Fatal opioid-detected overdoses in Connecticut, 2018-2022
Race
2018
2019
2020
2021
2022
Sex
2018
2019
2020
2021
2022
Race and sex
2018
2019
2020
2021
2022
Age
The black lines on the plots represent modes. The approach I used to calculate modes creates a continuous approximation of the age distribution [which admittedly is not the best approach here since age is a discrete variable]. I’ll be looking for a better approach.
2018
2019
2020
2021
2022
Day of week
2018
2019
2020
2021
2022
Month
2018
2019
2020
2021
2022
Town of OD
2018
2019
2020
2021
2022
Maps of OD towns
2018
2019
2020
2021
2022
Injury town v. residence town
I chose towns based on either what regional harm reduction workers wanted, based on towns with overdose prevention infrastructure or based on towns with the most ODs in the past few years. I can change this easily to show all towns. I’m looking at instances where people who OD’d in a town didn’t live 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 10Injury location
2018
2019
2020
2021
2022
Are people dying in their own homes?
2018
## [1] "There were 625 people who OD'd in their own residence."## [1] "The 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."Most common substances
2018
## Rows: 5
## Columns: 32
## 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, EtOH [4]
## $ cocaine <int> 0, 1, 0, 0, 1
## $ combo..her.pharm.or.fent..OR.pharm.fent <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 <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
## $ buprenorphine <int> 0, 0, 0, 0, 0
## $ fentanyl..4ANPP.too. <int> 1, 1, 1, 1, 1
## $ Frankens <int> 0, 0, 0, 0, 0
## $ fent.or.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
## $ otherop <int> 0, 0, 0, 0, 0
## $ pharma_w_meth_no_fent.or.op.analogs.or.cod.or..other.op. <int> 0, 0, 0, 0, 0
## $ pharma_nobupnometh <int> 0, 0, 0, 0, 0
## $ other <int> 0, 0, 0, 1, 1
## $ benzos <int> 0, 0, 0, 0, 0
## $ amphetamine <int> 0, 0, 0, 0, 0
## $ EtOH <int> 0, 0, 0, 0, 0
## $ THC <int> 0, 0, 1, 0, 0
## $ n <int> 46, 35, 17, 1~The top 5 substance combinations for 2018 are:
- fent only
- cocaine, fent only
- fent only, weed
- fent only, other
- cocaine, fent only, other
2019
## Rows: 5
## Columns: 33
## 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, EtOH [5]
## $ 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 <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, 1, 0, 1
## $ benzos <int> 0, 0, 0, 0, 0
## $ cocaine <int> 1, 0, 1, 0, 1
## $ amphetamine <int> 0, 0, 0, 0, 0
## $ EtOH <int> 0, 0, 0, 1, 1
## $ THC <int> 0, 0, 0, 0, 0
## $ n <int> 52, 37, 28, 27, 24The top 5 substance combinations for 2019 are:
- fent only, cocaine
- fent only
- fent only, other, cocaine
- fent only, alcohol
- fent only, other, cocaine, alcohol
2020
## Rows: 5
## Columns: 32
## 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, EtOH [5]
## $ 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, 1, 0
## $ 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, 0, 1
## $ THC <dbl> 0, 0, 0, 0, 0
## $ n <int> 65, 57, 57, 48, 42The top 5 substance combinations for 2020 are:
- fent only, cocaine
- fent only
- fent only, other, cocaine
- fent only, other
- fent only, alcohol
2021
## Rows: 5
## Columns: 32
## 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., EtOH [5]
## $ 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> 1, 0, 1, 0, 1
## $ benzos <int> 0, 0, 0, 0, 0
## $ cocaine <int> 1, 1, 0, 1, 1
## $ amphetamine..including.eutylone. <int> 0, 0, 0, 0, 0
## $ EtOH <int> 0, 0, 0, 1, 1
## $ THC <int> 0, 0, 0, 0, 0
## $ n <int> 87, 63, 56, 54, 52The top 5 substance combinations for 2021 are:
- fent only, other, cocaine
- fent only, cocaine
- fent only, other
- fent only, cocaine, alcohol
- fent only, other, cocaine, alcohol
2022
## Rows: 5
## Columns: 32
## 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, EtOH [5]
## $ 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> 1, 0, 1, 0, 1
## $ benzos <int> 0, 0, 0, 0, 0
## $ cocaine <int> 1, 1, 0, 1, 1
## $ amphetamine.including.eutylone.MDMA <int> 0, 0, 0, 0, 0
## $ EtOH <int> 0, 0, 0, 1, 1
## $ THC <int> 0, 0, 0, 0, 0
## $ n <int> 119, 86, 61, 59, 54The top 5 substance combinations for 2022 are:
- fent only, other, cocaine
- fent only, cocaine
- fent only, other
- fent only, cocaine, alcohol
- fent only, other, cocaine, alcohol
How many people were using chronically?
I identify chronic users crudely – I look for entries where the words “chronic” [can sometimes indicate chronic pain, meaning opioid scripts], “Chronic”, “user”, “drug use”, “drug user”, “drug abuse”, “snort”, “snorts”, “drug abuse”, “addict”, “addicted”, “rehab”, “sober house”, “clean” or “abuse” 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]
2018
## [1] "There were at least 300 people with known chronic use out of 965 decedents in 2018, which is 31.088083 percent of all decedents."2019
## [1] "There were at least 458 people with known chronic use out of 1140 decedents in 2019, which is 40.175439 percent of all decedents."2020
## [1] "There were at least 673 people with known chronic use out of 1280 decedents in 2020, which is 52.578125 percent of all decedents."2021
## [1] "There were at least 741 people with known chronic use out of 1427 decedents in 2021, which is 51.927120 percent of all decedents."2022
## [1] "There were at least 706 people with known chronic use out of 1353 decedents in 2022, which is 52.180340 percent of all decedents."Who did decedents live with?
Please note: this field and all fields explored below are only available up to 2020.
2018
2019
2020
% cases found in <24h and >=24h
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
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
