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J Gen Intern Med. 2013 January; 28(1): 82–90.
Published online 2012 August 16. doi:  10.1007/s11606-012-2189-z
PMCID: PMC3539026

Receipt of Opioid Analgesics by HIV-Infected and Uninfected Patients



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


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


Cross-sectional analysis of the Veterans Aging Cohort Study.


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


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.


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).


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: opioid, pain, HIV, narcotics, veterans


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.


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


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).


Patient Characteristics (Table 1)

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.

Table 1
Patient Characteristics by Receipt of Any Opioids and HIV Status, n = 78,748

Prescription Opioid Receipt Profiles (Table 2)

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).

Table 2
Patterns of Opioid Analgesic Receipt Among Those Receiving Any Opioids

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

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

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.

Table 5
Unadjusted and Adjusted Odds Ratios for Long-Term Opioid Therapy, Stratified by HIV Serostatus, Among Patients Receiving Opioids

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.


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.


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.


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.


We made the following assumptions to determine the milligram of morphine equivalents for each opioid: 1) If the quantity of pills was less than the intended days supply of the prescription, then days supply was considered equal to the quantity of pills as we assumed patients did not take less than one pill; 2) Each fentanyl patch was dispensed over 72 h, consistent with previous literature;30 3) Cough elixirs were prescribed to be taken as 7.5 mL (between 5 and 10 mL) every 4–6 h (37.5 mL per day) for 7 days; 4) We assumed that patients were taking no less than 10 mL per day of a opioid given solution (excluding cough elixirs); if a quantity of solution/days supply was less than 10 mL, then days supply was set equal to the quantity divided by 10 and we assumed that an equal amount was taken on each day. For a quantity of one, we assumed this was equal to one bottle of 500 mL of solution; and 5) For solutions (1 % of all formulations), we accounted for differences in concentration such that for XX mg opioid in YY mL solution, then quantity was divided by YY.


1. Trescot AM, Helm S, Hansen H, et al. Opioids in the management of chronic non-cancer pain: an update of American Society of the Interventional Pain Physicians’ (ASIPP) Guidelines. Pain Phys. 2008;11(2 Suppl):S5–S62. [PubMed]
2. Olsen Y, Daumit GL, Ford DE. Opioid prescriptions by U.S. primary care physicians from 1992 to 2001. J Pain. 2006;7(4):225–235. doi: 10.1016/j.jpain.2005.11.006. [PubMed] [Cross Ref]
3. Reid MC, Engles-Horton LL, Weber MB, Kerns RD, Rogers EL, O’Connor PG. Use of opioid medications for chronic noncancer pain syndromes in primary care. J Gen Intern Med. 2002;17(3):173–179. doi: 10.1046/j.1525-1497.2002.10435.x. [PMC free article] [PubMed] [Cross Ref]
4. Braden JB, Fan MY, Edlund MJ, Martin BC, DeVries A, Sullivan MD. Trends in use of opioids by noncancer pain type 2000–2005 among Arkansas Medicaid and HealthCore enrollees: results from the TROUP study. J Pain. 2008;9(11):1026–1035. doi: 10.1016/j.jpain.2008.06.002. [PMC free article] [PubMed] [Cross Ref]
5. Pletcher MJ, Kertesz SG, Kohn MA, Gonzales R. Trends in opioid prescribing by race/ethnicity for patients seeking care in US emergency departments. JAMA. 2008;299(1):70–78. doi: 10.1001/jama.2007.64. [PubMed] [Cross Ref]
6. Caudill-Slosberg MA, Schwartz LM, Woloshin S. Office visits and analgesic prescriptions for musculoskeletal pain in US: 1980 vs. 2000. Pain. 2004;109(3):514–519. doi: 10.1016/j.pain.2004.03.006. [PubMed] [Cross Ref]
7. Onen NF, Barrette EP, Shacham E, Taniguchi T, Donovan M, Overton ET.A review of opioid prescribing practices and associations with repeat opioid prescriptions in a contemporary outpatient HIV clinic. Pain Pract. Nov 22 2011. [PubMed]
8. Martell BA, O’Connor PG, Kerns RD, et al. Systematic review: opioid treatment for chronic back pain: prevalence, efficacy, and association with addiction. Ann Intern Med. 2007;146(2):116–127. [PubMed]
9. Becker WC, Fiellin DA, Gallagher RM, Barth KS, Ross JT, Oslin DW. The association between chronic pain and prescription drug abuse in Veterans. Pain Med. 2009;10(3):531–536. doi: 10.1111/j.1526-4637.2009.00584.x. [PubMed] [Cross Ref]
10. Edlund MJ, Martin BC, Fan MY, Devries A, Braden JB, Sullivan MD. Risks for opioid abuse and dependence among recipients of chronic opioid therapy: results from the TROUP study. Drug Alcohol Depend. 2010;112(1–2):90–98. doi: 10.1016/j.drugalcdep.2010.05.017. [PMC free article] [PubMed] [Cross Ref]
11. Chou R, Fanciullo GJ, Fine PG, et al. Clinical guidelines for the use of chronic opioid therapy in chronic noncancer pain. J Pain. 2009;10(2):113–130. doi: 10.1016/j.jpain.2008.10.008. [PubMed] [Cross Ref]
12. Silverberg MJ, Ray GT, Saunders K, et al.Prescription long-term opioid use in HIV-infected patients. Clin J Pain. Jun 14 2011. [PMC free article] [PubMed]
13. Koeppe J, Armon C, Lyda K, Nielsen C, Johnson S. Ongoing pain despite aggressive opioid pain management among persons with HIV. Clin J Pain. 2010;26(3):190–198. doi: 10.1097/AJP.0b013e3181b91624. [PubMed] [Cross Ref]
14. Koeppe J, Lichtenstein K, Armon C, et al.Factors associated with initiation of prolonged analgesic use among patients in the HIV Outpatient Study (HOPS). Clin J Pain. May 2 2011. [PubMed]
15. Miaskowski C, Penko JM, Guzman D, Mattson JE, Bangsberg DR, Kushel MB.Occurrence and characteristics of chronic pain in a community-based cohort of indigent adults living with HIV infection. J Pain. Jun 17 2011. [PMC free article] [PubMed]
16. Marcus KS, Kerns RD, Rosenfeld B, Breitbart W. HIV/AIDS-related pain as a chronic pain condition: implications of a biopsychosocial model for comprehensive assessment and effective management. Pain Med. 2000;1(3):260–273. doi: 10.1046/j.1526-4637.2000.00033.x. [PubMed] [Cross Ref]
17. Hewitt DJ, McDonald M, Portenoy RK, Rosenfeld B, Passik S, Breitbart W. Pain syndromes and etiologies in ambulatory AIDS patients. Pain. 1997;70(2–3):117–123. doi: 10.1016/S0304-3959(96)03281-2. [PubMed] [Cross Ref]
18. Roy S, Ninkovic J, Banerjee S, et al.Opioid drug abuse and modulation of immune function: consequences in the susceptibility to opportunistic infections. J Neuroimmune Pharmacol. Jul 26 2011. [PMC free article] [PubMed]
19. Miller M, Sturmer T, Azrael D, Levin R, Solomon DH. Opioid analgesics and the risk of fractures in older adults with arthritis. J Am Geriatr Soc. 2011;59(3):430–438. doi: 10.1111/j.1532-5415.2011.03318.x. [PMC free article] [PubMed] [Cross Ref]
20. Solomon DH, Rassen JA, Glynn RJ, et al. The comparative safety of opioids for nonmalignant pain in older adults. Arch Intern Med. 2010;170(22):1979–1986. doi: 10.1001/archinternmed.2010.450. [PubMed] [Cross Ref]
21. Solomon DH, Rassen JA, Glynn RJ, Lee J, Levin R, Schneeweiss S. The comparative safety of analgesics in older adults with arthritis. Arch Intern Med. 2010;170(22):1968–1976. doi: 10.1001/archinternmed.2010.391. [PubMed] [Cross Ref]
22. McCance-Katz EF, Sullivan LE, Nallani S. Drug interactions of clinical importance among the opioids, methadone and buprenorphine, and other frequently prescribed medications: a review. Am J Addict. 2010;19(1):4–16. doi: 10.1111/j.1521-0391.2009.00005.x. [PMC free article] [PubMed] [Cross Ref]
23. Curtis LH, Stoddard J, Radeva JI, et al. Geographic variation in the prescription of schedule II opioid analgesics among outpatients in the United States. Health Serv Res. 2006;41(3 Pt 1):837–855. doi: 10.1111/j.1475-6773.2006.00511.x. [PMC free article] [PubMed] [Cross Ref]
24. Tetrault JM, Tate JP, McGinnis KA, et al.Hepatic safety and antiretroviral effectiveness in HIV-infected patients receiving naltrexone. Alcohol Clin Exp Res. Jul 28 2011. [PMC free article] [PubMed]
25. Butt AA, Chang CC, Kuller L, et al. Risk of heart failure with human immunodeficiency virus in the absence of prior diagnosis of coronary heart disease. Arch Intern Med. 2011;171(8):737–743. doi: 10.1001/archinternmed.2011.151. [PMC free article] [PubMed] [Cross Ref]
26. Freiberg MS, Chang CC, Skanderson M, et al. The risk of incident coronary heart disease among veterans with and without HIV and hepatitis C. Circ Cardiovasc Qual Outcomes. 2011;4(4):425–432. doi: 10.1161/CIRCOUTCOMES.110.957415. [PMC free article] [PubMed] [Cross Ref]
27. Fultz SL, Skanderson M, Mole LA, et al. Development and verification of a “virtual” cohort using the National VA Health Information System. Med Care. 2006;44(8 Suppl 2):S25–S30. doi: 10.1097/01.mlr.0000223670.00890.74. [PubMed] [Cross Ref]
28. Smith MW, Joseph GJ. Pharmacy data in the VA health care system. Med Care Res Rev. 2003;60(3 Suppl):92S–123S. doi: 10.1177/1077558703256726. [PubMed] [Cross Ref]
29. Centers for Medicare and Medicaid Services. ICD-9 Provider and Diagnostic Codes. 2011; Accessed 7-14-2012.
30. von Korff M, Saunders K, Thomas Ray G, et al. De facto long-term opioid therapy for noncancer pain. Clin J Pain. 2008;24(6):521–527. doi: 10.1097/AJP.0b013e318169d03b. [PMC free article] [PubMed] [Cross Ref]
31. Agency Medical Directors’ Group. Interagency guideline on opioid dosing for chronic non-cancer pain. June 2010.
32. Bohnert AS, Valenstein M, Bair MJ, et al. Association between opioid prescribing patterns and opioid overdose-related deaths. JAMA. 2011;305(13):1315–1321. doi: 10.1001/jama.2011.370. [PubMed] [Cross Ref]
33. Martin BC, Fan MY, Edlund MJ, Devries A, Braden JB, Sullivan MD. Long-term chronic opioid therapy discontinuation rates from the TROUP study. J Gen Intern Med. 2011;26(12):1450–1457. doi: 10.1007/s11606-011-1771-0. [PMC free article] [PubMed] [Cross Ref]
34. U.S. Drug Enforcement Adminstration. Controlled Substance Schedules., 2010. Accessed 7.14.2012.
35. Hart LG, Larson EH, Lishner DM. Rural definitions for health policy and research. Am J Public Health. 2005;95(7):1149–1155. doi: 10.2105/AJPH.2004.042432. [PubMed] [Cross Ref]
36. Panel on Antiretroviral Guidelines for Adults and Adolescents. Guidelines for Adults and Adolescents D. Guidelines for the Use of Antiretroviral Agents in HIV-1-Infected Adults and Adolescents. In: (OARAC) OoARAC, ed2012.
37. McKinnell JA, Willig JH, Westfall AO, et al. Antiretroviral prescribing patterns in treatment-naive patients in the United States. AIDS Patient Care STDS. 2010;24(2):79–85. doi: 10.1089/apc.2009.0220. [PMC free article] [PubMed] [Cross Ref]
38. Whitehead AJ, Dobscha SK, Morasco BJ, Ruimy S, Bussell C, Hauser P. Pain, substance use disorders and opioid analgesic prescription patterns in veterans with hepatitis C. J Pain Symptom Manage. 2008;36(1):39–45. doi: 10.1016/j.jpainsymman.2007.08.013. [PubMed] [Cross Ref]
39. Braden JB, Sullivan MD, Ray GT, et al. Trends in long-term opioid therapy for noncancer pain among persons with a history of depression. Gen Hosp Psychiatry. 2009;31(6):564–570. doi: 10.1016/j.genhosppsych.2009.07.003. [PMC free article] [PubMed] [Cross Ref]
40. Edlund MJ, Martin BC, Devries A, Fan MY, Braden JB, Sullivan MD. Trends in use of opioids for chronic noncancer pain among individuals with mental health and substance use disorders: the TROUP study. Clin J Pain. 2010;26(1):1–8. doi: 10.1097/AJP.0b013e3181b99f35. [PMC free article] [PubMed] [Cross Ref]
41. Sullivan MD, Edlund MJ, Fan MY, Devries A, Brennan Braden J, Martin BC. Trends in use of opioids for non-cancer pain conditions 2000–2005 in commercial and Medicaid insurance plans: the TROUP study. Pain. 2008;138(2):440–449. doi: 10.1016/j.pain.2008.04.027. [PMC free article] [PubMed] [Cross Ref]
42. Sinnott PL, Siroka AM, Shane AC, Trafton JA, Wagner TH.Identifying neck and back pain in administrative data: defining the right cohort. Spine (Phila Pa 1976). Nov 26 2011. [PubMed]
43. Becker WC, Sullivan LE, Tetrault JM, Desai RA, Fiellin DA. Non-medical use, abuse and dependence on prescription opioids among U.S. adults: psychiatric, medical and substance use correlates. Drug Alcohol Depend. 2008;94(1–3):38–47. doi: 10.1016/j.drugalcdep.2007.09.018. [PubMed] [Cross Ref]
44. Sullivan MD, Edlund MJ, Fan MY, Devries A, Brennan Braden J, Martin BC. Risks for possible and probable opioid misuse among recipients of chronic opioid therapy in commercial and medicaid insurance plans: the TROUP Study. Pain. 2010;150(2):332–339. doi: 10.1016/j.pain.2010.05.020. [PMC free article] [PubMed] [Cross Ref]
45. Tsao JC, Stein JA, Dobalian A. Pain, problem drug use history, and aberrant analgesic use behaviors in persons living with HIV. Pain. 2007;133(1–3):128–137. doi: 10.1016/j.pain.2007.03.016. [PMC free article] [PubMed] [Cross Ref]
46. Edelman EJ, Gordon K, Justice AC.Patient and provider-reported symptoms in the post-cART Era. AIDS Behav. May 20 2010. [PMC free article] [PubMed]
47. Womack JA, Goulet JL, Gibert C, et al. Increased risk of fragility fractures among HIV infected compared to uninfected male veterans. PLoS One. 2011;6(2):e17217. doi: 10.1371/journal.pone.0017217. [PMC free article] [PubMed] [Cross Ref]
48. Cintron A, Morrison RS. Pain and ethnicity in the United States: a systematic review. J Palliat Med. 2006;9(6):1454–1473. doi: 10.1089/jpm.2006.9.1454. [PubMed] [Cross Ref]
49. Moskowitz D, Thom DH, Guzman D, Penko J, Miaskowski C, Kushel M. Is primary care providers’ trust in socially marginalized patients affected by race? J Gen Intern Med. 2011;26(8):846–851. doi: 10.1007/s11606-011-1672-2. [PMC free article] [PubMed] [Cross Ref]
50. Justice AC, Dombrowski E, Conigliaro J, et al. Veterans Aging Cohort Study (VACS): overview and description. Med Care. 2006;44(8 Suppl 2):S13–S24. doi: 10.1097/01.mlr.0000223741.02074.66. [PMC free article] [PubMed] [Cross Ref]

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