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The contribution of hospital-acquired infections to the COVID-19 epidemic in England in the first half of 2020.

ABSTRACT: Background SARS-CoV-2 is known to transmit in hospital settings, but the contribution of infections acquired in hospitals to the epidemic at a national scale is unknown. Methods We used comprehensive national English datasets to determine the number of COVID-19 patients with identified hospital-acquired infections (with symptom onset >7 days after admission and before discharge) in acute English hospitals up to August 2020. As patients may leave the hospital prior to detection of infection or have rapid symptom onset, we combined measures of the length of stay and the incubation period distribution to estimate how many hospital-acquired infections may have been missed. We used simulations to estimate the total number (identified and unidentified) of symptomatic hospital-acquired infections, as well as infections due to onward community transmission from missed hospital-acquired infections, to 31 ^st July 2020. Results In our dataset of hospitalised COVID-19 patients in acute English hospitals with a recorded symptom onset date (n = 65,028), 7% were classified as hospital-acquired. We estimated that only 30% (range across weeks and 200 simulations: 20-41%) of symptomatic hospital-acquired infections would be identified, with up to 15% (mean, 95% range over 200 simulations: 14.1%-15.8%) of cases currently classified as community-acquired COVID-19 potentially linked to hospital transmission. We estimated that 26,600 (25,900 to 27,700) individuals acquired a symptomatic SARS-CoV-2 infection in an acute Trust in England before 31st July 2020, resulting in 15,900 (15,200-16,400) or 20.1% (19.2%-20.7%) of all identified hospitalised COVID-19 cases. Conclusions Transmission of SARS-CoV-2 to hospitalised patients likely caused approximately a fifth of identified cases of hospitalised COVID-19 in the "first wave" in England, but less than 1% of all infections in England. Using time to symptom onset from admission for inpatients as a detection method likely misses a substantial proportion (>60%) of hospital-acquired infections.

SUBMITTER: Knight G

PROVIDER: S-EPMC8902876 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundFew small studies have described hospital-acquired infections (HAIs) occurring in patients with COVID-19.Research questionWhat characteristics in critically ill patients with COVID-19 are associated with HAIs and how are HAIs associated with outcomes in these patients?Study design and methodsMulticenter retrospective analysis of prospectively collected data including adult patients with severe COVID-19 admitted to eight Italian hub hospitals from February 20, 2020, through May 20, 2020. Descriptive statistics and univariate and multivariate Weibull regression models were used to assess incidence, microbial cause, resistance patterns, risk factors (ie, demographics, comorbidities, exposure to medication), and impact on outcomes (ie, ICU discharge, length of ICU and hospital stays, and duration of mechanical ventilation) of microbiologically confirmed HAIs.ResultsOf the 774 included patients, 359 patients (46%) demonstrated 759 HAIs (44.7 infections/1,000 ICU patient-days; 35% multidrug-resistant [MDR] bacteria). Ventilator-associated pneumonia (VAP; n = 389 [50%]), bloodstream infections (BSIs; n = 183 [34%]), and catheter-related BSIs (n = 74 [10%]) were the most frequent HAIs, with 26.0 (95% CI, 23.6-28.8) VAPs per 1,000 intubation-days, 11.7 (95% CI, 10.1-13.5) BSIs per 1,000 ICU patient-days, and 4.7 (95% CI, 3.8-5.9) catheter-related BSIs per 1,000 ICU patient-days. Gram-negative bacteria (especially Enterobacterales) and Staphylococcus aureus caused 64% and 28% of cases of VAP, respectively. Variables independently associated with infection were age, positive end expiratory pressure, and treatment with broad-spectrum antibiotics at admission. Two hundred thirty-four patients (30%) died in the ICU (15.3 deaths/1,000 ICU patient-days). Patients with HAIs complicated by septic shock showed an almost doubled mortality rate (52% vs 29%), whereas noncomplicated infections did not affect mortality. HAIs prolonged mechanical ventilation (median, 24 days [interquartile range (IQR), 14-39 days] vs 9 days [IQR, 5-13 days]; P < .001), ICU stay (24 days [IQR, 16-41 days] vs 9 days [IQR, 6-14 days]; P = .003), and hospital stay (42 days [IQR, 25-59 days] vs 23 days [IQR, 13-34 days]; P < .001).InterpretationCritically ill patients with COVID-19 are at high risk for HAIs, especially VAPs and BSIs resulting from MDR organisms. HAIs prolong mechanical ventilation and hospitalization, and HAIs complicated by septic shock almost double mortality.Trial registryClinicalTrials.gov; No.: NCT04388670; URL: www.clinicaltrials.gov.

Project description:BackgroundHospital settings are at increased risk of spreading Coronavirus Disease 2019 (COVID-19) infections, hence non-pharmaceutical prevention interventions (NPPIs) and prioritized vaccination of healthcare workers and resident patients are critical. The status of COVID-19 hospital acquired infections (HAIs) in low-income settings is unclear. We aimed to identify and summarize the existing evidence on COVID-19 HAIs amongst patients, prior to the rollout of vaccines in countries worldwide.MethodsWe conducted a scoping review of English peer-reviewed literature in PubMed, Web of Science and Scopus using a combination of selected search terms. Full texts articles presenting results on COVID-19 HAIs in hospitalised patients before the rollout of vaccines in countries worldwide were eligible. Data extracted from eligible articles included estimates of COVID-19 HAIs, country, and type of hospital setting, and was summarized narratively. Quality assessment of included articles was not possible.ResultsLiterature searches generated a total of 5920 articles, and 45 were eligible for analysis. Eligible articles were from Europe, North America, Asia, and Brazil and none were from low-income countries. The proportion of COVID-19 HAIs ranged from 0% when strict NPPIs were applied, to 65% otherwise. The estimates of COVID-19 HAIs did not differ by country but were lower in studies conducted after implementation of NPPIs and in specialized hospital settings for operative surgery. Studies conducted before the implementation of NPPIs or in long-term care and psychiatric wards often reported high estimates of HAI. Although there was no clear trend in general wards, those situated in academic hospitals managed to reduce HAI rates under strict NPPI protocols. Operative surgery settings, unlike psychiatric settings, effectively prevented COVID-19 HAI using tailored NPPIs.ConclusionThe available evidence shows a high risk of COVID-19 HAIs, the feasibility of preventing HAIs in different healthcare settings and the importance of appropriately tailored NPPIs. There were no data from low-income settings, therefore, it is unclear whether the reported NPPI approaches could be equally effective elsewhere. We recommend routine monitoring of COVID-19 HAIs in countries with low vaccination coverage, to identify and close gaps in NPPIs and understand gains made from vaccinating healthcare workers and hospitalized patients.

Project description:BackgroundUnderstanding the characteristics and natural history of novel pathogens is crucial to inform successful control measures. Japan was one of the first affected countries in the COVID-19 pandemic reporting their first case on 14 January 2020. Interventions including airport screening, contact tracing, and cluster investigations were quickly implemented. Here we present insights from the first 3 months of the epidemic in Japan based on detailed case data.MethodsWe conducted descriptive analyses based on information systematically extracted from individual case reports from 13 January to 31 March 2020 including patient demographics, date of report and symptom onset, symptom progression, travel history, and contact type. We analysed symptom progression and estimated the time-varying reproduction number, Rt, correcting for epidemic growth using an established Bayesian framework. Key delays and the age-specific probability of transmission were estimated using data on exposures and transmission pairs.ResultsThe corrected fitted mean onset-to-reporting delay after the peak was 4 days (standard deviation: ± 2 days). Early transmission was driven primarily by returning travellers with Rt peaking at 2.4 (95% CrI: 1.6, 3.3) nationally. In the final week of the trusted period (16-23 March 2020), Rt accounting for importations diverged from overall Rt at 1.1 (95% CrI: 1.0, 1.2) compared to 1.5 (95% CrI: 1.3, 1.6), respectively. Household (39.0%) and workplace (11.6%) exposures were the most frequently reported potential source of infection. The estimated probability of transmission was assortative by age with individuals more likely to infect, and be infected by, contacts in a similar age group to them. Across all age groups, cases most frequently onset with cough, fever, and fatigue. There were no reported cases of patients < 20 years old developing pneumonia or severe respiratory symptoms.ConclusionsInformation collected in the early phases of an outbreak are important in characterising any novel pathogen. The availability of timely and detailed data and appropriate analyses is critical to estimate and understand a pathogen's transmissibility, high-risk settings for transmission, and key symptoms. These insights can help to inform urgent response strategies.

Project description:ObjectivesTo develop a regional model of COVID-19 dynamics for use in estimating the number of infections, deaths and required acute and intensive care (IC) beds using the South West England (SW) as an example case.DesignOpen-source age-structured variant of a susceptible-exposed-infectious-recovered compartmental mathematical model. Latin hypercube sampling and maximum likelihood estimation were used to calibrate to cumulative cases and cumulative deaths.SettingSW at a time considered early in the pandemic, where National Health Service authorities required evidence to guide localised planning and support decision-making.ParticipantsPublicly available data on patients with COVID-19.Primary and secondary outcome measuresThe expected numbers of infected cases, deaths due to COVID-19 infection, patient occupancy of acute and IC beds and the reproduction ('R') number over time.ResultsSW model projections indicate that, as of 11 May 2020 (when 'lockdown' measures were eased), 5793 (95% credible interval (CrI) 2003 to 12 051) individuals were still infectious (0.10% of the total SW population, 95% CrI 0.04% to 0.22%), and a total of 189 048 (95% CrI 141 580 to 277 955) had been infected with the virus (either asymptomatically or symptomatically), but recovered, which is 3.4% (95% CrI 2.5% to 5.0%) of the SW population. The total number of patients in acute and IC beds in the SW on 11 May 2020 was predicted to be 701 (95% CrI 169 to 1543) and 110 (95% CrI 8 to 464), respectively. The R value in SW was predicted to be 2.6 (95% CrI 2.0 to 3.2) prior to any interventions, with social distancing reducing this to 2.3 (95% CrI 1.8 to 2.9) and lockdown/school closures further reducing the R value to 0.6 (95% CrI 0.5 to 0.7).ConclusionsThe developed model has proved a valuable asset for regional healthcare services. The model will be used further in the SW as the pandemic evolves, and-as open-source software-is portable to healthcare systems in other geographies.

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The contribution of hospital-acquired infections to the COVID-19 epidemic in England in the first half of 2020.

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