The Influence of Social Determinants on Receiving Outpatient Treatment with Monoclonal Antibodies, Disease Risk, and Effectiveness for COVID-19.

BACKGROUND: Limited research has studied the influence of social determinants of health (SDoH) on the receipt, disease risk, and subsequent effectiveness of neutralizing monoclonal antibodies (nMAbs) for outpatient treatment of COVID-19. OBJECTIVE: To examine the influence of SDoH variables on receiving nMAb treatments and the risk of a poor COVID-19 outcome, as well as nMAb treatment effectiveness across SDoH subgroups. DESIGN: Retrospective observational study utilizing electronic health record data from four health systems. SDoH variables analyzed included race, ethnicity, insurance, marital status, Area Deprivation Index, and population density. PARTICIPANTS: COVID-19 patients who met at least one emergency use authorization criterion for nMAb treatment. MAIN MEASURE: We used binary logistic regression to examine the influence of SDoH variables on receiving nMAb treatments and risk of a poor outcome from COVID-19 and marginal structural models to study treatment effectiveness. RESULTS: The study population included 25,241 (15.1%) nMAb-treated and 141,942 (84.9%) non-treated patients. Black or African American patients were less likely to receive treatment than white non-Hispanic patients (adjusted odds ratio (OR) = 0.86; 95% CI = 0.82-0.91). Patients who were on Medicaid, divorced or widowed, living in rural areas, or living in areas with the highest Area Deprivation Index (most vulnerable) had lower odds of receiving nMAb treatment, but a higher risk of a poor outcome. For example, compared to patients on private insurance, Medicaid patients had 0.89 (95% CI = 0.84-0.93) times the odds of receiving nMAb treatment, but 1.18 (95% CI = 1.13-1.24) times the odds of a poor COVID-19 outcome. Age, comorbidities, and COVID-19 vaccination status had a stronger influence on risk of a poor outcome than SDoH variables. nMAb treatment benefited all SDoH subgroups with lower rates of 14-day hospitalization and 30-day mortality. CONCLUSION: Disparities existed in receiving nMAbs within SDoH subgroups despite the benefit of treatment across subgroups.

Neutralizing Monoclonal Antibody Use and COVID-19 Infection Outcomes.

Importance: Evidence on the effectiveness and safety of COVID-19 therapies across a diverse population with varied risk factors is needed to inform clinical practice. Objective: To assess the safety of neutralizing monoclonal antibodies (nMAbs) for the treatment of COVID-19 and their association with adverse outcomes. Design, Setting, and Participants: This retrospective cohort study included 167 183 patients from a consortium of 4 health care systems based in California, Minnesota, Texas, and Utah. The study included nonhospitalized patients 12 years and older with a positive COVID-19 laboratory test collected between November 9, 2020, and January 31, 2022, who met at least 1 emergency use authorization criterion for risk of a poor outcome. Exposure: Four nMAb products (bamlanivimab, bamlanivimab-etesevimab, casirivimab-imdevimab, and sotrovimab) administered in the outpatient setting. Main Outcomes and Measures: Clinical and SARS-CoV-2 genomic sequence data and propensity-adjusted marginal structural models were used to assess the association between treatment with nMAbs and 4 outcomes: all-cause emergency department (ED) visits, hospitalization, death, and a composite of hospitalization or death within 14 days and 30 days of the index date (defined as the date of the first positive COVID-19 test or the date of referral). Patient index dates were categorized into 4 variant epochs: pre-Delta (November 9, 2020, to June 30, 2021), Delta (July 1 to November 30, 2021), Delta and Omicron BA.1 (December 1 to 31, 2021), and Omicron BA.1 (January 1 to 31, 2022). Results: Among 167 183 patients, the mean (SD) age was 47.0 (18.5) years; 95 669 patients (57.2%) were female at birth, 139 379 (83.4%) were White, and 138 900 (83.1%) were non-Hispanic. A total of 25 241 patients received treatment with nMAbs. Treatment with nMAbs was associated with lower odds of ED visits within 14 days (odds ratio [OR], 0.76; 95% CI, 0.68-0.85), hospitalization within 14 days (OR, 0.52; 95% CI, 0.45-0.59), and death within 30 days (OR, 0.14; 95% CI, 0.10-0.20). The association between nMAbs and reduced risk of hospitalization was stronger in unvaccinated patients (14-day hospitalization: OR, 0.51; 95% CI, 0.44-0.59), and the associations with hospitalization and death were stronger in immunocompromised patients (hospitalization within 14 days: OR, 0.31 [95% CI, 0.24-0.41]; death within 30 days: OR, 0.13 [95% CI, 0.06-0.27]). The strength of associations of nMAbs increased incrementally among patients with a greater probability of poor outcomes; for example, the ORs for hospitalization within 14 days were 0.58 (95% CI, 0.48-0.72) among those in the third (moderate) risk stratum and 0.41 (95% CI, 0.32-0.53) among those in the fifth (highest) risk stratum. The association of nMAb treatment with reduced risk of hospitalizations within 14 days was strongest during the Delta variant epoch (OR, 0.37; 95% CI, 0.31-0.43) but not during the Omicron BA.1 epoch (OR, 1.29; 95% CI, 0.68-2.47). These findings were corroborated in the subset of patients with viral genomic data. Treatment with nMAbs was associated with a significant mortality benefit in all variant epochs (pre-Delta: OR, 0.16 [95% CI, 0.08-0.33]; Delta: OR, 0.14 [95% CI, 0.09-0.22]; Delta and Omicron BA.1: OR, 0.10 [95% CI, 0.03-0.35]; and Omicron BA.1: OR, 0.13 [95% CI, 0.02-0.93]). Potential adverse drug events were identified in 38 treated patients (0.2%). Conclusions and Relevance: In this study, nMAb treatment for COVID-19 was safe and associated with reductions in ED visits, hospitalization, and death, although it was not associated with reduced risk of hospitalization during the Omicron BA.1 epoch. These findings suggest that targeted risk stratification strategies may help optimize future nMAb treatment decisions.

An external stability audit framework to test the validity of personality prediction in AI hiring.

Automated hiring systems are among the fastest-developing of all high-stakes AI systems. Among these are algorithmic personality tests that use insights from psychometric testing, and promise to surface personality traits indicative of future success based on job seekers' resumes or social media profiles. We interrogate the validity of such systems using stability of the outputs they produce, noting that reliability is a necessary, but not a sufficient, condition for validity. Crucially, rather than challenging or affirming the assumptions made in psychometric testing - that personality is a meaningful and measurable construct, and that personality traits are indicative of future success on the job - we frame our audit methodology around testing the underlying assumptions made by the vendors of the algorithmic personality tests themselves. Our main contribution is the development of a socio-technical framework for auditing the stability of algorithmic systems. This contribution is supplemented with an open-source software library that implements the technical components of the audit, and can be used to conduct similar stability audits of algorithmic systems. We instantiate our framework with the audit of two real-world personality prediction systems, namely, Humantic AI and Crystal. The application of our audit framework demonstrates that both these systems show substantial instability with respect to key facets of measurement, and hence cannot be considered valid testing instruments.

Quantifying Empiric Antibiotic Use in US Children's Hospitals.

OBJECTIVES: Empirical broad-spectrum antibiotics are routinely administered for short durations to children with suspected bacteremia while awaiting blood culture results. Our aim for this study was to estimate the proportion of broad-spectrum antibiotic use accounted for by these "rule-outs." METHODS: The Pediatric Health Information System was used to identify children aged 3 months to 20 years hospitalized between July 2016 and June 2017 who received broad-spectrum antibiotics for suspected bacteremia. Using an electronic definition for a rule-out, we estimated the proportion of all broad-spectrum antibiotic days of therapy accounted for by this indication. Clinical and demographic characteristics, as well as antibiotic choice, are reported descriptively. RESULTS: A total of 67 032 episodes of suspected bacteremia across 42 hospitals were identified. From these, 34 909 (52%) patients were classified as having received an antibiotic treatment course, and 32 123 patients (48%) underwent an antibiotic rule-out without a subsequent treatment course. Antibiotics prescribed for rule-outs accounted for 12% of all broad-spectrum antibiotic days of therapy. Third-generation cephalosporins and vancomycin were the most commonly prescribed antibiotics, and substantial hospital-level variation in vancomycin use was identified (range: 16%-58% of suspected bacteremia episodes). CONCLUSIONS: Broad-spectrum intravenous antibiotic use for rule-out infections appears common across children's hospitals, with substantial hospital-level variation in the use of vancomycin in particular. Antibiotic stewardship programs focused on intervening on antibiotics prescribed for longer durations may consider this novel opportunity to further standardize antibiotic regimens and reduce antibiotic exposure.