Abstract
Introduction:
Evidence suggests that United States (US) dentists excessively prescribe opioids. There are limited national data on recent trends in opioid prescribing by US dentists. This study examined trends of opioid prescribing by general dentists and dental specialists in the US from 2012–2019.
Methods:
Dispensed prescriptions for oral opioid analgesics written by dentists were identified in the IQVIA Longitudinal Prescription Dataset from January 2012 through December 2019. Autoregressive integrated moving average and joinpoint regression models described monthly population-based prescribing rates (prescriptions [Rx]/100,000 persons), dentist-based prescribing rates (Rx/1,000 dentists), and opioid dosages (mean daily morphine milligram equivalents [MME/day]). All analyses were performed in 2020.
Results:
Over the eight years, dentists prescribed >87.2 million opioid prescriptions. Population- and dentist-based prescribing rates declined monthly by −1.97 Rx/100,000 persons (95% confidence intervals [CI] −9.98, −0.97) and −39.12 Rx/1,000 dentists (95%CI −58.63, −17.65), respectively. Opioid dosages declined monthly by −0.08MME/day (95%CI −0.13,−0.04). Joinpoint regression identified four time-points (February 2016, May 2017, December 2018, and March 2019) at which monthly prescribing rate trends were often decreasing in greater magnitude compared to the prior time-segment.
Conclusion:
Following national trends, dentists became more conservative in prescribing opioids. Greater magnitude of decline occurred post-2016, following the implementation of strategies aimed to further regulate opioid prescribing. Understanding factors that influence prescribing trends can aid in development of tailored resources to encourage and support dentists’ conservative approach to prescribing opioids.
Keywords: Opioid, dentist, prescribing behavior, time-series analysis, ARIMA, joinpoint
Introduction
The American Dental Association (ADA) recommends using nonsteroidal anti-inflammatory drugs (NSAIDs) as first-line agents and to only prescribe the lowest dose and shortest duration of opioids in the management of acute dental-related pain.1,2 However, evidence suggests that United States’ (US) dentists may be overprescribing opioids (e.g. unnecessary provision of opioids, excessive quantities prescribed, opioid dosages exceeding recommended morphine milligram equivalents [MME] thresholds, >3-days’ supply).3–5 Short-term exposure to opioids increases the risk for future or persistent opioid use and adverse opioid outcomes (e.g. misuse, abuse, morbidity, mortality).6–8
Risk mitigation strategies to reduce adverse opioid outcomes had been implemented at the federal-, state- and local-levels, targeting all opioid prescribers, including dentists.9 Examples include the 2014 Drug Enforcement Administration (DEA) rescheduling of hydrocodone and 2016 Centers for Disease Control and Prevention (CDC) guidelines for pain management.2,10 Research exploring the impact of strategies on opioid prescribing and associated adverse opioid outcomes are inconsistent, with varying levels of magnitude, and typically conducted at the state- or institution-level.9,11,12 Regardless, existing literature suggest reduced opioid prescribing rates across all prescribers during the period these strategies were implemented.7,13–17 Consistent with national trends, recent literature demonstrated that opioid prescribing had declined amongst Medicaid and privately insured populations, and across dental surgical and non-surgical procedures, in the years ranging from 2011–2019.18–20 However, while these studies have described the trends of opioids dispensed following a dental service, no study to date has evaluated recent trends at the national level using a comprehensive dataset that includes all dentist prescribed opioids regardless of payer status.
Therefore, to address this gap, this study evaluated trends in opioid prescribing by general dentists and dental specialists in the US from 2012–2019 using a comprehensive national database. Furthermore, time points of significant changes in the prescribing trends were mapped to possible risk mitigation strategies.
Methods
Study Sample
A time-series analysis was conducted using the IQVIA Longitudinal Prescription Dataset (LRx), from January 1, 2012 to December 31, 2019. This dataset encompasses approximately 92% of all dispensed outpatient prescriptions in the US, inclusive of prescriptions dispensed without insurance reimbursement. The de-identified dataset was restricted to prescriptions written by a dentist (general, pediatric, endodontist, orthodontist, periodontist, prosthodontist, dental anesthesiologist). Oral and maxillofacial surgeons (OMFS) were not included in the dataset. Variables included prescription (National Drug Code [NDC], strength, dosage form, quantity dispensed, days’ supply, date dispensed), prescriber (prescriber identifier, five-digit zip code), and patient data (age at dispensed prescription, sex). This study was reviewed and determined to be exempt by the university’s institutional review board.
Prescriptions for an oral solid dosage form (e.g. capsules or tablets) of an opioid analgesic (identified using NDCs) and patients between 1–99 years on date of dispensing were included. Opioids used for cough suppressant (e.g. promethazine/codeine) or buprenorphine-containing products (e.g. buprenorphine/naloxone) were excluded from analysis as these opioids are not primarily indicated for pain management. Prescriptions were excluded for liquid and topical opioids, if any patient, provider or prescription variables were missing, and if the days’ supply and/or quantity dispensed were ≤0 or >99th percentile.
Covariates included patient age group (1–17, 18–39, 40–64, 65–99 years), patient sex (female/male), Census Bureau region (Northeast, Midwest, South, West), and payer (cash, commercial, Medicaid, Medicare). Urban-rural classification was based on zip codes; zip codes in counties with <2,500 individuals were classified as rural and all others as urban.21 Opioid categories were grouped as hydrocodone, oxycodone, codeine, tramadol, and other (i.e. fentanyl, hydromorphone, levorphanol, meperidine, methadone, morphine, oxymorphone and tapentadol). Opioids with extended-release (ER) or long-acting (LA) formulations (i.e. levorphanol, tapentadol, tramadol, methadone, morphine, oxymorphone, hydromorphone, oxycodone) were categorized as ER/LA, and all others as immediate-release opioid formulations.22 Proportion of prescriptions were described as exceeding days’ supply (>3 or >7-days) and high opioid dosages (≥50 or ≥90MME/day) thresholds defined by the CDC.2 Dentist type were grouped into general or specialist (i.e. pediatric, endodontist, orthodontist, periodontist, prosthodontist, dental anesthesiologist) using the prescriber specialty identifier.
Measures
Outcome measures included monthly population-based (Rx/100,000 individuals) and dentist-based prescribing rates (Rx/1,000 dentists), and opioid dosages (mean MME/day). Population-based prescribing rates were calculated by dividing the monthly number of opioid prescriptions by the annual population size, as reported by the Census Bureau.23 Dentist-based prescribing rates were calculated by dividing the monthly number of opioid prescriptions by the annual number of actively prescribing dentists. Actively prescribing dentists were defined as dentists who prescribed ≥20 prescriptions of any medication (e.g. opioids, antibiotics, etc) in a calendar year within the LRx. Opioid dosages were calculated by multiplying the total daily doses by the opioid strength and MME conversion factor.2
Statistical Analysis
Prescription characteristics were described annually by patient, prescriber, and prescription variables. Means with standard deviations (SD) described continuous variables, and frequencies described categorical variables. Statistical significance was determined as p-value≤0.05.
Autoregressive integrated moving average (ARIMA) models described monthly trends of opioid use outcomes. ARIMA models are denoted as ARIMA(q,d,p), where q=autoregression (AR), d=integration, and p=moving average (MA).24 The Durban-Watson test for autocorrelation, and augmented Dickey-Fuller test for stationarity were utilized. Presence of non-stationarity resulted in the time-series being differenced (d=1) to ensure stationarity. The AR(q) and MA(p) values were determined separately for each model using the autocorrelation function (ACF) and the partial autocorrelation function (PACF). Selection of the best-fitting ARIMA models were based on the model with the lowest AR and MA terms with significant coefficients, lowest variance of the error terms, goodness-of-fit assessment (highest log-likelihood statistics; lowest Akaike and Bayesian information criterion), and visual inspection of the residual ACF and PACF. In this study, prescribing rates were fitted by ARIMA(1,1,4) and opioid dosage were fitted by ARIMA(0,1,0) models, unless otherwise indicated.
Joinpoint regressions identified time segments at which there were changes in the slope of all outcome measures. Analyses were performed using the Joinpoint Trend Analysis Software v4.8.0.1 from the National Cancer Institute.25,26 The grid search method identified all possible combinations of joinpoints. The software then selected the best-fitting model as the model with the optimal number of joinpoints, from zero to five, at the lowest sum of square errors. The permutation test assessed models for best-fit, and p-values were adjusted for multiple comparisons by Bonferroni correction. Each joinpoint model reported the average monthly percent change (MPC) for the entire time segment and a MPC for each time segment.
Subgroup analyses, of ARIMA and joinpoint regressions, were conducted for dentist-based prescribing rates and opioid dosages by opioid categories, patient age groups, payer type and US regions. The denominator value for region subgroups were calculated as the number of actively prescribing dentists within each region of the US. The denominators for opioid categories, patient age groups, and payer type subgroups were not adjusted, as all dentists have the chance for prescribing an opioid within those subgroups. Analyses were conducted using SAS v9.4 and StataMP v16, in 2020.
Results
Over 87.2 million opioids were prescribed by dentists from January 2012 to December 2019 (Appendix Figure 1). Across each of the eight years, hydrocodone was the most commonly prescribed opioid, 75.5% of the 12.6 million prescriptions written in 2012, and 52.8% of the 6.9 million prescriptions in 2019 (Table 1). From 2012–2019, there were decreases in the proportion of prescriptions with days’ supply >3 (−5.8%) and >7 (−2.6%), and opioid dosages ≥50MME/day (−12.2%) and ≥90MME/day (−1.8%).
Table 1.
Prescription characteristics by year, 2012–2019
| Prescription characteristics | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 |
|---|---|---|---|---|---|---|---|---|
| n = 12,610,859 | n = 12,663,081 | n = 12,612,200 | n = 12,211,335 | n = 11,691,260 | n = 10,479,179 | n = 8,092,092 | n = 6,913,201 | |
| Patients’ Age, mean (SD) | 43.2 (15.8) | 43.8 (16.0) | 44.1 (16.1) | 44.5 (16.3) | 44.8 (16.5) | 45.2 (16.7) | 45.7 (16.9) | 46.2 (17.2) |
| Patient Age groups, % | ||||||||
| 1–17 years | 2.9% | 2.8% | 2.7% | 2.7% | 2.8% | 2.8% | 2.7% | 2.7% |
| 18–39 years | 40.7% | 39.8% | 39.8% | 39.3% | 38.8% | 38.3% | 37.5% | 36.5% |
| 40–64 years | 46.2% | 46.2% | 46.0% | 45.7% | 45.3% | 44.8% | 44.6% | 44.3% |
| 65–99 years | 10.1% | 11.1% | 11.6% | 12.3% | 13.1% | 14.1% | 15.2% | 16.5% |
| Gender, % | ||||||||
| Female | 54.4% | 54.3% | 54.3% | 54.3% | 54.4% | 54.3% | 54.4% | 54.3% |
| Male | 45.3% | 45.4% | 45.5% | 45.6% | 45.5% | 45.6% | 45.6% | 45.7% |
| Dentist Type a, % | ||||||||
| General | 99.2% | 99.2% | 99.1% | 99.1% | 99.0% | 99.0% | 98.9% | 98.8% |
| Specialist b | 0.8% | 0.8% | 0.9% | 0.9% | 1.0% | 1.0% | 1.1% | 1.2% |
| Payer Type, % | ||||||||
| Commercial/Third Party | 69.6% | 69.8% | 70.6% | 72.0% | 72.7% | 72.8% | 72.4% | 72.5% |
| Medicaid | 5.9% | 4.9% | 5.6% | 5.2% | 4.9% | 4.6% | 4.0% | 3.6% |
| Medicare/Part D | 7.8% | 9.1% | 9.5% | 10.3% | 10.9% | 11.7% | 12.6% | 13.5% |
| Cash | 16.7% | 16.2% | 14.2% | 12.5% | 11.5% | 10.9% | 11.0% | 10.4% |
| US Region, % | ||||||||
| Northeast | 13.1% | 12.5% | 12.0% | 12.1% | 11.3% | 9.7% | 9.2% | 9.2% |
| Midwest | 19.7% | 19.7% | 19.9% | 20.4% | 20.3% | 20.4% | 20.4% | 20.0% |
| South | 44.7% | 45.2% | 44.8% | 44.6% | 45.7% | 47.0% | 47.7% | 48.3% |
| West | 22.5% | 22.6% | 23.3% | 23.0% | 22.8% | 22.8% | 22.6% | 22.4% |
| Geographic classification, % | ||||||||
| Urban | 99.1% | 99.1% | 99.1% | 99.1% | 99.2% | 99.2% | 99.2% | 99.3% |
| Rural | 0.9% | 0.9% | 0.9% | 0.9% | 0.8% | 0.8% | 0.8% | 0.7% |
| Opioid Categories, % | ||||||||
| Hydrocodone | 75.5% | 75.3% | 72.7% | 64.1% | 63.2% | 61.1% | 57.0% | 52.8% |
| Codeine | 13.5% | 13.6% | 15.3% | 21.1% | 22.3% | 24.4% | 29.4% | 31.9% |
| Oxycodone | 8.0% | 8.0% | 8.3% | 9.6% | 9.4% | 9.0% | 6.8% | 7.8% |
| Tramadol | 2.5% | 2.8% | 3.4% | 4.8% | 4.8% | 5.3% | 6.6% | 7.4% |
| Other c | 0.4% | 0.4% | 0.3% | 0.3% | 0.3% | 0.2% | 0.2% | 0.2% |
| Opioid Formulation, % | ||||||||
| Immediate-release | 99.88% | 99.90% | 99.90% | 99.90% | 99.91% | 99.92% | 99.93% | 99.94% |
| Extended-release/Long-acting | 0.12% | 0.10% | 0.10% | 0.10% | 0.09% | 0.08% | 0.07% | 0.06% |
| Quantity dispensed, mean (SD) | 18.6 (11.7) | 17.6 (9.2) | 17.4 (8.5) | 17.5 (8.2) | 17.2 (7.7) | 16.8 (7.7) | 16.0 (7.8) | 15.0 (7.1) |
| Days’ Supply, mean (SD) | 3.6 (3.0) | 3.5 (2.5) | 3.5 (2.3) | 3.5 (2.3) | 3.4 (2.2) | 3.4 (2.1) | 3.3 (2.1) | 3.2 (1.9) |
| Prescriptions with days’ supply >3, % | 36.6% | 35.7% | 37.0% | 38.4% | 37.8% | 36.4% | 33.4% | 30.8% |
| Prescriptions with days’ supply >7, % | 3.5% | 2.5% | 2.3% | 2.3% | 2.0% | 1.8% | 1.4% | 0.9% |
| MME/day, mean (SD) | 36.3 (18.9) | 35.4 (17.5) | 34.5 (16.8) | 33.8 (16.5) | 33.3 (16.0) | 32.5 (15.6) | 30.3 (14.1) | 28.6 (12.5) |
| Prescriptions with MME/day≥50, % | 18.4% | 17.7% | 17.0% | 15.8% | 14.8% | 13.6% | 9.6% | 6.2% |
| Prescriptions with MME/day≥90, % | 2.2% | 1.6% | 1.3% | 1.3% | 1.2% | 1.1% | 0.6% | 0.4% |
Other dentist type: dental technologist or hygienist (<1%, so not shown)
Specialist: endodontist, orthodontist, pedodontics, periodontist, prosthodontist, and anesthesiologist
Other opioids: morphine, fentanyl, levorphanol, meperidine, methadone, and tapentadol
Abbreviation: SD = Standard deviations; MME/day = morphine milligram equivalents per day
From ARIMA models, population-based prescribing rates decreased by −1.97Rx/100,000 individuals per month (95% confidence interval [CI] −9.98, −0.97), from 347.10 to 156.88Rx/100,000 individuals in January 2012 to December 2019, respectively. In January 2012, dentists prescribed 6,806.84Rx/1,000 dentists. This decreased monthly by −38.12Rx/1,000 dentists (95%CI −58.63, −17.65) to 3,140.75Rx/1,000 dentists in December 2019. Opioid dosages decreased monthly at −0.08MME/day (95%CI −0.13, −0.04), from an average of 36.07MME/day in January 2012 to 28.15MME/day in December 2019.
Each joinpoint regression identified five time segments with different trends for each opioid outcome measure. Joinpoint regressions were similar for population- and dentist-based prescribing rates: average MPC was −0.8%; MPC from January 2012 to February 2016 (−0.2%) and February 2016 to May 2017 (−0.8%) time segments were small; larger reductions in MPC occurred from May 2017 to December 2018 (−2.3% for population- and −2.2% for dentist-based) and from March 2019 to December 2019 (−2.4% for population- and −2.3% for dentist-based); a non-significant increase in MPC occurred from December 2016 to March 2019 (Figure 1A and B, Appendix Table 1). For opioid dosages, the average MPC was −0.3%. MPCs across each of the five time segments were small, with larger MPC reduction occurring between October 2017 to October 2019 at −0.8%, and no change occurring from January 2012 to March 2013 (Figure 1C, Appendix Table 1).
Figure 1. Trend of monthly opioid outcomes (prescribing rates and opioid dosages), January 2012 to December 2019.
Figures display results from the Jointpoint regression as monthly percent change (MPC) with 95% confidence intervals (95%CI) for each of the five time segments. *Significance at p<0.05; Rx=prescriptions
For subgroup analyses, ARIMA models are presented in Table 2, and joinpoint regressions are presented in Appendix Tables 2 and 3.
Table 2.
Autoregressive integrated moving average models of dentist-based prescribing rates and opioid dosages for subgroups, January 2012 to December 2019
| Dentist-based opioid prescribing rate | Opioid Dosages | |||||
|---|---|---|---|---|---|---|
|
| ||||||
| Subgroups | Jan 2012 Rx/1,000 dentist (95%CI) | Dec 2019 Rx/1,000 dentist (95%CI) | Monthly Rx/1,000 dentist (95%CI)a | Jan 2012 Mean [SD] | Dec 2019 Mean [SD] | Monthly Mean MME/day (95%CI)b |
|
| ||||||
| Opioid Categories | ||||||
|
| ||||||
| Hydrocodone | 5,139.0 (5,127.9, 5,150.2) |
1,628.3 (1,622.1, 1,634.5) |
−37.75
*** (−53.39, −22.11) |
36.1 [16.3] | 29.5 [11.4] |
−0.07
*** (−0.11, −0.03)d |
| Codeine | 928.5 (923.8, 933.3) |
1,029.5 (1,024.6, 1,034.4) |
1.67 (−5.28, 8.61) |
27.4 [12.8] | 23.7 [9.1] |
−0.04
*** (−0.06, −0.02) |
| Oxycodone | 546.0 (542.4, 549.7) |
247.8 (245.4, 250.2) |
−3.12 (−6.31, 0.07) |
52.8 [29.2] | 40.4 [17.8] |
−0.13
*** (−0.19, −0.07) |
| Tramadol | 164.1 (162.1, 166.0) |
231.9 (229.5, 234.2) |
0.83 (−1.23, 2.89) |
28.4 [13.6] | 25.1 [8.2] |
−0.03
* (−0.06, −0.01) |
|
| ||||||
| Age groups | ||||||
|
| ||||||
| 18–39 years | 2,801.3 (2,793.1, 2,809.5) |
1,120.1 (1,115.0, 1,125.2) |
−17.36
*** (−26.97, −7.75) |
36.2 [18.8] | 27.8 [12.0] |
−0.09
*** (−0.13, −0.04)d |
| 40–64 years | 3,144.1 (3,135.4, 3,152.8) |
1,409.9 (1,404.1, 1,415.6) |
−18.40
*** (−26.62, −10.18) |
36.5 [18.7] | 28.4 [12.5] |
−0.08
*** (−0.11, −0.06) |
| 65–99 years | 699.8 (695.7, 703.9) |
497.4 (494.0, 500.8) |
−1.82 (−6.89, 3.25) |
34.7 [16.7] | 28.3 [11.8] |
−0.07
*** (−0.10, −0.04) |
|
| ||||||
| Payer Type | ||||||
|
| ||||||
| Commercial | 4,781.4 (4,770.6, 4,792.1) |
2,323.6 (2,316.2, 2,331.0) |
−25.35
** (−44.06, −6.65) |
35.5 [16.9] | 28.3 [12.1] |
−0.08
*** (−0.11, −0.05) |
| Medicaid | 444.8 (441.5, 448.0) |
97.5 (96.0, 99.0) |
−3.38
*** (−5.16, −1.59) |
32.8 [16.4] | 26.9 [11.3] |
−0.06
* (−0.11, −0.01) |
| Medicare/Part D | 469.2 (465.8, 472.6) |
416.8 (413.7, 419.9) |
−0.64
(−3.60, 2.33) |
33.6 [17.7] | 27.8 [11.8] |
−0.06
*** (−0.09, −0.03) |
| Cash | 1,111.5 (1,106.4, 1,116.7) |
302.9 (300.2, 305.5) |
−9.04
*** (−11.44, −6.65) |
40.8 [24.6] | 28.2 [13.2] |
−0.14
*** (−0.19, −0.09)e |
|
| ||||||
| US Region | ||||||
|
| ||||||
| Northeast | 4,503.3 (4,480.3, 4,526.3) |
1,395.7 (1,382.8, 1,408.5) |
−31.61
*** (−45.08, −18.14) |
33.0 [17.6] | 26.6 [12.9] |
−0.07
*** (−0.09, −0.04) |
| Midwest | 6,693.9 (6,665.6, 6,722.2) |
3,159.9 (3,140.7, 3,179.1) |
−36.16
** (−60.99, −11.33) |
34.7 [16.9] | 27.0 [11.1] |
−0.08
*** (−0.12, −0.04)c |
| South | 11,663.3 (11,630.6, 11,696.1) |
5,786.5 (5,763.6, 5,809.4) |
−60.29
** (−98.44, −22.14) |
37.8 [19.2] | 28.8 [12.1] |
−0.09
*** (−0.15, −0.04)c |
| West | 5,139.0 (5,127.9, 5,150.2) |
1,628.3 (1,622.1, 1,634.5) |
−27.25
** (−44.18, −10.32) |
35.6 [18.8] | 28.3 [12.7] |
−0.08
*** (−0.11, −0.04) |
Abbreviations: Rx = Number of prescriptions; CI = Confidence intervals; MME/day = morphine milligram equivalents per day; ARIMA = autoregressive integrated moving average
Boldface indicated statistical significance (p<0.05)
p<0.05,
p<0.01,
p<0.001
ARIMA(1,1,4) for all models
ARIMA(0,1,0) for all models, unless otherwise specified
ARIMA(0,1,1)
ARIMA(1,1,1)
ARIMA(0,1,2)
Monthly prescribing rates decreased for hydrocodone (−37.75Rx/1,000 dentists; 95%CI −53.39, −22.11), with slight reductions for oxycodone (−3.12; 95%CI −6.31, 0.07), and non-significant increases for codeine (1.67; −5.28, 8.61) and tramadol (0.83; 95%CI −1.23, 2.89) (Figure 2A). The larger MPCs occurred from August 2014 to November 2014 for hydrocodone (−8.8%), codeine (+14.6%), and tramadol (+16.3%), though not statistically significant. MPC increased for oxycodone from February 2014 to July 2015 (+1.1%). Overall, opioid dosages decreased monthly across all opioid categories, but MPC increased for codeine from August 2014 to November 2014 (+1.1%) (Figure 2B).
Figure 2.
Trend of dentist-based prescribing rates and opioid dosages by subgroups, January 2012 to December 2019
Monthly prescribing rates decreased across all age groups (18–39 years: −17.36Rx/1,000 dentists; 95%CI −26.97, −7.75; and 40–64 years: −18.40; 95%CI −26.62, −10.18), but were not significant for the oldest age group (−1.82; 95%CI −6.89, 3.25) (Figure 2C). The only change in prescribing rates for the oldest age group occurred in September of 2016 (−1.1%). Prior to September 2016, prescribing rates amongst the oldest age group had been increasing monthly (+0.3%). Average MPC in opioid dosages were similar across the age groups (−0.2 to −0.3%) (Figure 2D). Due to the exclusion of liquid opioids, patient age group 1–17 years was not included in the main subgroup analysis but is available in Appendix Tables 4 and 5.
Prescribing rates declined monthly across all payer types (range −0.64 to −25.35Rx/1,000 dentists), but was not significant for Medicare (Figure 2E). For Medicaid, prescribing rates decreased overall, except for a sharp non-significant increase from December 2013 to April 2014 (+7.8%). Amongst Medicare, prescribing rates increased from January 2012 (+0.3% to +1.4%) until October 2016 when rates began to decline (−1.1%). Average MPC in opioid dosages was similar across payer types (−0.2% to −0.4%) (Figure 2F).
Monthly prescribing rates declined similarly across regions (range −27.25 to −36.16Rx/1,000 dentists), except the South which declined almost twice that of the other regions (−60.99; 95%CI −98.44, −22.14) (Figure 2G). Steeper declines in prescribing rates began in April 2016 for the Northeast (−2.4%) and Midwest (−3.6%), in February 2017 for the South (−1.7%), and April 2017 for the West (−2.4%). The larger decreases in opioid dosages started in December 2017 for the West (−.8%) and in August 2017 for all other regions (range −0.7% to 0.8%) (Figure 2H).
Discussion
In the US, opioid prescriptions written by general dentists and specialist dentists decreased by 54.8% from 2012–2019. The largest absolute reduction in prescribing rates occurred for hydrocodone, patients 40–64 years, reimbursement by commercial plans, and dentists located in the South. Frequent significant changes in prescribing trends occurred after 2016. Only modest reductions in opioid dosages (<1MME/day) were observed over the study period.
Our study suggests an increasingly conservative approach of prescribing opioids amongst dentists, similar to the decreases in opioid prescribing rates and high dosage prescriptions by all prescribers from 2006–2017.7 Greater reductions in prescribing rates were observed in early 2016 through late 2017. This coincides with the CDC’s opioid guidelines published in March 2016 and the subsequent ADA statements on opioid use for dental-related pain in October 2016.1,2 The ADA updated their opioid prescribing statements in March 2018, which may have contributed to additional declines in prescribing rates in late 2018.27 Similar to another study, our results demonstrated an annual decrease in the proportion of prescriptions with long days’ supply and high opioid dosages.18 The majority of prescriptions from 2012–2019 were for <3-days’ supply and on average <50MME/day, which suggests dentists are commonly prescribing within recommendations.
Between 2016–2017, twenty-one states, mainly in the Northeast, implemented opioid prescribing limit laws that restricted opioid prescribing and dispensing based on days’ supply, quantity dispensed and/or opioid dosages.28 In our study, the greatest reduction in prescribing rates within the Northeast and Midwest regions began in April 2016, which aligned with the implementation of these state laws. However, the majority of opioid prescriptions by dentists were dispensed in the South. Prescribing rates in the South decreased by nearly 50% from 2012–2019. To date, one study found that opioid limiting laws in twenty-six states were not associated with additional reduction in opioid prescriptions compared to states without such laws.12 It is beyond the scope of this study to determine if changes in opioid prescribing in these regions are a result of these state laws. Nevertheless, the downward prescribing rates across all US regions is encouraging.
In October 2014, the DEA rescheduled hydrocodone from a schedule III to a schedule II medication, as a strategy to restrict hydrocodone prescribing.10 As a schedule II, hydrocodone prescriptions could no longer be telephoned into pharmacies, were limited to a 30-day supply and were non-refillable. Dentists commonly prescribe hydrocodone with acetaminophen after third molar removals or impacted tooth extractions.3,6 This study observed monthly decreases in prescribing of hydrocodone and increases in codeine- (schedule III) and tramadol-containing products (schedule IV) from August to November 2014, along with increased monthly prescribing of oxycodone from January 2014 to July 2015. It is possible that the stricter regulations on hydrocodone prompted dentists to substitute with codeine and tramadol, and prescribed at higher dosages to be equianalgesic to hydrocodone. However, the increase in oxycodone prescribing, a schedule II medication like hydrocodone, is counter-intuitive and difficult to explain. While it cannot be concluded that the rescheduling resulted in the observed prescribing trends, it is reassuring that dentists are prescribing hydrocodone less frequently and using lower potency opioids (e.g. codeine).
Under provisions of the Affordable Care Act in January 2014, twenty-six states expanded Medicaid eligibility, which included expansion of dental coverage.29 Coinciding with this timeframe, dental opioid prescribing rates increased from December 2013 to April 2014 in our study’s Medicaid subgroup. Some studies suggested the Medicaid expansion increased healthcare utilization and reimbursement for prescriptions, which could explain the increased opioids.30 However, others reported increase in opioids across states regardless of Medicaid expansion implementation.31 Regardless, prescribing rates for opioids reimbursed by Medicaid began to decrease monthly after April 2014.
In this study’s Medicare subgroup, opioid prescribing rates began to decline after October 2016. The Medicare prescriber enrollment introduced by the Centers for Medicare and Medicaid Services (CMS) in 2014, could be one of many reasons to explain this decline.32 The CMS ruling required dentists to enroll as a Medicare provider by the end of 2015, but this ruling was rescinded in 2018.33 During this period, dentists who opted-out of enrollment could not get reimbursement for their dental services and patients could not get their prescriptions reimbursed under Medicare Part D. This may explain the decreasing trend in opioids reimbursed by Medicare, and for the similar declining trend for opioids prescribed to individuals 65–99 years. These trends may also reflect recommendations to limit the use of high risk medications, like opioids, amongst older adults.34 However, while NSAIDs are preferred analgesics for dental-related pain, older adults with certain comorbidities (e.g. kidney disease, ulcers, uncontrolled hypertension) are at an increased risk for adverse events (e.g. gastrointestinal bleeds, acute kidney injury).34 This complexity of analgesic prescribing to the Medicare and older adult populations may explain the overall minimal monthly decline in the dentist-based prescribing rates, and suggests the need for geriatric-specific pain management guidelines.
Adolescents and young adults are commonly first exposed to opioids through dentists, and are at an increased risk of future opioid use and abuse.6,35 The exclusion of liquid formulation opioids in this analysis presents a conservative reporting of opioids in this younger age group. However, regardless of the opioid dosage form, it is concerning that opioids are being prescribed to individuals less than 17 years. In the ADA dental pain management clinical practice guidelines currently available for public comment, opioids are not recommended for patients less than 12 years and non-opioid analgesics are recommended for patients age 12 to 17.36 Therefore, it is important to continue minimizing younger individuals’ exposure to unnecessary opioids.
Several studies have demonstrated that NSAIDs, alone or in combination with acetaminophen, are more effective for pain relief after dental surgeries, with lower abuse risk and side effect profile, compared to opioids.37,38 However, since patients can obtain NSAIDs and acetaminophen without a prescription, there is an underestimation of the use of these medications in prescription claims databases. Furthermore, adjunctive pain management strategies, such as patients taking NSAIDs in clinic prior to surgery or dentists’ use of long-acting local anesthesia, are not captured in outpatient prescription claims datasets. Therefore, there are challenges to determine if reduction in opioid prescribing resulted in increased prescribing of non-opioid therapies.
Limitations
This study has limitations. The LRx dataset lacked patient-level information (e.g. comorbidities, oral health status) and pain severity or dental procedure performed to evaluate appropriateness of opioid usage. Generalizability is limited to general dentists and specialist dentists as prescriptions written by OMFS were not available in the LRx dataset. Opioid prescribing trends may be conservative due to exclusion of liquid opioids. As the dispensing of an opioid does not equate to consumption, this limits the capacity to evaluate for unused pills per prescription. Due to study methodology, associations between risk mitigation strategies and declining opioid trends cannot be drawn.
Conclusions
During 2012–2019, US dentists prescribed fewer opioids and at lower opioid dosages. Based on the trends observed, there are suggestions that federal and state risk mitigation strategies may have influenced dentists’ prescribing. The growing awareness of the opioid epidemic may also be a driving factor. While current strategies target all prescribers, understanding how they influence dentists can aid in development of strategies specific to dentists, especially to high opioid prescribers. Future research should focus on understanding dentists’ perspective on risk mitigation strategies, identifying factors associated with decreased opioid prescribing, and determine if reductions in dental opioid-prescribing results in clinically meaningful decreases in opioid-related outcomes.
Supplementary Material
Acknowledgements:
The content presented is the sole responsibility of the authors and does not necessarily represent the official views of the AHRQ, Department of Veterans Affairs, the US government, or IQVIA or any of its affiliated entities. The statements, findings, conclusions, views and opinions contained and expressed in this study are based in part on data obtained under license from IQVIA: Longitudinal Prescription January 2012 to December 2019, IQVIA, Inc).
Research reported was supported by the Agency for Healthcare Research and Quality (AHRQ) under grant R01 HS25177 (PI: Katie Suda). The study sponsor had no role in the design or conduct of the study, data collection, management, analysis, and interpretation of the data, preparation, review or appraisal of the manuscript and decision to submit the manuscript for publication.
Footnotes
Authorship:
Connie H. Yan conceived study design, completed the analysis and manuscript writing.
Todd A. Lee assisted with the study design, analysis, and critical evaluation of the results and writing.
Lisa K. Sharp assisted with the study design, and critical evaluation of the results and writing.
Colin C. Hubbard assisted with the study design, and critical evaluation of the results and writing.
Charlesnika T. Evans assisted with the study design, and critical evaluation of the results and writing.
Gregory S. Calip assisted with the study design, and critical evaluation of the results and writing.
Jessina C. McGregor assisted with the study design, and critical evaluation of the results and writing.
Walid F. Gellad assisted with the study design, and critical evaluation of the results and writing.
Katie J. Suda supervised and assisted with the study design, and critical evaluation of the results and writing.
This work was presented in part at the Virtual International Society of Pharmacoeconomic and Outcomes Research (ISPOR) Annual Meeting, held virtually in May 2021.
Financial Disclosures
Connie H. Yan reports current employment with AbbVie Inc. at the time of submission.
Todd A. Lee has no financial disclosures.
Lisa K. Sharp has no financial disclosures.
Colin C. Hubbard reports receiving support from grants from Pfizer outside the submitted work.
Charlesnika T. Evans has no financial disclosures.
Gregory S. Calip discloses employment with Flatiron Health, which is an independent subsidiary of the Roche group; stock ownership in Roche; and research grants from Pfizer unrelated to this study.
Susan Rowan has no financial disclosures.
Jessina C. McGregor has no financial disclosures.
Walid F. Gellad has no financial disclosures.
Katie J. Suda has no financial disclosures.
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