| Literature DB >> 35156106 |
Gabriel Vallecillo1,2, Francina Fonseca1,2,3, Lina Oviedo1,2, Xavier Durán2, Ignacio Martinez4, Alexandra García-Guix1,2, Claudio Castillo1,2, Marta Torrens1,2,4, Santiago Llana1, Albert Roquer1, Maria de la Cabeza Martinez1, Sandra Aguelo1, Irene Canosa1.
Abstract
INTRODUCTION: During the COVID-19 pandemic, limited access to health care augmented COVID-19 risk in subjects with opioid use disorder (OUD). The aim of the study was to compare COVID-19 incidence in individuals with OUD receiving continuous clinical care with that of the general population.Entities:
Year: 2022 PMID: 35156106 PMCID: PMC8760741 DOI: 10.1016/j.dadr.2022.100027
Source DB: PubMed Journal: Drug Alcohol Depend Rep ISSN: 2772-7246
Social and substance use characteristics of 366 people with opioid use disorders.
| Characteristics | Total | Non-COVID-19 | COVID-19 | |
|---|---|---|---|---|
| n | 366 | 356 | 10 | |
| Age | 48.23 (8.89) | 48.13 (8.84) | 51.50 (10.5%) | 0.30 |
| Sex | ||||
| men | 280 (76.5%) | 273 (76.7%) | 7 (70.0%) | 0.71 |
| Origin | ||||
| Spanish | 283 (77.3%) | 275 (77.2%) | 8 (80.0%) | 1 |
| Education level | ||||
| primary school | 240 (65.6%) | 232 (65.2%) | 8 (80.0%) | 0.17 |
| secondary school | 113 (30.9%) | 112 (31.5%) | 1 (10.0%) | |
| post secondary school | 13 (3.6%) | 12 (3.4%) | 1 (10.0%) | |
| Housing | ||||
| homeless | 41 (11.2%) | 41 (11.5%) | 0 (0.0%) | 0.67 |
| shelter | 27 (7.4%) | 27 (7.6%) | 0 (0.0%) | |
| home | 298 (81.4%) | 288 (80.9%) | 10 (100%) | |
| Employment | ||||
| Employed | 94 (25.7%) | 89 (25.0%) | 5 (50.0%) | 0.14 |
| unemployed | 171 (46.7%) | 169 (47.5%) | 2 (20.0%) | |
| inactive | 101 (27.6%) | 98 (27.5%) | 3 (30.0%) | |
| Marital status | ||||
| single | 249 (68.0%) | 244 (68.5%) | 5 (50.0%) | 0.11 |
| couple | 110 (30.1%) | 106 (29.8%) | 4 (40.0%) | |
| widow | 7 (1.9%) | 6 (1.7%) | 1 (10.0%) | |
| Criminal recordsyes | 187 (51.1%) | 182 (51.1%) | 5 (50.0%) | 1 |
| Substance use disorders | ||||
| heroine | 366(100%) | 356(100%) | 10(10%) | 1 |
| cocaine | 319 (87.2%) | 309 (86.8%) | 10 (100.0%) | 0.37 |
| cannabis | 213 (58.4%) | 205 (57.7%) | 8 (80.0%) | 0.20 |
| alcohol | 160 (43.7%) | 153 (43.0%) | 7 (70.0%) | 0.11 |
| Route administration | ||||
| injected | 208 (56.8%) | 201 (56.5%) | 7 (70.0%) | 0.40 |
| nasal | 82 (22.4%) | 81 (22.8%) | 1 (10.0%) | |
| pulmonary | 61 (16.7%) | 60 (16.9%) | 1 (10.0%) | |
| oral | 15 (4.1%) | 14 (3.9%) | 1 (10.0%) | |
| Agonist therapy | ||||
| methadone | 302 (82.5%) | 294 (82.6%) | 8 (80.0%) | 0.68 |
| buprenorphine/naloxone | 64 (17.5%) | 62 (17.4%) | 2 (20.0%) | |
| Drug urine test negative | 232 (63.4%) | 224 (62.9%) | 8 (80.0%) | 0.33 |
Data are presented as No. (%) unless otherwise indicated.
Data presented as mean ± standard deviation.
Clinical characteristics of 366 people with opioid use disorders.
| Condition | Total | Non-COVID-19 | COVID-19 | |
|---|---|---|---|---|
| Dual diagnosis | 207 (56.6%) | 202 (56.7%) | 5 (50.0%) | 0.75 |
| HIV infection | 237 (64.8%) | 229 (64.3%) | 8 (80.0%) | 0.50 |
| Hepatitis C infectionIgG antibodies | 237 (64.8%) | 229 (64.3%) | 8 (80.0%) | 0.50 |
| RNA | 46 (12.6%) | 43 (12.1%) | 3 (30.0%) | 0.11 |
| Hypertension | 58 (15.8%) | 53 (14.9%) | 5 (50.0%) | 0.01 |
| Diabetes | 27 (7.4%) | 26 (7.3%) | 1 (10.0%) | 0.54 |
| Liver chronic diseases | 24 (6.6%) | 24 (6.7%) | 0 (0.0%) | 1.00 |
| Respiratory chronic diseases | 57 (15.6%) | 54 (15.2%) | 3 (30.0%) | 0.19 |
| Cardiovascular chronic diseases | 10 (2.7%) | 8 (2.2%) | 2 (20.0%) | 0.02 |
| Kidney chronic diseases | 11 (3.0%) | 10 (2.8%) | 1 (10.0%) | 0.26 |
| Cancer | 10 (2.7%) | 10 (2.8%) | 0 (0.0%) | 1.00 |
Data are presented as No. (%) unless otherwise indicated.
Abbreviations: RNA, ribonucleic acid