Publications

BACKGROUND: Opioids are overprescribed in the outpatient dental setting. Therefore, opportunities exist for opioid stewardship.

OBJECTIVES: The purpose of this pilot study was to test the feasibility of an academic detailing (AD) intervention to promote appropriate prescribing of opioids in outpatient dentistry.

METHODS: We implemented an AD intervention targeting management of acute oral pain in a Midwestern Veterans Affairs outpatient dental facility. The intervention targeted dentists who actively prescribed opioids at the time of the study. The pilot study tested feasibility, adoption, and acceptance of the AD campaign. Visit-based prescribing rates were obtained from the Veterans Health Administration's Corporate Data Warehouse for baseline and postintervention using difference-in-differences analyses to detect potential changes in health service outcomes.

RESULTS: Results indicate moderate levels of feasibility through participation rates (n = 5, 55.5%) and high levels of organizational readiness for change (average of 88.6% agree to strongly agree). Furthermore, fidelity of the AD intervention was high. Adoption measures show moderate indication of motivation to change, and trends suggest that participating dentists decreased their visit-based opioid prescribing rates (P > 0.05).

CONCLUSION: The intervention demonstrated feasibility with some indications of adoption of intervention techniques and decrease in opioid prescribing. We further recommend working closely with frontline providers to gather feedback and buy-in before scaling and implementing the AD campaign.

BACKGROUND: Although statins are a class I recommendation for prevention of atherosclerotic cardiovascular disease and its complications, their use is suboptimal. Differential underuse may mediate disparities in cardiovascular health for systematically marginalized persons.

OBJECTIVE: To estimate disparities in statin use by race-ethnicity-gender and to determine whether these potential disparities are explained by medical appropriateness of therapy and structural factors.

DESIGN: Cross-sectional analysis.

SETTING: National Health and Nutrition Examination Survey from 2015 to 2020.

PARTICIPANTS: Persons eligible for statin therapy based on 2013 and 2018 American College of Cardiology/American Heart Association blood cholesterol guidelines.

MEASUREMENTS: The independent variable was race-ethnicity-gender. The outcome of interest was use of a statin. Using the Institute of Medicine framework for examining unequal treatment, we calculated adjusted prevalence ratios (aPRs) to estimate disparities in statin use adjusted for age, disease severity, access to health care, and socioeconomic status relative to non-Hispanic White men.

RESULTS: For primary prevention, we identified a lower prevalence of statin use that was not explained by measurable differences in disease severity or structural factors among non-Hispanic Black men (aPR, 0.73 [95% CI, 0.59 to 0.88]) and non-Mexican Hispanic women (aPR, 0.74 [CI, 0.53 to 0.95]). For secondary prevention, we identified a lower prevalence of statin use that was not explained by measurable differences in disease severity or structural factors for non-Hispanic Black men (aPR, 0.81 [CI, 0.64 to 0.97]), other/multiracial men (aPR, 0.58 [CI, 0.20 to 0.97]), Mexican American women (aPR, 0.36 [CI, 0.10 to 0.61]), non-Mexican Hispanic women (aPR, 0.57 [CI, 0.33 to 0.82), non-Hispanic White women (aPR, 0.69 [CI, 0.56 to 0.83]), and non-Hispanic Black women (aPR, 0.75 [CI, 0.57 to 0.92]).

LIMITATION: Cross-sectional data; lack of geographic, language, or statin-dose data.

CONCLUSION: Statin use disparities for several race-ethnicity-gender groups are not explained by measurable differences in medical appropriateness of therapy, access to health care, and socioeconomic status. These residual disparities may be partially mediated by unobserved processes that contribute to health inequity, including bias, stereotyping, and mistrust.

PRIMARY FUNDING SOURCE: National Institutes of Health.

PURPOSE: Opioids, benzodiazepines and sedatives can manage dental pain, fear and anxiety but have a narrow margin of safety in children. General dentists may inappropriately prescribe gabapentin and stimulants. National evidence on dispensing rates of these high-alert medicines by dentists to children is limited.

METHODS: We utilize join-point regression to identify changes in fills for opioids, sedatives, benzodiazepines, gabapentin, and stimulants to children <18 years from 2012 to 2019 in a national dataset comprising 92% of dispensed outpatient prescriptions by dentists.

RESULTS: From 2012 to 2019, 3.8 million children filled prescriptions for high-alert drugs from general dentists. National quarterly dispensing of high-alert drugs decreased 63.1%, from 10456.0 to 3858.8 days per million. Opioids accounted for 69.4% of high-alert prescriptions. From 2012 to 2019, fills for opioids, sedatives, benzodiazepines, and stimulants decreased by 65.2% (7651.8 to 2662.7), 43.4% (810.9 to 458.7), 43.6% (785.7 to 442.7) and 89.3% (825.6 to 88.6 days per million), respectively. Gabapentin increased 8.1% (121.8 to 131.7 days per million). A significant decrease in high-alert fills occurred in 2016, (-6.0% per quarter vs. -1.6% pre-2016, P-value<0.001), especially for opioids (-7.0% vs. -1.2%, P-value<0.001). Older teenagers (15-17 years) received 42.5% of high-alert prescriptions. Low-income counties in the South were overrepresented among top-prescribing areas in 2019.

CONCLUSIONS: We found promising national decreases in fills for high-alert medicines to children by general dentists from 2012 to 2019. However, older teenagers and children in some counties continued to receive dental opioids at high rates. Future efforts should address non-evidence-based pain management in these groups.

BACKGROUND: Atrial fibrillation (AF) is associated with a five-fold increased risk of stroke and a two-fold increased risk of death. We aimed to quantify changes in new diagnoses of AF following the onset of the COVID-19 pandemic. Investigating changes in new diagnoses of AF is of relevance because delayed diagnosis interferes with timely treatment to prevent stroke, heart failure, and death.

METHODS: Using De-identified Optum's Clinformatics® Data Mart, we identified 19,500,401 beneficiaries continuously enrolled for 12 months in 2016-Q3 2020 with no history of AF. The primary outcome was new AF diagnoses per 30-day interval. Secondary outcomes included AF diagnosis in the inpatient setting, AF diagnosis in the outpatient setting, and ischemic stroke as initial manifestation of AF. We constructed seasonal autoregressive integrated moving average models to quantify changes in new AF diagnoses after the onset of the COVID-19 pandemic (3/11/2020, date of pandemic declaration). We tested whether changes in the new AF diagnoses differed by race and ethnicity.

RESULTS: The average age of study participants was 51.0±18.5 years, and 52% of the sample was female. During the study period, 2.7% of the study sample had newly-diagnosed AF. New AF diagnoses decreased by 35% (95% CI, 21%-48%) after the onset of the COVID-19 pandemic, from 1.14 per 1000 individuals (95% CI, 1.05-1.24) to 0.74 per 1000 (95% CI, 0.64 to 0.83, p-value<0.001). New AF diagnoses decreased by 37% (95% CI, 13%- 55%) in the outpatient setting and by 29% (95% CI, 14%-43%) in the inpatient setting. The decrease in new AF diagnoses was similar across racial and ethnic subgroups.

CONCLUSION: In a nationwide cohort of 19.5 million individuals, new diagnoses of AF decreased substantially following the onset of the COVID-19 pandemic. Our findings evidence pandemic disruptions in access to care for AF, which are concerning because delayed diagnosis interferes with timely treatment to prevent complications.

IMPORTANCE: Despite the political salience of insulin prices, no study to date has quantified trends in insulin prices that account for manufacturer discounts (net prices).

OBJECTIVE: To describe trends in insulin list prices and net prices faced by payers from 2012 to 2019 and estimate changes in net prices after the 2015 to 2017 entry of new insulin products.

DESIGN, SETTING, AND PARTICIPANTS: This longitudinal study included an analysis of Medicare, Medicaid, and SSR Health drug pricing data from January 1, 2012, to December 31, 2019. Data analyses were performed from June 1, 2022, to October 31, 2022.

EXPOSURES: US sales of insulin products.

MAIN OUTCOMES AND MEASURES: Net prices faced by payers were estimated for insulin products as list prices minus manufacturer discounts negotiated in commercial and Medicare Part D markets (ie, commercial discounts). Trends in net prices were evaluated before and after the entry of new insulin products.

RESULTS: Net prices of long-acting insulin products increased at an annual rate of 23.6% from 2012 to 2014 but decreased at an annual rate of 8.3% after the introduction of insulin glargine (Toujeo and Basaglar) and degludec (Tresiba) in 2015. Net prices of short-acting insulin increased at an annual rate of 5.6% from 2012 to 2017 but then decreased from 2018 to 2019 after the introduction of insulin aspart (Fiasp) and lispro (Admelog). For human insulin products, which did not experience entry of new products, net prices increased at an annual rate of 9.2% from 2012 to 2019. From 2012 to 2019, commercial discounts increased from 22.7% to 64.8% for long-acting insulin products, from 37.9% to 66.1% for short-acting insulin products, and from 54.9% to 63.1% for human insulin products.

CONCLUSIONS AND RELEVANCE: In this longitudinal study of US insulin products, results suggest that insulin prices substantially increased from 2012 to 2015, even after accounting for discounts. The introduction of new insulin products was followed by substantial discounting practices that lowered net prices faced by payers.

BACKGROUND: The COVID-19 pandemic profoundly disrupted the delivery of medical care. It remains unclear whether individuals diagnosed with new onset disease during the pandemic were less likely to initiate treatments after diagnosis. We sought to evaluate changes in the treatment initiation of patients newly diagnosed with atrial fibrillation (AF) after the onset of the COVID-19 pandemic.

METHODS: In this retrospective cohort study, we identified individuals with incident AF from 01/01/2016-09/30/2021 using Optum's de-identified Clinformatics® Data Mart Database. The primary outcome was initiation of oral anticoagulation (OAC) within 30 days of AF diagnosis. Secondary outcomes included initiation of OAC within 180 days of diagnosis, initiation of warfarin, direct oral anticoagulants (DOACs), rhythm control medications and electrical cardioversion within 30 days of diagnosis. We constructed interrupted time series analyses to examine changes in the outcomes following the onset of the pandemic.

RESULTS: A total of 573,524 patients (age 73.0 ± 10.9 years) were included in the study. There were no significant changes in the initiation of OAC, DOAC, and rhythm control medications associated with the onset of the pandemic. There was a significant decrease in initiation of electrical cardioversion associated with the onset of the pandemic. The rate of electronic cardioversion within 30 days of diagnosis decreased by 4.9% per 1,000 patients after the onset of the pandemic and decreased by about 35% in April 2020, compared to April 2019, from 5.53% to 3.58%.

CONCLUSION: The COVID-19 pandemic did not affect the OAC initiation within 30 days of AF diagnosis but was associated with a decline in the provision of procedures for patients newly diagnosed with AF.

OBJECTIVES: To develop and validate a machine-learning algorithm to predict fatal overdose using Pennsylvania Prescription Drug Monitoring Program (PDMP) data.

METHODS: The training/testing (n = 3020,748) and validation (n = 2237,701) cohorts included Pennsylvania residents with a prescription dispensing from February 2018-September 2021. Potential predictors (n = 222) were measured in the 6 months prior to a random index date. Using a gradient boosting machine, we developed a 20-variable model to predict risk of fatal drug overdose in the 6 months after the index date.

RESULTS: Beneficiaries in the training (n = 1,812,448), testing (n = 1,208,300), and validation (n = 2,237,701) samples had similar age, with low rates of fatal overdose during 6-month follow up (0.12%, 0.12%, 0.04%, respectively). The validation c-statistic was 0.86 for predicting fatal overdose using 20 PDMP variables. When ranking individuals based on risk score, the prediction model more accurately identified fatal overdose at 6 months compared to using opioid dosage or opioid/benzodiazepine overlap, although the percentage of individuals in the highest risk percentile who died at 6 months was less than 1%.

CONCLUSIONS AND POLICY IMPLICATIONS: A gradient boosting machine algorithm predicting fatal overdose derived from twenty variables performed well in discriminating risk across testing and validation samples, improving on single factor risk measures like opioid dosage.

INTRODUCTION: The opioid epidemic has exacted a significant toll in rural areas, yet adoption of medications for opioid use disorder (MOUD) lags. The Rural Access to Medication Assisted Treatment in Pennsylvania (RAMP) Project facilitated adoption of MOUD in rural primary care clinics. The purpose of this study was to gain a better understanding of the barriers and facilitators operating at multiple levels to access or provide MOUD in rural Pennsylvania.

METHODS: In total, the study conducted 35 semi-structured interviews with MOUD patients and MOUD providers participating in RAMP. Qualitative analysis incorporated both deductive and inductive approaches. The study team coded interviews and performed thematic analysis. Using a modified social-ecological framework, themes from the qualitative interviews are organized in five nested levels: individual, interpersonal, health care setting, community, and public policy.

RESULTS: Patients and providers agreed on many barriers (e.g., lack of providers, lack of transportation, insufficient rapport and trust in patient-provider relationship, and cost, etc.); however, their interpretation of the barrier, or indicated solution, diverged in meaningful ways. Patients described their experiences in broad terms pointing to the social determinants of health, as they highlighted their lives outside of the therapeutic encounter in the clinic. Providers focused on their professional roles, responsibilities, and operations within the primary care setting.

CONCLUSIONS: Providers may want to discuss barriers to treatment related to social determinants of health with patients, and pursue partnerships with organizations that seek to address those barriers. The findings from these interviews point to potential opportunities to enhance patient experience, increase access to and optimize processes for MOUD in rural areas, and reduce stigma against people with opioid use disorder (OUD) in the wider community.