.139423. "## [1] "Here's a more in-depth look at out-of-town ODs in specific towns:"## Town name Out-of-town ODs Total ODs Proportion of out-of-town ODs
## 1 New Haven 9 81 0.11
## 2 North Haven 2 4 0.5
## 3 East Haven 3 14 0.21
## 4 West Haven 3 17 0.18
## 5 Orange 0 2 02021
## [1] "There were 54 instances where a person's residence town didn't match up with their injury town, out of 302 ODs in 2021 in region 2. This is 0.178808. "## [1] "Here's a more in-depth look at out-of-town ODs in specific towns:"## Town name Out-of-town ODs Total ODs Proportion of out-of-town ODs
## 1 New Haven 27 135 0.2
## 2 North Haven 1 6 0.17
## 3 East Haven 2 14 0.14
## 4 West Haven 6 39 0.15
## 5 Orange 0 1 02022
## [1] "There were 45 instances where a person's residence town didn't match up with their injury town, out of 270 ODs in 2022 in region 2. This is 0.166667. "## [1] "Here's a more in-depth look at out-of-town ODs in specific towns:"## Town name Out-of-town ODs Total ODs Proportion of out-of-town ODs
## 1 New Haven 25 125 0.2
## 2 North Haven 2 5 0.4
## 3 East Haven 3 19 0.16
## 4 West Haven 5 27 0.19
## 5 Orange 0 2 02023
## [1] "There were 48 instances where a person's residence town didn't match up with their injury town, out of 245 ODs in 2023 in region 2. This is 0.195918. "## [1] "Here's a more in-depth look at out-of-town ODs in specific towns:"## Town name Out-of-town ODs Total ODs Proportion of out-of-town ODs
## 1 New Haven 27 126 0.21
## 2 North Haven 0 2 0
## 3 East Haven 0 15 0
## 4 West Haven 4 25 0.16
## 5 Orange 0 0 NaNInjury location
Please note that I have taken the original OCME data and aggregated the injury location categories somewhat [e.g. “wooded area” and “outdoors” are both listed under “outdoors”].
2018
2019
2020
2021
2022
2023
Are people dying in their own homes?
Most deaths occur in some sort of residence. I check to see how many deaths have the same injury and residence address, which would mean that the decedent died at home.
2018
## [1] "There were 80 people who OD'd in their own residence."## [1] "The total proportion of decedents ODing in their own residence was 0.666667."## [1] "Out of everyone who OD'd in a residence, 0.816327 of people OD'd in their own residence."2019
## [1] "There were 128 people who OD'd in their own residence."## [1] "The proportion of decedents ODing in their own residence was 0.731429."## [1] "Out of everyone who OD'd in a residence, 0.907801 of people OD'd in their own residence."2020
## [1] "There were 144 people who OD'd in their own residence."## [1] "The proportion of decedents ODing in their own residence was 0.669767."## [1] "Out of everyone who OD'd in a residence, 0.867470 of people OD'd in their own residence."2021
## [1] "There were 215 people who OD'd in their own residence."## [1] "The proportion of decedents ODing in their own residence was 0.673981."## [1] "Out of everyone who OD'd in a residence, 0.856574 of people OD'd in their own residence."2022
## [1] "There were 195 people who OD'd in their own residence."## [1] "The proportion of decedents ODing in their own residence was 0.696429."## [1] "Out of everyone who OD'd in a residence, 0.836910 of people OD'd in their own residence."2023
## [1] "There were 160 people who OD'd in their own residence."## [1] "The proportion of decedents ODing in their own residence was 0.632411."## [1] "Out of everyone who OD'd in a residence, 0.869565 of people OD'd in their own residence."Proportion of deaths with substances present
Upon request, I am sharing the proportion of deaths with the following substances detected in tox: fentanyl, pharmaceutical opioids [there is a “pharma” category in the data], xylazine, alcohol and gabapentin/pregabalin. I combine gabapentin and pregabalin based on danbury’s recommendation since they are part of the same drug class. More tox data for 2009-2023 to be published in the future!
Fentanyl
Pharmaceutical opioids
Xylazine
Alcohol
Gabapentin/pregabalin
Top 5 substance combinations by year
These are substance combinations found within the same decedent based on their tox report. “Other” is a catch-all category the OCME data uses to identify substances that are uncommon/not usually looked for in a tox report for an accidental overdose [e.g. benadryl, prescriptions for an unrelated condition].
2018
## Rows: 5
## Columns: 31
## Groups: cocaine, combo..her.pharm.or.fent..OR.pharm.fent, heronly, pharmonly, fentonly, heroin, X6.mam, morphine, hermor_nocod, codeine, cod_w_no_hermor, di.H.codeine, hydromorphone, oxymorphone, hydrocodone, oxycodone, methadone, buprenorphine, fentanyl..4ANPP.too., Frankens, fent.or.frankens, tramadol, opioid.analogs..e.g...U47700., otherop, pharma_w_meth_no_fent.or.op.analogs.or.cod.or..other.op., pharma_nobupnometh, other, benzos, amphetamine [5]
## $ cocaine <int> 0, 0, 0, 0, 0
## $ combo..her.pharm.or.fent..OR.pharm.fent <int> 0, 0, 0, 0, 0
## $ heronly <int> 0, 0, 0, 0, 0
## $ pharmonly <int> 1, 0, 0, 1, 1
## $ fentonly <int> 0, 1, 1, 0, 0
## $ heroin <int> 0, 0, 0, 0, 0
## $ X6.mam <int> 0, 0, 0, 0, 0
## $ morphine <int> 0, 0, 0, 0, 0
## $ hermor_nocod <int> 0, 0, 0, 0, 0
## $ codeine <int> 0, 0, 0, 0, 0
## $ cod_w_no_hermor <int> 0, 0, 0, 0, 0
## $ di.H.codeine <int> 0, 0, 0, 0, 0
## $ hydromorphone <int> 0, 0, 0, 0, 0
## $ oxymorphone <int> 0, 0, 0, 0, 1
## $ hydrocodone <int> 0, 0, 0, 0, 0
## $ oxycodone <int> 0, 0, 0, 0, 1
## $ methadone <int> 1, 0, 0, 1, 0
## $ buprenorphine <int> 0, 0, 0, 0, 0
## $ fentanyl..4ANPP.too. <int> 0, 1, 1, 0, 0
## $ Frankens <int> 0, 0, 0, 0, 0
## $ fent.or.frankens <int> 0, 1, 1, 0, 0
## $ tramadol <int> 0, 0, 0, 0, 0
## $ opioid.analogs..e.g...U47700. <int> 0, 0, 0, 0, 0
## $ otherop <int> 0, 0, 0, 0, 0
## $ pharma_w_meth_no_fent.or.op.analogs.or.cod.or..other.op. <int> 1, 0, 0, 1, 1
## $ pharma_nobupnometh <int> 0, 0, 0, 0, 1
## $ other <int> 1, 0, 1, 0, 1
## $ benzos <int> 0, 0, 0, 1, 0
## $ amphetamine <int> 0, 0, 0, 0, 0
## $ EtOH <int> 0, 0, 0, 0, 0
## $ n <int> 3, 2, 2, 2, 2The top 5 substance combinations for 2018 are:
- fentanyl
- cocaine, fentanyl
- fentanyl, other
- cocaine, fentanyl, other
- fentanyl, alcohol
2019
## Rows: 5
## Columns: 32
## Groups: xyla, combo, heronly, pharmonly, fentonly, Heroin, X6.mam, Morphine, hermor_nocod, Codeine, cod.w.no.hermor, di.H.codeine, Hydromorphone, oxymorphone, Hydrocodone, Oxycodone, Methadone, bup, fentanyl..4.ANPP.too., frankens, fent...frankens, tramadol, opioid.analogs..e.g...U47700., Other.Op, pharma.w.meth.bup.no.fent.or.other.op, pharma_nobupnometh, other, benzos, cocaine, amphetamine [4]
## $ xyla <int> 0, 0, 0, 0, 0
## $ combo <int> 0, 0, 1, 0, 1
## $ heronly <int> 0, 0, 0, 0, 0
## $ pharmonly <int> 0, 0, 0, 0, 0
## $ fentonly <int> 1, 1, 0, 1, 0
## $ Heroin <int> 0, 0, 1, 0, 1
## $ X6.mam <int> 0, 0, 1, 0, 1
## $ Morphine <int> 0, 0, 1, 0, 1
## $ hermor_nocod <int> 0, 0, 1, 0, 1
## $ Codeine <int> 0, 0, 0, 0, 0
## $ cod.w.no.hermor <int> 0, 0, 0, 0, 0
## $ di.H.codeine <int> 0, 0, 0, 0, 0
## $ Hydromorphone <int> 0, 0, 0, 0, 0
## $ oxymorphone <int> 0, 0, 0, 0, 0
## $ Hydrocodone <int> 0, 0, 0, 0, 0
## $ Oxycodone <int> 0, 0, 0, 0, 0
## $ Methadone <int> 0, 0, 0, 0, 0
## $ bup <int> 0, 0, 0, 0, 0
## $ fentanyl..4.ANPP.too. <int> 1, 1, 1, 1, 1
## $ frankens <int> 0, 0, 0, 0, 0
## $ fent...frankens <int> 1, 1, 1, 1, 1
## $ tramadol <int> 0, 0, 0, 0, 0
## $ opioid.analogs..e.g...U47700. <int> 0, 0, 0, 0, 0
## $ Other.Op <int> 0, 0, 0, 0, 0
## $ pharma.w.meth.bup.no.fent.or.other.op <int> 0, 0, 0, 0, 0
## $ pharma_nobupnometh <int> 0, 0, 0, 0, 0
## $ other <int> 0, 0, 0, 0, 0
## $ benzos <int> 0, 0, 0, 0, 0
## $ cocaine <int> 1, 0, 1, 1, 0
## $ amphetamine <int> 0, 0, 0, 0, 0
## $ EtOH <int> 0, 0, 0, 1, 0
## $ n <int> 6, 5, 5, 4, 4The top 5 substance combinations for 2019 are:
- fentanyl, cocaine
- fentanyl
- fentanyl, alcohol
- fentanyl, cocaine, other
- fentanyl, other
2020
## Rows: 5
## Columns: 31
## Groups: combo, heronly, pharmonly, fentonly, Heroin, X6.mam, Morphine, hermor_nocod, Codeine, cod.w.no.hermor, di.H.codeine, Hydromorphone, oxymorphone, Hydrocodone, Oxycodone, Methadone, bup, fentanyl..4.ANPP.too., frankens, fent...frankens, tramadol, opioid.analogs..e.g...U47700., Other.Op, pharma.w.meth.bup.no.fent.or.other.op, pharma_nobupnometh, other, benzos, cocaine, amphetamine [3]
## $ combo <chr> "0", "0", "0", "0", "0"
## $ heronly <chr> "0", "0", "0", "0", "0"
## $ pharmonly <chr> "0", "0", "0", "0", "0"
## $ fentonly <chr> "1", "1", "1", "1", "1"
## $ Heroin <dbl> 0, 0, 0, 0, 0
## $ X6.mam <dbl> 0, 0, 0, 0, 0
## $ Morphine <dbl> 0, 0, 0, 0, 0
## $ hermor_nocod <dbl> 0, 0, 0, 0, 0
## $ Codeine <dbl> 0, 0, 0, 0, 0
## $ cod.w.no.hermor <dbl> 0, 0, 0, 0, 0
## $ di.H.codeine <dbl> 0, 0, 0, 0, 0
## $ Hydromorphone <dbl> 0, 0, 0, 0, 0
## $ oxymorphone <dbl> 0, 0, 0, 0, 0
## $ Hydrocodone <dbl> 0, 0, 0, 0, 0
## $ Oxycodone <dbl> 0, 0, 0, 0, 0
## $ Methadone <dbl> 0, 0, 0, 0, 0
## $ bup <dbl> 0, 0, 0, 0, 0
## $ fentanyl..4.ANPP.too. <dbl> 1, 1, 1, 1, 1
## $ frankens <dbl> 0, 0, 0, 0, 0
## $ fent...frankens <dbl> 1, 1, 1, 1, 1
## $ tramadol <dbl> 0, 0, 0, 0, 0
## $ opioid.analogs..e.g...U47700. <dbl> 0, 0, 0, 0, 0
## $ Other.Op <dbl> 0, 0, 0, 0, 0
## $ pharma.w.meth.bup.no.fent.or.other.op <dbl> 0, 0, 0, 0, 0
## $ pharma_nobupnometh <dbl> 0, 0, 0, 0, 0
## $ other <dbl> 0, 0, 1, 0, 0
## $ benzos <dbl> 0, 0, 0, 0, 0
## $ cocaine <dbl> 1, 1, 1, 0, 0
## $ amphetamine <dbl> 0, 0, 0, 0, 0
## $ EtOH <dbl> 1, 0, 0, 0, 1
## $ n <int> 17, 13, 13, 12, 8The top 5 substance combinations for 2020 are:
- fentanyl, cocaine
- fentanyl
- fentanyl, other, cocaine
- fentanyl, alcohol
- fentanyl, other
2021
## Rows: 5
## Columns: 31
## Groups: combo, her.only, pharm.only, fent.only, Heroin, X6.mam, Morphine, hermor_nocod, Codeine, cod.w.no.hermor, di.H.codeine, Hydromorphone, oxymorphone, Hydrocodone, Oxycodone, Methadone, bup, fentanyl..4.ANPP.despropionyl.fent.too., frankens, fent...frankens, tramadol, opioid.analogs.e.g.mitragynine, Other.Op, pharma.w.meth.bup.no.fent.or.other.op, pharma_nobupnometh, other, benzos, cocaine, amphetamine..including.eutylone. [4]
## $ combo <int> 0, 0, 0, 0, 0
## $ her.only <int> 0, 0, 0, 0, 0
## $ pharm.only <int> 0, 0, 0, 0, 0
## $ fent.only <int> 1, 1, 1, 1, 1
## $ Heroin <int> 0, 0, 0, 0, 0
## $ X6.mam <int> 0, 0, 0, 0, 0
## $ Morphine <int> 0, 0, 0, 0, 0
## $ hermor_nocod <int> 0, 0, 0, 0, 0
## $ Codeine <int> 0, 0, 0, 0, 0
## $ cod.w.no.hermor <int> 0, 0, 0, 0, 0
## $ di.H.codeine <int> 0, 0, 0, 0, 0
## $ Hydromorphone <int> 0, 0, 0, 0, 0
## $ oxymorphone <int> 0, 0, 0, 0, 0
## $ Hydrocodone <int> 0, 0, 0, 0, 0
## $ Oxycodone <int> 0, 0, 0, 0, 0
## $ Methadone <int> 0, 0, 0, 0, 0
## $ bup <int> 0, 0, 0, 0, 0
## $ fentanyl..4.ANPP.despropionyl.fent.too. <int> 1, 1, 1, 1, 1
## $ frankens <int> 0, 0, 0, 0, 0
## $ fent...frankens <int> 1, 1, 1, 1, 1
## $ tramadol <int> 0, 0, 0, 0, 0
## $ opioid.analogs.e.g.mitragynine <int> 0, 0, 0, 0, 0
## $ Other.Op <int> 0, 0, 0, 0, 0
## $ pharma.w.meth.bup.no.fent.or.other.op <int> 0, 0, 0, 0, 0
## $ pharma_nobupnometh <int> 0, 0, 0, 0, 0
## $ other <int> 0, 1, 0, 0, 1
## $ benzos <int> 0, 0, 0, 0, 0
## $ cocaine <int> 1, 1, 0, 1, 0
## $ amphetamine..including.eutylone. <int> 0, 0, 0, 0, 0
## $ EtOH <int> 0, 0, 0, 1, 0
## $ n <int> 29, 22, 20, 19, 17The top 5 substance combinations for 2021 are:
- fentanyl, other, cocaine
- fentanyl, cocaine
- fentanyl, other
- fentanyl
- fentanyl, other, cocaine, alcohol
2022
## Rows: 5
## Columns: 31
## Groups: combo, her.only, pharm.only, fent.only, Heroin, X6.mam, Morphine, hermor_nocod, Codeine, cod.w.no.hermor, di.H.codeine, Hydro.morphone, oxy.morphone, Hydro.codone, Oxy.codone, Methadone, bup, fentanyl..4.ANPP.despropionyl.fent.too., frankens, fent...frankens, tramadol, opioid.analogs.e.g.mitragynine, Other.Op, pharma.w.meth.bup.no.fent.or.other.op, pharma_nobupnometh, other, benzos, cocaine, amphetamine.including.eutylone.MDMA [4]
## $ combo <int> 0, 0, 0, 0, 0
## $ her.only <int> 0, 0, 0, 0, 0
## $ pharm.only <int> 0, 0, 0, 0, 0
## $ fent.only <int> 1, 1, 1, 1, 1
## $ Heroin <int> 0, 0, 0, 0, 0
## $ X6.mam <int> 0, 0, 0, 0, 0
## $ Morphine <int> 0, 0, 0, 0, 0
## $ hermor_nocod <int> 0, 0, 0, 0, 0
## $ Codeine <int> 0, 0, 0, 0, 0
## $ cod.w.no.hermor <int> 0, 0, 0, 0, 0
## $ di.H.codeine <int> 0, 0, 0, 0, 0
## $ Hydro.morphone <int> 0, 0, 0, 0, 0
## $ oxy.morphone <int> 0, 0, 0, 0, 0
## $ Hydro.codone <int> 0, 0, 0, 0, 0
## $ Oxy.codone <int> 0, 0, 0, 0, 0
## $ Methadone <int> 0, 0, 0, 0, 0
## $ bup <int> 0, 0, 0, 0, 0
## $ fentanyl..4.ANPP.despropionyl.fent.too. <int> 1, 1, 1, 1, 1
## $ frankens <int> 0, 0, 0, 0, 0
## $ fent...frankens <int> 1, 1, 1, 1, 1
## $ tramadol <int> 0, 0, 0, 0, 0
## $ opioid.analogs.e.g.mitragynine <int> 0, 0, 0, 0, 0
## $ Other.Op <int> 0, 0, 0, 0, 0
## $ pharma.w.meth.bup.no.fent.or.other.op <int> 0, 0, 0, 0, 0
## $ pharma_nobupnometh <int> 0, 0, 0, 0, 0
## $ other <int> 0, 1, 0, 1, 0
## $ benzos <int> 0, 0, 0, 0, 0
## $ cocaine <int> 1, 1, 1, 0, 0
## $ amphetamine.including.eutylone.MDMA <int> 0, 0, 0, 0, 0
## $ EtOH <int> 0, 0, 1, 0, 1
## $ n <int> 30, 29, 18, 18, 15The top 5 substance combinations for 2022 are:
- fentanyl, other, cocaine
- fentanyl, cocaine
- fentanyl, other
- fentanyl, cocaine, alcohol
- fentanyl, other, cocaine, alcohol
2023
## Rows: 5
## Columns: 31
## Groups: combo, her.only, pharm.only, fent.only, Heroin, X6.mam, Morphine, hermor_nocod, Codeine, cod.w.no.hermor, di.H.codeine, Hydro.morphone, oxy.morphone, Hydro.codone, Oxy.codone, Methadone, bup, fentanyl..4.ANPP.despropionyl.fent.too., frankens, fent...frankens, tramadol, opioid.analogs.e.g.mitragynine, Other.Op, pharma.w.meth.bup.no.fent.or.other.op, pharma_nobupnometh, other, benzos, cocaine, amphetamine.including.eutylone.MDMA [3]
## $ combo <dbl> 0, 0, 0, 0, 0
## $ her.only <dbl> 0, 0, 0, 0, 0
## $ pharm.only <dbl> 0, 0, 0, 0, 0
## $ fent.only <dbl> 1, 1, 1, 1, 1
## $ Heroin <dbl> 0, 0, 0, 0, 0
## $ X6.mam <dbl> 0, 0, 0, 0, 0
## $ Morphine <dbl> 0, 0, 0, 0, 0
## $ hermor_nocod <dbl> 0, 0, 0, 0, 0
## $ Codeine <dbl> 0, 0, 0, 0, 0
## $ cod.w.no.hermor <dbl> 0, 0, 0, 0, 0
## $ di.H.codeine <dbl> 0, 0, 0, 0, 0
## $ Hydro.morphone <dbl> 0, 0, 0, 0, 0
## $ oxy.morphone <dbl> 0, 0, 0, 0, 0
## $ Hydro.codone <dbl> 0, 0, 0, 0, 0
## $ Oxy.codone <dbl> 0, 0, 0, 0, 0
## $ Methadone <dbl> 0, 0, 0, 0, 0
## $ bup <dbl> 0, 0, 0, 0, 0
## $ fentanyl..4.ANPP.despropionyl.fent.too. <dbl> 1, 1, 1, 1, 1
## $ frankens <dbl> 0, 0, 0, 0, 0
## $ fent...frankens <dbl> 1, 1, 1, 1, 1
## $ tramadol <dbl> 0, 0, 0, 0, 0
## $ opioid.analogs.e.g.mitragynine <dbl> 0, 0, 0, 0, 0
## $ Other.Op <dbl> 0, 0, 0, 0, 0
## $ pharma.w.meth.bup.no.fent.or.other.op <dbl> 0, 0, 0, 0, 0
## $ pharma_nobupnometh <dbl> 0, 0, 0, 0, 0
## $ other <dbl> 0, 1, 0, 0, 1
## $ benzos <dbl> 0, 0, 0, 0, 0
## $ cocaine <dbl> 1, 1, 0, 1, 1
## $ amphetamine.including.eutylone.MDMA <dbl> 0, 0, 0, 0, 0
## $ EtOH <dbl> 0, 0, 0, 1, 1
## $ n <int> 39, 29, 14, 13, 12The top 5 substance combinations for 2022 are:
- fentanyl, cocaine
- fentanyl, other, cocaine
- fentanyl, cocaine, alcohol
- fentanyl
- fentanyl, other
How many people were using chronically?
This is based on request and should not be used for anything but a rough indication of chronic use. I identify chronic users crudely and approximately – I look for entries where the words “chronic” [can sometimes also indicate chronic pain, meaning opioid scripts], “Chronic”, “drug use”, “drug user”, “drug abuse”, “snort”, “snorts”, “addict”, “addicted”, “rehab”, or “sober house” pops up in either the notes field or immediate cause of death, because that generally means that the person had chronic drug use [based on my look at the data, I am including alcohol in chronic use].
2018
## [1] "There were at least 13 people with suspected chronic use out of 120 decedents in 2018, which is 10.833333 percent of all decedents."2019
## [1] "There were at least 31 people with suspected chronic use out of 175 decedents in 2019, which is 17.714286 percent of all decedents."2020
## [1] "There were at least 43 people with suspected chronic use out of 215 decedents in 2020, which is 20.000000 percent of all decedents."2021
## [1] "There were at least 64 people with suspected chronic use out of 319 decedents in 2021, which is 20.062696 percent of all decedents."2022
## [1] "There were at least 150 people with suspected chronic use out of 280 decedents in 2022, which is 53.571429 percent of all decedents."2023
## [1] "There were at least 44 people with suspected chronic use out of 253 decedents in 2023, which is 17.391304 percent of all decedents."Who did decedents live with?
Please note: this field and all fields explored below are only available in the data up to 2020.
2018
2019
2020
% cases found in <24h and >=24h
Please note that this is an estimation based on the death narrative – it’s calculated as \(\text{Time last known alive} - \text{time when the body was found}\). The last known alive time is based on witness statements and may be an overestimation, especially in cases where friends and family had not seen the decedent in a number of weeks/months.
2018
## [1] "There were 79 people found within 24 hours, which is 0.658333 of all decedents."## [1] "There were 22 people found in over 24 hours, which is 0.183333 of all decedents."2019
## [1] "There were 129 people found within 24 hours, which is 0.737143 of all decedents."## [1] "There were 32 people found in over 24 hours, which is 0.182857 of all decedents."2020
## [1] "There were 159 people found within 24 hours, which is 0.739535 of all decedents."## [1] "There were 33 people found in over 24 hours, which is 0.153488 of all decedents."Race of <24h and >=24h decedents
2018
Under 24h
Over 24h
2019
Under 24h
Over 24h
2020
Under 24h
Over 24h
Town of OD of <24h and >=24h decedents
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2018
Under 24h
Over 24h
2019
Under 24h
Over 24h
2020
Under 24h
Over 24h
Injury location for <24h and >=24h
2018
Under 24h
Over 24h
2019
Under 24h
Over 24h
2020
Under 24h
Over 24h
