Covid-19: Asymptomatic/presymptomatic and the next patient?

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File:CDC 2019-nCoV Laboratory Test Kit.jpg - Wikimedia Commons

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Bottom line

To date we are seeing a small reduction in Covid-19 prevalence in England, the number of asymptomatic patients being about 13% of those who test positive for SARS-CoV-2. Applying a prediction interval to the latest systematic review suggests the chance of encountering an asymptomatic patient varies between 1% and 78% of those who would test positive. Without rapid and accurate virus and antibody testing at present extreme care still needs to be taken regarding dental aerosol generating procedures to protect both patients and staff. Self-reporting infection without a confirmatory test is about 3% accurate.

Introduction

As with all Covid-19 related data more information is appearing and being updated every day. In this opinion paper I would like to discuss the differences between Covid-19 positive patients who are asymptomatic/presympromatic, and symptomatic; what the latest data locally and globally shows, and how to interpret the data presented when making clinical decisions of individual patient risk.

Methods

The definition of a presymptomatic patient is a person where SARS-CoV-2 is detected before symptom onset, and asymptomatic are persons where SARS-CoV-2 is detected but symptoms never develop. Symptomatic is therefore those persons where SARS-CoV-2 is detected, and they have symptoms (Furukawa et al., 2020). In a cross-sectional study, one cannot separate the two categories of asymptomatic and presymptomatic as the data collected only refers to a specific time point such as the 3rd of May. If a longitudinal study is undertaken over a couple of weeks, then it may be possible to detect the patients who later go on to develop symptoms.

The latest data from the ONS, updated on the 28th May revised its prevalence estimate down to 0.24% of the community with COVID-19 (95% confidence interval: 0.11% to 0.46%) based on tests performed on 18,913 people in 8,799 households (ONS, 2020). Between the 26th April and the 25th May of those participants who reported symptoms 2.62% (95% CI: 1.56% to 4.11%) also tested positive, compared to an estimate of 0.35% of those not reporting any symptoms on the day of the test (95% CI: 0.30% to 0.47%). From this we could conclude that the sensitivity of self-reporting  coronavirus infection without a confirmatory test is 2.62% accurate, and of those individuals with a positive test 13.36% were asymptomatics on the day (both true asymptomatics and presymptomatics).

A recent systematic review by Byambasuren and co-workers set out to estimate the extent of true asymptomatic individuals and the associated risk of community spread (Byambasuren et al., 2020). Their inclusion criteria required  people of any age who were at-risk of contracting SARS-CoV-2 virus, and diagnosed by laboratory-based real time quantitative reverse transcription polymerase chain reaction (RT-qPCR) or serological tests to be positive. These people remained symptomless throughout the follow up period of at least 7 days to distinguish them from pre-symptomatic cases. From the 5 papers that fulfilled these criteria they undertook a meta-analysis and concluded using a fixed-effect model that the proportion of asymptomatic cases was 16% (95% CI: 12% – 20%) (See Figure 1.)

Figure 1.Fixed effects pooled estimates of proportion of asymptomatic carriers

Asymp_fixed

The authors used a fixed effects model possibly due to the small number of studies with similar methodologies. If, however a prediction interval (Borenstein et al., 2011) was added to the same data using the ‘meta’ package in R the summary estimate remains the same but the distribution of true effect sizes one could expect in future studies could be as low as 1% and as high as 78% (See Figure 2.).

Figure 2. Fixed effects pooled estimates of proportion of asymptomatic carriers plus prediction interval

Asmpto_fixed

Discussion

When interpreting the summary estimate within a systematic review it must be remembered this represents the mean and error variability between the different study results, and not the true distribution of asymptomatic Covid-19 carries that one may encounter tomorrow in your practice. In other words, it would not be impossible for you to encounter a group of patients (say a family) where 78% are true asymptomatic patients. As researchers produce more high-quality studies (preferably ≥24) this prediction interval will drop sharply down but unlike a confidence interval never approach zero.

References

BORENSTEIN, M., HEDGES, L. V., HIGGINS, J. P. & ROTHSTEIN, H. R. 2011. Prediction IntervalsI. Introduction to meta-analysis. John Wiley & Sons.

BYAMBASUREN, O., CARDONA, M., BELL, K., CLARK, J., MCLAWS, M.-L. & GLASZIOU, P. 2020. Estimating the extent of true asymptomatic COVID-19 and its potential for community transmission: systematic review and meta-analysis. medRxiv.

FURUKAWA, N., BROOKS, J. & SOBEL, J. 2020. Evidence Supporting Transmission of Severe Acute Respiratory Syndrome Coronavirus 2 While Presymptomatic or Asymptomatic. Emerging infectious diseases, 26.

ONS. 2020. Coronavirus (COVID-19) Infection Surveypilot: England, 28 May 2020 [Online]. Available: https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/conditionsanddiseases/bulletins/coronaviruscovid19infectionsurveypilot/28may2020 [Accessed].

Disclaimer:  The article has not been peer-reviewed; it should not replace individual clinical judgement, and the sources cited should be checked. The views expressed in this commentary represent the views of the author and not necessarily those of the host institution. The views are not a substitute for professional advice.

 

Interesting Data from Face Shield Studies

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Mask - Wikipedia

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Bottom Line

Face shields are highly effective at preventing large droplet contamination of the operators face and mask but they will not reduce aerosol penetration, so a good fitting mask is imperative. The advantages in this form of PPE can quickly be lost with poor attention to doffing and donning procedures especially when not undertaking AGPs.

Background

In the world before Covid-19 the majority of the dental profession had never heard of the words ‘aerosol generating procedure’(AGP) in relation to the use of low and high-speed dental handpieces, ultrasonic scalers, air polishers, and triple syringes. In late 2019 the Covid-19 virus brought with it the risk of potentially fatal respiratory infection via droplet and aerosol spread (Meng et al., 2020). To counter the risk of droplet infection the majority of international emergency dental care guidance almost universally recommended the use of face shields to protect the clinical team (Cochrane, 2020). Up until now the dental team had mainly been using safety glasses as eye protection, so what additional information can we learn from the research relating to face-shields?

Methods

To simplify the literature review process when there are some many non-peer reviewed unindexed preprint papers available on line I limited my search to a review by Roberge to locate some of the primary literature (Roberge, 2016). 8 studies were extracted from the review for further appraisal, 6 were simulation studies (Shoham et al., Bentley et al., 1994, Rusin et al., 2002, Lindsley et al., 2014, Brady et al., 2017, Weber et al., 2019) , one observational study of personal protective equipment (PPE) usage by operating theatre staff (Herron et al., 2019)  and a further review (Jones et al., 2020).

The simplest of the simulation studies sprayed fluorescent dye at various combinations of glasses, goggles, and a face shield. As one would expect the face shield plus FFP2 mask effectively blocked all droplet contamination to the eyes, nose and mouth (Shoham et al.).

A dental simulation study using similar fluorescent dye demonstrated  aerosols penetration of a single layered face mask behind a face shields to enter the nose indicating the importance of a well-fitting mask to protect against aerosol that can flow round a face shield (Bentley et al., 1994).  Weber and co-workers conducted a far more sophisticated simulation study using a total of 39 participants who performed 74 experiments, involving 10–12 experimental trials for each healthcare activity. Their first interesting finding was that with face shields, facemask contamination was higher among non-AGPs rather than AGPs. The authors concluded that:

‘The lack of correlation between the fluorescein mass on the face shield and fluorescein aerosol concentration suggests that projected droplets or transfer upon contact by contaminated gloves were more likely the source of contamination than particles suspended air.’

A further simulation study by Brady also concluded that improper doffing and reuse of masks increased infected droplet transfer to the hands from 7% to 15.5% (Brady et al., 2017), the 3 to 5 times higher droplet transfer as opposed to nuclei transfer (dehydrated droplet) may be associated with the fluid resistant surface of the FFP2 mask holding the droplets on the surface. Rusin  demonstrated  3300 to 6600 fold increase in transfer efficiency of virus (PRD-1 phage) when comparing a porous surface with a fluid resistant surface (Rusin et al., 2002).

Continuing on the theme of cross contamination Herron observed 1036 operating theatre staff on their technique of applying masks and face shields and found only 18% (190/1034) of surgically scrubbed staff fully complied with the CDC guidelines on the application of a face mask, and face-shields were worn correctly 3.6% of the time (37/1034). The most recent review of PPE for infectious diseases prior to Covid-19 focused on lessons learned from Ebola, SARS, and MRSA,  and in line with the above studies concluded that face shields were susceptible to being by-passed by splash or touch (Jones et al., 2020)

Discussion

From this rapid review we can conclude that face shields are highly effective at preventing large droplet contamination of the operators face and mask. The lack of seal round the periphery however will not prevent aerosol contaminating the inside of an improperly fitting face mask in simulation studies. The two most interesting observations however relate to the major source of contamination of the mask coming from the operator’s hands, and the reduced contamination during AGP procedures. I would hypothesize that the reduced contamination during the AGPs could be attributed to the more focused workflow keeping operators hands more closely confined to the surgical site, and increasing awareness of hand contamination. As with all these reviews there is a need for more high quality long-term real-world studies to evaluate the effectiveness of enhanced PPE usage.

References

BENTLEY, C. D., BURKHART, N. W. & CRAWFORD, J. J. 1994. Evaluating spatter and aerosol contamination during dental procedures. J Am Dent Assoc, 125, 579-84.

BRADY, T. M., STRAUCH, A. L., ALMAGUER, C. M., NIEZGODA, G., SHAFFER, R. E., YORIO, P. L. & FISHER, E. M. 2017. Transfer of bacteriophage MS2 and fluorescein from N95 filtering facepiece respirators to hands: Measuring fomite potential. J Occup Environ Hyg, 14, 898-906.

COCHRANE. 2020. Recommendations for the re-opening of dental services: a rapid review of international sources [Online]. Available: https://oralhealth.cochrane.org/news/recommendations-re-opening-dental-services-rapid-review-international-sources [Accessed].

HERRON, J. B. T., KUHT, J. A., HUSSAIN, A. Z., GENS, K. K. & GILLIAM, A. D. 2019. Do theatre staff use face masks in accordance with the manufacturers’ guidelines of use? J Infect Prev, 20, 99-106.

JONES, R. M., BLEASDALE, S. C., MAITA, D., BROSSEAU, L. M. & PROGRAM, C. D. C. P. E. 2020. A systematic risk-based strategy to select personal protective equipment for infectious diseases. Am J Infect Control, 48, 46-51.

LINDSLEY, W. G., NOTI, J. D., BLACHERE, F. M., SZALAJDA, J. V. & BEEZHOLD, D. H. 2014. Efficacy of face shields against cough aerosol droplets from a cough simulator. Journal of occupational and environmental hygiene, 11, 509-518.

MENG, L., HUA, F. & BIAN, Z. 2020. Coronavirus Disease 2019 (COVID-19): Emerging and Future Challenges for Dental and Oral Medicine. Journal of Dental Research.

ROBERGE, R. J. 2016. Face shields for infection control: A review. J Occup Environ Hyg, 13, 235-42.

RUSIN, P., MAXWELL, S. & GERBA, C. 2002. Comparative surface‐to‐hand and fingertip‐to‐mouth transfer efficiency of gram‐positive bacteria, gram‐negative bacteria, and phage. Journal of Applied Microbiology, 93, 585-592.

SHOHAM, S., ACUNA-VILLAORDUNA, C. & COTTON, M. Comparison of Protection against Ocular Contamination with Disposable Eyewear Products [Online]. Available: www.medonyx.com/media/MedStarFullClinicalPoster.pdf [Accessed].

WEBER, R. T., PHAN, L. T., FRITZEN-PEDICINI, C. & JONES, R. M. 2019. Environmental and Personal Protective Equipment Contamination during Simulated Healthcare Activities. Ann Work Expo Health, 63, 784-796.

Disclaimer:  The article has not been peer-reviewed; it should not replace individual clinical judgement, and the sources cited should be checked. The views expressed in this commentary represent the views of the author and not necessarily those of the host institution. The views are not a substitute for professional advice.

The Tyranny of Average and Guideline Development for PPE

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‘All models are wrong, but some are useful’ George Box

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3D_medical_animation_corona_virus

The Bottom Line

It is important when developing national and local guidelines to appreciate the extremely high variability  in clinical risk that Covid-19 poses to the dental team, and the patients they serve. Regarding the use of PPE and aerosol generating procedures there should be sufficient flexibility in the national guidelines to allow clinicians to safely apply their local knowledge so that the PPE selected matches the degree of risk for the individuals. Applying a rigid, unsustainable PPE policy will waste valuable resources while increasing potential harm to staff and patients.

Background

During the Q&A section of a webinar I was recently presenting about evidence-based dentistry one of the participants made a very interesting point. As a surgeon working in the front line of the Covid-19 pandemic not all areas have been effected the same, in some areas of the country we have hardly seen any deaths in the community, and in a North London hospital the staff there are seeing many severe cases a day. This point that the infection is not evenly distributed either geographically nor demographically is important, as it highlights the ‘tyranny of the average’(Merlo et al., 2017) in terms of healthcare policy and guideline development. From the data this virus is not an ‘equal opportunities killer’ as the highest mortality rates  are effecting the frail, disadvantaged, and highlighting strong racial/ethnic inequalities (Webb et al., 2020, Everson et al., 2002). A clear example of this is that out of 23,804 Covid-19 related deaths 31.4% (7466) occurred in people with Type II diabetes (prevalence in pop. approx. 6%). 15% of  Covid-19 admissions  to hospital had no reported medical conditions, but 50% had three or more significant comorbidities (ISARIC, 2020) (See Figure 1.).

Figure 1.The distribution of combinations of the four most common chronic diseases (ISARIC 2020)

Comorbidities

An example of this variation can be seen in regional mortality figures for England. Mortality with a diagnosis of Covid-19 is the only reliable endpoint measurement  until we have large scale reliable track and trace/antibody tests. From the background information we can deduce that there appears to be significant variation between geographic locations with lower risk in the more rural counties and smaller cities by as much as a factor of 2.5.(Table 1).

Table 1. Differences in regional mortality up to 8th May

Location Population Population density/km2 Covid-19 deaths % of population die of Covid-19 Risk Ratio
Plymouth 262100 8500 57 0.021 1
Lincolnshire 761224 127 207 0.027 1.29
Manchester 552858 4716 289 0.052 2.48
Tower Hamlets 324745 16000 172 0.053 2.52

Methods

The following calculations are a Fermi problem after Enrico Fermi, where an estimate is made with little or no data and used to identify orders of magnitude rather than point estimates. The methodology undertaken uses the mathematical principles laid out by Pólya in his famous book ‘How to Solve it’ (Polya, 1945). These are

  • Understand the problem.
  • Find the connection between the data and the unknown and devise a plan.
  • Run the model.
  • Examine the solution.

In a previous post I looked at base rates as a point estimate by using the mean without a confidence interval (Howe, 2020). Since then two reviews have been published  investigating the infection fatality rate (IFR) (Ioannidis, 2020, Meyerowitz-Katz and Merone, 2020). The IFR estimates the overall mortality rather than the number of deaths as a proportion of confirmed cases (CFR). Due to variations in analysis the two reviews produce significantly different results, Ioannidis’s review produces as IFR range of 0.02% to 0.40%, and the Meyerowitz-Katz review a mean of 0.75% (95% CI: 0.49 to 1.01). The results from the  Office of National Statistics (ONS) data for infection in England was  0.27% (95% CI: 0.17% to 0.41%). To calculate the risk range of dying following a dental aerosol generating procedure (AGP) is based on the assumption that there is a 100% chance of becoming infected in a single exposure to the virus, the second assumption is that we work with both the minima and the maxima as presented. The summary estimate for asymptomatic individuals in the population was previously calculated as 0.27% (95% CI: 0.12% to 0.45%) (Howe, 2020).

Figure 2. Frequency tree for maximum  and minimum AGP risk

Minimax

With this new range of asymptomatic risk as 1:540 to1:5000 we can calculate the risk that this infected patient passes on the virus to another patient or member of staff and they unfortunately die. We do not treat symptomatic Covid-19 patients in this model. The final range of AGP related death ranges from 1: 54000 to 1:25,000,000 (See Table 3.).

Table 3. Minimum and maximum infection fatality risk

IFR Review paper Minimum and maximum risk of death(natural frequency)
Ioannidis, 2020 1: 135,000 to 1:25,000,000
Meyerowitz-Katz and Merone, 2020 1: 54,000   to 1:1,020,000

Discussion

From the data presented we can make broad assumptions about the population most at risk form corona virus, namely the urban elderly with multiple chronic health conditions. We can also make a similar broad assumption that rural and less densely populated areas have had lower mortality rates. From the current IFR data the variability in trying to make an estimate of risk become incredibly wide from 1:54,000 to 1:25,000,000 which is a 462 times increase in risk. From this result we can see that even in the worst-case scenario the risk of a dental AGP being directly linked to an individual patients death is very low if good cross infection policy is employed which includes PPE as a component of the process. High level PPE (Fluid resistant disposable gowns with plastic aprons, FFP3 and visors) can soon lose its protective benefits with prolonged use, lack of comfort, complicated workflow and difficulties in removing (Phan et al., 2019a, Phan et al., 2019b). There is a huge variance in the volume of aerosol generated in a dental procedure from polishing a composite to removing a broken bridge. It is important in that case that any guidance developed around the use of PPE should consider this variability and set an absolute minimum standard, thus allowing the clinician to tailor the PPE requirement to local conditions/risk. Low risk AGPs could require a fluid resistant face mask and face shield/visor, and as the risk increases higher levels of PPE are added rather than going to maximum PPE in all circumstances (high volume suction is common to all procedures). Wasted resources will harm health care provision to the most vulnerable social groups disproportionally, so high quality studies will need to be rapidly developed to assess these risks.

 

EVERSON, S. A., MATY, S. C., LYNCH, J. W. & KAPLAN, G. A. 2002. Epidemiologic evidence for the relation between socioeconomic status and depression, obesity, and diabetes. Journal of psychosomatic research, 53, 891-895.

HOWE, M. S. 2020. Are we sleepwalking into PPE paralysis? [Online]. Available: https://www.nationalelfservice.net/dentistry/dental-workforce/sleepwalking-ppe-paralysis/ [Accessed].

IOANNIDIS, J. P. 2020. The infection fatality rate of COVID-19 inferred from seroprevalence data.

ISARIC. 2020. Data PlatformCOVID-19 Report 6 MAY 20 [Online]. Available: https://media.tghn.org/medialibrary/2020/05/ISARIC_Data_Platform_COVID-19_Report_6MAY20.pdf [Accessed].

MERLO, J., MULINARI, S., WEMRELL, M., SUBRAMANIAN, S. V. & HEDBLAD, B. 2017. The tyranny of the averages and the indiscriminate use of risk factors in public health: The case of coronary heart disease. SSM Popul Health, 3, 684-698.

MEYEROWITZ-KATZ, G. & MERONE, L. 2020. A systematic review and meta-analysis of published research data on COVID-19 infection-fatality rates. medRxiv.

PHAN, L. T., MAITA, D., MORTIZ, D. C., WEBER, R., FRITZEN-PEDICINI, C., BLEASDALE, S. C., JONES, R. M. & PROGRAM, C. P. E. 2019a. Personal protective equipment doffing practices of healthcare workers. Journal of occupational and environmental hygiene, 16, 575-581.

PHAN, L. T., SWEENEY, D., MAITA, D., MORITZ, D. C., BLEASDALE, S. C., JONES, R. M. & PROGRAM, C. D. C. P. E. 2019b. Respiratory viruses on personal protective equipment and bodies of healthcare workers. Infect Control Hosp Epidemiol, 40, 1356-1360.

POLYA, G. 1945. How to solve it: A new aspect of mathematical method, Princeton university press.

WEBB, H. M., NÁPOLES, A. & PÉREZ-STABLE, E. 2020. COVID-19 and Racial/Ethnic Disparities. JAMA.

Disclaimer:  The article has not been peer-reviewed; it should not replace individual clinical judgement, and the sources cited should be checked. The views expressed in this commentary represent the views of the author and not necessarily those of the host institution. The views are not a substitute for professional advice.