Computer informatics is integral to infection control, a role that will grow as surveillance and reporting become increasingly automated. Surveillance applications include automated infection monitoring, device utilization calculations, and outbreak detection. Prevention uses include identifying multidrug-resistant organism carriers at admission, flagging high-risk patients, reducing unnecessary device use, and supporting antimicrobial stewardship. Public health integration enhances electronic reporting, regional communication. and outbreak response. This article reviews current and emerging applications of informatics in infection prevention and control, including generative artificial intelligence and methods for public health clinical data transmission.

INTRODUCTION:People experiencing homelessness (PEH) face significant risks of early cancer-related mortality due to barriers of care for complex treatments. We describe our pilot study for PEH with advanced cancer undergoing treatment at a large safety-net hospital system. METHODS:We performed a retrospective case series of PEH with advanced cancer undergoing medical respite from 2020 to 2024. Descriptive statistics were used to describe patient characteristics and outcomes. RESULTS:Nineteen patients participated in medical respite program while undergoing cancer treatment. Patients were referred from hospitals, clinics, and prisons; 74% were men and 67% under age 65. The most common primary cancers involved the lung, colon or rectum, and oropharynx-53% with known metastatic disease. All patients were able to complete cancer-related treatment with 42% achieving treatment response and transitioning to long-term housing. CONCLUSION:We demonstrated that temporary housing and support facilitated cancer treatment, which is important as states adopt medical respite to address social needs.

BACKGROUND:Early identification of patients colonized with multidrug-resistant organisms (MDROs) facilitates infection control interventions. We assessed a Public Health Risk Model's ability to predict carbapenem-resistant Enterobacterales and other MDROs. METHODS:We retrospectively analyzed a medical intensive care unit patient cohort screened at time of admission for MDRO carriage (1/2017-1/2018). Encounters were linked to Illinois Hospital Discharge Data and assigned a public health risk model probability score. We compared the model's performance to traditional screening strategies that use variables locally available to clinicians at time of admission (i.e., transfer from other hospital, tracheostomy, gastrostomy, pressure ulcer). Model discrimination was evaluated by quantifying the area under the curve (AUC). For each approach, we assessed sensitivity, specificity, and number needed to screen (NNS) to detect one MDRO carrier. RESULTS:Model probability calculation was successful in 1237/1250 (98.9%) admissions. The model identified carbapenem-resistant Enterobacterales colonization well (AUC 0.82) and generalized to predict colonization with other healthcare-associated MDROs, including carbapenem-resistant Pseudomonas aeruginosa (AUC 0.82) and vancomycin-resistant enterococci (AUC 0.76). The model did not predict MDROs with known local community reservoirs, i.e., third-generation cephalosporin-resistant Enterobacterales (AUC 0.61) and methicillin-resistant Staphylococcus aureus (AUC 0.59). At the same NNS, the model had higher sensitivity compared to use of traditional screening strategies (68% versus 41%). CONCLUSION:A risk model using patient-level healthcare exposure data from a state public health dataset identified critically ill patients likely to harbor healthcare-associated MDROs at the time of admission.

This study analyzed COVID-19 disease severity distributions among different age, racial, and ethnic groups for pre-Omicron and Omicron variant periods from May 2020 to October 2022. Disease severity categories were defined by ICD-10-CM diagnostic codes recorded in the electronic health record in the 7 days preceding and 13 days following the SARS-CoV-2 positive laboratory record (index date) and were grouped into 4 mutually exclusive categories: severe complications, high, moderate, and low disease severity. Low severity was defined as the absence of codes for any of the other categories. Among 1,613,706 included patients, there was a lower prevalence of disease severity during the Omicron variant period across all race and ethnicity groups (P<0.001) compared with the pre-Omicron variant period; however, the Omicron period had a higher prevalence of severe complications (P<0.05). Relative to White patients with high disease severity, Black patients and patients of other races had 37.1% and 52.4% (Pt<0.0001) greater risk of having high disease severity, respectively, in the pre-Omicron period, but high disease severity was similar across racial groups during the Omicron period. During pre-Omicron, mean monthly relative differences among Hispanic patients with high disease severity and severe complications compared with non-Hispanic patients were -5.17% and -39.4%, respectively, which shifted to 24.4% and 44.1% in the Omicron period (Pt<0.0001). These findings provide valuable insight into patterns of COVID-19 disease severity, especially for marginalized populations, and highlight the need for targeted public health strategies as variant-specific trends evolve over time.

This study analyzed COVID-19 disease severity distributions among different age, racial, and ethnic groups for pre-Omicron and Omicron variant periods from May 2020 to October 2022. Disease severity categories were defined by ICD-10-CM diagnostic codes recorded in the electronic health record in the 7 days preceding and 13 days following the SARS-CoV-2 positive laboratory record (index date) and were grouped into 4 mutually exclusive categories: severe complications, high, moderate, and low disease severity. Low severity was defined as the absence of codes for any of the other categories. Among 1,613,706 included patients, there was a lower prevalence of disease severity during the Omicron variant period across all race and ethnicity groups ( P <0.001) compared with the pre-Omicron variant period; however, the Omicron period had a higher prevalence of severe complications ( P <0.05). Relative to White patients with high disease severity, Black patients and patients of other races had 37.1% and 52.4% ( P t <0.0001) greater risk of having high disease severity, respectively, in the pre-Omicron period, but high disease severity was similar across racial groups during the Omicron period. During pre-Omicron, mean monthly relative differences among Hispanic patients with high disease severity and severe complications compared with non-Hispanic patients were −5.17% and −39.4%, respectively, which shifted to 24.4% and 44.1% in the Omicron period ( P t <0.0001). These findings provide valuable insight into patterns of COVID-19 disease severity, especially for marginalized populations, and highlight the need for targeted public health strategies as variant-specific trends evolve over time.

Objective: We aimed to determine whether benchmarking antimicrobial use (AU) to antimicrobial resistance (AR) using select AU/AR ratios is more informative than AU metrics in isolation.Design: We retrospectively measured AU (antimicrobial therapy days per 1,000 days present) and AU/AR ratios (specific antimicrobial therapy days per corresponding AR event) in two hospitals during 2020 through 2022. We then had antimicrobial stewardship committee members evaluate each AU and corresponding AU/AR value and indicate whether they believed it represented potential overuse, appropriate use, or potential underuse of the antimicrobials, or whether they could not provide an assessment.Setting: Two acute-care hospitals.Patients: Hospitalized patients.Results: In semi-annual facility-wide analyses, echinocandins had a median AU/AR ratio of 658.5 therapy days per fluconazole-resistant Candida event in Hospital A, IV vancomycin had a median AU/AR ratio of 114.9 and 108.2 therapy days per methicillin-resistant Staphylococcus aureus event in Hospital A and B, respectively, and linezolid had a median AU/AR ratio of 33.8 and 88.0 therapy days per vancomycin-resistant Enterococcus event in Hospital A and B, respectively. When AU and AU/AR values were evaluated by stewardship committees, more respondents were able to assess antimicrobial use based on AU/AR values compared to AU values. Based on AU/AR ratios, most respondents identified potential overuse of echinocandins and IV vancomycin in Hospital A, and potential overuse of linezolid and IV vancomycin in Hospital B.Conclusion: Select AU/AR ratios provided informative metrics to antimicrobial stewardship personnel, which can be used to motivate audits of antimicrobial administration to determine appropriateness.

Introduction:This study was aimed to describe the epidemiology of colon cancer screening in an underserved population. This is a cohort study, conducted in a primary care clinic in a safety-net public hospital. Methods:Patients aged 49-78 years were categorized by initial screening status and followed over 28 months. Status was categorized into the following 3 groups with 8 subgroups: screening eligible (up to date or not up to date), non-screening eligible (Personal history of colon cancer, surveillance for colon cancer, work-up in progress, poor health status, and indeterminate), and refusal. Patient egress was defined as no primary care visit for 12 months or <2 primary care visits separated by 60 days over 24 months. Results:Of 1,074 patients reviewed, 856 (80%) were screening eligible, 177 (16%) were non-screening eligible, and 41 (4%) refused screening. Over 28 months, most patients retained in the practice did not change category (n=949; 88%). Of 125 patients who changed, most (n=101) went from screening eligible to non-screening eligible, most commonly to surveillance and work-up in progress subgroups. Egress was common; nearly half of patients (47%) were not evaluated owing to egress. Conclusions:Maintaining a colon cancer registry was complex owing to the dynamic nature of screening status and unstable primary care attendance. Regional integration of electronic health records could substantially reduce manual effort.

This quasi-experimental before-and-after intervention study evaluated an indication-based electronic prescribing alert in a 450-bed tertiary care hospital. Implementation reduced meropenem use by 41% without compromising patient safety, demonstrating the effectiveness of this targeted antimicrobial stewardship strategy.

Background: External comparisons of antimicrobial use (AU) may be more informative if adjusted for encounter characteristics. Optimal methods to define input variables for encounter-level risk-adjustment models of AU are not established. Methods: This retrospective analysis of electronic health record data included 50 US hospitals in 2020-2021. We used NHSN definitions for all antibacterials days of therapy (DOT), including adult and pediatric encounters with at least 1 day present in inpatient locations. We assessed 4 methods to define input variables: 1) diagnosis-related group (DRG) categories by Yu et al., 2) adjudicated Elixhauser comorbidity categories by Goodman et al., 3) all Clinical Classification Software Refined (CCSR) diagnosis and procedure categories, and 4) adjudicated CCSR categories where codes not appropriate for AU risk-adjustment were excluded by expert consensus, requiring review of 867 codes over 4 months to attain consensus. Data were split randomly, stratified by bed size as follows: 1) training dataset including two-thirds of encounters among two-thirds of hospitals; 2) internal testing set including one-third of encounters within training hospitals, and 3) external testing set including the remaining one-third of hospitals. We used a gradient-boosted machine (GBM) tree-based model and two-staged approach to first identify encounters with zero DOT, then estimate DOT among those with >0.5 probability of receiving antibiotics. Accuracy was assessed using mean absolute error (MAE) in testing datasets. Correlation plots compared model estimates and observed DOT among testing datasets. The top 20 most influential variables were defined using modeled variable importance. Results: Our datasets included 629,445 training, 314,971 internal testing, and 419,109 external testing encounters. Demographic data included 41% male, 59% non-Hispanic White, 25% non-Hispanic Black, 9% Hispanic, and 5% pediatric encounters. DRG was missing in 29% of encounters. MAE was lower in pediatrics as compared to adults, and lowest for models incorporating CCSR inputs (Figure 1). Performance in internal and external testing was similar, though Goodman/Elixhauser variable strategies were less accurate in external testing and underestimated long DOT outliers (Figure 2). Agnostic and adjudicated CCSR model estimates were highly correlated; their influential variables lists were similar (Figure 3). Conclusion: Larger numbers of CCSR diagnosis and procedure inputs improved risk-adjustment model accuracy compared with prior strategies. Variable importance and accuracy were similar for agnostic and adjudicated approaches. However, maintaining adjudications by experts would require significant time and potentially introduce personal bias. If findings are confirmed, the need for expert adjudication of input variables should be reconsidered.

Background: The incidence of sudden unexpected infant death (SUID) in the United States has persisted at roughly thesame level since the mid-2000s, despite intensive prevention efforts around safe sleep. Disparities in outcomes across racialand socioeconomic lines also persist. These disparities are reflected in the spatial distribution of cases across neighborhoods.Strategies for prevention should be targeted precisely in space and time to further reduce SUID and correct disparities. Objective: We sought to aid neighborhood-level prevention efforts by characterizing communities where SUID occurred inCook County, IL, from 2015 to 2019 and predicting where it would occur in 2021-2025 using a semiautomated, reproducibleworkflow based on open-source software and data. Methods: This cross-sectional retrospective study queried geocoded medical examiner data from 2015-2019 to identifySUID cases in Cook County, IL, and aggregated them to "communities" as the unit of analysis. We compared demographicfactors in communities affected by SUID versus those unaffected using Wilcoxon rank sum statistical testing. We usedsocial vulnerability indicators from 2014 to train a negative binomial prediction model for SUID case counts in each givencommunity for 2015-2019. We applied indicators from 2020 to the trained model to make predictions for 2021-2025. Results: Validation of our query of medical examiner data produced 325 finalized cases with a sensitivity of 95% (95% CI93%-97%) and a specificity of 98% (95% CI 94%-100%). Case counts at the community level ranged from a minimum of 0 toa maximum of 17. A map of SUID case counts showed clusters of communities in the south and west regions of the county. Allcommunities with the highest case counts were located within Chicago city limits. Communities affected by SUID exhibitedlower median proportions of non-Hispanic White residents at 17% versus 60% (P<.001) and higher median proportions ofnon-Hispanic Black residents at 32% versus 3% (P<.001). Our predictive model showed moderate accuracy when assessedon the training data (Nagelkerke R-2=70.2% and RMSE=17.49). It predicted Austin (17 cases), Englewood (14 cases), AuburnGresham (12 cases), Chicago Lawn (12 cases), and South Shore (11 cases) would have the largest case counts between 2021and 2025. Conclusions: Sharp racial and socioeconomic disparities in SUID incidence persisted within Cook County from 2015 to2019. Our predictive model and maps identify precise regions within the county for local health departments to target forintervention. Other jurisdictions can adapt our coding workflows and data sources to predict which of their own communitieswill be most affected by SUID.

Background:Data modernization efforts to strengthen surveillance capacity could help assess trends in use of preventive services and diagnoses of new chronic disease during the COVID-19 pandemic, which broadly disrupted health care access. Methods:This cross-sectional study examined electronic health record data from US adults aged 21 to 79 years in a large national research network (PCORnet), to describe use of 8 preventive health services (N = 30,783,825 patients) and new diagnoses of 9 chronic diseases (N = 31,588,222 patients) during 2018 through 2022. Joinpoint regression assessed significant trends, and health debt was calculated comparing 2020 through 2022 volume to prepandemic (2018 and 2019) levels. Results:From 2018 to 2022, use of some preventive services increased (hemoglobin A1c and lung computed tomography, both P < .05), others remained consistent (lipid testing, wellness visits, mammograms, Papanicolaou tests or human papillomavirus tests, stool-based screening), and colonoscopies or sigmoidoscopies declined (P < .01). Annual new chronic disease diagnoses were mostly stable (6% hypertension; 4% to 5% cholesterol; 4% diabetes; 1% colonic adenoma; 0.1% colorectal cancer; among women, 0.5% breast cancer), although some declined (lung cancer, cervical intraepithelial neoplasia or carcinoma in situ, cervical cancer, all P < .05). The pandemic resulted in health debt, because use of most preventive services and new diagnoses of chronic disease were less than expected during 2020; these partially rebounded in subsequent years. Colorectal screening and colonic adenoma detection by age group aligned with screening recommendation age changes during this period. Conclusion:Among over 30 million patients receiving care during 2018 through 2022, use of preventive services and new diagnoses of chronic disease declined in 2020 and then rebounded, with some remaining health debt. These data highlight opportunities to augment traditional surveillance with EHR-based data.

Background: Early identification of patients colonized with MDROs can help healthcare facilities improve infection control and treatment. We evaluated whether a model previously validated to predict carbapenem-resistant Enterobacterales (CRE) carriage on hospital admission (area under the curve [AUC]=0.86, Lin et al. OFID 2019) would generalize to predict a patient’s likelihood of CRE and non-CRE MDRO colonization at the time of medical intensive care unit (MICU) admission. Methods: We analyzed data collected previously in a retrospective observational cohort study of patients admitted to Rush University Medical Center’s MICU from 1/2017-1/2018 and screened within the first two days for rectal MDRO colonization. Organisms of interest included CRE, carbapenem-resistant Pseudomonas aeruginosa (CRPA), vancomycin-resistant enterococci (VRE), and third-generation cephalosporin-resistant Enterobacterales (3GCR-E). Methicillin-resistant Staphylococcus aureus (MRSA) nasal colonization at admission was determined by routine clinical screening. Each patient’s first MICU admission during the study period was linked to Illinois’ hospital discharge database and assigned a CRE colonization risk probability using the existing model. Model covariates were age, and during the prior 365 days, number of short-term acute care hospitalizations (STACH) and mean STACH length of stay, number of long-term acute care hospitalizations (LTACH) and mean LTACH length of stay, prior hospital admission with an ICD-10 diagnosis code indicating bacterial infection, and current admission to LTACH. Predictive value of the model was evaluated by receiver operating characteristic (ROC) curves. Results: We analyzed 1237 MICU admissions. MDRO admission prevalence is shown in the Table. The model performed well to predict carriage of healthcare-associated MDROs, including CRE, CRPA, composite CR-MDROs (CRE & CRPA), and VRE. However, the model performed poorly for MDROs with known community reservoirs, including 3GCR-E and MRSA (Table). In general, MDRO admission prevalence increased in parallel with predicted CRE colonization risk (Figure). The number needed to screen (NNS) to detect one healthcare-associated MDRO carrier was inversely related to the CRE colonization risk score. For example, NNS in the total cohort compared to those with CRE risk score of >0.5% was: CRE 111 vs 32 patients, CRPA 333 vs 42 patients, composite CR-MDRO 83 vs 18 patients, and VRE 12 vs 4 patients. However, higher CRE risk score cutoff was inversely related to screening sensitivity. Conclusion: A prediction model using prior healthcare exposure information successfully discriminated patients likely to harbor healthcare-associated MDROs upon MICU admission. Prediction scores generated by a public-health accessible database could be used to target screening/isolation or enact protective measures for high-risk patients.

This cohort study assesses the performance of International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) Z59 codes for identifying housing instability during health care encounters.

ObjectiveTo introduce Linkja, an open-source, certified, Privacy-Preserving Record Linkage (PPRL) tool with the primary objective of facilitating secure and efficient record linkage across diverse datasets, while preserving individual privacy, providing a comprehensive and privacy-centric approach to program assessments. ApproachLinkja has been used to evaluate Chicago’s Flexible Housing Pool program by securely linking various datasets, including Homeless Management Information Systems (HMIS), Electronic Health Records, Jail records, Medical Examiner reports, and Public Health data. Data contributors used separate honest brokers for record linkage, and generation of salt and cryptography keys. ResultsLinkja enabled this multi-sector linkage by facilitating legal and data sharing agreements, avoided licensing fees and procurement processes for all parties, and provided a de-identified for analysis. The insights gleaned from the analysis motivated the FHP program to plan a follow-up evaluation that will add emergency serviced records from a separate City of Chicago department. The successful linkage provided a nuanced understanding of the association between stable housing and reduced healthcare utilization. ConclusionsLinkja's cryptographic certification ensures the highest standards of security during data linkage, safeguarding sensitive information. The matching rules, validated on hard-to-match populations, prove essential in maintaining accuracy and reliability in the linkage process. ImplicationsThe open-source nature of Linkja promotes transparency and collaboration in the development and enhancement of privacy-preserving record linkage techniques. This tool has broad implications for sectors dealing with sensitive data, fostering responsible data-sharing practices, and enabling secure collaborative research initiatives without compromising individual privacy.

Objective:We sought to determine whether increased antimicrobial use (AU) at the onset of the coronavirus disease 2019 (COVID-19) pandemic was driven by greater AU in COVID-19 patients only, or whether AU also increased in non-COVID-19 patients.Design:In this retrospective observational ecological study from 2019 to 2020, we stratified inpatients by COVID-19 status and determined relative percentage differences in median monthly AU in COVID-19 patients versus non-COVID-19 patients during the COVID-19 period (March-December 2020) and the pre-COVID-19 period (March-December 2019). We also determined relative percentage differences in median monthly AU in non-COVID-19 patients during the COVID-19 period versus the pre-COVID-19 period. Statistical significance was assessed using Wilcoxon signed-rank tests.Setting:The study was conducted in 3 acute-care hospitals in Chicago, Illinois.Patients:Hospitalized patients.Results:Facility-wide AU for broad-spectrum antibacterial agents predominantly used for hospital-onset infections was significantly greater in COVID-19 patients versus non-COVID-19 patients during the COVID-19 period (with relative increases of 73%, 66%, and 91% for hospitals A, B, and C, respectively), and during the pre-COVID-19 period (with relative increases of 52%, 64%, and 66% for hospitals A, B, and C, respectively). In contrast, facility-wide AU for all antibacterial agents was significantly lower in non-COVID-19 patients during the COVID-19 period versus the pre-COVID-19 period (with relative decreases of 8%, 7%, and 8% in hospitals A, B, and C, respectively).Conclusions:AU for broad-spectrum antimicrobials was greater in COVID-19 patients compared to non-COVID-19 patients at the onset of the pandemic. AU for all antibacterial agents in non-COVID-19 patients decreased in the COVID-19 period compared to the pre-COVID-19 period.

Highly transmissible infections with short serial intervals, such as SARS-Cov-2 and influenza, can quickly overwhelm healthcare resources in institutional settings such as jails. We assessed the impact of intake screening measures on the risk of SARS-CoV-2 outbreaks in this setting. We identified which elements of the intake process created the largest reductions in caseload. We implemented an individual-based simulation representative of SARS-Cov-2 transmission in a large urban jail utilizing testing at entry, quarantine, and post-quarantine testing to protect its general population from mass infection. We tracked the caseload under each scenario and quantified the impact of screening steps by varying quarantine duration, removing testing, and using a range of test sensitivities. We repeated the simulations under a range of transmissibility and community prevalence levels to evaluate the sensitivity of our results. We found that brief quarantine of newly incarcerated individuals separate from the existing population of the jail to permit pre-quarantine and end-of-quarantine tests reduced SARS-CoV-2 caseload 30-70% depending on test sensitivity. These results were robust to variation in the transmissibility. Further quarantine (up to 14 days) on average created only a 5% further reduction in caseload. A multilayered intake process is necessary to limit the spread of highly transmissible pathogens with short serial intervals. The pre-symptomatic phase means that no single strategy can be effective. We also show that shorter durations of quarantine combined with testing can be nearly as effective at preventing spread as longer-duration quarantine up to 14 days.

Variation in availability, format, and standardization of patient attributes across health care organizations impacts patient-matching performance. We report on the changing nature of patient-matching features available from 2010-2020 across diverse care settings. We asked 38 health care provider organizations about their current patient attribute data-collection practices. All sites collected name, date of birth (DOB), address, and phone number. Name, DOB, current address, social security number (SSN), sex, and phone number were most commonly used for cross-provider patient matching. Electronic health record queries for a subset of 20 participating sites revealed that DOB, first name, last name, city, and postal codes were highly available (>90%) across health care organizations and time. SSN declined slightly in the last years of the study period. Birth sex, gender identity, language, country full name, country abbreviation, health insurance number, ethnicity, cell phone number, email address, and weight increased over 50% from 2010 to 2020. Understanding the wide variation in available patient attributes across care settings in the United States can guide selection and standardization efforts for improved patient matching in the United States.

Abstract Background MDROs frequently contaminate hospital environments. We performed a multicenter cluster-randomized, crossover trial of two methods for intensive monitoring of terminal cleaning effectiveness at reducing infection and colonization with MDROs within ICUs. Methods Six medical and surgical ICUs at three medical centers received both intensive monitoring interventions sequentially, in a randomized order. The intervention included surveying a minimum of 10 surfaces each in 5 rooms weekly, after terminal cleaning, with adenosine triphosphate (ATP) monitoring or an ultraviolet fluorescent marker (UV/F). Results were delivered to environmental services (EVS) staff in real-time, with failing surfaces recleaned. The primary study outcome was the monthly rate of infection or colonization with MDROs, including methicillin-resistant Staphylococcus aureus, Clostridioides difficile, vancomycin-resistant Enterococcus, and multidrug-resistant gram-negative bacilli (MDR-GNB), assessed during a 12-month baseline comparison period and sequential 6-month intervention periods, separated by a 2-month washout. Outcomes during each intervention period were compared to the combined baseline period plus the alternative intervention period using mixed-effects Poisson regression, with study hospital as a random effect. Results The primary outcome rate varied by hospital and ICU (Figure 1). The ATP method was associated with a relative reduction in the incidence rate of infection or colonization with MDROs (incidence rate ratio (IRR) 0.887, 95% confidence-interval (CI) 0.811–0.969, P=0.008) (Table 1), infection with MDROs (IRR 0.924, 95% CI 0.855–0.998, P=0.04), and infection or colonization limited to multidrug-resistant MDR-GNB (IRR 0.856, 95% CI 0.825–0.887, P< 0.001). The UV/F intervention was not associated with a statistically significant impact on these outcomes. Room turn-around time was increased by a median of one minute with the ATP intervention and 4.5 minutes with the UV/F intervention compared to baseline. Conclusion Intensive monitoring of ICU terminal room cleaning with an ATP modality is associated with a relative reduction of infection and colonization with MDROs with a negligible impact on TAT. Disclosures Hilary Babcock, MD, MPH, FIDSA, FSHEA (nothing to disclose), David K. Warren, MD, MPH, Homburg & Partner (consultant), Ebbing Lautenbach, MD, MPH, MSCE (nothing to disclose), Jennifer Han, MD, MSCE, GlaxoSmithKline (employee, shareholder).

In December 2021 and early 2022, four medications received emergency use authorization (EUA) by the Food and Drug Administration for outpatient treatment of mild-to-moderate COVID-19 in patients who are at high risk for progressing to severe disease; these included nirmatrelvir/ritonavir (Paxlovid) and molnupiravir (Lagevrio) (both oral antivirals), expanded use of remdesivir (Veklury; an intraveneous antiviral), and bebtelovimab (a monoclonal antibody [mAb]).* Reports have documented disparities in mAb treatment by race and ethnicity (1) and in oral antiviral treatment by zip code-level social vulnerability (2); however, limited data are available on racial and ethnic disparities in oral antiviral treatment.† Using electronic health record (EHR) data from 692,570 COVID-19 patients aged ≥20 years who sought medical care during January-July 2022, treatment with Paxlovid, Lagevrio, Veklury, and mAbs was assessed by race and ethnicity, overall and among high-risk patient groups. During 2022, the percentage of COVID-19 patients seeking medical care who were treated with Paxlovid increased from 0.6% in January to 20.2% in April and 34.3% in July; the other three medications were used less frequently (0.7%-5.0% in July). During April-July 2022, when Paxlovid use was highest, compared with White patients, Black or African American (Black) patients were prescribed Paxlovid 35.8% less often, multiple or other race patients 24.9% less often, American Indian or Alaska Native and Native Hawaiian or other Pacific Islander (AIAN/NHOPI) patients 23.1% less often, and Asian patients 19.4% less often; Hispanic patients were prescribed Paxlovid 29.9% less often than non-Hispanic patients. Racial and ethnic disparities in Paxlovid treatment were generally somewhat higher among patients at high risk for severe COVID-19, including those aged ≥50 years and those who were immunocompromised. The expansion of programs focused on equitable awareness of and access to outpatient COVID-19 treatments, as well as COVID-19 vaccination, including updated bivalent booster doses, can help protect persons most at risk for severe illness and facilitate equitable health outcomes.

ObjectiveTo measure a baseline rate of gun homicide deaths for adults on probation in Cook County, Illinois, USA. To compare this rate to the rates for the population of the City of Chicago and for all of Cook County, which includes Chicago and suburban Cook County. ApproachIn March of 2022, two data contributing partners, Cook County Adult Probation Department (APD) and Cook County Medical Examiner’s Office (CCME), partnered with Cook County Health for salt and crypto generation and the Medical Research Analytics and Informatics Alliance (MRAIA) as the data generator. We used the open source Linkja program (https://linkja.github.io/) to match adult probation records with gun homicide death records from CCME. For our study period, CY 2018 through 2021, APD submitted a hashed roster of 53,969 unique individuals active on probation, which was matched to 307 records of gun homicide deaths. ResultsOverall, there were 3,728 gun homicides in the CCME data, 8% (n=307) involved individuals on probation. The average age was 34 years at death, almost 91% were male (n=3,374), 71% Black (n=2,650), and 14% Hispanic (n=536). Among individuals who died from gun violence, compared to those not in the probation data, probation clients tended to be younger (29 vs 33, p<.001) and more likely to be Black (89% vs. 69.5%; p<.001), We found that the rate of gun homicide deaths per 100,000 probation clients was 569, ~22 times higher than the rate for the City of Chicago in 2020 (rate=25 per 100,000) and ~33 times higher than the rate for Cook County in 2020 (rate=17 per 100,000). ConclusionPrivacy-preserving record linkage software enabled a cross-departmental data sharing project that heretofore was not realized due to privacy concerns and existing data use agreements. Our results highlight an excessive risk of homicide among adult probation clients, particularly among young black males.