| Literature DB >> 35460142 |
Tara Gomes1,2,3,4, Katherine Callaway Kim5,6, Katie J Suda5,7, Ria Garg8, Mina Tadrous2,4.
Abstract
PURPOSE: We sought to compare trends in opioid purchasing between developed and developing economies to understand patterns of opioid consumption, and how they were impacted by the COVID-19 pandemic.Entities:
Keywords: drug policy; pain; prescription opioids
Mesh:
Substances:
Year: 2022 PMID: 35460142 PMCID: PMC9088547 DOI: 10.1002/pds.5443
Source DB: PubMed Journal: Pharmacoepidemiol Drug Saf ISSN: 1053-8569 Impact factor: 2.732
Changes in rates of retail opioid purchases (units per 1000 population), by development status and class, 2019 versus 2014
| Variable | All countries ( | Developed economies ( | Developing economies ( | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Units per 1000 in July–December | % Change (95% CI) | Units per 1000 in July–December | % Change (95% CI) | Units per 1000 in July–December | % Change (95% CI) | ||||
| 2014 | 2019 | 2014 | 2019 | 2014 | 2019 | ||||
| All Opioids | 3195.6 | 2521.4 | −21.1 (−32.6, 4.6) | 14734.7 | 11221.1 | −23.8 (−34.7, 3.6) | 449.1 | 517.6 | 15.2 (4.6, 35.6) |
| Codeine | 939.3 | 766.1 | −18.4 (−35.1, −8.0) | 3857.7 | 3018.4 | −21.8 (−42.8, −9.7) | 244.7 | 247.3 | 1.1 (−9.6, 23.3) |
| Tramadol | 853.5 | 809.2 | −5.2 (−18.3, 22.2) | 3668.7 | 3251.5 | −11.4 (−23.7, 19.4) | 183.5 | 246.7 | 34.5 (18.2, 66.4) |
| Oxycodone | 521.9 | 387.4 | −25.8 (−32.9, 25.7) | 2701.4 | 2052.6 | −24 (−31.4, 30.1) | 3.1 | 3.8 | 23.7 (−14.9, 144.5) |
| Hydrocodone | 593.9 | 298.8 | −49.7 (−54.9, 124.4) | 3086.9 | 1592.6 | −48.4 (−48.9, −48.1) | 0.53 | 0.82 | 55.8 (−71.9, 447.5) |
| Morphine | 123.7 | 100.1 | −19.0 (−36.3, 6.5) | 635.2 | 519.9 | −18.2 (−35.9, 8.1) | 1.9 | 3.4 | 79.3 (46.1, 164.3) |
| Other | 87.3 | 97.9 | 12.0 (−16.1, 79.0) | 391.1 | 457.9 | 17.1 (−16.5, 113.2) | 15.1 | 14.9 | −0.7 (−18.0, 145.2) |
| Hydromorphone | 55.7 | 44.7 | −19.7 (−46.8, 38.9) | 289.0 | 238.4 | −17.5 (−45.2, 44.7) | 0.12 | 0.06 | −50.4 (−83.2, −10.1) |
| Fentanyl | 20.3 | 17.2 | −15.3 (−40.3, 15.9) | 104.7 | 89.9 | −14.1 (−40.4, 18.7) | 0.24 | 0.48 | 96.4 (11.7, 322.3) |
Source: Authors' analysis of MIDAS drug purchases, July 2014–December 2019.
FIGURE 1Rates of retail opioid purchases (units per 1000 population), July 2014–August 2020, by development status and class.
FIGURE 2Percentage change in population‐adjusted rates of retail opioid purchases by jurisdiction, July–December 2019 versus July–December 2014.
Results of time series analysis of impact of COVID‐19 pandemic on rates of retail opioid purchases
| March 2020 pulse intervention | April to May 2020 pulse intervention | ||||||
|---|---|---|---|---|---|---|---|
| Outcome | ARIMA model | Estimate | 95% Confidence Interval |
| Estimate | 95% Confidence Interval |
|
| Developing economies | |||||||
| Opioid units purchased |
(2,1,0)(0,1,0)12 no intercept | 10.9 units/1000 | 6.1 to 15.6 units/1000 |
| −14.8 units/1000 | −17.9 to −11.7 units/1000 |
|
| Developed economies | |||||||
| Opioid units purchased |
(2,1,0)(0,1,1)12 no intercept | 145.5 units/1000 | 83.4 to 207.6 units/1000 |
| −129.2 units/1000 | −171.8 to 86.6 units/1000 |
|
Note: Boldface indicates statistical significance (p < 0.05).
Changes in rates of retail opioid purchases (units per 1000 population) during the first stage of the COVID‐19 pandemic, by development status and class
| Variable | All countries ( | Developed economies ( | Developing economies ( | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Units per 1000 in March–August | % Change (95% CI) | Units per 1000 in March–August | % Change (95% CI) | Units per 1000 in March–August | % Change (95% CI) | ||||
| 2019 | 2020 | 2019 | 2020 | 2019 | 2020 | ||||
| All opioids | 2532.9 | 2401.0 | −5.2 (−6.7, −2.8) | 11317.6 | 10733.5 | −5.2 (−6.6, −2.7) | 509.5 | 493.8 | −3.1 (−8.2, 0.9) |
| Codeine | 764.0 | 719.6 | −5.8 (−10.6, −3.6) | 3018.4 | 2831.3 | −6.2 (−12.2, −3.8) | 244.7 | 236.2 | −3.5 (−13.2, −0.3) |
| Tramadol | 807.1 | 772.2 | −4.3 (−6.3, −1.0) | 3281.2 | 3111.8 | −5.2 (−7.0, −1.8) | 237.3 | 236.6 | −0.3 (−4.9, 4.5) |
| Oxycodone | 392.9 | 371.6 | −5.4 (−6.3, −2.0) | 2079.5 | 1980.1 | −4.8 (−5.7, −1.0) | 4.4 | 3.5 | −20.6 (−54.8, 14.6) |
| Hydrocodone | 306.3 | 283.4 | −7.5 (−42.5, −0.5) | 1632.8 | 1518.3 | −7.0 (−7.2, −6.9) | 0.8 | 0.73 | −9.2 (−48.5, 21.8) |
| Morphine | 100.9 | 98.1 | −2.7 (−6.5, 4.7) | 525.6 | 510.8 | −2.8 (−6.6, 4.1) | 3.1 | 3.7 | 19.5 (2.7, 40.5) |
| Other | 100.5 | 95.2 | −5.3 (−18.7, 2.3) | 455.4 | 456.7 | 0.3 (−3.9, 5.3) | 18.8 | 12.5 | −33.4 (−45.0, 7.4) |
| Hydromorphone | 44.0 | 44.2 | 0.4 (−6.1, 7.0) | 234.7 | 236.8 | 0.9 (−5.7, 7.7) | 0.061 | 0.065 | 6.5 (−22.2, 23.4) |
| Fentanyl | 17.3 | 16.8 | −2.7 (−7.6, 1.0) | 90.2 | 87.7 | −2.7 (−7.9, 1.0) | 0.45 | 0.54 | 19.0 (−6.5, 42.0) |
Source: Authors' analysis of MIDAS drug purchases, March 2019–August 2020.
FIGURE 3Percent changes in population‐adjusted rates of retail opioid purchases (units per 1000 population) by jurisdiction, July–December 2019 versus July–December 2014.