Publications

BACKGROUND AND AIMS: Barrett's esophagus (BE) screening is not highly utilized in the United States, and there are few data describing providers' approach to screening. To fill this gap and guide the implementation of future BE screening strategies, we studied evaluation practice patterns for gastroesophageal reflux disease (GERD) by nongastroenterologists.

METHODS: We performed a retrospective cohort study of patients with chronic GERD using health claims data from the United States between 2005 and 2019. We used up to 5 years of data after the diagnosis of chronic GERD to determine patient factors associated with completion of a gastroenterology encounter. We also identified patient factors associated with whether the first gastroenterology encounter was a direct-access upper endoscopy or an office visit.

RESULTS: We identified 484,023 patients diagnosed with chronic GERD by a nongastroenterology provider. The cumulative incidence of completing a gastroenterology encounter within 5 years was 38.7%. Gastrointestinal symptoms, such as dysphagia (adjusted hazard ratio [aHR] = 2.11, 95% confidence interval [CI] = 1.94-2.30), abdominal pain (aHR = 1.89, 95% CI = 1.85-1.94), and melena (aHR = 1.73, 95% CI = 1.65-1.82), were strongly associated with completion of a gastroenterology encounter. The patient factors strongly associated with direct-access upper endoscopy included dysphagia (aHR = 1.68, 95% CI = 1.52-1.85), weight loss (aHR = 1.46, 95% CI = 1.28-1.63), and melena (aHR = 1.42, 95% CI = 1.28-1.56).

CONCLUSION: A total of 38.7% of patients with chronic GERD complete a gastroenterology encounter within 5 years of diagnosis, and gastrointestinal alarm symptoms are the most strongly associated factors for receiving gastroenterology care. These findings highlight the importance of incorporating primary care providers in the development of new BE screening programs.

PURPOSE: We examined the prevalence of, and factors associated with, serious opioid-related adverse drug events (ORADEs) that led to an emergency department (ED) visit or hospitalization among patients with chronic gastrointestinal (GI) symptoms and disorders dually enrolled in the Department of Veterans Affairs (VA) and Medicare Part D.

METHODS: In this retrospective cohort study, we used linked national patient-level data (April 1, 2011, to October 31, 2014) from the VA and Centers for Medicare and Medicaid Services to identify serious ORADEs among dually enrolled veterans with a chronic GI symptom or disorder. Outcome measures included serious ORADEs, defined as an ED visit attributed to an ORADE or a hospitalization where the principal or secondary reason for admission involved an opioid. We used multiple logistic regression models to determine factors independently associated with a serious ORADE.

RESULTS: We identified 3,430 veterans who had a chronic GI symptom or disorder; were dually enrolled in the VA and Medicare Part D; and had a serious ORADE that led to an ED visit, hospitalization, or both. The period prevalence of having a serious ORADE was 2.4% overall and 4.4% among veterans with chronic opioid use (≥90 consecutive days). Veterans with serious ORADEs were more likely to be less than 40 years old, male, white, and to have chronic abdominal pain, functional GI disorders, chronic pancreatitis, or Crohn's disease. They were also more likely to have used opioids chronically and at higher daily doses.

CONCLUSION: There may be a considerable burden of serious ORADEs among patients with chronic GI symptoms and disorders. Future quality improvement efforts should target this vulnerable population.

BACKGROUND AND AIMS: One-third of opioid (OPI) overdose deaths involve concurrent benzodiazepine (BZD) use. Little is known about concurrent opioid and benzodiazepine use (OPI-BZD) most associated with overdose risk. We aimed to examine associations between OPI-BZD dose and duration trajectories, and subsequent OPI or BZD overdose in US Medicare.

DESIGN: Retrospective cohort study.

SETTING: US Medicare.

PARTICIPANTS: Using a 5% national Medicare data sample (2013-16) of fee-for-service beneficiaries without cancer initiating OPI prescriptions, we identified 37 879 beneficiaries (age ≥ 65 = 59.3%, female = 71.9%, white = 87.6%, having OPI overdose = 0.3%).

MEASUREMENTS: During the 6 months following OPI initiation (i.e. trajectory period), we identified OPI-BZD dose and duration patterns using group-based multi-trajectory models, based on average daily morphine milligram equivalents (MME) for OPIs and diazepam milligram equivalents (DME) for BZDs. To label dose levels in each trajectory, we defined OPI use as very low (< 25 MME), low (25-50 MME), moderate (51-90 MME), high (91-150 MME) and very high (>150 MME) dose. Similarly, we defined BZD use as very low (< 10 DME), low (10-20 DME), moderate (21-40 DME), high (41-60 DME) and very high (> 60 DME) dose. Our primary analysis was to estimate the risk of time to first hospital or emergency department visit for OPI overdose within 6 months following the trajectory period using inverse probability of treatment-weighted Cox proportional hazards models.

FINDINGS: We identified nine distinct OPI-BZD trajectories: group A: very low OPI (early discontinuation)-very low declining BZD (n = 10 598; 28.0% of the cohort); B: very low OPI (early discontinuation)-very low stable BZD (n = 4923; 13.0%); C: very low OPI (early discontinuation)-medium BZD (n = 4997; 13.2%); D: low OPI-low BZD (n = 5083; 13.4%); E: low OPI-high BZD (n = 3906; 10.3%); F: medium OPI-low BZD (n = 3948; 10.4%); G: very high OPI-high BZD (n = 1371; 3.6%); H: very high OPI-very high BZD (n = 957; 2.5%); and I: very high OPI-low BZD (n = 2096; 5.5%). Compared with group A, five trajectories (32.3% of the study cohort) were associated with increased 6-month OPI overdose risks: E: low OPI-high BZD [hazard ratio (HR) = 3.27, 95% confidence interval (CI) = 1.61-6.63]; F: medium OPI-low BZD (HR = 4.04, 95% CI = 2.06-7.95); G: very high OPI-high BZD (HR = 6.98, 95% CI = 3.11-15.64); H: very high OPI-very high BZD (HR = 4.41, 95% CI = 1.51-12.85); and I: very high OPI-low BZD (HR = 6.50, 95% CI = 3.15-13.42).

CONCLUSIONS: Patterns of concurrent opioid and benzodiazepine use most associated with overdose risk among fee-for-service US Medicare beneficiaries initiating opioid prescriptions include very high-dose opioid use (MME > 150), high-dose benzodiazepine use (DME > 40) or medium-dose opioid with low-dose benzodiazepine use.

IMPORTANCE: Prescription opioids can treat acute pain in primary care but have potential for unsafe use and progression to prolonged opioid prescribing.

OBJECTIVE: To compare clinician-facing interventions to prevent unsafe opioid prescribing in opioid-naive primary care patients with acute noncancer pain.

DESIGN SETTING AND PARTICIPANTS: We conducted a multisite, cluster-randomized, 2 × 2 factorial, clinical trial in 3 health care systems that comprised 48 primary care practices and 525 participating clinicians from September 2018 through January 2021. Patient participants were opioid-naive outpatients, 18 years or older, who presented for a qualifying clinic visit with acute noncancer musculoskeletal pain or nonmigraine headache.

INTERVENTIONS: Practices randomized to: (1) control; (2) opioid justification; (3) monthly clinician comparison emails; or (4) opioid justification and clinician comparison. All groups received opioid prescribing guidelines via the electronic health record at the time of a new opioid prescription.

MAIN OUTCOMES AND MEASURES: Primary outcome measures were receipt of an initial opioid prescription at the qualifying clinic visit. Other outcomes were opioid prescribing for more than 3 months and a concurrent opioid/benzodiazepine prescription over 12-month follow-up.

RESULTS: Among 22 616 enrolled patient participants (9740 women [43.1%]; 64 American Indian/Alaska Native [0.3%]; 590 Asian [2.6%], 1120 Black/African American [5.0%], 1777 Hispanic [7.9%], 225 Native Hawaiian/Pacific Islander [1.0%], and 18 981 White [83.9%] individuals), the initial opioid prescribing rates at the qualifying clinic visit were 3.1% in the total sample, 4.2% in control, 3.6% in opioid justification, 2.6% in clinician comparison, and 1.9% in opioid justification and clinician comparison. Compared with control, the adjusted odds ratio (aOR) for a new opioid prescription was 0.74 (95% CI, 0.46-1.18; P = .20) for opioid justification and 0.60 (95% CI, 0.38-0.96; P = .03) for clinician comparison. Compared with control, clinician comparison was associated with decreased odds of opioid therapy of more than 3 months (aOR, 0.79; 95% CI, 0.69-0.91; P = .001) and concurrent opioid/benzodiazepine prescription (aOR, 0.85; 95% CI, 0.72-1.00; P = .04), whereas opioid justification did not have a significant effect.

CONCLUSIONS AND RELEVANCE: In this cluster randomized clinical trial, comparison emails decreased the proportion of opioid-naive patients with acute noncancer pain who received an opioid prescription, progressed to treatment with long-term opioid therapy, or were exposed to concurrent opioid and benzodiazepine therapy. Health care systems can consider adding clinician-targeted nudges to other initiatives as an efficient, scalable approach to further decrease potentially unsafe opioid prescribing.

TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03537573.

IMPORTANCE: Older US veterans commonly receive health care outside of the US Veterans Health Administration (VHA) through Medicare, which may increase receipt of low-value care and subsequent care cascades.

OBJECTIVE: To characterize the frequency, cost, and source of low-value prostate-specific antigen (PSA) testing and subsequent care cascades among veterans dually enrolled in the VHA and Medicare and to determine whether receiving a PSA test through the VHA vs Medicare is associated with more downstream services.

DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study used VHA and Medicare administrative data from fiscal years (FYs) 2017 to 2018. The study cohort consisted of male US veterans dually enrolled in the VHA and Medicare who were aged 75 years or older without a history of prostate cancer, elevated PSA, prostatectomy, radiation therapy, androgen deprivation therapy, or a urology visit. Data were analyzed from December 15, 2020, to October 20, 2022.

EXPOSURES: Receipt of low-value PSA testing.

MAIN OUTCOMES AND MEASURES: Differences in the use and cost of cascade services occurring 6 months after receipt of a low-value PSA test were assessed for veterans who underwent low-value PSA testing in the VHA and Medicare compared with those who did not, adjusted for patient- and facility-level covariates.

RESULTS: This study included 300 393 male US veterans at risk of undergoing low-value PSA testing. They had a mean (SD) age of 82.6 (5.6) years, and the majority (264 411 [88.0%]) were non-Hispanic White. Of these veterans, 36 459 (12.1%) received a low-value PSA test through the VHA, which was associated with 31.2 (95% CI, 29.2 to 33.2) additional cascade services per 100 veterans and an additional $24.5 (95% CI, $20.8 to $28.1) per veteran compared with the control group. In the same cohort, 17 981 veterans (5.9%) received a PSA test through Medicare, which was associated with 39.3 (95% CI, 37.2 to 41.3) additional cascade services per 100 veterans and an additional $35.9 (95% CI, $31.7 to $40.1) per veteran compared with the control group. When compared directly, veterans who received a PSA test through Medicare experienced 9.9 (95% CI, 9.7 to 10.1) additional cascade services per 100 veterans compared with those who underwent testing within the VHA.

CONCLUSIONS AND RELEVANCE: The findings of this cohort study suggest that US veterans dually enrolled in the VHA and Medicare commonly experienced low-value PSA testing and subsequent care cascades through both systems in FYs 2017 and 2018. Care cascades occurred more frequently through Medicare compared with the VHA. These findings suggest that low-value PSA testing has substantial downstream implications for patients and may be especially challenging to measure when care occurs in multiple health care systems.

BACKGROUND: Little is known about whether machine-learning algorithms developed to predict opioid overdose using earlier years and from a single state will perform as well when applied to other populations. We aimed to develop a machine-learning algorithm to predict 3-month risk of opioid overdose using Pennsylvania Medicaid data and externally validated it in two data sources (ie, later years of Pennsylvania Medicaid data and data from a different state).

METHODS: This prognostic modelling study developed and validated a machine-learning algorithm to predict overdose in Medicaid beneficiaries with one or more opioid prescription in Pennsylvania and Arizona, USA. To predict risk of hospital or emergency department visits for overdose in the subsequent 3 months, we measured 284 potential predictors from pharmaceutical and health-care encounter claims data in 3-month periods, starting 3 months before the first opioid prescription and continuing until loss to follow-up or study end. We developed and internally validated a gradient-boosting machine algorithm to predict overdose using 2013-16 Pennsylvania Medicaid data (n=639 693). We externally validated the model using (1) 2017-18 Pennsylvania Medicaid data (n=318 585) and (2) 2015-17 Arizona Medicaid data (n=391 959). We reported several prediction performance metrics (eg, C-statistic, positive predictive value). Beneficiaries were stratified into risk-score subgroups to support clinical use.

FINDINGS: A total of 8641 (1·35%) 2013-16 Pennsylvania Medicaid beneficiaries, 2705 (0·85%) 2017-18 Pennsylvania Medicaid beneficiaries, and 2410 (0·61%) 2015-17 Arizona beneficiaries had one or more overdose during the study period. C-statistics for the algorithm predicting 3-month overdoses developed from the 2013-16 Pennsylvania training dataset and validated on the 2013-16 Pennsylvania internal validation dataset, 2017-18 Pennsylvania external validation dataset, and 2015-17 Arizona external validation dataset were 0·841 (95% CI 0·835-0·847), 0·828 (0·822-0·834), and 0·817 (0·807-0·826), respectively. In external validation datasets, 71 361 (22·4%) of 318 585 2017-18 Pennsylvania beneficiaries were in high-risk subgroups (positive predictive value of 0·38-4·08%; capturing 73% of overdoses in the subsequent 3 months) and 40 041 (10%) of 391 959 2015-17 Arizona beneficiaries were in high-risk subgroups (positive predictive value of 0·19-1·97%; capturing 55% of overdoses). Lower risk subgroups in both validation datasets had few individuals (≤0·2%) with an overdose.

INTERPRETATION: A machine-learning algorithm predicting opioid overdose derived from Pennsylvania Medicaid data performed well in external validation with more recent Pennsylvania data and with Arizona Medicaid data. The algorithm might be valuable for overdose risk prediction and stratification in Medicaid beneficiaries.

FUNDING: National Institute of Health, National Institute on Drug Abuse, National Institute on Aging.

BACKGROUND: Health systems are increasingly implementing interventions to reduce older patients' use of low-value medications. However, prescribers' perspectives on medication value and the acceptability of interventions to reduce low-value prescribing are poorly understood.

OBJECTIVE: To identify the characteristics that affect the value of a medication and those factors influencing low-value prescribing from the perspective of primary care physicians.

DESIGN: Qualitative study using semi-structured interviews.

SETTING: Academic and community primary care practices within University of Pittsburgh Medical Center health system.

PARTICIPANTS: Sixteen primary care physicians.

MEASUREMENTS: We elicited 16 prescribers' perspectives on definitions and examples of low-value prescribing in older adults, the factors that incentivize them to engage in such prescribing, and the characteristics of interventions that would make them less likely to engage in low-value prescribing.

RESULTS: We identified three key themes. First, prescribers viewed low-value prescribing among older adults as common, characterized both by features of the medications themselves and of the particular patients to whom they were prescribed. Second, prescribers described the causes of low-value prescribing as multifactorial, with factors related to patients, prescribers, and the health system as a whole, making low-value prescribing a default practice pattern. Third, interventions addressing low-value prescribing must minimize the cognitive load and time pressures that make low-value prescribing common. Interventions increasing time pressure or cognitive load, such as increased documentation, were considered less acceptable.

CONCLUSIONS: Our findings demonstrate that low-value prescribing is a well-recognized phenomenon, and that interventions to reduce low-value prescribing must consider physicians' perspectives and address the specific patient, prescriber and health system factors that make low-value prescribing a default practice.