Identification of risk factors of Long COVID and predictive modeling in the RECOVER EHR cohorts.

BACKGROUND: SARS-CoV-2-infected patients may develop new conditions in the period after the acute infection. These conditions, the post-acute sequelae of SARS-CoV-2 infection (PASC, or Long COVID), involve a diverse set of organ systems. Limited studies have investigated the predictability of Long COVID development and its associated risk factors. METHODS: In this retrospective cohort study, we used electronic healthcare records from two large-scale PCORnet clinical research networks, INSIGHT (~1.4 million patients from New York) and OneFlorida+ (~0.7 million patients from Florida), to identify factors associated with having Long COVID, and to develop machine learning-based models for predicting Long COVID development. Both SARS-CoV-2-infected and non-infected adults were analysed during the period of March 2020 to November 2021. Factors associated with Long COVID risk were identified by removing background associations and correcting for multiple tests. RESULTS: We observed complex association patterns between baseline factors and a variety of Long COVID conditions, and we highlight that severe acute SARS-CoV-2 infection, being underweight, and having baseline comorbidities (e.g., cancer and cirrhosis) are likely associated with increased risk of developing Long COVID. Several Long COVID conditions, e.g., dementia, malnutrition, chronic obstructive pulmonary disease, heart failure, PASC diagnosis U099, and acute kidney failure are well predicted (C-index > 0.8). Moderately predictable conditions include atelectasis, pulmonary embolism, diabetes, pulmonary fibrosis, and thromboembolic disease (C-index 0.7-0.8). Less predictable conditions include fatigue, anxiety, sleep disorders, and depression (C-index around 0.6). CONCLUSIONS: This observational study suggests that association patterns between investigated factors and Long COVID are complex, and the predictability of different Long COVID conditions varies. However, machine learning-based predictive models can help in identifying patients who are at risk of developing a variety of Long COVID conditions.

Most people who develop COVID-19 make a full recovery, but some go on to develop post-acute sequelae of SARS-CoV-2 infection, commonly known as Long COVID. Up to now, we did not know why some people are affected by Long COVID whilst others are not. We conducted a study to identify risk factors for Long COVID and developed a mathematical modeling approach to predict those at risk. We find that Long COVID is associated with some factors such as experiencing severe acute COVID-19, being underweight, and having conditions including cancer or cirrhosis. Due to the wide variety of symptoms defined as Long COVID, it may be challenging to come up with a set of risk factors that can predict the whole spectrum of Long COVID. However, our approach could be used to predict a variety of Long COVID conditions.

Excess burden of respiratory and abdominal conditions following COVID-19 infections during the ancestral and Delta variant periods in the United States: An EHR-based cohort study from the RECOVER program.

IMPORTANCE: The frequency and characteristics of post-acute sequelae of SARS-CoV-2 infection (PASC) may vary by SARS-CoV-2 variant. OBJECTIVE: To characterize PASC-related conditions among individuals likely infected by the ancestral strain in 2020 and individuals likely infected by the Delta variant in 2021. DESIGN: Retrospective cohort study of electronic medical record data for approximately 27 million patients from March 1, 2020-November 30, 2021. SETTING: Healthcare facilities in New York and Florida. PARTICIPANTS: Patients who were at least 20 years old and had diagnosis codes that included at least one SARS-CoV-2 viral test during the study period. EXPOSURE: Laboratory-confirmed COVID-19 infection, classified by the most common variant prevalent in those regions at the time. MAIN OUTCOME(S) AND MEASURE(S): Relative risk (estimated by adjusted hazard ratio [aHR]) and absolute risk difference (estimated by adjusted excess burden) of new conditions, defined as new documentation of symptoms or diagnoses, in persons between 31-180 days after a positive COVID-19 test compared to persons without a COVID-19 test or diagnosis during the 31-180 days after the last negative test. RESULTS: We analyzed data from 560,752 patients. The median age was 57 years; 60.3% were female, 20.0% non-Hispanic Black, and 19.6% Hispanic. During the study period, 57,616 patients had a positive SARS-CoV-2 test; 503,136 did not. For infections during the ancestral strain period, pulmonary fibrosis, edema (excess fluid), and inflammation had the largest aHR, comparing those with a positive test to those without a COVID-19 test or diagnosis (aHR 2.32 [95% CI 2.09 2.57]), and dyspnea (shortness of breath) carried the largest excess burden (47.6 more cases per 1,000 persons). For infections during the Delta period, pulmonary embolism had the largest aHR comparing those with a positive test to a negative test (aHR 2.18 [95% CI 1.57, 3.01]), and abdominal pain carried the largest excess burden (85.3 more cases per 1,000 persons). CONCLUSIONS AND RELEVANCE: We documented a substantial relative risk of pulmonary embolism and a large absolute risk difference of abdomen-related symptoms after SARS-CoV-2 infection during the Delta variant period. As new SARS-CoV-2 variants emerge, researchers and clinicians should monitor patients for changing symptoms and conditions that develop after infection.

Evaluating completion rates of COVID-19 contact tracing surveys in New York City.

IMPORTANCE: Contact tracing is the process of identifying people who have recently been in contact with someone diagnosed with an infectious disease. During an outbreak, data collected from contact tracing can inform interventions to reduce the spread of infectious diseases. Understanding factors associated with completion rates of contact tracing surveys can help design improved interview protocols for ongoing and future programs. OBJECTIVE: To identify factors associated with completion rates of COVID-19 contact tracing surveys in New York City (NYC) and evaluate the utility of a predictive model to improve completion rates, we analyze laboratory-confirmed and probable COVID-19 cases and their self-reported contacts in NYC from October 1st 2020 to May 10th 2021. METHODS: We analyzed 742,807 case investigation calls made during the study period. Using a log-binomial regression model, we examined the impact of age, time of day of phone call, and zip code-level demographic and socioeconomic factors on interview completion rates. We further developed a random forest model to predict the best phone call time and performed a counterfactual analysis to evaluate the change of completion rates if the predicative model were used. RESULTS: The percentage of contact tracing surveys that were completed was 79.4%, with substantial variations across ZIP code areas. Using a log-binomial regression model, we found that the age of index case (an individual who has tested positive through PCR or antigen testing and is thus subjected to a case investigation) had a significant effect on the completion of case investigation - compared with young adults (the reference group,24 years old < age < = 65 years old), the completion rate for seniors (age > 65 years old) were lower by 12.1% (95%CI: 11.1% - 13.3%), and the completion rate for youth group (age < = 24 years old) were lower by 1.6% (95%CI: 0.6% -2.6%). In addition, phone calls made from 6 to 9 pm had a 4.1% (95% CI: 1.8% - 6.3%) higher completion rate compared with the reference group of phone calls attempted from 12 and 3 pm. We further used a random forest algorithm to assess its potential utility for selecting the time of day of phone call. In counterfactual simulations, the overall completion rate in NYC was marginally improved by 1.2%; however, certain ZIP code areas had improvements up to 7.8%. CONCLUSION: These findings suggest that age and time of day of phone call were associated with completion rates of case investigations. It is possible to develop predictive models to estimate better phone call time for improving completion rates in certain communities.

Reduction of airborne and surface-borne bacteria in a medical center burn intensive care unit using active, upper-room, germicidal ultraviolet (GUV) disinfection.

OBJECTIVE: To determine the effectiveness of active, upper-room, germicidal ultraviolet (GUV) devices in reducing bacterial contamination in patient rooms in air and on surfaces as a supplement to the central heating, ventilation, and air conditioning (HVAC) air handling unit (AHU) with MERV 14 filters and UV-C disinfection. METHODS: This study was conducted in an academic medical center, burn intensive care unit (BICU), for 4 months in 2022. Room occupancy was monitored and recorded. In total, 402 preinstallation and postinstallation bacterial air and non-high-touch surface samples were obtained from 10 BICU patient rooms. Airborne particle counts were measured in the rooms, and bacterial air samples were obtained from the patient-room supply air vents and outdoor air, before and after the intervention. After preintervention samples were obtained, an active, upper-room, GUV air disinfection system was deployed in each of the patient rooms in the BICU. RESULTS: The average levels of airborne bacteria of 395 CFU/m3 before GUV device installation and 37 CFU/m3 after installation indicated an 89% overall decrease (P < .0001). Levels of surface-borne bacteria were associated with a 69% decrease (P < .0001) after GUV device installation. Outdoor levels of airborne bacteria averaged 341 CFU/m3 in March before installation and 676 CFU/m3 in June after installation, but this increase was not significant (P = .517). CONCLUSIONS: Significant reductions in air and surface contamination occurred in all rooms and areas and were not associated with variations in outdoor air concentrations of bacteria. The significant decrease of surface bacteria is an unexpected benefit associated with in-room GUV air disinfection, which can potentially reduce overall bioburden.

Community transmission of SARS-CoV-2 during the Delta wave in New York City.

BACKGROUND: Understanding community transmission of SARS-CoV-2 variants of concern (VOCs) is critical for disease control in the post pandemic era. The Delta variant (B.1.617.2) emerged in late 2020 and became the dominant VOC globally in the summer of 2021. While the epidemiological features of the Delta variant have been extensively studied, how those characteristics shaped community transmission in urban settings remains poorly understood. METHODS: Using high-resolution contact tracing data and testing records, we analyze the transmission of SARS-CoV-2 during the Delta wave within New York City (NYC) from May 2021 to October 2021. We reconstruct transmission networks at the individual level and across 177 ZIP code areas, examine network structure and spatial spread patterns, and use statistical analysis to estimate the effects of factors associated with COVID-19 spread. RESULTS: We find considerable individual variations in reported contacts and secondary infections, consistent with the pre-Delta period. Compared with earlier waves, Delta-period has more frequent long-range transmission events across ZIP codes. Using socioeconomic, mobility and COVID-19 surveillance data at the ZIP code level, we find that a larger number of cumulative cases in a ZIP code area is associated with reduced within- and cross-ZIP code transmission and the number of visitors to each ZIP code is positively associated with the number of non-household infections identified through contact tracing and testing. CONCLUSIONS: The Delta variant produced greater long-range spatial transmission across NYC ZIP code areas, likely caused by its increased transmissibility and elevated human mobility during the study period. Our findings highlight the potential role of population immunity in reducing transmission of VOCs. Quantifying variability of immunity is critical for identifying subpopulations susceptible to future VOCs. In addition, non-pharmaceutical interventions limiting human mobility likely reduced SARS-CoV-2 spread over successive pandemic waves and should be encouraged for reducing transmission of future VOCs.

Excess burden of respiratory and abdominal conditions following COVID-19 infections during the ancestral and Delta variant periods in the United States: An EHR-based cohort study from the RECOVER Program.

Importance: The frequency and characteristics of post-acute sequelae of SARS-CoV-2 infection (PASC) may vary by SARS-CoV-2 variant. Objective: To characterize PASC-related conditions among individuals likely infected by the ancestral strain in 2020 and individuals likely infected by the Delta variant in 2021. Design: Retrospective cohort study of electronic medical record data for approximately 27 million patients from March 1, 2020-November 30, 2021. Setting: Healthcare facilities in New York and Florida. Participants: Patients who were at least 20 years old and had diagnosis codes that included at least one SARS-CoV-2 viral test during the study period. Exposure: Laboratory-confirmed COVID-19 infection, classified by the most common variant prevalent in those regions at the time. Main Outcomes and Measures: Relative risk (estimated by adjusted hazard ratio [aHR]) and absolute risk difference (estimated by adjusted excess burden) of new conditions, defined as new documentation of symptoms or diagnoses, in persons between 31-180 days after a positive COVID-19 test compared to persons with only negative tests during the 31-180 days after the last negative test. Results: We analyzed data from 560,752 patients. The median age was 57 years; 60.3% were female, 20.0% non-Hispanic Black, and 19.6% Hispanic. During the study period, 57,616 patients had a positive SARS-CoV-2 test; 503,136 did not. For infections during the ancestral strain period, pulmonary fibrosis, edema (excess fluid), and inflammation had the largest aHR, comparing those with a positive test to those with a negative test, (aHR 2.32 [95% CI 2.09 2.57]), and dyspnea (shortness of breath) carried the largest excess burden (47.6 more cases per 1,000 persons). For infections during the Delta period, pulmonary embolism had the largest aHR comparing those with a positive test to a negative test (aHR 2.18 [95% CI 1.57, 3.01]), and abdominal pain carried the largest excess burden (85.3 more cases per 1,000 persons). Conclusions and Relevance: We documented a substantial relative risk of pulmonary embolism and large absolute risk difference of abdomen-related symptoms after SARS-CoV-2 infection during the Delta variant period. As new SARS-CoV-2 variants emerge, researchers and clinicians should monitor patients for changing symptoms and conditions that develop after infection.

Planning For The Next Pandemic: Lab Systems Need Policy Shift To Speed Emerging Infectious Disease Warning And Tracking.

Early detection and ongoing monitoring of infectious diseases depends on diagnostic testing. The US has a large, diverse system of public, academic, and private laboratories that develop new diagnostic tests; perform routine testing; and conduct specialized reference testing, such as genomic sequencing. These laboratories operate under a complex mix of laws and regulations at the federal, state, and local levels. The COVID-19 pandemic exposed major weaknesses in the nation's laboratory system, some of which were seen again during the global mpox outbreak in 2022. In this article we review how the US laboratory system has been designed to detect and monitor emerging infections, describe what gaps were revealed during COVID-19, and propose specific steps that policy makers can take both to strengthen the current system and to prepare the US for the next pandemic.

Risk Factors and Predictive Modeling for Post-Acute Sequelae of SARS-CoV-2 Infection: Findings from EHR Cohorts of the RECOVER Initiative.

Background: Patients who were SARS-CoV-2 infected could suffer from newly incidental conditions in their post-acute infection period. These conditions, denoted as the post-acute sequelae of SARS-CoV-2 infection (PASC), are highly heterogeneous and involve a diverse set of organ systems. Limited studies have investigated the predictability of these conditions and their associated risk factors. Method: In this retrospective cohort study, we investigated two large-scale PCORnet clinical research networks, INSIGHT and OneFlorida+, including 11 million patients in the New York City area and 16.8 million patients from Florida, to develop machine learning prediction models for those who are at risk for newly incident PASC and to identify factors associated with newly incident PASC conditions. Adult patients aged 20 with SARS-CoV-2 infection and without recorded infection between March 1st, 2020, and November 30th, 2021, were used for identifying associated factors with incident PASC after removing background associations. The predictive models were developed on infected adults. Results: We find several incident PASC, e.g., malnutrition, COPD, dementia, and acute kidney failure, were associated with severe acute SARS-CoV-2 infection, defined by hospitalization and ICU stay. Older age and extremes of weight were also associated with these incident conditions. These conditions were better predicted (C-index >0.8). Moderately predictable conditions included diabetes and thromboembolic disease (C-index 0.7-0.8). These were associated with a wider variety of baseline conditions. Less predictable conditions included fatigue, anxiety, sleep disorders, and depression (C-index around 0.6). Conclusions: This observational study suggests that a set of likely risk factors for different PASC conditions were identifiable from EHRs, predictability of different PASC conditions was heterogeneous, and using machine learning-based predictive models might help in identifying patients who were at risk of developing incident PASC.

Advancing detection and response capacities for emerging and re-emerging pathogens in Africa.

Recurrent disease outbreaks caused by a range of emerging and resurging pathogens over the past decade reveal major gaps in public health preparedness, detection, and response systems in Africa. Underlying causes of recurrent disease outbreaks include inadequacies in the detection of new infectious disease outbreaks in the community, in rapid pathogen identification, and in proactive surveillance systems. In sub-Saharan Africa, where 70% of zoonotic outbreaks occur, there remains the perennial risk of outbreaks of new or re-emerging pathogens for which no vaccines or treatments are available. As the Ebola virus disease, COVID-19, and mpox (formerly known as monkeypox) outbreaks highlight, a major paradigm shift is required to establish an effective infrastructure and common frameworks for preparedness and to prompt national and regional public health responses to mitigate the effects of future pandemics in Africa.

Contact tracing reveals community transmission of COVID-19 in New York City.

Understanding SARS-CoV-2 transmission within and among communities is critical for tailoring public health policies to local context. However, analysis of community transmission is challenging due to a lack of high-resolution surveillance and testing data. Here, using contact tracing records for 644,029 cases and their contacts in New York City during the second pandemic wave, we provide a detailed characterization of the operational performance of contact tracing and reconstruct exposure and transmission networks at individual and ZIP code scales. We find considerable heterogeneity in reported close contacts and secondary infections and evidence of extensive transmission across ZIP code areas. Our analysis reveals the spatial pattern of SARS-CoV-2 spread and communities that are tightly interconnected by exposure and transmission. We find that locations with higher vaccination coverage and lower numbers of visitors to points-of-interest had reduced within- and cross-ZIP code transmission events, highlighting potential measures for curtailing SARS-CoV-2 spread in urban settings.

The effect of a proof-of-vaccination requirement, incentive payments, and employer-based mandates on COVID-19 vaccination rates in New York City: a synthetic-control analysis.

BACKGROUND: COVID-19 vaccines have been available to all adults in the USA since April, 2021, but many adults remain unvaccinated. We aimed to assess the joint effect of a proof-of-vaccination requirement, incentive payments, and employer-based mandates on rates of adult vaccination in New York City (NYC). METHODS: We constructed a synthetic control group for NYC composed of other counties in the core of large, metropolitan areas in the USA. The vaccination outcomes for NYC were compared against those of the synthetic control group from July 26, 2021, to Nov 1, 2021, to determine the differential effects of the policies. Analyses were conducted on county-level vaccination data reported by the Centers for Disease Control and Prevention. The synthetic control group was constructed by matching on county-level preintervention vaccination outcomes, partisanship, economic attributes, demographics, and metropolitan area population. Statistical inference was conducted using placebo tests for non-treated counties. FINDINGS: The synthetic control group resembled NYC across attributes used in the matching process. The cumulative adult vaccination rate for NYC (in adults aged 18 years or older who received at least one dose of an authorised COVID-19 vaccine) increased from 72·5% to 89·4% (+16·9 percentage points [pp]) during the intervention period, compared with an increase from 72·5% to 83·2% (+10·7 pp) for the synthetic control group, a difference of 6·2 pp (95% CI 1·4-10·7), or 410 201 people (90 966-706 532). Daily vaccinations for NYC were consistently higher than those in the synthetic control group, a pattern that started shortly after the start of the intervention period. INTERPRETATION: The combination of a proof-of-vaccination requirement, incentive payments, and vaccine mandates increased vaccination rates among adults in NYC compared with jurisdictions that did not use the same measures. Whether the impact of these measures occurred by inducing more people to get vaccinated, or by accelerating vaccinations that would have occurred later, the increase in vaccination rates likely averted illness and death. FUNDING: None.

Contact tracing reveals community transmission of COVID-19 in New York City.

Understanding SARS-CoV-2 transmission within and among communities is critical for tailoring public health policies to local context. However, analysis of community transmission is challenging due to a lack of high-resolution surveillance and testing data. Here, using contact tracing records for 644,029 cases and their contacts in New York City during the second pandemic wave, we provide a detailed characterization of the operational performance of contact tracing and reconstruct exposure and transmission networks at individual and ZIP code scales. We find considerable heterogeneity in reported close contacts and secondary infections and evidence of extensive transmission across ZIP code areas. Our analysis reveals the spatial pattern of SARS-CoV-2 spread and communities that are tightly interconnected by exposure and transmission. We find that higher vaccination coverage and reduced numbers of visitors to points-of-interest are associated with fewer within- and cross-ZIP code transmission events, highlighting potential measures for curtailing SARS-CoV-2 spread in urban settings.

Comparison of national antimicrobial treatment guidelines, African Union.

OBJECTIVE: To identify and compare antimicrobial treatment guidelines from African Union (AU) Member States. METHODS: We reviewed national government agency and public health institutes' websites and communicated with country or regional focal points to identify existing treatment guidelines from AU Member States. We included guidelines if they contained disease-, syndrome- or pathogen-specific treatment recommendations and if those recommendations included antimicrobial name or class, dosage and therapy duration. The scope of the review was limited to infections and clinical syndromes that often have a bacterial cause. We assessed treatment guidelines for alignment with the Grading of Recommendations Assessment, Development and Evaluation (GRADE) criteria. We compared treatment recommendations for various common bacterial infections or clinical syndromes described across national guidelines and those described in three World Health Organization guidelines. FINDINGS: We identified 31 treatment guidelines from 20 of the 55 (36%) AU Member States; several countries had more than one treatment guideline that met our inclusion criteria. Fifteen (48%) guidelines from 10 countries have been published or updated since 2015. Methods used to develop the guidelines were not well described. No guidelines were developed according to the GRADE approach. Antimicrobial selection, dosage and duration of recommended therapies varied widely across guidelines for all infections and syndromes. CONCLUSION: AU Member States lack antimicrobial treatment guidelines that meet internationally accepted methods and that draw from local evidence about disease burden and antimicrobial susceptibility.

Antiseizure Medication Withdrawal in Seizure-Free Patients: Practice Advisory Update Summary: Report of the AAN Guideline Subcommittee.

OBJECTIVE: To update a 1996 American Academy of Neurology practice parameter. METHODS: The authors systematically reviewed literature published from January 1991 to March 2020. RESULTS: The long-term (24-60 months) risk of seizure recurrence is possibly higher among adults who have been seizure-free for 2 years and taper antiseizure medications (ASMs) vs those who do not taper ASMs (15% vs 7% per the 1 Class I article addressing this issue). In pediatric patients, there is probably no significant difference in seizure recurrence between those who begin tapering ASMs after 2 years vs 4 years of seizure freedom, and there is insufficient evidence of significant difference in risk of seizure recurrence between those who taper ASMs after 18 months of seizure freedom and those tapering after 24 months. There is insufficient evidence that the rate of seizure recurrence with ASM withdrawal following epilepsy surgery after 1 year of seizure freedom vs after 4 years is not significantly different than maintaining patients on ASMs. An epileptiform EEG in pediatric patients increases the risk of seizure recurrence. ASM withdrawal possibly does not increase the risk of status epilepticus in adults. In seizure-free adults, ASM weaning possibly does not change quality of life. Withdrawal of ASMs at 25% every 10 days to 2 weeks is probably not significantly different from withdrawal at 25% every 2 months in children who are seizure-free in more than 4 years of follow-up. RECOMMENDATIONS: Fourteen recommendations were developed.

Macro level influences on strategic responses to the COVID-19 pandemic - an international survey and tool for national assessments.

BACKGROUND: Variation in the approaches taken to contain the SARS-CoV-2 (COVID-19) pandemic at country level has been shaped by economic and political considerations, technical capacity, and assumptions about public behaviours. To address the limited application of learning from previous pandemics, this study aimed to analyse perceived facilitators and inhibitors during the pandemic and to inform the development of an assessment tool for pandemic response planning. METHODS: A cross-sectional electronic survey of health and non-health care professionals (5 May - 5 June 2020) in six languages, with respondents recruited via email, social media and website posting. Participants were asked to score inhibitors (-10 to 0) or facilitators (0 to +10) impacting country response to COVID-19 from the following domains - Political, Economic, Sociological, Technological, Ecological, Legislative, and wider Industry (the PESTELI framework). Participants were then asked to explain their responses using free text. Descriptive and thematic analysis was followed by triangulation with the literature and expert validation to develop the assessment tool, which was then compared with four existing pandemic planning frameworks. RESULTS: 928 respondents from 66 countries (57% health care professionals) participated. Political and economic influences were consistently perceived as powerful negative forces and technology as a facilitator across high- and low-income countries. The 103-item tool developed for guiding rapid situational assessment for pandemic planning is comprehensive when compared to existing tools and highlights the interconnectedness of the 7 domains. CONCLUSIONS: The tool developed and proposed addresses the problems associated with decision making in disciplinary silos and offers a means to refine future use of epidemic modelling.

COVID-19 Infections Among Students and Staff in New York City Public Schools.

BACKGROUND: The 2019 novel coronavirus disease (COVID-19) pandemic led many jurisdictions to close in-person school instruction. METHODS: We collected data about COVID-19 cases associated with New York City (NYC) public schools from polymerase chain reaction testing performed in each school on a sample of asymptomatic students and staff and from routine reporting. We compared prevalence from testing done in schools to community prevalence estimates from statistical models. We compared cumulative incidence for school-associated cases to all cases reported to the city. School-based contacts were monitored to estimate the secondary attack rate and possible direction of transmission. RESULTS: To assess prevalence, we analyzed data from 234 132 persons tested for severe acute respiratory syndrome coronavirus 2 infection in 1594 NYC public schools during October 9 to December 18, 2020; 986 (0.4%) tested positive. COVID-19 prevalence in schools was similar to or less than estimates of prevalence in the community for all weeks. To assess cumulative incidence, we analyzed data for 2231 COVID-19 cases that occurred in students and staff compared with the 86 576 persons in NYC diagnosed with COVID-19 during the same period; the overall incidence was lower for persons in public schools compared with the general community. Of 36 423 school-based close contacts, 191 (0.5%) subsequently tested positive for COVID-19; the likely index case was an adult for 78.0% of secondary cases. CONCLUSIONS: We found that in-person learning in NYC public schools was not associated with increased prevalence or incidence overall of COVID-19 infection compared with the general community.

Antimicrobial resistance control efforts in Africa: a survey of the role of Civil Society Organisations.

Background: Antimicrobial resistance (AMR) is a growing public health threat in Africa. AMR prevention and control requires coordination across multiple sectors of government and civil society partners. Objectives: To assess the current role, needs, and capacities of CSOs working in AMR in Africa. Methods: We conducted an online survey of 35 CSOs working in 37 countries across Africa. The survey asked about priorities for AMR, current AMR-specific activities, monitoring practices, training needs, and preferences for sharing information on AMR. Further data were gathered on the main roles of the organisations, the length of time engaged in and budget spent on AMR-related activities, and their involvement in the development and implementation of National Action Plans (NAPs). Results were assessed against The Africa Centres for Disease Control and Prevention (Africa CDC) Framework for Antimicrobial Resistance (2018-2023). Results: CSOs with AMR-related activities are working in all four areas of Africa CDC's Framework: improving surveillance, delaying emergence, limiting transmission, and mitigating harm from infections caused by AMR microorganisms. Engagement with the four objectives is mainly through advocacy, followed by accountability and service delivery. There were limited monitoring activities reported by CSOs, with only seven (20%) providing an example metric used to monitor their activities related to AMR, and 27 (80%) CSOs reporting having no AMR-related strategy. Half the CSOs reported engaging with the development and implementation of NAPs; however, only three CSOs are aligning their work with these national strategies. Conclusion: CSOs across Africa are supporting AMR prevention and control, however, there is potential for more engagement. Africa CDC and other government agencies should support the training of CSOs in strategies to control AMR. Tailored training programmes can build knowledge of AMR, capacity for monitoring processes, and facilitate further identification of CSOs' contribution to the AMR Framework and alignment with NAPs and regional strategies.

Healthcare-associated outbreaks of bacterial infections in Africa, 2009-2018: A review.

BACKGROUND: Healthcare-associated infections (HAIs) are a major global public health problem, increasing the transmission of drug-resistant infections. In Africa, the prevalence of HAIs among all hospital inpatients is estimated to be between 3% and 15%, but outbreaks are infrequently reported. Failure to detect and/or report outbreaks can increase the risk of ongoing infections and recurrent outbreaks. METHODS: A search of the PubMed, Web of Science, Cochrane Library, and other outbreak databases was performed to identify published literature on bacterial HAI outbreaks in Africa (January 2009 to December 2018). Details of the outbreak characteristics, hospital environment, and the control measures implemented were extracted. RESULTS: Twenty-two studies published over the 10-year period were identified. These reported 31 distinct outbreaks and a total of 31 causative pathogens, including Klebsiella pneumoniae (six outbreaks, 19%), Staphylococcus aureus (six outbreaks, 19%), and Enterococcus (five outbreaks, 16%). Most outbreaks were reported from university (n = 8, 26%) and tertiary hospitals (n = 11, 55%), from South Africa (n = 9, 41%) and Tunisia (n = 4, 18%). Interventions to control the outbreaks were described in 27 (90%) outbreaks, and all instituted or recommended enhancing hand hygiene and education. CONCLUSIONS: Few facilities in Africa reported HAI outbreaks over the 10-year period, suggesting substantial under-detection and under-reporting. The quality and timeliness of reporting require improvement to ensure changes in public health practice.