Low frequency of allergy referral for penicillin allergy evaluation in an urban Boston primary care setting.

Background: A key strategy to combat the public health crisis of antimicrobial resistance is to use appropriate antibiotics, which is difficult in patients with a penicillin allergy label. Objective: Our aim was to investigate racial and ethnic differences related to penicillin allergy labeling and referral to allergy/immunology in primary care. Methods: This was a retrospective study of Tufts Medical Center's Boston-based primary care patients in 2019. Univariable and multivariable logistic regression models were used to examine demographic associations with (1) penicillin allergy label and (2) allergist referral. Results: Of 21,918 primary care patients, 2,391 (11%) had a penicillin allergy label; of these, 249 (10%) had an allergist referral. In multivariable logistic regression models, older age (adjusted odds ratio [aOR] = 1.06 [95% CI = 1.04-1.09]) and female sex (aOR = 1.58 [95% CI = 1.44-1.74]) were associated with higher odds of penicillin allergy label carriage. Black race (aOR = 0.77 [95% CI = 0.69-0.87]) and Asian race (aOR = 0.47 [95% CI = 0.41-0.53]) were associated with lower odds of penicillin allergy label carriage. In multivariable regression, allergist referral was associated with female sex (aOR = 1.52 [95% CI = 1.10-2.10]) and Black race (aOR = 1.74 [95% CI = 1.25-2.45]). Of 93 patients (37%) who completed their allergy visit, 26 (28%) had received penicillin allergy evaluation or were scheduled to receive a penicillin allergy evaluation at a future visit. Conclusions: There were racial differences in penicillin allergy labeling and referral. Allergy referral for penicillin allergy assessment was rare. Larger studies are needed to assess penicillin allergy labeling and delabeling with an equity focus on optimizing patient health outcomes.

Development of a COVID-19-Related Anti-Asian Tweet Data Set: Quantitative Study.

BACKGROUND: Since the advent of the COVID-19 pandemic, individuals of Asian descent (colloquial usage prevalent in North America, where "Asian" is used to refer to people from East Asia, particularly China) have been the subject of stigma and hate speech in both offline and online communities. One of the major venues for encountering such unfair attacks is social networks, such as Twitter. As the research community seeks to understand, analyze, and implement detection techniques, high-quality data sets are becoming immensely important. OBJECTIVE: In this study, we introduce a manually labeled data set of tweets containing anti-Asian stigmatizing content. METHODS: We sampled over 668 million tweets posted on Twitter from January to July 2020 and used an iterative data construction approach that included 3 different stages of algorithm-driven data selection. Finally, we found volunteers who manually annotated the tweets by hand to arrive at a high-quality data set of tweets and a second, more sampled data set with higher-quality labels from multiple annotators. We presented this final high-quality Twitter data set on stigma toward Chinese people during the COVID-19 pandemic. The data set and instructions for labeling can be viewed in the Github repository. Furthermore, we implemented some state-of-the-art models to detect stigmatizing tweets to set initial benchmarks for our data set. RESULTS: Our primary contributions are labeled data sets. Data Set v3.0 contained 11,263 tweets with primary labels (unknown/irrelevant, not-stigmatizing, stigmatizing-low, stigmatizing-medium, stigmatizing-high) and tweet subtopics (eg, wet market and eating habits, COVID-19 cases, bioweapon). Data Set v3.1 contained 4998 (44.4%) tweets randomly sampled from Data Set v3.0, where a second annotator labeled them only on the primary labels and then a third annotator resolved conflicts between the first and second annotators. To demonstrate the usefulness of our data set, preliminary experiments on the data set showed that the Bidirectional Encoder Representations from Transformers (BERT) model achieved the highest accuracy of 79% when detecting stigma on unseen data with traditional models, such as a support vector machine (SVM) performing at 73% accuracy. CONCLUSIONS: Our data set can be used as a benchmark for further qualitative and quantitative research and analysis around the issue. It first reaffirms the existence and significance of widespread discrimination and stigma toward the Asian population worldwide. Moreover, our data set and subsequent arguments should assist other researchers from various domains, including psychologists, public policy authorities, and sociologists, to analyze the complex economic, political, historical, and cultural underlying roots of anti-Asian stigmatization and hateful behaviors. A manually annotated data set is of paramount importance for developing algorithms that can be used to detect stigma or problematic text, particularly on social media. We believe this contribution will help predict and subsequently design interventions that will significantly help reduce stigma, hate, and discrimination against marginalized populations during future crises like COVID-19.

Primary Care Population Management for COVID-19 Patients.

BACKGROUND: Most patients infected with SARS-CoV-2 have mild to moderate symptoms manageable at home; however, up to 20% develop severe illness requiring additional support. Primary care practices performing population management can use these tools to remotely assess and manage COVID-19 patients and identify those needing additional medical support before becoming critically ill. AIM: We developed an innovative population management approach for managing COVID-19 patients remotely. SETTING: Development, implementation, and evaluation took place in April 2020 within a large urban academic medical center primary care practice. PARTICIPANTS: Our panel consists of 40,000 patients. By April 27, 2020, 305 had tested positive for SARS-CoV-2 by RT-qPCR. Outreach was performed by teams of doctors, nurse practitioners, physician assistants, and nurses. PROGRAM DESCRIPTION: Our innovation includes an algorithm, an EMR component, and a twice daily population report for managing COVID-19 patients remotely. PROGRAM EVALUATION: Of the 305 patients with COVID-19 in our practice at time of submission, 196 had returned to baseline; 54 were admitted to hospitals, six of these died, and 40 were discharged. DISCUSSION: Our population management strategy helped us optimize at-home care for our COVID-19 patients and enabled us to identify those who require inpatient medical care in a timely fashion.

A multicomponent intervention to improve adherence to opioid prescribing and monitoring guidelines in primary care.

OBJECTIVE: Guidelines for appropriate management of chronic opioid therapy are underutilized by primary care physicians (PCPs). The authors hypothesized that developing a multicomponent, team-based opioid management system with electronic health record (EHR) support would allow our clinicians to improve adherence to chronic opioid prescribing and monitoring guidelines. DESIGN: This was a retrospective pre-post study. SETTING: The authors performed this intervention at our large, urban, academic primary care practice. PATIENTS, PARTICIPANTS: All patients with the diagnosis of "chronic pain, opioid requiring (ICD-10 F11.20)" on their primary care EHR problem lists were included in this study. INTERVENTION: The authors implemented a five-pronged strategy to improve our system of opioid prescribing, including (1) a patient registry with regular dissemination of reports to PCPs; (2) standardization of policies regarding opioid prescribing and monitoring; (3) development of a risk-assessment algorithm and riskstratified monitoring guidelines; (4) a team-based approach to care with physician assistant care managers; and (5) an EHR innovation to facilitate communication and guideline adherence. MAIN OUTCOME MEASURES: The authors measured percent adherence to opioid prescribing guidelines, including annual patient-provider agreements, biannual urine drug screens (UDSs), and prescription monitoring program (PMP) verification. RESULTS: Between September 2015 and September 2016, the percentage of patients on chronic opioid therapy with a signed controlled substances agreement within the preceding year increased from 46 to 76 percent (p < 0.0001), while the percentage of patients with a UDS done within the past 6 months rose from 23 to 79 percent (p < 0.0001). The percentage of patients whose state PMPs profile had been checked by a primary care team member in the past year rose from 45 to 97 percent (p < 0.0001). CONCLUSION: A comprehensive strategy to standardize chronic opioid prescribing in our primary care practice coincided with an increase in adherence to opioid management guidelines.

Systemic obstacles to battered women's participation in the judicial system: when will the status quo change?

The Canadian government has introduced numerous policies, guidelines, and mandates at the federal and provincial levels that recognize woman abuse as a serious social problem and violation of the law. Nonetheless, recent feminist research continues to expose laws and practices that fail woman abuse victims. The present study examined the experiences of women victims in domestic violence cases and the barriers they faced in dealing with the police, the courts, and social service agencies. Despite government initiatives, the study results corroborate previous findings indicating that many battered women feel further traumatized by ambivalent or discriminatory attitudes and practices prevalent within the system.