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. 2025 Jun 19;29(6):e70063. doi: 10.1002/ejp.70063

Socioeconomic Position and Chronic Opioid Use After Hip Fracture Surgery: A Danish Population‐Based Cohort Study

Nickolaj Risbo 1, Vera Ehrenstein 1,2, Per Hviid Gundtoft 3, Jan‐Erik Gjertsen 4, Alma Becic Pedersen 1,✉
PMCID: PMC12178151  PMID: 40536348

ABSTRACT

Background

Chronic opioid use is a common and serious consequence of hip fracture. We examined the association between socioeconomic position (SEP) and chronic opioid use after hip fracture surgery.

Methods

Using nationwide Danish registries, we included patients aged ≥ 65 years undergoing hip fracture surgery in 2012–2021 (n = 52,801). Cohabitation, liquid assets, and education were markers of SEP. Chronic opioid use was defined as ≥ 2 prescriptions of opioids 31–365 days post‐surgery. For the same period, all opioid doses were converted to morphine milligram equivalents (MME), mg/day. We used log‐binomial regression to estimate adjusted risk ratios (aRR) with 95%‐confidence intervals (CI) comparing patients within each SEP marker, adjusting for relevant confounding.

Results

The 1‐year risks of chronic opioid use were 33% for patients living alone versus 30% for patients cohabiting (aRR 1.05 [CI 1.02–1.09]), 37% for low versus 28% for high levels of liquid assets (aRR 1.28 [CI 1.23–1.34]), and 33% for low versus 28% for high education (aRR 1.19 [CI 1.14–1.25]). Patients living alone used 11.5 MME mg/day versus 9.8 mg/day in patients cohabiting, patients with low liquid assets used 14.8 versus 7.9 mg/day in patients with high liquid assets, and patients with low education used 11.8 versus 7.9 mg/day in patients with high education.

Conclusions

About a third of hip fracture patients are using opioids continuously in the year after surgery. Living alone, less liquid assets, and low education were associated with a higher risk of opioid use and dosage of use, both in preoperative opioid users and non‐users.

Significance Statement

This study shows that among patients undergoing hip fracture surgery, low socioeconomic position measured by living alone, having less liquid assets or low education is associated with a higher risk of chronic opioid use and higher dosage of use in the first year postoperatively. Clinicians should consider socioeconomic position when prescribing opioids after hip fracture. The integration of less addictive opioids and non‐pharmacological approaches in the pain management may reduce opioid use and improve patient safety.

1. Introduction

Hip fracture is a painful and serious condition requiring acute surgery (Bierbaum et al. 1999). These patients are on average above 80 years of age and have several comorbidities (Kristensen et al. 2023). Almost 10% of hip fracture patients die and 30% develop medical or surgical complications within the first month postoperatively (Pedersen et al. 2017). The mortality rate is 2‐3‐fold higher in patients developing complications (Kjorholt et al. 2019; Pedersen et al. 2016). Mortality within 1 year of hip fracture ranges from 11% to 28% (Hjelholt et al. 2022; Sing et al. 2023).

Opioids are used for managing acute pain after hip fracture surgery (Simoni et al. 2019). The aim is to achieve good control of postoperative pain to improve early mobilisation and rehabilitation, thereby reducing the risks of cardiopulmonary complications, prolonged recovery (Gan 2017), morbidity, and mortality (Kristensen, Thillemann, Soballe et al. 2016). However, treatment with opioids is associated with an array of challenges and negative consequences. Opioid use in the elderly increases the risks of respiratory depression, dizziness and postoperative delirium (Benyamin et al. 2008), as well as risks of falls, fractures (Yue et al. 2020) and mortality (Stone et al. 2023). Opioid dosage is challenging in older patients due to physiological decline and polypharmacy (Degenhardt et al. 2019; Matos et al. 2020). Furthermore, patients can have difficulties in stopping opioids after surgery (Hereford et al. 2022). An earlier study showed that 17% of opioid‐naive hip fracture patients are prescribed opioids several times within one year after surgery (Simoni et al. 2019).

The association between socioeconomic position (SEP) and opioid use has previously been reported in general population among both people in working age and older people (Nestvold et al. 2024). Although social inequalities in hip fracture treatment and mortality have been described (Valentin et al. 2020, 2021), little evidence exists on the socioeconomic inequality in chronic opioid use after hip fracture (Stone et al. 2023; Hereford et al. 2022; Edwards et al. 2021). These studies are limited by low sample size, short duration, slightly older study period, and methodology not specific to study causality. Knowledge about the social factors of chronic opioid use is of importance for healthcare professionals and policymakers to prevent opioid‐related adverse effects and improve patient outcome and safety (Edwards et al. 2021).

Therefore, the present study's aim was to examine the association between markers of SEP and the risk of chronic opioid use after hip fracture surgery.

2. Material and Methods

2.1. Setting and Study Population

We conducted a population‐based cohort study in Denmark using linked registries and databases (Schmidt et al. 2019). Denmark has a source population of approximately 5.9 million (2021), and all residents receive a unique civil registration number at birth or upon immigration through the Danish Civil Registration System, enabling individual‐level linkage of data across multiple databases. The study population included all Danish patients undergoing surgery for first‐time hip fracture during 2012–2021 as registered in the Danish Multidisciplinary Hip Fracture Database (DMHFD) and followed up for at least one year after surgery (Kristensen et al. 2020).

2.2. Exposure—SEP

As markers of SEP, we included cohabitation, liquid assets, and educational level. Information on cohabitation was available in the Danish Civil Registration System and defined as living alone or cohabiting (married or living with a partner). Because our population includes individuals > 65 years of age, family income may not reflect economic resources. We therefore calculated liquid assets which refer to the economic assets or valuables available to a person for conversion to cash (Denmark Statistics 2025). In Denmark, access to individual‐level data on healthcare, wealth and debt including assessments of all liquid assets such as real estate, bank deposits, the value of cars, stocks and bonds, pension savings, and debt are available for research purposes after application to Statistic Denmark (Schmidt et al. 2019). For each patient, we linked socioeconomic data with data from other national registries and analysed them in pseudonymized form, applying privacy‐preserving reporting according to Danish legislation. For each patient we calculated mean liquid assets for 5 years before surgery, which goes beyond income data. Subsequently, patients were divided into low (< 33,600 USD), medium (33,600–198,300 USD), or high liquid (> 198,300 USD) assets group based on tertiles using the end of 2024 exchange rate. Educational level was defined using highest attained education at the year of surgery as recorded in the Population Education Registry. Educational level was categorised into low (elementary school), medium (more than elementary school but less than university degree), high (university degree) or unknown.

2.3. Outcome—Opioid Use

Data on dispensation of 13 common types of opioid prescriptions were obtained from the Danish National Prescription Registry using Anatomical Therapeutic Chemical (ATC) codes (Table S1). The primary outcome was chronic opioid use, defined as ≥ 2 prescriptions of opioids in the period from 31 to 365 days after hip fracture surgery (main analysis). The first month after surgery was not counted since opioids represent a standard post‐surgical pain treatment. The definition of chronic opioid use was guided by recent reviews and clinical expertise (Jivraj et al. 2020; Karmali et al. 2020).

The secondary outcome was opioid dosage. All prescribed doses were converted to morphine milligram equivalents (MME) using a conversion factor corresponding to the specific opioid type prescription (Table S2) (Nielsen et al. 2016). Based on previous research (Melsen et al. 2024), doses and conversion factors were available for > 95% of the prescriptions included, while the rest were excluded from the MME analysis. For the six most common opioids, > 99% of prescriptions were included due to the availability of conversion factors and doses.

In a sensitivity analysis, chronic opioid use was defined by ≥ 2 opioid prescriptions in 2 out of the last 3 quarters during the first postoperative year, thus ≥ 2 prescriptions for 92–365 days after surgery. This was done to assess the robustness of the primary outcome definition, since currently there is no standard definition of chronic opioid use (Jivraj et al. 2020; Karmali et al. 2020).

2.4. Covariates

We obtained data on sex and age from the Danish Civil Registration system. Data on year of surgery (in categories 2012–2014, 2015–2016, 2017–2018, 2019–2021), type of surgery (as osteosynthesis or total/hemi arthroplasty), and body mass index (BMI) and pre‐fracture basic mobility was obtained from the DMHFD. The BMI (in kg per sqm) was categorised as underweight (< 20), normal (20–24.9), overweight (25–29.9), obese patients (≥ 30), and those with missing data on BMI. Pre‐fracture mobility was assessed with the Cumulated Ambulation Score (CAS) and categorised as CAS of 0–3 points, 4–5 points, 6 points or missing, with higher score indicating better ambulation (Kristensen et al. 2024).

Data on comorbidity history were obtained from the Danish National Patient Registry. Comorbidity was measured with Charlson Comorbidity Index (CCI) score (low CCI = 0, medium CCI = 1–2, high CCI ≥ 3) (Charlson et al. 1987). In addition, we included some previously reported very common conditions in hip fracture patients such as history of depression, anxiety or psychotic illness 10 years before surgery, history of fluid and electrolyte disorders 1 year before surgery, history of pain and inflammatory conditions defined through use of analgesic drugs 1 year before surgery, and history of osteoporosis defined through use of anti‐osteoporosis drugs 10 years before surgery (Gadgaard et al. 2024).

Preoperative opioid use was defined as minimum 2 opioid prescriptions in the 6 months before hip fracture surgery.

2.5. Statistics

Patient characteristics by each SEP marker were summarised using counts and percentages for categorical variables and medians with interquartile range for continuous variables.

We computed crude risks of chronic opioid use for each level of the SEP markers. We used log‐binomial regression to calculate crude and adjusted risk ratios (aRRs) with 95% confidence intervals (CIs) comparing patients within each SEP marker. CIs in the adjusted analyses were calculated using 500 bootstrap samples. We accounted for mortality during the follow‐up using inverse probability of censoring weights (Hernán and Robins 2013). Thus, only patients with one year of follow‐up after surgery had a fully observable outcome. The remaining patients, who either died or emigrated, were censored for the adjusted analyses. We calculated inverse probability of censoring weights using all available confounders data in a logistic regression and then these weights were applied in the adjusted analyses, using only the uncensored patients. Based on Directed Acyclic Graphs, we included slightly different confounders in the adjusted models. This method allows for better insights into the assumed causal mechanisms and can assist in the selection of confounders to adjust for in the multivariate regression model. Using this method, we identify a minimum but a sufficient set of factors to adjust for their confounding effects. Thus, comparing educational levels, we adjusted for age, sex and year of surgery. Comparing cohabitation status, we adjusted for age, sex, year of surgery, education, liquid assets, CCI, psychiatric comorbidity, and medication for pain or inflammation. Comparing liquid assets, we adjusted for age, sex, year of surgery, education, cohabitation, CCI, psychiatric comorbidity, and medication for pain or inflammation (Figures S1 and S2).

We calculate the total MME for each patient based on opioids prescriptions during 31–365 days after surgery and present it as MME mg per day by each marker of SES.

To evaluate whether the observed overall association between markers of SEP and chronic opioid use varied across patient subgroups on a multiplicative scale, we calculated RRs while stratifying by age, sex, type of surgery, year of surgery and preoperative opioid user status. For example, we repeated the analysis comparing patients living alone to those cohabiting (reference) among female and male patients separately.

In a sensitivity analysis, crude risks, crude and adjusted RRs, and MME mg/day were calculated by SES based on opioid prescription for the period from 92 to 365 days.

The study was reported to the Danish Data Protection Agency through registration at Aarhus University (record number: AU‐2016‐051‐000001, sequential number 880). Ethical approval is not required in Denmark for studies on routinely collected registry data.

3. Results

We included 52,801 hip fracture patients during 2012–2021. The median age of the study population was 83 years, 69% were female, 38% had a low CCI score, and the median length of hospital stay was 7 days. Regarding SEP markers, 65% were living alone, 50% had low education, and 33% had low liquid assets. Patients who had low SEP were slightly older, more likely to be female, had lower pre‐fracture basic mobility, but higher prevalence of depression, anxiety or psychotic illness and fluid/electrolyte disorders. Distributions of BMI, type of surgery, and use of medication for pain/inflammation were rather similar across SEP groups except for patients living alone who were more underweight (Table 1).

TABLE 1.

Patient characteristics by markers of socioeconomic position. N (%) unless otherwise stated.

Cohabitation status Liquid assets Education status
Living alone Cohabitating Low Medium High Low Medium High Missing
n 34,082 18,719 17,599 17,604 17,598 26,485 16,474 6232 3610
Age in years, median (IQR) 85 (79–90) 79 (73–85) 83 (77–89) 84 (77–89) 82 (75–88) 84 (78–89) 81 (74–86) 81 (74–87) 95 (92–97)
Gender—female 25,899 (76.0) 10,428 (55.7) 12,988 (73.8) 12,169 (69.1) 11,170 (63.5) 19,938 (75.3) 9708 (58.9) 3913 (62.8) 2768 (76.7)
Year of surgery
2012–2014 11,845 (34.8) 6022 (32.2) 6247 (35.5) 5903 (33.5) 5717 (32.5) 9166 (34.6) 4847 (29.4) 1786 (28.7) 2068 (57.3)
2015–2016 7585 (22.3) 4141 (22.1) 3929 (22.3) 3968 (22.5) 3829 (21.8) 5990 (22.6) 3629 (22.0) 1351 (21.7) 756 (20.9)
2017–2018 7256 (21.3) 4077 (21.8) 3656 (20.8) 3768 (21.4) 3909 (22.2) 5682 (21.5) 3775 (22.9) 1443 (23.2) 433 (12.0)
2019–2021 7396 (21.7) 4479 (23.9) 3767 (21.4) 3965 (22.5) 4143 (23.5) 5647 (21.3) 4223 (25.6) 1652 (26.5) 353 (9.8)
Type of surgery
Osteosynthesis 21,768 (63.9) 11,912 (63.6) 11,311 (64.3) 11,245 (63.9) 11,124 (63.2) 16,890 (63.8) 10,487 (63.7) 3884 (62.3) 2419 (67.0)
Total or hemi hip arthroplasty 12,314 (36.1) 6807 (36.4) 6288 (35.7) 6359 (36.1) 6474 (36.8) 9595 (36.2) 5987 (36.3) 2348 (37.7) 1191 (33.0)
BMI, in kg/sqm
Underweight 4485 (13.2) 1914 (10.2) 2138 (12.1) 2137 (12.1) 2124 (12.1) 3164 (11.9) 1941 (11.8) 777 (12.5) 517 (14.3)
Normal 13,212 (38.8) 6925 (37.0) 6410 (36.4) 6848 (38.9) 6879 (39.1) 9994 (37.7) 6243 (37.9) 2415 (38.8) 1485 (41.1)
Overweight 7023 (20.6) 4614 (24.6) 3907 (22.2) 3991 (22.7) 3739 (21.2) 6120 (23.1) 3646 (22.1) 1251 (20.1) 620 (17.2)
Obese 2193 (6.4) 1567 (8.4) 1485 (8.4) 1303 (7.4) 972 (5.5) 2172 (8.2) 1114 (6.8) 304 (4.9) 170 (4.7)
Missing 7169 (21.0) 3699 (19.8) 3659 (20.8) 3325 (18.9) 3884 (22.1) 5035 (19.0) 3530 (21.4) 1485 (23.8) 818 (22.7)
Prefacture mobility (CAS‐score)
0–3 2979 (8.7) 1150 (6.1) 1517 (8.6) 1410 (8.0) 1202 (6.8) 2064 (7.8) 1264 (7.7) 466 (7.5) 335 (9.3)
4–5 1927 (5.7) 769 (4.1) 1041 (5.9) 843 (4.8) 812 (4.6) 1370 (5.2) 801 (4.9) 288 (4.6) 237 (6.6)
6 24,130 (70.8) 14,559 (77.8) 12,416 (70.5) 12,929 (73.4) 13,344 (75.8) 19,440 (73.4) 12,318 (74.8) 4696 (75.4) 2235 (61.9)
Missing 5046 (14.8) 2241 (12.0) 2625 (14.9) 2422 (13.8) 2240 (12.7) 3611 (13.6) 2091 (12.7) 782 (12.5) 803 (22.2)
Chronic opioid use 6 months before surgery 6870 (20.2) 3053 (16.3) 4101 (23.3) 3262 (18.5) 2560 (14.5) 5331 (20.1) 2932 (17.8) 952 (15.3) 708 (19.6)
Comorbiditites
Depression, anxiety or psychotic illness 10 years before surgery 11,241 (33.0) 4654 (24.9) 6296 (35.8) 5098 (29.0) 4501 (25.6) 8263 (31.2) 4887 (29.7) 1676 (26.9) 1069 (29.6)
Fluid and electrolyte disorders 1 year before surgery 2088 (6.1) 806 (4.3) 1105 (6.3) 960 (5.5) 829 (4.7) 1507 (5.7) 873 (5.3) 297 (4.8) 217 (6.0)
Medication for pain/inflammation 1 year before surgery 5227 (15.3) 3389 (18.1) 2956 (16.8) 2753 (15.6) 2907 (16.5) 4197 (15.8) 2853 (17.3) 1014 (16.3) 552 (15.3)
Osteoporosis medication 10 years before surgery 6716 (19.7) 3236 (17.3) 3410 (19.4) 3241 (18.4) 3301 (18.8) 5251 (19.8) 2944 (17.9) 1206 (19.4) 551 (15.3)
Charlson Comorbidity Index score
0 12,767 (37.5) 7051 (37.7) 5880 (33.4) 6734 (38.3) 7204 (40.9) 9867 (37.3) 5956 (36.2) 2452 (39.3) 1543 (42.7)
1–2 14,280 (41.9) 7120 (38.0) 7391 (42.0) 7135 (40.5) 6874 (39.1) 10,843 (40.9) 6679 (40.5) 2421 (38.8) 1457 (40.4)
3+ 7035 (20.6) 4548 (24.3) 4328 (24.6) 3735 (21.2) 3520 (20.0) 5775 (21.8) 3839 (23.3) 1359 (21.8) 610 (16.9)
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Note: Patients were divided into low (< 33,600 USD), medium (33,600–198,300 USD), or high liquid (> 198,300 USD) assets group based on tertiles using 2024 exchange rate and 5 years liquid assets history; education status: low (elementary school), medium (more than elementary school but less than university degree), and high (university degree).

Abbreviations: BMI, Body Mass Index; CAS, Cumulative Ambulation Score.

One‐year risks of post‐operative chronic opioid use (≥ 2 prescription 31–365 days after surgery) were 33% among patients living alone and 30% among those cohabiting, corresponding to aRR of 1.05 (CI: 1.02–1.09). Risks of chronic opioid use were 37% among patients with low liquid assets versus 28% among those with high liquid assets (aRR = 1.28, CI: 1.23–1.34), and 33% among patients with low educational level versus 28% among those with high educational level (aRR = 1.19, CI: 1.14–1.25) (Figure 1).

FIGURE 1.

FIGURE 1

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Risk ratios (RR) for chronic opioid use defined as ≥ 2 prescriptions during 31–365 days after surgery with 95%‐confidence intervals (CI) comparing patients within each socioeconomic position marker of cohabitation, liquid assets, and education. Comparing cohabitation status, we adjusted for age, sex, year of surgery, education, liquid assets, Charlson Comorbidity index, history of depression, anxiety or psychotic illness as well as prior use of pain/inflammation drugs. Comparing liquid assets, we adjusted for age, sex, year of surgery, education, cohabitation, Charlson Comorbidity index, history of depression, anxiety or psychotic illness as well as prior use of pain/inflammation drugs. Comparing educational levels, we adjusted for age, sex, and year of surgery. Patients were divided into low (< 33,600 USD), medium (33,600–198,300 USD), or high liquid (> 198,300 USD) assets groups based on tertiles using the 2024 exchange rate and 5 years liquid assets history; education status: low (elementary school), medium (more than elementary school but less than university degree), and high (university degree).

The MMEs calculated as mg/day (based on ≥ 2 prescriptions 31–365 days after surgery) are presented in Figure 2 and show that patients living alone used 11.5 MME mg/day versus 9.8 mg/day used by patients cohabitating, patients with low liquid assets used 14.8 versus 7.9 mg/day used by patients with high liquid assets, and patients with low education used 11.8 versus 7.9 mg/day used by patients with high education.

FIGURE 2.

FIGURE 2

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Morphine milligram equivalents (MME) mg per day comparing patients within each socioeconomic position marker of cohabitation, liquid assets and education. All opioids prescription during 31–365 days after surgery were included in the calculation. Patients were divided into low (< 33,600 USD), medium (33,600–198,300 USD), or high liquid (> 198,300 USD) assets group based on tertiles using 2024 exchange rate and 5 years liquid assets history; education status: low (elementary school), medium (more than elementary school but less than university degree), and high (university degree).

Analyses stratified on age, sex, type of surgery, history of opioid use and year of surgery showed the same association between markers of SEP and chronic opioid use as found in the overall analyses. However, the risks were in general 10%‐points lower among opioid‐naïve patients than in the overall population (Figure 3, Table S3).

FIGURE 3.

FIGURE 3

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Risk ratios (RR) for chronic opioid use defined as ≥ 2 prescriptions during 31–365 days after surgery stratified on age, sex, type of surgery and preoperative opioid history. Within each of age groups, sex, type of surgery groups and preoperative opioid history groups, we compared patients by cohabitation (living alone vs. cohabiting [ref.]), liquid assets (low or medium vs. high liquid assets [ref.]), and education (low or medium vs. high education [ref.]). In general, we adjusted as following, except for the variables we stratified on: Comparing cohabitation status, we adjusted for age, sex, year or surgery, and education, liquid assets, Charlson Comorbidity index, history of depression, anxiety or psychotic illness as well as prior use of pain/inflammation drugs. Comparing liquid assets, we adjusted for age, sex, year of surgery, education, cohabitation, Charlson Comorbidity index, history of depression, anxiety or psychotic illness as well as prior use of pain/inflammation drugs. Comparing educational levels, we adjusted for age, sex, and year of surgery. Patients were divided into low (< 33,600 USD), medium (33,600–198,300 USD), or high liquid (> 198,300 USD) assets group based on tertiles using 2024 exchange rate and 5 years liquid assets history; education status: low (elementary school), medium (more than elementary school but less than university degree), and high (university degree).

The sensitivity analysis based on chronic opioid use defined as ≥ 2 prescriptions for 92–365 days after surgery showed that the associations between markers of SEP and chronic opioid use were slightly stronger (thus, all aRR were higher in the sensitivity analyses than in the main analyses [Figure 4]). The MMEs in sensitivity analyses were slightly lower being 10.8 mg/day in patients living alone versus 9.2 mg/day in those cohabitating, 14.1 mg/day in patients with low liquid assets versus 7.3 mg/day in those with high, and 11.1 mg/day in patients with low education versus 7.2 mg/day in those with high education.

FIGURE 4.

FIGURE 4

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Sensitivity analysis. Risk ratios (RR) for chronic opioid use defined as ≥ 2 prescriptions in two different quartiles during 92–365 days after surgery comparing patients within each socioeconomic position marker of cohabitation, liquid assets, and education. Comparing cohabitation status, we adjusted for age, sex, year of surgery, and education, liquid assets, Charlson Comorbidity index, history of depression, anxiety or psychotic illness as well as prior use of pain/inflammation drugs. Comparing liquid assets, we adjusted for age, sex, year of surgery, education, cohabitation, Charlson Comorbidity index, history of depression, anxiety or psychotic illness as well as prior use of pain/inflammation drugs. Comparing educational levels, we adjusted for age, sex, and year of surgery. Patients were divided into low (< 33,600 USD), medium (33,600–198,300 USD), or high liquid (> 198,300 USD) assets group based on tertiles using 2024 exchange rate and 5 years liquid assets history; education status: low (elementary school), medium (more than elementary school but less than university degree), and high (university degree).

4. Discussion

Based on 52,801 patients aged > 65 years, we found that living alone, less liquid assets, and low education were associated with a higher risk of chronic opioid use and higher MME doses in the first year after hip fracture surgery. The results stand irrespective of the definition of chronic opioid use and preoperative opioid use status. The results are clinically relevant as they may help healthcare professionals at hospitals and in primary care focus on socially vulnerable patients and design future interventions aiming to reduce social gradients in opioid use among frail hip fracture patients and thereby improve their outcome.

4.1. Comparison With Existing Literature and Interpretation

Several studies have reported on risk factors for chronic opioid use after hip fracture surgery (Stone et al. 2023; Hereford et al. 2022; Edwards et al. 2021) but SEP factors were not considered. However, our finding of the association between low education and chronic opioid use is in line with studies on hip and knee arthroplasty patients (Pryymachenko et al. 2020; Kleno et al. 2024). In addition, the association between low education and a high level of pain (Feldman et al. 2015) as well as poor quality of life and length of hospital stay (Mesterton et al. 2020) after hip and knee arthroplasty has been reported. Since the complexity of both patients (due to comorbidity, frailty and polypharmacy) and surgery (due to more fragile bones), as well as the average age, is higher among acute hip fracture patients compared to elective hip and knee arthroplasty patients, in addition to the fact that the main indication for arthroplasty surgery is to reduce hip/knee pain, it is likely that the risks of chronic opioid use are higher in hip fracture patients in general, but the relative risks by SEP do not have to be affected.

Our results are in line with the results from a study based on American Medicare data where adults > 65 years of age reported an association between income inequality and the opioid prescribing rate which was largely explained by residential stability and social isolation (Yang et al. 2021).

The association between low SEP and increased risk of chronic opioid use after hip fracture surgery can be attributed to several factors and mechanisms. Patients with lower SEP had a higher burden of comorbidities compared to patients with higher SEP (Wong et al. 2022). While we adjusted for CCI in our analysis, we did not have information on the severity of all diagnoses, which could complicate recovery and increase the need for pain management. While we know from data that the use of medication for pain/inflammation 1 year before surgery was rather similar across SEP groups, we do not know whether similarity also exists for type of anaesthesia and multimodal analgesia during hospitalisation for hip fracture. However, in Denmark, type of anaesthesia and multimodal analgesia follows department‐specific enhanced recovery protocols, independently of SEP. Also, patients with lower SEP could have some diseases treated by general practitioners with or without medication, which could lead to prolonged opioid use (Edwards et al. 2021). Some (Wong et al. 2022) but not all studies (Kristensen, Thillemann, Pedersen, et al. 2016) have shown that low SEP is associated with longer time to surgery and longer length of hospital stay after hip fracture which is likely to affect pain management of patients. Low SEP has further been found to be associated with higher risk of readmission due to various reasons after hip fracture surgery (Kristensen, Thillemann, Pedersen, et al. 2016; Patel et al. 2021), which could increase the need for extended opioid use. Social deprivation has also been found to be associated with decreased likelihood to participate in outpatient physical therapy for musculoskeletal conditions (Stephens et al. 2023; Stonner et al. 2022) which is a key factor for hip fracture recovery, leading to a greater reliance on opioids. Lower SEP or rather poverty is often associated with higher levels of psychosocial stress (Knifton and Inglis 2020), which can exacerbate pain perception (Edwards et al. 2016) and increase the likelihood of opioid use.

Inadequate health literacy and weaker support systems are strongly associated with low SEP, making it harder to manage pain effectively without opioids (Svendsen et al. 2014, 2020). These factors contribute to the increased risk of chronic opioid use among hip fracture patients with lower SEP. We cannot change the SEP of our patients, but we can offer a more stratified approach to counselling and treatment interventions, tailoring the needs of patients with lower SEP. This approach can be divided into preoperative, intraoperative and postoperative strategies. Preoperative strategies are limited since hip fracture is an acute event but could include education and counselling on pain management in the general population, addressing side effects of opioids and psychological factors contributing to pain and opioid use.

Intraoperative strategies could include a multimodal approach to pain treatment prioritising non‐opioid analgesics and opioids such as morphine, which is known to be less addictive than oxycodone, fentanyl, and tramadol (Simoni et al. 2020). The use of intraoperative local infiltration anaesthesia has been shown to reduce opioid consumption in hip fracture patients treated with a hemiarthroplasty (Hofstad et al. 2022). In addition, the type of surgery should be considered since total hip arthroplasty is associated with a lower risk of chronic opioid use than internal fixation (Edwards et al. 2021). Postoperative strategies could include early mobilisation (Tudorache et al. 2025), early comprehensive geriatric assessment (Mazarello Paes et al. 2025), enhanced postoperative recovery programmes (Kehlet 2020), and regular follow‐up appointments to monitor rehabilitation progress, pain levels, opioid use and overall recovery. In addition, ensuring access to social support in the postoperative period by connecting patients with community resources and support groups, and continuous education for patients, families, and carers about opioid side effects and care after a hip fracture (registry AaNZHF 2025) could help in the early identification of opioid‐related problems. Future research should prioritise the evaluation of these interventions to determine their effectiveness in reducing chronic opioid use among patients with low SEP following hip fracture surgery.

4.2. Methodological Considerations

The key strength of this study stems from our use of a comprehensive population‐based dataset of hip fracture patients, increasing the generalizability of our findings and avoiding selection bias both into and out of the study population. Validity of diagnosis‐ and surgery‐codes for hip fracture is high (Hjelholt et al. 2020). We had individual‐level data on SEP markers of high validity (Baadsgaard and Quitzau 2011; Jensen and Rasmussen 2011) and complete data on opioid prescriptions and dosage both before and after hip fracture surgery (Pottegard et al. 2017). We were able to include several relevant variables to describe the study population and adjust for confounding.

Our study has several limitations. We lacked data on indication for opioid therapy. However, previous research has shown that 80% of patients took opioids for pain improvement, while other indications were sleep improvement and unspecified reasons (Stark et al. 2017). We did not have data on opioid use during the index hospital stay for hip fracture, which could have an impact on opioid use after discharge. This study reports on opioid prescriptions while actual opioid ingestion (e.g., compliance with opioids) is unknown. However, evidence suggests that electronic prescription databases provide a reliable measure of medication intake (Schneeweiss and Avorn 2005). We did not consider data on other surgical procedures that might have been performed during the follow‐up because these procedures are mediators of the examined association rather than confounders. Our study faces challenges due to the absence of a universally accepted definition of chronic opioid use following surgery. Consequently, our findings should be interpreted within the framework of the specific definition of chronic opioid use that we have employed. Our study provides evidence of a significant association between SEP and chronic opioid use following hip fracture surgery. However, establishing the causal effect of SEP on opioid use remains challenging. The generalisation of our findings is higher for countries with tax‐supported, free‐of‐charge healthcare systems, and opioid access similar to those in Denmark.

In conclusion, living alone, less liquid assets, and low level of education were associated with a higher risk of chronic opioid use and higher MMEs in the first year after hip fracture surgery. The results stand irrespective of the definition of chronic opioid used and preoperative opioid use status. Interventions targeting patients identified as being at greatest risk are important to reduce social inequality, side effects of opioid use including falls and secondary fractures, and subsequently mortality.

Author Contributions

Conceptualization: A.B.P., N.R. Statistical analyses: N.R. Project administration: A.B.P., N.R. Supervision and input to statistical analyses: all authors. Writing – original draft: A.B.P. Writing – review and editing: all authors.

Supporting information

Data S1.

EJP-29-0-s001.docx (107.5KB, docx)

Risbo, N. , Ehrenstein V., Gundtoft P. H., Gjertsen J.-E., and Pedersen A. B.. 2025. “Socioeconomic Position and Chronic Opioid Use After Hip Fracture Surgery: A Danish Population‐Based Cohort Study.” European Journal of Pain 29, no. 7: e70063. 10.1002/ejp.70063.

Data Availability Statement

Original data are not available due to Danish legislation governing data access.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Data S1.

EJP-29-0-s001.docx (107.5KB, docx)

Data Availability Statement

Original data are not available due to Danish legislation governing data access.

Articles from European Journal of Pain (London, England) are provided here courtesy of Wiley

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