What is the most appropriate gown/apron for preventing Covid-19 contaminated fluids transfer in dental practice?

Originally posted on the Dental Elf

L0028811 A nurse and a surgeon, both wearing gown and mask. Etching b


Which gown/apron combination provides the best protection in the dental practice?

Bottom-line answer:

From this reanalysis of the primary data the reusable cotton surgical gown may be more practical in the dental environment in the long-term than the disposable fluid resistant gown due to its reduce potential for cross contamination during use. The plastic apron creates the most cross contamination and should only be used if there is significant risk of fluid contamination.


This paper is a reanalysis of a recent systematic review (Verbeek et al., 2020) on personal protective equipment (PPE), reframing the question to fit into the new clinical workflow created by Covid-19, and dental aerosol generating procedures (AGPs). I have covered face masks in a previous post.

Much has been written on the epidemiology of Covid-19 and its transmissibility via contact, droplets, aerosols, or faeco-oral route. The main concern within dentistry being the aerosol generated during many routine dental procedures (Coulthard, 2020). To reduce this contamination risk Public Health England’s guidance document for personal protective equipment updated 3 May 2020 Section 10.4 (GOV.UK, 2020) states that:-

‘Disposable fluid repellent coveralls or long-sleeved gowns must be worn when a disposable plastic apron provides inadequate cover of staff uniform or clothes for the procedure or task being performed, and when there is a risk of splashing of body fluids such as during AGPs in higher risk areas or in operative procedures. If non-fluid-resistant gowns are used, a disposable plastic apron should be worn’.

As this advice is generic and the workflow within a critical care unit differs from a dental practice it is important to evaluate the best available evidence from a primary care rather than secondary care perspective.


To save unnecessary duplication of search strategies and risk of bias/quality assessments I utilised the most up to date Cochrane Review of PPE for preventing highly infectious diseases due to exposure to contaminated body fluids in healthcare staff (Verbeek et al., 2020). This systematic review included 17 studies with 1950 participants evaluating 21 interventions. The authors concluded:

‘We found low- to very low-certainty evidence that covering more parts of the body leads to better protection but usually comes at the cost of more difficult donning or doffing and less user comfort, and may therefore even lead to more contamination. More breathable types of PPE may lead to similar contamination but may have greater user satisfaction.’

The authors conclusion helped to focus the next stage of analysis which was based around the levels of contamination and wearability. From the included studies two randomised simulation trials, one of a parallel design (Wong et al., 2004) and a second of a cross-over design (Guo et al., 2014) were selected as they contained sufficient primary data to undertake a meta-analysis. A simulation trial utilises aerosolised fluorescent dye sprayed on the PPE instead of true viral contamination. It was possible to extract data on contamination of  fluid resistant disposable gowns, standard cotton surgical gowns, and plastic aprons. The data was placed in Excel and then transferred to R for meta-analysis using the ’meta’ package. A random effect model was used with a Hartung Knapp conversion due to variability within the studies. Prediction intervals were included to facilitate the estimation for future studies.


The first meta-analysis compared a fluid resistant disposable gown with a standard cotton gown for both Wong and Guo. In Guo’s study the group tried two methods of doffing PPE: their individual accustomed removal method (IARM), and gown removal methods recommended by the Centers for Disease Control and Prevention (CDC).

The overall result favoured the cotton gown, but the result was non-significant, the mean difference (MD) was 0.91 (95%CI: -0.34 to 2.66) (See Figure 1.). The second meta-analysis showed significantly less contamination of the cotton surgical gowns compared with the plastic apron, the  MD was 8.4 (95%CI: 0.59 to 16.2) (See Figure 2.). The final analysis looked at the  contamination of the clinician post PPE removal showing equal contamination between the different PPE types MD was -0.02 (95% CI: -1.43 to 1.40) (See Figure 3).

Figure 1.Forest plot of disposable fluid resistant gown vs cotton gown


Figure 2. Forest plot of plastic apron vs cotton gown


Figure 3. Forest plot of body contamination



From the results of the meta-analysis there is little difference between the disposable fluid resistant gown and the reusable cotton surgical gown in terms of contamination/protection of both the wearer, patient, and clinical environment. The results favour the cotton gown as cotton through its material and properties can absorb droplet contaminants and thereby reduce opportunities for such contaminants to spread to the environment. The plastic apron performed worst and may significantly increase the risk of cross contamination both to the clinician and patient and should only be necessary where there is a risk of serious fluid contamination.

There is an interesting paper recently published by Phan and co-workers (Phan et al., 2019) who observed that ‘90% of observed doffing was incorrect, with respect to the doffing sequence, doffing technique, or use of appropriate PPE. Common errors were doffing gown from the front, removing face shield of the mask, and touching potentially contaminated surfaces and PPE during doffing’.

These results presented are hypothetical and due to the lack of specific studies of virus penetration through gowns in dentistry and are based on surrogate, and composite outcomes. There is an urgent need for specific studies to address PPE performance in the dental surgery environment.

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.


COULTHARD, P. 2020. Dentistry and coronavirus (COVID-19) – moral decision-making. Br Dent J, 228, 503-505.

GOV.UK. 2020. COVID-19 ( personal protective equipment (PPE) [Online]. Available: https://www.gov.uk/government/publications/wuhan-novel-coronavirus-infection-prevention-and-control/covid-19-personal-protective-equipment-ppe [Accessed].

GUO, Y. P., LI, Y. & WONG, P. L. 2014. Environment and body contamination: a comparison of two different removal methods in three types of personal protective clothing. Am J Infect Control, 42, e39-45.

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

VERBEEK, J. H., RAJAMAKI, B., IJAZ, S., SAUNI, R., TOOMEY, E., BLACKWOOD, B., TIKKA, C., RUOTSALAINEN, J. H. & KILINC BALCI, F. S. 2020. Personal protective equipment for preventing highly infectious diseases due to exposure to contaminated body fluids in healthcare staff. Cochrane Database Syst Rev, 4, CD011621.

WONG, T. K., CHUNG, J. W., LI, Y., CHAN, W. F., CHING, P. T., LAM, C. H., CHOW, C. B. & SETO, W. H. 2004. Effective personal protective clothing for health care workers attending patients with severe acute respiratory syndrome. American journal of infection control, 32, 90-96.

Other  links

Personal protective equipment: a commentary for the dental and oral health care team on  Verbeek et al .

Four Ways to Optimise an Outcome



As a dental surgeon, I have spent my entire career trying to keep up-to-date with the latest evidence as surgical techniques have evolved. Over the past 10 years I have started to question the validity of some of this evidence as I was seeing more complications relating to dental implant treatment than the research would suggest. To explore this hypothetical mismatch between clinical and research outcomes I chose to undertake an updated systematic review (SR) and sensitivity meta-analysis on the ‘Long-term survival of titanium dental implants’ [1].

Observations from the research

Following completion of my SR there were four areas where the previous SRs had potentially over optimised their conclusions:

  1. Definitions of implant failure
    Problem: Most of the research defined the failure of a dental implant using the most extreme outcome (loss from the oral cavity). In clinical practice it is generally accepted that an implant has failed if it causes pain or is mobile when in use, lost most of its supporting bone or presents with uncontrollable infection.
    Solution: By universally adopting these real-world definitions of implant failure the research will produce results closer to what a patient might consider a failure.
  2. Patients lost to follow-up
    : In all the papers reviewed the researchers had assumed that any patient unavailable for assessment at 10-years was ‘missing at random’, that is to say that their absence had nothing to do with the treatment they received and therefore the data was ignorable and only complete data would be analysed. In clinical practice there is anecdotal evidence and handful of research papers showing that patient who don’t come back for review may have had a higher failure rate (up to ten times higher) for clinical, psychological or financial reasons [2,3].
    Solution: In the real-world clinical environment there is less control over patient monitoring, and it is not plausible to either assume all the patients are missing at random or that all patients lost to follow-up had complete success or complete failure. There needs to be some plausible imputation model to account for the missing data. In my review we set the relative implant failure rate at 5x higher than the authors published result, based on previous lost to follow-up studies and then imputed the number of additionally failed implant this would add in if all the patients had been followed up [4]. One could argue about using a multiplier of 5 but it is more plausible than ignoring the patient altogether or substituting a probability of 0 or 1 for the missing outcome (Cromwell’s Law) [5].
  3. Risk of Bias (RoB) assessment
    : In the initial review most of the previously published SR’s did not employ a risk of bias tool. If one was used it was either Cochrane Collaborations tool for assessing risk of bias in randomised trials or the Newcastle Ottawa Scale for comparing non-randomised studies. The problem with both these tools is that there was no comparator group to assess, so neither of the tools are suitable to assess the risk of bias in these SR’s. By using an inappropriate RoB tool there is a risk or presenting the evidence in a better light by concentrating on the internal validity of the study.
    Solution: I used a risk of bias tool specifically designed for prevalence studies so there is no comparator group [6]. This tool places an emphasis on the external validity (how closely the group under study represent the national population that may benefit from the treatment).
  4. Presentation of the prediction interval
    : The results of the meta-analysis were only presented as a summary estimate and 95% confidence interval. It must be remembered that this is the mean survival rate of all the studies and the 95% confidence interval represents the precision of that estimate. This does not help us predict the possible outcome of a future study conducted in a similar fashion.
    Solution: It is possible to add a prediction interval (PI) to the summary estimate, which represents distribution of the true effects and the heterogeneity in the same metric as the original effect size measure [7,8].


A traditional analysis produced similar 10-year survival estimates to previous systematic reviews. A more realistic sensitivity meta-analysis accounting for loss to follow-up data and the calculation of prediction intervals demonstrated a possible doubling of the risk of implant loss in the older age groups.

Link to CEBM blog  “https://www.cebm.net/2019/05/four-ways-that-a-systematic-review-can-over-optimise-an-outcome/”

[1] M.-S. Howe, W. Keys, D. Richards, Long-term (10-year) dental implant survival: A systematic review and sensitivity meta-analysis, J. Dent. VO – 84. 84 (2019) 9–21. doi:10.1016/j.jdent.2019.03.008.

[2] A.C.P. Sims, The importance of a high tracking rate in long term medical follow-up studies, Lancet. 302 (1973) 433–435.

[3] E.H. Geng, N. Emenyonu, M.B. Bwana, , Sampling-Based Approach to Determining Outcomes of Patients Lost to Follow-Up in Antiretroviral Therapy Scale-Up Programs in Africa, J. Am. Mediacal Assocoation. 300 (2008) 506–507. doi:10.1001/jama.300.5.506.

[4] E.A. Akl, M. Briel, J.J. You, , Potential impact on estimated treatment effects of information lost to follow-up in randomised controlled trials (LOST-IT): systematic review, Br. Med. J. 344 (2012) e2809–e2809. doi:10.1136/bmj.e2809.

[5] D. V Lindley, Understanding uncertainty. [electronic book], in: Hoboken, New Jersey : Wiley, 2014., 2014: pp. 129–130.

[6] D. Hoy, P. Brooks, A. Woolf, , Assessing risk of bias in prevalence studies: Modification of an existing tool and evidence of interrater agreement, J. Clin. Epidemiol. 65 (2012) 934–939. doi:10.1016/j.jclinepi.2011.11.014.

[7] M. Borenstein, L. V Hedges, J.P.T. Higgins, Introduction to Meta-Analysis, Wiley & Sons, Chichester, UK, 2009.

[8] J. IntHout, J.P.A. Ioannidis, M.M. Rovers, Plea for routinely presenting prediction intervals in meta-analysis, Br. Med. J. Open. 6 (2016) e010247. doi:10.1136/bmjopen-2015-010247.