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The Society for Healthcare Epidemiology of America (SHEA) is a leading medical society for infection prevention and antibiotic stewardship. This descriptive study evaluated speaker demographics at the annual SHEA Spring conferences from 2019 to 2022.
This was a retrospective, descriptive analysis of the demographic composition of speakers at the annual SHEA Spring conferences between 2019 and 2022, excluding the cancelled 2020 conference. Self-reported demographics were available for gender, race, ethnicity, age, primary practice setting, and professional degrees in speaker and membership categories.
In total, 447 speaker slots were filled by 305 unique speakers over 3 years. Average annual membership included 55.2% female, 44.8% male, 69.3% White, 21.4% Asian, 6.0% Hispanic/Latino, 2.9% Black, and 0.4% American Indian/Alaska Native or Native Hawaiian/Pacific Islander (AIAN/NHPI); 48.9% did not report a race or ethnicity. Speakers during the same period were 63.5% female, 36.5% male, 68.2% White, 13.3% Asian, 3.8% Black, 3.4% Hispanic/Latino, 0.8% AIAN/NHPI; 13.4% did not report race or ethnicity. In 2021, pharmacists represented 11.6% of speakers (and 2.9% of members) and members with nondoctoral degrees represented 11.6% of speakers (and 21.5% of members) (P < .0001). In each year, we detected underrepresentation of community and private-practice speakers relative to membership (eg, in 2022, 4.3% of speakers vs 15.7% of members; P < .05).
The SHEA Spring conferences demonstrated an increase in pharmacist speakers over time, but speakers from community hospitals and with nondoctoral degrees remain underrepresented relative to membership. Racial and ethnic minoritized individuals remain underrepresented as members and speakers. Intentional interventions are needed to consistently achieve equitable speaker representation across multiple demographic groups.
Background: The Society for Healthcare Epidemiology of America (SHEA) serves as a national platform for infection prevention and antibiotic stewardship. Like many professional healthcare societies over the last decade, the SHEA has pledged to provide equitable opportunities to individuals in the organization. The impact of these efforts remains undetermined. This study evaluated trends in speaker demographics at the annual SHEA Spring Conference from 2019 to 2022. Methods: SHEA leadership or staff provided demographic information on SHEA members and Spring conference speakers (excluding poster sessions) from 2019 to 2022. We excluded 2020 due to conference cancellation. Data were summarized using descriptive statistics, and χ2 analysis was used to evaluate changes over time. Individual speakers were compared with member demographics. Self-reported SHEA speaker and member demographics were available for sex, race or ethnicity, age, primary practice setting, and professional degrees. Speaker professional degree was not available for 2022. Results: In total, 447 speaker slots were filled by 218 unique speakers over the 3-year period. The SHEA average annual membership between 2019 and 2022 with self-reported demographics included 55.2% female and 44.8% male members, with race reported as follows: 69.3% White, 21.4% Asian, 6.0% Hispanic or Latino, 2.9% Black, 0.4% American Indian/Alaska Native/Native Hawaiian/Pacific Islander (AIAN/NHPI). However, almost half of the members did not report a race or ethnicity. The SHEA speakers during the same period were 63.5% female and 36.5% male, with 68.2% White, 13.3% Asian, 3.8% Black, 3.4% Hispanic/Latino, and 0.8% AIAN/NHPI. Only 13.4% of speakers did not report race or ethnicity. Every year, there were fewer than 6 speakers in each of the Black, Hispanic or Latino, AIAN/NHPI race or ethnicity categories. In 2019, 49.2% of speakers were aged 41–50 years, compared with 28.6% of members in that age group (P = 0.0029). By 2022, 35.6% of speakers were aged 41–50 years, compared with 29.3% of members in that age group (P = .074). In 2021, pharmacists represented 11.9% of speakers compared with 2.9% of members, and members with nondoctoral degrees represented 11.1% of speakers compared with 21.4% of members (P < .0001). In each year, there was a statistically significant association between primary practice setting and speaker or member representation, with underrepresentation of community or private-practice speakers relative to their proportion of membership: 2019 (7.5% speakers vs 14.3% members), 2021 (6.5% speakers vs 15.2% members), 2022 (4.3% speakers vs 15.7% members) (P < .05) . Conclusions: Although there has been more equitable speaker age representation and an increase in pharmacist speakers at the SHEA Spring Conference over time, practitioners from community settings and those with nondoctoral degrees remain underrepresented relative to the SHEA membership. Racial or ethnic minoritized individuals remain underrepresented as members and speakers compared with the general US population. Intentional interventions are needed to consistently achieve equitable speaker representation across multiple demographic groups at the SHEA Spring Conference.
Background: Socioeconomic barriers or divergent implementation of prevention measures may impact risk of healthcare-associated infections by racial groups. We utilized a previously studied cohort of patients to quantify disparities in central-line–associated bloodstream infection (CLABSI) risk by race accounting for inherent differences in risk related to device utilization. Methods: In a retrospective cohort of adult patients at 4 hospitals (range, 110–733 beds) from 2012 to 2017, we linked central-line data to patient encounter data: race, age, comorbidities, total parenteral nutrition (TPN), chemotherapy, CLABSI. Analysis was limited to patients with >2 central-line days and <3 concurrent central lines. Patient exposures were calculated for each central-line episode (defined by insertion and removal dates); analysis of central-line episode-specific risk of CLABSI among Black versus White patients adjusted for clinical factors, duration of central-line episode, and central-line risk category (ie, low: single port, dialysis or PICC; medium: single temporary or nontunneled; or high: any concurrent central-lines) in Cox proportional hazards regression of time to CLABSI. Results: In total, 526 CLABSIs occurred a median of 14 days after insertion among 57,642 central-line episodes in 32,925 patients. CLABSIs occurred in similar frequency across racial groups: 217 (1.7%) among Black patients, 256 (1.6%) among White patients, and 11 (1.6%) among Hispanic patients (also 42 among unknown or other race). Duration of central-line episode was similar between racial groups (median, 5 days). Black patients were less likely to have medium-risk central lines (34%) compared to white patients (RR, 0.82; 95% CI, 0.79–0.84), but they had a similar frequency of high-risk central lines (21%; RR, 1.0; 95% CI, 1.0–1.1). Compared with low-risk central lines, risk of CLABSI was increased among medium-risk central lines (RR, 1.3; 95% CI, 1.0–1.7) and high-risk central lines (RR, 2.2; 95% CI, 1.8–2.7). CLABSIs were more likely in TPN central lines (RR, 2.3; 95% CI, 1.9–2.7) than others, but they were not more likely among Black patients than White patients (RR, 0.9; 95% CI, 0.1–1.1). In survival analysis, there were 24,700 central-line episodes among Black patients compared to 26,648 episodes among White patients; adjusting for central-line risk and TPN, the risk of CLABSI was similar during the first 21 days of central-line use (adjusted hazard ratio, 1.08; 95% CI, 0.88–01.32) (Fig. 1). Conclusions: After accounting for central-line configuration, Black patients did not have a higher risk of CLABSI within 21 central-line days. Further evaluation is warranted to assess racial disparities in risks of other healthcare-associated infections and to determine whether a lack of CLABSI-specific racial disparities can be replicated in other regions and healthcare systems.
Background: Provider-specific prescribing metrics can be used for benchmarking and feedback to reduce unnecessary antibiotic use; however, metrics must be credible. To improve credibility of a recently described risk-adjusted antibiotic prescribing metric for hospital medicine service (HMS) providers, we assessed whether providers who initially prescribed excess antibiotics continued to prescribe antibiotics excessively. Methods: We linked administration and billing data among patients at 4 acute-care hospitals (1,571 beds) to calculate days of therapy (DOT) ordered by individual hospitalists for each of 3 NHSN antibiotic groupings: broad-spectrum hospital onset (BS-HO), broad-spectrum community-onset (BS-CO), or anti-MRSA for each patient day billed from January 2020 to June 2021. To incorporate repeated measures by provider, mixed models adjusted for patient-mix characteristics (eg, % encounters with urinary tract infection, etc) were used to calculate serial, bimonthly, provider-specific, observed-to-expected ratios (OERs). An OER of 1.25 indicates that the prescribing rate observed was 25% higher than predicted, adjusting for patient mix. We then used log binomial generalized estimating equations to assess whether a high prescribing rate (defined as an OER ≥ 1.25) for an individual provider in an earlier bimonthly period was associated with a persistent high rate for that provider in the following period. Results: Overall, 975 bimonthly periods were evaluated from 136 hospitalists. Most (58%) contributed data the entire 18-month study period. Median OERs were similar between hospitals: 0.94 (IQR, 0.65–1.28) for BS-HO antibiotic use, 0.99 (IQR, 0.73–1.24) for BS-CO antibiotic use, and 0.95 (IQR, 0.65–1.28) for anti-MRSA antibiotic use. At the individual prescriber level, roughly one-quarter of bimonthly OERs (range varied by group and hospital from 21% to 31%) were categorized as high. At 3 of the 4 hospitals, a provider with a high OER for either BS-HO or BS-CO antibiotic use in any bimonthly period was more likely to have a high OER in the subsequent period (Fig. 1). These observed risk ratios were statistically significant for BS-HO antibiotic use at only 2 hospitals: hospital A risk ratio (RR) was 1.54 (95% CI, 1.10–2.16); hospital B RR was 1.28 (95% CI, 0.90–1.82); hospital C RR was 0.76 (95% CI, 0.39–1.48); and ospital D RR was 1.71 (95% CI, 1.09–2.68). Conclusions: Our findings suggest that hospitalists with a higher than expected 2-month period of antibiotic prescribing are likely to continue to have elevated prescribing rates in the following period, particularly for BS-HO antibiotics. These findings increase the credibility of using a 2-month prescribing metric for BS-HO antibiotic stewardship efforts; further work is needed to evaluate utility for other antibiotic groupings.
To determine the impact of an inpatient stewardship intervention targeting fluoroquinolone use on inpatient and postdischarge Clostridioides difficile infection (CDI).
We used an interrupted time series study design to evaluate the rate of hospital-onset CDI (HO-CDI), postdischarge CDI (PD-CDI) within 12 weeks, and inpatient fluoroquinolone use from 2 years prior to 1 year after a stewardship intervention.
An academic healthcare system with 4 hospitals.
All inpatients hospitalized between January 2017 and September 2020, excluding those discharged from locations caring for oncology, bone marrow transplant, or solid-organ transplant patients.
Introduction of electronic order sets designed to reduce inpatient fluoroquinolone prescribing.
Among 163,117 admissions, there were 683 cases of HO-CDI and 1,104 cases of PD-CDI. In the context of a 2% month-to-month decline starting in the preintervention period (P < .01), we observed a reduction in fluoroquinolone days of therapy per 1,000 patient days of 21% after the intervention (level change, P < .05). HO-CDI rates were stable throughout the study period. In contrast, we also detected a change in the trend of PD-CDI rates from a stable monthly rate in the preintervention period to a monthly decrease of 2.5% in the postintervention period (P < .01).
Our systemwide intervention reduced inpatient fluoroquinolone use immediately, but not HO-CDI. However, a downward trend in PD-CDI occurred. Relying on outcome measures limited to the inpatient setting may not reflect the full impact of inpatient stewardship efforts.
To evaluate whether a series of quality improvement interventions to promote safe perioperative use of cephalosporins in penicillin-allergic patients improved use of first-line antibiotics and decreased costs.
Before-and-after trial following several educational interventions.
Academic medical center.
This study included patients undergoing a surgical procedure involving receipt of a perioperative antibiotic other than a penicillin or carbapenem between January 1, 2017, and August 31, 2019. Patients with and without a penicillin allergy label in their electronic medical record were compared with respect to the percentage who received a cephalosporin and average antibiotic cost per patient.
A multidisciplinary team from infectious diseases, allergy, anesthesiology, surgery, and pharmacy surveyed anesthesiology providers about their use of perioperative cephalosporins in penicillin-allergic patients. Using findings from that survey, the team designed a decision-support algorithm for safe utilization and provided 2 educational forums to introduce this algorithm, emphasizing the safety of cefazolin or cefuroxime in penicillin-allergic patients without history of a severe delayed hypersensitivity reaction.
The percentage of penicillin-allergic patients receiving a perioperative cephalosporin improved from ∼34% to >80% following algorithm implementation and the associated educational interventions. This increase in cephalosporin use was associated with a ∼50% reduction in antibiotic cost per penicillin-allergic patient. No significant adverse reactions were reported.
An educational antibiotic stewardship intervention produced a significant change in clinician behavior. A simple intervention can have a significant impact, although further study is needed regarding whether this response is sustained and whether an educational intervention is similarly effective in other healthcare systems.
Background: Effective inpatient stewardship initiatives can improve antibiotic prescribing, but impact on outcomes like Clostridioides difficile infections (CDIs) is less apparent. However, the effect of inpatient stewardship efforts may extend to the postdischarge setting. We evaluated whether an intervention targeting inpatient fluoroquinolone (FQ) use in a large healthcare system reduced incidence of postdischarge CDI. Methods: In August 2019, 4 acute-care hospitals in a large healthcare system replaced standalone FQ orders with order sets containing decision support. Order sets redirected prescribers to syndrome order sets that prioritize alternative antibiotics. Monthly patient days (PDs) and antibiotic days of therapy (DOT) administered for FQs and NHSN-defined broad-spectrum hospital-onset (BS-HO) antibiotics were calculated using patient encounter data for the 23 months before and 13 months after the intervention (COVID-19 admissions in the previous 7 months). We evaluated hospital-onset CDI (HO-CDI) per 1,000 PD (defined as any positive test after hospital day 3) and 12-week postdischarge (PDC- CDI) per 100 discharges (any positive test within healthcare system <12 weeks after discharge). Interrupted time-series analysis using generalized estimating equation models with negative binomial link function was conducted; a sensitivity analysis with Medicare case-mix index (CMI) adjustment was also performed to control for differences after start of the COVID-19 pandemic. Results: Among 163,117 admissions, there were 683 HO-CDIs and 1,009 PDC-CDIs. Overall, FQ DOT per 1,000 PD decreased by 21% immediately after the intervention (level change; P < .05) and decreased at a consistent rate throughout the entire study period (−2% per month; P < .01) (Fig. 1). There was a nonsignificant 5% increase in BS-HO antibiotic use immediately after intervention and a continued increase in use after the intervention (0.3% per month; P = .37). HO-CDI rates were stable throughout the study period, with a nonsignificant level change decrease of 10% after the intervention. In contrast, there was a reversal in the trend in PDC-CDI rates from a 0.4% per month increase in the preintervention period to a 3% per month decrease in the postintervention period (P < .01). Sensitivity analysis with adjustment for facility-specific CMI produced similar results but with wider confidence intervals, as did an analysis with a distinct COVID-19 time point. Conclusion: Our systemwide intervention using order sets with decision support reduced inpatient FQ use by 21%. The intervention did not significantly reduce HO-CDI but significantly decreased the incidence of CDI within 12 weeks after discharge. Relying on outcome measures limited to inpatient setting may not reflect the full impact of inpatient stewardship efforts and incorporating postdischarge outcomes, such as CDI, should increasingly be considered.
This consensus statement by the Society for Healthcare Epidemiology of America (SHEA) and the Society for Post-Acute and Long-Term Care Medicine (AMDA), the Association for Professionals in Epidemiology and Infection Control (APIC), the HIV Medicine Association (HIVMA), the Infectious Diseases Society of America (IDSA), the Pediatric Infectious Diseases Society (PIDS), and the Society of Infectious Diseases Pharmacists (SIDP) recommends that coronavirus disease 2019 (COVID-19) vaccination should be a condition of employment for all healthcare personnel in facilities in the United States. Exemptions from this policy apply to those with medical contraindications to all COVID-19 vaccines available in the United States and other exemptions as specified by federal or state law. The consensus statement also supports COVID-19 vaccination of nonemployees functioning at a healthcare facility (eg, students, contract workers, volunteers, etc).
Background: Hospitalists play a critical role in antimicrobial stewardship as the primary antibiotic prescriber for many inpatients. We sought to describe antibiotic prescribing variation among hospitalists within a healthcare system. Methods: We created a novel metric of hospitalist-specific antibiotic prescribing by linking hospitalist billing data to hospital medication administration records in 4 hospitals (two 500-bed academic (AMC1 and AMC2), one 400-bed community (CH1), and one 100-bed community (CH2)) from January 2016 to December 2018. We attributed dates that a hospitalist electronically billed for a given patient as billed patient days (bPD) and mapped an antibiotic day of therapy (DOT) to a bPD. Each DOT was classified according to National Healthcare Safety Network antibiotic categories: broad-spectrum hospital-onset (BS-HO), broad-spectrum community-onset (BS-CO), anti-MRSA, and highest risk for Clostridioides difficile infection (CDI). DOT and bPD were pooled to calculate hospitalist-specific DOT per 1,000 bPD. Best subsets regression was performed to assess model fit and generate hospital and antibiotic category-specific models adjusting for patient-level factors (eg, age ≥65, ICD-10 codes for comorbidities and infections). The models were used to calculate predicted hospitalist-specific DOT and observed-to-expected ratios (O:E) for each antibiotic category. Kruskal-Wallis tests and pairwise Wilcoxon rank-sum tests were used to determine significant differences between median DOT per 1,000 bPD and O:E between hospitals for each antibiotic category. Results: During the study period, 116 hospitalists across 4 hospitals contributed a total of 437,303 bPD. Median DOT per 1,000 bPD varied between hospitals (BS-HO range, 46.7–84.2; BS-CO range, 63.3–100; anti-MRSA range, 48.4–65.4; CDI range, 82.0–129.4). CH2 had a significantly higher median DOT per 1,000 bPD compared to the academic hospitals (all antibiotic categories P < .001) and CH1 (BS-HO, P = .01; anti-MRSA, P = .02) (Fig. 1A). The 4 antibiotic groups at 4 hospitals resulted in 16 models, with good model fit for CH2 (R2 > 0.55 for all models), modest model fit for AMC2 (R2 = 0.46–0.55), fair model fit for CH1 (R2 = 0.19–0.35), and poor model fit for AMC1 (R2 < 0.12 for all models). Variation in hospitalist-specific O:E was moderate (IQR, 0.9–1.1). AMC1 showed greater variation than other hospitals, but we detected no significant differences in median O:E between hospitals (all antibiotic categories P > .10) (Fig. 1B). Conclusions: Adjusting for patient-level factors significantly reduced much of the variation in hospitalist-specific DOT per 1,000 bPD in some but not all hospitals, suggesting that unmeasured factors may drive antibiotic prescribing. This metric may represent a target for stewardship intervention, such as hospitalist-specific feedback of antibiotic prescribing practices.
Disclosures: Scott Fridkin, consulting fee - vaccine industry (various) (spouse)
Background: There is great enthusiasm for the potential of decision support tools embedded in the electronic medical record to improve antimicrobial use in hospitals. Yet they are often limited in their ability to change prescriber behavior. Analyzing these tools using an interactive sociotechnical approach (ISTA) can identify barriers and facilitators to the implementation of electronic decision support (EDS) in antimicrobial stewardship. Objective: To examine prescriber and antimicrobial steward perceptions of EDS using an ISTA approach in the preimplementation phase of an antimicrobial stewardship intervention. Methods: We conducted semistructured interviews with prescribers and stewards from 4 hospitals in 2 health systems in the context of a multicomponent intervention to improve the use of fluoroquinolones and extended-spectrum cephalosporins. Sites planned to implement various EDS elements including order sets, antimicrobial time outs, and audit with feedback stewardship notes in the medical record. Interviews elicited respondent perceptions about the planned intervention. Two analysts systematically coded transcripts using an ISTA framework in NVivo12 software. Results: Interviews with 64 respondents were conducted: 38 physicians, 7 nurses, 6 advanced practice providers, and 13 pharmacists. We identified 4 key sociotechnical interaction types likely to influence stewardship EDS implementation. First, EDS changes the communication patterns and practices of antimicrobial stewards in a way that improves efficiency but decreases vital social interaction with prescribers to facilitate behavior change. Second, there is a gap between what stewards envision for EDS and that which is possible to build in a timely manner by hospital information technology specialists. As a result, there is often a months- to years-long delay from proposal to implementation, which negatively affects intervention acceptance. Third, prescribers expressed great enthusiasm for stewardship EDS that would simplify their workload, allow them to complete important work tasks, and save time. They strongly objected to stewardship EDS that was disruptive without a compelling purpose or did not integrate smoothly with pre-existing technology infrastructure. Fourth, physician prescribers attributed social and emotional meaning to stewardship EDS, suggesting that these tools can undermine professional authority, autonomy, and confidence. Conclusions: Implementing stewardship EDS in a way that improves the use of antimicrobials while minimizing unintended negative consequences requires attention to the interplay between new EDS and an organization’s existing workflow, culture, social interactions and technologies. Implementing EDS in stewardship will require attention to these domains to realize the full potential of these tools and to avoid negative unintended consequences.
To determine the effect of an electronic medical record (EMR) nudge at reducing total and inappropriate orders testing for hospital-onset Clostridioides difficile infection (HO-CDI).
An interrupted time series analysis of HO-CDI orders 2 years before and 2 years after the implementation of an EMR intervention designed to reduce inappropriate HO-CDI testing. Orders for C. difficile testing were considered inappropriate if the patient had received a laxative or stool softener in the previous 24 hours.
Four hospitals in an academic healthcare network.
All patients with a C. difficile order after hospital day 3.
Orders for C. difficile testing in patients administered a laxative or stool softener in <24 hours triggered an EMR alert defaulting to cancellation of the order (“nudge”).
Of the 17,694 HO-CDI orders, 7% were inappropriate (8% prentervention vs 6% postintervention; P < .001). Monthly HO-CDI orders decreased by 21% postintervention (level-change rate ratio [RR], 0.79; 95% confidence interval [CI], 0.73–0.86), and the rate continued to decrease (postintervention trend change RR, 0.99; 95% CI, 0.98–1.00). The intervention was not associated with a level change in inappropriate HO-CDI orders (RR, 0.80; 95% CI, 0.61–1.05), but the postintervention inappropriate order rate decreased over time (RR, 0.95; 95% CI, 0.93–0.97).
An EMR nudge to minimize inappropriate ordering for C. difficile was effective at reducing HO-CDI orders, and likely contributed to decreasing the inappropriate HO-CDI order rate after the intervention.
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