Journal of General Internal Medicine

, Volume 28, Issue 1, pp 82–90

Receipt of Opioid Analgesics by HIV-Infected and Uninfected Patients

Authors

    • Department of Internal MedicineYale University School of Medicine
    • Center for Interdisciplinary Research on AIDSYale University School of Public Health
  • Kirsha Gordon
    • VA Connecticut Healthcare System
  • William C. Becker
    • Department of Internal MedicineYale University School of Medicine
    • VA Connecticut Healthcare System
  • Joseph L. Goulet
    • VA Connecticut Healthcare System
    • Department of PsychiatryYale University School of Medicine
    • Pain Research, Informatics, Medical Comorbidities and Education (PRIME) CenterVA Connecticut Healthcare System
  • Melissa Skanderson
    • VA Connecticut Healthcare System
    • VA Pittsburgh Healthcare System
  • Julie R. Gaither
    • Yale University School of Public Health
  • Jennifer Brennan Braden
    • Department of PsychiatryUniversity of Washington
  • Adam J. Gordon
    • Department of MedicineUniversity of Pittsburgh School of Medicine
    • VA Pittsburgh Healthcare System
  • Robert D. Kerns
    • Department of PsychiatryYale University School of Medicine
    • Pain Research, Informatics, Medical Comorbidities and Education (PRIME) CenterVA Connecticut Healthcare System
    • Departments of Neurology and PsychologyYale University School of Medicine
  • Amy C. Justice
    • Department of Internal MedicineYale University School of Medicine
    • Center for Interdisciplinary Research on AIDSYale University School of Public Health
    • VA Connecticut Healthcare System
    • Pain Research, Informatics, Medical Comorbidities and Education (PRIME) CenterVA Connecticut Healthcare System
  • David A. Fiellin
    • Department of Internal MedicineYale University School of Medicine
    • Center for Interdisciplinary Research on AIDSYale University School of Public Health
Original Research

DOI: 10.1007/s11606-012-2189-z

Cite this article as:
Edelman, E.J., Gordon, K., Becker, W.C. et al. J GEN INTERN MED (2013) 28: 82. doi:10.1007/s11606-012-2189-z

ABSTRACT

BACKGROUND

Opioids are increasingly prescribed, but there are limited data on opioid receipt by HIV status.

OBJECTIVES

To describe patterns of opioid receipt by HIV status and the relationship between HIV status and receiving any, high-dose, and long-term opioids.

DESIGN

Cross-sectional analysis of the Veterans Aging Cohort Study.

PARTICIPANTS

HIV-infected (HIV+) patients receiving Veterans Health Administration care, and uninfected matched controls.

MAIN MEASURES

Pain-related diagnoses were determined using ICD-9 codes. Any opioid receipt was defined as at least one opioid prescription; high-dose was defined as an average daily dose ≥120 mg of morphine equivalents; long-term opioids was defined as ≥90 consecutive days, allowing a 30 day refill gap. Multivariable models were used to assess the relationship between HIV infection and the three outcomes.

KEY RESULTS

Among the HIV+ (n = 23,651) and uninfected (n = 55,097) patients, 31 % of HIV+ and 28 % of uninfected (p < 0.001) received opioids. Among patients receiving opioids, HIV+ patients were more likely to have an acute pain diagnosis (7 % vs. 4 %), but less likely to have a chronic pain diagnosis (53 % vs. 69 %). HIV+ patients received a higher mean daily morphine equivalent dose than uninfected patients (41 mg vs. 37 mg, p = 0.001) and were more likely to receive high-dose opioids (6 % vs. 5 %, p < 0.001). HIV+ patients received fewer days of opioids than uninfected patients (median 44 vs. 60, p < 0.001), and were less likely to receive long-term opioids (31 % vs. 34 %, p < 0.001). In multivariable analysis, HIV+ status was associated with receipt of any opioids (AOR 1.40, 95 % CI 1.35, 1.46) and high-dose opioids (AOR 1.22, 95 % CI 1.07, 1.39), but not long-term opioids (AOR 0.94, 95 % CI 0.88, 1.01).

CONCLUSIONS

Patients with HIV infection are more likely to be prescribed opioids than uninfected individuals, and there is a variable association with pain diagnoses. Efforts to standardize approaches to pain management may be warranted in this highly complex and vulnerable patient population.

KEY WORDS

opioidpainHIVnarcoticsveterans

INTRODUCTION

In the United States, there has been a dramatic increase in the use of prescription opioids for treating pain.16 This has occurred despite limited evidence demonstrating opioid efficacy for chronic, non-cancer pain7,8 and evidence of their risks, including side effects,3 and potential for abuse and dependence.9,10 Guidelines recommend careful consideration of risks and benefits of treatment when initiating opioids for pain, especially for populations with a history of substance use disorders.11

Prescribing opioid analgesics to HIV-infected patients, who have both a higher underlying prevalence of substance use disorders and pain conditions, is complicated.1215 The benefits of opioids for treating painful conditions, such as neuropathy and osteonecrosis, which may occur more commonly among HIV-infected patients, must be balanced against16,17 the possibility of increased risk for harm. Opioids may impact immune function,18 be associated with toxicities1921 and interact with antiretrovirals.22

There are limited recent data examining factors associated with opioid prescribing to HIV-infected patients. Existing studies focus on long-term therapy;12,14 lack an uninfected comparison group;7,14,15 and do not consider opioid indication.12,14 Furthermore, some of these studies were conducted within a single site,7,15 which may limit their generalizability.23 Therefore, we conducted a cross-sectional study to: 1) describe opioid analgesic receipt in a large sample of HIV-infected and uninfected patients; 2) determine whether opioid receipt varies by HIV status; and 3) identify factors associated with receipt of any opioid analgesics, high-dose opioids and long-term opioids.

METHODS

Study Overview

We used data from fiscal year (FY) 2006 in the Veterans Aging Cohort Study—Virtual Cohort (VACS-VC), described elsewhere.2427 Briefly, the VACS-VC is a cohort of HIV-infected patients and uninfected patients matched on age, sex, race/ethnicity and site of care identified from the United States Veterans Health Administration (VHA) administrative data. Data for this cohort include information from the Clinical Case Registry, which is the VHA HIV registry, and the Decision Support System.28,29 The study was approved by the Human Investigations Committee at Yale University and the VA Connecticut Healthcare System; it was granted a waiver for informed consent and is HIPAA compliant.

Study Population

VACS-VC has 40,594 HIV-infected and 81,188 matched uninfected patients with available clinical data in fiscal year (FY) 2006, October 1, 2005 through September 30, 2006. Patients were excluded if they met any of the following criteria: 1) ambiguous HIV status; 2) cancer diagnosis, excluding non-epithelial skin cancers; 3) no inpatient or outpatient visit in FY2006, suggesting not currently receiving care from the VHA system; or 4) unclear opioid pharmacy data.

Prescription Opioid Types

We determined receipt of all oral and transdermal opioids, including codeine, hydrocodone, oxycodone, oxycodone sustained action (SA), morphine, morphine SA, fentanyl, hydromorphone, and methadone. We collapsed the following opioids into a single low potency opioid category: dihydrocodeine, meperidine, pentazocine, propoxyphene, levorphanol, tramadol, and tapentadol. Non-formulary medications, including hydrocodone SA; hydromorphone SA; oxymorphone; oxymorphone SA; and codeine SA, were not dispensed. Medications for the treatment of opioid dependence (methadone via opioid treatment programs and buprenorphine) were excluded. Opioids were included in the analysis, regardless of the provider characteristics (e.g. primary care vs. specialist), formulation or indication.

Prescription Opioid Use Profiles

Days of opioid receipt were calculated based on prescription information, assuming the prescription was taken as directed. Total morphine equivalents were calculated by multiplying the quantity of each prescription by the strength of the prescription (milligram of opioid per unit dispensed). Standard conversion factors were used to estimate the number of milligrams of morphine equivalents dispensed.30 To determine the milligrams of morphine equivalents of special formulations, including cough elixirs, transdermal fentanyl, and solutions, we relied on existing literature and then reached consensus, as described in Appendix 1.

Average daily morphine equivalent dose was calculated by dividing total milligrams of morphine equivalents by days supplied. Patients were considered to have received any opioid therapy if they had received at least one prescription for any outpatient opioid in FY2006. Patients may have started opioids before FY2006 or continued them after FY2006; we captured here only days supplied during FY2006. For prescriptions that spanned FY2006, only dates during FY2006 were included. High-dose opioid therapy was defined as an average daily dose of at least 120 mg of morphine equivalents. This threshold is consistent with an existing clinical guideline,31 evidence for increased risk of death32 and opioid abuse and dependence,10 and decreased likelihood of long-term opioid therapy discontinuation in this range.33 Long-term opioid therapy was defined as 90 consecutive days of opioid, allowing for a 30-day refill window.30 Opioids were categorized into short-acting Schedule II; long-acting Schedule II; and non-schedule II opioids, according to Drug Enforcement Administration classifications.34

Covariates

Socio-demographic variables included gender, race/ethnicity, age and urbanicity based on site of care using rural-urban commuting area codes.35 Clinical variables, based on ICD-9 codes, included alcohol and drug use disorders, including opioid use disorder; mental illness, including major depression, bipolar disorder, post-traumatic stress disorder, and schizophrenia; and pain-related diagnosis. Hepatitis C Virus (HCV) status was based on ICD-9 codes and laboratory data. We categorized pain-related diagnoses as acute pain-related diagnosis if the patient had abdominal pain, chest pain, fracture, or kidney stones; and chronic pain-related if the patient had back pain, extremity pain, headache, menstrual pain, neck pain, neuropathy, osteoarthritis, other pain, rheumatoid arthritis, or temporomandibular pain. Patients were considered to have a particular diagnosis if they had at least one inpatient or two outpatient codes in FY2006. As a marker of disease severity, we included number of days hospitalized within the VHA and proportion who died during FY2006. In the models restricted to HIV-infected patients only, we included average CD4 count and HIV-1 RNA viral load during FY2006 and combination antiretroviral therapy (cART) use (defined as any three antiretroviral agents).36,37 CD4 count and HIV-1 RNA viral load were not normally distributed, and were therefore transformed to square root CD4 count and log10 HIV-1 RNA.

Data Quality

To verify the validity of pharmacy data in extreme outliers, we completed a chart review of the 20 patients with the highest morphine equivalent dose. This review verified the accuracy of the prescription information. In addition, we performed a chart review of 15 HIV-infected patients who received opioids in FY2006, but lacked a documented pain diagnosis. This confirmed that none of the ICD-9 pain codes diagnoses had been recorded within FY2006. Also, we dropped records with presumed data entry errors (e.g. six records for tramadol with negative quantities).

Statistical Analyses

Descriptive statistics were performed. We calculated the proportion of patients who received different opioids and descriptive statistics of opioid use profiles. We used t-test for continuous variables, or a nonparametric counterpart for non-normally distributed continuous variables, and chi-square for categorical variables to compare characteristics by HIV status, considering p < 0.05 as statistically significant. Multivariable logistic regression models were constructed to assess the relationship between HIV status and receiving any opioids, high-dose opioids and long-term opioids for all patients with complete data. All models were run unadjusted and then adjusted for patient characteristics (gender, age, race/ethnicity, HCV status, pain-related diagnosis, mental illness, substance use disorder) and urbanicity. The models for HIV-infected patients additionally adjusted for HIV specific variables. As we anticipated an effect modification on the association between HIV status and opioid analgesic receipt by race/ethnicity, we tested for an interaction between race/ethnicity and HIV status, which was significant (p = 0.0003). Therefore, we performed stratified analyses by HIV status and present the combined and stratified models, with standardized odds ratios. Statistical analyses were performed using SAS version 9.1.3 (SAS Institute Inc., North Carolina).

RESULTS

Patient Characteristics (Table 1)

Table 1

Patient Characteristics by Receipt of Any Opioids and HIV Status, n = 78,748

Characteristic

Total (n = 78,748)

No Opioids Received (n = 56,206)

Opioids Received (n = 22,542)

p value*

HIV− (n = 39,863)

HIV+ (n = 16,343)

HIV− (n = 15,234)

HIV+ (n = 7,308)

Gender, n(%)

     

0.0001

 Male

76,671 (97)

38,835 (97)

15,943 (98)

14,836 (97)

7,057 (96)

 

 Female

2,077 (3)

1,028 (2)

400 (2)

398 (3)

251 (3)

 

Race/Ethnicity, n(%)

     

<0.001

 White

30,883 (39)

14,957 (38)

6,077 (37)

6,815 (45)

3,034 (42)

 

 Black

37,627 (48)

19,377 (49)

8,024 (49)

6,731 (44)

3,495 (48)

 

 Hispanic

6,017 (8)

3,110 (8)

1,176 (7)

1,217 (8)

514 (7)

 

 Other

4,221 (5)

2,419 (6)

1,066 (6)

471 (3)

265 (4)

 

Age mean (SD)

46 (10)

47 (10)

46 (10)

46 (9)

45 (9)

<0.001

Urban, n(%)

65,541 (86)

32,428 (84)

14,635 (92)

12,002 (82)

6,476 (91)

<0.001

Days Hospitalized, median (IQR)

6 (3, 15)

6 (3, 14)

7 (3, 15)

6 (3, 14)

7 (3, 15)

0.01

Died, n(%)

4,251 (5)

1,315 (3)

1,377 (8)

804 (5)

755 (10)

<0.001

HCV, n(%)

16,719 (21)

5,527 (14)

5,302 (32)

2,910 (19)

2,980 (41)

<0.001

Pain-Related Diagnosis, n(%)

     

<0.001

 No Pain Diagnosis

46,054 (58)

26,478 (66)

12,712 (78)

3,992 (26)

2,872 (39)

 

 Acute Pain

3,438 (4)

1,570 (4)

654 (4)

668 (4)

546 (7)

 

 Chronic Pain

29,256 (37)

11,815 (30)

2,977 (18)

10,574 (69)

3,890 (53)

 

Serious Mental Illness, n(%)

15,973 (20)

8,154 (20)

2,177 (13)

4,081 (27)

1,561 (21)

<0.001

 Major Depression

4,222 (5)

1,632 (4)

797 (5)

1,157 (8)

636 (9)

<0.001

 PTSD

7,175 (9)

3,473 (9)

768 (5)

2,312 (15)

622 (8)

<0.001

 Bipolar Disorder

3,289 (4)

1,580 (4)

539 (3)

788 (5)

382 (5)

<0.001

Schizophrenia

4,011 (5)

2,674 (7)

473 (3)

646 (4)

218 (3)

<0.001

Alcohol Use Disorder, n(%)

8,078 (10)

3,847 (10)

1,574 (10)

1,706 (11)

951 (13)

<0.001

Drug Use Disorder, n(%)

8,413 (11)

3,504 (9)

2,057 (12)

1,585 (10)

1,267 (17)

<0.001

 Opioid Use Disorder, n(%)

2,163 (3)

783 (2)

595 (4)

402 (3)

383 (5)

<0.001

cART 2006, n(%)‡,§

15,648 (66)

n/a

10,634 (65)

n/a

5,014 (69)

<0.001

 cART if CD4 ≤200

7,414 (55)

n/a

5,099 (54)

n/a

2,315 (58)

<0.001

 cART if CD4 >200

8,234 (80)

n/a

5,535 (80)

n/a

2,699 (81)

0.48

HIV-1 RNA, Log 10 Viral Load, median, IQR§

3 (2, 4)

n/a

3 (2, 4)

n/a

3(2, 4)

<0.001

CD4 count cells/μL median, IQR§

398 (216, 594)

n/a

403 (218, 598)

n/a

386 (213, 589)

0.06

*Chi-square test (χ²) or Kruskal-Wallis test were used to compare overall differences across the four groups

Serious Mental Illness includes major depression, post-traumatic stress disorder (PTSD), bipolar disorder and schizophrenia

cART is combination antiretroviral therapy

§Calculated for HIV-infected patients only

We excluded patients with an ambiguous HIV status (n = 37); cancer diagnosis other than non-epithelial skin cancers (n = 4,014); no inpatient or outpatient visit in FY2006 (n = 38,979); and unclear opioid pharmacy data (n = 4). Our final analytic sample included 78,748 patients, 30 % of whom were HIV-infected and 29 % of who had received at least one opioid prescription. Overall, our analytic sample was a racially/ethnically diverse sample (39 % white, 48 % black, 8 % Hispanic) of male patients (97 %) with a mean age of 46 years, who received care in urban settings (86 %). Among those hospitalized, the mean length of hospitalization was 6 days, and 5 % died during FY2006. These patients had a high prevalence of comorbid disease. An acute pain-related diagnosis was recorded in 4 % and chronic pain-related diagnosis in 37 %. Among patients receiving opioids, 5 % received high-dose opioids and 33 % received long-term opioids.

Prescription Opioid Receipt Profiles (Table 2)

Table 2

Patterns of Opioid Analgesic Receipt Among Those Receiving Any Opioids

Characteristic

Total (n = 22,542)

HIV− (n = 15,234)

HIV+ (n = 7,308 )

p value

Average Daily Dose (mg morphine eq)

 IQR

20 (14, 36)

21 (14, 37)

20 (14, 36)

0.02

 Mean (SD)

38 (70)

37 (66)

41 (76)

0.001

High Dose Opioids, n (%)

1,190 (5)

742 (5)

448 (6)

<0.001

Days Prescribed

 IQR

55 (15, 204)

60 (17, 212)

44 (14, 189)

<0.001

 Mean (SD)

114 (122)

118 (123)

108 (121)

<0.001

Long-term Opioid Therapy, n (%)

7,545 (33)

5,253 (34)

2,292 (31)

<0.001

Non-schedule II Short-Acting, n (%)

18,399 (82)

12,652 (83)

5,747 (80)

<0.001

Schedule II Long-Acting, n (%)

2,998 (13)

1,976 (13)

1,022 (14)

0.04

Schedule II Short-Acting, n (%)

5,919 (26)

3,727 (24)

2,192 (30)

<0.001

*Interquartile range (IQR) = median (25th, 75th) quartiles; standard deviation - SD

Among patients who received opioids, the mean average daily dose was higher in HIV-infected compared to uninfected patients (41 mg vs. 37 mg, p = 0.001). HIV-infected patients were more likely to receive high-dose opioid therapy than uninfected patients (6 % vs. 5 %; p < 0.001). In contrast, HIV-infected patients received fewer days of opioids, IQR 44 (14, 189) compared to uninfected patients, IQR 60 (17, 212) (p < 0.001) and were less likely to receive long-term opioid therapy (31 % vs. 34 %; p < 0.001). Non-schedule II short-acting opioids were the most common opioid type received by both HIV-infected and uninfected patients (80 % vs. 83 %; p < 0.001). Among all received opioids, hydrocodone (39 %), codeine (26 %) and oxycodone (23 %) were the three most commonly received opioids. The patterns were similar by HIV status (data not shown). Low potency opioids were received by 25 % HIV-infected patients and 34 % uninfected patients (p < 0.001).

Factors Associated with Receiving Any Opioids

Compared to patients who did not receive opioids, both HIV-infected and uninfected patients who received opioids had a higher prevalence of comorbid disease across all measures (p < 0.001), including HCV, alcohol and drug abuse/dependence, major depression, PTSD, and both acute and chronic pain-related diagnoses (Table 1). Among those who received opioids, HIV-infected patients, relative to uninfected patients, were more likely to have major depression (9 % vs. 8 %), alcohol (13 % vs. 11 %) and drug (17 % vs. 10 %) abuse/dependence, but were less likely to have PTSD (8 % vs. 15 %). HIV-infected patients were also more likely to have an acute pain-related diagnosis (7 % vs. 4 %), but less likely to have a chronic pain-related diagnosis (53 % vs. 69 %).

HIV status was associated with receipt of any opioids in both unadjusted and adjusted analyses (Table 3). HCV, acute and chronic pain-related diagnoses, PTSD, and major depression were also significantly associated with receiving any opioids. These results were similar by HIV status in terms of strength and direction of the association.
Table 3

Unadjusted and Adjusted Odds Ratios for Receipt of Any Opioids, Stratified by HIV Serostatus

Characteristic

All, Unadjusted

All, Adjusted§

HIV−§

HIV+§

Odds Ratios (95 % CI)

Odds Ratios (95 % CI)

Odds Ratios (95 % CI)

Odds Ratios (95 % CI)

HIV-infected

1.17 (1.13, 1.21)

1.40 (1.35, 1.46)

n/a

n/a

Male Sex

 

1.08 (0.98, 1.20)

1.22 (1.07, 1.39)

0.74 (0.57, 0.95)

Age (10 year increments)

 

0.86 (0.85, 0.88)

0.84 (0.83, 0.86)

0.94 (0.90, 0.99)

Race

 White

 

ref

ref

ref

 Black

 

0.75 (0.72, 0.77)

0.71 (0.68, 0.75)

0.80 (0.73, 0.87)

 Hispanic

 

0.83 (0.77, 0.90)

0.84 (0.77, 0.92)

0.81 (0.68, 0.96)

 Other

 

0.54 (0.50, 0.59)

0.53 (0.47, 0.59)

0.65 (0.53, 0.80)

Urban

 

0.86 (0.82, 0.91)

0.87 (0.82, 0.92)

0.85 (0.73, 0.98)

HCV

 

1.50 (1.43, 1.56)

1.54 (1.45, 1.63)

1.50 (1.37, 1.64)

Pain-Related Diagnosis

 No Pain

 

ref

ref

ref

 Acute Pain

 

3.10 (2.87, 3.35)

2.81 (2.54, 3.10)

3.54 (3.00, 4.17)

 Chronic Pain

 

5.80 (5.60, 6.03)

5.86 (5.61, 6.13)

5.40 (4.95, 5.90)

Serious Mental Illness

 PTSD

 

1.27 (1.20, 1.35)

1.31 (1.23, 1.39)

1.26 (1.05, 1.51)

 Bipolar Disorder

 

1.04 (0.96, 1.13)

1.02 (0.92, 1.13)

1.14 (0.93, 1.40)

 Major Depression

 

1.28 (1.19, 1.37)

1.29 (1.18, 1.41)

1.19 (1.01, 1.39)

 Schizophrenia

 

0.62 (0.57, 0.68)

0.60 (0.54, 0.66)

0.87 (0.66, 1.14)

Alcohol Use Disorder

 

0.88 (0.82, 0.93)

0.86 (0.79, 0.93)

0.85 (0.72, 1.00)

Drug Use Disorder

 

0.97 (0.91, 1.04)

0.95 (0.87, 1.03)

1.06 (0.92, 1.23)

cART Treatment*

   

1.16 (1.03, 1.29)

HIV-1 RNA

   

1.10 (1.06, 1.14)

CD4 count‡†

   

1.00 (1.00, 1.01)

*cART is combination antiretroviral therapy Log 10 HIV-1 RNA viral load Square root CD4 count

§Variables included in the model are all those variables for which there are listed odds ratios and corresponding confidence intervals

In the stratified analysis, among HIV-infected patients, cART treatment and HIV-1 RNA were associated with receipt of any opioids, while CD4 count was not.

The associations with gender varied by HIV status: among HIV-infected patients, male gender was associated with not receiving any opioids, while among uninfected patients, male gender was associated with receipt of opioids.

Factors Associated with Receipt of High-Dose Opioids

HIV status was associated with receipt of high-dose opioids in both unadjusted and adjusted analyses (see Table 4). HCV and chronic pain-related diagnoses were associated with receipt of high-dose opioids among all patients.
Table 4

Unadjusted and Adjusted Odds Ratios for High-Dose Opioids, Stratified by HIV Serostatus, Among Patients Receiving Opioids

Characteristic

All, Unadjusted

All, Adjusted§

HIV−§

HIV+§

Odds Ratios (95 % CI)

Odds Ratios (95 % CI)

Odds Ratios (95 % CI)

Odds Ratios (95 % CI)

HIV-infected

1.28 (1.13, 1.44)

1.22 (1.07, 1.39)

n/a

n/a

Male Sex

 

1.15 (0.78, 1.69)

0.80 (0.51, 1.25)

2.68 (0.83, 8.63)

Age (10 year increments)

 

0.95 (0.88, 1.02)

0.93 (0.85, 1.02)

0.92 (0.77, 1.08)

Race/Ethnicity

 White

 

Ref

ref

ref

 Black

 

0.36 (0.31, 0.41)

0.34 (0.28, 0.40)

0.34 (0.25, 0.47)

 Hispanic

 

0.50 (0.38, 0.67)

0.39 (0.27, 0.58)

0.58 (0.33, 0.99)

 Other

 

0.33 (0.21, 0.53)

0.29 (0.15, 0.54)

0.24 (0.08, 0.77)

Urban

 

1.10 (0.93, 1.31)

1.15 (0.95, 1.40)

1.12 (0.70, 1.79)

HCV

 

2.09 (1.83, 2.38)

2.03 (1.70, 2.42)

2.21 (1.66, 2.94)

Pain-Related Diagnosis

 No Pain

 

Ref

ref

ref

 Acute Pain

 

0.73 (0.50, 1.05)

0.68 (0.38, 1.19)

0.64 (0.30, 1.36)

 Chronic Pain

 

1.88 (1.62, 2.17)

1.92 (1.57, 2.35)

2.21 (1.62, 3.02)

Serious Mental Illness

 PTSD

 

1.21 (1.03, 1.44)

1.25 (1.03, 1.52)

1.01 (0.62, 1.65)

 Bipolar Disorder

 

0.94 (0.72, 1.22)

0.97 (0.70, 1.35)

0.93 (0.53, 1.65)

 Major Depression

 

1.12 (0.91, 1.37)

1.34 (1.04, 1.72)

1.11 (0.72, 1.73)

 Schizophrenia

 

0.37 (0.22, 0.61)

0.32 (0.16, 0.62)

0.67 (0.24, 1.88)

Alcohol Use Disorder

 

0.48 (0.38, 0.60)

0.45 (0.33, 0.62)

0.67 (0.41, 1.11)

Drug Use Disorder

 

1.53 (1.25, 1.86)

1.44 (1.09, 1.90)

1.47 (0.97, 2.22)

cART Treatment*

   

1.71 (1.09, 2.67)

HIV-1 RNA

   

0.95 (0.83, 1.08)

CD4 count

   

1.01 (0.99, 1.03)

*cART is combination antiretroviral therapy Log 10 HIV-1 RNA viral load Square root CD4 count

§Variables included in the model are all those variables for which there are listed odds ratios and corresponding confidence intervals

Table 5

Unadjusted and Adjusted Odds Ratios for Long-Term Opioid Therapy, Stratified by HIV Serostatus, Among Patients Receiving Opioids

Characteristic

All, Unadjusted

All, Adjusted§

HIV−§

HIV+§

Odds Ratios (95 % CI)

Odds Ratios (95 % CI)

Odds Ratios (95 % CI)

Odds Ratios (95 % CI)

HIV-infected

0.87 (0.82, 0.92)

0.94 (0.88, 1.01)

n/a

n/a

Male Sex

 

1.33 (1.11, 1.59)

1.31 (1.05, 1.65)

1.58 (1.04, 2.41)

Age (10 year increments)

 

0.99 (0.96, 1.02)

0.94 (0.91, 0.98)

1.11 (1.02, 1.20)

Race

 White

 

Ref

ref

ref

 Black

 

0.54 (0.51, 0.57)

0.51 (0.47, 0.55)

0.67 (0.58, 0.78)

 Hispanic

 

0.53 (0.47, 0.61)

0.52 (0.44, 0.61)

0.54 (0.40, 0.72)

 Other

 

0.60 (0.50, 0.71)

0.52 (0.42, 0.65)

0.85 (0.59, 1.22)

Urban

 

0.71 (0.66, 0.77)

0.72 (0.66, 0.78)

0.67 (0.53, 0.83)

HCV

 

1.54 (1.43, 1.65)

1.43 (1.31, 1.57)

1.70 (1.48, 1.97)

Pain-Related Diagnosis

 No Pain

 

Ref

ref

ref

 Acute Pain

 

0.66 (0.56, 0.77)

0.62 (0.50, 0.77)

0.67 (0.50, 0.91)

 Chronic Pain

 

1.73 (1.62, 1.85)

1.70 (1.56, 1.84)

1.69 (1.46, 1.96)

Serious Mental Illness

 PTSD

 

1.28 (1.17, 1.39)

1.31 (1.18, 1.44)

0.88 (0.68, 1.14)

 Bipolar Disorder

 

1.01 (0.89, 1.16)

0.99 (0.84, 1.16)

1.18 (0.87, 1.60)

Major Depression

 

1.24 (1.11, 1.38)

1.36 (1.19, 1.55)

1.02 (0.81, 1.30)

 Schizophrenia

 

0.77 (0.66, 0.91)

0.82 (0.68, 0.99)

0.84 (0.54, 1.32)

Alcohol Use Disorder

 

0.79 (0.71, 0.88)

0.81 (0.71, 0.92)

0.88 (0.68, 1.13)

Drug Use Disorder

 

0.89 (0.80, 0.99)

0.89 (0.77, 1.02)

1.05 (0.84, 1.31)

cART Treatment*

   

1.37 (1.12, 1.67)

HIV-1 RNA

   

0.91 (0.86, 0.98)

CD4 count

   

1.01 (1.00, 1.02)

*cART is combination antiretroviral therapy Log 10 HIV-1 RNA viral load Square root CD4 count

§Variables included in the model are all those variables for which there are listed odds ratios and corresponding confidence intervals

In the stratified analysis, among HIV-infected patients, cART treatment was associated with receiving high dose opioids. Among uninfected patients only, PTSD, major depression and drug use disorder were associated with receipt of high-dose opioids. These factors were not associated with receipt of high-dose opioids among HIV-infected patients.

Factors Associated with Receipt of Long-Term Opioids

HIV status was associated with lower odds of receiving long-term opioids in the unadjusted model, which was marginally significant in the adjusted model (Table 5). Male gender, rural location, HCV and chronic pain-related diagnoses were associated with receiving long-term opioids among all patients.

Among HIV-infected patients, age, and cART treatment were also associated with receipt of long-term opioids. Among uninfected patients, PTSD and major depression were also associated with receiving long-term opioids.

DISCUSSION

We found that HIV-infected and uninfected patients commonly received opioids. Consistent with previous literature, patients receiving opioids had a greater prevalence of HCV, major depression, alcohol and drug use disorders and pain-related diagnoses.4,7,12,3841 Unique to our cohort, we demonstrated that these conditions were more common among HIV-infected patients who were prescribed opioids in comparison to their age/sex/race/ethnicity/site-matched controls. Generally, non-schedule II short-acting medications were the most common types of opioids received. After adjustment for established factors known to be associated with opioid analgesic receipt,4,5,23,40 HIV-infected patients were 40 % more likely to receive any opioids than uninfected patients. These associations were less pronounced for high-dose and absent for long-term opioid receipt. Pain-related diagnoses, HCV status and race/ethnicity were also important determinants of opioid receipt among both HIV-infected and uninfected patients. To our knowledge, this is the first study to examine patterns of opioid receipt among HIV-infected patients and matched controls in a Veteran sample.

We believe that the overwhelming majority of opioids were received for pain management, although a small number of prescriptions (e.g. codeine) may have been for cough. That a substantial proportion of both HIV-infected patients and uninfected patients lacked a documented pain-related diagnosis, may in part be explained by the limitations of administrative data,42 but the difference by HIV status was unexpected. The ICD-9 codes we included may have missed some pain syndromes which more commonly affect HIV-infected patients, such as herpes zoster or avascular necrosis. Coding practices among Infectious Disease vs. General Medicine providers may differ. These data are consistent with a prior study, which found that only 57 % of HIV-infected patients initiating opioid treatment had a documented indication for opioids.7 Our finding that HIV-infected patients were more likely to receive any and high-dose opioids, and less likely to receive long-term opioids are unlikely to be explained by differences in pain-related diagnoses alone. In fact, recent data from our group found that HIV-infected patients were less likely to report moderate or severe pain than uninfected patients (34 % vs. 49 %, p < 0.05). These data suggest that variation among primary care providers and specialists in their approach to pain management may exist.

The increased prevalence of substance use disorders (and to a lesser extent major depression) among HIV-infected patients receiving opioids in our sample is of potential concern, as these characteristics are associated with opioid analgesic misuse.9,10,4345 These findings contrast with data demonstrating that in patients with HCV, opioid analgesic misuse was not more common among patients with substance use disorders.38 Similar to prior studies, we found that being HCV-infected was associated with opioid analgesic receipt.38 Whether opioid receipt and outcomes differ among HIV-monoinfected and HIV/HCV-coinfected patients remains to be determined.

The prevalence of any opioid receipt and long-term receipt among HIV-infected patients deserves consideration. While HIV-infected patients frequently experience pain and are in need of effective treatment,13,17,46 data suggest there may be potential harm from opioids. For example, data indicate that compared to other analgesics, opioids are associated with increased cardiovascular events and fractures19,21 both of which occur more commonly among HIV-infected patients.26,47 Similarly, some evidence suggests that opioids may adversely impact the immune system.18 Our univariate analysis demonstrated a trend towards lower CD4 cell counts among patients on opioids, a finding that may be confounded by indication and did not persist in multivariable analyses. Prior studies are inconsistent in their findings about the association between receipt of opioids and HIV biomarkers.7,12,14 Whether in vitro effects of opioids on the immune system translate into clinical phenomena warrants further investigation.18 The potential for medication interactions with antiretroviral agents also requires consideration when considering some opioid analgesics, particularly methadone.22 In our sample, almost 5 % of the HIV-infected patients received methadone (data not otherwise shown).

The differences by race/ethnicity and receipt of any opioids, high-dose opioids and long-term opioid therapy are notable. The odds of receiving an opioid prescription was 20 % less in black patients compared with white patients. This difference by race/ethnicity is consistent with trends in existing literature in both general populations5,48 and HIV-infected patients in particular.12,14 Whether this reflects a difference in disease manifestations, patient experience of pain and preferences, indications for opioids for pain management, patient-provider trust,49 or a health disparity48 cannot be ascertained from these data, but deserves further investigation. HIV status, however, seems to mitigate differences observed by race/ethnicity, as the odds ratios were generally less pronounced among HIV-infected patients compared to uninfected patients.

Our study has some limitations. First, we were unable to capture pain severity and indication for opioids directly; instead, we relied upon ICD-9 codes. Second, ICD-9 codes may lead to under-reporting of conditions, which we used to identify pain-related diagnoses and comorbidities.42 Third, as our data were restricted to opioids obtained from a VHA pharmacy, we were unable to assess opioid analgesics received from outside sources. The VHA, however, offers generous pharmacy benefits28 and almost all HIV-infected patients receive their antiretroviral agents through the VHA.50 We did not assess provider types that prescribed the opioid medications. Finally, these findings may not be generalizable to female patients or to patients receiving healthcare in rural settings or outside the VHA.

In conclusion, opioid analgesics are commonly received by both HIV-infected and uninfected patients who have a high prevalence of comorbid diseases. Differences in documented pain-related diagnoses and opioid-receipt by HIV-infected and uninfected patients exist. HIV status remained an important determinant of opioid receipt even after adjusting for pain-related diagnoses, HCV status and race/ethnicity. Future work should be aimed at understanding these differences in opioid receipt by HIV status, by examining patient and provider-level factors that may contribute to this variation. In addition, work evaluating the associations between opioid analgesic receipt and important health outcomes among HIV-infected patients receiving opioids, such as the risk of developing opioid use disorders, infection, fracture and hypogonadism, is needed. Finally, given the prevalence and variations in opioid prescribing observed, future research should investigate the role of interventions to standardize documentation of pain diagnoses, dosing and duration of opioids across different clinical settings.

Acknowledgements

This work was generously supported by the Society of General Internal Medicine’s Lawrence Linn Award, the Robert Wood Johnson Foundation Clinical Scholars Program, the Department of Veterans Affairs and the Veterans Aging Cohort study, funded by the National Institute on Alcohol Abuse and Alcoholism (U10 AA 13566).

This work was presented as oral presentations in earlier versions at the Veterans Aging Cohort Study Scientific Meeting, October 13th, 2011, Washington, D.C. and the Society of General Internal Medicine 35th National Annual Meeting, May 12th, 2012, Orlando, FL.

Disclosures

The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs.

Conflict of Interest

The authors declare that they do not have a conflict of interest.

Copyright information

© Society of General Internal Medicine 2012