Estimation of the impact of hospital-onset SARS-CoV-2 infections on length of stay in English hospitals using causal inference.

BACKGROUND: From March 2020 through August 2021, 97,762 hospital-onset SARS-CoV-2 infections were detected in English hospitals. Resulting excess length of stay (LoS) created a potentially substantial health and economic burden for patients and the NHS, but we are currently unaware of any published studies estimating this excess. METHODS: We implemented appropriate causal inference methods to determine the extent to which observed additional hospital stay is attributable to the infection rather than the characteristics of the patients. Hospital admissions records were linked to SARS-CoV-2 test data to establish the study population (7.5 million) of all non-COVID-19 admissions to English hospitals from 1st March 2020 to 31st August 2021 with a stay of at least two days. The excess LoS due to hospital-onset SARS-CoV-2 infection was estimated as the difference between the mean LoS observed and in the counterfactual where infections do not occur. We used inverse probability weighted Kaplan-Meier curves to estimate the mean survival time if all hospital-onset SARS-CoV-2 infections were to be prevented, the weights being based on the daily probability of acquiring an infection. The analysis was carried out for four time periods, reflecting phases of the pandemic differing with respect to overall case numbers, testing policies, vaccine rollout and prevalence of variants. RESULTS: The observed mean LoS of hospital-onset cases was higher than for non-COVID-19 hospital patients by 16, 20, 13 and 19 days over the four phases, respectively. However, when the causal inference approach was used to appropriately adjust for time to infection and confounding, the estimated mean excess LoS caused by hospital-onset SARS-CoV-2 was: 2.0 [95% confidence interval 1.8-2.2] days (Mar-Jun 2020), 1.4 [1.2-1.6] days (Sep-Dec 2020); 0.9 [0.7-1.1] days (Jan-Apr 2021); 1.5 [1.1-1.9] days (May-Aug 2021). CONCLUSIONS: Hospital-onset SARS-CoV-2 is associated with a small but notable excess LoS, equivalent to 130,000 bed days. The comparatively high LoS observed for hospital-onset COVID-19 patients is mostly explained by the timing of their infections relative to admission. Failing to account for confounding and time to infection leads to overestimates of additional length of stay and therefore overestimates costs of infections, leading to inaccurate evaluations of control strategies.

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

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 31st 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.

Quantifying the contribution of pathways of nosocomial acquisition of COVID-19 in English hospitals.

BACKGROUND: Despite evidence of the nosocomial transmission of novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in hospitals worldwide, the contributions of the pathways of transmission are poorly quantified. METHODS: We analysed national records of hospital admissions and discharges, linked to data on SARS-CoV-2 testing, using an individual-based model that considers patient-to-patient, patient-to-healthcare worker (HCW), HCW-to-patient and HCW-to-HCW transmission. RESULTS: Between 1 March 2020 and 31 December 2020, SARS-CoV-2 infections that were classified as nosocomial were identified in 0.5% (0.34-0.74) of patients admitted to an acute National Health Service trust. We found that the most likely route of nosocomial transmission to patients was indirect transmission from other infected patients, e.g. through HCWs acting as vectors or contaminated fomites, followed by direct transmission between patients in the same bay. The risk of transmission to patients from HCWs over this time period is low, but can contribute significantly when the number of infected inpatients is low. Further, the risk of a HCW acquiring SARS-CoV-2 in hospital is approximately equal to that in the community, thereby doubling their overall risk of infection. The most likely route of transmission to HCWs is transmission from other infected HCWs. CONCLUSIONS: Current control strategies have successfully reduced the transmission of SARS-CoV-2 between patients and HCWs. In order to reduce the burden of nosocomial COVID-19 infections on health services, stricter measures should be enforced that would inhibit the spread of the virus between bays or wards in the hospital. There should also be a focus on inhibiting the spread of SARS-CoV-2 between HCWs. The findings have important implications for infection-control procedures in hospitals.

Effectiveness of infection prevention and control interventions, excluding personal protective equipment, to prevent nosocomial transmission of SARS-CoV-2: a systematic review and call for action.

Many infection prevention and control (IPC) interventions have been adopted by hospitals to limit nosocomial transmission of SARS-CoV-2. The aim of this systematic review is to identify evidence on the effectiveness of these interventions. We conducted a literature search of five databases (OVID MEDLINE, Embase, CENTRAL, COVID-19 Portfolio (pre-print), Web of Science). SWIFT ActiveScreener software was used to screen English titles and abstracts published between 1st January 2020 and 6th April 2021. Intervention studies, defined by Cochrane Effective Practice and Organisation of Care, that evaluated IPC interventions with an outcome of SARS-CoV-2 infection in either patients or healthcare workers were included. Personal protective equipment (PPE) was excluded as this intervention had been previously reviewed. Risks of bias were assessed using the Cochrane tool for randomised trials (RoB2) and non-randomized studies of interventions (ROBINS-I). From 23,156 screened articles, we identified seven articles that met the inclusion criteria, all of which evaluated interventions to prevent infections in healthcare workers and the majority of which were focused on effectiveness of prophylaxes. Due to heterogeneity in interventions, we did not conduct a meta-analysis. All agents used for prophylaxes have little to no evidence of effectiveness against SARS-CoV-2 infections. We did not find any studies evaluating the effectiveness of interventions including but not limited to screening, isolation and improved ventilation. There is limited evidence from interventional studies, excluding PPE, evaluating IPC measures for SARS-CoV-2. This review calls for urgent action to implement such studies to inform policies to protect our most vulnerable populations and healthcare workers.

Ct threshold values, a proxy for viral load in community SARS-CoV-2 cases, demonstrate wide variation across populations and over time.

Background: Information on SARS-CoV-2 in representative community surveillance is limited, particularly cycle threshold (Ct) values (a proxy for viral load). Methods: We included all positive nose and throat swabs 26 April 2020 to 13 March 2021 from the UK's national COVID-19 Infection Survey, tested by RT-PCR for the N, S, and ORF1ab genes. We investigated predictors of median Ct value using quantile regression. Results: Of 3,312,159 nose and throat swabs, 27,902 (0.83%) were RT-PCR-positive, 10,317 (37%), 11,012 (40%), and 6550 (23%) for 3, 2, or 1 of the N, S, and ORF1ab genes, respectively, with median Ct = 29.2 (~215 copies/ml; IQR Ct = 21.9-32.8, 14-56,400 copies/ml). Independent predictors of lower Cts (i.e. higher viral load) included self-reported symptoms and more genes detected, with at most small effects of sex, ethnicity, and age. Single-gene positives almost invariably had Ct > 30, but Cts varied widely in triple-gene positives, including without symptoms. Population-level Cts changed over time, with declining Ct preceding increasing SARS-CoV-2 positivity. Of 6189 participants with IgG S-antibody tests post-first RT-PCR-positive, 4808 (78%) were ever antibody-positive; Cts were significantly higher in those remaining antibody negative. Conclusions: Marked variation in community SARS-CoV-2 Ct values suggests that they could be a useful epidemiological early-warning indicator. Funding: Department of Health and Social Care, National Institutes of Health Research, Huo Family Foundation, Medical Research Council UK; Wellcome Trust.

Preferences for Medical Consultations from Online Providers: Evidence from a Discrete Choice Experiment in the United Kingdom.

BACKGROUND: In the UK, consultations for prescription medicines are available via private providers such as online pharmacies. However, these providers may have lower thresholds for prescribing certain drugs. This is a particular concern for antibiotics, given the increasing burden of antimicrobial resistance. Public preferences for consultations with online providers are unknown, hence the impact of increased availability of online consultations on antibiotic use and population health is unclear. OBJECTIVE: To conduct a discrete choice experiment survey to understand UK public preferences for seeking online consultations, and the factors that influence these preferences, in the context of having symptoms for which antibiotics may be appropriate. METHODS: In a survey conducted between July and August 2018, general population respondents completed 16 questions in which they chose a primary care consultation via either their local medical centre or an online provider. Consultations were described in terms of five attributes, including cost and similarity to traditional 'face-to-face' appointments. Choices were modelled using regression analysis. RESULTS: Respondents (n = 734) placed a high value on having a consultation via their local medical centre rather than an online provider, and a low value on consultations by phone or video. However, respondents characterised as 'busy young professionals' showed a lower strength of preference for traditional consultations, with a higher concern for convenience. CONCLUSION: Before COVID-19, the UK public had limited appetite for consultations with online providers, or for consultations that were not face-to-face. Nevertheless, prescriptions from online providers should be monitored going forward, particularly for antibiotics, and in key patient groups.

Community prevalence of SARS-CoV-2 in England from April to November, 2020: results from the ONS Coronavirus Infection Survey.

BACKGROUND: Decisions about the continued need for control measures to contain the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) rely on accurate and up-to-date information about the number of people testing positive for SARS-CoV-2 and risk factors for testing positive. Existing surveillance systems are generally not based on population samples and are not longitudinal in design. METHODS: Samples were collected from individuals aged 2 years and older living in private households in England that were randomly selected from address lists and previous Office for National Statistics surveys in repeated cross-sectional household surveys with additional serial sampling and longitudinal follow-up. Participants completed a questionnaire and did nose and throat self-swabs. The percentage of individuals testing positive for SARS-CoV-2 RNA was estimated over time by use of dynamic multilevel regression and poststratification, to account for potential residual non-representativeness. Potential changes in risk factors for testing positive over time were also assessed. The study is registered with the ISRCTN Registry, ISRCTN21086382. FINDINGS: Between April 26 and Nov 1, 2020, results were available from 1 191 170 samples from 280 327 individuals; 5231 samples were positive overall, from 3923 individuals. The percentage of people testing positive for SARS-CoV-2 changed substantially over time, with an initial decrease between April 26 and June 28, 2020, from 0·40% (95% credible interval 0·29-0·54) to 0·06% (0·04-0·07), followed by low levels during July and August, 2020, before substantial increases at the end of August, 2020, with percentages testing positive above 1% from the end of October, 2020. Having a patient-facing role and working outside your home were important risk factors for testing positive for SARS-CoV-2 at the end of the first wave (April 26 to June 28, 2020), but not in the second wave (from the end of August to Nov 1, 2020). Age (young adults, particularly those aged 17-24 years) was an important initial driver of increased positivity rates in the second wave. For example, the estimated percentage of individuals testing positive was more than six times higher in those aged 17-24 years than in those aged 70 years or older at the end of September, 2020. A substantial proportion of infections were in individuals not reporting symptoms around their positive test (45-68%, dependent on calendar time. INTERPRETATION: Important risk factors for testing positive for SARS-CoV-2 varied substantially between the part of the first wave that was captured by the study (April to June, 2020) and the first part of the second wave of increased positivity rates (end of August to Nov 1, 2020), and a substantial proportion of infections were in individuals not reporting symptoms, indicating that continued monitoring for SARS-CoV-2 in the community will be important for managing the COVID-19 pandemic moving forwards. FUNDING: Department of Health and Social Care.