Hydromorphone and the risk of infective endocarditis among people who inject drugs: a population-based, retrospective cohort study

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Hydromorphone and the risk of infective endocarditis among people who inject drugs: a population-based, retrospective cohort study Michael Silverman, Justin Slater, Racquel Jandoc, Sharon Koivu, Amit X Garg, Matthew A Weir

Summary

Background The incidence of infective endocarditis related to injection drug use is increasing. On the basis of clinical practice and epidemiological and in-vitro data, we postulated that exposure to controlled-release hydromorphone is associated with an increased risk of infective endocarditis among people who inject drugs. Methods We used linked health administrative databases in Ontario, Canada, to assemble a retrospective cohort of adults (aged 18–55 years) who inject drugs for the period of April 1, 2006, to Sept 30, 2015. Cases of infective endocarditis among this cohort were identified using International Classification of Diseases 10 codes. We estimated exposure to hydromorphone and risk of infective endocarditis among this cohort in two ways. First, in a populationlevel analysis, we identified patients living in regions with high (≥25%) and low (≤15%) hydromorphone prescription rates and, after matching 1:1 on various baseline characteristics, compared their frequency of infective endocarditis. Second, in a patient-level analysis including only those with prescription drug data, we identified those who had filled prescriptions (ie, received the drug from the pharmacy) for controlled-release or immediate-release hydromorphone and, after matching 1:1 on various baseline characteristics, compared their frequency of infective endocarditis with that of patients who had filled prescriptions for other opioids. Results Between April 1, 2006, and Sept 30, 2015, 60 529 patients had evidence of injection drug use, 733 (1·2%, 95% CI 1·1–1·3) of whom had infective endocarditis. In the population-level analysis of 32 576 matched patients, we identified 254 (1·6%) admissions with infective endocarditis in regions with high hydromorphone use and 113 (0·7%) admissions in regions with low use (adjusted odds ratio [OR] 2·2, 95% CI 1·8–2·8, p<0·0001). In the patient-level analysis of 3884 matched patients, the frequency of infective endocarditis was higher among patients who filled prescriptions for hydromorphone than among those who filled prescriptions for nonhydromorphone opioids (2·8% [109 patients] vs 1·1% [41 patients]; adjusted OR 2·5, 95% CI 1·8–3·7, p<0·0001). This significant association was seen for controlled-release hydromorphone (3·9% [73 of 1895 patients] vs 1·1% [20 of 1895]; adjusted OR 3·3, 95% CI 2·1–5·6, p<0·0001), but not for immediate-release hydromorphone (1·8% [36 of 1989] vs 1·1% [21 of 1989]; 1·7, 0·9–3·6, p=0·072.

Lancet Infect Dis 2020 Published Online January 22, 2020 https://doi.org/10.1016/ S1473-3099(19)30705-4 See Online/Comment https://doi.org/10.1016/ S1473-3099(19)30754-6 Division of Infectious Diseases (M Silverman MD) and Division of Nephrology (Prof A X Garg MD, M A Weir MD), Department of Medicine, Western University, London, ON, Canada; Department of Family Medicine (S Koivu MD) and Department of Epidemiology and Biostatistics (Prof A X Garg, M A Weir), Western University, London, ON, Canada; and Institute for Clinical Evaluative Sciences, Toronto, ON, Canada (J Slater MSc, R Jandoc MSc, Prof A X Garg, M A Weir) Correspondence to: Dr Matthew A Weir, University Hospital, London, 339 Windermere Rd, London, ON N6A 5A5, Canada [email protected]

Interpretation Among people who inject drugs, the risk of infective endocarditis is significantly higher for those exposed to controlled-release hydromorphone than to other opioids. This association might be mediated by the controlled-release mechanism and should be the subject of further investigation. Funding Ontario Ministry of Health and Long-Term Care, Academic Medical Organization of Southwestern Ontario, Schulich School of Medicine and Dentistry (Western University), and Lawson Health Research Institute. Copyright © 2020 Elsevier Ltd. All rights reserved.

Introduction Prescription opioids form a substantial part of the current opioid epidemic, accounting for 40% of deaths from opioids worldwide.1,2 Among the many complications associated with injecting prescription opioids, infective endocarditis stands out because in Sweden,3 Australia,4 the USA,5–9 and Canada,10 its incidence is increasing faster than that of injection drug use. The reasons for this increase are not clear, although potential contributors include the reduced availability of controlled-release oxycodone (OxyContin; Purdue Pharma, Stamford, CT, USA) and a shift to other controlled-release opioids. When OxyContin was removed from the Canadian

market we observed a substantial increase in the pre­ scription rate of hydromorphone and a simultaneous increase in the risk of infective endocarditis related to injection drug use.10 The role of hydromorphone in infective endocarditis might relate to how its controlled-release formulation is prepared for intravenous use. To inject prescription opioids, pills are often crushed in so-called metal cookers and then heated with water to produce an aqueous solution. Compared with OxyContin, which was readily soluble in water,11 the controlled-release hydromorphone available in Ontario (Hydromorph Contin, Purdue Pharma or Apo Hydromorphone CR, Apotex, Toronto, ON, Canada)

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Research in context Evidence before this study Infective endocarditis is one of the most serious complications for people who inject drugs, and for unknown reasons, its incidence has been increasing in the USA, Europe, Australia, and Canada. In our jurisdiction (Ontaria, Canada), the rise of infective endocarditis related to injection drug use has paralleled the increased use of hydromorphone that followed the removal of controlled-release oxycodone (OxyContin; Purdue Pharma, Stamford, CT, USA) from the Canadian market. This subsequent increase in use of hydromorphone included its controlled-release formulation. Unlike the readily soluble, controlled-release formulation of oxycodone, the polymer beads that provide controlled release of hydromorphone are difficult to crush for intravenous injection, leaving residual drug in the injection equipment. This residue promotes equipment storage and reuse, which increases the risk of bacterial contamination. Controlled-release hydromorphone also supports the survival of Staphylococcus aureus in injection equipment. To identify studies describing relationships between specific opioids and infective endocarditis we searched PubMed, EMBASE, and PubMed Central from Jan 1, 1990, to Sept 30, 2017, using terms including generic names of opioids, infective endocarditis, and endocarditis. We updated this search through to Feb 1, 2019, and besides our own preliminary work on this topic, we found no published data in either search

See Online for appendix

is difficult to completely dissolve because its polymercoated beads resist crushing.12 As a result, cookers used to prepare controlled-release hydro­ morphone con­ tain residual drug, which promotes their storage and reuse12,13 and increases the risk of bacterial contamination. In support of this theory, we showed that equipment used by people who inject drugs to prepare controlled-release hydromorphone is frequently contaminated with Staphylo­ coccus aureus.14 To determine whether increased exposure to controlledrelease hydromorphone is associated with an increased risk of infective endocarditis among people who inject drugs, we conducted a population-based, retrospective cohort study using health administrative data in Ontario, Canada. We used administrative data to assemble a cohort of people who inject drugs. Within this cohort, we assessed exposure to opioids using both regional and individual prescription drug data. We then identified admissions to hospital with infective endocarditis and determined whether people with exposure to hydro­ morphone and its different preparations were more likely to experience this outcome than were those exposed to other opioids.

Methods

Study design and participants We conducted a population-based, retrospective cohort analysis using the linked health administrative databases 2

describing a relationship between controlled-release hydromorphone and infective endocarditis. Added value of this study We postulated that the properties of controlled-release hydromorphone would increase the risk of infective endocarditis among people who inject drugs. In this population-based, retrospective cohort study, we showed a significant increase in the risk of infective endocarditis among people who inject drugs who were prescribed controlled-release hydromorphone compared with those prescribed other opioids. We did not observe an increased risk with immediate-release hydromorphone, suggesting that the controlled-release polymer beads influence the risk of infective endocarditis. This study provides the first evidence of a possible cause of the widely described increasing incidence of infective endocarditis among people who inject drugs. Implications of all the available evidence The association that we observed should prompt similar investigations in regions where an increasing risk of infective endocarditis among people who inject drugs has been described. Additionally, future studies should investigate whether other opioids that have similar controlled-release mechanisms to hydromorphone are associated with a similarly increased risk of infective endocarditis.

of Ontario, Canada, for the period of April 1, 2006 (when the Institute for Clinical Evaluative Services changed the way mental health diagnoses are captured), to Sept 30, 2015. Citizens of Ontario have universal access to physician services and inpatient care, and a subset also receive universal prescription drug coverage through the Ontario Drug Benefits programme. The use of data in this project was authorised under section 45 of the Personal Health Information Protection Act and did not require review by a research ethics board. We report this study according to guidelines for observational studies using routinely collected data (appendix pp 2–6).15 We identified our study population using a previously designed and validated definition for people who inject drugs, which comprises a set of administrative codes and characteristics (appendix p 7).16 This definition included patients aged 18–55 years who had hospital admissions in the preceding 6 months (with cohort entry beginning on April 1, 2006) who had any diagnosis of substance, opioid, stimulant, or combined drug abuse, or infection with hepatitis C. We used another list of codes to identify baseline characteristics (appendix pp 8–9). In previous studies and our own validation study, most people who inject drugs were younger than 55 years.7,17 The definition of people who inject drugs has a positive predictive value of 83% against the standard of chart review17 and has been used in other retrospective studies of infective endocarditis related to injection drug use.9,10

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104 144 valid hospital admissions with evidence of injection drug use (2006–15)

35 294 aged ≤17 years or ≥56 years

68 850 hospital admissions with evidence of injection drug use, restricted to target age range (18–55 years)

8321 had repeated admissions within 30 days

60 529 people who inject drugs

60 529 people assessed for eligibility for inclusion in population-level exposure analysis

14 024 people in intermediate hydromorphone use region

46 505 in the population-level exposure analysis 17 908 in low hydromorphone region 28 597 in high hydromorphone region

13 929 excluded in matching process 1620 in low hydromorphone region 12 309 in high hydromorphone region

32 576 in the regional exposure analysis 16 288 in high hydromorphone region 16 288 in low hydromorphone region

60 529 people assessed for eligibility for inclusion in patient-level exposure analysis

46 706 had no opioid prescriptions

13 823 in the patient-level exposure analysis 4672 with hydromorphone prescriptions 9151 with other opioid prescriptions

6055 excluded in matching process 788 with hydromorphone prescriptions 5267 with other opioid prescriptions

7768 in the patient-level exposure analysis 3884 with hydromorphone prescriptions 3884 with other opioid prescriptions

Figure: Study profile

Data sources We used five databases that are linked using unique en­ coded identifiers and analysed at the Institute for Clinical Evaluative Sciences at Western University (London, ON, Canada). We accessed data on hospital and emergency department admissions through the Canadian Institute for Health Information Discharge Abstract Database and the National Ambulatory Care Reporting System database. Trained personnel at hospitals in Ontario review the medical charts of all patients with health-care encounters and enter into the hospital admission and emergency room visit databases’ diagnosis and procedure codes recorded by physicians. On the basis of rules and guidelines provided by the Canadian Institute for Health Information, all diagnoses and procedures are encoded using the International Classification of Diseases system . We used the Ontario Health Insurance Plan database to obtain data on physician services and the Registered Persons Database for demographic data and vital statistics. To assess outpatient prescription drug use, we

used the Ontario Drug Benefits database, which records dispensed medications with high accuracy.16 Typically, studies using this database are limited to people older than 65 years, because at this age all residents receive outpatient prescription drugs free of charge (although with a dispensing fee); however, Ontario Drug Benefits also provides coverage for people receiving provincial financial assistance, people enrolled in disability support prog­ rammes, and people with high prescription drug costs relative to income (appendix p 7). Therefore, despite restricting our cohort to people aged 55 years or younger, we expected some participants to have accessible prescrip­ tion drug data.

Estimation of exposure to opioids Because of the difficulty in estimating exposure to hydro­ morphone among a population of people who inject drugs, we estimated exposure in two ways. The first approach was to assess population-level hydromorphone exposure (using prescription rates for 2012–15 throughout

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Unmatched cohort Low prescribing (n=17 908)

Matched cohort High prescribing (n=28 597)

Standardised difference (%)

Low prescribing (n=16 288)

High prescribing (n=16 288)

Standardised difference (%)

Demographics Age at cohort entry (years) Mean (SD)

39·9 (10·5)

39·2 (10·7)

7%

40·8 (10·6)

40·8 (10·6)

0%

Median (IQR)

42 (31–49)

41 (30–49)

··

43 (32–50)

43 (32–50)

··

7635 (42·6%)

Sex, n (%) 12 998 (45·5%)

6%

6841 (42·0%)

7102 (43·6%)

3%

10 273 (57·4%)

15 599 (54·5)

6%

9447 (58·0%)

9186 (56·4%)

3%

Urban

15 981 (89·2%)

23 892 (83·5%)

17%

14 822 (91·0%)

14 838 (91·1%)

0%

Rural

1905 (10·6%)

4700 (16·4%)

17%

1450 (8·9%)

1450 (8·9%)

0%

4%

16 (0·1%)

Female Male Location, n (%)

Missing data

22 (0·1%)

<6

<6

4%

Socioeconomic status, n (%) 1 (poorest)

5961 (33·3%)

12 284 (43·0%)

20%

5603 (34·4%)

5603 (34·4%)

0%

2

3530 (19·7%)

6174 (21·6%)

5%

3469 (21·3%)

3469 (21·3%)

0%

3

3019 (16·9%)

4327 (15·1%)

5%

2818 (17·3%)

2948 (18·1%)

2%

4

2636 (14·7%)

3269 (11·4%)

10%

2313 (14·2%)

2313 (14·2%)

0%

5 (richest)

2413 (13·5%)

2167 (7·6%)

19%

1743 (10·7%)

1743 (10·7%)

0%

349 (1·9%)

374 (1·3%)

5%

326 (2·0%)

212 (1·3%)

6%

1208 (6·7%)

1765 (6·2%)

2%

1108 (6·8%)

928 (5·7%)

4%

210 (1·2%)

322 (1·1%)

0%

179 (1·1%)

179 (1·1%)

0%

1386 (7·7%)

1933 (6·8%)

4%

1287 (7·9%)

1010 (6·2%)

6%

Congestive heart failure

361 (2·0%)

532 (1·9%)

1%

309 (1·9%)

261 (1·6%)

3%

Coronary artery disease

40 (0·2%)

157 (0·5%)

5%

33 (0·2%)

81 (0·5%)

5%

3091 (17·3%)

5948 (20·8%)

9%

Missing data Comorbidities, n (%) Alcoholism Bipolar disorder Chronic liver disease

Depression or anxiety disorder, or both Hepatitis B

2948 (18·1%)

3%

90 (0·3%)

1%

49 (0·3%)

33 (0·2%)

2%

2164 (12·1%)

4846 (16·9%)

14%

1873 (11·5%)

1873 (11·5%)

0%

HIV

952 (5·3%)

1038 (3·6%)

8%

798 (4·9%)

472 (2·9%)

10%

Schizophrenia or other psychotic disorder

285 (1·6%)

555 (1·9%)

3%

244 (1·5%)

277 (1·7%)

2%

2368 (13·2%)

4148 (14·5%)

4%

2117 (13·0%)

2166 (13·3%)

1% 0%

Hepatitis C

Self harm

45 (0·3%)

2769 (17·0%)

Medication use, n (%) Antibiotics

3929 (21·9%)

7435 (26·0%)

10%

3502 (21·5%)

3534 (21·7%)

Anticoagulants

187 (1·0%)

267 (0·9%)

1%

163 (1·0%)

147 (0·9%)

2%

Antipsychotics

2129 (11·9%)

4967 (17·4%)

16%

1873 (11·5%)

2329 (14·3%)

9%

Benzodiazepines

3332 (18·6%)

6467 (22·6%)

10%

2964 (18·2%)

3111 (19·1%)

2%

112 (0·6%)

362 (1·3%)

7%

98 (0·6%)

179 (1·1%)

5% 11%

Lithium

5735 (32·0%)

12 113 (42·4%)

22%

5180 (31·8%)

6027 (37·0%)

Codeine

1451 (8·1%)

2478 (8·7%)

2%

1287 (7·9%)

1222 (7·5%)

2%

Fentanyl

299 (1·7%)

769 (2·7%)

7%

261 (1·6%)

391 (2·4%)

5%

Hydromorphone

14%

Opioids

877 (4·9%)

2698 (9·4%)

18%

766 (4·7%)

1301 (8·0%)

Merperidine

43 (0·2%)

103 (0·4%)

2%

33 (0·2%)

33 (0·2%)

Methadone

2052 (11·5%)

4860 (17·0%)

16%

1906 (11·7%)

2394 (14·7%)

1% 9%

Morphine

736 (4·1%)

1392 (4·9%)

4%

635 (3·9%)

652 (4·0%)

1%

Oxycodone

2459 (13·7%)

3840 (13·4%)

1%

2199 (13·5%)

1889 (11·6%)

6%

(Table 1 continues on next page)

the province, determined by the Narcotics Atlas using Ontario Drug Benefits data). The adminis­tration of health care in Ontario is divided into 14 geographical sectors (referred to as Local Health Integration Networks). The 4

2017 Ontario Narcotics Atlas identified significant diffe­ rences in the prescription rates of different opioids across these sectors (appendix p 11).18 Using these data, we identified sectors with high (≥25% of the total opioid

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Unmatched cohort Low prescribing (n=17 908)

Matched cohort High prescribing (n=28 597)

Standardised difference (%)

Low prescribing (n=16 288)

High prescribing (n=16 288)

Standardised difference (%)

(Continued from previous page) Estimates of comorbidity Unique drugs used in past 12 months Mean (SD)

3·6 (5·5)

4·4 (5·6)

14%

3·5 (5·3)

3·6 (5·2)

3%

Median (IQR)

0 (0–6)

2 (0–7)

··

0 (0–6)

0 (0–6)

·· 11%

Hospitalisations in past 12 months Mean (SD)

1·3 (2·6)

1·1 (2·1)

8%

1·3 (2·6)

1·0 (2·0)

Median (IQR)

0 (0–2)

0 (0–1)

··

0 (0–2)

0 (0–1)

··

Emergency room visits in past 12 months Mean (SD)

5·0 (10·8)

4·4 (7·1)

7%

4·9 (10·5)

3·9 (6·3)

Median (IQR)

2 (1–5)

2 (1–5)

··

2 (1–5)

2 (1–5)

12% ·· 6%

Family physician visits in past 12 months Mean (SD)

20·6 (24·2)

20·5 (25·4)

0%

20·5 (23·9)

19·1 (24·5)

Median (IQR)

12 (5–27)

11 (4–25)

··

12 (5–27)

10 (4–23)

··

4%

2655 (16·3%)

2280 (14·0%)

6%

Electrocardiograms, n (%)

2929 (16·4%)

4276 (15·0%)

Standardised differences are differences in group means as a percentage of the pooled SD. Standardised differences greater than 10% are meaningful imbalances. To minimise the risk of re-identification, values of 5 or less are reported as <6.

Table 1: Baseline characteristics of patients living in regions of low and high hydromorphone prescription

prescriptions) and low (≤15% of the total opioid pre­ scriptions) hydromorphone prescription rates. We did not include sectors with intermediate prescription rates in our analysis. The 2017 Ontario Narcotics Atlas does not differentiate between controlled-release and immediaterelease formulations of hydro­ morphone and does not capture data on illicit drug use. In the second approach, we assessed patient-level hydromorphone exposure in patients with available prescription drug data. To determine the opioid to which a patient was probably exposed, we identified the date on which each patient met the definition of a person who injects drugs and then looked at the preceding 120 days for any opioid prescription. The Ontario Drug Benefits programme allows a maximum dispensation of 100 days worth of pills. By using a 120-day period, we captured all active prescriptions and included a 20-day grace period to account for any delays in patients seeking refills. We classified as exposed people who filled a hydromorphone prescription (ie, took a prescription to the pharmacy and received the drug), regardless of any other opioid prescription filled during that time period. We classified people who filled a prescription for any non-hydromorphone opioid as unexposed; we excluded patients with no opioid prescriptions from this analysis because we could not be confident in making inferences about a group for which we had no means of estimating their exposure level. We categorised patients who were prescribed both immediate-release and controlled-release hydromor­ phone in the 120 days as being exposed to controlledrelease hydromorphone. We did not do a dose-response analysis because we expected its precision to be limited

by a low event rate and an inability to accurately estimate doses of injected opioids on the basis of prescription drug data.

Outcome Our main outcome of interest was incidence of infective endocarditis related to injection drug use, which required admission to hospital. We defined hospital admissions with infective endocarditis using codes that have been shown in our databases to have a sensitivity of 93% and a positive predictive value of 83% (appendix p 10).19 We defined admissions to hospital with infective endo­ carditis as related to injection drug use if the admission occurred during or within 6 months following an admission that qualified the patient as a person who injects drugs. We considered admissions with infective endocarditis occurring within 30 days of a previous admission with infective endocarditis to be relapses and therefore excluded them from the analysis.

Statistical analysis For each analysis, we compared the prevalence of basel­ ine characteristics between exposed and nonexposed groups using standardised differences, which describe a difference between group means as a percentage of the variable’s pooled SD. Standardised differences greater than 10% represent meaningful imbalances.20,21 To compare frequencies of infective endocarditis related to injection drug use between sectors with high and low prescription rates, we matched patients in regions with low hydromorphone use to those in regions with high hydromorphone use 1:1 on initial hospital admission date

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Unmatched cohort Other opioids (n=9151)

Matched cohort

Hydromorphone (n=4672)

Standardised difference (%)

Other opioids (n=3884)

Hydromorphone Standardised (n=3884) difference (%)

Demographics Age at cohort entry (years) Mean (SD)

43·9 (8·6)

43·7 (8·9)

2%

44·4 (8·4)

44·4 (8·4)

1%

Median (IQR)

46 (39–51)

46 (38–51)

··

46 (39–51)

46 (39–51)

··

Sex, n (%) Female

3877 (42·4%)

2034 (43·5%)

2%

1729 (44·5%)

1679 (43·2%)

3%

Male

5274 (57·6%)

2638 (56·5%)

2%

2155 (55·5%)

2205 (56·8%)

3%

Urban

8102 (88·5%)

4204 (90·0%)

5%

3582 (92·2%)

3582 (92·2%)

0%

Rural

1045 (11·4%)

468 (10·0%)

5%

302 (7·8%)

302 (7·8%)

0%

Location, n (%)

Missing data

<6

<6

3%

<6

<6

0%

Socioeconomic status, n (%) 1 (poorest)

4029 (44·0%)

1955 (41·8%)

4%

1753 (45·1%)

1685 (43·4%)

4%

2

2047 (22·4%)

1119 (24·0%)

4%

958 (24·7%)

960 (24·7%)

0%

3

1340 (14·6%)

680 (14·6%)

0%

534 (13·7%)

549 (14·1%)

1%

4

897 (9·8%)

546 (11·7%)

6%

369 (9·5%)

436 (11·2%)

6%

5 (richest)

749 (8·2%)

339 (7·3%)

3%

240 (6·2%)

227 (5·8%)

1%

89 (1·0%)

33 (0·7%)

3%

30 (0·8%)

27 (0·7%)

1%

Alcoholism

682 (7·5%)

315 (6·7%)

3%

312 (8·0%)

270 (7·0%)

4%

Bipolar disorder

133 (1·5%)

58 (1·2%)

2%

84 (2·2%)

47 (1·2%)

7%

Chronic liver disease

796 (8·7%)

603 (12·9%)

14%

386 (9·9%)

517 (13·3%)

11%

Congestive heart failure

277 (3·0%)

238 (5·1%)

10%

164 (4·2%)

182 (4·7%)

2%

Coronary artery disease

69 (0·8%)

65 (1·4%)

6%

35 (0·9%)

51 (1·3%)

4%

2144 (23·4%)

1098 (23·5%)

Missing data Comorbidities, n (%)

Depression or anxiety disorder, or both

0%

963 (24·8%)

897 (23·1%)

4%

Hepatitis B

47 (0·5%)

28 (0·6%)

1%

12 (0·3%)

25 (0·6%)

5%

Hepatitis C

1954 (21·4%)

1199 (25·7%)

10%

917 (23·6%)

999 (25·7%)

5%

HIV

633 (6·9%)

349 (7·5%)

2%

261 (6·7%)

286 (7·4%)

3%

Schizophrenia or other psychotic disorder

126 (1·4%)

52 (1·1%)

2%

60 (1·5%)

37 (1·0%)

5%

1579 (17·3%)

719 (15·4%)

5%

705 (18·2%)

580 (14·9%)

9% 2%

Self harm Medication use, n (%) Antibiotics

4671 (51·0%)

2647 (56·7%)

11%

2141 (55·1%)

2178 (56·1%)

Anticoagulants

183 (2·0%)

171 (3·7%)

10%

13 (0·3%)

16 (0·4%)

1%

Antipsychotics

2300 (25·1%)

1248 (26·7%)

4%

1059 (27·3%)

1033 (26·6%)

2%

Benzodiazepines

4640 (50·7%)

2296 (49·1%)

3%

2037 (52·4%)

1959 (50·4%)

4%

126 (1·4%)

44 (0·9%)

4%

50 (1·3%)

41 (1·1%)

2%

Lithium

(Table 2 continues on next page)

(within up to a year before and a year after), income quintile, urban versus rural residency, number of unique prescriptions filled in the past 120 days (0–3, 5–9, 10–14, 15–19, or ≥20), and history of hepatitis C. To compare frequencies of infective endocarditis between participants exposed to immediate-release or controlled-release hydro­ morphone and those unexposed, we matched patients on initial hospitalisation date, age (within 2 years), low versus high hydromorphone use region, number of hospitalisations in the past year (0, 1, or >2), and the number of unique prescriptions filled in the past 120 days. We estimated ORs and 95% CIs between the exposed and unexposed groups using conditional logistic regression, 6

controlling for previous physician visits and history of electrocardiograms. We did not adjust estimates of risk for previous valve surgery, congenital heart disease, or intravascular shunts because they were present in less than 0·5% of our cohort, which is consistent with previous data.22 With the observed frequencies, ORs can be interpreted as relative risks. We did all analyses with SAS version 9.4.

Role of the funding source The sponsor of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full

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Unmatched cohort Other opioids (n=9151)

Matched cohort

Hydromorphone (n=4672)

Standardised difference (%)

Other opioids (n=3884)

Hydromorphone Standardised (n=3884) difference (%)

(Continued from previous page) Estimates of comorbidity Unique drugs used in past 12 months Mean (SD)

9·5 (5·5)

11·1 (6·0)

27%

10·6 (5·6)

11·0 (6·0)

7%

Median (IQR)

9 (5–13)

10 (7–14)

··

10 (7–14)

10 (7–14)

··

Mean (SD)

1·6 (2·6)

2·7 (3·5)

35%

2·1 (2·8)

2·4 (3·3)

10%

Median (IQR)

1 (0–2)

2 (0–4)

··

1 (0–3)

1 (0–3)

··

Mean (SD)

6·6 (10·2)

8·6 (12·1)

18%

7·3 (10·6)

8·0 (11·1)

7%

Median (IQR)

4 (1–8)

5 (2–11)

··

4 (2–9)

5 (2–10)

·· 9%

Hospitalisations in past 12 months

Emergency room visits in past 12 months

Family physician visits in past 12 months Mean (SD)

25·4 (24·1)

29·8 (27·2)

17%

26·8 (25·5)

29·1 (26·9)

Median (IQR)

18 (9–33)

21 (11–41)

··

18 (9–35)

21 (10–40)

35%

897 (23·1%)

Electrocardiograms, n (%)

1639 (17·9%)

1542 (33·0%)

1221 (31·4%)

·· 19%

Standardised differences describe a difference between group means as a percentage of the pooled SD. Standardised differences greater than 10% represent meaningful imbalances. To minimise the risk of re-identification, values of 5 or less are reported as <6.

Table 2: Baseline characteristics of patients prescribed non-hydromorphone opioids and patients prescribed hydromorphone

access to all the data in the study and had final res­ ponsibility for the decision to submit for publication.

Results Between April 1, 2006, and Sept 30, 2015, 60 529 eligible patients had a hospital admission with injection drug use (figure); 733 (1·2%, 95% CI 1·1–1·3) had evidence of infective endocarditis related to injection drug use. 46 505 (76·8%) patients were eligible for inclusion in the population-level exposure analysis and 13 823 (22·8%) for the patient-level exposure analysis (figure). In the population-level analysis, we identified 28 597 (61·5%) patients living in regions with high hydro­ morphone prescription rates and 17 908 (38·5%) living in regions with low rates. Matching was possible for 16 288 (91·0%) of 17 908 patients in the unexposed, low-prescribing group and improved the balance in baseline characteristics between groups (table 1). Within the matched cohort, we observed 254 (1·6%) of 16 288 admissions with infective endocarditis related to injection drug use in sectors with high hydromorphone pres­cription rates and 113 (0·7%) of 16 288 admissions in sectors with low prescription rates (adjusted OR 2·2, 95% CI 1·8–2·8, p<0·0001). In the patient-level exposure analysis, we identified 13 823 people who inject drugs who filled prescriptions for opioids. Of these patients, 4672 (33·8%) filled at least one prescription for hydromorphone and 9151 (66·2%) filled prescriptions for at least one of fentanyl, mepe­ ridine, methadone, morphine, or oxycodone. We were able to match 3884 (83·1%) of 4672 patients who filled pres­ criptions for hydromorphone to patients who filled pres­ criptions for non-hydromorphone opioids (table 2).

Among the matched cohort, we observed 109 (2·8%) admissions with infective endocarditis among patients who filled pres­ criptions for hydromorphone compared with 41 (1·1%) admissions among those who filled prescriptions for non-hydromorphone opioids (adjusted OR 2·5, 95% CI 1·8–3·7, p<0·0001). Within the matched cohort of patients with known individual exposure, we assessed immediate-release and controlled-release hydromorphone separately (table 3). We observed 36 (1·8%) admissions with infective endo­ carditis among 1989 patients who filled prescriptions for imme­ diate-release hydro­ morphone and 21 (1·1%) admissions among 1989 matched patients who filled prescriptions of non-hydromorphone opioids (adjusted OR 1·7, 95% CI 0·9–3·6, p=0·072). For controlledrelease hydromorphone, we observed 73 (3·9%) admi­ ssions com­ pared with 20 (1·1%) admissions among 1895 matched patients who filled prescriptions for nonhydromorphone opioids (adjusted OR 3·3, 95% CI 2·1–5·6, p <0·0001).

Discussion In this retrospective cohort study, we showed that the risk of infective endocarditis related to injection drug use was significantly higher among people living in regions with high hydromorphone prescription rates than among those living in low prescription regions. Among patients for whom prescription drug data were available, we showed that filling a prescription for controlled-release hydromorphone was associated with a risk of infective endocarditis that was three times higher than that of other opioids. This association was not seen for the immediate-release formulation of hydro­morphone.

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Immediate-release hydromorphone analysis (matched cohort) Other opioids (n=1989)

Immediate-release hydromorphone (n=1989)

Standardised difference (%)

Controlled-release hydromorphone analysis (matched cohort) Other opioids (n=1895)

Controlled-release hydromorphone (n=1895)

Standardised difference (%)

Demographics Age at cohort entry Mean (SD)

44·2 (8·5)

44·2 (8·5)

2%

44·6 (8·3)

44·6 (8·4)

1%

Median (IQR)

46 (39–51)

46 (39–51)

··

47 (40–52)

47 (40–51)

··

Sex, n (%) Female

885 (44·5%)

873 (43·9%)

2%

844 (44·5%)

806 (42·5%)

3%

1104 (55·5%)

1116 (56·1%)

2%

1051 (55·5%)

1089 (57·5%)

3%

Urban

1853 (93·2%)

1853 (93·2%)

5%

1729 (91·2%)

1729 (91·2%)

0%

Rural

136 (6·8%)

136 (6·8%)

5%

166 (8·8%)

166 (8·8%)

0%

1 (poorest)

902 (45·3%)

843 (42·4%)

6%

851 (44·9%)

843 (44·5%)

1%

2

485 (24·4%)

517 (26·0%)

4%

473 (25·0%)

443 (23·4%)

4%

Male Location, n (%)

Socioeconomic status, n (%)

3

271 (13·6%)

277 (13·9%)

1%

263 (13·9%)

272 (14·4%)

1%

4

199 (10·0%)

220 (11·1%)

4%

170 (9·0%)

215 (11·4%)

8%

5 (richest)

118 (5·9%)

118 (5·9%)

0%

122 (6·4%)

109 (5·8%)

3%

14 (0·7%)

14 (0·7%)

0%

16 (0·8%)

13 (0·7%)

1%

168 (8·4%)

166 (8·3%)

0%

144 (7·6%)

104 (5·5%)

8%

42 (2·1%)

23 (1·2%)

7%

42 (2·2%)

24 (1·3%)

7%

204 (10·3%)

303 (15·2%)

15%

182 (9·6%)

214 (11·3%)

6%

Congestive heart failure

81 (4·1%)

88 (4·4%)

1%

83 (4·4%)

94 (5·0%)

3%

Coronary artery disease

18 (0·9%)

34 (1·7%)

7%

17 (0·9%)

17 (0·9%)

0%

487 (24·5%)

463 (23·3%)

3%

476 (25·1%)

434 (22·9%)

5%

Missing data Comorbidities, n (%) Alcoholism Bipolar disorder Chronic liver disease

Depression or anxiety disorder, or both Hepatitis B

18 (0·9%)

6%

Hepatitis C

492 (24·7%)

527 (26·5%)

4%

425 (22·4%)

472 (24·9%)

6%

HIV

136 (6·8%)

142 (7·1%)

1%

125 (6·6%)

144 (7·6%)

4%

28 (1·4%)

20 (1·0%)

4%

32 (1·7%)

17 (0·9%)

7%

382 (19·2%)

326 (16·4%)

7%

323 (17·0%)

254 (13·4%)

10%

1121 (56·4%)

1143 (57·5%)

2%

1020 (53·8%)

1035 (54·6%)

2%

51 (2·6%)

54 (2·7%)

1%

34 (1·8%)

79 (4·2%)

14%

Schizophrenia or other psychotic disorder Self harm

7 (0·4%)

<6%

<6%

2%

Medication use, n (%) Antibiotics Anticoagulants Antipsychotics Benzodiazepines Lithium

522 (26·2%)

529 (26·6%)

4%

537 (28·3%)

504 (26·6%)

4%

1026 (51·6%)

974 (49·0%)

3%

1011 (53·4%)

985 (52·0%)

3%

24 (1·2%)

20 (1·0%)

2%

26 (1·4%)

21 (1·1%)

3%

(Table 3 continues on next page)

Our findings fit well into a growing understanding of the role that some controlled-release opioids might have in infective endocarditis related to injection drug use. Controlled-release opioids such as OxyContin are favoured among people who inject drugs because of the large amount of opioid per pill.23 The removal of this drug from other markets has driven people who inject drugs to use other opioids,24,25 and this shift is the probable explanation for the growing use of hydro­ morphone in our jurisdiction, Ontario.10 8

Despite their similarities, OxyContin and controlledrelease hydromorphone differ substantially in the ease with which they can be prepared for intravenous injection; whereas OxyContin’s waxes can be readily separated from the dissolved drug with gentle heating,11 the polymercoated beads found in capsules of controlled-release hydromorphone are difficult to crush, and attempts at dissolution result in an inhomogeneous slurry that is difficult to inject and leaves residual drug in the injection equipment.11,12,26 In a previous study,27 we showed that

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Immediate-release hydromorphone analysis (matched cohort) Other opioids (n=1989)

Immediate-release hydromorphone (n=1989)

Standardised difference (%)

Controlled-release hydromorphone analysis (matched cohort) Other opioids (n=1895)

Controlled-release hydromorphone (n=1895)

Standardised difference (%)

(Continued from previous page) Estimates of comorbidity Unique drugs used in past 12 months Mean (SD)

10·6 (5·6)

10·9 (5·9)

5%

10·7 (5·6)

11·2 (6·0)

8%

Median (IQR)

10 (6–14)

10 (6–14)

··

10 (7–14)

10 (7–15)

··

Mean (SD)

2·2 (2·7)

2·6 (3·2)

14%

2·0 (2·9)

2·2 (3·3)

7%

Median (IQR)

2 (0–3)

2 (0–3)

··

1 (0–3)

1 (0–3)

··

Hospitalisations in past 12 months

Emergency room visits in past 12 months Mean (SD)

7·5 (10·2)

8·8 (11·4)

12%

7·1 (11·1)

7·3 (10·8)

1%

Median (IQR)

4 (2–9)

5 (3–11)

··

4 (2–8)

4 (2–9)

··

Family physician visits in past 12 months Mean (SD)

27·1 (25·0)

31·2 (27·8)

16%

26·6 (25·9)

26·9 (25·7)

Median (IQR)

19 (10–36)

22 (11–45)

··

18 (9–35)

19 (10–35)

··

469 (23·6%)

603 (30·3%)

15%

428 (22·6%)

618 (32·6%)

23%

Electrocardiograms, n (%)

1%

Standardised differences describe a difference between group means as a percentage of the pooled SD. Standardised differences greater than 10% represent meaningful imbalances. To avoid the risk of re-identification, cells with small values are reported as <6%.

Table 3: Baseline characteristics of the matched cohort of patients prescribed immediate-release or controlled-release hydromorphone compared with those prescribed other opioids

injection equipment used to prepare controlled-release hydromorphone retained 45% of the initial dose, which could be harvested by a further addition of water. This residual drug increases the risk of infective endocarditis in at least two ways. First, it promotes storage of injection equipment for future use, a practice that has been shown to be much more common with controlled-release than with immediate-release hydromorphone (93 [96%] of 97 vs 41 [49%] of 84 participants, p<0·001).13 This storage probably contributes to the high risk of S aureus contamination observed in equipment used to prepare controlled-release hydromorphone.14 Second, when the residual drug is injected, it is more likely to contain S aureus because, unlike oxycodone and immediaterelease hydromorphone, solutions of controlled-release hydromorphone enhance the survival of S aureus within injection equipment.14 Importantly, the increased risk of infective endocarditis is not likely to be related to hydromorphone per se, but rather to the polymer-coated beads that provide the controlled-release property and result in the residual drug and subsequent equipment reuse. Although the formulation of controlled-release hydromorphone we studied is not available in all countries that report increased risks of infective endocarditis, popular formulations of morphine, such as Kadian (Allergan Madison, NJ, USA), use similar controlledrelease mechanisms; further research is warranted to investigate the association of other controlled-release opioids with infective endo­ carditis related to injection drug use (appendix p 10).

Our study has several strengths. Ontario’s health administrative databases are large enough to allow us to study uncommon outcomes, such as infective endo­ carditis, and inclusive enough to capture data on marginalised populations, such as people who inject drugs. For many variables, including our exposure and outcome variables, these databases have been shown to be reliable in specific validation studies.17,19 Our hypothesis was based on clinical observations,12 previously identified associations,10 and in-vitro data,14 and our findings were consistent across two different estimates of hydro­ morphone exposure (population and patient levels). However, we also recognise our study’s limitations. As with all observational studies, we can only identify associations, not causal relationships, although in this subject area it would be difficult to conduct experimental studies. Although we used a validated administrative definition of people who inject drugs, the definition relied on admissions to hospital and probably under­ estimated the true number of people who inject drugs; however, among this population, the distributions of characteristics such as injection frequency and drugs used have been shown to be similar for people who are and are not admitted to hospital.28 Although we excluded people who had infective endocarditis within 30 days of a previous admission with infective endocarditis, some patients might have had infective endocarditis longer ago and would therefore not be truly incident cases. In our population-level analysis, we might have intro­ duced an ecological fallacy by using regional-level data on

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hydromorphone exposure to infer individual-level asso­ ciations. In addition, the regional distribution of opioid prescriptions was largely determined by pres­criptions for patients older than 65 years; despite the frequency with which prescription opioids are diverted for illicit use, this might not have been an accurate representation of the opioids available to our younger cohort. Moreover, drug exposure data at the level of the region could not differentiate between immediate-release and controlledrelease hydromor­phone. Our measurement of individual-level exposure also had limitations. Only half of our original cohort that met provincial criteria for prescription drug coverage had accessible prescription drug data. Because those criteria include comorbidities, the patients included in this analysis were less healthy than those who were not. This factor limits the generalisability of our findings to patients with a comorbidity profile similar to that shown in table 2. Furthermore, although prescription drug data in Ontario is highly accurate,16 it identifies only prescriptions that were filled, not whether or how drugs were taken. Although we took steps to minimise imbalances between groups, the influence of residual confounding could not be completely eliminated. If physicians had noticed an association between injection of controlledrelease hydro­morphone and infective endocarditis, they might have been more likely to investigate the patient for endocarditis. It is possible that the use of hydromorphone is a marker for other high-risk behaviours that predispose people to infective endocarditis. For example, people who inject hydromorphone are more likely than those who do not use hydromorphone to use heroin because of its pharm­ acodynamic similarities,29–31 and this could compound their risk of adverse outcomes, including infective endocarditis. Injection is a preferred route of administration for immediate-release hydromorphone,32 so our findings might have simply identified a risk of infective endocar­ ditis associated with this route of administration. However, this preference for injection does not appear to apply to the controlled-release formulation of hydro­morphone. People who inject drugs have been described as reluctant to undertake the slow process of dissolving controlled-release hydromorphone.12 Although we have postulated that the incomplete dissolution of controlled-release hydromorphone is the causal factor in its higher risk of contamination, some online resources warn against heating solutions of this drug because the resulting product will become too thick to inject.26 This preference to forego heating is an alternative explanation for the elevated contamination rates of controlled-release hydromorphone. Some opioids exert immunosuppressive effects, which could increase the risk of infective endocarditis, but hydromorphone does not exhibit this property.33,34 Despite the breadth of our administrative data, some variables were not available to us, including the frequency of injection or the use of 10

black-market opioids, which might have differed systematically between users and non-users of controlledrelease hydromorphone. The possibility that crushing controlled-release polymer beads leads to the injection of fragments that can damage heart valves and predispose people to infective endocarditis is an alternative explanation that we are currently studying. We also considered the possible influence of dose. Because opioid exposure has been associated with an increased infection risk in some studies, our findings could have been the result of the high potency of controlledrelease hydromorphone. However, this explanation is less plausible than our hypothesis for various reasons. First, prescriptions for common opioids in Ontario during the study period were, on average, dispensed at similar potency levels.35 Second, higher potency opioids are probably injected at a lower daily frequency than lower potency opioids are, which is associated with a reduced risk of infective endocarditis.36 Third, although the association between pneumonia and opioid exposure has been shown repeatedly and has a plausible cough-suppression mech­ anism,33,37,38 these studies have important limi­ tations,34,39 and evidence from the largest cohort study to examine the association between opioids and infection suggests that the association with infection is related to the immuno­ suppressive properties of some opioids (morphine, fentanyl, and methadone) and not to the prescribed dose.40 In conclusion, our study suggests that among people who inject drugs, exposure to controlled-release hydro­ morphone is associated with a significant increase in the risk of infective endocarditis. The possible association between other controlled-release opioid formulations and infective endocarditis should be the subject of further research. Contributors MS, SK, and MAW did the literature search. MS and SK were responsible for the concept of the study, and JS, RJ, AXG, and MAW were responsible for its design. Data were collected by JS; analysed by JS, RJ, and MAW; and interpreted by MS, AXG, and MAW. MS, RJ, SK, AXG, and MAW wrote the manuscript. Declaration of interests We declare no competing interests. Acknowledgments This study was supported by the Institute for Clinical Evaluative Sciences (ICES), Western site. ICES is funded by an annual grant from the Ontario Ministry of Health and Long-Term Care (MOHLTC). Core funding for ICES Western is provided by the Academic Medical Organization of Southwestern Ontario (AMOSO), the Schulich School of Medicine and Dentistry (SSMD), Western University, and the Lawson Health Research Institute (LHRI). The opinions, results, and conclusions are our own and are independent from the funding sources. No endorsement by ICES, AMOSO, SSMD, LHRI, or the MOHLTC is intended or should be inferred. Parts of this material are based on data or information compiled and provided by the Canadian Institute for Health Information (CIHI). However, the analyses, conclusions, opinions, and statements expressed in this Article are those of the authors, and not necessarily those of the CIHI. We also thank IMS Brogan for use of their Drug Information Database. AXG was supported by the Dr Adam Linton Chair in Kidney Health Analytics and a Clinician Investigator Award from the Canadian Institutes of Health Research.

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References 1 Belzak L, Halverson J. The opioid crisis in Canada: a national perspective. Health Promot Chronic Dis Prev Can 2018; 38: 224–33. 2 Scholl L, Seth P, Kariisa M, Wilson N, Baldwin G. Drug and opioid-involved overdose deaths—United States, 2013–2017. MMWR Morb Mortal Wkly Rep 2018; 67: 1419–27. 3 Asgeirsson H, Thalme A, Weiland O. Low mortality but increasing incidence of Staphylococcus aureus endocarditis in people who inject drugs: experience from a Swedish referral hospital. Medicine (Baltimore) 2016; 95: e5617. 4 Tung MKY, Light M, Giri R, et al. Evolving epidemiology of injecting drug use-associated infective endocarditis: a regional centre experience. Drug Alcohol Rev 2015; 34: 412–17. 5 Cooper HLF, Brady JE, Ciccarone D, Tempalski B, Gostnell K, Friedman SR. Nationwide increase in the number of hospitalizations for illicit injection drug use-related infective endocarditis. Clin Infect Dis 2007; 45: 1200–03. 6 Hartman L, Barnes E, Bachmann L, Schafer K, Lovato J, Files DC. Opiate injection-associated infective endocarditis in the southeastern United States. Am J Med Sci 2016; 352: 603–08. 7 Wurcel AG, Anderson JE, Chui KKH, et al. Increasing infectious endocarditis admissions among young people who inject drugs. Open Forum Infect Dis 2016; 3: ofw157. 8 Gray ME, Rogawski McQuade ET, Scheld WM, Dillingham RA. Rising rates of injection drug use associated infective endocarditis in Virginia with missed opportunities for addiction treatment referral: a retrospective cohort study. BMC Infect Dis 2018; 18: 532. 9 Schranz AJ, Fleischauer A, Chu VH, Wu L-T, Rosen DL. Trends in drug use-associated infective endocarditis and heart valve surgery, 2007 to 2017: a study of statewide discharge data. Ann Intern Med 2019; 170: 31–40. 10 Weir MA, Slater J, Jandoc R, Koivu S, Garg AX, Silverman M. The risk of infective endocarditis among people who inject drugs: a retrospective, population-based time series analysis. CMAJ 2019; 191: e93–99. 11 Cone EJ. Ephemeral profiles of prescription drug and formulation tampering: evolving pseudoscience on the internet. Drug Alcohol Depend 2006; 83 (suppl 1): S31–39. 12 Roy E, Arruda N, Bourgois P. The growing popularity of prescription opioid injection in downtown Montréal: new challenges for harm reduction. Subst Use Misuse 2011; 46: 1142–50. 13 Ball LJ, Puka K, Speechley M, et al. Sharing of injection drug preparation equipment is associated with HIV infection: a cross-sectional study. J Acquir Immune Defic Syndr 2019; 81: e99–103. 14 Kasper KJ, Manoharan I, Hallam B, et al. A controlled-release oral opioid supports S aureus survival in injection drug preparation equipment and may increase bacteremia and endocarditis risk. PLoS One 2019; 14: 1–12. 15 Nicholls SG, Quach P, von Elm E, et al. The reporting of studies conducted using observational routinely-collected health data (RECORD) statement: methods for arriving at consensus and developing reporting guidelines. PLoS One 2015; 10: e0125620. 16 Levy AR, O’Brien BJ, Sellors C, Grootendorst P, Willison D. Coding accuracy of administrative drug claims in the Ontario Drug Benefit database. Can J Clin Pharmacol 2003; 10: 67–71. 17 Ball LJ, Sherazi A, Laczko D, et al. Validation of an algorithm to identify infective endocarditis in people who inject drugs. Med Care 2018; 56: e70–75. 18 Hillmer M, Wong A, eds. Ontario Narcotics Atlas. Toronto: Ontario Public Drug Programs Division, 2016. 19 Tan C, Hansen M, Cohen G, Boyle K, Daneman N, Adhikari NKJ. Accuracy of administrative data for identification of patients with infective endocarditis. Int J Cardiol 2016; 224: 162–64. 20 Austin PC. Using the standardized difference to compare the prevalence of a binary variable between two groups in observational research. Commun Stat Simul Comput 2009; 38: 1228–34.

21 Mamdani M, Sykora K, Li P, et al. Reader’s guide to critical appraisal of cohort studies: 2. assessing potential for confounding. BMJ 2005; 330: 960–62. 22 Rodger L, Glockler-Lauf SD, Shojaei E, et al. Clinical characteristics and factors associated with mortality in first-episode infective endocarditis among persons who inject drugs. JAMA Netw Open 2018; 1: e185220. 23 Butler SF, Benoit C, Budman SH, et al. Development and validation of an Opioid Attractiveness Scale: a novel measure of the attractiveness of opioid products to potential abusers. Harm Reduct J 2006; 3: 5. 24 Larance B, Dobbins T, Peacock A, et al. The effect of a potentially tamper-resistant oxycodone formulation on opioid use and harm: main findings of the National Opioid Medications Abuse Deterrence (NOMAD) study. Lancet Psychiatry 2018; 5: 155–66. 25 Jauncey M, Livingston M, Salmon AM, Dietze P. The impact of OxyContin reformulation at the Sydney Medically Supervised Injecting Centre: pros and cons. Int J Drug Policy 2018; 53: 17–22. 26 Insite. Insite 2010 cookbook. 2010 (updated 2018). https://rnao.ca/ sites/rnao-ca/files/Insite_2010_2018_Cookbook.pdf (accessed March 29, 2019). 27 Ball LJ, Venner C, Tirona RG, et al. Heating injection drug preparation equipment used for opioid injection may reduce HIV transmission associated with sharing equipment. J Acquir Immune Defic Syndr 2019; 81: e127–34. 28 Palepu A, Tyndall MW, Leon H, et al. Hospital utilization and costs in a cohort of injection drug users. CMAJ 2001; 165: 415–20. 29 Smith MY, Haddox JD, Di Marino ME. Correlates of nonmedical use of hydromorphone and hydrocodone: results from a national household survey. J Pain Palliat Care Pharmacother 2007; 21: 5–17. 30 Dunn KE, Brands B, Marsh DC, Bigelow GE. Characterizing the subjective, observer-rated, and physiological effects of hydromorphone relative to heroin in a human laboratory study. Psychopharmacology (Berl) 2018; 235: 971–81. 31 Oviedo-Joekes E, Guh D, Brissette S, et al. Double-blind injectable hydromorphone versus diacetylmorphine for the treatment of opioid dependence: a pilot study. J Subst Abuse Treat 2010; 38: 408–11. 32 Young AM, Havens JR, Leukefeld CG. Route of administration for illicit prescription opioids: a comparison of rural and urban drug users. Harm Reduct J 2010; 7: 24. 33 Edelman EJ, Gordon KS, Crothers K, et al. Association of prescribed opioids with increased risk of community-acquired pneumonia among patients with and without HIV. JAMA Intern Med 2019; 179: 297–304. 34 Plein LM, Rittner HL. Opioids and the immune system—friend or foe. Br J Pharmacol 2018; 175: 2717–25. 35 Kahan M, Mailis-Gagnon A, Wilson L, Srivastava A. Canadian guideline for safe and effective use of opioids for chronic noncancer pain: clinical summary for family physicians, part 1: general population. Can Fam Physician 2011; 57: 1257–66. 36 Wilson LE, Thomas DL, Astemborski J, Freedman TL, Vlahov D. Prospective study of infective endocarditis among injection drug users. J Infect Dis 2002; 185: 1761–66. 37 Dublin S, Walker RL, Jackson ML, et al. Use of opioids or benzodiazepines and risk of pneumonia in older adults: a population-based case-control study. J Am Geriatr Soc 2011; 59: 1899–907. 38 Wiese AD, Griffin MR, Schaffner W, Stein CM, Grijalva CG. Opioid analgesic use and risk for invasive pneumococcal diseases. Ann Intern Med 2018; 168: 396–404. 39 Khosrow-Khavar F, Kurteva S, Cui Y, Filion KB, Douros A. Opioids and the risk of infection: a critical appraisal of the pharmacologic and clinical evidence. Expert Opin Drug Metab Toxicol 2019; 15: 565–75. 40 Wiese AD, Griffin MR, Schaffner W, et al. Long-acting opioid use and the risk of serious infections: a retrospective cohort study. Clin Infect Dis 2019; 68: 1862–69.

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