Opioid Prescribing Among Adults With Disabilities in the United States After the 2014 Federal Hydrocodone Rescheduling Regulation

Victor Liaw; Yong-Fang Kuo; Mukaila A Raji; Jacques Baillargeon

doi:10.1177/0033354919892638

. 2019 Dec 13;135(1):114–123. doi: 10.1177/0033354919892638

Opioid Prescribing Among Adults With Disabilities in the United States After the 2014 Federal Hydrocodone Rescheduling Regulation

Victor Liaw ¹, Yong-Fang Kuo ^2,^3,^4,⁵, Mukaila A Raji ^2,³, Jacques Baillargeon ^3,^4,^5,^✉

PMCID: PMC7119258 PMID: 31835012

Abstract

Objectives:

Deaths from prescription opioid overdoses have reached an epidemic level in the United States, particularly among persons with disabilities. The 2014 federal rescheduling regulation is associated with reduced opioid prescribing in the general US population; however, to date, no data have been published on this regulation’s effect on persons with disabilities. We examined whether the 2014 hydrocodone rescheduling change was associated with reduced opioid prescribing among adult Medicare beneficiaries with disabilities.

Methods:

We identified 680 876 Medicare beneficiaries with disabilities aged 21-64 in 2013 and 657 687 in 2015 from a 20% national sample. We examined changes in the monthly opioid-prescribing rates from January 1, 2013, through December 31, 2015. We also compared opioid-prescribing rates in 2013 with rates in 2015.

Results:

In 2014, the percentage of Medicare beneficiaries with disabilities who received hydrocodone prescriptions decreased by 0.154% per month (95% confidence interval [CI], –0.186 to –0.121, P < .001). The percentage of Medicare beneficiaries with disabilities who received hydrocodone prescriptions decreased from 32.2% in 2013 to 27.7% in 2015, whereas rates of any opioid prescribing, prolonged prescribing (≥90-day supply), and high-dose prescribing (≥100 morphine milligram equivalents per day for >30 days) decreased only modestly, from 50.2% to 49.0%, from 27.4% to 26.5%, and from 7.5% to 7.0%, respectively.

Conclusions:

The 2014 federal rescheduling of hydrocodone was associated with only minor changes in overall and potentially high-risk opioid-prescribing rates. Neither state variation in long-term prescribing nor beneficiary characteristics explained the changes in persistently high opioid-prescribing rates among adults with disabilities after the 2014 regulation. Future studies should examine patient and provider characteristics underlying the persistent high-risk prescribing patterns in this population.

Keywords: hydrocodone, regulation, Medicare, disability, opioid

The number of deaths from prescription opioid overdoses is rising in the United States.¹ Medicare beneficiaries with disabilities are at particularly high risk of opioid overdose.^2-5 This population is more likely than the general US population to have at least 2 risk factors for exposure to prescription opioids: mental health disorders and musculoskeletal pain syndromes. About 30% of adults with disabilities receive Medicare because of a mental health disorder,⁶ and persons in the United States who have mental health disorders receive more than half of all prescribed opioids.⁷ Another 30% of Medicare beneficiaries with disabilities have chronic musculoskeletal pain, a condition that increases their risk of receiving prescription opioids.^6,8,9 With multiple opportunities for exposure to prescription opioids, it is not surprising that Medicare beneficiaries with disabilities—approximately 3% of the total US population—accounted for up to 25% of annual prescription-opioid overdose deaths in the United States in 2008.^6,10

These deaths reflect the high prevalence of prescription opioid use in this population, with approximately 50% of Medicare beneficiaries with disabilities filling at least 1 opioid prescription each year.^10-12 Moreover, 20% of Medicare beneficiaries with disabilities fill a prescription of ≥90 days per year, compared with 3% to 4% of the general adult US population.^10-12 Several studies have shown increased susceptibility to opioid use disorders from prescription opioid exposure, with prolonged prescriptions resulting in a substantially increased risk for opioid-related toxicities.^13-15 In view of these findings, opioid use among adults with disabilities is a growing public health concern, and intervention efforts occur at state and federal levels.^10,16-23

Most efforts to combat the opioid epidemic occur at the state level. Findings from studies examining the effect of state laws on reducing opioid misuse and opioid overdose are mixed.^10,16-19 In October 2014, the federal government enacted a regulation mandating that the Drug Enforcement Administration (DEA) move all hydrocodone-combination products from schedule III to schedule II, which reduced their prescription limit to a 30-day supply with no refills.²⁴ This federal regulation is associated with lower rates of both hydrocodone and overall opioid prescribing in the general population.^21-23,25 Although it is difficult to determine the extent to which additional factors (eg, increased public and provider awareness, insurance companies’ implementation of more stringent prescription policies, publication of new prescribing guidelines and regulations) contribute to this decrease,^26-29 the association between hydrocodone rescheduling and reduced prescribing rates is apparent and warrants further investigation. To date, no data have been published on this regulation’s effect on persons with disabilities.

The objective of our study was to examine whether the 2014 federal hydrocodone rescheduling regulation was associated with a decrease in opioid-prescribing rates among adults with disabilities. Specifically, we examined changes in the rates of hydrocodone-combination and overall opioid prescribing from 2013 to 2015, with particular attention to prolonged and high-dose patterns of prescribing that are prevalent in this population.^2,10,11

Methods

Data Source

We conducted a retrospective cohort study using enrollment and claims data for a 20% national sample of Medicare beneficiaries, which included Medicare beneficiary summary files, Medicare Provider Analysis and Review files, Outpatient Standard Analytic Files, Medicare Carrier files, and Prescription Drug Event files.³⁰ Our research was approved by the University of Texas Medical Branch Institutional Review Board.

Study Cohorts

Medicare enrollment files provided information on original entitlement. We selected beneficiaries aged 21-64 who had disability as their original entitlement and were enrolled in Medicare Part D (n = 650 000-680 000). The DEA’s ruling was published in August 2014 and implemented in October 2014. Therefore, the first study cohort was established 1 year before the rescheduling (2013), and the second cohort was established 1 year after the rescheduling (2015).

Measures

We used the National Drug Code, product name, therapeutic class description, and DEA class code from the 2015 Red Book Select Extracts database³¹ to categorize opioid treatments into 4 classes: hydrocodone (specifically hydrocodone-combination products), nonhydrocodone schedule II opioids, nonhydrocodone schedule III opioids, and tramadol. We did not include opioids given by injection, because prescribing rates for these inpatient treatments do not reflect outpatient opioid prescription use. We also studied 3 patterns of opioid receipt: any, prolonged, and high dose. We defined any opioid use as receipt of at least 1 opioid prescription in 1 study year. We defined prolonged opioid use as receipt of an opioid prescription for ≥90 days in 1 study year. We defined high-dose opioid use as receipt of an opioid supply of ≥100 morphine milligram equivalents (MME) per day for >30 days.²⁰ The formula for MME is ([strength per unit] × [quantity prescribed] × [MME conversion factor])/(daily supply). We also conducted a sensitivity analysis in which we defined a prolonged opioid use as receipt of an opioid prescription for ≥180 days in a study year.³²

Beneficiary and Regional Characteristics

Medicare enrollment files also provided data on age, sex, race, and race/ethnicity. We assessed Medicaid enrollment, which is a widely used proxy for low-income status in health services research.¹⁰ We obtained data on education and household income from the 2011-2015 American Community Survey by ZIP code linkage and divided household income into quartiles.³³ We generated Elixhauser comorbidity measures from inpatient and outpatient claims in the 12 months before the study year and categorized them according to the number of morbidities (0, 1, 2, ≥3).³⁴ We divided residential area into metropolitan, nonmetropolitan urban, and rural by using rural–urban continuum codes.³⁵

Statistical Analyses

We calculated the proportions of beneficiaries who received an opioid prescription for each category (any, hydrocodone, nonhydrocodone schedule II, nonhydrocodone schedule III, tramadol) per month from January 1, 2013, through December 31, 2015. For each beneficiary during each study month, we used a 90-day look-back period to find the most recent prescription date and the total days’ supply of that prescription. The numerator of the estimated prevalence for each month consisted of beneficiaries who had at least 1 day of opioid supply available in that month, and the denominator was the number of disabled enrollees who had Medicare Part A, Medicare Part B, and Medicare Part D without health maintenance organization coverage during that month. We then analyzed these aggregated monthly prevalence trends by using an interrupted time-series model. We examined changes across 3 periods: preimplementation (2013), implementation (2014), and postimplementation (2015).

In addition, we conducted patient-level analyses and calculated the proportion of beneficiaries who received prolonged or high-dose prescriptions in 2013 and 2015. We further stratified these values by patient and for each study year. We constructed modified Poisson regression models—adjusted for the fixed effect of states, months of follow-up, and clustering effect within beneficiaries—to evaluate whether the change in prevalence ratio in these prescriptions measured from 2013 through 2015 varied by patient characteristics. We also conducted a longitudinal study of beneficiaries who received prolonged opioid prescriptions and those who received high-dose opioid prescriptions. We then examined changes in prescription trends in 2015 compared with 2013. All tests of significance were 2-sided, with P < .05 considered significant. We performed analyses by using SAS Enterprise version 7.12.³⁶ We constructed maps to examine opioid-prescribing patterns across states and by time period by using ArcGIS version 9.3.³⁷

Results

The largest month-to-month decrease for the percentage of any opioids and hydrocodone occurred from October to November 2014 (Figure 1). During this same period, nonhydrocodone schedule II prescriptions overtook hydrocodone as the most prescribed opioid category and remained so for the rest of the study period. Among the 4 opioid prescription classes in 2014, the percentage of hydrocodone prescriptions decreased the most (–0.154% per month; 95% confidence interval [CI], –0.186 to –0.121; P < .001). In contrast, the opioid prescription classes with the greatest increases in 2014 were found in nonhydrocodone schedule II (0.033% per month; 95% CI, 0.024-0.041; P < .001) and nonhydrocodone schedule III (0.032% per month; 95% CI, 0.023-0.041; P < .001) prescriptions.

The percentage of adult Medicare beneficiaries with disabilities who received at least 1 hydrocodone prescription decreased from 32.2% in 2013 to 27.7% in 2015 (Figure 2). From 2013 to 2015, the percentage of these beneficiaries who received nonhydrocodone schedule II opioid prescriptions increased from 22.5% to 23.5%, and the percentage who received schedule III opioid prescriptions increased from 4.2% to 5.6%. The percentage of adult Medicare beneficiaries with disabilities who received any opioid prescription decreased from 50.2% in 2013 to 49.0% in 2015.

Figure 2. — Percentage of adult Medicare beneficiaries with disabilities aged 21-64 who received opioid prescriptions, by opioid class or pattern of opioid receipt, 2013 and 2015, United States. Errors bars indicate 95% confidence intervals. Any opioid use was defined as receipt of at least 1 opioid prescription in 1 study year. Prolonged opioid use was defined as receipt of an opioid prescription for ≥90 days in 1 study year. High-dose opioid use was defined as receipt of an opioid supply of ≥100 morphine milligram equivalents per day for >30 days. Based on the 20% national Medicare database. Data source: Mues et al.³⁰

From 2013 to 2015, the percentage of prolonged opioid users decreased from 27.4% to 26.5% (relative percentage difference, –3.3%) and the percentage of high-dose opioid users decreased from 7.5% to 7.0% (relative percentage difference, 6.9%; Figure 2). The longitudinal analysis showed that 18.0% (25 322 of 140 589) of adult Medicare beneficiaries with disabilities who received prolonged opioid prescriptions in 2013 no longer received prolonged opioid prescriptions in 2015. By contrast, 5.9% (21 529 of 367 114) of the 2013 cohort who did not have a prolonged opioid prescription in 2013 received a ≥90-day opioid prescription in 2015. Similarly, 29.0% (11 000 of 37 883) of adult Medicare beneficiaries with disabilities who received a high-dose opioid prescription in 2013 did not receive a high-dose opioid prescription in 2015, and 1.7% (8253 of 490 814) of adult Medicare beneficiaries who did not have a high-dose opioid prescription in 2013 initiated one in 2015.

In an examination of variation—according to demographic, clinical, and regional characteristics—in the absolute and relative changes in the percentages of prolonged opioid prescriptions in 2013 vs 2015, the observed changes did not vary substantially by sex, income, rurality, or morbidity but did vary by age (P < .001; Table 1). The following subpopulations of beneficiaries had relative percentage differences, between the smallest and largest decreases over time, that were significant: age (50-64 years [–2.8] vs 21-29 years [–17.8]), race (Hispanic [–1.7] vs non-Hispanic white [–4.1]), dual eligible (no [–0.3] vs yes [–5.0]), and education (quartile 1 [–2.5] vs quartile 2 [–5.0]). A sensitivity analysis, in which we defined prolonged use as ≥180 days, yielded comparable results. Likewise, in an examination of variation—according to demographic, clinical, and regional characteristics—in the absolute and relative changes in the percentages of high-dose prescriptions in 2013 vs 2015, the observed changes did not vary substantially by sex or income level but did vary significantly by age (P < .001), race/ethnicity (P < .001), dual eligibility (P < .001), education (P = .01), rurality (P = .003), and morbidity (P = .003; Table 2). The following subpopulations of beneficiaries had relative percentage differences, between the smallest and largest decreases over time, that were significant: age (50-64 years [–5.5] vs 21-29 years [–24.9]), race (Hispanic [–1.8] vs other [–12.1]), dual eligible (no [–4.2] vs yes [–9.1]), education (quartile 4 [–5.3] vs quartile 3 [–9.3]), rural/urban (rural [–5.0] vs urban [–9.4]), and morbidity (0 conditions [–5.1] vs ≥3 conditions [–9.6]).

Table 1.

Absolute and relative changes in percentages of prolonged (≥90 days) opioid prescriptions for Medicare beneficiaries with disabilities aged 21-64,^a by patient characteristics, United States, 2013 and 2015^b

Characteristic	2013		2015		Change		P Value^d
Characteristic	Total, No.	No.^c (%) With Prolonged Opioid Prescriptions	Total	No.^c (%) With Prolonged Opioid Prescriptions	Absolute, %	Relative, %	P Value^d
Age, y							<.001
21-29	37 408	2501 (6.7)	32 979	1812 (5.5)	–1.2	–17.8
30-39	91 577	14 766 (16.1)	85 776	12 251 (14.3)	–1.8	–11.4
40-49	158 434	41 907 (26.5)	137 220	34 158 (24.9)	–1.6	–5.9
50-64	401 765	129 686 (32.3)	401 032	125 806 (31.4)	–0.9	–2.8
Sex							.17
Male	347 659	80 778 (23.2)	329 931	74 273 (22.5)	–0.7	–3.1
Female	341 525	108 082 (31.6)	327 076	99 754 (30.5)	–1.1	–3.6
Race/ethnicity							.01
Non-Hispanic white	467 369	138 763 (29.7)	455 829	129 835 (28.5)	–1.2	–4.1
Non-Hispanic black	138 732	33 030 (23.8)	125 132	28 943 (23.1)	–0.7	–2.9
Hispanic	58 057	12 160 (20.9)	51 373	10 576 (20.6)	–0.4	–1.7
Other^e	25 026	4907 (19.6)	24 672	4673 (18.9)	–0.7	–3.4
Dual eligible^f							<.001
Yes	513 648	136 892 (26.7)	475 325	120 380 (25.3)	–1.3	–5.0
No	175 536	51 968 (29.6)	181 682	53 647 (29.5)	–0.1	–0.3
ZIP code education^g							<.001
Quartile 1	224 879	66 396 (29.5)	225 868	64 997 (28.8)	–0.8	–2.5
Quartile 2	200 259	58 180 (29.1)	188 717	52 112 (27.6)	–1.4	–5.0
Quartile 3	161 126	41 881 (26.0)	148 038	36 983 (25.0)	–1.0	–3.9
Quartile 4	102 920	22 403 (21.8)	94 384	19 935 (21.1)	–0.7	–3.0
ZIP code income^g							.24
Quartile 1	237 277	71 835 (30.3)	237 047	68 960 (29.1)	–1.2	–3.9
Quartile 2	190 030	54 842 (28.9)	181 263	50 073 (27.6)	–1.2	–4.3
Quartile 3	155 114	40 284 (26.0)	143 927	36 169 (25.1)	–0.8	–3.2
Quartile 4	106 762	21 899 (20.5)	94 769	18 825 (19.9)	–0.7	–3.2
Rural/urban^h							.05
Metropolitan	520 060	133 463 (25.7)	489 152	121 102 (24.8)	–0.9	–3.5
Urban	150 969	48 925 (32.4)	150 082	46 686 (31.1)	–1.3	–4.0
Rural	18 155	6472 (35.6)	17 774	6239 (35.1)	–0.5	–1.5
Morbidityⁱ							.23
0	229 452	42 323 (18.4)	213 754	37 531 (17.6)	–0.8	–4.8
1	194 008	51 291 (26.4)	181 880	46 164 (25.4)	–1.1	–4.0
2	118 225	36 956 (31.3)	114 431	34 630 (30.3)	–1.00	–3.2
≥3	147 499	58 290 (39.5)	146 941	55 702 (37.9)	–1.6	–4.1

Open in a new tab

^a Beneficiaries with continuous enrollment in Medicare Part A, Part B, and Part D without a health maintenance organization in previous year and in the study year or until death. Only beneficiaries alive after April 1 of the study year were included.

^b Based on the 20% national Medicare database. Data source: Mues et al.³⁰

^c The numbers given are the number of Medicare beneficiaries with disabilities with each characteristic who received prolonged opioid prescriptions.

^d From the modified Poisson regression model to examine the interaction between each characteristic and year of study. The model tested whether the relative changes in each category differed from each other. P < .05 was considered significant.

^e Includes Asian/Pacific Islander, American Indian/Alaska Native, and unknown.

^f Enrolled for ≥1 month in the Medicaid program.

^g Education and household income for ZIP code areas were obtained from the 2011-2015 American Community Survey 5-year estimators and categorized by quartiles (1 = lowest quartile, 4 = highest quartile).³³

^h Type of residential area (metropolitan, nonmetropolitan urban, rural) using rural–urban continuum codes.³⁵

ⁱ Elixhauser comorbidity index, the total number of comorbid conditions based on the International Classification of Diseases, Ninth Revision codes, for each enrollee were generated from all claims in the 12 months before each study year.³⁴

Table 2.

Absolute and relative changes in the percentages of high-dose (≥100 morphine milligram equivalents per day for >30 days in 1 year) opioid prescriptions for Medicare beneficiaries with disabilities aged 21-64,^a by patient characteristics, United States, 2013 and 2015^b

Characteristic	2013		2015		Change
Characteristic	Total	No.^c (%) With Prolonged Opioid Prescriptions	Total	No.^c (%) With Prolonged Opioid Prescriptions	Absolute, %	Relative, %	P Value^d
Age, y							<.001
21-29	37 408	702 (1.9)	32 979	465 (1.4)	–0.5	–24.9
30-39	91 577	4406 (4.8)	85 776	3482 (4.1)	–0.8	–15.6
40-49	158 434	12 779 (8.1)	137 220	10 046 (7.3)	–0.7	–9.2
50-64	401 765	33 954 (8.5)	401 032	32 039 (8.0)	–0.5	–5.5
Sex							.11
Male	347 659	24 397 (7.0)	329 931	21 717 (6.6)	–0.4	–6.2
Female	341 525	27 444 (8.0)	327 076	24 315 (7.4)	–0.6	–7.5
Race/ethnicity							<.001
Non-Hispanic white	467 369	40 834 (8.7)	455 829	36 561 (8.0)	–0.7	–8.2
Non-Hispanic black	138 732	6925 (5.0)	125 132	5927 (4.7)	–0.3	–5.1
Hispanic	58 057	2733 (4.7)	51 373	2375 (4.6)	–0.1	–1.8
Other^e	25 026	1349 (5.4)	24 672	1169 (4.7)	–0.7	–12.1
Dual eligible^f							<.001
Yes	513 648	35 770 (7.0)	475 325	30 094 (6.3)	–0.6	–9.1
No	175 536	16 071 (9.2)	181 682	15 938 (8.8)	–0.4	–4.2
ZIP code education^g							.01
Quartile 1	224 879	15 529 (6.9)	225 868	14 750 (6.5)	–0.4	–5.4
Quartile 2	200 259	15 731 (7.9)	188 717	13 867 (7.3)	–0.5	–6.5
Quartile 3	161 126	13 061 (8.1)	148 038	10 883 (7.3)	–0.8	–9.3
Quartile 4	102 920	7520 (7.3)	94 384	6532 (6.9)	–0.4	–5.3
ZIP code income^h							.27
Quartile 1	237 277	16 557 (7.0)	237 047	15 442 (6.5)	–0.5	–6.7
Quartile 2	190 030	14 857 (7.8)	181 263	13 078 (7.2)	–0.6	–7.7
Quartile 3	155 114	12 450 (8.0)	143 927	10 940 (7.6)	–0.4	–5.3
Quartile 4	106 762	7977 (7.5)	94 769	6571 (6.9)	–0.5	–7.2
Rural/urban^h							.003
Metropolitan	520 060	39 041 (7.5)	489 152	34 464 (7.0)	–0.5	–6.2
Urban	150 969	11 421 (7.6)	150 082	10 285 (6.9)	–0.7	–9.4
Rural	18 155	1379 (7.6)	17 774	1283 (7.2)	–0.4	–5.0
Morbidityⁱ							.003
0	229 452	12 017 (5.2)	213 754	10 624 (5.0)	–0.3	–5.1
1	194 008	13 408 (6.9)	181 880	11 754 (6.5)	–0.5	–6.5
2	118 225	9706 (8.2)	114 431	8614 (7.5)	–0.7	–8.3
≥3	147 499	16 710 (11.3)	146 941	15 040 (10.2)	–1.1	–9.6

Open in a new tab

^a Beneficiaries with continuous enrollment in Medicare Part A, Part B, and Part D without a health maintenance organization in previous year and in the study year or until death.

^b Based on the 20% national Medicare database. Data source: Mues et al.³⁰

^c The numbers given are the number of disabled adult Medicare enrollees with each characteristic who received prolonged opioid prescriptions.

^d From the modified Poisson regression model to examine the interaction between each characteristic and year of study. The model tested whether the relative changes in each category were different from each other. P < .05 was considered significant.

^e Includes Asian/Pacific Islander, American Indian/Alaska Native, and unknown.

^f Enrolled for ≥1 month in the Medicaid program.

^h Type of residential area (metropolitan, nonmetropolitan urban, rural) using rural–urban continuum codes.³⁵

The percentage of prolonged opioid users decreased in 38 states from 2013 to 2015, but all changes were minor; most states stayed in the same quintile as in 2013. Illinois and North Dakota had the largest absolute increase (1.8%) and West Virginia had the largest absolute decrease (3.6%) in the percentage of Medicare beneficiaries with disabilities who received prolonged opioid prescriptions (Figure 3).

Figure 3. — Prolonged opioid use by Medicare beneficiaries with disabilities aged 21-64, United States, 2013 and 2015. Rates of prescriptions for prolonged opioid use (≥90 days) before and after hydrocodone rescheduling in the United States. Based on the 20% national Medicare database. Data source: Mues et al.³⁰

Discussion

The 2014 federal rescheduling of hydrocodone was associated with a decrease in the proportion of adult Medicare beneficiaries with disabilities receiving hydrocodone prescriptions, most notably around the time of its implementation. This decrease in the percentage of Medicare beneficiaries with disabilities receiving hydrocodone-combination prescriptions appeared to be partially compensated by increases in the percentage of Medicare beneficiaries with disabilities receiving nonhydrocodone schedule II and III prescriptions. It is likely that these increases were also partially responsible for the minor changes in overall and prolonged opioid-prescribing rates.

Although it is reasonable to infer that the reductions in opioid prescribing found in our study were associated with the federal rescheduling of hydrocodone, these decreases were smaller than expected when compared with decreases in opioid prescribing among commercially insured and older Medicare populations described in previous studies.^21,25-27,38 Overall opioid-prescribing rates for adults with disabilities decreased by only 2.5%, whereas rates were reported to have decreased by 5.5% among the older Medicare population and 11.4% among commercially insured adults.^21,25 Likewise, prolonged opioid-prescribing rates decreased by 3.3% among adults with disabilities, whereas rates are reported to have decreased by 10.2% in the older Medicare population.²⁵ In addition, high-dose opioid-prescribing rates decreased by 6.9% among adults with disabilities, whereas rates decreased by 8.2% among the older Medicare population.²⁵ Furthermore, the increases in nonhydrocodone schedule II and III prescribing rates in our study were larger than increases seen in other national populations.^21,25 Our finding that the prescribing rates for nonhydrocodone schedule II opioids surpassed the prescribing rates for hydrocodone is a phenomenon unique to adult Medicare beneficiaries with disabilities. These findings suggest that the 2014 federal regulation had a minimal effect on opioid-prescribing patterns among adult Medicare beneficiaries with disabilities, a finding that is consistent with the reported lack of effectiveness of state laws in curbing high-risk prescription opioid prescribing in this population.¹⁰

In 2015, after the 2014 regulation, adults with disabilities received a higher percentage of hydrocodone prescriptions (27.7%) than older Medicare (18.3%) and commercially insured (2.0%) adults.^21,25 We saw a similar pattern for rates of any opioid prescription (49.0% for adults with disabilities vs 35.0% for older Medicare adults and 4.2% for commercially insured adults).^21,25 The rates of any opioid prescription were also consistent with a previous study (52% for adults with disabilities vs 26% for older Medicare adults and 14% for commercially insured adults).¹¹ About 1 in 5 adults with disabilities with a prolonged opioid prescription in 2013 had no prolonged opioid prescription in 2015 (n = 25 322), but this number was nearly equivalent to those newly starting a prolonged opioid prescription in 2015 (n = 21 529). Overall, adults with disabilities comprised only 17.4% of the Medicare population in 2015 but accounted for 34.1% of prolonged opioid prescriptions and 60.3% of high-dose opioid prescriptions that same year.^21,25 Data from studies of state laws showed a similar persistence of high opioid-prescribing rates among adults with disabilities after the enactment of new state laws.^10,11

The percentage of prolonged opioid prescriptions in each state did not change substantially in 2015 compared with 2013. However, some patient characteristics (age ≥40, lack of Medicaid coverage, education level, Hispanic ethnicity) were significantly associated with smaller decreases in both prolonged and high-dose prescribing. The associations between these characteristics and high-risk opioid prescribing warrant further investigation.

Persons with disabilities have a higher rate of prescription opioid use than the general population, which greatly increases their risk of developing opioid use disorders and having an opioid overdose.^{10,11,13-15,39} Opioid use is associated with some of the most prevalent disabilities in this population (mental health disorders, musculoskeletal pain).^3,6-9,39 However, several studies, including the 2016 Centers for Disease Control and Prevention’s Guideline for Prescribing Opioids for Chronic Pain, have suggested that long-term opioid therapy for noncancer pain is not necessarily associated with meaningful long-term benefits.^8,40,41 Furthermore, persons with disabilities have a lower rate of treatment for opioid use disorder than persons without disabilities (11% vs 32%).⁴² This lower treatment rate may be attributable to physical inaccessibility of treatment centers, limited insurance coverage, and policies that limit access to medication-assisted treatments.⁴² Thus, exploring pain management plans that combine both pharmacologic and nonopioid treatment alternatives for chronic noncancer pain (eg, back pain from spine arthritis) may be one way to stem the prevalence of high-risk opioid prescribing among adults with disabilities. Particular attention should be given to assessing opioid withdrawal syndrome and tapering plans.⁴³

Limitations

This study had several limitations. First, because we used Medicare Part D data, we could not determine whether beneficiaries actually took their prescribed opioids; as such, we may have overestimated opioid use in this population. Furthermore, indications for opioid prescriptions and data on pain severity were not available, which limited our ability to distinguish opioid misuse from necessary use. Illegally obtained opioids, injected opioids, and diversion to other users also were not considered. As such, it is likely that our findings underestimate total opioid use. Second, the study focused exclusively on fee-for-service Medicare Part D enrollees; as such, our results may not be generalizable to other Medicare populations. Third, the 2013 and 2015 cohorts differed in patient characteristics, which could have confounded our ability to assess the impact of the regulation on opioid use. However, we controlled for these differences in our multivariable analyses. Fourth, states were considered as a fixed effect in our multivariable analyses, which limited our ability to assess the extent to which the impact of the regulation varied according to states. Fifth, our comparison of 2 one-year periods—2013 and 2015—did not permit assessment of a possible underlying temporal trend occurring over a broader time period. Finally, we did not consider the effect from providers, who have the ultimate role in opioid prescribing.

Conclusions

Among Medicare beneficiaries with disabilities, the 2014 federal regulation was associated with decreases in hydrocodone prescribing but only minor decreases in overall and high-risk opioid prescribing. This decrease in hydrocodone prescribing was countered by a substantial increase in nonhydrocodone schedule II and III prescribing. In view of these findings, future research—particularly studies that examine the physical and intellectual disabilities associated with persistent overall and high-risk opioid use—is needed to further inform the development of opioid guidelines and policies for effective and safe management of pain among adults with disabilities.

Footnotes

Declaration of Conflicting Interests: The authors declared the following potential conflict of interest with respect to the research, authorship, and/or publication of this article: Dr Jacques Baillargeon has received payment for consulting with AbbVie, GlaxoSmithKline, Auxilium Pharmaceuticals, and Endo Pharmaceuticals.

Funding: The authors declared the following financial support with respect to the research, authorship, and/or publication of this article: R01DA039192 (National Institute on Drug Abuse), RP160674 (Cancer Prevention and Research Institute of Texas), and T32HS02613301 (Agency for Healthcare Research and Quality). The funding organizations had no role in the design or conduct of the study; in the collection, analysis, or interpretation of data; or in the preparation, review, or approval of the article.

ORCID iD: Jacques Baillargeon, PhD Inline graphic https://orcid.org/0000-0002-3297-653X

References

1. Rudd RA, Seth P, David F, Scholl L. Increases in drug and opioid-involved overdose deaths—United States, 2010-2015 [published correction appears in MMWR Morb Mortal Wkly Rep. 2017;66:35]. MMWR Morb Mortal Wkly Rep. 2016;65(50-51):1445–1452. doi:10.15585/mmwr.mm655051e1 [DOI] [PubMed] [Google Scholar]
2. Peters JL, Durand WM, Monteiro KA, Dumenco L, George P. Opioid overdose hospitalizations among Medicare-disability beneficiaries. J Am Board Fam Med. 2018;31(6):881–896. doi:10.3122/jabfm.2018.06.180152 [DOI] [PubMed] [Google Scholar]
3. Webster LR. Risk factors for opioid-use disorder and overdose. Anesth Analg. 2017;125(5):1741–1748. doi:10.1213/ANE.00000000000002496 [DOI] [PubMed] [Google Scholar]
4. Song Z. Mortality quadrupled among opioid-driven hospitalizations, notably within lower-income and disabled white populations. Health Aff (Millwood). 2017;36(12):2054–2061. doi:10.1377/hlthaff.2017.0689 [DOI] [PMC free article] [PubMed] [Google Scholar]
5. Roland CL, Ye X, Stevens V, Oderda GM. The prevalence and cost of Medicare beneficiaries diagnosed and at risk for opioid abuse, dependence, and poisoning. J Manag Care Spec Pharm. 2019;25(1):18–27. doi:10.18553/jmcp.2019.25.1.018 [DOI] [PMC free article] [PubMed] [Google Scholar]
6. Social Security Administration. Annual Statistical Report on the Social Security Disability Insurance Program, 2016—Beneficiaries in Current-Payment Status. SSA Pub No 13-11826 Washington, DC: Social Security Administration; 2018. [Google Scholar]
7. Davis MA, Lin LA, Liu H, Sites BD. Prescription opioid use among adults with mental health disorders in the United States. J Am Board Fam Med. 2017;30(4):407–417. doi:10.3122/jabfm.2017.04.170112 [DOI] [PubMed] [Google Scholar]
8. Deyo RA, Von Korff M, Duhrkoop D. Opioids for low back pain. BMJ. 2015;350:g6380 doi:10.1136/bmj.g6380 [DOI] [PMC free article] [PubMed] [Google Scholar]
9. Hudson TJ, Edlund MJ, Steffick DE, Tripathi SP, Sullivan MD. Epidemiology of regular prescribed opioid use: results from a national, population-based survey. J Pain Symptom Manag. 2008;36(3):280–288. doi:10.1016/j.jpainsymman.2007.10.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
10. Meara E, Horwtiz JR, Powell W, et al. State legal restrictions and prescription-opioid use among disabled adults. N Engl J Med. 2016;375(1):44–53. doi:10.1056/NEJMsa1514387 [DOI] [PMC free article] [PubMed] [Google Scholar]
11. Jeffery MM, Hooten WM, Henk HJ, et al. Trends in opioid use in commercially insured and Medicare advantage populations in 2007-16: retrospective cohort study. BMJ. 2018;362:k2833 doi:10.1136/bjm.k2833 [DOI] [PMC free article] [PubMed] [Google Scholar]
12. Boudreau D, Von Kroff M, Rutter CM, et al. Trends in long-term opioid therapy for chronic non-cancer pain. Pharmacoepidemiol Drug Saf. 2009;18(12):1166–1175. doi:10.1002/pds.1833 [DOI] [PMC free article] [PubMed] [Google Scholar]
13. Edlund MJ, Martin BC, Russo JE, DeVries A, Braden JB, Sullivan MD. The role of opioid prescription in incident opioid abuse and dependence among individuals with chronic noncancer pain: the role of opioid prescription. Clin J Pain. 2014;30(7):557–564. doi:10.1097/AJP.000 000 000 000 021 [DOI] [PMC free article] [PubMed] [Google Scholar]
14. 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 [DOI] [PubMed] [Google Scholar]
15. Coutinho AD, Gandhi K, Fuldeore RM, Landsman-Blumberg PB, Gandhi S. Long-term opioid users with chronic noncancer pain: assessment of opioid abuse risk and relationship with healthcare resource use. J Opioid Manag. 2018;14(2):131–141. doi:10.5055/jom.2018.0440 [DOI] [PubMed] [Google Scholar]
16. Moyo P, Simoni-Wastila L, Griffin BA, et al. Impact of prescription drug monitoring programs (PDMPs) on opioid utilization among Medicare beneficiaries in 10 US states. Addiction. 2017;112(10):1784–1796. doi:10.1111/add.13860 [DOI] [PubMed] [Google Scholar]
17. Patrick SW, Fry CE, Jones TF, Buntin MB. Implementation of prescription drug monitoring programs associated with reductions in opioid-related death rates. Health Aff (Millwood). 2016;35(7):1324–1332. doi:10.1377/hlthaff.2015.1496 [DOI] [PMC free article] [PubMed] [Google Scholar]
18. Popovici I, Maclean JC, Hijazi B, Radakrishnan S. The effect of state laws designed to prevent nonmedical prescription opioid use on overdose deaths and treatment. Health Econ. 2018;27(2):294–305. doi:10.1002/hec.3548 [DOI] [PubMed] [Google Scholar]
19. Johnson H, Paulozzi L, Porucznik C, Mack K, Herter B. Decline in drug overdose deaths after state policy changes—Florida, 2010-2012. MMWR Morb Mortal Wkly Rep. 2014;63(26):569–574. [PMC free article] [PubMed] [Google Scholar]
20. Kuo YF, Raji MA, Liaw V, Baillargeon J, Goodwin JS. Opioid prescriptions in older Medicare beneficiaries after the 2014 federal rescheduling of hydrocodone products. J Am Geriatr Soc. 2018;66(5):945–953. doi:10.1111/jgs.15332 [DOI] [PMC free article] [PubMed] [Google Scholar]
21. Raji MA, Kuo YF, Adhikari D, Baillargeon J, Goodwin JS. Decline in opioid prescribing after federal rescheduling of hydrocodone products. Pharmacoepidemiol Drug Saf. 2018;27(5):513–519. doi:10.1002/pds.4376 [DOI] [PMC free article] [PubMed] [Google Scholar]
22. Jones CM, Lurie PG, Throckmorton DC. Effect of US Drug Enforcement Administration’s rescheduling of hydrocodone combination analgesic products on opioid analgesic prescribing. JAMA Intern Med. 2016;176(3):339–402. doi:10.1001/jamainternmed.2015.7799 [DOI] [PubMed] [Google Scholar]
23. Bernhardt MB, Taylor RS, Hagan JL, et al. Evaluation of opioid prescribing after rescheduling of hydrocodone-containing products. Am J Health Syst Pharm. 2017;74(24):2046–2053. doi:10.2146/ajhp160548 [DOI] [PubMed] [Google Scholar]
24. US Department of Justice, Drug Enforcement Administration. Schedules of controlled substances: rescheduling of hydrocodone combination products from schedule III to schedule II. Fed Regist. 2014;79(163):49661–49682. [PubMed] [Google Scholar]
25. Kuo YF, Raji MA, Liaw V, Baillargeon J, Goodwin JS. Opioid prescriptions in older Medicare beneficiaries after the 2014 federal rescheduling of hydrocodone products. J Am Geriatr Soc. 2018;66(5):945–953. doi:10.1111/jgs.15332 [DOI] [PMC free article] [PubMed] [Google Scholar]
26. Haider A, Zhukovsky DS, Meng YC, et al. Opioid prescription trends among patients with cancer referred to outpatient palliative care over a 6-year period. J Oncol Pract. 2017;13(12):e972–e981. doi:10.1200/JOP.2017.024901 [DOI] [PMC free article] [PubMed] [Google Scholar]
27. Schultz S, Chamberlain C, Vulcan M, Rana H, Patel B, Alexander JC. Analgesic utilization before and after rescheduling of hydrocodone in a large academic level 1 trauma center. J Opioid Manag. 2016;12(2):119–122. doi:10.5055/jom.2016.0323 [DOI] [PubMed] [Google Scholar]
28. Meisenberg BR, Grover J, Campbell C, Korpon D. Assessment of opioid prescribing practices before and after implementation of a health system intervention to reduce opioid overprescribing. JAMA Netw Open. 2018;1(5):e18290 8. doi:10.1001/jamanetworkopen.2018.2908 [DOI] [PMC free article] [PubMed] [Google Scholar]
29. Kahlenberg CA, Stepan JG, Premkumar A, Lovecchio FD, Cross MB. Institutional guidelines can decrease the amount of opioids prescribed after total joint replacement. HSS J. 2019;15(1):27–30. doi:10.1007/s11420-018-9632-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
30. Mues KE, Liede A, Liu J, et al. Use of the Medicare database in epidemiologic and health services research: a valuable source of real-world evidence on the older and disabled populations in the US. Clin Epidemiol. 2017;9:267–277. doi:10.2147/CLEP.S105613 [DOI] [PMC free article] [PubMed] [Google Scholar]
31. Truven Health Analytics. Red Book Select Extracts. http://truvenhealth.com/Portals/0/Assets/AWP/PROV_19263_0818_Red_Book_Flat_File_Data_Sheet_FINAL.pdf. Published 2014. Accessed October 15, 2019.
32. Barnett ML, Olenski AR, Jena AB. Opioid-prescribing patterns of emergency physicians and risk of long-term use. N Engl J Med. 2017;376(7):663–673. doi:10.1056/NEJMsa1610524 [DOI] [PMC free article] [PubMed] [Google Scholar]
33. US Census Bureau. 2011-2015 American Community Survey 5-year estimates: 2015 selected social characteristics. https://www.census.gov/programs-surveys/acs/technical-documentation/table-and-geography-changes/2015/5-year.html. Accessed October 15, 2019.
34. Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care. 1998;36(1):8–27. doi:10.1097/00005650-199801000-00004 [DOI] [PubMed] [Google Scholar]
35. US Department of Agriculture. Rural–urban continuum codes. https//www.ers.usda.gov/data-products/rural-urban-continuum-codes. Published 2016. Accessed October 15, 2019.
36. SAS Enterprise [statistical software]. Version 7.12. Cary, NC: SAS Institute Inc; 2014. [Google Scholar]
37. ArcGIS Desktop [software]. Version 9.3. Redlands, CA: Environmental Systems Research Institute; 2017. [Google Scholar]
38. Seago S, Hayek A, Pruszynski J, Newman MG. Change in prescription habits after federal rescheduling of hydrocodone combination products. Proc (Bayl Univ Med Cent). 2016;29(3):268–270. doi:10.1080/08998280.2016.11929431 [DOI] [PMC free article] [PubMed] [Google Scholar]
39. Morden NE, Munson JC, Colla CH, et al. Prescription opioid use among disabled Medicare beneficiaries: intensity, trends, and regional variation. Med Care. 2014;52(9):852–859. doi:10.1097/MLR.0000000000000183 [DOI] [PMC free article] [PubMed] [Google Scholar]
40. Dowell D, Haegerich TM, Chou R. CDC guideline for prescribing opioids for chronic pain—United States, 2016. JAMA. 2016;315(15):1624–1645. doi:10.1001/jama.2016.1464 [DOI] [PMC free article] [PubMed] [Google Scholar]
41. Abdel Shaheed C, Maher CG, Williams KA, Day R, McLachlan AJ. Efficacy, tolerability, and dose-dependent effects of opioid analgesics for low back pain: a systematic review and meta-analysis. JAMA Intern Med. 2016;176(7):958–968. doi:10.1001/jamainternmed.2016.1251 [DOI] [PubMed] [Google Scholar]
42. National Institute on Disability, Independent Living, and Rehabilitation Research. Summary of responses from a request for information: people with disabilities and opioid use disorder. https://acl.gov/sites/default/files/news%202018-05/20180502NIDILRROpioidRFIFindings.pdf. Published 2018. Accessed September 30, 2019.
43. Pergolizzi JV, Jr, LeQuang JA, Berger GK, Raffa RB. The basic pharmacology of opioids informs the opioid discourse about misuse and abuse: a review. Pain Ther. 2017;6(1):1–16. doi:10.1007/s40122-017-0068-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr1-0033354919892638] 1. Rudd RA, Seth P, David F, Scholl L. Increases in drug and opioid-involved overdose deaths—United States, 2010-2015 [published correction appears in MMWR Morb Mortal Wkly Rep. 2017;66:35]. MMWR Morb Mortal Wkly Rep. 2016;65(50-51):1445–1452. doi:10.15585/mmwr.mm655051e1 [DOI] [PubMed] [Google Scholar]

[bibr2-0033354919892638] 2. Peters JL, Durand WM, Monteiro KA, Dumenco L, George P. Opioid overdose hospitalizations among Medicare-disability beneficiaries. J Am Board Fam Med. 2018;31(6):881–896. doi:10.3122/jabfm.2018.06.180152 [DOI] [PubMed] [Google Scholar]

[bibr3-0033354919892638] 3. Webster LR. Risk factors for opioid-use disorder and overdose. Anesth Analg. 2017;125(5):1741–1748. doi:10.1213/ANE.00000000000002496 [DOI] [PubMed] [Google Scholar]

[bibr4-0033354919892638] 4. Song Z. Mortality quadrupled among opioid-driven hospitalizations, notably within lower-income and disabled white populations. Health Aff (Millwood). 2017;36(12):2054–2061. doi:10.1377/hlthaff.2017.0689 [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr5-0033354919892638] 5. Roland CL, Ye X, Stevens V, Oderda GM. The prevalence and cost of Medicare beneficiaries diagnosed and at risk for opioid abuse, dependence, and poisoning. J Manag Care Spec Pharm. 2019;25(1):18–27. doi:10.18553/jmcp.2019.25.1.018 [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr6-0033354919892638] 6. Social Security Administration. Annual Statistical Report on the Social Security Disability Insurance Program, 2016—Beneficiaries in Current-Payment Status. SSA Pub No 13-11826 Washington, DC: Social Security Administration; 2018. [Google Scholar]

[bibr7-0033354919892638] 7. Davis MA, Lin LA, Liu H, Sites BD. Prescription opioid use among adults with mental health disorders in the United States. J Am Board Fam Med. 2017;30(4):407–417. doi:10.3122/jabfm.2017.04.170112 [DOI] [PubMed] [Google Scholar]

[bibr8-0033354919892638] 8. Deyo RA, Von Korff M, Duhrkoop D. Opioids for low back pain. BMJ. 2015;350:g6380 doi:10.1136/bmj.g6380 [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr9-0033354919892638] 9. Hudson TJ, Edlund MJ, Steffick DE, Tripathi SP, Sullivan MD. Epidemiology of regular prescribed opioid use: results from a national, population-based survey. J Pain Symptom Manag. 2008;36(3):280–288. doi:10.1016/j.jpainsymman.2007.10.003 [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr10-0033354919892638] 10. Meara E, Horwtiz JR, Powell W, et al. State legal restrictions and prescription-opioid use among disabled adults. N Engl J Med. 2016;375(1):44–53. doi:10.1056/NEJMsa1514387 [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr11-0033354919892638] 11. Jeffery MM, Hooten WM, Henk HJ, et al. Trends in opioid use in commercially insured and Medicare advantage populations in 2007-16: retrospective cohort study. BMJ. 2018;362:k2833 doi:10.1136/bjm.k2833 [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr12-0033354919892638] 12. Boudreau D, Von Kroff M, Rutter CM, et al. Trends in long-term opioid therapy for chronic non-cancer pain. Pharmacoepidemiol Drug Saf. 2009;18(12):1166–1175. doi:10.1002/pds.1833 [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr13-0033354919892638] 13. Edlund MJ, Martin BC, Russo JE, DeVries A, Braden JB, Sullivan MD. The role of opioid prescription in incident opioid abuse and dependence among individuals with chronic noncancer pain: the role of opioid prescription. Clin J Pain. 2014;30(7):557–564. doi:10.1097/AJP.000 000 000 000 021 [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr14-0033354919892638] 14. 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 [DOI] [PubMed] [Google Scholar]

[bibr15-0033354919892638] 15. Coutinho AD, Gandhi K, Fuldeore RM, Landsman-Blumberg PB, Gandhi S. Long-term opioid users with chronic noncancer pain: assessment of opioid abuse risk and relationship with healthcare resource use. J Opioid Manag. 2018;14(2):131–141. doi:10.5055/jom.2018.0440 [DOI] [PubMed] [Google Scholar]

[bibr16-0033354919892638] 16. Moyo P, Simoni-Wastila L, Griffin BA, et al. Impact of prescription drug monitoring programs (PDMPs) on opioid utilization among Medicare beneficiaries in 10 US states. Addiction. 2017;112(10):1784–1796. doi:10.1111/add.13860 [DOI] [PubMed] [Google Scholar]

[bibr17-0033354919892638] 17. Patrick SW, Fry CE, Jones TF, Buntin MB. Implementation of prescription drug monitoring programs associated with reductions in opioid-related death rates. Health Aff (Millwood). 2016;35(7):1324–1332. doi:10.1377/hlthaff.2015.1496 [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr18-0033354919892638] 18. Popovici I, Maclean JC, Hijazi B, Radakrishnan S. The effect of state laws designed to prevent nonmedical prescription opioid use on overdose deaths and treatment. Health Econ. 2018;27(2):294–305. doi:10.1002/hec.3548 [DOI] [PubMed] [Google Scholar]

[bibr19-0033354919892638] 19. Johnson H, Paulozzi L, Porucznik C, Mack K, Herter B. Decline in drug overdose deaths after state policy changes—Florida, 2010-2012. MMWR Morb Mortal Wkly Rep. 2014;63(26):569–574. [PMC free article] [PubMed] [Google Scholar]

[bibr20-0033354919892638] 20. Kuo YF, Raji MA, Liaw V, Baillargeon J, Goodwin JS. Opioid prescriptions in older Medicare beneficiaries after the 2014 federal rescheduling of hydrocodone products. J Am Geriatr Soc. 2018;66(5):945–953. doi:10.1111/jgs.15332 [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr21-0033354919892638] 21. Raji MA, Kuo YF, Adhikari D, Baillargeon J, Goodwin JS. Decline in opioid prescribing after federal rescheduling of hydrocodone products. Pharmacoepidemiol Drug Saf. 2018;27(5):513–519. doi:10.1002/pds.4376 [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr22-0033354919892638] 22. Jones CM, Lurie PG, Throckmorton DC. Effect of US Drug Enforcement Administration’s rescheduling of hydrocodone combination analgesic products on opioid analgesic prescribing. JAMA Intern Med. 2016;176(3):339–402. doi:10.1001/jamainternmed.2015.7799 [DOI] [PubMed] [Google Scholar]

[bibr23-0033354919892638] 23. Bernhardt MB, Taylor RS, Hagan JL, et al. Evaluation of opioid prescribing after rescheduling of hydrocodone-containing products. Am J Health Syst Pharm. 2017;74(24):2046–2053. doi:10.2146/ajhp160548 [DOI] [PubMed] [Google Scholar]

[bibr24-0033354919892638] 24. US Department of Justice, Drug Enforcement Administration. Schedules of controlled substances: rescheduling of hydrocodone combination products from schedule III to schedule II. Fed Regist. 2014;79(163):49661–49682. [PubMed] [Google Scholar]

[bibr25-0033354919892638] 25. Kuo YF, Raji MA, Liaw V, Baillargeon J, Goodwin JS. Opioid prescriptions in older Medicare beneficiaries after the 2014 federal rescheduling of hydrocodone products. J Am Geriatr Soc. 2018;66(5):945–953. doi:10.1111/jgs.15332 [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr26-0033354919892638] 26. Haider A, Zhukovsky DS, Meng YC, et al. Opioid prescription trends among patients with cancer referred to outpatient palliative care over a 6-year period. J Oncol Pract. 2017;13(12):e972–e981. doi:10.1200/JOP.2017.024901 [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr27-0033354919892638] 27. Schultz S, Chamberlain C, Vulcan M, Rana H, Patel B, Alexander JC. Analgesic utilization before and after rescheduling of hydrocodone in a large academic level 1 trauma center. J Opioid Manag. 2016;12(2):119–122. doi:10.5055/jom.2016.0323 [DOI] [PubMed] [Google Scholar]

[bibr28-0033354919892638] 28. Meisenberg BR, Grover J, Campbell C, Korpon D. Assessment of opioid prescribing practices before and after implementation of a health system intervention to reduce opioid overprescribing. JAMA Netw Open. 2018;1(5):e18290 8. doi:10.1001/jamanetworkopen.2018.2908 [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr29-0033354919892638] 29. Kahlenberg CA, Stepan JG, Premkumar A, Lovecchio FD, Cross MB. Institutional guidelines can decrease the amount of opioids prescribed after total joint replacement. HSS J. 2019;15(1):27–30. doi:10.1007/s11420-018-9632-6 [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr30-0033354919892638] 30. Mues KE, Liede A, Liu J, et al. Use of the Medicare database in epidemiologic and health services research: a valuable source of real-world evidence on the older and disabled populations in the US. Clin Epidemiol. 2017;9:267–277. doi:10.2147/CLEP.S105613 [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr31-0033354919892638] 31. Truven Health Analytics. Red Book Select Extracts. http://truvenhealth.com/Portals/0/Assets/AWP/PROV_19263_0818_Red_Book_Flat_File_Data_Sheet_FINAL.pdf. Published 2014. Accessed October 15, 2019.

[bibr32-0033354919892638] 32. Barnett ML, Olenski AR, Jena AB. Opioid-prescribing patterns of emergency physicians and risk of long-term use. N Engl J Med. 2017;376(7):663–673. doi:10.1056/NEJMsa1610524 [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr33-0033354919892638] 33. US Census Bureau. 2011-2015 American Community Survey 5-year estimates: 2015 selected social characteristics. https://www.census.gov/programs-surveys/acs/technical-documentation/table-and-geography-changes/2015/5-year.html. Accessed October 15, 2019.

[bibr34-0033354919892638] 34. Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care. 1998;36(1):8–27. doi:10.1097/00005650-199801000-00004 [DOI] [PubMed] [Google Scholar]

[bibr35-0033354919892638] 35. US Department of Agriculture. Rural–urban continuum codes. https//www.ers.usda.gov/data-products/rural-urban-continuum-codes. Published 2016. Accessed October 15, 2019.

[bibr36-0033354919892638] 36. SAS Enterprise [statistical software]. Version 7.12. Cary, NC: SAS Institute Inc; 2014. [Google Scholar]

[bibr37-0033354919892638] 37. ArcGIS Desktop [software]. Version 9.3. Redlands, CA: Environmental Systems Research Institute; 2017. [Google Scholar]

[bibr38-0033354919892638] 38. Seago S, Hayek A, Pruszynski J, Newman MG. Change in prescription habits after federal rescheduling of hydrocodone combination products. Proc (Bayl Univ Med Cent). 2016;29(3):268–270. doi:10.1080/08998280.2016.11929431 [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr39-0033354919892638] 39. Morden NE, Munson JC, Colla CH, et al. Prescription opioid use among disabled Medicare beneficiaries: intensity, trends, and regional variation. Med Care. 2014;52(9):852–859. doi:10.1097/MLR.0000000000000183 [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr40-0033354919892638] 40. Dowell D, Haegerich TM, Chou R. CDC guideline for prescribing opioids for chronic pain—United States, 2016. JAMA. 2016;315(15):1624–1645. doi:10.1001/jama.2016.1464 [DOI] [PMC free article] [PubMed] [Google Scholar]

[bibr41-0033354919892638] 41. Abdel Shaheed C, Maher CG, Williams KA, Day R, McLachlan AJ. Efficacy, tolerability, and dose-dependent effects of opioid analgesics for low back pain: a systematic review and meta-analysis. JAMA Intern Med. 2016;176(7):958–968. doi:10.1001/jamainternmed.2016.1251 [DOI] [PubMed] [Google Scholar]

[bibr42-0033354919892638] 42. National Institute on Disability, Independent Living, and Rehabilitation Research. Summary of responses from a request for information: people with disabilities and opioid use disorder. https://acl.gov/sites/default/files/news%202018-05/20180502NIDILRROpioidRFIFindings.pdf. Published 2018. Accessed September 30, 2019.

[bibr43-0033354919892638] 43. Pergolizzi JV, Jr, LeQuang JA, Berger GK, Raffa RB. The basic pharmacology of opioids informs the opioid discourse about misuse and abuse: a review. Pain Ther. 2017;6(1):1–16. doi:10.1007/s40122-017-0068-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Opioid Prescribing Among Adults With Disabilities in the United States After the 2014 Federal Hydrocodone Rescheduling Regulation

Victor Liaw

Yong-Fang Kuo, PhD

Mukaila A Raji, MD

Jacques Baillargeon, PhD