Abstract
Importance.
Family members are cited as a common source of prescription opioids used for nonmedical reasons. Yet, the overdose risk associated with exposure to opioids prescribed to family members among adolescents and young adults (youth) is not well-established.
Objective.
To assess the association between opioids prescribed to family members and pharmaceutical opioid overdose among youth.
Design, Setting, and Cohort.
A retrospective cohort study of family units with youth ages 11 to 26 enrolled in a Kaiser Permanente Colorado health plan in 2006, followed through June 2018. Time to first overdose was modeled using Cox regression.
Exposures.
Opioid prescriptions and dosage dispensed to family members and youth in the past month.
Outcome.
Fatal and non-fatal pharmaceutical opioid overdoses identified in vital records and emergency department and inpatient settings, respectively.
Results.
The study population consisted of 72 040 adolescents and young adults (mean age across follow-up: 19.3 years; 50.9% female) nested in 45 145 family units. Youth were more commonly exposed to prescription opioids dispensed to a family member than through their own prescription. Across follow-up, 36.5% of youth filled at least one opioid prescription, and 65.9% of youth had one or more family members with a prescription. Exposure to family members with opioid prescriptions in the past month was associated with increased pharmaceutical opioid overdose risk (adjusted hazard ratio [aHR], 2.17; 95% CI, 1.24–3.79), independent of opioids prescribed to youth (aHR, 6.62; 95% CI, 3.39–12.91). Concurrent exposure to opioid prescriptions from youth and family members was associated with significantly increased overdose risk (aHR, 12.99; 95% CI, 5.08–33.25). High dosage of total morphine milligram equivalents (MME) prescribed to family members in the past month was associated with youth overdose (0 MME [reference], >0-<200 MME [aHR, 1.39; 95% CI, 0.51–3.81], 200-<600 MME [aHR, 1.49; 95% CI, 0.59–3.77], and ≥600 MME [aHR, 2.93; 95% CI, 1.55–5.56]).
Conclusion and Relevance.
Among youth linked to family members, exposure to family members prescribed opioids was associated with increased pharmaceutical opioid overdose, independent of opioids prescribed to youth. Further interventions targeting youth and families are needed, including counseling patients about risks of opioids to youth in their families.
Introduction
Opioid exposures and overdoses among adolescents and young adults (termed youth) represent a significant public health problem.1–3 Despite state and national efforts to reduce opioid prescribing4–6, the volume of opioids prescribed to youth and adults remains substantial.7–9 In 2017, more than 191 million opioid prescriptions were dispensed in the United States, more than three times the number prescribed in 1999.9 While overdose risks associated with a number of opioid prescribing patterns at the individual-level, such as high-dose, have been established10–13, less is known about opioid exposures from family members.
Growing evidence suggests that the family is an important factor in the spread and consumption of prescription opioids by providing access to opioids and facilitating the sharing of information and practices related to their use.14–18 Thus, opioids prescribed to youth and their family members represent two distinct but potentially linked sources of exposure that together put youth at risk of overdose. Prevention efforts may need to target both youth and their families in order to adequately address overdose risk. Yet, the relative associations of opioids prescribed to youth and their family members and overdose have not been well-established. We conducted a retrospective cohort study that linked youth and their family members in a health plan and tested the association of opioid prescribing to the family and pharmaceutical opioid overdose among youth. Our objectives were to assess overdose risk associated with past-month opioid prescriptions to youth and to their family members, opioid dosages, and timing of exposures.
Methods
Study Design and Cohort
We conducted a retrospective cohort study of family units enrolled in a Kaiser Permanente Colorado (KPCO) health care plan. KPCO is an integrated healthcare plan and delivery system that serves approximately 640,000 members. The cohort consisted of index youth and their family members. Index youth were all individuals ages 11 to 26 currently or newly enrolled in 2006 who obtained their insurance coverage as a dependent of a primary health plan member. Individuals up to age 26 are typically eligible for insurance coverage as a dependent. We did not require youth healthcare utilization as a criterion for inclusion because our focus was on opioid exposure in the family. Index youth were linked to at least one other family member using the primary subscriber’s unique plan identifier. We excluded 124 youths who enrolled as a dependent member but did not match to any family members. Youth were followed through age 26, first observed pharmaceutical opioid overdose, disenrollment from KPCO, death, or the end of the observation period on June 30, 2018, whichever occurred first. Youth with gaps in insurance coverage reentered the cohort upon re-enrollment, and time disenrolled was excluded from the analysis. The KPCO Institutional Review Board approved the study, with a waiver of informed consent. The study followed the Strengthening the Reporting of Cohort Studies in Epidemiology (STROBE) reporting guideline.
Exposure
During each index youth’s follow-up, we identified opioid prescriptions dispensed in the past month to youth and separately to members of their family unit between study entry and June 30, 2018 using National Drug Codes (NDC) in outpatient pharmacy dispensings and billing claims. For dosage, we summed the total morphine milligram equivalents (MME) of all prescription opioids dispensed in the past month to all family members and separately to index youth themselves. Total MME in the past month was subsequently divided into approximate terciles separately for family members (>0-<200 MME, 200-<600 MME, ≥600 MME) and index youth (>0-<120 MME, 120-<225 MME, ≥225 MME). Exposures were constructed as time-varying measures and updated each month of the index youth’s follow-up.
Covariates
We adjusted models for clinical factors shown to be associated with overdose in prior studies, including: drug use disorder, alcohol use disorder, and tobacco use; major depressive, anxiety, and mood disorders; and pain.19–22 A history of each was treated as a time-varying measure and was updated monthly during follow-up as youth acquired the clinical diagnosis. Models also included time-varying measures of ever having one or more comorbidities in the Quan-Deyo Modified Charlson Comorbidities Index (CCI)23,24 during follow-up and separate indictors of common psychotropic medications (benzodiazepines, stimulants, and anti-depressants) dispensed to youth and family members in the past month. The CCI measure was dichotomized due to little variation in the number of comorbidities among index youth. Models were also adjusted for demographic factors, including gender, race (white, non-white, missing race), ethnicity (Hispanic and non-Hispanic), and age at each month of follow-up (11–15, 16–20, 21–26). Youth with missing data on race (44.1%) were grouped in a separate category. Finally, models included subscriber unit size and tract-level median family income based on the 2006–2010 American Community Survey 5-Year estimates.
Outcome
We identified non-fatal and fatal pharmaceutical opioid overdoses in emergency department and inpatient settings among index youth using the International Classification of Diseases, Ninth (ICD-9) and Tenth (ICD-10) Revision codes from medical billing claims and state vital records. For non-fatal pharmaceutical opioid overdoses, we used ICD-9 codes 965.0, 965.00, 965.02, 965.09, E850.1, and E850.2 and ICD-10 codes T40.0X1-T40.0X4, T40.2X1-T40.2X4, T40.3X1-T40.3X4, and T40.4X1-T40.4X4. For fatal pharmaceutical opioid overdoses, we used ICD-10 codes that indicate drug poisoning as the underlying cause of death (X40-X44, X60-X64, X85, Y10-Y14) and pharmaceutical opioids involvement as a contributing cause (T40.2-T40.3). The study analyzed the first incident pharmaceutical opioid overdose during follow-up.
Statistical Analyses
To assess the association between exposure to prescription opioids and time to first pharmaceutical opioid overdose among index youth, we fitted Cox proportional-hazards models to calculate adjusted hazard ratios (aHRs) and 95% confidence intervals (CI). We used the counting process method to model opioid prescriptions as a time-varying four-level exposure: no opioid prescription, prescriptions to only youth, prescriptions to only family members, or prescriptions to both youth and family members in the past month. This allowed us to assess the association of prescribed opioids in the family in the past month and pharmaceutical opioid overdose among index youth, independent of and concurrent with opioids prescriptions to index youth themselves. We also tested a dose-response association by examining total MME dispensed to all family members and youth themselves in the past month in a separate model. Robust standard errors were calculated by clustering at the family unit level to adjust for correlation of survival times among index youth in the same family. The proportional hazards assumption for time-constant covariates was assessed using tests of Schoenfeld residuals.
We conducted secondary analysis to examine the timing of exposure from most recent to more distant, defined as one or more opioid prescriptions dispensed to family members, youth themselves, or both in the past three, six, or twelve months. To assess the influence of drug use disorder on our estimates, we also conducted a sensitivity analysis that excluded youth with a drug use disorder prior to their first opioid prescription.
All statistical analyses were performed using Stata version 15.1. Statistical significance was based on two-sided tests with a threshold of p<0.05.
Results
The study consisted of 72 040 index adolescents and young adults aged 11 to 26 (mean [standard deviation (SD)] age across follow-up, 19.3 years [3.7]; 36 646 [50.9%] females [Table 1]) nested in 45 145 family units (mean [SD] number of family members, 4 [1.4]). The mean (SD) length of follow-up for index youth was 4.9 (3.8) person-years. During the entire follow-up, 4 347 (6.0%) individual youth had a drug use disorder, 9 498 (13.2%) had a major depressive disorder, 9 365 (13.0%) had an anxiety disorder, and 31 357 (43.5%) had one or more acute or chronic pain diagnoses. The chief pain diagnoses were headaches/migraines, low back pain, and knee pain. Comorbidities were more common among youth exposed to opioid prescriptions (their own or from family members) compared to the full cohort.
Table 1.
Demographic and Clinical Characteristics of Adolescents and Young Adults (Ages 11–26) in Study Cohort, By Exposure
| Full Cohort (N=72 040) | Youth with ≥1 Own Opioid Prescriptions (N=26 284)a | Youth with ≥1 Family Opioid Prescriptions (N=47 461)a | Youth with ≥1 Own and Family Opioid Prescriptions (N=4 278)a | |
|---|---|---|---|---|
| No. (%) | No. (%) | No. (%) | No. (%) | |
| Opioid Prescriptions Across Entire Follow-up | ||||
| Youth with at least one opioid prescription | 26 284 (36.5) | - | - | - |
| Youth with family members who had at least one opioid prescription | 47 461 (65.9) | - | - | - |
| Youth with at least one opioid prescription and with family members who had at least one prescription | 4 278 (5.9) | - | - | - |
| Diagnosis History Across Entire Follow-up | ||||
| 1 or More CCI Conditions | 14 944 (20.7) | 8 244 (31.4) | 11 773 (24.8) | 1 760 (41.1) |
| Drug Use Disorder | 4 347 (6.0) | 2 694 (10.2) | 3 616 (7.6) | 658 (15.4) |
| Alcohol Use Disorder | 944 (1.3) | 612 (2.3) | 790 (1.7) | 144 (3.4) |
| Tobacco Use | 6 032 (8.4) | 3 846 (14.6) | 4 885 (10.3) | 978 (22.9) |
| Major Depressive Disorder | 9 498 (13.2) | 5 566 (21.2) | 7 688 (16.2) | 1 269 (29.7) |
| Anxiety Disorder | 9 365 (13.0) | 5 639 (21.5) | 7 671 (16.2) | 1 308 (30.6) |
| Mood Disorder | 2 778 (3.9) | 1 586 (6.0) | 2 234 (4.7) | 430 (10.1) |
| Painb | 31 357 (43.5) | 17 804 (67.7) | 24 639 (51.9) | 3 329 (77.8) |
| Psychotropic Medications Across Entire Follow-up | ||||
| Benzodiazepines | ||||
| Youth with at least one prescription | 4 980 (6.9) | 3 643 (13.9) | 4 076 (8.6) | 982 (23.0) |
| Youth with family members who had at least one prescription | 18 554 (25.8) | 8 912 (33.9) | 16 576 (34.9) | 2 423 (56.6) |
| Stimulants | ||||
| Youth with at least one prescription | 3 851 (5.3) | 2 057 (7.8) | 3 172 (6.7) | 438 (10.2) |
| Youth with family members who had at least one prescription | 3 184 (4.4) | 1 539 (5.9) | 2 763 (5.8) | 403 (9.4) |
| Anti-depressants | ||||
| Youth with at least one prescription | 11 634 (16.1) | 7 083 (26.9) | 9 292 (19.6) | 1 664 (38.9) |
| Youth with family members who had at least one prescription | 29 073 (40.4) | 12 903 (49.1) | 24 217 (51.0) | 3 106 (72.6) |
| Social and Demographic Factors | ||||
| Female | 36 646 (50.9) | 14 338 (54.6) | 23 408 (49.3) | 2 344 (54.8) |
| Race | ||||
| White | 31 092 (43.2) | 10 935 (56.2) | 16 175 (47.3) | 2 432 (56.8) |
| Non-White | 9 195 (12.8) | 3 809 (14.5) | 6 538 (13.8) | 593 (13.9) |
| Missingc | 31 753 (44.1) | 7 704 (29.3) | 18 473 (38.9) | 1 253 (29.3) |
| Hispanic | 8 739 (12.1) | 3 836 (14.6) | 6 275 (13.2) | 658 (15.4) |
| Baseline Age | ||||
| 11–15 years | 31 135 (43.2) | 12 875 (49.0) | 23 647 (49.8) | 2 189 (51.2) |
| 16–20 years | 28 018 (38.9) | 10 377 (39.5) | 18 765 (39.5) | 1 738 (40.6) |
| 21–26 years | 12 887 (17.9) | 3 032 (11.5) | 5 049 (10.6) | 351 (8.2) |
| Subscriber Unit Size (Mean) | 4.4 | 4.4 | 4.6 | 4.7 |
| Tract-Level Median Family Income | ||||
| <$50,000 | 13 026 (18.1) | 4 114 (15.7) | 8 152 (17.2) | 715 (16.7) |
| $50,000 - $64,999 | 10 113 (14.0) | 3 653 (13.9) | 6 710 (14.1) | 644 (15.1) |
| $65,000 - $79,999 | 15 000 (20.8) | 5 518 (21.0) | 9 919 (20.9) | 977 (22.8) |
| $80,000 - $94,999 | 13 295 (18.5) | 4 931 (18.8) | 8 827 (18.6) | 828 (19.4) |
| $≥95,000 | 20 606 (28.6) | 8 068 (30.7) | 13 853 (29.2) | 1 114 (26.0) |
Abbreviation: CCI = Quan-Deyo Modified Charlson Comorbidity Index
Exposure is time-varying, and groups are not mutually exclusive
Represents one or more chronic or acute pain diagnoses
In the full cohort, 12.5% of those with missing race information are Hispanic
Opioid Prescriptions
There were 647 767 total opioid prescriptions dispensed to index youth (103 489 [16.0%]) prescriptions) or members of their family (544 278 [84.0%] prescriptions) during follow-up. The most common opioids among index youth were hydrocodone (46.0% of opioid prescriptions), short-acting oxycodone (32.7%), and tramadol (6.9%). Similarly, among family members, the leading opioids were hydrocodone (39.2%), short-acting oxycodone (33.0%), and tramadol (10.6%). During follow-up, 36.5% of index youth filled at least one opioid prescription, 65.9% had a family member with at least one prescription, and 5.9% filled their own prescription and had a family member with a prescription in the same month. Among youth with at least one family member who had an opioid prescription, the median total MME (interquartile range [IQR]) in the family over the past month was 300 (150–896), and among youth who had at least one prescription themselves the median total MME (IQR) in the past month was 150 (100–300).
Pharmaceutical Opioid Overdose Incidence Rate
Among index youth there were 103 incident prescription opioid overdose events during follow-up (incidence rate, 29.0 per 100 000 person-years), of which 14 were fatal (Table 2). Compared to no exposure to an opioid prescription in the past month, prescriptions to one or more family members (unadjusted incidence rate ratio, 2.7; 95% CI, 1.5–4.8) and to index youth themselves (unadjusted incidence rate ratio, 14.8; 95% CI, 7.8–26.2) were associated with higher rates of overdose. While concurrent exposure to opioid prescriptions to both family members and youth themselves in the past month was rare (0.2% of total person-years), it was associated with markedly higher rates of overdose compared to no exposure (unadjusted incidence rate ratio, 40.7; 95% CI, 15.8–88.8).
Table 2.
Incidence Rates of Fatal and Non-Fatal Prescription Opioid Overdose Among Adolescents and Young Adults
| Opioid Prescriptions in the Past Month | Person-Years (PY) | Overdoses, No. | Rate (95% CI), Per 100 000 PY | Unadjusted Incidence Rate Ratio (IRR) (95% CI) |
|---|---|---|---|---|
| No Prescription | 320 519 | 65 | 20.3 (15.9–25.9) | [Reference] |
| Prescriptions to Only Family Members | 28 912 | 16 | 55.3 (33.9–90.3) | 2.7 (1.5–4.8) |
| Prescriptions to Only Youth | 5 001 | 15 | 299.9 (180.8–497.5) | 14.8 (7.8–26.2) |
| Prescriptions to Both Youth and Family Members | 848 | 7 | 825.5 (393.7–1 732.3) | 40.7 (15.8–88.8) |
| Overall | 355 280 | 103 | 29.0 (23.9–35.2) | |
Opioid Prescriptions and Risk of Pharmaceutical Opioid Overdose Among Youth
In adjusted Cox proportional-hazards models (Table 3, Model 2), compared no exposure to opioid prescriptions, prescriptions to one or more family members in the past month was independently associated with a two-fold increase in the risk of overdose among index youth (aHR, 2.17; 95% CI, 1.24–3.79). Prescriptions to index youth themselves in the past month was associated with more than a seven-fold increase in the risk of overdose (aHR, 6.62; 95% CI, 3.39–12.91). Concurrent prescriptions to both family members and youth in the past month was associated with a fifteen-fold increase in overdose risk (aHR, 12.99; 95% CI, 5.08–33.25).
Table 3.
Unadjusted and Adjusted Association of Opioid Prescriptions and Pharmaceutical Opioid Overdose Among Adolescents and Young Adults
| Model 1 | Model 2 | |
|---|---|---|
| Adjusted | Adjusted | |
| aHR (95% CI)a,b | aHR (95% CI)a,c | |
| Opioid Prescription in the Past Month | ||
| No Prescription | [Reference] | |
| Prescriptions to Family Members | 2.17 (1.24 – 3.79) | |
| Prescriptions to Youth | 6.62 (3.39 – 12.91) | |
| Prescriptions to Youth and Family Members | 12.99 (5.08 – 33.25) | |
| Total MME to Family Members in the Past Month | ||
| None | [Reference] | |
| >0-<200 MME | 1.39 (0.51 – 3.81) | |
| 200-<600 MME | 1.49 (0.59 – 3.77) | |
| ≥600 MME | 2.93 (1.55 – 5.56) | |
| Total MME to Youth in the Past Month | ||
| None | [Reference] | |
| >0-<120 MME | 2.67 (0.63 – 11.28) | |
| 120-<225 MME | 5.22 (1.87 – 14.60) | |
| ≥225 MME | 8.77 (4.05 – 18.99) |
Abbreviations: HR = hazard ratio; aHR = adjusted hazard ratio; CI = confidence interval; MME = morphine milligram equivalents
Covariates in adjusted models include one or more conditions in the Quan-Deyo Modified Charlson Comorbidity Index, drug use disorder, alcohol use disorder, tobacco use, major depressive disorder, anxiety disorder, mood disorder, pain diagnosis, benzodiazepines, stimulants, anti-depressants, gender, race, ethnicity, age, subscriber unit size, and tract-level median family income
Model 1 specifies exposure to opioid prescriptions in the past month as a four-level time-varying measure
Model 2 includes total opioid dosage (MME) in the past month for family members and youth as time-varying measures grouped into categories of approximately equal size
An analysis of dose response indicated that only the highest total dosage of opioids prescribed to family members in the past month was associated with risk of overdose (Table 2, Model 3): 0 MME (reference), >0-<200 MME (aHR, 1.39; 95% CI, 0.51–3.81), 200-<600 MME (aHR, 1.49; 95% CI, 0.59–3.77), and ≥600 MME (aHR, 2.93; 95% CI, 1.55–5.56). In the same model, the total dosage of opioids prescribed to youth themselves similarly showed an apparent dose-response association: 0 MME (reference), >0-<120 MME (aHR 2.67; 95% CI, 0.63–11.28), 120-<225 MME (aHR 5.22; 95% CI, 1.87–14.60), and ≥225 MME (aHR 8.77; 95% CI, 4.05–18.99).
In secondary analysis (Table 4), one or more opioid prescriptions in the past three months to family members (aHR, 1.85; 95% CI, 1.11–3.11), youth themselves (aHR, 5.86; 95% CI, 3.33–10.30), or both (aHR, 6.56; 95% CI, 2.87–14.99) were associated with lower risks compared to prescriptions in the past month. The overdose risk diminished for youth prescriptions in the past six months (aHR, 4.65; 95% CI, 2.69–8.04) and past twelve months (aHR, 3.93; 95% CI, 2.23–6.92), while it was variable for prescriptions to family members or both groups in the past six months (family members [aHR, 1.93; 95% CI, 1.19–3.13]; both [aHR, 3.68; 95% CI, 1.71–7.89]) and past twelve months (family members [aHR, 1.74; 95% CI, 1.02–2.98]; both [aHR, 4.46; 95% CI, 2.36–8.43]). While the risk of overdose persisted with exposure in the past 12 months, the month following an opioid prescription was associated with the most pronounced risk.
Table 4.
Association of Opioid Prescriptions and Pharmaceutical Opioid Overdose During Past 3-Month, 6-Month, and 12-Month Exposures
| Past 3 Months | Past 6 Months | Past 12 Months | |
|---|---|---|---|
| aHR (95% CI)a | aHR (95% CI)a | aHR (95% CI)a | |
| Opioid Prescriptions | |||
| No Prescription | [Reference] | [Reference] | [Reference] |
| Prescriptions to Family Members | 1.85 (1.11 – 3.11) | 1.93 (1.19 – 3.13) | 1.74 (1.02 – 2.98) |
| Prescriptions to Youth | 5.86 (3.33 – 10.30) | 4.65 (2.69 – 8.04) | 3.93 (2.23 – 6.92) |
| Prescriptions to Youth and Family Members | 6.56 (2.87 – 14.99) | 3.68 (1.71 – 7.89) | 4.46 (2.36 – 8.43) |
Abbreviations: aHR = adjusted hazard ratio; CI = confidence interval
Covariates in adjusted models include one or more conditions in the Quan-Deyo Modified Charlson Comorbidity Index, drug use disorder, alcohol use disorder, tobacco use, major depressive disorder, anxiety disorder, mood disorder, pain diagnosis, benzodiazepines, stimulants, and anti-depressants, gender, race, ethnicity, age, subscriber unit size, and tract-level median family income
In sensitivity analysis (eTable 1 in the Supplement), excluding youth with a diagnosed drug use disorder prior to their first opioid prescription (n=2 518) did not alter the study conclusion that opioid prescriptions were associated with youth overdose.
Discussion
Exposure to family members with opioid prescriptions in the past month was associated with increased risk of pharmaceutical opioid overdose, independent of youth being prescribed opioids. In addition, concurrent opioid prescriptions among youth and family members in the past month, time immediately following prescriptions, and high dose were all associated with elevated risks. Findings suggest that exposure to prescribed opioids in the family pose serious risk of pharmaceutical opioid overdose to youth.
Several potential mechanisms could explain the association between prescribed opioids from family members and overdose among youth. First, and most directly, opioid prescriptions to family members may provide ready access to opioids. Unsecured and leftover medications may be misused, and medications may also be shared among family members.18 Second, family exposure may reflect parents and siblings who model behaviors, attitudes, and norms regarding medical and nonmedical use of opioids.14 This can influence youth preferences and contribute to initiation of opioid use, misuse, and opioid-related complications. Third, family exposure may also be a marker for problems related to parents’ opioid use – such as aberrant pain medication seeking behaviors, intermittent over-sedation, and the development of opioid use disorders (OUD) – that may impair parenting ability, diminish the quality of parent-child relationships, and induce stress and instability in the home.25 Poor parental discipline skills, neglect, and mistreatment have been shown to be associated with risk of adolescent substance use problems.26 Finally, there may be shared risk factors that link patterns of opioid use youth and their family members, including genetic predisposition, common health conditions, social stressors (e.g., housing instability, financial insecurity, and marital dissolution), and local availability of opioids.16,27–30
Our study contributes to an early but growing literature on the risks of opioid exposures in the family and opioid initiation16, long-term opioid use15, and overdose31. We tested and quantified the risk associated with prescribed opioids in the family, while adjusting for opioids prescribed to youth. Since youth may be widely exposed to family members with prescription opioids, the associated overdose risk for youth, even if modest relative to the risk associated with youth’s own prescriptions, may be significant at the population-level. Findings also highlight the need for greater attention to the magnified overdose risk associated with concurrent opioids use among youth and their family members. Greater combined opioid dosage from concurrent exposure may contribute to such risk.
Our findings suggest that clinicians prescribing opioids in adult medicine should consider counselling patients about the risks of opioids to youth in their families. Reducing opioid prescriptions to adults has been a focus of recent prevention efforts and can contribute to decreasing opioid exposure in the home. The Centers for Disease Control and Prevention (CDC) established guidelines recommending prescribing limits in the treatment of chronic pain with regard to opioid initiation, dosage, and duration to curb unnecessary exposure and excess supply.6 Limiting access to opioids, however, should be balanced against the risks of complications induced by volatile dosing and undertreatment of pain.32,33
In response to concerns that prescribed opioids in the home may be misused among youth, federal agencies have supported common-sense educational campaigns to encourage safe storage and proper disposal of medications.34,35 The evidence base to support such interventions, however, is not currently robust.18,36 While home safety interventions may serve as an effective primary prevention for youth who have not yet initiated opioid use, such interventions may have unintended consequences. For example, a change in access to opioids in the home may push youth who are already misusing into the illicit drug market, where unknown quality and potency of heroin and fentanyl could magnify overdose risk.37 Thus, further research on best practices and potential adverse impacts of well-intended interventions are needed.
Interventions that focus on restricting and controlling access to prescription opioids alone are just one of several efforts needed to curb the current epidemic that is increasingly centered on illicit opioids.37,38 Undiagnosed and untreated opioid use disorder (OUD) may contribute to prescription misuse and high overdose risk. Efforts should include increased screening and treatment paired with interventions to address social and economic determinants of OUD more broadly.29,39,40 Given the prevalence of nonmedical use of prescription opioids and rates of overdoses9,41, harm reduction strategies are also needed, such as increased access to overdose reversal medication through standing orders and co-dispensing.
In the current epidemic, addressing early exposures to opioids, including those from opioids prescribed to family members, among youth may be important to preventing OUD and use of illicit opioids.42 Adolescence and young adulthood are periods in the life course when drug use is usually initiated43, and nonmedical use of opioids in this population is associated subsequent heroin use and OUD.44–47
Limitations and Strengths
Our study has certain limitations. We were unable to distinguish whether prescription opioids from family members, youth themselves, or elsewhere directly contributed to overdoses. The study did not capture all potential sources of prescription opioids, including prescriptions that were paid by cash or another health plan or were obtained from peers, family members not enrolled in the same health plan, or informal drug markets, which together may underestimate the extent of exposure to prescription opioids. On the other hand, we were unable to verify whether health plan subscriber members resided in the same household, implying that access to prescription opioids may be overstated. The study also did not account for shared genetic, social, and environmental risk factors that may put individuals in the same family at risk of overdose and could confound the relationship between exposure to opioids in household members and risk of overdose. Finally, since the study was conducted in a single health system in Colorado, findings should be replicated in other settings.
Our study also has several strengths. We leveraged electronic health records to link a large cohort of youth to their family members to assess exposure in the family. Use of pharmacy dispensing data allowed us to determine the degree and timing of opioid exposures with precision.
Conclusion
In a retrospective study of adolescents and young adults, exposure to prescription opioids in families was common. Opioids prescribed to family members were associated with increased risk of pharmaceutical opioid overdose, independent of opioids prescribed to youth. Further intervention efforts targeting youth and families are needed, including counseling patients about risks of opioids to youth in their families.
Supplementary Material
Key Points.
Question:
Are opioids prescribed to family members associated with pharmaceutical opioid overdose among adolescents and young adults?
Findings:
In this retrospective cohort study that included 72 040 adolescents and young adults, exposure to family members with opioid prescriptions in the past month was associated with a two-fold increase in the risk of overdose, and youth’s own prescriptions were associated with more than a six-fold increase. Concurrent exposure to prescriptions to family members and youth themselves was associated with a fifteen-fold increase in overdose risk.
Meaning:
Opioid prescriptions to family members as well as those to youth were associated with overdose.
Acknowledgement
Funding/Support: This study was supported by the National Institute on Drug Abuse under grants R01DA042059 and R01DA042059-04S1.
Role of the Funder/Sponsor: The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Footnotes
Conflict of Interest: Dr. Binswanger receives royalties for educational content on the health of incarcerated persons from Uptodate. No other authors declare any conflicts of interest.
References
- 1.Gomes T, Tadrous M, Mamdani MM, Paterson JM, Juurlink DN. The burden of opioid-related mortality in the United States. JAMA Netw Open. 2018;1(2):e180217–e180217. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Gaither JR, Leventhal JM, Ryan SA, Camenga DR. National trends in hospitalizations for opioid poisonings among children and adolescents, 1997 to 2012. JAMA Pediatr 2016;170(12):1195–1201. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Allen JD, Casavant MJ, Spiller HA, Chounthirath T, Hodges NL, Smith GA. Prescription opioid exposures among children and adolescents in the United States: 2000–2015. Pediatrics. 2017;139(4):e20163382. [DOI] [PubMed] [Google Scholar]
- 4.Murphy K, Becker M, Locke J, Kelleher C, McLeod J, Isasi F. Finding Solutions to the Prescription Opioid and Heroin Crisis: A Road Map for States. Washington, D.C.; 2016. [Google Scholar]
- 5.Christie C, Baker C, Cooper R, Kennedy PJ, Madras B, Bondi P. The President’s Commission on Combating Drug Addiction and the Opioid Crisis. Washington, D.C.; 2017. [Google Scholar]
- 6.Dowell D, Haegerich TM, Chou R. CDC guideline for prescribing opioids for chronic pain—United States, 2016. JAMA 2016;315(15):1624–1645. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Fortuna RJ, Robbins BW, Caiola E, Joynt M, Halterman JS. Prescribing of controlled medications to adolescents and young adults in the United States. Pediatrics. 2010:peds-2010. [DOI] [PubMed]
- 8.Groenewald CB, Rabbitts JA, Gebert T, Palermo TM. Trends in opioid prescriptions among children and adolescents in the United States: a nationally representative study from 1996 to 2012. Pain. 2016;157(5):1021. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Centers for Disease Control and Prevention. 2018 Annual Surveillance Report of Drug-Related Risks and Outcomes--United States.; 2018.
- 10.Bohnert ASB, Valenstein M, Bair MJ, et al. Association between opioid prescribing patterns and opioid overdose-related deaths. Jama 2011;305(13):1315–1321. [DOI] [PubMed] [Google Scholar]
- 11.Dunn KM, Saunders KW, Rutter CM, et al. Opioid Prescriptions for Chronic Pain and Overdose. Ann Intern Med 2010;152(2):85. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Paulozzi LJ. Prescription drug overdoses: A review. J Safety Res 2012;43(4):283–289. [DOI] [PubMed] [Google Scholar]
- 13.National Academies of Sciences, Engineering and M. Pain Management and the Opioid Epidemic (Bonnie RJ, Ford MA, Phillips JK, eds.). Washington, D.C.: National Academies Press; 2017. doi: 10.17226/24781 [DOI] [PubMed] [Google Scholar]
- 14.de Vaan M, Stuart T. Does Intra-household Contagion Cause an Increase in Prescription Opioid Use? Am Sociol Rev 2019:1–32.
- 15.Harbaugh CM, Lee JS, Chua K-P, et al. Association between long-term opioid use in family members and persistent opioid use after surgery among adolescents and young adults. JAMA Surg 2019:e185838–e185838. [DOI] [PMC free article] [PubMed]
- 16.Seamans MJ, Carey TS, Westreich DJ, et al. Association of household opioid availability and prescription opioid initiation among household members. JAMA Intern Med 2018;178(1):102–109. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Chua K-P, Brummett CM, Conti RM, Haffajee RL, Prosser LA, Bohnert ASB. Assessment of Prescriber and Pharmacy Shopping Among the Family Members of Patients Prescribed Opioids. JAMA Netw Open. 2019;2(5):e193673. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Binswanger IA, Glanz JM. Pharmaceutical Opioids in the Home and Youth: Implications for Adult Medical Practice. Subst Abus 2015;36(2):141–143. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Glanz JM, Narwaney KJ, Mueller SR, et al. Prediction Model for Two-Year Risk of Opioid Overdose Among Patients Prescribed Chronic Opioid Therapy. J Gen Intern Med January 2018. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Brady JE, Giglio R, Keyes KM, DiMaggio C, Li G. Risk markers for fatal and non-fatal prescription drug overdose: a meta-analysis. Inj Epidemiol 2017;4(1):24. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.King S, Chambers CT, Huguet A, et al. The epidemiology of chronic pain in children and adolescents revisited: A systematic review. Pain. 2011;152(12):2729–2738. [DOI] [PubMed] [Google Scholar]
- 22.Centers for Disease Control and Prevention (CDC). Quality Improvement and Care Coordination: Implementing the CDC Guideline for Prescribing Opioids for Chronic Pain.; 2018. https://www.cdc.gov/drugoverdose/pdf/prescribing/CDC-DUIP-QualityImprovementAndCareCoordination-508.pdf. Accessed January 22, 2020.
- 23.Quan H, Sundararajan V, Halfon P, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005;43(11):1130–1139. [DOI] [PubMed] [Google Scholar]
- 24.Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol 1992;45(6):613–619. [DOI] [PubMed] [Google Scholar]
- 25.Barnard M, McKeganey N. The impact of parental problem drug use on children: what is the problem and what can be done to help? Addiction. 2004;99(5):552–559. [DOI] [PubMed] [Google Scholar]
- 26.Austin AE, Shanahan ME, Zvara BJ. Association of childhood abuse and prescription opioid use in early adulthood. Addict Behav 2018;76:265–269. [DOI] [PubMed] [Google Scholar]
- 27.Saxon AJ, Oreskovich MR, Brkanac Z. Genetic determinants of addiction to opioids and cocaine. Harv Rev Psychiatry 2005;13(4):218–232. [DOI] [PubMed] [Google Scholar]
- 28.King NB, Fraser V, Boikos C, Richardson R, Harper S. Determinants of increased opioid-related mortality in the United States and Canada, 1990–2013: a systematic review. Am J Public Health. 2014;104(8):e32–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Dasgupta N, Beletsky L, Ciccarone D. Opioid Crisis: No Easy Fix to Its Social and Economic Determinants. Am J Public Health. 2018;108(2):182–186. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Friedman J, Kim D, Schneberk T, et al. Assessment of Racial/Ethnic and Income Disparities in the Prescription of Opioids and Other Controlled Medications in California. JAMA Intern Med 2019;179(4):469–476. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Khan NF, Bateman BT, Landon JE, Gagne JJ. Association of Opioid Overdose With Opioid Prescriptions to Family Members. JAMA Intern Med 2019. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Glanz JM, Binswanger IA, Shetterly SM, Narwaney KJ, Xu S. Association between opioid dose variability and opioid overdose among adults prescribed long-term opioid therapy. JAMA Netw open. 2019;2(4):e192613–e192613. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Rubin R Limits on Opioid Prescribing Leave Patients With Chronic Pain Vulnerable. JAMA 2019;321(21):2059. [DOI] [PubMed] [Google Scholar]
- 34.U.S. Food & Drug Administration. Safe Opioid Disposal - Remove the Risk Outreach Toolkit. https://www.fda.gov/drugs/ensuring-safe-use-medicine/safe-opioid-disposal-remove-risk-outreach-toolkit. Published 2019. Accessed September 19, 2019.
- 35.Centers for Disease Control and Prevention. Put Your Medicines Up and Away and Out of Sight. https://www.cdc.gov/Features/MedicationStorage/. Published 2019. Accessed September 19, 2019.
- 36.National Academies of Sciences. Pain Management and the Opioid Epidemic National Academies Press (US); 2017. doi: 10.17226/24781 [DOI] [PubMed] [Google Scholar]
- 37.Beletsky L, Davis CS. Today’s fentanyl crisis: Prohibition’s Iron Law, revisited. Int J Drug Policy. 2017;46:156–159. [DOI] [PubMed] [Google Scholar]
- 38.Samet JH, Kertesz SG. Suggested paths to fixing the opioid crisis: directions and misdirections. JAMA Netw open. 2018;1(2):e180218–e180218. [DOI] [PubMed] [Google Scholar]
- 39.Daniel H, Bornstein SS, Kane GC. Addressing Social Determinants to Improve Patient Care and Promote Health Equity: An American College of Physicians Position Paper. Ann Intern Med 2018;168(8):577. [DOI] [PubMed] [Google Scholar]
- 40.Kolodny A, Courtwright DT, Hwang CS, et al. The Prescription Opioid and Heroin Crisis: A Public Health Approach to an Epidemic of Addiction. Annu Rev Public Health. 2015;36(1):559–574. [DOI] [PubMed] [Google Scholar]
- 41.Substance Abuse and Mental Health Services Administration. Key Substance Use and Mental Health Indicators in the United States: Results from the 2017 National Survey on Drug Use and Health. Rockville, MD; 2018. [Google Scholar]
- 42.Compton WM, Jones CM, Baldwin GT, Harding FM, Blanco C, Wargo EM. Targeting Youth to Prevent Later Substance Use Disorder: An Underutilized Response to the US Opioid Crisis. Am J Public Health. 2019;109(S3):S185–S189. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Kandel DB, Logan JA. Patterns of drug use from adolescence to young adulthood: I. Periods of risk for initiation, continued use, and discontinuation. Am J Public Health. 1984;74(7):660–666. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.McCabe SE, Veliz P, Schulenberg JE. Adolescent context of exposure to prescription opioids and substance use disorder symptoms at age 35: a national longitudinal study. Pain. 2016;157(10):2173–2178. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Cerdá M, Santaella J, Marshall BDL, Kim JH, Martins SS. Nonmedical prescription opioid use in childhood and early adolescence predicts transitions to heroin use in young adulthood: a national study. J Pediatr 2015;167(3):605–612. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Kelley-Quon LI, Cho J, Strong DR, et al. Association of nonmedical prescription opioid use with subsequent heroin use initiation in adolescents. JAMA Pediatr 2019;173(9):e191750–e191750. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Compton WM, Jones CM, Baldwin GT. Relationship between nonmedical prescription-opioid use and heroin use. N Engl J Med 2016;374(2):154–163. [DOI] [PubMed] [Google Scholar]
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