key: cord-269408-6qncy0nd
authors: Khonyongwa, Kirstin; Taori, Surabhi K.; Soares, Ana; Desai, Nergish; Sudhanva, Malur; Bernal, William; Schelenz, Silke; Curran, Lisa A.
title: Incidence and outcomes of healthcare-associated COVID-19 infections: significance of delayed diagnosis and correlation with staff absence
date: 2020-10-13
journal: J Hosp Infect
DOI: 10.1016/j.jhin.2020.10.006
sha: 
doc_id: 269408
cord_uid: 6qncy0nd

BACKGROUND: The sudden increase in COVID-19 admissions in hospitals during the SARS-CoV2 pandemic of 2020 led to onward transmissions among vulnerable inpatients. AIMS: This study was performed to evaluate the prevalence and clinical outcomes of Healthcare-associated COVID-19 infections (HA-COVID-19) during the 2020 epidemic and study factors which may promote or correlate with its incidence and transmission in a Teaching Hospital NHS Trust in London, England. METHODS: Electronic laboratory, patient and staff self-reported sickness records were interrogated from 1(st) March to 18(th) April 2020. HA-COVID-19 was defined as COVID-19 with symptom onset >14 days of admission. Test performance of a single combined throat and nose swab (CTNS) for patient placement was calculated. The effect of delayed RNA positivity (DRP, defined as >48h delay), staff self-reported COVID-19 sickness absence, hospital bed occupancy, and community incidence of COVID-19 was compared for HA-COVID-19. The incidence of other significant hospital-acquired bacterial infections (HAB) was compared to previous years. RESULTS: 58 HA-COVID-19 (7.1%) cases were identified. When compared to community-acquired admitted cases (CA-COVID-19), significant differences were observed in age (p=0.018), ethnicity (p<0.001) and comorbidity burden (p<0.001) but not in 30 d mortality. CTNS negative predictive value was 60.3%. DRP was associated with greater mortality (p=0.034) and incidence of HA-COVID-19 correlated positively with DRP (R=0.7108) and staff sickness absence (R=0.7815). For the study period HAB rates were similar to previous 2 years. CONCLUSION: Early diagnosis and isolation of COVID-19 patients would help reduce transmission. A single CTNS has limited value in segregating patients into positive and negative pathways.

The COVID-19 (SARS-CoV-2) outbreak started in China in late December 2019 and was declared a Public Health Emergency of International Concern on 30 th January, 2020. The first cases identified in the UK were in international travellers but local transmission was soon observed. London experienced the largest number of COVID-19 cases in any UK region (1) while a large part of the total burden of disease was in south-east London.

To increase capacity for intensive care of severely ill COVID-19 patients in our hospital, elective work was minimised. Subsequently specific wards and intensive care units (ICUs) became cohort areas for affected patients and only non COVID-19 emergency work continued. Due to the high prevalence of infection during the peak of the outbreak, one of the suggested strategies to prevent healthcare transmission was to screen all patients on admission with a single combined nose and throat swab. This was assessed for SARS-CoV-2 RNA to enable segregation into COVID-19 positive and non COVID-19 cohort wards.

Recent publications have identified advanced age, comorbidities and male gender as major risk factors for severity and mortality in COVID-19 (2, 3) and the impact of ethnicity is being explored (4) . Healthcare-associated COVID-19 infections (HA-COVID-19) have been reported in other studies (5) but the literature on epidemiology, risk factors and outcomes of acquisition is lacking.

This study was performed to determine the burden, risk factors and clinical outcomes of adult HA-COVID-19 infections and evaluate factors which may correlate with the incidence and transmission of HA-COVID-19. Factors studied included the utility of a single combined throat and nose swab (CTNS) for patient placement, delayed RNA positivity (DRP), selfreported COVID-19 sickness absence among hospital staff, total hospital bed occupancy, community incidence of COVID-19 (CIC19) and the change in incidence of other significant hospital-acquired bacterial infections (HAB).

Setting: This study was conducted at the main site of a tertiary care teaching hospital in south-east London from 1 st March to 18 th April 2020. Before the COVID-19 outbreak, the capacity was 960 beds including 73 adult intensive care beds. The hospital caters to a wide mix of specialities including haemato-oncology, liver transplantation, neurosciences, women's health, paediatrics, renal, respiratory and endocrinology and serves a socioeconomically deprived region of London.

All patients who were SARS-CoV-2 RNA positive from a respiratory sample and who were admitted to hospital for at least an overnight stay were included in the study.

Case definitions: J o u r n a l P r e -p r o o f COVID-19 infection: either clinical or radiological evidence of pneumonia or acute respiratory distress syndrome, or influenza-like illness with fever ≥37.8°C and acute onset of at least one of the following: persistent cough (with or without sputum), hoarseness, nasal discharge or congestion, shortness of breath, sore throat, wheezing, sneezing within the PHE estimated incubation period (range 1 to 14 days, median 5 days) (6, 7) .

Community-associated COVID-19 infection (CA-COVID-19):

1. All symptoms on admission in keeping with above symptoms of COVID-19 AND 2. Respiratory sample positive for SARS-CoV-2 RNA at some point in their admission corresponding with the above symptoms Infection prevention and control (IPC) measures: All patients suspected of having a respiratory illness compatible with COVID-19 were triaged at the emergency department. If inpatient care was required, they were admitted to a ward designated as a holding area (18 en-suite single rooms) while awaiting results of investigations. If a swab was SARS-CoV-2 RNA positive, then they were kept in a side room with IPC precautions or in a designated COVID-19 cohort ward/ICU. If the swab was SARS-CoV-2 RNA negative, they were placed with other non-COVID-19 patients.

Local SARS-CoV-2 RNA testing by real time PCR with mean turn-around time of 6 hours was introduced on 29th February 2020. This enabled rapid movement of patients from the holding area. When the requirement for beds with ventilator support increased, other areas of the hospital were repurposed to accommodate 102 ICU beds.

Negative pressure rooms were limited in the hospital and a risk assessment was done to reduce the impact of aerosols. A separate area for donning and doffing personal protective equipment (PPE) was established for each ward. When side room availability reached capacity, parts of the ward were created as cohort areas with bed spaces segregated from the other, curtains always to be closed, and where possible, allotted a separate toilet and dedicated nurse. There is a large variation in the design of each ward in the hospital. Cohort areas were segregated by at least a passageway and the beds allocated to COVID-19 patients clearly signposted as the cohort area. For the majority of the duration of the study, whole wards were either COVID-19 cohort wards or non COVID-19 wards. Staff members were advised to wear PPE and FFP3 masks as appropriate. Training and mask fit testing sessions were organised continuously from February 2020. Public Health England (PHE) guidance and updates were followed for all other aspects of infection prevention and control.

Cleaning of environmental surfaces and clinical equipment was implemented as per PHE recommendations. Curtains were changed if a non COVID-19 patient was to be admitted to the bed space vacated by a COVID-19 patient.

When a HA-COVID-19 case was identified, actions included staff refresher training for correct PPE usage, rapid transfer of patients to a COVID-19 positive cohort ward, deep cleaning (washing walls and carpets) followed by increasing the cleaning frequency until no further transmission was seen (defined as no new symptom onset within 2 weeks of last known case and in haematology and geriatrics a CNTS was tested for SARS-CoV-2 RNA twice weekly for all contacts up to 2 weeks from last positive case regardless of symptoms).

Virological methods: A combined throat and nose swab (CTNS) in VTM, or respiratory fluid such as bronchoalveolar lavage (BAL), were assessed using RdRp gene for SARS CoV-2 RNA. Local interpretation of PCR curves was performed using PCR: AI machine-learning software

Clinical, laboratory and outcome data: SARS-CoV-2 RNA results data was extracted from the laboratory information management system (WinPath) and clinical and demographic details from electronic patient records (EPR). Age, gender and ethnicity (as Black Asian and Minority Ethnicities (BAME) or non-BAME), chronic kidney disease (CKD), hypertension, malignancy, dementia, chronic obstructive pulmonary disease (COPD), diabetes and the Charlson Comorbidity Index (CCI-age adjusted) were noted. Patients without electronic clinical records were excluded. Patients were followed up for 30 days. Duration of hospital stay, readmission after discharge and 7, 14 and 30d mortality were recorded. ICU admission within 7 days of COVID-19 diagnosis was used as a marker for severe disease and a subset analysis was performed.

Test performance of single CNTS: This was calculated to evaluate the efficiency of a single CTNS on admission to segregate patients into positive and negative pathways. True positives were all cases fitting the definition of CA-COVID-19 and positive on the first CTNS (taken within 48h of admission), true negatives were patients with a negative CTNS swab within 48h of admission and had a minimum follow up stay of 14 days wherein they remained SARS-CoV-2 RNA negative and /or did not develop symptoms of COVID-19. All positive PCR reactivity was reviewed by virologists before releasing the results to rule out contamination. If contamination was suspected samples were retested on a new PCR run and a repeat sample was requested. Hence false positives were ruled out prior to result authorisation. False negatives were all cases fitting the definition of CA-COVID-19 (and RNA positive after 48h) but were negative on the first CTNS (within 48h).

Delayed SARS-CoV-2 RNA positivity (DRP) was defined as: The outcomes of patients with DRP were compared with those without a delay.

Source and incubation period of HA-COVID-19 and late indeterminate cases: If an index case patient was found in the same ward as a HA-COVID-19 case (within 14d prior) they were recorded as a potential source. Duration of exposure of each HA-COVID-19 case to a known positive patient was calculated and the incubation period was determined using the midpoint of the exposure period up to the development of symptoms and expressed as a range from the earliest and latest contact with the known positive. Asymptomatic cases were excluded from this analysis.

Staff self-reported absence: The Workforce Development database (Health-Roster) was interrogated for staff absences due to 1: COVID-19, 2: cold, cough and flu-like illness, 3: chest and respiratory problems. This did not include those self-isolating or shielding as per UK government advice (9) . Absences due to 2 and 3 above from 2019 were evaluated for comparison. Staff were grouped as per patient facing roles (G1 -nurses, doctors, additional clinical services and allied health professionals), non-patient facing, high nosocomial exposure risk (G2 -estates and ancillary, Scientific and Technical and Healthcare Scientists) and non-clinical (G3 -Administrative and Clerical) to compare association with healthcare contact. Only one episode of sickness was recorded per 14 day period for each staff member. Staff members deployed to other roles were excluded. Records for staff not directly employed by the hospital Trust (e.g. porters and catering staff) were not available.

Hospital bed occupancy data, CIC19 and HAB : Bed occupancy was derived from the Business Intelligence Unit of the Hospital and community incidence of COVID-19 (CIC19) was derived from national population data (10) and Department of Health reports of COVID-19 cases (1). HAB included bloodstream infections due to Staphylococcus aureus (MRSA and MSSA), Escherichia coli, Klebsiella spp, Pseudomonas aeruginosa, and vancomycin-resistant enterococci and toxigenic Clostridioides difficile infections detected after 48h of admission. The incidence for the duration of this study was compared to the average incidence from 2018 and 2019 during March, April and May.

Statistical methods:

Categorical variables were compared using chi square or Fisher's exact test. Continuous variables not normally distributed were presented as median with interquartile range and Mann Whitney U test used for significance testing. Logistic regression was applied to explore risk factors associated with the outcomes and route of acquisition. To build the logistic regression model, forward selection was used and variables with p<0.10 were entered in the model. Results were not adjusted for BAME, because of unevenly distributed missing data. Likelihood ratio test was used to determine significant effect on the outcome after adjusting for the other variables in the model. Kaplan Meier plots were utilised to assess mortality.

Pearson test was used for correlations between HA-COVID-19 and hospital bed occupancy, DRP, CIC19 and staff self-reported COVID-19 sickness.

All data was collated in excel and analysed in STATA (version 16).

On 25th Feb 2020 the first case of COVID-19 was recorded in the hospital. Subsequently, until 18 th April, 2020, approximately 5000 people were tested, of which 1729 tested positive ( Figure 1 ). Of these, 865 (50%) were admitted to hospital within 14d of testing for at least an overnight stay or already admitted to hospital at the time of testing. Due to the possibility of multiple acquisition sites, patients who had a second hospital admission within 14d prior to testing (n=32) were excluded. Patients < 18 years of age (n=9) and those where no clinical data was available (n=3) were also excluded. Full classification of COVID-19 cases based on acquisition are described in Figure 1 .

A total of 775 (82.8%) admitted COVID-19 cases were classified as CA-COVID-19, 58 (7.1%) as HA-COVID-19 and 32(3.7%) as indeterminate. Fifteen (1.7%) patients were classed as asymptomatic (2 SARS-CoV-2 RNA positive 48h-7 days of admission, 2 cases 8-14 days post admission and 11 cases >14d of admission). For HA-COVID-19, time from admission to symptom onset ranged from 15 to 250 days (median 32.5 and IQR 21-65 days). Figure 2 illustrates the primary reason for admission of HA-COVID-19 and indeterminate cases. Incidence of HA-COVID-19 during this period was 133 per 100,000 bed-days. During the study period 551 patients who stayed as inpatients >14 days were classed as not having acquired COVID-19 ( tested on an average 1.5 times , range:0-8 ).

Tables 1A and 1B summarise the clinical features and outcomes of CA-and HA-COVID-19.

Overall, the HA-COVID-19 population was more likely to be >65years and have a CCI ≥5, but less likely to be BAME. Diabetes, CKD, malignancy and a longer post-COVID-19 length of stay (median 28d, p<0.001) were common with HA-COVID-19 but no overall difference in mortality was observed (Table 1A and Supplemental Figure S1 ).

Risk factors were similar for the 245 patients admitted to ICU (supplement table S1A and S1B) and there were no statistically significant differences in any outcome measures. A DRP was seen in 53 patients (Table 2A ). In 14 cases the cause was delay in sampling and in 39 cases samples was taken within 48h but SARS-CoV-2 RNA was negative. For DRP patients, age >65, non BAME ethnicity, diabetes and malignancy, CCI>5 were more common as was 30d mortality (p=0.01). This association remained significant in the multivariate model (Table 2B) . 45 cases (85%) were not isolated appropriately as a result of the negative RNA test.

For the 58 HA-COVID-19 patients, a potential source patient was found for 44 cases and 14 late indeterminate and 11 asymptomatic late indeterminate (Table 3) . CA-COVID-19 with DRP was the largest single contributing group of HA-COVID-19 and late indeterminate cases (34.5 %). Figure S2A shows the incidence of self-reported staff sickness from 2019 and 2020 and figure S2B shows the self-reported COVID-19 sickness absence for groups with high (G1 and G2 combined) and low (G3) risk of nosocomial exposure.

Bed occupancy varied during the study period as seen in Figure S3 , with an early fall in bed occupancy reflecting the reduction of elective activity and expedited discharge of non-COVID-19 patients in anticipation of increasing COVID-19 admissions.

Correlation between weekly incidence of HA-COVID-19 (including late indeterminate cases) and staff self-reported sickness absence, DRP cases, CIC19 and Trust COVID-19 bed occupancy is displayed in Figure 3 . Significant correlation was observed with the former two but neither with COVID-19 bed occupancy nor the incidence in the community.

The incidence of HAB during the study period was not significant when compared to the average of the previous two years ( Figure S4 ).

Tables S3-S4 provide risk factors and outcome analysis including for the indeterminate cases. Table S5 provides additional outcome measures for DRP associated cases. Table S6 and Figure S5 provide the estimated incubation period for 44 HA-COVID-19 patients.

A recent survey from 46 acute hospitals in the UK reported an average of approximately 8-9% of patients with a positive COVID-19 whose diagnosis was identified 14 d after admission (inter-quartile range 3.8% to 12%) (Written communication). In our study 7.1% were symptomatic HA-COVID-19 and an additional 2.2% were symptomatic late indeterminate cases. In addition, 11 asymptomatic cases were identified after 14 d and 2 were identified 8-14d from admission. We identified asymptomatic cases as part of contact screening in high transmission risk situations, but if outbreak management programmes include consistent testing of all asymptomatic contacts, potentially more cases would be identified.

Both in the overall analysis and in those admitted to ICU, distinct differences in the risk factors between CA-COVID-19 and HA-COVID-19 were observed. The HA-COVID-19 group was older with more comorbidities and may represent the population hospitalised at that time. However, the lower 7d mortality (14.5% vs 6.9%) and lower proportion admitted to ICU (32.4 vs 20.4%) may be because CA-COVID-19 cases were only admitted to hospital if they were severe. Overall 30d mortality appears similar between CA-COVID-19 and HA-COVID-19. We also observed a trend in CA-COVID-19 for more cases of BAME origin (67.2% vs 33.9% p<0.001) but since data were missing for 72 patients, results are not conclusive.

In our setting, the sensitivity of a single CTNS taken within 48h to predict the potential to transmit SARS-CoV-2 was 92.2% and the negative predictive value was 60.3%. Wang et al (11) highlighted the variability in detection of the viral RNA by PCR in different sample types with maximum positivity rate in bronchoalveolar lavage (93%), followed by sputum (72%) and nasal swabs (63%).

In the present study, 53 patients had a DRP result in keeping with the recent report of Kucirka et al (12) who reviewed the variation in false negative SARS-CoV-2 RT PCR results from upper respiratory tract samples. They concluded that the probability of a falsenegative result in an infected person varied with days from symptom onset; the median false-negative rate varying from 38% (CI, 18% to 65%) on day of symptom onset to 20% (CI, 12% to 30%) 3 days after symptom onset. Since patients can present at any stage of the illness, we conclude one CTNS is insufficient to prevent onward transmission, if the decision to segregate patients is based on this result alone. Gao et al (5) have described two nosocomial outbreaks in Wuhan where the index was a misdiagnosed case of CA-COVID-19.

Our results also suggest that 34.5% of all HA-COVID-19 and late indeterminate infections could be traced back to cases where the acquisition was from a community case but an RNA based diagnosis could not be made within 48h of admission ( Table 3 ). The weekly incidence of DRP also correlated with the incidence of HA-COVID-19.

Among DRP cases, co-morbidities (CCI>5) were higher than in those without a delay (56.6% vs 43.3% p=0.058) and may explain the difficulty in making a clear diagnosis in the presence of multiple clinical features. However, the higher 30d mortality (p=0.01) (Tables 2A and 2B) in this patient group emphasises the need to identify a more accurate method of ruling out COVID-19 in the initial stages of presentation. A combination of detailed history taking, successive swabs, deeper respiratory sample and radiology and biochemical markers should be evaluated in the future to help determine the best strategy to reduce onward transmission in hospital settings.

During Feb-April 2020, reported staff sickness due to cold, cough, flu and chest and respiratory problems decreased from the second week of the study probably due to the similarity of symptoms with COVID-19 and the introduction of a new staff sickness code (S13 COVID-19, Figure S2A ). COVID-19 sickness, on the other hand, increased in the first three weeks of the study and then started decreasing from week 4 which coincides with the Government introduction of community wide restriction on movement and closure of nonessential businesses. This finding is similar to the recent report by Hunter et al (13) which found an increase in staff positivity from 5 to 20% from 10 th to 31 st March 2020. They compared positivity rates amongst staff in patient-facing, non-patient facing and non-clinical roles and found no significant difference between these groups suggesting nosocomial transmission from patients to staff was not an important factor during the study period. However in their study, data on clinical roles were only available for 1/3 of included staff.

Our study compared staff self-reported COVID-19 sickness rates between patient-facing high nosocomial risk and non-clinical staff and found the difference increased after social distancing was implemented. We hypothesize that working for home may be an easier option for non-patient-facing staff that may not need to take sickness leave even if mild symptoms are present.

Previous work has shown that healthcare staffing potentially influences the incidence of HAIs (14) . In the present study, the incidence of HA-COVID-19 correlated positively with healthcare staff absence due to COVID-19. The reasons for this correlation are likely to be multifactorial since the reduction in healthcare staff to patient ratios may have a negative influence on appropriate IPC measures, but may independently be a reason for SARS-CoV-2 transmission from infected healthcare staff to patients.

Dona et al (15) recently discussed the potential impact of COVID-19 on hospital transmission of MDR organisms, based on how risk factors such a healthcare absence, hospital overcrowding, PPE usage and patient demographics are distributed in a healthcare setting. In our study, the effect of the measures taken and the demographics did not have a significant impact on the rate of MDR healthcare-associated infections when compared to the average of the previous two years.

Our study does have some limitations. Staff members were tested for SARS-CoV-2 via a regulated pathway from 27th March 2020. Prior to this, testing was on special request only and has not been included in this report. Due to the high community prevalence at the time it was not possible to determine the source of the infection linked to staff members. Recent reports suggest SARS-CoV-2 can be transmitted by asymptomatic carriers (16, 17) . We have observed this in HA-COVID-19 clusters but this study did not extend to include detection of asymptomatic infections. It is possible our HA-COVID-19 rates are lower during this period because a large proportion of elective work had stopped which limited the number of susceptible patients in the hospital. Cases with suspicion of COVID-19 but with multiple RNA negative samples were out of the scope of this analysis.

This study shows that hospital transmission of COVID-19 can be initiated by carriers who may not show symptoms, and could be admitted for other reasons. Screening for asymptomatic or early infection on admission is one approach recently advocated to segregate COVID-19 and non COVID-19 patients. However the use of a single CTNS for this purpose is limited. Further work on appropriate use of resources in patient pathways to limit transmission is recommended.

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