Fatal opioid-detected overdoses in Connecticut, 2018-2023
Use the sidebar on the left to find different ways to explore fatal overdose data from 2018-2023. All data is from the Connecticut Office of the Chief Medical Examiner and complies with their data-sharing policies. 2024 data will be added when available.
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
Maps of OD towns
Please consult the below map to find your town. Interactive capabilities coming soon! This map is from CT DEEP and can be found here.
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 158 instances where a person's residence town didn't match up with their injury town, out of 893 ODs in 2018 where the injury and residence town were both known. This is 0.176932 of all such 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 Percent of out-of-town ODs
## 1 Danbury 3 15 20
## 2 Waterbury 5 66 7.6
## 3 Torrington 0 23 0
## 4 New Haven 5 38 13.2
## 5 West Haven 2 12 16.7
## 6 Hartford 33 89 37.1
## 7 Norwich 2 12 16.7
## 8 New London 1 14 7.1
## 9 Bridgeport 5 51 9.8
## 10 New Britain 7 44 15.9
## 11 Litchfield 0 3 0
## 12 Meriden 3 19 15.8
## 13 Norwalk 1 8 12.5
## 14 Stamford 2 12 16.7
## 15 Windham 0 10 02019
## [1] "There were 173 instances where a person's residence town didn't match up with their injury town, out of 1035 ODs in 2019 where the injury and residence town were both known. This is 0.167150 of all such 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 Percent of out-of-town ODs
## 1 Danbury 3 16 18.8
## 2 Waterbury 15 91 16.5
## 3 Torrington 4 25 16
## 4 New Haven 9 55 16.4
## 5 West Haven 6 24 25
## 6 Hartford 21 108 19.4
## 7 Norwich 5 26 19.2
## 8 New London 5 23 21.7
## 9 Bridgeport 9 60 15
## 10 New Britain 1 32 3.1
## 11 Litchfield 0 1 0
## 12 Meriden 3 20 15
## 13 Norwalk 0 13 0
## 14 Stamford 1 6 16.7
## 15 Windham 1 17 5.92020
## [1] "There were 191 instances where a person's residence town didn't match up with their injury town, out of 1174 ODs in 2020 where the injury and residence town were both known. This is 0.162692 of all such 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 Percent of out-of-town ODs
## 1 Danbury 2 23 8.7
## 2 Waterbury 12 87 13.8
## 3 Torrington 2 23 8.7
## 4 New Haven 9 81 11.1
## 5 West Haven 3 17 17.6
## 6 Hartford 26 101 25.7
## 7 Norwich 4 34 11.8
## 8 New London 2 17 11.8
## 9 Bridgeport 7 65 10.8
## 10 New Britain 6 35 17.1
## 11 Litchfield 0 1 0
## 12 Meriden 2 30 6.7
## 13 Norwalk 2 17 11.8
## 14 Stamford 2 19 10.5
## 15 Windham 4 22 18.22021
## [1] "There were 241 instances where a person's residence town didn't match up with their injury town, out of 1296 ODs in 2021 where the injury and residence town were both known. This is 0.185957 of all such 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 Percent of out-of-town ODs
## 1 Danbury 6 23 26.1
## 2 Waterbury 12 88 13.6
## 3 Torrington 0 18 0
## 4 New Haven 27 135 20
## 5 West Haven 6 39 15.4
## 6 Hartford 27 124 21.8
## 7 Norwich 6 24 25
## 8 New London 6 39 15.4
## 9 Bridgeport 12 83 14.5
## 10 New Britain 7 40 17.5
## 11 Litchfield 0 1 0
## 12 Meriden 5 23 21.7
## 13 Norwalk 1 13 7.7
## 14 Stamford 3 21 14.3
## 15 Windham 0 12 02022
## [1] "There were 203 instances where a person's residence town didn't match up with their injury town, out of 1206 ODs in 2022 where the injury and residence town were both known. This is 0.168325 of all such 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 Percent of out-of-town ODs
## 1 Danbury 4 21 19
## 2 Waterbury 18 99 18.2
## 3 Torrington 1 20 5
## 4 New Haven 25 125 20
## 5 West Haven 5 27 18.5
## 6 Hartford 24 131 18.3
## 7 Norwich 4 32 12.5
## 8 New London 4 25 16
## 9 Bridgeport 9 80 11.2
## 10 New Britain 5 46 10.9
## 11 Litchfield 0 1 0
## 12 Meriden 3 29 10.3
## 13 Norwalk 3 23 13
## 14 Stamford 1 20 5
## 15 Windham 2 20 102023
## [1] "There were 204 instances where a person's residence town didn't match up with their injury town, out of 1141 ODs in 2023 where the injury and residence town were both known. This is 0.178791 of all such 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 Percent of out-of-town ODs
## 1 Danbury 1 27 3.7
## 2 Waterbury 21 98 21.4
## 3 Torrington 0 16 0
## 4 New Haven 27 126 21.4
## 5 West Haven 4 25 16
## 6 Hartford 33 131 25.2
## 7 Norwich 3 19 15.8
## 8 New London 2 13 15.4
## 9 Bridgeport 12 92 13
## 10 New Britain 4 45 8.9
## 11 Litchfield 0 0 NaN
## 12 Meriden 7 35 20
## 13 Norwalk 3 16 18.8
## 14 Stamford 4 25 16
## 15 Windham 0 11 0Injury 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 625 people who OD'd in their own residence."## [1] "The total proportion of decedents ODing in their own residence was 0.647668."## [1] "Out of everyone who OD'd in a residence, 0.840054 of people OD'd in their own residence."2019
## [1] "There were 741 people who OD'd in their own residence."## [1] "The proportion of decedents ODing in their own residence was 0.650000."## [1] "Out of everyone who OD'd in a residence, 0.818785 of people OD'd in their own residence."2020
## [1] "There were 831 people who OD'd in their own residence."## [1] "The proportion of decedents ODing in their own residence was 0.649219."## [1] "Out of everyone who OD'd in a residence, 0.823588 of people OD'd in their own residence."2021
## [1] "There were 948 people who OD'd in their own residence."## [1] "The proportion of decedents ODing in their own residence was 0.664331."## [1] "Out of everyone who OD'd in a residence, 0.861818 of people OD'd in their own residence."2022
## [1] "There were 883 people who OD'd in their own residence."## [1] "The proportion of decedents ODing in their own residence was 0.652624."## [1] "Out of everyone who OD'd in a residence, 0.869094 of people OD'd in their own residence."2023
## [1] "There were 776 people who OD'd in their own residence."## [1] "The proportion of decedents ODing in their own residence was 0.631408."## [1] "Out of everyone who OD'd in a residence, 0.855568 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 OCME’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 [4]
## $ cocaine <dbl> 0, 1, 0, 1, 0
## $ combo..her.pharm.or.fent..OR.pharm.fent <dbl> 0, 0, 0, 0, 0
## $ heronly <dbl> 0, 0, 0, 0, 0
## $ pharmonly <dbl> 0, 0, 0, 0, 0
## $ fentonly <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
## $ 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
## $ buprenorphine <dbl> 0, 0, 0, 0, 0
## $ fentanyl..4ANPP.too. <dbl> 1, 1, 1, 1, 1
## $ Frankens <dbl> 0, 0, 0, 0, 0
## $ fent.or.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
## $ otherop <dbl> 0, 0, 0, 0, 0
## $ pharma_w_meth_no_fent.or.op.analogs.or.cod.or..other.op. <dbl> 0, 0, 0, 0, 0
## $ pharma_nobupnometh <dbl> 0, 0, 0, 0, 0
## $ other <dbl> 0, 0, 1, 1, 0
## $ benzos <dbl> 0, 0, 0, 0, 0
## $ amphetamine <dbl> 0, 0, 0, 0, 0
## $ EtOH <dbl> 0, 0, 0, 0, 1
## $ n <int> 63, 45, 24, 2~The 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, 0, 0, 0
## $ heronly <int> 0, 0, 0, 0, 0
## $ pharmonly <int> 0, 0, 0, 0, 0
## $ fentonly <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 <dbl> 0, 0, 0, 0, 0
## $ di.H.codeine <dbl> 0, 0, 0, 0, 0
## $ Hydromorphone <dbl> 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 <dbl> 0, 0, 0, 0, 0
## $ other <int> 0, 0, 0, 1, 1
## $ benzos <int> 0, 0, 0, 0, 0
## $ cocaine <int> 1, 0, 0, 1, 0
## $ amphetamine <int> 0, 0, 0, 0, 0
## $ EtOH <int> 0, 0, 1, 0, 0
## $ n <int> 75, 58, 40, 39, 36The 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 [4]
## $ 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, 1
## $ benzos <dbl> 0, 0, 0, 0, 0
## $ cocaine <dbl> 1, 0, 1, 0, 0
## $ amphetamine <dbl> 0, 0, 0, 0, 0
## $ EtOH <dbl> 0, 0, 0, 1, 0
## $ n <int> 95, 89, 76, 66, 62The 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 <dbl> 0, 0, 0, 0, 0
## $ di.H.codeine <int> 0, 0, 0, 0, 0
## $ Hydromorphone <int> 0, 0, 0, 0, 0
## $ oxymorphone <dbl> 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> 1, 0, 1, 0, 1
## $ benzos <dbl> 0, 0, 0, 0, 0
## $ cocaine <int> 1, 1, 0, 0, 1
## $ amphetamine..including.eutylone. <dbl> 0, 0, 0, 0, 0
## $ EtOH <dbl> 0, 0, 0, 0, 1
## $ n <int> 124, 92, 83, 77, 73The 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 [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> 1, 0, 1, 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> 160, 117, 99, 80, 78The 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, 1, 0, 1
## $ amphetamine.including.eutylone.MDMA <dbl> 0, 0, 0, 0, 0
## $ EtOH <dbl> 0, 0, 1, 0, 1
## $ n <int> 168, 148, 66, 63, 60The 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 162 people with suspected chronic use out of 965 decedents in 2018, which is 16.787565 percent of all decedents."2019
## [1] "There were at least 225 people with suspected chronic use out of 1140 decedents in 2019, which is 19.736842 percent of all decedents."2020
## [1] "There were at least 323 people with suspected chronic use out of 1280 decedents in 2020, which is 25.234375 percent of all decedents."2021
## [1] "There were at least 303 people with suspected chronic use out of 1427 decedents in 2021, which is 21.233357 percent of all decedents."2022
## [1] "There were at least 706 people with suspected chronic use out of 1353 decedents in 2022, which is 52.180340 percent of all decedents."2023
## [1] "There were at least 229 people with suspected chronic use out of 1229 decedents in 2023, which is 18.633035 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 678 people found within 24 hours, which is 0.702591 of all decedents."## [1] "There were 164 people found in over 24 hours, which is 0.169948 of all decedents."2019
## [1] "There were 850 people found within 24 hours, which is 0.745614 of all decedents."## [1] "There were 194 people found in over 24 hours, which is 0.170175 of all decedents."2020
## [1] "There were 935 people found within 24 hours, which is 0.730469 of all decedents."## [1] "There were 207 people found in over 24 hours, which is 0.161719 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
