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Results correlate with prior studies in which non-pediatric clinicians and white race/ethnicity were predictive of antibiotic prescription, while association with older patient age has not been previously reported. Findings illustrate the promise of linking electronic health records with community data to evaluate health care disparities.

BACKGROUND AND OBJECTIVE: For febrile infants, predictive models to detect bacterial infections are available, but clinical adoption remains limited by implementation barriers. There is a need for predictive models using widely available predictors. Thus, we previously derived 2 novel predictive models (machine learning and regression) by using demographic and clinical factors, plus urine studies. The objective of this study is to refine and externally validate the predictive models. METHODS: This is a cross-sectional study of infants initially evaluated at one pediatric emergency department from January 2011 to December 2018. Inclusion criteria were age 0 to 90 days, temperature ≥38°C, documented gestational age, and insurance type. To reduce potential biases, we derived models again by using derivation data without insurance status and tested the ability of the refined models to detect bacterial infections (ie, urinary tract infection, bacteremia, and meningitis) in the separate validation sample, calculating areas-under-the-receiver operating characteristic curve, sensitivities, and specificities. RESULTS: Of 1419 febrile infants (median age 53 days, interquartile range = 32–69), 99 (7%) had a bacterial infection. Areas-under-the-receiver operating characteristic curve of machine learning and regression models were 0.92 (95% confidence interval [CI] 0.89–0.94) and 0.90 (0.86–0.93) compared with 0.95 (0.91–0.98) and 0.96 (0.94–0.98) in the derivation study. Sensitivities and specificities of machine learning and regression models were 98.0% (94.7%–100%) and 54.2% (51.5%–56.9%) and 96.0% (91.5%–99.1%) and 50.0% (47.4%–52.7%). CONCLUSIONS: Compared with the derivation study, the machine learning and regression models performed similarly. Findings suggest a clinical-based model can estimate bacterial infection risk. Future studies should prospectively test the models and investigate strategies to optimize clinical adoption.

The SARS-CoV-2 pandemic has had a disproportionate effect on Black and Indigenous people, racial and ethnic minorities, and other historically marginalized groups. 1 Two antiviral oral drugs, Paxlovid (Pfizer; nirmatrelvir copackaged with ritonavir) and molnupiravir have received emergency use authorization when taken within 5 days for symptomatic, high-risk COVID-19 infection. Paxlovid and molnupiravir reduce hospitalization by 89% and 31%, respectively. 2 These drugs complement COVID-19 infusion therapeutics, eg, remdesivir and sotrovimab. 2 To date, inequities have emerged in receipt of COVID-19 vaccines and monoclonal antibody (mAb) treatment. 3,4 Prescription of oral antiviral drugs among primary care and other clinicians could improve access to marginalized populations by avoiding the logistic challenges associated with time-sensitive appointments at infusion centers. If equitably deployed, these new treatments could mitigate COVID-19-related inequities in hospitalizations and deaths in racial and ethnic minority communities. Ethical principles (ie, maximize benefit, equal concern, and mitigate health inequities) and procedural fairness and transparency developed for allocation of COVID-19 vaccines are relevant to COVID-19 therapeutics. 1 Operationalization of these principles involves unique challenges. These include fluctuations in patient demand based on COVID-19 surges and public awareness of therapeutic options, and time-sensitive COVID-19 testing and treatment windows, in addition to fluctuations in production and supply of therapeutics. Operationalization of these ethical principles is further hindered by current US Department of Health and Human Services (HHS) policy that allocates therapeutics to states based on population size rather than need, 5 with states in turn establishing their distribution policies and public awareness campaigns.Informed by models that highlight cascading disparities at critical steps in the care continuum, (ie, from patient awareness, demand, and care-seeking to receiving medication), 6 we discuss key barriers to equity and corresponding strategies to overcome them (Table ). Patient DemandPatient demand is driven by pandemic dynamics, the public perceptions of risk, supply scarcity, and patient awareness of relevant symptoms, treatment options, treatment windows, and the logistics of obtaining treatment, ie, testing, obtaining appropriate care, and treatment by prescription or infusion.Low awareness is accentuated by limited information, even online. The HHS website provides links to mAb sites but does not specifically discuss how to access oral antiviral medications. State health department websites vary widely in the breadth, depth, and usability of information they provide with few providing information on oral antiviral drugs or pharmacies that stock them.Although reliable data are lacking, clinical and prior experience suggest comparatively low awareness of novel COVID-19 therapeutics among socially disadvantaged racial and ethnic minority communities.These awareness...

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To examine the association between cough status and bacterial infections (BIs) to more accurately stratify risk and predict BIs in febrile infants.METHODS: A retrospective cohort study was performed by identifying all infants #60 days old with temperature $38°C at an urban pediatric emergency department from 2014 to 2016. The Rochester Risk model was used to stratify risk. Cough status (with or without) was the main covariate of interest. The primary outcome was a BI, including urinary tract infection, bacteremia, or meningitis. Analyses consisted of descriptive statistics, simple and multiple regression to compare the odds of BI on the basis of cough status, as well as x 2 statistics to compare the BI rates among high-risk infants with and without cough.RESULTS: Of 508 febrile infants #60 days old, 46 (9.1%) had a BI, 13 of which were either bacteremia or meningitis. There were no BIs among low-risk infants with a cough. The odds of BI increased progressively, peaking at 14.6 (95% confidence interval: 4.3-49.7) for high-risk infants without a cough. The adjusted odds of BI among infants with cough was 0.47 (95% confidence interval: 0.22-0.99). CONCLUSIONS:In our findings, an inverse relationship is demonstrated between presence of cough and odds of BI, suggesting that cough status may be a useful marker of viral infections in febrile infants. Considering that detecting cough status is noninvasive, inexpensive, and immediately available, it represents an attractive value-based risk factor to enhance current BI prediction models.