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Prescribing of Opioid Analgesics in Germany: Opposite Trends When Different Quantification Measures are Used
Authors Scholle OHF 
, Jobski K, Viebrock J , Haug U
Received 11 November 2025
Accepted for publication 16 March 2026
Published 30 March 2026 Volume 2026:18 580510
DOI https://doi.org/10.2147/CLEP.S580510
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr E Horváth-Puhó
Oliver HF Scholle,1 Kathrin Jobski,2 Jost Viebrock,3 Ulrike Haug1,4
1Department of Clinical Epidemiology, Leibniz Institute for Prevention Research and Epidemiology – BIPS, Bremen, Germany; 2Department of Health Services Research, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany; 3Department of Statistical Methods in Epidemiology, Leibniz Institute for Prevention Research and Epidemiology – BIPS, Bremen, Germany; 4Faculty of Human and Health Sciences, University of Bremen, Bremen, Germany
Correspondence: Oliver HF Scholle, Department of Clinical Epidemiology, Leibniz Institute for Prevention Research and Epidemiology – BIPS, Achterstrasse 30, Bremen, 28359, Germany, Tel +49 421 218 56878, Fax +49 421 218 56821, Email [email protected]
Purpose: To describe trends in prescribing of opioid analgesics in Germany using different quantification measures.
Patients and Methods: We conducted annual cross-sectional studies for the years 2005 to 2020 based on the so-called German Pharmacoepidemiological Research Database (GePaRD; claims data covering ~20% of the German population). Opioid prescribing was assessed based on outpatient dispensations of opioid analgesics. For each year, we calculated the age- and sex-specific and -standardized prescription prevalence (number of persons with ≥ 1 prescription per 1000 persons) as well as the sum of oral morphine equivalents (OMEs) in mg per 1000 person-years.
Results: The standardized prescription prevalence per 1000 persons showed a relative decrease by 14.1% between 2005 (52.3/1000) and 2016 (44.9/1000) and a further decrease by 5.6% until 2020 (42.4/1000). Based on the standardized OME per 1000 person-years, there was a relative increase by 51.4% until 2016 (2005: 191,987 mg; 2016: 290,733 mg) and a decrease by 5.3% thereafter (2020: 275,218 mg).
Conclusion: In Germany, opposite trends for opioid prescription prevalence and OMEs were observed between 2005 and 2016, while after 2016, both quantification measures showed a decreasing trend. Our study illustrates that trends in prescribing of opioid analgesics can vary depending on the quantification measure; therefore, it is advisable to calculate both measures when monitoring opioid prescribing.
Plain Language Summary: This study looked at opioid pain medicines prescribed in Germany between 2005 and 2020. We used data from a large health insurance database covering about one fifth of the German population. To track changes over time, we applied two different ways of measuring prescribing: (1) the amount of people receiving at least one prescription per year (prescription prevalence) and (2) the total amount of opioids prescribed, taking into account the different strengths of opioids; this is expressed in a standard unit called oral morphine equivalents (OMEs). When we looked at prescription prevalence, the proportion of people receiving opioids went down steadily over time. In contrast, when we looked at OME, the total quantity of opioids prescribed increased until 2016, after which it declined slightly. These different results show that trends in opioid prescribing depend on how they are measured. Our findings highlight the importance of using both measures for a complete understanding of prescribing patterns.
Keywords: opioids, drug utilization, pharmacoepidemiology, quantification measure
Introduction
Opioid overdose is a major public health concern worldwide, partly attributable to inappropriate prescribing. Therefore, monitoring opioid prescribing, followed by actions where necessary, is of vital importance.1 Routinely collected healthcare data such as claims data offer a robust source for monitoring drug utilization, avoiding recall and non-responder bias.
Various quantification measures are used in drug utilization studies to evaluate trends in prescribing. Among these, prescription prevalence is a fundamental metric.2 This measure provides insights into the proportion of a population receiving a specific medication. For the evaluation of drug quantities or volume, the Defined Daily Dose (DDD) defined by the World Health Organization (WHO) is useful in drug utilization studies to standardize drug consumption measurements across different populations and time periods.3
Specifically, when quantifying utilization of opioid analgesics, oral morphine equivalents (OMEs)—which standardize the doses of different opioids based on the analgesic effect—are of particular interest.4,5 Previous studies based on claims data showed a positive association between the average daily OME and opioid-related mortality.6,7 In Germany, however, no study has examined trends in opioid prescribing based on OMEs.
In the past two decades, most opioid prescriptions in Germany were issued for patients with non-cancer pain.8 National clinical guidelines relevant to this context include the German guideline on long-term opioid therapy for chronic non-cancer pain, first issued in 2009 and updated in 2015 and 2020.9–11 Previous studies including data up to 2016 and using quantification measures other than OME concluded that there was no evidence of an opioid epidemic in Germany.8,11–13
In this study, we aimed to describe trends in opioid prescribing in Germany from 2005 to 2020 using OMEs in comparison to the prescription prevalence.
Materials and Methods
Using the so-called German Pharmacoepidemiological Research Database (GePaRD), we conducted annual cross-sectional studies for the years 2005 to 2020.
Data Source
GePaRD is based on claims data from four statutory health insurance providers in Germany covering more than 25 million persons who have been insured with one of the participating providers since 2004 or later.14 Per data year, there is information on approximately 20% of the general population and all geographical regions of Germany are represented. GePaRD includes, among others, information on demographics and prescribed drugs dispensed in community pharmacies.14
Study Population
For each year from 2005 to 2020, we included persons with available information on sex (female or male) and birth year (proportion of persons with missing sex and/or birth year: <0.3%). Further, persons were required to have continuous insurance coverage in the respective year. Persons dying in the respective year were included if they had continuous insurance until death in that year.
Prescriptions of Opioid Analgesics
We identified prescriptions based on the German modification of the WHO Anatomical Therapeutic Chemical (ATC) classification system (version from April 2022). For this study, all outpatient prescriptions of opioid analgesics (ATC N02A) with a prescription date between January 1 and December 31 of the respective year were included. For each prescription, we determined the OME—ie, the amount of equianalgesic oral morphine in mg—by multiplying the total amount of opioid (in mg) in the package with the respective equianalgesic factor to morphine considering the package-specific route of application (eg, sublingual, transdermal, oral, etc; see Table S1).
Analysis
We calculated the prescription prevalence, defined as the number of persons with at least one prescription per 1000 persons. In addition, we calculated the sum of OMEs in mg per 1000 person-years. In addition to total opioid prescribing, we also calculated the prescription prevalence for the most frequent opioid analgesics on the ATC 5th level. As other quantification measures, we calculated the DDDs per 1000 person-years and the number of prescriptions per 1000 person-years (prescription rate).
Separately for each of the years 2005 to 2020, we calculated the quantification measures stratified by sex and age group (0–19, 20–39, 40–59, 60–79, and ≥80 years). To account for differences in population structure regarding age and sex over time, we applied direct standardization using the German population on December 31, 2020, as the reference population.15 This approach ensures comparability of the estimates across years by adjusting for variations in age and sex distribution. Analyses were conducted using SAS (version 9.4; SAS Institute, Cary, NC, USA).
Results
Per year, the analyses included between 12,042,018 (2005) and 17,181,878 (2020) persons (Tables 1, S2 and S3). The age- and sex-standardized prescription prevalence of opioid analgesics per 1000 persons was 52.3 in 2005, 44.9 in 2016 (relative decrease between 2005 and 2016: 14.1%), and 42.4 in 2020 (relative decrease between 2016 and 2020: 5.6%) (Figure 1). The age- and sex-standardized OMEs per 1000 person-years were 191,987 mg in 2005, 290,733 mg in 2016 (relative increase between 2005 and 2016: 51.4%), and 275,218 mg in 2020 (relative decrease between 2016 and 2020: 5.3%).
 |
Table 1 Standardized Prescription Prevalence and Oral Morphine Equivalents in 2005, 2016, and 2020, Overall and by Age Group for Females and Males |
 |
Figure 1 Trends in prescribing of opioid analgesics from 2005 to 2020 based on prescription prevalence and oral morphine equivalents. Both measures were directly sex- and age-standardized to the German population. |
The analyses on trends in the prescription prevalence between 2005 and 2020 at the ATC 5th level (Figure S1) showed a decrease for tramadol (from 20.9 to 9.5 per 1000 persons; relative decrease: 55%), for codeine combinations with paracetamol (from 13.8 to 2.2 per 1000 persons; relative decrease: 84%), and for codeine combinations with other non-opioid analgesics (from 9.1 to 0.9 per 1000 persons; relative decrease: 90%). During the same period, the prevalence for the combination of tilidine and naloxone increased (from 9.6 to 19.6 per 1000 persons; relative increase: 104%).
Regarding trends stratified by age and sex (Table 1), a decreasing prescription prevalence from 2005 to 2016 was observed in all age and sex groups except for females and males aged ≥80 years. From 2016 to 2020, the prescription prevalence decreased in all age and sex groups. The OMEs per 1000 person-years increased in all age and sex groups between 2005 and 2016 except for females aged 0–19 years. From 2016 to 2020, the OMEs per 1000 person-years decreased in all age and sex groups except for females and males aged 0–19 years.
Regarding the other quantification measures, the age- and sex-standardized DDDs per 1000 person-years steadily increased until 2012 and decreased thereafter (Figure S2). The age- and sex-standardized prescription rate also increased until 2012, with a steep increase until 2007 followed by a flat increase afterwards (Figure S3).
Discussion
We found decreasing trends in opioid prescribing between 2005 and 2016 in Germany based on the prescription prevalence, whereas the opposite (ie, an increase) was observed when oral morphine equivalents were considered. From 2016 to 2020, opioid prescribing decreased based on both quantification measures.
Prior Research
Prior trend studies on opioid prescribing in Germany only used data up to 2016. In line with our study, they found no evidence of an opioid epidemic in Germany.8,12,13 As these studies did not consider oral morphine equivalents, a detailed comparison with our study is not possible. For the previously not studied period from 2016 to 2020, our study provides further reassuring results demonstrating that opioid prescribing decreased based on all standard quantification measures in this period.
Internationally, we identified some studies that assessed opioid prescribing using both prescription prevalence and oral morphine equivalents, such as Xie et al16 (data from Catalonia, Spain; study period 2007–2019), Karanges et al17 (Australia; 2006–2015), and Nissen et al18 (Denmark; 1999–2017). While these studies observed differences by quantification measure, none found opposite trends as we observed for the period 2005–2016. Curtis et al19 (England; 1998–2018) and Svendsen et al5 (Norway; 2004–2008) acknowledged the possibility of different interpretations in trends depending on the quantification measure; however, they did not evaluate the prescription prevalence and, like the studies mentioned before, found no opposite trends depending on the measure used.
When comparing trends in the quantification measures between different countries, it must be considered that the underlying prescribing patterns may differ. In the studies by Xie et al16 and Nissen et al,18 for example, the observed increases were mainly driven by a pronounced rise in tramadol prescribing, which by the end of the respective study periods had become the opioid with the highest prescription prevalence. In contrast, Karanges et al17 reported a marked increase and a relatively high absolute prescription prevalence for oxycodone. We observed substantial increases in the prescription prevalence for the combination of tilidine and naloxone; however, this was accompanied by a parallel substantial decrease in the prescription prevalence of tramadol and codeine combinations. Further, it must be considered that there are some methodological differences between these studies. The study by Xie et al16 was restricted to one region within Spain and Nissen et al18 focused on selected opioids with highest prevalence, while our analyses were conducted without regional restrictions and included all reimbursed outpatient opioid prescriptions. In addition, Karanges et al17 used different data sources for volume- and person-based measures.
Implications
According to the WHO, monitoring of opioid prescribing is one specific strategy to prevent opioid overdoses.1 In this context, our study implies that opioid prescribing should be monitored by both the prescription prevalence and oral morphine equivalents given that results on trends in opioid prescribing could even be opposite depending on the quantification measure used. Calculating both measures is also more informative from a clinical perspective. Our results, for example, suggest that between 2005 and 2016, fewer persons received opioid analgesics in Germany. However, as oral morphine equivalents increased, those actually receiving them may have done so for a longer duration, in higher doses, or in higher potency, or a combination thereof. We also demonstrated that the decrease in overall prescription prevalence was primarily driven by the declining prescription prevalence of codeine combinations, which—due to the low analgesic potency—had a lesser impact on OMEs.
Strengths and Limitations
The strengths of this study include the use of a large healthcare database, covering 20% of the population, with no recall or non-responder bias. In addition, the use of the German modification of the WHO ATC classification system was an advantage in this study. This modification, for example, enables a distinction regarding the different indications (“severe pain” versus “opioid dependence”) for preparations with the active ingredient levomethadone. Given the very large study population, we did not perform formal statistical tests for trends, as even minimal changes would be expected to reach statistical significance and might distract from clinically and public-health relevant differences. Trends were therefore reported descriptively along with 95% confidence intervals (Tables S2 and S3) illustrating the precision of the estimates. A limitation of our study is that our analysis exclusively captured opioid analgesics dispensed in community pharmacies and reimbursed by statutory health insurances. Our study was merely descriptive and thus not designed to investigate determinants of trends such as changes in prescribing policies or in healthcare utilization patterns over time.
Conclusions
In Germany, opposite trends for opioid prescription prevalence and OMEs were observed between 2005 and 2016, while after 2016, both quantification measures showed a decreasing trend. Our study illustrates that trends in prescribing of opioid analgesics can vary depending on the quantification measure, ie, it is advisable to calculate both measures when monitoring opioid prescribing.
Abbreviations
ATC, Anatomical Therapeutic Chemical; DDD, Defined Daily Dose; GePaRD, German Pharmacoepidemiological Research Database; OME(s), oral morphine equivalent(s); WHO, World Health Organization.
Data Sharing Statement
As we are not the owners of the data, we are not legally entitled to grant access to the data of the German Pharmacoepidemiological Research Database. In accordance with German data protection regulations, access to the data is granted only to employees of the Leibniz Institute for Prevention Research and Epidemiology – BIPS on the BIPS premises and in the context of approved research projects. Third parties may only access the data in cooperation with BIPS and after signing an agreement for guest researchers at BIPS.
Ethics Approval and Informed Consent
In Germany, the utilization of health insurance data for scientific research is regulated by the Code of Social Law. All involved health insurance providers as well as the Federal Office for Social Security and the Senator for Health, Women and Consumer Protection in Bremen as their responsible authorities approved the use of GePaRD data for this study. Informed consent for studies based on claims data is required by law unless obtaining consent appears unacceptable and would bias results, which was the case in this study. According to the Ethics Committee of the University of Bremen studies based on GePaRD are exempt from institutional review board review.
Acknowledgments
The authors would like to thank all statutory health insurance providers which provided data for this study, namely AOK Bremen/Bremerhaven, DAK-Gesundheit, Techniker Krankenkasse (TK), and hkk Krankenkasse. They would also like to thank Alina Ludewig and Fabian Gesing for the statistical programming of the data and Dr. Heike Gerds for proofreading the final manuscript. 39th International Conference on Pharmacoepidemiology & Therapeutic Risk Management (ICPE), 25–27 August 2023, Halifax, Canada; published as an abstract in Pharmacoepidemiology and Drug Safety 2023;32(S1):102 and the conference presentation is available at https://de170d6b23836ee9498a-9e3cbe05dc55738dcbe22366a8963ae7.ssl.cf1.rackcdn.com/2432924-1062768-007.pdf; 30th Annual Meeting of the German Drug Utilisation Research Group (GAA), 09–10 November 2023, Cologne, Germany; published as an abstract available from https://doi.org/10.3205/23gaa23.
Author Contributions
All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.
Funding
This study was funded by the Federal Institute for Drugs and Medical Devices (Bundesinstitut für Arzneimittel und Medizinprodukte, BfArM; grant number V-2021.1/1516 68605 / 2022-2023).
Disclosure
OHFS, JV, and UH are working at an independent, non-profit research institute, the Leibniz Institute for Prevention Research and Epidemiology – BIPS. Unrelated to this study, BIPS occasionally conducts studies financed by the pharmaceutical industry. These are post-authorization safety studies (PASS) requested by health authorities. The design and conduct of these studies as well as the interpretation and publication are not influenced by the pharmaceutical industry. The study presented was not funded by the pharmaceutical industry. KJ declares no conflicts of interest in this work.
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