Disparities in use of physical restraints at an urban, minority-serving hospital emergency department.

BACKGROUND: Recent reports have identified associations between patient race and ethnicity and use of physical restraint while receiving care in the emergency department (ED). However, no study has assessed this relationship in hospitals primarily treating patients of color and underserved populations. The primary objective of this study was to evaluate the association between race/ethnicity and the use of restraints in an ED population at a minority-serving, safety-net institution. METHODS: For this cross-sectional study, chart review identified all adult patients presenting to the Boston Medical Center ED between January 2018 and April 2021. Generalized estimating equation logistic regression modeling was conducted to evaluate associations between race and use of restraints. RESULTS: Of 348,384 ED visits (22.9% White, 46.7% Black, 23.1% Hispanic), 1852 (0.5%) had an associated physical restraint order. Multivariable models showed significant interactions (p = 0.02) between race/ethnicity, behavioral health diagnosis, and sex on the primary outcome of physical restraint. Stratified analysis revealed that among patients with no behavioral health diagnoses, Black (odds ratio [OR] 0.50, 95% confidence interval [CI] 0.34-0.72, p = 0.0003) and Hispanic (OR 0.35, 95% CI 0.20-0.63, p = 0.0004) patients had lower odds of restraint than White patients. Among female patients with a mental health and/or substance use disorder diagnosis, Black (OR 1.95, 95% CI 1.49-2.54, p < 0.0001) and Hispanic (OR 2.13, 95% CI 1.49-3.03, p < 0.0001) patients had higher odds of restraint than White patients. Similar trends were observed for Black male patients (OR 1.60, 95% CI 1.34-1.91, p < 0.0001) but not for Hispanic male patients (OR 0.96, 95% CI 0.73-1.26, p = 0.77) with behavioral health diagnoses who had similar odds of restraint to White patients. Additional factors associated with physical restraint include younger age, public or lack of insurance, and ED visits during the pandemic. CONCLUSIONS: Racial disparities exist in restraint utilization at this minority-serving safety-net hospital; however, these disparities are modified by sex and by behavioral health diagnoses. The reasons for these disparities may be multifactorial and warrant further investigation.

Patient navigation to address sociolegal barriers for patients with cancer: A comparative-effectiveness study.

BACKGROUND: Sociolegal barriers to cancer care are defined as health-related social needs like affordable healthy housing, stable utility service, and food security that may be remedied by public policy, law, regulation, or programming. Legal support has not been studied in cancer care. METHODS: The authors conducted a randomized controlled trial of patients who had newly diagnosed cancer at a safety-net medical center in Boston from 2014 through 2017, comparing standard patient navigation versus enhanced navigation partnered with legal advocates to identify and address sociolegal barriers. English-speaking, Spanish-speaking, or Haitian Creole-speaking patients with breast and lung cancer were eligible within 30 days of diagnosis. The primary outcome was timely treatment within 90 days of diagnosis. Secondary outcomes included patient-reported outcomes (distress, cancer-related needs, and satisfaction with navigation) at baseline and at 6 months. RESULTS: In total, 201 patients with breast cancer and 19 with lung cancer enrolled (response rate, 78%). The mean patient age was 55 years, 51% of patients were Black and 22% were Hispanic, 20% spoke Spanish and 8% spoke Haitian Creole, 73% had public health insurance, 77% reported 1 or more perceived sociolegal barrier, and the most common were barriers to housing and employment. Ninety-six percent of participants with breast cancer and 73% of those with lung cancer initiated treatment within 90 days. No significant effect of enhanced navigation was observed on the receipt of timely treatment among participants with breast cancer (odds ratio, 0.88; 95% CI, 0.17-4.52) or among those with lung cancer (odds ratio, 4.00; 95% CI, 0.35-45.4). No differences in patient-reported outcomes were observed between treatment groups. CONCLUSIONS: Navigation enhanced by access to legal consultation and support had no impact on timely treatment, patient distress, or patient needs. Although most patients reported sociolegal barriers, few required intensive legal services that could not be addressed by navigators. LAY SUMMARY: In patients with cancer, the experience of sociolegal barriers to care, such as unstable housing, utility services, or food insecurity, is discussed. Addressing these barriers through legal information and assistance may improve care. This study compares standard patient navigation versus enhanced navigation partnered with legal advocates for patients with breast and lung cancers. Almost all patients in both navigation groups received timely care and also reported the same levels of distress, needs, and satisfaction with navigation. Although 75% of patients in the study had at least 1 sociolegal barrier identified, few required legal advocacy beyond what a navigator who received legal information and coaching could provide.

Calculating power for the Finkelstein and Schoenfeld test statistic for a composite endpoint with two components.

The Finkelstein and Schoenfeld (FS) test is a popular generalized pairwise comparison approach to analyze prioritized composite endpoints (eg, components are assessed in order of clinical importance). Power and sample size estimation for the FS test, however, are generally done via simulation studies. This simulation approach can be extremely computationally burdensome, compounded by increasing number of composite endpoints and with increasing sample size. Here we propose an analytical solution to calculate power and sample size for commonly encountered two-component hierarchical composite endpoints. The power formulas are derived assuming underlying distributions in each of the component outcomes on the population level, which provide a computationally efficient and practical alternative to the standard simulation approach. Monte Carlo simulation results demonstrate that performance of the proposed power formulas are consistent with that of the simulation approach, and have generally desirable objective properties including robustness to mis-specified distributional assumptions. We demonstrate the application of the proposed formulas by calculating power and sample size for the Transthyretin Amyloidosis Cardiomyopathy Clinical Trial.

Racial equity in linkage to inpatient opioid use disorder treatment in patients that received emergency care.

OBJECTIVES: Opioid use disorder (OUD) is a national epidemic, and Black and Hispanic patients are less likely to receive treatment when compared to white patients. In this study, race was used as a proxy to assess potential effects of racism on the referral process for OUD treatment. Our primary aim was to examine whether Black or Hispanic patients experienced increased barriers to inpatient OUD detoxification (detox) placement at a community-integrated, substance use disorder support program based in an emergency department (ED). Our secondary aim was to determine if Black and Hispanic patients were more likely to have >3 referrals. METHODS: This retrospective cohort study was conducted at a large urban safety-net hospital and included patients seen in the ED from July 2018 to September 2019 with ICD-10 codes for an opioid-related visit and who sought placement to inpatient detox. A generalized linear mixed model controlling for multiple visits, age, sex, insurance, time, day of week, and time of year was used to assess the association between race/ethnicity and hypothesized barriers to placement. The proportion of patients with >3 visits for referral to inpatient detox was compared between Black and Hispanic patients and white patients using a chi-squared test. RESULTS: We identified 1733 encounters from 782 unique patients seeking connection to inpatient detox for OUD. Of the 1733 encounters, 45% were among Black and Hispanic patients. Hispanic and Black men had significantly lower odds of having a barrier to inpatient OUD detox than white men (OR = 0.734, 95% CI 0.542-0.995). No significant difference was found for Hispanic and Black women (OR = 1.212, 95% CI 0.705-2.082). More Black and Hispanic patients experienced >3 referrals to inpatient detox compared to white patients (19.2% vs 12.9%, p = 0.016). CONCLUSIONS: This study suggests in the context of near-universal health insurance coverage, an ED-based OUD support program staffed by diverse community members can mitigate inequities in access to inpatient detox. However, the increased number of ED visits for OUD detox placement by Black and Hispanic patients suggests racial inequities in OUD treatment exist after linkage to care. Additional research should explore the causes, specifically structural and interpersonal racism, and determine solutions to address racial inequities in detox placement as well as maintenance in treatment programs.

Persistent inter-observer variability of breast density assessment using BI-RADS® 5th edition guidelines.

OBJECTIVES: Due to most states' legislation, mammographic density categorization has potentially far-reaching implications, but remains subjective based on BIRADS® guidelines. We aimed to determine 1) effect of BI-RADS® 5th edition (5th-ed) vs 4th-edition (4th-ed) guidelines on reader agreement regarding density assessment; 2) 5th-ed vs 4th-ed density distribution, and visual vs quantitative assessment agreement; 3) agreement between experienced vs less experienced readers. METHODS: In a retrospective review, six breast imaging radiologists (BIR) (23-30 years' experience) visually assessed density of 200 screening mammograms performed September 2012-January 2013 using 5th-ed guidelines. Results were compared to 2016 data of the same readers evaluating the same mammograms using 4th-ed guidelines after a training module. 5th-ed density categorization by seven junior BIR (1-5 years' experience) was compared to eight experienced BIR. Nelson et al.'s kappas (κm, κw), Fleiss' κF, and Cohen's κ were calculated. Quantitative density using Volpara was compared with reader assessments. RESULTS: Inter-reader weighted agreement using 5th-ed is moderately strong, 0.73 (κw, s.e. = 0.01), similar to 4th-ed, 0.71 (κw, s.e. = 0.03). Intra-reader Cohen's κ is 0.23-0.34, similar to 4th-ed. Binary not-dense vs dense categorization, using 5th-ed results in higher dense categorization vs 4th-ed (p < 0.001). 5th-ed density distribution results in higher numbers in categories B/C vs 4th-ed (p < 0.001). Distribution for 5th-ed does not differ based on reader experience (p = 0.09). Reader vs quantitative weighted agreement is similar (5th-ed, Cohen's κ = 0.76-0.85; 4th-ed, Cohen's κ = 0.68-0.83). CONCLUSION: There is persistent subjectivity of visually assessed mammographic density using 5th-ed guidelines; experience does not correlate with better inter-reader agreement.

Modeling rater diagnostic skills in binary classification processes.

Many disease diagnoses involve subjective judgments by qualified raters. For example, through the inspection of a mammogram, MRI, or ultrasound image, the clinician himself becomes part of the measuring instrument. To reduce diagnostic errors and improve the quality of diagnoses, it is necessary to assess raters' diagnostic skills and to improve their skills over time. This paper focuses on a subjective binary classification process, proposing a hierarchical model linking data on rater opinions with patient true disease-development outcomes. The model allows for the quantification of the effects of rater diagnostic skills (bias and magnifier) and patient latent disease severity on the rating results. A Bayesian Markov chain Monte Carlo (MCMC) algorithm is developed to estimate these parameters. Linking to patient true disease outcomes, the rater-specific sensitivity and specificity can be estimated using MCMC samples. Cost theory is used to identify poor- and strong-performing raters and to guide adjustment of rater bias and diagnostic magnifier to improve the rating performance. Furthermore, diagnostic magnifier is shown as a key parameter to present a rater's diagnostic ability because a rater with a larger diagnostic magnifier has a uniformly better receiver operating characteristic (ROC) curve when varying the value of diagnostic bias. A simulation study is conducted to evaluate the proposed methods, and the methods are illustrated with a mammography example.

Opioid education and nasal naloxone rescue kits in the emergency department.

INTRODUCTION: Emergency departments (EDs) may be high-yield venues to address opioid deaths with education on both overdose prevention and appropriate actions in a witnessed overdose. In addition, the ED has the potential to equip patients with nasal naloxone kits as part of this effort. We evaluated the feasibility of an ED-based overdose prevention program and described the overdose risk knowledge, opioid use, overdoses, and overdose responses among participants who received overdose education and naloxone rescue kits (OEN) and participants who received overdose education only (OE). METHODS: Program participants were surveyed by telephone after their ED visit about their substance use, overdose risk knowledge, history of witnessed and personal overdoses, and actions in a witnessed overdose including use of naloxone. RESULTS: A total of 415 ED patients received OE or OEN between January 1, 2011 and February 28, 2012. Among those, 51 (12%) completed the survey; 37 (73%) of those received a naloxone kit, and 14 (27%) received OE only. Past 30-day opioid use was reported by 35% OEN and 36% OE, and an overdose was reported by 19% OEN and 29% OE. Among 53% (27/51) of participants who witnessed another individual experiencing an overdose, 95% OEN and 88% OE stayed with victim, 74% OEN and 38% OE called 911, 26% OEN and 25% OE performed rescue breathing, and 32% OEN (n=6) used a naloxone kit to reverse the overdose. We did not detect statistically significant differences between OEN and OE-only groups in opioid use, overdose or response to a witnessed overdose. CONCLUSION: This is the first study to demonstrate the feasibility of ED-based opioid overdose prevention education and naloxone distribution to trained laypersons, patients and their social network. The program reached a high-risk population that commonly witnessed overdoses and that called for help and used naloxone, when available, to rescue people. While the study was retrospective with a low response rate, it provides preliminary data for larger, prospective studies of ED-based overdose prevention programs.

Clinician impression versus prescription drug monitoring program criteria in the assessment of drug-seeking behavior in the emergency department.

STUDY OBJECTIVE: We compare emergency provider impression of drug-seeking behavior with objective criteria from a state prescription drug monitoring program, assess change in opioid pain reliever prescribing after prescription drug monitoring program review, and examine clinical factors associated with suspected drug-seeking behavior. METHODS: This was a prospective observational study of emergency providers assessing a convenience sample of patients aged 18 to 64 years who presented to either of 2 academic medical centers with chief complaint of back pain, dental pain, or headache. Drug-seeking behavior was objectively defined as present when a patient had greater than or equal to 4 opioid prescriptions by greater than or equal to 4 providers in the 12 months before emergency department evaluation. Emergency providers completed data forms recording their impression of the likelihood of drug-seeking behavior, patient characteristics, and plan for prescribing pre- and post-prescription drug monitoring program review. Descriptive statistics were generated. We calculated agreement between emergency provider impression of drug-seeking behavior and prescription drug monitoring program definition, and sensitivity, specificity, and positive predictive value of emergency provider impression, using prescription drug monitoring program criteria as the criterion standard. A multivariate logistic regression analysis was conducted to determine clinical factors associated with drug-seeking behavior. RESULTS: Thirty-eight emergency providers with prescription drug monitoring program access participated. There were 544 patient visits entered into the study from June 2011 to January 2013. There was fair agreement between emergency provider impression of drug-seeking behavior and prescription drug monitoring program (κ=0.30). Emergency providers had sensitivity 63.2% (95% confidence interval [CI] 54.8% to 71.7%), specificity 72.7% (95% CI 68.4% to 77.0%), and positive predictive value 41.2% (95% CI 34.4% to 48.2%) for identifying drug-seeking behavior. After exposure to prescription drug monitoring program data, emergency providers changed plans to prescribe opioids at discharge in 9.5% of cases (95% CI 7.3% to 12.2%), with 6.5% of patients (n=35) receiving opioids not previously planned and 3.0% (n=16) no longer receiving opioids. Predictors for drug-seeking behavior by prescription drug monitoring program criteria were patient requests opioid medications by name (odds ratio [OR] 1.91; 95% CI 1.13 to 3.23), multiple visits for same complaint (OR 2.5; 95% CI 1.49 to 4.18), suspicious history (OR 1.88; 95% CI 1.1 to 3.19), symptoms out of proportion to examination (OR 1.83; 95% CI 1.1 to 3.03), and hospital site (OR 3.1; 95% CI 1.76 to 5.44). CONCLUSION: Emergency providers had fair agreement with objective criteria from the prescription drug monitoring program in suspecting drug-seeking behavior. Program review changed management plans in a small number of cases. Multiple clinical factors were predictive of drug-seeking behavior.

Statistical models for autocorrelated count data.

A generalized linear mixed model is an increasingly popular choice for the modelling of correlated, non-normal responses in a regression setting. A number of methods are currently available for fitting a generalized linear mixed model including Monte-Carlo Markov-Chain maximum likelihood algorithms, approximate maximum likelihood (PQL), iterative bias correction, and others. Of interest in this paper is to compare the parameter estimation of the various methods in the modelling of a count data set, the incidence of polio in the USA over the period 1970-1983, using a longlinear generalized linear mixed model with an autoregressive correlation structure. Despite the fact that all of these methods are considered valid modelling techniques, we find that parameter estimates and standard errors differ substantially between analyses, particularly in the estimation of the parameters describing the random effects distribution. A small simulation study is helpful in understanding some of these differences. The methods lead to reasonably similar predictions for future observations, with small differences observed in some monthly counts.