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Abstract Background The CDC recently reported that firearm homicide rates in the United States increased in 2020, particularly among Black/African American individuals and men 25–44 years old. It is unclear whether firearm hospitalizations also increased, and more importantly, what impact the COVID-19 pandemic and COVID-related policies had. Using the North Carolina Trauma Registry, a statewide registry of trauma admissions to eighteen North Carolina hospitals, we calculated weekly GSW hospitalization rates from 1/2019 to 12/2020, overall and stratified by race-ethnicity, age, and sex. Interrupted time-series design and segmented linear regression were used to estimate changes in weekly hospitalization rates over time after (1) U.S. declaration of a public health emergency; (2) statewide Stay-at-Home order; (3) Stay-at-Home order lifted with restrictions (Phase 2: Safer-at-Home); and (4) further lifting of restrictions (Phase 2.5: Safer-at-Home). Non-GSW assault hospitalizations were used as a control to assess whether trends were observed across all assault hospitalizations or if effects were specific to gun violence. Findings Overall, 47.3% (n = 3223) of assault hospitalizations were GSW. Among GSW hospitalizations, median age was 27 years old (interquartile range [IQR] 21–25), 86.2% were male, and 49.5% occurred after the U.S. declared a public health emergency. After the Stay-at-Home order was implemented, weekly GSW hospitalization rates began increasing substantially among Black/African American residents (weekly trend change = 0.775, 95% CI = 0.254 to 1.296), peaking at an average 15.6 hospitalizations per 1,000,000 residents. Weekly hospitalization rates declined after restrictions were lifted but remained elevated compared to pre-COVID levels in this group (average weekly rate 10.6 per 1,000,000 at the end of 2020 vs. 8.9 per 1,000,000 pre-pandemic). The Stay-at-Home order was also associated with increasing GSW hospitalization rates among males 25–44 years old (weekly trend change = 1.202, 95% CI = 0.631 to 1.773); rates also remained elevated among 25–44-year-old males after restrictions were lifted in 2020 (average weekly rate 10.1 vs. 7.9 per 1,000,000). Non-GSW hospitalization rates were relatively stable in 2020. Conclusions The COVID-19 pandemic and statewide Stay-at-Home orders appeared to have placed Black/African American residents and men ages 25–44 at higher risk for GSW hospitalizations, exacerbating pre-existing disparities. Persistent gun violence disparities must be addressed.

Abstract Background To combat the coronavirus pandemic, states implemented several public health policies to reduce infection and transmission. Increasing evidence suggests that these prevention strategies also have had a profound impact on non-COVID healthcare utilization. The goal of this study was to determine the impact of a statewide Stay-at-Home order and other COVID-related policies on trauma hospitalizations, stratified by race/ethnicity, age, and sex. Methods We used the North Carolina Trauma Registry, a statewide registry of trauma hospitalizations for 18 hospitals across North Carolina, including all North Carolina trauma centers, to calculate weekly rates of assault, self-inflicted, unintentional motor vehicle collision (MVC), and other unintentional injury hospitalizations between January 1, 2019, and December 31, 2020. Interrupted time-series design and segmented linear regression were used to estimate changes in hospitalization rates after several COVID-related executive orders, overall and stratified by race/ethnicity, age, and sex. Changes in hospitalization rates were assessed after 1) USA declaration of a public health emergency; 2) North Carolina statewide Stay-at-Home order; 3) Stay-at-Home order lifted with restrictions (Phase 2: Safer-at-Home); and 4) further lifting of restrictions (Phase 2.5: Safer-at-Home). Results There were 70,478 trauma hospitalizations in North Carolina, 2019–2020. In 2020, median age was 53 years old and 59% were male. Assault hospitalization rates (per 1,000,000 NC residents) increased after the Stay-at-Home order, but substantial increases were only observed among Black/African American residents (weekly trend change = 1.147, 95% CI = 0.634 to 1.662) and 18–44-year-old males (weekly trend change = 1.708, 95% CI = 0.870 to 2.545). After major restrictions were lifted, assault rates decreased but remained elevated compared to pre-COVID levels. Unintentional non-MVC injury hospitalizations decreased after the USA declared a public health emergency, especially among women ≥ 65 years old (weekly trend change = -4.010, 95% CI = -6.166 to -1.855), but returned to pre-pandemic levels within several months. Conclusions Statewide Stay-at-Home orders placed Black/African American residents at higher risk of assault hospitalizations, exacerbating pre-existing disparities. Males 18–44 years old were also at higher risk of assault hospitalization. Fear of COVID-19 may have led to decreases in unintentional non-MVC hospitalization rates, particularly among older females. Policy makers must anticipate policy-related harms that may disproportionately affect already disadvantaged communities and develop mitigation approaches.

Background: Patients receiving outpatient parenteral antimicrobial therapy (OPAT) are often medically complex and require carefully tailored treatments to address severe and often concomitant infections. Our objective was to illustrate the heterogeneity in antimicrobials used for patients in OPAT, within and across infection diagnosis groups. Methods: We abstracted electronic health record data regarding day-level treatment into a registry of 2,358 OPAT courses (n = 2,072 unique patients) treated in the University of North Carolina Medical Center OPAT program during 2015–2022 (total, 11,861 person weeks; average, 7 OPAT weeks per patient). We classified infection diagnoses into 10 hierarchical or mutually exclusive categories (eg, bacteremia only, diabetic foot infection (DFI) only, osteomyelitis only) (Fig., vertical axes). Accounting for 64 antimicrobial medications and 520 cocktails administered for at least 1 patient day in our OPAT registry, we also defined 18 hierarchical or mutually exclusive classifications of treatment (eg, “daptomycin alone” or “daptomycin and any other antibiotic(s)” (Fig. key). We conducted 2 stratified analyses to describe the heterogeneity across infection diagnoses with respect (1) to medications used at OPAT initiation (patient as unit of analysis) and (2) to medications used throughout OPAT (person time as unit of analysis, allowing for differential OPAT course to other treatment classifications during follow-up). We present stacked bar charts to visualize the intersection between infection diagnosis and treatment group. Results: Among patients in this OPAT registry, 34.6% had osteomyelitis and/or DFI, 4.8% had bacteremia, and 44.6% had multiple infections (Fig. 1). The most common medications in initial OPAT regimens were vancomycin (30.8% of OPAT patients), ceftriaxone (15.0%), and daptomycin (10.9%). We observed overall similarity between the distribution of treatment groups at initiation compared to cumulative person-time during the OPAT course (Figs. 1 and 2). However, we observed heterogeneity in medications by infection diagnosis (Figs. 1 and 2); for example, vancomycin was used in 39% of osteomyelitis cases but only 14% for endocarditis (Fig. 2). For several infection groups (eg, osteomyelitis, DFI, multiple infections, “other” single infections), no treatment classification exceeded 20% use (Figs. 1 and 2). Conclusions: Day-level data on medication use in this monitored registry of patients provided evidence of heterogeneity in the types of medications used throughout treatment in OPAT, which varies within and across infection diagnoses. These data highlight the need for multilayered ascertainment of medication exposure in this medically complex patient population to inform surveillance for adverse effects and guide comparative effectiveness research for postdischarge antibiotic treatment.

BackgroundDigital health solutions have been shown to enhance outcomes for individuals with chronic medical illnesses, but few have been validated for surgical patients. The digital health platform ManageMySurgery (MMS) has been validated for spine surgery as a feasible method for patients along their surgical journey through in-app education and completion of patient-reported outcomes surveys. ObjectiveThe aim of this study is to determine the rates of 90-day emergency room (ER) visits, readmissions, and complications in patients undergoing spine surgery using MMS compared to patients using traditional perioperative care alone. MethodsPatients undergoing spine surgery at a US-based academic hospital were invited to use MMS perioperatively between December 2017 and September 2021. All patients received standard perioperative care and were classified as MMS users if they logged into the app. Demographic information and 90-day outcomes were acquired via electronic health record review. The odds ratios of having 90-day ER visits, readmissions, mild complications, and severe complications between the MMS and non-MMS groups were estimated using logistic regression models. ResultsA total of 1015 patients were invited, with 679 using MMS. MMS users and nonusers had similar demographics: the average ages were 57.9 (SD 12.5) years and 61.5 (SD 12.7) years, 54.1% (367/679) and 47.3% (159/336) were male, and 90.1% (612/679) and 88.7% (298/336) had commercial or Medicare insurance, respectively. Cervical fusions (559/1015, 55.07%) and single-approach lumbar fusions (231/1015, 22.76%) were the most common procedures for all patients. MMS users had a lower 90-day readmission rate (55/679, 8.1%) than did nonusers (30/336, 8.9%). Mild complications (MMS: 56/679, 8.3%; non-MMS: 32/336, 9.5%) and severe complications (MMS: 66/679, 9.7%; non-MMS: 43/336, 12.8%) were also lower in MMS users. MMS users had a lower 90-day ER visit rate (MMS: 62/679, 9.1%; non-MMS: 45/336, 13.4%). After adjustments were made for age and sex, the odds of having 90-day ER visits for MMS users were 32% lower than those for nonusers, but this difference was not statistically significant (odds ratio 0.68, 95% CI 0.45-1.02; P=.06). ConclusionsThis is one of the first studies to show differences in acute outcomes for people undergoing spine surgery who use a digital health app. This study found a correlation between MMS use and fewer postsurgical ER visits in a large group of spine surgery patients. A planned randomized controlled trial will provide additional evidence of whether this digital health tool can be used as an intervention to improve patient outcomes.

Background: Urinary tract infections (UTIs) are common in the inpatient, observation, and emergency department settings. Although many UTI-causing pathogens are susceptible to oral β-lactams, these agents are not tested directly, and susceptibility is extrapolated from other agents. To improve the use of these agents, the University of North Carolina Medical Center (UNCMC) added cephalexin to the susceptibility profile generated with urine culture results in the electronic health record (EHR). We evaluated prescribing trends of cephalexin, other oral β-lactams, fluoroquinolones, and other antibiotics for UTIs in the inpatient setting, before and after the susceptibility reporting change. Methods: An interrupted time-series analysis was conducted. Among 1,491 patients who had positive urine cultures with susceptibilities and received at least 1 antibiotic with a listed UTI indication during their inpatient stay at UNCMC, we measured the weekly prevalence (%) of patients who received each antibiotic group: cephalexin, other oral β-lactams (amoxicillin-clavulanate, cefdinir, cefuroxime), fluoroquinolones (levofloxacin, ciprofloxacin), and ceftriaxone. The study comprised a preintervention period (September 2018–March 2019) and a postintervention period (September 2019–March 2020). The prevalence of each antibiotic or group was plotted over time, and segmented linear regression was used to estimate the impact of the intervention on each antibiotic groups’ time trend. Results: At study baseline in September 2018, the weekly prevalence of antibiotic use was 11% for cephalexin, 26% for other oral β-lactams, 51% for ceftriaxone, and 29% for fluoroquinolones. Fluoroquinolone use decreased steadily throughout the study period, by 11% during the 7-month preintervention period (95% CI, −17% to −5%) and by 8% (95% CI, −13% to −3%) after the intervention (P for trend deflection, .70). In contrast, during the preintervention period, trends were flat for cephalexin, ceftriaxone, and other oral β-lactams (all P for nonzero preintervention slope were >.40). During the postintervention period, use increased for ceftriaxone (6%; 95% CI, 3%–9%). Post-intervention use also increased for cephalexin (5%; 95% CI, −3% to 12%) and other oral β-lactams (4%; 95% CI, −8%, 15%), but these trends were imprecise and not statistically significant at α = .05. Fig. 1 displays trends and raw data for each antibiotic group. Conclusions: The urine culture susceptibility reporting change was associated with small increases in cephalexin and ceftriaxone use, coincident with continued decreasing use of fluoroquinolones, for hospitalized patients with positive urine cultures and a listed UTI indication. Low-resource EHR-based interventions may confer considerable benefit for antimicrobial stewardship efforts in this clinical setting, and larger real-world studies are needed to replicate and contextualize these findings.