The Trends in Opioid Use in Castile and Leon, Spain: A Population-Based Registry Analysis of Dispensations in 2015 to 2018
Abstract
:1. Introduction
2. Experimental Section
3. Results
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Cicero, T.J.; Ellis, M.S. Understanding the demand side of the prescription opioid epidemic: Does the initial source of opioids matter? Drug. Alcohol. Depend. 2017, 173 (Suppl. 1), S4–S10. [Google Scholar] [CrossRef]
- Rosner, B.; Neicun, J.; Yang, J.C.; Roman-Urrestarazu, A. Opioid prescription patterns in Germany and the global opioid epidemic: Systematic review of available evidence. PLoS ONE 2019, 14, e0221153. [Google Scholar] [CrossRef] [PubMed]
- Van Amsterdam, J.; van den Brink, W. The Misuse of Prescription Opioids: A Threat for Europe? Curr. Drug Abuse Rev. 2015, 8, 3–14. [Google Scholar] [CrossRef] [PubMed]
- Gomes, T.; Tadrous, M.; Mamdani, M.M.; Paterson, J.M.; Juurlink, D.N. The Burden of Opioid-Related Mortality in the United States. JAMA Netw. Open 2018, 1, e180217. [Google Scholar] [CrossRef] [PubMed]
- Islam, M.M.; Wollersheim, D. Who Are Dispensed the Bulk Amount of Prescription Opioids? J. Clin. Med. 2019, 8, 293. [Google Scholar] [CrossRef] [Green Version]
- Compton, W.M.; Jones, C.M. Epidemiology of the U.S. opioid crisis: The importance of the vector. Ann. N. Y. Acad. Sci. 2019, 1451, 130–143. [Google Scholar] [CrossRef]
- Li, G.; Chihuri, S. Prescription opioids, alcohol and fatal motor vehicle crashes: A population-based case-control study. Inj. Epidemiol. 2019, 6, 11. [Google Scholar] [CrossRef] [Green Version]
- Herrera-Gómez, F.; García-Mingo, M.; Colás, M.; González-Luque, J.C.; Álvarez, F.J. Opioids in oral fluid of Spanish drivers. Drug. Alcohol Depend. 2018, 187, 35–39. [Google Scholar] [CrossRef]
- Chihuri, S.; Li, G. Use of prescription opioids and motor vehicle crashes: A meta analysis. Accid. Anal. Prev. 2017, 109, 123–131. [Google Scholar] [CrossRef]
- Cheatle, M.D.; Barker, C. Improving opioid prescription practices and reducing patient risk in the primary care setting. J. Pain. Res. 2014, 7, 301–311. [Google Scholar] [CrossRef] [Green Version]
- Chihuri, S.; Li, G. Use of Prescription Opioids and Initiation of Fatal 2-Vehicle Crashes. JAMA Netw. Open. 2019, 2, e188081. [Google Scholar] [CrossRef] [PubMed]
- Schumacher, M.B.; Jongen, S.; Knoche, A.; Petzke, F.; Vuurman, E.F.; Vollrath, M.; Ramaekers, J.G. Effect of chronic opioid therapy on actual driving performance in non-cancer pain patients. Psychopharmacology 2017, 234, 989–999. [Google Scholar] [CrossRef] [PubMed]
- Fierro, I.; Colás, M.; González-Luque, J.C.; Álvarez, F.J. Roadside opioid testing of drivers using oral fluid: The case of a country with a zero tolerance law, Spain. Subst. Abuse Treat. Prev. Policy 2017, 12, 22. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Gutierrez-Abejón, E.; Herrera-Gómez, F.; Criado-Espegel, P.; Alvarez, F.J. Use of driving-impairing medicines by a Spanish population: A population-based registry study. BMJ Open 2017, 7, e017618. [Google Scholar] [CrossRef] [Green Version]
- Herrera-Gómez, F.; Gutierrez-Abejón, E.; Criado-Espegel, P.; Álvarez, F.J. The Problem of Benzodiazepine Use and Its Extent in the Driver Population: A Population-Based Registry Study. Front. Pharmacol. 2018, 9, 408. [Google Scholar] [CrossRef] [Green Version]
- Herrera-Gómez, F.; Gutierrez-Abejón, E.; Álvarez, F.J. Antipsychotics in the general population and the driver population: Comparisons from a population-based registry study. Int. Clin. Psychopharmacol. 2019, 34, 184–188. [Google Scholar] [CrossRef]
- Benchimol, E.I.; Smeeth, L.; Guttmann, A.; Harron, K.; Moher, D.; Petersen, I.; Sørensen, H.T.; von Elm, E.; Langan, S.M.; RECORD Working Committee. The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement. PLoS Med. 2015, 12, e1001885. [Google Scholar] [CrossRef]
- Brady, K.T.; McCauley, J.L.; Back, S.E. Prescription Opioid Misuse, Abuse, and Treatment in the United States: An Update. Am. J. Psychiatry 2016, 173, 18–26. [Google Scholar] [CrossRef] [Green Version]
- Herrera-Gómez, F.; García-Mingo, M.; Colás, M.; González-Luque, J.C.; Alvarez, F.J. Drivers who tested positive for cannabis in oral fluid: A longitudinal analysis of administrative data for Spain between 2011 and 2016. BMJ Open 2019, 9, e026648. [Google Scholar] [CrossRef]
- Minsterio De Sanidad Servicios Sociales e lgualdad. Utilización de medicamentos opioides en España durante el periodo 2008-2015; Agencia Española de Medicamentos y Productos Sanitarios INFORME DE UTILIZACIÓN DE MEDICAMENTOS U/OPI/V1/13022017: Madrid, Spain, 21 February 2017.
- Jacob, L.; Kostev, K. Prevalence of pain medication prescriptions in France, Germany, and the UK—A cross-sectional study including 4,270,142 patients. Postgrad. Med. 2018, 130, 32–36. [Google Scholar] [CrossRef]
- Schubert, I.; Ihle, P.; Sabatowski, R. Increase in Opiate Prescription in Germany between 2000 and 2010. Dtsch. Ärztebl. Int. 2013, 110, 45–51. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Werber, A.; Marschall, U.; L’hoest, H.; Hauser, W.; Moradi, B.; Schiltenwolf, M. Opioid therapy in the treatment of chronic pain conditions in Germany. Pain. Physician 2015, 18, E323–E331. [Google Scholar] [PubMed]
- Cragg, A.; Hau, J.P.; Woo, S.A.; Kitchen, S.A.; Liu, C.; Doyle-Waters, M.M.; Hohl, C.M. Risk Factors for Misuse of Prescribed Opioids: A Systematic Review and Meta-Analysis. Ann. Emerg. Med. 2019, 74, 634–646. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Sherman, R.E.; Anderson, S.A.; dal Pan, G.J.; Gray, G.W.; Gross, T.; Hunter, N.L.; LaVange, L.; Marinac-Dabic, D.; Marks, P.W.; Robb, M.A.; et al. Real-World Evidence—What Is It and What Can It Tell Us? N. Engl. J. Med. 2016, 375, 2293–2297. [Google Scholar] [CrossRef] [Green Version]
Population Using Opioids % (95 CI) | Drivers Using Opioids % (95 CI) | |||||
---|---|---|---|---|---|---|
Total | Opioids only | Opioids in Association | Total | Opioids only | Opioids in Association | |
Total | 11.44 (11.32–11.56) | 1.78 (1.66–1.91) | 10.29 (10.17–10.41) | 8.72 (8.57–8.88) | 1.04 (0.88–1.2) | 8.3 (8.14–8.45) |
Male | 9.07 (8.9–9.24) | 1.19 (1.01–1.37) | 8.27 (8.1–8.45) | 9.17 (8.97–9.37) | 1.17 (0.96–1.38) | 8.59 (8.39–8.8) |
Female | 13.73 (13.56–13.89) | 2.35 (2.18–2.53) | 12.24 (12.07–12.41) | 8.06 (7.81–8.3) | 0.84 (0.59–1.1) | 7.85 (7.6–8.1) |
χ2, p | 3144.861, p = 0.001 | 1570.787, p = 0.001 | 2003.986, p = 0.001 | 16,871.0, p = 0.001 | 1979.537, p = 0.001 | 15,501.654, p = 0.001 |
Type of use | ||||||
Chronic | ||||||
Total | 3.2 (3.13–3.26) | 1.03 (0.93–1.12) | 2.69 (2.62–2.75) | 1.7 (1.63–1.77) | 0.51 (0.4–0.63) | 2.15 (2.07–2.23) |
Male | 2.04 (1.95–2.12) | 0.63 (0.5–0.76) | 2.14 (2.05–2.23) | 1.92 (1.83–2.02) | 0.6 (0.45–0.74) | 2.03 (1.93–2.14) |
Female | 4.32 (4.22–4.42) | 1.41 (1.27–1.55) | 3.21 (3.12–3.3) | 1.37 (1.27–1.48) | 0.39 (0.22–0.56) | 2.07 (1.94–2.2) |
χ2, p | 1985.911, p = 0.001 | 1170.328, p = 0.001 | 1085.825, p = 0.001 | 4601.091, p = 0.001 | 1108.628, p = 0.001 | 3676.290, p = 0.001 |
Subacute | ||||||
Total | 2.98 (2.91–3.04) | 0.58 (0.5–0.65) | 2.31 (2.25–2.37) | 2.34 (2.26–2.42) | 0.4 (0.3–0.5) | 1.33 (1.27–1.4) |
Male | 2.39 (2.3–2.48) | 0.43 (0.32–0.54) | 1.48 (1.41–1.56) | 2.46 (2.35–2.56) | 0.44 (0.31–0.57) | 1.17 (1.09–1.24) |
Female | 3.55 (3.46–3.63) | 0.72 (0.62–0.82) | 3.11 (3.02–3.2) | 2.16 (2.03–2.29) | 0.34 (0.18–0.5) | 1.22 (1.12–1.32) |
χ2, p | 328.39, p = 0.001 | 293.255, p = 0.001 | 239.633, p = 0.001 | 4662.045, p = 0.001 | 614.929, p = 0.001 | 4406.436, p = 0.001 |
Acute | ||||||
Total | 5.26 (5.18–5.35) | 0.18 (0.14–0.22) | 5.3 (5.21–5.38) | 4.68 (4.57–4.8) | 0.13 (0.07–0.19) | 4.67 (4.55–4.79) |
Male | 4.65 (4.52–4.77) | 0.13 (0.07–0.19) | 4.65 (4.52–4.78) | 4.79 (4.65–4.94) | 0.14 (0.07–0.21) | 4.62 (4.47–4.77) |
Female | 5.86 (5.75–5.98) | 0.22 (0.17–0.28) | 5.92 (5.8–6.04) | 4.52 (4.33–4.71) | 0.12 (0.02–0.21) | 4.61 (4.41–4.8) |
χ2, p | 214.78. p = 0.001 | 76.966. p = 0.001 | 239.790. p = 0.001 | 7414.013. p = 0.001 | 258.198. p = 0.001 | 7365.078. p = 0.001 |
Daily use | ||||||
Total | 0.24 (0.12–0.37) | 0.13 (0.01–0.27) | 0.11 (0.01–0.21) | 0.13 (0.04–0.25) | 0.07 (0.02–0.13) | 0.08 (0.01–0.13) |
Male | 0.14 (0.01–0.30) | 0.08 (0.01–0.17) | 0.07 (0.01–0.13) | 0.14 (0.07–0.28) | 0.08 (0.03–0.14) | 0.07 (0.01–0.15) |
Female | 0.34 (0.16–0.52) | 0.18 (0.01–0.36) | 0.16 (0.06–0.26) | 0.1 (0.05–0.19) | 0.05 (0.01–0.11) | 0.05 (0.01–0.09) |
χ2, p | 317.198, p = 0.001 | 255.709, p = 0.001 | 266.803, p = 0.001 | 357.012, p = 0.001 | 160.226, p = 0.001 | 506.183, p = 0.00 |
Average of driving-impairing medicines; population opioid use | ||||||
Total | 2.54 (2.53–2.54) | 3.16 (3.14–3.18) | 2.49 (2.49–2.50) | 2.34 (2.33–2.36) | 3.04 (3.01–3.07) | 2.31 (2.30–2.33) |
Male | 2.28 (2.26–2.29) | 2.87 (2.83–2.9) | 2.24 (2.23–2.26) | 2.26 (2.24–2.27) | 2.86 (2.82–2.9) | 2.22 (2.20–2.23) |
Female | 2.66 (2.65–2.67) | 3.29 (3.27–3.32) | 2.63 (2.61–2.64) | 2.49 (2.47–2.51) | 3.36 (3.3–3.42) | 2.45 (2.43–2.47) |
t, p | −30.520, p = 0.001 | −19.564, p = 0.001 | −41.237, p = 0.001 | −14.153, p = 0.001 | −14.425, p = 0.001 | −16.290, p = 0.001 |
Average of driving-impairing medicines; population daily opioid use | ||||||
Total | 3.52 (3.46–3.58) | 3.77 (3.7–3.83) | 3.23 (3.17–3.29) | 3.67 (3.55–3.79) | 3.93 (3.8–4.06) | 3.31 (3.21–3.42) |
Male | 3.29 (3.16–3.41) | 3.57 (3.44–3.69) | 2.91 (2.8–3.02) | 3.40 (3.25–3.54) | 3.7 (3.55–3.85) | 2.99 (2.87–3.12) |
Female | 3.60 (3.53–3.67) | 3.86 (3.78–3.94) | 3.35 (3.29–3.42) | 4.18 (3.96–4.2) | 4.41 (4.17–4.65) | 3.82 (3.63–4.01) |
t, p | −3.561, p = 0.012 | −3.657, p = 0.001 | −6.321, p = 0.001 | −5.580, p = 0.001 | −4.769, p = 0.001 | −7.094, p = 0.001 |
Population Using Opioids % (95 CI) | Drivers Using Opioids % (95 CI) | |||||||
---|---|---|---|---|---|---|---|---|
2015 | 2016 | 2017 | 2018 | 2015 | 2016 | 2017 | 2018 | |
Total | 10.59 (10.47–10.71) | 11.31 (11.19–11.43) | 11.25 (11.13–11.37) | 12.6 (12.48–12.72) | 7.86 (7.71–8.02) | 8.63 (8.47–8.78) | 8.59 (8.43–8.74) | 9.82 (9.67–9.97) |
Male | 8.41 (8.23–8.58) | 8.87 (8.7–9.05) | 8.84 (8.67–9.02) | 10.16 (9.99–10.33) | 8.44 (8.24–8.64) | 9 (8.8–9.2) | 8.95 (8.75–9.15) | 10.29 (10.09–10.49) |
Female | 12.7 (12.54–12.87) | 13.66 (13.49–13.83) | 13.57 (13.4–13.74) | 14.97 (14.8–15.13) | 6.99 (6.74–7.23) | 8.07 (7.83–8.32) | 8.04 (7.79–8.28) | 9.13 (8.88–9.37) |
χ2, p | 3022.886, p = 0.001 | 2623.495, p = 0.001 | 2850.157, p = 0.001 | 3144.861, p = 0.001 | 15,215.257, p = 0.001 | 15,624.29, p = 0.001 | 15,862.716, p = 0.001 | 16,871, p = 0.001 |
Type of use | ||||||||
Chronic | ||||||||
Total | 2.32 (2.26–2.38) | 3.16 (3.1–3.23) | 3.42 (3.35–3.49) | 3.88 (3.81–3.96) | 1.09 (1.03–1.15) | 1.64 (1.57–1.71) | 1.87 (1.8–1.95) | 2.2 (2.12–2.27) |
Male | 1.39 (1.32–1.46) | 1.98 (1.89–2.06) | 2.19 (2.1–2.28) | 2.59 (2.5–2.69) | 1.26 (1.18–1.34) | 1.86 (1.76–1.95) | 2.1 (2–2.2) | 2.48 (2.37–2.58) |
Female | 3.21 (3.13–3.3) | 4.31 (4.21–4.41) | 4.61 (4.51–4.71) | 5.13 (5.03–5.24) | 0.83 (0.75–0.92) | 1.32 (1.22–1.43) | 1.55 (1.44–1.66) | 1.79 (1.68–1.9) |
χ2, p | 1558.149, p = 0.001 | 1617.673, p = 0.001 | 1907.393, p = 0.001 | 1985.911, p = 0.001 | 2548.811, p = 0.001 | 3635.145, p = 0.001 | 3924.455, p = 0.001 | 4601.091, p = 0.001 |
Subacute | ||||||||
Total | 2.84 (2.78––2.91) | 2.91 (2.84–2.97) | 2.92 (2.86–2.98) | 3.24 (3.17–3.31) | 2.07 (1.99–2.16) | 2.28 (2.2––2.36) | 2.34 (2.25–2.42) | 2.66 (2.58–2.74) |
Male | 2.24 (2.15–2.33) | 2.3 (2.21–2.4) | 2.34 (2.25–2.43) | 2.67 (2.58–2.77) | 2.25 (2.14–2.35) | 2.36 (2.26–2.47) | 2.43 (2.32–2.54) | 2.78 (2.67–2.89) |
Female | 3.43 (3.33–3.52) | 3.49 (3.4–3.58) | 3.48 (3.39–3.57) | 3.79 (3.69–3.88) | 1.81 (1.68–1.94) | 2.16 (2.03–2.29) | 2.2 (2.07–2.33) | 2.49 (2.35–2.62) |
χ2, p | 434.257, p = 0.001 | 220.048, p = 0.001 | 254.568, p = 0.001 | 328.39, p = 0.001 | 4265.404, p = 0.001 | 4359.183, p = 0.001 | 4528.862, p = 0.001 | 4662.045, p = 0.001 |
Acute | ||||||||
Total | 5.43 (5.34–5.52) | 5.24 (5.15–5.32) | 4.91 (4.83–4.99) | 5.48 (5.39–5.57) | 4.7 (4.57–4.82) | 4.7 (4.59–4.82) | 4.37 (4.26–4.49) | 4.96 (4.85–5.07) |
Male | 4.78 (4.65–4.91) | 4.59 (4.47–4.72) | 4.32 (4.19–4.44) | 4.89 (4.76–5.02) | 4.93 (4.77–5.08) | 4.78 (4.63–4.93) | 4.43 (4.28–4.57) | 5.04 (4.89–5.18) |
Female | 6.06 (5.95–6.18) | 5.86 (5.74–5.97) | 5.48 (5.37–5.59) | 6.05 (5.93–6.16) | 4.34 (4.14–4.54) | 4.59 (4.4–4.78) | 4.3 (4.11–4.48) | 4.85 (4.67–5.03) |
χ2, p | 313.76, p = 0.001 | 196.406, p = 0.001 | 208.622, p = 0.001 | 214.78, p = 0.001 | 8349.901, p = 0.001 | 7441.297, p = 0.001 | 7286.667, p = 0.001 | 7414.013, p = 0.001 |
Daily use | ||||||||
Total | 0.13 (0.01–0.26) | 0.22 (0.1–0.35) | 0.28 (0.15–0.41) | 0.34 (0.21–0.46) | 0.06 (0–0.11) | 0.11 (0.01–0.21) | 0.15 (0.05–0.25) | 0.18 (0.02–0.34) |
Male | 0.08 (0–0.16) | 0.13 (0.04–0.25) | 0.16 (0.06–0.26) | 0.21 (0.02–0.39) | 0.07 (0.02–0.13) | 0.12 (0.02–0.23) | 0.16 (0.06–0.26) | 0.2 (0.1–0.35) |
Female | 0.19 (0.01–0.36) | 0.32 (0.14–0.49) | 0.39 (0.21–0.57) | 0.46 (0.28–0.64) | 0.05 (0–0.1) | 0.09 (0.04–0.16) | 0.12 (0.02–0.23) | 0.15 (0.05–0.25) |
χ2, p | 196.231, p = 0.001 | 256.032, p = 0.001 | 265.263, p = 0.001 | 317.198, p = 0.001 | 132.567, p = 0.001 | 214.021, p = 0.001 | 307.247, p = 0.001 | 357.012, p = 0.001 |
Average of driving-impairing medicines; population opioid use | ||||||||
Total | 2.51 (2.50–2.52) | 2.55 (2.55–2.56) | 2.53 (2.52–2.53) | 2.55 (2.54–2.56) | 2.30 (2.29–2.32) | 2.35 (2.34–2.37) | 2.36 (2.34–2.37) | 2.38 (2.37–2.40) |
Male | 2.22 (2.21–2.24) | 2.30 (2.28–2.31) | 2.28 (2.26––2.29) | 2.33 (2.32–2.34) | 2.21 (2.10–2.22) | 2.27 (2.25–2.28) | 2.27 (2.25–2.28) | 2.30 (2.29–2.32) |
Female | 2.65 (2.64–2.66) | 2.68 (2.67–2.69) | 2.65 (2.64–2.66) | 2.67 (2.66–2.68) | 2.47 (2.44–2.49) | 2.49 (2.47–2.51) | 2.50 (2.47–2.52) | 2.51 (2.49–2.53) |
t, p | −45.757, p = 0.001 | −41.513, p = 0.001 | −40.116, p = 0.001 | −38.597, p = 0.001 | −16.855, p = 0.001 | −15.636, p = 0.001 | −16.042, p = 0.001 | −15.142, p = 0.001 |
Average of driving-impairing medicines; population daily opiois use | ||||||||
Total | 3.65 (3.57–3.73) | 3.55 (3.49–3.61) | 3.44 (3.39–3.50) | 3.44 (3.34–3.49) | 3.80 (3.63–3.87) | 3.75 (3.63–3.87) | 3.54 (3.43–3.65) | 3.57 (3.47–3.67) |
Male | 3.42 (3.26–3.58) | 3.42 (3.29–3.54) | 3.15 (3.05––3.25) | 3.17 (3.07–3.27) | 3.53 (3.33–3.72) | 3.51 (3.36–3.65) | 3.27 (3.15–3.40) | 3.27 (3.16–3.39) |
Female | 3.73 (3.64–3.83) | 3.60 (3.53–3.67) | 3.55 (3.49–3.62) | 3.55 (3.48–3.61) | 4.39 (4.05–4.73) | 4.22 (4–4.24) | 4.01 (3.81–4.21) | 4.11 (3.92–4.29) |
t, p | −3.263, p = 0.001 | −2.553, p = 0.011 | −6.410, p = 0.001 | −6.443, p = 0.001 | −4.579, p = 0.001 | −5.230, p = 0.001 | −6.110, p = 0.001 | −7.451, p = 0.001 |
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Herrera-Gómez, F.; Gutierrez-Abejón, E.; Ayestarán, I.; Criado-Espegel, P.; Álvarez, F.J. The Trends in Opioid Use in Castile and Leon, Spain: A Population-Based Registry Analysis of Dispensations in 2015 to 2018. J. Clin. Med. 2019, 8, 2148. https://doi.org/10.3390/jcm8122148
Herrera-Gómez F, Gutierrez-Abejón E, Ayestarán I, Criado-Espegel P, Álvarez FJ. The Trends in Opioid Use in Castile and Leon, Spain: A Population-Based Registry Analysis of Dispensations in 2015 to 2018. Journal of Clinical Medicine. 2019; 8(12):2148. https://doi.org/10.3390/jcm8122148
Chicago/Turabian StyleHerrera-Gómez, Francisco, Eduardo Gutierrez-Abejón, Ignacio Ayestarán, Paloma Criado-Espegel, and F. Javier Álvarez. 2019. "The Trends in Opioid Use in Castile and Leon, Spain: A Population-Based Registry Analysis of Dispensations in 2015 to 2018" Journal of Clinical Medicine 8, no. 12: 2148. https://doi.org/10.3390/jcm8122148