Socioeconomic Disparities and the Prevalence of Antimicrobial Resistance.

BACKGROUND: The increased prevalence of antimicrobial resistant (AMR) infections is a significant global health threat, resulting in increased morbidity, mortality, and costs. The drivers of AMR are complex and potentially impacted by socioeconomic factors. We investigated the relationships between geographic and socioeconomic factors and AMR. METHODS: We collected select patient bacterial culture results from 2015 to 2020 from electronic health records (EHR) of two expansive healthcare systems within the Dallas-Fort Worth, TX (DFW) metropolitan area. Among individuals with EHR records who resided in the four most populus counties in DFW, culture data were aggregated. Case counts for each organism studied were standardized per 1,000 persons per area population. Using residential addresses, the cultures were geocoded and linked to socioeconomic index values. Spatial autocorrelation tests identified geographic clusters of high and low AMR organism prevalence and correlations with established socioeconomic indices. RESULTS: We found significant clusters of AMR organisms in areas with high levels of deprivation, as measured by the Area Deprivation Index (ADI). We found a significant spatial autocorrelation between ADI and the prevalence of AMR organisms, particularly for AmpC and MRSA with 14% and 13%, respectively, of the variability in prevalence rates being attributable to their relationship with the ADI values of the neighboring locations. CONCLUSIONS: We found that areas with a high ADI are more likely to have higher rates of AMR organisms. Interventions that improve socioeconomic factors such as poverty, unemployment, decreased access to healthcare, crowding, and sanitation in these areas of high prevalence may reduce the spread of AMR.

Mortality in hospitalized SARS-CoV-2 patients with contemporaneous bacterial and fungal infections.

Background: The interplay between SARS-CoV-2 and contemporaneous bacterial or fungal culture growth may have crucial implications for clinical outcomes of hospitalized patients. This study aimed to quantify the effect of microbiological culture positivity on mortality among hospitalized patients with SARS-CoV-2. Methods: In this retrospective cohort study, we included adult hospitalized patients from OPTUM COVID-19 specific data set, who tested positive for SARS-CoV-2 within 14 days of hospitalization between 01/20/2020 and 01/20/2022. We examined outcomes of individuals with organisms growing on cultures from the bloodstream infections (BSIs), urinary tract, and respiratory tract, and a composite of the three sites. We used propensity score matching on covariates included demographics, comorbidities, and hospitalization clinical parameters. In a sensitivity analysis, we included same covariates but excluded critical care variables such as length of stay, intensive care unit stays, mechanical ventilation, and extracorporeal membrane oxygenation. Results: The cohort included 104,560 SARS-CoV-2 positive adult hospitalized patients across the United States. The unadjusted mortality odds increased significantly with BSIs (98.7%) and with growth on respiratory cultures (RC) (176.6%), but not with growth on urinary cultures (UC). Adjusted analyses showed that BSIs and positive RC independently contribute to mortality, even after accounting for critical care variables. Conclusions: In SARS-CoV-2-positive hospitalized patients, positive bacterial and fungal microbiological cultures, especially BSIs and RC, are associated with an increased risk of mortality even after accounting for critical care variables associated with disease severity. These findings underscore the importance of stringent infection control and the effective management of secondary infections to improve patient outcomes.

A pandemic of COVID-19 mis- and disinformation: manual and automatic topic analysis of the literature.

Objective: Social media's arrival eased the sharing of mis- and disinformation. False information proved challenging throughout the coronavirus disease 2019 (COVID-19) pandemic with many clinicians and researchers analyzing the "infodemic." We systemically reviewed and synthesized COVID-19 mis- and disinformation literature, identifying the prevalence and content of false information and exploring mitigation and prevention strategies. Design: We identified and analyzed publications on COVID-19-related mis- and disinformation published from March 1, 2020, to December 31, 2022, in PubMed. We performed a manual topic review of the abstracts along with automated topic modeling to organize and compare the different themes. We also conducted sentiment (ranked -3 to +3) and emotion analysis (rated as predominately happy, sad, angry, surprised, or fearful) of the abstracts. Results: We reviewed 868 peer-reviewed scientific publications of which 639 (74%) had abstracts available for automatic topic modeling and sentiment analysis. More than a third of publications described mitigation and prevention-related issues. The mean sentiment score for the publications was 0.685, and 56% of studies had a negative sentiment (fear and sadness as the most common emotions). Conclusions: Our comprehensive analysis reveals a significant proliferation of dis- and misinformation research during the COVID-19 pandemic. Our study illustrates the pivotal role of social media in amplifying false information. Research into the infodemic was characterized by negative sentiments. Combining manual and automated topic modeling provided a nuanced understanding of the complexities of COVID-19-related misinformation, highlighting themes such as the source and effect of misinformation, and strategies for mitigation and prevention.

Assessing Racial and Ethnic Bias in Text Generation for Healthcare-Related Tasks by ChatGPT1.

Large Language Models (LLM) are AI tools that can respond human-like to voice or free-text commands without training on specific tasks. However, concerns have been raised about their potential racial bias in healthcare tasks. In this study, ChatGPT was used to generate healthcare-related text for patients with HIV, analyzing data from 100 deidentified electronic health record encounters. Each patient's data were fed four times with all information remaining the same except for race/ethnicity (African American, Asian, Hispanic White, Non-Hispanic White). The text output was analyzed for sentiment, subjectivity, reading ease, and most used words by race/ethnicity and insurance type. Results showed that instructions for African American, Asian, Hispanic White, and Non-Hispanic White patients had an average polarity of 0.14, 0.14, 0.15, and 0.14, respectively, with an average subjectivity of 0.46 for all races/ethnicities. The differences in polarity and subjectivity across races/ethnicities were not statistically significant. However, there was a statistically significant difference in word frequency across races/ethnicities and a statistically significant difference in subjectivity across insurance types with commercial insurance eliciting the most subjective responses and Medicare and other payer types the lowest. The study suggests that ChatGPT is relatively invariant to race/ethnicity and insurance type in terms of linguistic and readability measures. Further studies are needed to validate these results and assess their implications.

Identifying the Optimal Look-back Period for Prior Antimicrobial Resistance Clinical Decision Support.

BACKGROUND: Lack of consensus on the appropriate look-back period for multi-drug resistance (MDR) complicates antimicrobial clinical decision support. We compared the predictive performance of different MDR look-back periods for five common MDR mechanisms (MRSA, VRE, ESBL, AmpC, CRE). METHODS: We mapped microbiological cultures to MDR mechanisms and labeled them at different look-back periods. We compared predictive performance for each look-back period-MDR combination using precision, recall, F1 scores, and odds ratios. RESULTS: Longer look-back periods resulted in lower odds ratios, lower precisions, higher recalls, and lower delta changes in precision and recall compared to shorter periods. We observed higher precision with more information available to clinicians. CONCLUSION: A previously positive MDR culture may have significant enough precision depending on the mechanism of resistance and varying information available. One year is a clinically relevant and statistically sound look-back period for empiric antimicrobial decision-making at varying points of care for the studied population.