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Antimicrobial resistance is a major threat to human health. Since the 2000s, computational tools for predicting infectious diseases have been greatly advanced; however, efforts to develop real-time forecasting models for antimicrobial-resistant organisms (AMROs) have been absent. In this perspective, we discuss the utility of AMRO forecasting at different scales, highlight the challenges in this field, and suggest future research priorities. We also discuss challenges in scientific understanding, access to high-quality data, model calibration, and implementation and evaluation of forecasting models. We further highlight the need to initiate research on AMRO forecasting using currently available data and resources to galvanize the research community and address initial practical questions.

BackgroundInflammation may affect long-term kidney function. Diet may play a role in chronic inflammation. We hypothesized that proinflammatory diets increase the risk of progression to kidney failure with replacement therapy (KFRT), and systemic inflammation is a mediator of the effect of diet on progression to KFRT.MethodsIn the 1988-1994 National Health and Nutrition Examination Survey linked to the national ESKD registry, in adults with CKD (eGFR 15-59 ml/min per 1.73 m2), aged ≥20 years, we calculated the Adapted Dietary Inflammatory Index (ADII) at baseline from a 24-hour dietary recall and an inflammation score (IS) using average of z scores of four inflammation biomarkers. We explored the association of the ADII and IS with risk of incident KFRT using Cox proportional model, adjusting for sociodemographics, physical activity, Framingham risk score, eGFR, and urinary ACR. We evaluated whether, and to what extent, IS mediated the effect of the ADII on KFRT incidence, using causal mediation analysis.ResultsOf 1084 adults with CKD, 109 (10%) developed KFRT. The ADII was associated with increased risk of KFRT (relative hazard [RH] per SD increase (2.56): 1.4 [1.04-1.78]). IS was also associated with KFRT (RH: 1.12; 95% CI, 1.02 to 1.25). Approximately 36% of the association between the ADII and KFRT was explained by IS.ConclusionsAmong adults with CKD, a proinflammatory diet was associated with risk of KFRT, and that association was partially explained by an increase in inflammatory markers. Dietary interventions that reduce inflammation may offer an approach for preventing KFRT.

Background:Prophylactic antibiotics are commonly prescribed at discharge for mastectomy, despite guidelines recommending against this practice. We investigated factors associated with postdischarge prophylactic antibiotic use after mastectomy with and without immediate reconstruction and the impact on surgical-site infection (SSI).Study design:We studied a cohort of women aged 18–64 years undergoing mastectomy between January 1, 2010, and June 30, 2015, using the MarketScan commercial database. Patients with nonsurgical perioperative infections were excluded. Postdischarge oral antibiotics were identified from outpatient drug claims. SSI was defined using International Classification of Diseases, Ninth Edition, Clinical Modification (ICD-9-CM) diagnosis codes. Generalized linear models were used to determine factors associated with postdischarge prophylactic antibiotic use and SSI.Results:The cohort included 38,793 procedures; 24,818 (64%) with immediate reconstruction. Prophylactic antibiotics were prescribed after discharge after 2,688 mastectomy-only procedures (19.2%) and 17,807 mastectomies with immediate reconstruction (71.8%). The 90-day incidence of SSI was 3.5% after mastectomy only and 8.8% after mastectomy with immediate reconstruction. Antibiotics with anti–methicillin-sensitive Staphylococcus aureus (MSSA) activity were associated with decreased SSI risk after mastectomy only (adjusted relative risk [aRR], 0.74; 95% confidence interval [CI], 0.55–0.99) and mastectomy with immediate reconstruction (aRR, 0.80; 95% CI, 0.73–0.88), respectively. The numbers needed to treat to prevent 1 additional SSI were 107 and 48, respectively.Conclusions:Postdischarge prophylactic antibiotics were common after mastectomy. Anti-MSSA antibiotics were associated with decreased risk of SSI for patients who had mastectomy only and those who had mastectomy with immediate reconstruction. The high numbers needed to treat suggest that potential benefits of postdischarge antibiotics should be weighed against potential harms associated with antibiotic overuse.

High case counts after the Gamma (P. 1) variant of severe acute respiratory syndrome coronavirus 2 emerged in Brazil raised concerns that previously infected persons might become reinfected. Investigation of a cluster of coronavirus disease cases in Parintins, in the Brazilian Amazon, suggested household transmission but did not identify high rates of reinfection.

Objective:Ventilator-capable skilled nursing facilities (vSNFs) are critical to the epidemiology and control of antibiotic-resistant organisms. During an infection prevention intervention to control carbapenem-resistant Enterobacterales (CRE), we conducted a qualitative study to characterize vSNF healthcare personnel beliefs and experiences regarding infection control measures.Design:A qualitative study involving semistructured interviews.Setting:One vSNF in the Chicago, Illinois, metropolitan region.Participants:The study included 17 healthcare personnel representing management, nursing, and nursing assistants.Methods:We used face-to-face, semistructured interviews to measure healthcare personnel experiences with infection control measures at the midpoint of a 2-year quality improvement project.Results:Healthcare personnel characterized their facility as a home-like environment, yet they recognized that it is a setting where germs were ‘invisible’ and potentially ‘threatening.’ Healthcare personnel described elaborate self-protection measures to avoid acquisition or transfer of germs to their own household. Healthcare personnel were motivated to implement infection control measures to protect residents, but many identified structural barriers such as understaffing and time constraints, and some reported persistent preference for soap and water.Conclusions:Healthcare personnel in vSNFs, from management to frontline staff, understood germ theory and the significance of multidrug-resistant organism transmission. However, their ability to implement infection control measures was hampered by resource limitations and mixed beliefs regarding the effectiveness of infection control measures. Self-protection from acquiring multidrug-resistant organisms was a strong motivator for healthcare personnel both outside and inside the workplace, and it could explain variation in adherence to infection control measures such as a higher hand hygiene adherence after resident care than before resident care.

Objectives Best practice guidelines and quality metrics recommend immediate antibiotic treatment for all patients with suspected sepsis. However, little is known about how many patients given IV antibiotics in the emergency department are ultimately confirmed to have bacterial infection. Design, setting, and patients We performed a retrospective study of adult patients who presented to four Massachusetts emergency departments between June 2015 and June 2018 with suspected serious bacterial infection, defined as blood cultures drawn and broad-spectrum IV antibiotics administered. Structured medical record reviews were performed on a random sample of 300 cases to determine the post hoc likelihood of bacterial infection, categorized as definite, likely, unlikely, or definitely none. Interventions None. Measurements and main results Among the 300 patients with suspected serious bacterial infections, mean age was 68 years (SD 18), median hospital length of stay was 5 days (interquartile range, 3-8 d), 45 (15%) were admitted directly to ICU, and 14 (5%) died in hospital. Overall, 196 (65%) had definite (n = 115; 38%) or likely (n = 81; 27%) bacterial infection, whereas 104 (35%) were unlikely (n = 55; 18%) or definitely not infected (n = 49; 16%). Antibiotic treatment durations differed by likelihood of infection (median 15 days for definite, 9 for likely, 7 for unlikely, and 3 for definitely not infected). The most frequent post hoc diagnoses in patients with unlikely or definitely no bacterial infection included viral infections (28%), volume overload or cardiac disease (9%), drug effects (9%), and hypovolemia (7%). The likelihoods of infection were similar in the subset of 96 cases in whom emergency department providers explicitly documented possible or suspected sepsis and in the 45 patients admitted from the emergency department to the ICU. Conclusions One third of patients empirically treated with broad-spectrum antibiotics in the emergency department are ultimately diagnosed with noninfectious or viral conditions. These findings underscore the difficulty diagnosing serious infections in the emergency department and have important implications for guidelines and quality measures that compel immediate empiric antibiotics for all patients with possible sepsis.

As cases and deaths from coronavirus disease 2019 (COVID-19) in Europe rose sharply in late March, most European countries implemented strict mitigation policies, including closure of nonessential businesses and mandatory stay-at-home orders. These policies were largely successful at curbing transmission of SARS-CoV-2, the virus that causes COVID-19 (1), but they came with social and economic costs, including increases in unemployment, interrupted education, social isolation, and related psychosocial outcomes (2,3). A better understanding of when and how these policies were effective is needed. Using data from 37 European countries, the impact of the timing of these mitigation policies on mortality from COVID-19 was evaluated. Linear regression was used to assess the association between policy stringency at an early time point and cumulative mortality per 100,000 persons on June 30. Implementation of policies earlier in the course of the outbreak was associated with lower COVID-19-associated mortality during the subsequent months. An increase by one standard deviation in policy stringency at an early timepoint was associated with 12.5 cumulative fewer deaths per 100,000 on June 30. Countries that implemented stringent policies earlier might have saved several thousand lives relative to those countries that implemented similar policies, but later. Earlier implementation of mitigation policies, even by just a few weeks, might be an important strategy to reduce the number of deaths from COVID-19.

As of January 3, 2021, a total of 20,346,372 cases of coronavirus disease 2019 (COVID-19) and 349,246 associated deaths have been reported in the United States. Long-term sequalae of COVID-19 over the course of a lifetime currently are unknown; however, persistent symptoms and serious complications are being reported among COVID-19 survivors, including persons who initially experience a mild acute illness.* On December 11, 2020, the Food and Drug Administration (FDA) issued an Emergency Use Authorization (EUA) for Pfizer-BioNTech COVID-19 vaccine to prevent COVID-19, administered as 2 doses separated by 21 days. On December 12, 2020, the Advisory Committee on Immunization Practices (ACIP) issued an interim recommendation for use of Pfizer-BioNTech COVID-19 vaccine (1); initial doses were recommended for health care personnel and long-term care facility residents (2). As of December 23, 2020, a reported 1,893,360 first doses of Pfizer-BioNTech COVID-19 vaccine had been administered in the United States, and reports of 4,393 (0.2%) adverse events after receipt of Pfizer BioNTech COVID-19 vaccine had been submitted to the Vaccine Adverse Event Reporting System (VAERS). Among these, 175 case reports were identified for further review as possible cases of severe allergic reaction, including anaphylaxis. Anaphylaxis is a life-threatening allergic reaction that does occur rarely after vaccination, with onset typically within minutes to hours (3). Twenty-one cases were determined to be anaphylaxis (a rate of 11.1 per million doses administered), including 17 in persons with a documented history of allergies or allergic reactions, seven of whom had a history of anaphylaxis. The median interval from vaccine receipt to symptom onset was 13 minutes (range = 2-150 minutes). Among 20 persons with follow-up information available, all had recovered or been discharged home. Of the remaining case reports that were determined not to be anaphylaxis, 86 were judged to be nonanaphylaxis allergic reactions, and 61 were considered nonallergic adverse events. Seven case reports were still under investigation. This report summarizes the clinical and epidemiologic characteristics of case reports of allergic reactions, including anaphylaxis and nonanaphylaxis allergic reactions, after receipt of the first dose of Pfizer-BioNTech COVID-19 vaccine during December 14-23, 2020, in the United States. CDC has issued updated interim clinical considerations for use of mRNA COVID-19 vaccines currently authorized in the United States (4) and interim considerations for preparing for the potential management of anaphylaxis (5). In addition to screening for contraindications and precautions before administering COVID-19 vaccines, vaccine locations should have the necessary supplies available to manage anaphylaxis, should implement postvaccination observation periods, and should immediately treat persons experiencing anaphylaxis signs and symptoms with intramuscular injection of epinephrine (4,5).

On August 1, 2018, the Democratic Republic of the Congo Ministry of Health (DRC MoH) declared the tenth outbreak of Ebola virus disease (Ebola) in DRC, in the North Kivu province in eastern DRC on the border with Uganda, 8 days after another Ebola outbreak was declared over in northwest Equateur province. During mid- to late-July 2018, a cluster of 26 cases of acute hemorrhagic fever, including 20 deaths, was reported in North Kivu province.* Blood specimens from six patients hospitalized in the Mabalako health zone and sent to the Institut National de Recherche Biomedicale (National Biomedical Research Institute) in Kinshasa tested positive for Ebola virus. Genetic sequencing confirmed that the outbreaks in North Kivu and Equateur provinces were unrelated. From North Kivu province, the outbreak spread north to Ituri province, and south to South Kivu province (1). On July 17, 2019, the World Health Organization designated the North Kivu and Ituri outbreak a public health emergency of international concern, based on the geographic spread of the disease to Goma, the capital of North Kivu province, and to Uganda and the challenges to implementing prevention and control measures specific to this region (2). This report describes the outbreak in the North Kivu and Ituri provinces. As of November 17, 2019, a total of 3,296 Ebola cases and 2,196 (67%) deaths were reported, making this the second largest documented outbreak after the 2014-2016 epidemic in West Africa, which resulted in 28,600 cases and 11,325 deaths.† Since August 2018, DRC MoH has been collaborating with partners, including the World Health Organization, the United Nations Children's Fund, the United Nations Office for the Coordination of Humanitarian Affairs, the International Organization of Migration, The Alliance for International Medical Action (ALIMA), Medecins Sans Frontieres, DRC Red Cross National Society, and CDC, to control the outbreak. Enhanced communication and effective community engagement, timing of interventions during periods of relative stability, and intensive training of local residents to manage response activities with periodic supervision by national and international personnel are needed to end the outbreak.

As of December 14, 2020, children and adolescents aged

Coronavirus disease 2019 (COVID-19) is a viral respiratory illness caused by SARS-CoV-2. During January 21-July 25, 2020, in response to official requests for assistance with COVID-19 emergency public health response activities, CDC deployed 208 teams to assist 55 state, tribal, local, and territorial health departments. CDC deployment data were analyzed to summarize activities by deployed CDC teams in assisting state, tribal, local, and territorial health departments to identify and implement measures to contain SARS-CoV-2 transmission (1). Deployed teams assisted with the investigation of transmission in high-risk congregate settings, such as long-term care facilities (53 deployments; 26% of total), food processing facilities (24; 12%), correctional facilities (12; 6%), and settings that provide services to persons experiencing homelessness (10; 5%). Among the 208 deployed teams, 178 (85%) provided assistance to state health departments, 12 (6%) to tribal health departments, 10 (5%) to local health departments, and eight (4%) to territorial health departments. CDC collaborations with health departments have strengthened local capacity and provided outbreak response support. Collaborations focused attention on health equity issues among disproportionately affected populations (e.g., racial and ethnic minority populations, essential frontline workers, and persons experiencing homelessness) and through a place-based focus (e.g., persons living in rural or frontier areas). These collaborations also facilitated enhanced characterization of COVID-19 epidemiology, directly contributing to CDC data-informed guidance, including guidance for serial testing as a containment strategy in high-risk congregate settings, targeted interventions and prevention efforts among workers at food processing facilities, and social distancing.

The extent to which donor multidrug-resistant organisms (MDROs) affect organ utilization remains unclear. We performed a retrospective cohort study at 4 transplant centers between 2015 and 2016 to evaluate this question. All deceased donors who donated at least one organ were included. Exposed donors had at least one MDRO on culture. Unexposed donors had no MDRO-positive cultures. Only cultures obtained during the donor's terminal hospitalization were evaluated. Multivariable regression was used to determine the association between donor MDRO and (1) number of organs transplanted per donor and (2) the match run at which each organ was accepted. Subsequently, we restricted the analysis to donors with MDR-Gram-negative (GN) organisms. Of 440 total donors, 29 (7%) donors grew MDROs and 7 (2%) grew MDR-GNs. There was no significant association between donor MDRO and either measure of organ utilization. However, donor MDR-GNs were associated with a significant reduction in the number of organs transplanted per donor (incidence rate ratio 0.43, 95% confidence interval [CI] 0.39-0.48, P < .01), and organs were accepted significantly further down the match list (relative count 5.08, 95% CI 1.64-15.68, P = .01). Though donor MDR-GNs were infrequent in our study, their growing prevalence could meaningfully reduce the donor pool over time.

Community transmission of coronavirus disease 2019 (COVID-19) was first detected in the United States in February 2020. By mid-March, all 50 states, the District of Columbia (DC), New York City (NYC), and four U.S. territories had reported cases of COVID-19. This report describes the geographic distribution of laboratory-confirmed COVID-19 cases and related deaths reported by each U.S. state, each territory and freely associated state,* DC, and NYC during February 12-April 7, 2020, and estimates cumulative incidence for each jurisdiction. In addition, it projects the jurisdiction-level trajectory of this pandemic by estimating case doubling times on April 7 and changes in cumulative incidence during the most recent 7-day period (March 31-April 7). As of April 7, 2020, a total of 395,926 cases of COVID-19, including 12,757 related deaths, were reported in the United States. Cumulative COVID-19 incidence varied substantially by jurisdiction, ranging from 20.6 cases per 100,000 in Minnesota to 915.3 in NYC. On April 7, national case doubling time was approximately 6.5 days, although this ranged from 5.5 to 8.0 days in the 10 jurisdictions reporting the most cases. Absolute change in cumulative incidence during March 31-April 7 also varied widely, ranging from an increase of 8.3 cases per 100,000 in Minnesota to 418.0 in NYC. Geographic differences in numbers of COVID-19 cases and deaths, cumulative incidence, and changes in incidence likely reflect a combination of jurisdiction-specific epidemiologic and population-level factors, including 1) the timing of COVID-19 introductions; 2) population density; 3) age distribution and prevalence of underlying medical conditions among COVID-19 patients (1-3); 4) the timing and extent of community mitigation measures; 5) diagnostic testing capacity; and 6) public health reporting practices. Monitoring jurisdiction-level numbers of COVID-19 cases, deaths, and changes in incidence is critical for understanding community risk and making decisions about community mitigation, including social distancing, and strategic health care resource allocation.

Objective:To characterize the presence and magnitude of viruses in the air and on surfaces in the rooms of hospitalized patients with respiratory viral infections, and to explore the association between care activities and viral contamination.Design:Prospective observational study.Setting:Acute-care academic hospital.Participants:In total, 52 adult patients with a positive respiratory viral infection test within 3 days of observation participated. Healthcare workers (HCWs) were recruited in staff meetings and at the time of patient care, and 23 wore personal air-sampling devices.Methods:Viruses were measured in the air at a fixed location and in the personal breathing zone of HCWs. Predetermined environmental surfaces were sampled using premoistened Copan swabs at the beginning and at the end of the 3-hour observation period. Preamplification and quantitative real-time PCR methods were used to quantify viral pathogens.Results:Overall, 43% of stationary and 22% of personal air samples were positive for virus. Positive stationary air samples were associated with ≥5 HCW encounters during the observation period (odds ratio [OR], 5.3; 95% confidence interval [CI], 1.2–37.8). Viruses were frequently detected on all of the surfaces sampled. Virus concentrations on the IV pole hanger and telephone were positively correlated with the number of contacts made by HCWs on those surfaces. The distributions of influenza, rhinoviruses, and other viruses in the environment were similar.Conclusions:Healthcare workers are at risk of contracting respiratory virus infections when delivering routine care for patients infected with the viruses, and they are at risk of disseminating virus because they touch virus-contaminated fomites.

Healthcare-associated infection and antimicrobial resistance are major concerns. However, the extent to which antibiotic exposure affects transmission and detection of infections such as MRSA is unclear. Additionally, temporal trends are typically reported in terms of changes in incidence, rather than analysing underling transmission processes. We present a data-augmented Markov chain Monte Carlo approach for inferring changing transmission parameters over time, screening test sensitivity, and the effect of antibiotics on detection and transmission. We expand a basic model to allow use of typing information when inferring sources of infections. Using simulated data, we show that the algorithms are accurate, well-calibrated and able to identify antibiotic effects in sufficiently large datasets. We apply the models to study MRSA transmission in an intensive care unit in Oxford, UK with 7924 admissions over 10 years. We find that falls in MRSA incidence over time were associated with decreases in both the number of patients admitted to the ICU colonised with MRSA and in transmission rates. In our inference model, the data were not informative about the effect of antibiotics on risk of transmission or acquisition of MRSA, a consequence of the limited number of possible transmission events in the data. Our approach has potential to be applied to a range of healthcare-associated infections and settings and could be applied to study the impact of other potential risk factors for transmission. Evidence generated could be used to direct infection control interventions.

Background:Multidrug-resistant organisms (MDROs) colonizing the healthcare environment have been shown to contribute to risk for healthcare-associated infections (HAIs), with adverse effects on patient morbidity and mortality. We sought to determine how bacterial contamination and persistent MDRO colonization of the healthcare environment are related to the position of patients and wastewater sites.Methods:We performed a prospective cohort study, enrolling 51 hospital rooms at the time of admitting a patient with an eligible MDRO in the prior 30 days. We performed systematic sampling and MDRO culture of rooms, as well as 16S rRNA sequencing to define the environmental microbiome in a subset of samples.Results:The probability of detecting resistant gram-negative organisms, including Enterobacterales, Acinetobacter spp, and Pseudomonas spp, increased with distance from the patient. In contrast, Clostridioides difficile and methicillin-resistant Staphylococcus aureus were more likely to be detected close to the patient. Resistant Pseudomonas spp and S. aureus were enriched in these hot spots despite broad deposition of 16S rRNA gene sequences assigned to the same genera, suggesting modifiable factors that permit the persistence of these MDROs.Conclusions:MDRO hot spots can be defined by distance from the patient and from wastewater reservoirs. Evaluating how MDROs are enriched relative to bacterial DNA deposition helps to identify healthcare micro-environments and suggests how targeted environmental cleaning or design approaches could prevent MDRO persistence and reduce infection risk.

Purpose of reviewMathematical, statistical, and computational models provide insight into the transmission mechanisms and optimal control of healthcare-associated infections. To contextualize recent findings, we offer a summative review of recent literature focused on modeling transmission of pathogens in healthcare settings.Recent findingsThe COVID-19 pandemic has led to a dramatic shift in the modeling landscape as the healthcare community has raced to characterize the transmission dynamics of SARS-CoV-2 and develop effective interventions. Inequities in COVID-19 outcomes have inspired new efforts to quantify how structural bias impacts both health outcomes and model parameterization. Meanwhile, developments in the modeling of methicillin-resistant Staphylococcus aureus, Clostridioides difficile, and other nosocomial infections continue to advance. Machine learning continues to be applied in novel ways, and genomic data is being increasingly incorporated into modeling efforts.SummaryAs the type and amount of data continues to grow, mathematical, statistical, and computational modeling will play an increasing role in healthcare epidemiology. Gaps remain in producing models that are generalizable to a variety of time periods, geographic locations, and populations. However, with effective communication of findings and interdisciplinary collaboration, opportunities for implementing models for clinical decision-making and public health decision-making are bound to increase.

Background The profound changes wrought by coronavirus disease 2019 (COVID-19) on routine hospital operations may have influenced performance on hospital measures, including healthcare-associated infections (HAIs). We aimed to evaluate the association between COVID-19 surges and HAI and cluster rates. Methods In 148 HCA Healthcare-affiliated hospitals, from 1 March 2020 to 30 September 2020, and a subset of hospitals with microbiology and cluster data through 31 December 2020, we evaluated the association between COVID-19 surges and HAIs, hospital-onset pathogens, and cluster rates using negative binomial mixed models. To account for local variation in COVID-19 pandemic surge timing, we included the number of discharges with a laboratory-confirmed COVID-19 diagnosis per staffed bed per month. Results Central line-associated blood stream infections (CLABSI), catheter-associated urinary tract infections (CAUTI), and methicillin-resistant Staphylococcus aureus (MRSA) bacteremia increased as COVID-19 burden increased. There were 60% (95% confidence interval [CI]: 23–108%) more CLABSI, 43% (95% CI: 8–90%) more CAUTI, and 44% (95% CI: 10–88%) more cases of MRSA bacteremia than expected over 7 months based on predicted HAIs had there not been COVID-19 cases. Clostridioides difficile infection was not significantly associated with COVID-19 burden. Microbiology data from 81 of the hospitals corroborated the findings. Notably, rates of hospital-onset bloodstream infections and multidrug resistant organisms, including MRSA, vancomycin-resistant enterococcus, and Gram-negative organisms, were each significantly associated with COVID-19 surges. Finally, clusters of hospital-onset pathogens increased as the COVID-19 burden increased. Conclusions COVID-19 surges adversely impact HAI rates and clusters of infections within hospitals, emphasizing the need for balancing COVID-related demands with routine hospital infection prevention.

OBJECTIVES Widespread use and misuse of prescription and illicit opioids have exposed millions to health risks including serious infectious complications. Little is known, however, about the association between opioid use and sepsis. DESIGN Retrospective cohort study. SETTING About 373 U.S. hospitals. PATIENTS Adults hospitalized between January 2009 and September 2015. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS Sepsis was identified by clinical indicators of concurrent infection and organ dysfunction. Opioid-related hospitalizations were identified by the International Classification of Diseases, 9th Revision, Clinical Modification codes and/or inpatient orders for buprenorphine. Clinical characteristics and outcomes were compared by sepsis and opioid-related hospitalization status. The association between opioid-related hospitalization and all-cause, in-hospital mortality in patients with sepsis was assessed using mixed-effects logistic models to adjust for baseline characteristics and severity of illness.The cohort included 6,715,286 hospitalizations; 375,479 (5.6%) had sepsis, 130,399 (1.9%) had opioid-related hospitalizations, and 8,764 (0.1%) had both. Compared with sepsis patients without opioid-related hospitalizations (n = 366,715), sepsis patients with opioid-related hospitalizations (n = 8,764) were younger (mean 52.3 vs 66.9 yr) and healthier (mean Elixhauser score 5.4 vs 10.5), had more bloodstream infections from Gram-positive and fungal pathogens (68.9% vs 47.0% and 10.6% vs 6.4%, respectively), and had lower in-hospital mortality rates (10.6% vs 16.2%; adjusted odds ratio, 0.73; 95% CI, 0.60-0.79; p < 0.001 for all comparisons). Of 1,803 patients with opioid-related hospitalizations who died in-hospital, 928 (51.5%) had sepsis. Opioid-related hospitalizations accounted for 1.5% of all sepsis-associated deaths, including 5.7% of sepsis deaths among patients less than 50 years old. From 2009 to 2015, the proportion of sepsis hospitalizations that were opioid-related increased by 77% (95% CI, 40.7-123.5%). CONCLUSIONS Sepsis is an important cause of morbidity and mortality in patients with opioid-related hospitalizations, and opioid-related hospitalizations contribute disproportionately to sepsis-associated deaths among younger patients. In addition to ongoing efforts to combat the opioid crisis, public health agencies should focus on raising awareness about sepsis among patients who use opioids and their providers.

Background Inappropriate antibiotic prescribing is common in primary care (PC), particularly for respiratory tract diagnoses (RTDs). However, the optimal approach for improving prescribing remains unknown. Methods We conducted a stepped-wedge study in PC practices within a health system to assess the impact of a provider-targeted intervention on antibiotic prescribing for RTDs. RTDs were grouped into tiers based on appropriateness of antibiotic prescribing: tier 1 (almost always indicated), tier 2 (may be indicated), and tier 3 (rarely indicated). Providers received education on appropriate RTD prescribing followed by monthly peer comparison feedback on antibiotic prescribing for (1) all tiers and (2) tier 3 RTDs. A χ 2 test was used to compare the proportion of visits with antibiotic prescriptions before and during the intervention. Mixed-effects multivariable logistic regression analysis was performed to assess the association between the intervention and antibiotic prescribing. Results Across 30 PC practices and 185 755 total visits, overall antibiotic prescribing was reduced with the intervention, from 35.2% to 23.0% of visits (P Conclusions A provider-focused intervention reduced overall antibiotic prescribing for RTDs without affecting prescribing for infections that likely require antibiotics. Future research should examine the sustainability of such interventions, potential unintended adverse effects on patient health or satisfaction, and provider perceptions and acceptability.

Elective surgical patients routinely bathe with chlorhexidine gluconate (CHG) at home days prior to their procedures. However, the impact of home CHG bathing on surgical site CHG concentration is unclear. We examined 3 different methods of applying CHG and hypothesized that different application methods would impact resulting CHG skin concentration.

Background Bacterial resistance to first line antibiotics used to treat community-onset urinary tract infections (UTIs) continues to emerge. We sought to determine the association between extended-spectrum cephalosporin resistance (ESC-R) and recurrence among Enterobacteriaceae (EB) UTIs. Methods A retrospective cohort study was performed. All patients presenting to the Emergency Departments (EDs) or outpatient practices in a large health system with EB UTIs between 2010 and 2013 were included. Exposed patients had ESC-R EB UTIs. Unexposed patients had ESC-susceptible EB UTIs and were matched to exposed patients 1:1 on study year. Multivariable Cox proportional hazards regression analyses were performed to evaluate the association between ESC-R EB UTI and time to recurrent UTI within 12 months. Results A total of 302 patients with an index community-onset EB UTI were included, with 151 exposed and 151 unexposed. Overall, 163 (54%) patients experienced a recurrent UTI with a median time to recurrence of 69 days (interquartile range 25–183). On multivariable analyses, ESC-resistance was associated with an increased hazard of recurrent UTI (hazard ratio [HR] 1.39, 95% confidence interval [CI] 1.01–1.91, P = 0.04). Other variables that were independently associated with recurrence included a history of UTI prior to the index UTI and presence of a urinary catheter at the time of the index UTI. Secondarily, we found that when the treatment for the index UTI was adjusted for, there was no longer a significant association between ESC-R status and time to recurrent UTI (aHR 1.26, 95% CI 0.91–1.76, P = 0.17). Conclusions Community-onset UTI due to EB demonstrating ESC-resistance is associated with a significantly increased hazard of recurrent UTI within 12 months compared to ESC-susceptible EB, even after adjusting for baseline factors that predispose patients to UTI recurrence. This association appears to be driven primarily by delayed or inappropriate treatment for the index ESC-R EB UTI.

Objective:To determine metrics and provider characteristics associated with inappropriate antibiotic prescribing for respiratory tract diagnoses (RTDs).Design:Retrospective cohort study.Setting:Primary care practices in a university health system.Participants:Patients seen by an attending physician or advanced practice provider (APP) at their primary care office visit with International Classification of Disease, Tenth Revision, Clinical Modification (ICD-10-CM)–coded RTDs.Methods:Medical records were reviewed for 1,200 randomly selected office visits in which an antibiotic was prescribed to determine appropriateness. Based on this gold standard, metrics and provider characteristics associated with inappropriate antibiotic prescribing were determined.Results:Overall, 69% of antibiotics were inappropriate. Metrics utilizing prespecified RTDs most strongly associated with inappropriate prescribing were (1) proportion prescribing for RTDs for which antibiotics are almost never required (eg, bronchitis) and (2) proportion prescribing for any RTD. Provider characteristics associated with inappropriate antibiotic prescribing were APP versus physician (72% vs 58%; P = .02), family medicine versus internal medicine (76% vs 63%; P = .01), board certification 1997 or later versus board certification before 1997 (75% vs 63%; P = .02), nonteaching versus teaching practice (73% vs 51%; P < .01), and nonurban vs urban practice (77% vs 57%; P < .01).Conclusions:Metrics utilizing proportion prescribing for RTDs for which antibiotics are almost never required and proportion prescribing for any RTD were most strongly associated with inappropriate prescribing. APPs and clinicians with family medicine training, with board certification 1997 or later, and who worked in nonteaching or nonurban practices had higher proportions of inappropriate prescribing. These findings could inform design of interventions to improve prescribing and could represent an efficient way to track inappropriate prescribing.

COVID-19 vaccines are a critical tool for controlling the ongoing global pandemic. The Food and Drug Administration (FDA) has issued Emergency Use Authorizations for three COVID-19 vaccines for use in the United States.* In large, randomized-controlled trials, each vaccine was found to be safe and efficacious in preventing symptomatic, laboratory-confirmed COVID-19 (1-3). Despite the high level of vaccine efficacy, a small percentage of fully vaccinated persons (i.e. received all recommended doses of an FDA-authorized COVID-19 vaccine) will develop symptomatic or asymptomatic infections with SARS-CoV-2, the virus that causes COVID-19 (2-8).

We assessed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission between patients in shared rooms in an academic hospital between September 2020 and April 2021. In total, 11 290 patients were admitted to shared rooms, of whom 25 tested positive. Among 31 exposed roommates, 12 (39%) tested positive within 14 days. Transmission was associated with polymerase chain reaction (PCR) cycle thresholds ≤21.

Background Once antibiotic-resistant bacteria become established within the gut microbiota, they can cause infections in the host and be transmitted to other people and the environment. Currently, there are no effective modalities for decreasing or preventing colonization by antibiotic-resistant bacteria. Intestinal microbiota restoration can prevent Clostridioides difficile infection (CDI) recurrences. Another potential application of microbiota restoration is suppression of non-C. difficile multidrug-resistant bacteria and overall decrease in the abundance of antibiotic resistance genes (the resistome) within the gut microbiota. This study characterizes the effects of RBX2660, a microbiota-based investigational therapeutic, on the composition and abundance of the gut microbiota and resistome, as well as multidrug-resistant organism carriage, after delivery to patients suffering from recurrent CDI. Methods An open-label, multi-center clinical trial in 11 centers in the USA for the safety and efficacy of RBX2660 on recurrent CDI was conducted. Fecal specimens from 29 of these subjects with recurrent CDI who received either one (N = 16) or two doses of RBX2660 (N = 13) were analyzed secondarily. Stool samples were collected prior to and at intervals up to 6 months post-therapy and analyzed in three ways: (1) 16S rRNA gene sequencing for microbiota taxonomic composition, (2) whole metagenome shotgun sequencing for functional pathways and antibiotic resistome content, and (3) selective and differential bacterial culturing followed by isolate genome sequencing to longitudinally track multidrug-resistant organisms. Results Successful prevention of CDI recurrence with RBX2660 correlated with taxonomic convergence of patient microbiota to the donor microbiota as measured by weighted UniFrac distance. RBX2660 dramatically reduced the abundance of antibiotic-resistant Enterobacteriaceae in the 2 months after administration. Fecal antibiotic resistance gene carriage decreased in direct relationship to the degree to which donor microbiota engrafted. Conclusions Microbiota-based therapeutics reduce resistance gene abundance and resistant organisms in the recipient gut microbiome. This approach could potentially reduce the risk of infections caused by resistant organisms within the patient and the transfer of resistance genes or pathogens to others. Trial registration ClinicalTrials.gov, NCT01925417; registered on August 19, 2013.

Fitting stochastic transmission models to electronic patient data can offer detailed insights into the transmission of healthcare-associated infections and improve infection control. Pathogen whole-genome sequencing may improve the precision of model inferences, but computational constraints have limited modelling applications predominantly to small datasets and specific outbreaks, whereas large-scale sequencing studies have mostly relied on simple rules for identifying/excluding plausible transmission. We present a novel approach for integrating detailed epidemiological data on patient contact networks in hospitals with large-scale pathogen sequencing data. We apply our approach to study Clostridioides difficile transmission using a dataset of 1223 infections in Oxfordshire, UK, 2007–2011. 262 (21% [95% credibility interval 20–22%]) infections were estimated to have been acquired from another known case. There was heterogeneity by sequence type (ST) in the proportion of cases acquired from another case with the highest rates in ST1 (ribotype-027), ST42 (ribotype-106) and ST3 (ribotype-001). These same STs also had higher rates of transmission mediated via environmental contamination/spores persisting after patient discharge/recovery; for ST1 these persisted longer than for most other STs except ST3 and ST42. We also identified variation in transmission between hospitals, medical specialties and over time; by 2011 nearly all transmission from known cases had ceased in our hospitals. Our findings support previous work suggesting only a minority of C. difficile infections are acquired from known cases but highlight a greater role for environmental contamination than previously thought. Our approach is applicable to other healthcare-associated infections. Our findings have important implications for effective control of C. difficile., Author summary Preventing infections spreading in hospitals is a major priority for healthcare systems globally. Mathematical models can be used with electronic hospital records to reconstruct how infections spread, which in turn can help guide infection control interventions. Sequencing the genetic code (DNA) of the bacteria that cause infections can help to follow when transmission is occurring, as bacterial DNA from two patients infected from the same source will likely be very similar. Our paper describes a new statistical method for combining hospital records and sequencing data to track infections. We use our approach to study over 1200 infections of Clostridioides difficile (C. diff.), which is a common cause of diarrhoea in hospitals. We show that only a minority of infections are acquired from other unwell patients, but the amount of spread varies by the subtype of C. diff involved. We also show that different C. diff subtypes survive in the hospital environment for longer than others and may need enhanced control strategies. We also can detect differences in spread at different hospitals and show that by the end of the study we had largely eliminated transmission of C. diff from unwell patients in our hospitals.

The ongoing COVID-19 pandemic has overwhelmingly demonstrated the need to accurately evaluate the effects of implementing new or altering existing nonpharmaceutical interventions. Since these interventions applied at the societal level cannot be evaluated through traditional experimental means, public health officials and other decision makers must rely on statistical and mathematical epidemiological models. Nonpharmaceutical interventions are typically focused on contacts between members of a population, and yet most epidemiological models rely on homogeneous mixing which has repeatedly been shown to be an unrealistic representation of contact patterns. An alternative approach is individual based models (IBMs), but these are often time intensive and computationally expensive to implement, requiring a high degree of expertise and computational resources. More often, decision makers need to know the effects of potential public policy decisions in a very short time window using limited resources. This paper presents a computation algorithm for an IBM designed to evaluate nonpharmaceutical interventions. By utilizing recursive relationships, our method can quickly compute the expected epidemiological outcomes even for large populations based on any arbitrary contact network. We utilize our methods to evaluate the effects of various mitigation measures in the District of Columbia, USA, at various times and to various degrees.Rcode for our method is provided in the supplementry material, thereby allowing others to utilize our approach for other regions.

We describe coronavirus disease (COVID-19) among US food manufacturing and agriculture workers and provide updated information on meat and poultry processing workers. Among 742 food and agriculture workplaces in 30 states, 8,978 workers had confirmed COVID-19; 55 workers died. Racial and ethnic minority workers could be disproportionately affected by COVID-19.

Multiple guidelines recommend discontinuation of prophylactic antibiotics

This cohort study evaluates the incidence of nosocomial coronavirus disease 2019 (COVID-19) among patients admitted to a US medical center in the context of a comprehensive and progressive infection control program., Key Points Question What is the incidence of hospital-acquired coronavirus disease 2019 (COVID-19) at a large US academic medical center? Findings In this cohort study of 9149 patients admitted to a large US academic medical center over a 12-week period, 697 were diagnosed with COVID-19. In the context of a comprehensive and progressive infection control program, only 2 hospital-acquired cases were detected: 1 patient was likely infected by a presymptomatic spouse before visitor restrictions were implemented, and 1 patient developed symptoms 4 days after a 16-day hospitalization but without known exposures in the hospital. Meaning These findings suggest that overall risk of hospital-acquired COVID-19 was low and that rigorous infection control measures may be associated with minimized risk., Importance Some patients are avoiding essential care for fear of contracting coronavirus disease 2019 (COVID-19) in hospitals. There are few data, however, on the risk of acquiring COVID-19 in US hospitals. Objective To assess the incidence of COVID-19 among patients hospitalized at a large US academic medical center in the 12 weeks after the first inpatient case was identified. Design, Setting, and Participants This cohort study included all patients admitted to Brigham and Women’s Hospital (Boston, Massachusetts) between March 7 and May 30, 2020. Follow-up occurred through June 17, 2020. Medical records for all patients who first tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by reverse-transcription polymerase chain reaction (RT-PCR) on hospital day 3 or later or within 14 days of discharge were reviewed. Exposures A comprehensive infection control program was implemented that included dedicated COVID-19 units with airborne infection isolation rooms, personal protective equipment in accordance with US Centers for Disease Control and Prevention recommendations, personal protective equipment donning and doffing monitors, universal masking, restriction of visitors, and liberal RT-PCR testing of symptomatic and asymptomatic patients. Main Outcomes and Measures Whether infection was community or hospital acquired based on timing of tests, clinical course, and exposures. Results Over the 12-week period, 9149 patients (mean [SD] age, 46.1 [26.4] years; median [IQR] age, 51 years [30-67 years]; 5243 female [57.3%]) were admitted to the hospital, for whom 7394 SARS-CoV-2 RT-PCR tests were performed; 697 COVID-19 cases were confirmed, translating into 8656 days of COVID-19–related care. Twelve of the 697 hospitalized patients with COVID-19 (1.7%) first tested positive on hospital day 3 or later (median, 4 days; range, 3-15 days). Of these, only 1 case was deemed to be hospital acquired, most likely from a presymptomatic spouse who was visiting daily and diagnosed with COVID-19 before visitor restrictions and masking were implemented. Among 8370 patients with non–COVID-19–related hospitalizations discharged through June 17, 11 (0.1%) tested positive within 14 days (median time to diagnosis, 6 days; range, 1-14 days). Only 1 case was deemed likely to be hospital acquired, albeit with no known exposures. Conclusions and Relevance In this cohort study of patients in a large academic medical center with rigorous infection control measures, nosocomial COVID-19 was rare during the height of the pandemic in the region. These findings may inform practices in other institutions and provide reassurance to patients concerned about contracting COVID-19 in hospitals.