Background: Medicare claims are frequently used to study Clostridioides difficile infection (CDI) epidemiology. Categorizing CDI based on location of onset and potential exposure is critical in understanding transmission patterns and prevention strategies. While claims data are well-suited for identifying prior healthcare utilization exposures, they lack specimen collection and diagnosis dates to assign likely location of onset. Algorithms to classify CDI onset and healthcare association using claims data have been published, but the degree of misclassification is unknown. Methods: We linked patients with laboratory-confirmed CDI reported to four Emerging Infections Program (EIP) sites from 2016-2020 to Medicare beneficiaries using residence, birth date, sex, and hospitalization and/or healthcare exposure dates. Uniquely linked patients with fee-for-service Medicare A/B coverage and complete EIP case report forms were included. Patients with a claims CDI diagnosis code within ±28 days of a positive CDI test reported to EIP were categorized as hospital-onset (HO), long-term care facility onset (LTCFO), or community-onset (CO, either healthcare facility-associated [COHCFA] or community-associated [CA]) using a previously published algorithm based on claim type, ICD-10-CM code position, and duration of hospitalization (if applicable). EIP classifies CDI into these categories using positive specimen collection date and other information from chart review (e.g. admit/discharge dates). We assessed concordance of EIP and claims case classifications using Cohen’s kappa. Results: Of 10,002 eligible EIP-identified CDI cases, 7,064 were linked to a unique beneficiary; 3,451 met Medicare A/B fee-for-service coverage inclusion criteria. Of these, 650 (19%) did not have a claims diagnosis code ±28 days of the EIP specimen collection date (Table); 48% (313/650) of those without a claims diagnosis code were categorized by EIP as CA CDI. Among those with a CDI diagnosis code, concurrence of claims-based and EIP CDI classification was 68% (κ=0.56). Concurrence was highest for HO and lowest for COHCFA CDI. A substantial number of EIP-classified CO CDIs (30%, Figure) were misclassified as HO using the claims-based algorithm; half of these had a primary ICD-10 diagnosis code of sepsis (226/454; 50%). Conclusions: Evidence of CDI in claims data was found for 81% of EIP-reported CDI cases. Medicare classification algorithms concurred with the EIP classification in 68% of cases. Discordance was most common for community-onset CDI patients, many of whom were hospitalized with a primary diagnosis of sepsis. Misclassification of CO-CDI as HO may bias findings of claims-based CDI studies.

Empowering the Participant Voice (EPV) is an NCATS-funded six-CTSA collaboration to develop, demonstrate, and disseminate a low-cost infrastructure for collecting timely feedback from research participants, fostering trust, and providing data for improving clinical translational research. EPV leverages the validated Research Participant Perception Survey (RPPS) and the popular REDCap electronic data-capture platform. This report describes the development of infrastructure designed to overcome identified institutional barriers to routinely collecting participant feedback using RPPS and demonstration use cases. Sites engaged local stakeholders iteratively, incorporating feedback about anticipated value and potential concerns into project design. The team defined common standards and operations, developed software, and produced a detailed planning and implementation Guide. By May 2023, 2,575 participants diverse in age, race, ethnicity, and sex had responded to approximately 13,850 survey invitations (18.6%); 29% of responses included free-text comments. EPV infrastructure enabled sites to routinely access local and multi-site research participant experience data on an interactive analytics dashboard. The EPV learning collaborative continues to test initiatives to improve survey reach and optimize infrastructure and process. Broad uptake of EPV will expand the evidence base, enable hypothesis generation, and drive research-on-research locally and nationally to enhance the clinical research enterprise.

This study investigated the effects of Lacticaseibacillus rhamnosus HN001 supplementation on the architecture and gene expression in small intestinal tissues of piglets used as an animal model for infant humans. Twenty-four 10-d-old entire male piglets (4·3 (sd 0·59) kg body weight) were fed an infant formula (IF) (control) or IF supplemented with 1·3 × 105 (low dose) or 7·9 × 106 (high dose) colony-forming units HN001 per ml of reconstituted formula (n 8 piglets/treatment). After 24 d, piglets were euthanised. Samples were collected to analyse the histology and gene expression (RNAseq and qPCR) in the jejunal and ileal tissues, blood cytokine concentrations, and blood and faecal calprotectin concentrations. HN001 consumption altered (false discovery rate < 0·05) gene expression (RNAseq) in jejunal tissues but not in ileal tissues. The number of ileal goblet cells and crypt surface area increased quadratically (P < 0·05) as dietary HN001 levels increased, but no increase was observed in the jejunal tissues. Similarly, blood plasma concentrations of IL-10 and calprotectin increased linearly (P < 0·05) as dietary HN001 levels increased. In conclusion, supplementation of IF with HN001 affected the architecture and gene expression of small intestine tissue, blood cytokine concentration and frequencies, and blood calprotectin concentrations, indicating that HN001 modulated small intestinal tissue maturation and immunity in the piglet model.

Background: The 2014 US National Strategy for Combating Antibiotic-Resistant Bacteria aimed to reduce inappropriate inpatient antibiotic use by 20% for monitored conditions, such as community-acquired pneumonia (CAP), by 2020. Clinical guidelines recommend treating uncomplicated CAP with a minimum of 5 days of antibiotic therapy. Total length of therapy (LOT) >7 days or >3 days after clinical improvement is rarely necessary. In a previous study estimating LOT in uncomplicated CAP patients, 71% of patients ≥65 years exceeded recommended duration of antibiotics in 2012–2013 (Yi et al, 2018). We evaluated annual trends in LOT in adults ≥65 years hospitalized with uncomplicated CAP from 2013 to 2020. Methods: We conducted a retrospective cohort study among patients in the CMS database with a primary diagnosis of bacterial or unspecified pneumonia using International Classification of Diseases 9th and 10th Revision codes, length of stay (LOS) of 2–10 days, discharged home with self-care, and not rehospitalized in the 3 days following discharge. Discharge home was used as a surrogate for clinical improvement. Because inpatient LOT is not available in CMS data, we used linear regression to model inpatient LOT as a function of LOS using data on CAP patients ≥65 years from the PINC AI healthcare database. Postdischarge LOT was based on prescriptions filled following discharge. Total LOT was calculated by summing estimated inpatient LOT and actual postdischarge LOT (Fig. 1). Total LOT >7 days and postdischarge LOT >3 days were considered indicators of likely excessive LOT. We reported trends in the proportion of patients with likely excessive LOT during the study period. Results: From 2013 through 2020, there were 400,928 uncomplicated CAP hospitalizations among patients aged ≥65 years. Patients were more likely to be female (55%), and they had a median age of 76 years and a median LOS of 3 days. The median total LOT decreased from 9.5 days in 2013 to 7.7 days in 2020. The proportion of patients with total LOT >7 days decreased from 68% in 2013 to 50% in 2020 (% change, −27%); the proportion with postdischarge LOT >3 days decreased from 73% in 2013 to 62% in 2020 (% change, −16%) (Fig. 2). Conclusions: Likely excessive total LOT for adults ≥65 years hospitalized with uncomplicated CAP decreased by 27% in 2020, a considerable improvement from 2013. However, the high proportion of patients with likely excessive postdischarge LOT in 2020 (62%) demonstrates the need for antibiotic stewardship to optimize prescribing at hospital discharge.

Disclosures: None

OBJECTIVES/GOALS: Supported by the State of Alabama, the Alabama Genomic Health Initiative (AGHI) is aimed at preventing and treating common conditions with a genetic basis. This joint UAB Medicine-HudsonAlpha Institute for Biotechnology effort provides genomic testing, interpretation, and counseling free of charge to residents in each of Alabama’s 67 counties. METHODS/STUDY POPULATION: Launched in 2017, as a state-wide population cohort, AGHI (1.0) enrolled 6,331 Alabamians and returned individual risk of disease(s) related to the ACMG SF v2.0 medically actionable genes. In 2021, the cohort was expanded to include a primary care cohort. AGHI (2.0) has enrolled 750 primary care patients, returning individual risk of disease(s) related to the ACMG SF v3.1 gene list and pre-emptive pharmacogenetics (PGx) to guide medication therapy. Genotyping is done on the Illumina Global Diversity Array with Sanger sequencing to confirm likely pathogenic / pathogenic variants in medically actionable genes and CYP2D6 copy number variants using Taqman assays, resulting in a CLIA-grade report. Disease risk results are returned by genetic counselors and Pharmacogenetics results are returned by Pharmacists. RESULTS/ANTICIPATED RESULTS: We have engaged a statewide community (>7000 participants), returning 94 disease risk genetic reports and 500 PGx reports. Disease risk reports include increased predisposition to cancers (n=38), cardiac diseases (n=33), metabolic (n=12), other (n=11). 100% of participants harbor an actionable PGx variant, 70% are on medication with PGx guidance, 48% harbor PGx variants and are taking medications affected. In 10% of participants, pharmacists sent an active alert to the provider to consider/ recommend alternative medication. Most commonly impacted medications included antidepressants, NSAIDS, proton-pump inhibitors and tramadol. To enable the EMR integration of genomic information, we have developed an automated transfer of reports into the EMR with Genetics Reports and PGx reports viewable in Cerner. DISCUSSION/SIGNIFICANCE: We share our experience on pre-emptive implementation of genetic risk and pharmacogenetic actionability at a population and clinic level. Both patients and providers are actively engaged, providing feedback to refine the return of results. Real time alerts with guidance at the time of prescription are needed to ensure future actionability and value.

Gaps in the implementation of effective interventions impact nearly all cancer prevention and control strategies in the US including Massachusetts. To close these implementation gaps, evidence-based interventions must be rapidly and equitably implemented in settings serving racially, ethnically, socioeconomically, and geographically diverse populations. This paper provides a brief overview of The Implementation Science Center for Cancer Control Equity (ISCCCE) and describes how we have operationalized our commitment to a robust community-engaged center that aims to close these gaps. We describe how ISCCCE is organized and how the principles of community-engaged research are embedded across the center. Principles of community engagement have been operationalized across all components of ISCCCE. We have intentionally integrated these principles throughout all structures and processes and have developed evaluation strategies to assess whether the quality of our partnerships reflects the principles. ISCCCE is a comprehensive community-engaged infrastructure for studying efficient, pragmatic, and equity-focused implementation and adaptation strategies for cancer prevention in historically and currently disadvantaged communities with built-in methods to evaluate the quality of community engagement. This engaged research center is designed to maximize the impact and relevance of implementation research on cancer control in community health centers.

Among nursing home outbreaks of coronavirus disease 2019 (COVID-19) with ≥3 breakthrough infections when the predominant severe acute respiratory coronavirus virus 2 (SARS-CoV-2) variant circulating was the SARS-CoV-2 δ (delta) variant, fully vaccinated residents were 28% less likely to be infected than were unvaccinated residents. Once infected, they had approximately half the risk for all-cause hospitalization and all-cause death compared with unvaccinated infected residents.

Background: Previously, we reported decreasing postadmission urine-culture rates in hospitalized patients between 2012 and 2017, indicating a possible decrease in hospital-onset urinary tract infections or changes in diagnostic practices in acute-care hospitals (ACHs). In this study, we re-evaluated the trends using more recent data from 2017–2020 to assess whether new trends in hospital urine-culturing practices had emerged. Method: We conducted a longitudinal analysis of monthly urine-culture rates using microbiology data from 355 ACHs participating in the Premier Healthcare Database in 2017–2020. All cultures from the urinary tract collected on or before day 3 were defined as admission urine cultures and those collected on day 4 or later were defined as postadmission urine cultures. We included discharges from months where a hospital reported at least 1 urine culture with microbiology and antimicrobial susceptibility test results. Annual estimates of rates of admission culture and postadmission urine-culture rates were assessed using general estimating equation models with a negative binomial distribution accounting for hospital-level clustering and adjusting for hospital bed size, teaching status, urban–rural designation, discharge month, and census division. Estimated rate for each year (2018, 2019, and 2020) was compared to previous year’s estimated rate using rate ratios (RRs) and 95% confidence intervals (CIs) generated through the multivariable GEE models. Results: From 2017 to 2020, we included 8.7 million discharges and 1,943,540 urine cultures, of which 299,013 (15.4%) were postadmission urine cultures. In 2017–2020, unadjusted admission culture rates were 20.0, 19.6, 17.9, and 18.2 per 100 discharges respectively; similarly, unadjusted postadmission urine-culture rates were 8.6, 7.8, 7.0, and 7.5 per 1,000 patient days. In the multivariable analysis, adjusting for hospital characteristics, no significant changes in admission urine-culture rates were detected during 2017–2019; however, in 2020, admission urine-culture rates increased 6% compared to 2019 (RR, 1.06; 95% CI, 1.02–1.09) (Fig. 1). Postadmission urine-culture rates decreased 4% in 2018 compared to 2017 (RR, 0.96; 95% CI, 0.91–0.99) and 8% in 2019 compared to 2018 (RR, 0.92; 95% CI, 0.87–0.96). In 2020, postadmission urine-culture rates increased 10% compared to 2019 (RR, 1.10; 95% CI, 1.06–1.14) (Fig. 2). Factors significantly associated with postadmission urine-culture rates included discharge month and hospital bed size. For admission urine cultures, discharge month was the only significant factor. Conclusions: Between 2017–2019, postadmission urine-culture rates continued a decreasing trend, while admission culture rates remained unchanged. However, in 2020 both admission and postadmission urine culture rates increased significantly in comparison to 2019.

Funding: None

Disclosures: None

From 2014 to 2020, we compiled radiocarbon ages from the lower 48 states, creating a database of more than 100,000 archaeological, geological, and paleontological ages that will be freely available to researchers through the Canadian Archaeological Radiocarbon Database. Here, we discuss the process used to compile ages, general characteristics of the database, and lessons learned from this exercise in “big data” compilation.