Wurie F, Le Polain de Waroux O, Brande M et al. Characteristics of exhaled particle production in healthy volunteers: possible implications for infectious disease transmission [version 1; peer review: 2 approved]. F1000Research 2013, 2:14 (https://doi.org/10.12688/f1000research.2-14.v1)
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
Characteristics of exhaled particle production in healthy volunteers: possible implications for infectious disease transmission
[version 1; peer review: 2 approved]
Fatima Wurie1, Olivier Le Polain de Waroux1, Matthew Brande2, [...] Wesley DeHaan2, Katherine Holdgate1, Rishi Mannan1, Donald Milton3, Daniel Swerdlow4, Andrew Hayward1
Fatima Wurie1, Olivier Le Polain de Waroux1, [...] Matthew Brande2, Wesley DeHaan2, Katherine Holdgate1, Rishi Mannan1, Donald Milton3, Daniel Swerdlow4, Andrew Hayward1
1Centre of Infectious Disease Epidemiology, Research Department of Infection and Population Health, University College London, London, UK 2Pulmatrix Inc., Lexington, 02421, USA 3Maryland Institute of Applied Environmental Health, University of Maryland School of Public Health, College Park, 20742, USA 4Research Department of Epidemiology and Public Health, University College London, London, WC1E 6BT, UK
The size and concentration of exhaled particles may influence respiratory infection transmission risk. We assessed variation in exhaled particle production between individuals, factors associated with high production and stability over time. We measured exhaled particle production during tidal breathing in a sample of 79 healthy volunteers, using optical particle counter technology. Repeat measurements (several months after baseline) were obtained for 37 of the 79 participants. Multilevel linear regression models of log transformed particle production measures were used to assess risk factors for high production. Stability between measurements over time was assessed using Lin’s correlation coefficients. Ninety-nine percent of expired particles were <1μm in diameter. Considerable variation in exhaled particle production was observed between individuals and within individuals over time. Distribution of particle production was right skewed. Approximately 90% of individuals produce <150 particles per litre in normal breathing. A few individuals had measurements of over 1000 particles per litre (maximum 1456). Particle production increased with age (p<0.001) and was associated with high tree pollen counts. Particle production levels did not remain stable over time [rho 0.14 (95%CI -0.10, 0.38, p=0.238)]. Sub-micron particles conducive to airborne rather than droplet transmission form the great majority of exhaled particles in tidal breathing. There is a high level of variability between subjects but measurements are not stable over time. Production increases with age and may be influenced by airway inflammation caused by environmental irritants. Further research is needed to determine whether the observed variations in exhaled particle production affect transmission of respiratory infection.
Corresponding author:
Fatima Wurie
Competing interests:
WD and MB are current employees of Pulmatrix, Inc. which supplied the Exhalair equipment used in the study. Neither have any non-financial competing interests to declare. The remaining authors have no competing interests to declare.
Grant information:
The work was funded by a UCL discretionary account.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Exhaled particles serve as a vehicle of transmission for some pathogens. Respiratory infection transmission can be described as either droplet or airborne. Droplet transmission relates to larger particles that are expelled and rapidly settle to the ground, usually within 1 minute of production1. Droplet transmission therefore relies on relatively close proximity to the source case. These larger particles are generated from the upper respiratory tract during coughing or sneezing or during procedures such as suctioning or bronchoscopy2. Larger particles tend to deposit on external mucus membranes or high up in the respiratory tract. Settled droplets can also contribute to fomite transmission. Airborne transmission is caused by smaller expelled particles which can stay suspended in the air for long periods exposing a greater number of contacts at greater distance1–3. They are formed by the re-opening of closed airway passages, which de-stabilise the mucous surface layer4. These smaller particles penetrate further into the lower respiratory tract to alveolar level. It is not possible to define a cut-off particle diameter at which aerodynamic behaviour changes, however, the World Health Organisation use a 5 µm cut-off to distinguish between airborne and droplet transmission5.
Recently, the development of optical particle counter (OPC) technology has enabled researchers to measure both the density and the full spectrum of sizes of expired droplets, from the submicron level to larger droplets6,7. Studies using that technology have demonstrated that the majority of particles produced during normal breathing and talking are of submicron size. Although coughing and sneezing can produce 5 times more particles than normal breathing7, the latter accounts for the majority of expired bio-aerosols over the course of a day4,7–9. In addition, recent studies have shown that submicron particles exhaled during normal breathing can contain respiratory viruses6,10,11, suggesting that submicron particles could contribute to infectious disease transmission. The relative contributions of droplet and airborne transmission to the spread of different infections remains controversial but there is increasing recognition that airborne spread may be more important than previously thought for the transmission of respiratory viruses such as influenza12–17. For tuberculosis for example, airborne transmission is regarded as obligatory as mycobacteria need to reach alveolar levels to be taken up by macrophages18.
Early mathematical models of the spread of infectious diseases have tended to assume that infected individuals were largely homogenous within their age group with respect to transmission19,20. More recent modelling work shows substantial heterogeneity in transmission of SARS, measles, monkey pox and pneumonic plague21 suggesting the occurrence of “super-spreaders” of respiratory infections. Previous small-scale studies of exhaled particle production suggest that two distinct populations of particle producers exist: the majority of individuals are low producers (exhaling an average of less than 500 particles per litre during normal breathing) and a few are high producers (producing more than 500 particles per litre)4,7. It has been hypothesised that high level producers of exhaled particles (so-called “super-producers”) may be “super-spreaders” of respiratory infection. To date published studies of exhaled particle production have included small numbers of individuals, limiting the ability to describe the range of particle production and factors associated with high production and have not examined the long-term stability of exhaled particle production within individuals. For example one study with 16 volunteers22 examined the stability of exhaled particles only over the course of 2 months.
This study aimed to explore the characteristics of exhaled particle production in healthy individuals, its stability over time, and factors associated with high levels of particle production. Findings from this study may have implications for theories and models of infectious disease transmission through the respiratory route.
Methods for data collection
Ethical approval for this study was received by University College London Ethics Committee (Reference number 1564/001). We collected data from a convenience sample of workers from 4 departments of University College London (UCL). Measurements were obtained during three different sessions (one baseline session and two follow up sessions which were a few months apart) between November 2008 and June 2009. Three measurement cycles were obtained during each session.
Each participant session consisted of a 15-minute interview followed by a respiratory evaluation conducted by a study researcher. The latter consisted of the measurement of exhaled air using an optical particle counter, Exhalair (model 102580-AK), produced by Pulmatrix Incorporated, which measured aerosol size and concentration by optical particle counting technology coupled with respiratory flow rate and volume measurements. Once written informed consent was obtained, participants were asked to provide information regarding personal demographics, any chronic illnesses, prescribed medications, smoking status and any current symptoms of respiratory illness. Indoor and outdoor temperature and humidity readings were taken at the beginning of each session. The background aerosol count was recorded using a Lighthouse handheld 3013 Particle Counter, which measures the total number of particles greater than 0.3 micrometres in diameter per 0.1 cubic foot of air (also referred to as atmospheric-aerosol particle count).
Exhaled particle measurement
Participants breathe with a normal tidal breathing pattern into a disposable mouthpiece whilst wearing a nose clip to prevent nose breathing. Valves direct exhaled breath into the optical particle counter. One-way valves and bacterial/viral High Efficiency Particulate Air (HEPA) filters prevent inhalation of infectious particles, ambient or upstream contaminants or previously exhaled breath. Both the one-way valve and inhalation filter are replaced for each individual. The exhaled breath passes by a laser diode, which counts and sizes the particles in the airstream. Prior to exhaust, the airstream is passed through an additional internal large capacity HEPA filter to remove any contaminating elements.
Following initial calibration and a first washout period (which includes 3 deep breaths aimed at clearing any ambient particles from the respiratory tract), the Optical Particle Counter measures average size and concentration of exhaled particles in the range of 0.3 to 20µm in diameter over the course of 15 tidal breaths. A visual display provides feedback to participants allowing them to regulate their breathing within standard tidal breathing limits (the software takes the average tidal wash-out period into account and applies the following additional criteria during the sampling interval for a breath to be considered acceptable: peak inhale between 80–130% of average peak inhale and peak exhale between 80–139% of average peak exhale (with maximum exhale set at 28LPM). Minimum inhalation and exhalation volume = 60% of average inhalation and exhalation volumes respectively. This is due to the large variability in tidal volumes by a person so that they are held to being consistent from the tidal washout to the sampling interval. The process was repeated 3 times each session.
Statistical analysis
The dataset included 3 measurements per session for each participant, each representing the average number of particles per litre of exhaled breath over the course of 15 breaths. We plotted the particle count per litre during normal breathing at each attempt and each session for each individual included in the study.
Given the right skewed distribution of submicron bio-aerosol count/L, we log transformed the data and assessed normality through kernel density plots. We explored whether specific individual or environmental factors were associated with high particle production (i.e. ‘super-producers’), and defined high particle production as any particle count equal to or above the 90th percentile of particle count among study participants. The explanatory variables considered were individual factors such as age, sex, ethnicity, height and weight, medical history and flu-like symptoms on the day measurements were taken, and environmental factors which were thought to affect particle production including season, indoor and outdoor temperature, humidity measurements and pollen count. Given that multiple measurements were obtained for the same individuals and that each individual was included in the study for one or more sessions at different periods in time, crude and adjusted odds ratios (ORs) for high particle production were obtained by multilevel logistic regression analysis. Multilevel analysis was required to take the hierarchical structure of the data into account and the non-independence of observations. Univariable models were initially built, and we considered all variables associated with the outcome at p<0.10 for multivariable analysis. The least significant factor was dropped from each model in a stepwise fashion, until all variables remained significant at p<0.05. A sensitivity analysis was performed to explore how changes in the way super-producers were defined impacted on the associations found, using varying thresholds between the 85th and 95th percentile to define superproducers. All analyses were performed in STATA (STATA 12.0 IC, College Station, Texas, USA).
Respiratory/influenza like symptoms on the day of the measurement were defined as any two of the following symptoms: fever, sore throat, rhinitis or cough.
We explored the stability of bio-aerosol production for individuals between measurements during each session as well as between each session. We did this for each pair of measurements within a session (e.g. measurement 1 and 2 in session 1) as well as between pairs of summary measurements between sessions (e.g. mean measurement in sessions 1 and 2). We used Lin’s concordance correlation coefficient23, which is similar to a Pearson’s correlation coefficient for continuous variables, to assess the agreement between multiple continuous measurements on the same subject.
Results
Overall 79 individuals were included in this study, of which 56 (71%) were females (Table 1). The median age of the study participants was 32 years (range 22–62 years). More than half of them were researchers at UCL, and the rest were physicians, nurses, students, clerks and others (Table 1). Further information on the study participants can be found in Table 1. Thirty-seven individuals (47%) were followed up for a second session a few months later, and 13 (16%) were followed up twice (thus included in three different sessions), resulting in a total of 142 sessions. Of these, 50 (35%) were held during the summer, 12 (8%) were during autumn, 43 (30%) during winter and another 37 (26%) during spring. Each individual completed a series of 3 cycles of measurements for each session attended, which resulted in a total of 426 measurements of breathing cycles (59 of which were excluded due to incomplete data on particle size).
Table 1. Characteristics of the 79 individuals included in the study.
Variables
n
%
Age
20–29
31
39.24
30–39
24
30.38
40–49
15
18.99
50+
9
11.39
Sex
Male
23
29.11
Female
56
70.89
Ethnicity
White British
53
71.62
White other
10
13.51
South Asian
5
6.76
Other Asian
2
2.7
Black African
4
5.41
Occupation
Nurse
2
2.53
Physician (medical)
5
6.33
Researcher
41
51.9
Clerical worker
16
20.25
Student
6
7.59
Other
9
11.39
Asthmatic
No
65
82.28
Yes
14
17.72
Body Mass Index (kg/m2)
underweight (<19.0)
3
3.8
normal (19.0–24.9)
45
56.96
overweight (25.0–29.9)
26
32.91
obese (30+)
5
6.33
Smoking status
never smoked
49
62.03
stopped >10yrs ago
5
6.33
stopped <10yrs ago
15
18.99
current smoker
10
12.66
The median total particle count per litre was 38.3 (range 3.3–1456 particle count/L) with 99.9% of the total bio-aerosol production composed of particle sizes smaller than 1 micron and around 75% below 0.5 microns. Figure 1 shows the distribution of exhaled particle counts across all readings. The median sub-micron particle count was 37.3 counts/L (range 3.2–1456.4, 90th percentile 145.8/L).
Figure 1. Distribution of submicron particle count/L in exhaled air.
Table 2 shows results of the logistic regression analysis of the association between a range of exploratory variables and high particle production (i.e. >90th percentile of particle production). We found an association with age, with proportionally more ‘super-producers’ in older age groups compared to younger ones. This association was not confounded by BMI, height, weight, sex or any other factor. There was no association between particle count and respiratory/influenza-like symptoms at the time of measurement, including fever, dry and productive cough, runny nose, myalgia and headache (Table 2).
Table 2. Results of the multilevel linear regression analysis on factors associated with log transformed submicron bio-aerosol count/L.
* 2-sided Fisher’s exact test.
Variables
n
% super producers
Crude Odds Ratio (95% CI)
p-value
Adjusted Odds Ratio (95% CI)
p-value
Age
20–29
135
4.4
reference
30–39
117
9.4
2.61 (0.65–10.47)
0.175
2.45 (0.63–9.49)
0.196
40–49
78
19.2
6.47 (1.56–26.75)
0.010
6.33 (1.55–25.78)
0.010
50+
42
14.3
4.19 (0.76–23.07)
0.099
4.96 (0.94–26.12)
0.059
Gender
Male
93
9.7
reference
Female
279
10.4
1.04 (0.29–3.74)
0.953
Asthma
No
316
11.1
reference
Yes
56
5.4
0.34 (0.07–2.17)
0.281
Body Mass Index (kg/m2)
underweight (<19.0)
18
22.2
reference
normal (19.0–24.9)
215
7.9
0.20 (0.01–1.59)
0.123
overweight (25.0–29.9)
111
13.5
0.39 (0.04–3.55)
0.401
obese (30+)
28
7.1
0.14 (0.00–2.81)
0.198
Smoking status
never smoked
240
10.0
reference
stopped >10yrs ago
25
24.0
4.66 (0.73–29.65)
0.103
stopped <10yrs ago
64
4.7
0.41 (0.08–2.21)
0.301
current smoker
40
7.5
0.73 (0.12–4.53)
0.738
Number of respiratory illnesses in the last year
0
52
5.8
reference
1
137
12.4
2.70 (0.43–16.81)
0.288
2
87
14.9
3.95 (0.56–27.78)
0.167
3
41
4.9
0.81 (0.07–9.78)
0.865
4
55
5.4
0.82 (0.08–8.72)
0.872
Respiratory/Influenza-like symptoms at the time of measurement
Yes
37
0
No
335
11.3
NA
0.022*
Indoor temperature (degrees Celsius)
<21.5
85
5.9
reference
21.5–23.2
110
12.7
2.67 (0.57–16.78)
0.19
23.3–24.4
81
11.1
2.63 (0.45–17.57)
0.267
24.5+
96
10.4
2.05 (0.39–13.36)
0.364
Outdoor temperature (degrees Celsius)
<6.3
98
4.1
reference
reference
6.3–17.6
92
4.3
1.05 (0.18–6.24)
0.955
1.12 (0.21–5.96)
0.893
17.7–23.7
92
18.4
7.92 (1.68–37.13)
0.009
6.56 (1.56–27.49)
0.01
23.8+
90
14.4
5.11 (1.08–24.18)
0.039
5.88 (1.31–26.41)
0.021
Indoor humidity (%)
<30.0
86
19.8
reference
30.0–34.9
117
6.8
0.21 (0.05–0.86)
0.03
35.0–39.9
80
3.7
0.10 (0.01–0.62)
0.013
40.0+
89
11.2
0.41 (0.10–1.65)
0.21
Outdoor humidity (%)
<31.0
96
13.5
reference
31.0–34.4
83
9.6
0.49 (0.14–2.89)
0.548
34.5–37.9
94
6.4
0.29 (0.72–1.76)
0.207
38+
99
11.1
0.52 (0.16–2.95)
0.621
Season
Summer
136
7.3
reference
reference
Autumn
30
Winter
110
Spring
96
18.2
4.38 (1.32–14.56)
0.016
4.44 (1.45–13.57)
0.09
High tree pollen count
No
54
Yes
318
6.25 (1.58–24.64)
0.009
5.66 (1.62–19.72)
0.006
High grass pollen count
No
56
Yes
316
0.69 (0.12–3.88)
0.674
High nettle pollen count
No
279
Yes
93
0.70 (0.18–2.73)
0.603
Note: Due to collinearity, tree pollen count, spring season and outdoor temperature were adjusted for age separately. Age in table is adjusted for pollen count.
We also found a positive association with high tree pollen counts, which was not confounded by age hence the results in Table 2 are from the univariable analysis. We found no other environmental factor associated with high particle counts. Figure 2 shows the variation in pollen counts over the spring and summer study months.
Figure 2. 5-day moving average of daily average pollen counts during the 2009 pollen season.
The analysis with 85th and 95th centiles as the cut-off for defining super-producers yielded similar results and similar associations though point estimates and standard errors differed. Here we only present the results where the 90th centile was used as a cut-off to define super-producers.
We found that measurements repeated within a session were relatively stable with good agreement between particle counts (concordance coefficient rho ranging from 0.30 to 0.65, p-values <0.01) for all pairs of measurements within each session. However, we found little evidence that bio-aerosol production was stable over time, when comparing the geometric mean submicron particle counts/litre between each session (session 1 and 2: concordance coefficient rho 0.14 (95%CI -0.10, 0.38, p=0.238), session 1 and 3: rho 0.06 (95%CI -0.55–0.66, p=0.859), session 2 and 3: rho 0.36 (95%CI -0.13–0.85, p=0.148)). Figure 3 shows a scatter plot comparing results from session one and session 2 demonstrating minimal evidence of stability over time.
Figure 3. Stability of bio-aerosol production: scatterplot of mean submicron particle count per participant between sessions 1 and 2.
Discussion
During tidal breathing, 99.9% of the total exhaled particle production consisted of particles measuring less than 1µm in diameter, which has confirmed findings from previous studies4,8,9. In common with other studies, we observed high variability in the levels of exhaled particle production between individuals3,24,27. Unlike previous studies we were also able to assess stability over time and found that measurements taken several months apart were not well correlated. The size of our study enabled us to assess a range of putative predictors of exhaled particle production, including age, gender, height and weight, smoking history, chronic respiratory disease and acute respiratory symptoms. We found that high particle production was associated with older age, but not with any other individual factor, and also observed an ecological association between high particle production and high pollen count.
The predominance of sub-micron particles in exhaled breath underlines the potential importance of airborne transmission in respiratory infection. The high level of variation in particle production between individuals may account for the observed heterogeneity in transmission of respiratory infection21. The lack of stability of particle counts over time, however, suggests that individuals with high particle counts who may be more infectious during one episode of infection may not be as infectious during subsequent episodes of infection. The association with age suggests an age related deterioration of the respiratory system22,25 through decreased elasticity, lower levels of surfactant, age-associated increases in airways closure26 or increased likelihood of chronic inflammation, which may influence production of exhaled particles. There is no evidence from the literature, however, that older adults are more likely to transmit respiratory infections compared with younger adults. The association with pollen counts also suggests that airway irritation may increase the production of exhaled particles.
This is the largest study to date of exhaled particle production in healthy volunteers and the first to assess the stability of the population in a subset of participants. We did not attempt to gain a representative sample of the population, rather relying on recruiting colleagues who were more easily accessible. This potentially limits generalizability. No children or adults of post-retirement age were included, limiting the conclusions that can be made about age-related trends. Finally, since the hypothesis of an association with high pollen counts was developed post hoc following observations that particle counts tended to be higher in spring and summer months, this association should be treated with caution. The association is also ecological, and therefore potentially confounded by other variables not captured here. It is important that future studies of variation in production assess this over a wider age range, incorporate measures of stability and assess the impact of potential environmental factors on production.
Finally, this study was conducted among healthy volunteers. Whilst a small proportion of these “healthy” volunteers had mild respiratory symptoms at the time of measurement, the study was not designed to assess the impact of respiratory infections or other acute or chronic respiratory problems on exhaled particle production. It may be that particle production in individuals will change through the course of respiratory infections affecting transmission.
A better understanding of the role of airborne transmission in the spread of infections is critical to informing disease transmission models and control policy. For example in influenza, a high risk from airborne transmission may influence decisions about appropriate levels of social distancing, use of respirators rather than surgical masks and appropriate isolation facilities for patients with newly emergent strains28. Further studies focussing on measurements during the course of acute respiratory infections are needed to investigate infection-induced changes in particle production. In addition, studies are needed to explore whether variations in exhaled particle production are associated with an increased respiratory infection transmission risk. Given the lack of stability of production over time it will be important that such studies measure particle production and transmission risk over the same time period. Such studies are fundamental to our understanding of respiratory infection transmission.
Author contributions
FW led project management, data collection, collated and dealt with data management, wrote initial drafts of paper and presented the work at scientific conferences. OLPW developed the statistical strategy and analysis and contributed to the interpretation of results. RM, KH and DS completed the ethics application submission and collected the data. WD and MB provided expert technical support to the project. AH provided academic, strategic and statistical advice throughout the study period. DM and AH were responsible for the inception of the study. FW, OLPW, WD, MB, AH and DM refined the final drafts of the paper.
Competing interests
WD and MB are current employees of Pulmatrix, Inc. which supplied the Exhalair equipment used in the study. Neither have any non-financial competing interests to declare. The remaining authors have no competing interests to declare.
Grant information
The work was funded by a UCL discretionary account.
Acknowledgments
We thank the study participants from the UCL Department of Infection and Population Health, UCL Department of Primary Care and Population Health, The Bartlett School of Architecture, Building, Environmental Design & Planning, UCL Department of Civil, Environmental and Geomatic Engineering and UCL Department of Epidemiology and Public Health.
Faculty Opinions recommended
References
1.
Gralton J, Tovey E, McLaws ML, et al.:
The role of particle size in aerosolised pathogen transmission: a review.
J Infect.
2011; 62(1): 1–13. PubMed Abstract
| Publisher Full Text
7.
Fiegel J, Clarke R, Edwards DA, et al.:
Airborne infectious disease and the suppression of pulmonary bioaerosols.
Drug Discov Today.
2006; 11(1–2): 51–57. PubMed Abstract
| Publisher Full Text
8.
Papineni RS, Rosenthal FS:
The size distribution of droplets in the exhaled breath of healthy human subjects.
J Aerosol Med.
1997; 10(2): 105–116. PubMed Abstract
| Publisher Full Text
9.
Fairchild CI, Stampfer JF:
Particle concentration in exhaled breath.
Am Ind Hyg Assoc J.
1987; 48(11): 948–949. PubMed Abstract
| Publisher Full Text
10.
Stelzer-Braid S, Olive BG, Blazey AJ, et al.:
Exhalation of respiratory viruses by breathing, coughing, and talking.
J Med Virol.
2009; 81(9): 1674–1679. PubMed Abstract
| Publisher Full Text
11.
Huynh KN, Oliver BG, Stelzer S, et al.:
A new method for sampling and detection of exhaled respiratory virus aerosols.
Clin Infect Dis.
2008; 46(1): 93–95. PubMed Abstract
| Publisher Full Text
12.
Brankston G, Gitterman L, Hirji Z, et al.:
Transmission of influenza A in human beings.
Lancet Infect Dis.
2007; 7(4): 257–265. PubMed Abstract
| Publisher Full Text
14.
Nicas M, Nazaroff WW, Hubbard A:
Toward understanding the risk of secondary airborne infection: emission of respirable pathogens.
J Occup Environ Hyg.
2005; 2(3): 143–154. PubMed Abstract
| Publisher Full Text
16.
Wong BC, Lee N, Li Y, et al.:
Possible role of aerosol transmission in a hospital outbreak of influenza.
Clin Infect Dis.
2010; 51(10): 1176–1183. PubMed Abstract
| Publisher Full Text
17.
Atkinson MP, Wein LM:
Quantifying the routes of transmission for pandemic influenza.
Bull Math Biol.
2008; 70(3): 820–867. PubMed Abstract
| Publisher Full Text
18.
Roy CJ, Milton DK:
Airborne transmission of communicable infection–the elusive pathway.
N Engl J Med.
2004; 350(17): 1710–2. PubMed Abstract
| Publisher Full Text
19.
Anderson RM, May RM:
Infectious Diseases of Humans: Dynamics and Control. Oxford University Press, Oxford 1991: 757. Reference Source
20.
Garske T, Rhodes CJ:
The effect of superspreading on epidemic outbreak size distributions.
J Theor Biol.
2008; 253(2): 228–37. PubMed Abstract
| Publisher Full Text
21.
Lloyd-Smith JO, Schreiber SJ, Kopp PE, et al.:
Superspreading and the effect of individual variation on disease emergence.
Nature.
2005; 438(7066): 355–359. PubMed Abstract
| Publisher Full Text
22.
Schwarz K, Biller H, Windt H, et al.:
Characterization of exhaled particles from the healthy human lung–a systematic analysis in relation to pulmonary function variables.
J Aerosol Med Pulm Drug Deliv.
2010; 23(6): 371–379. PubMed Abstract
| Publisher Full Text
23.
Lawrence I-Kuei Lin:
A concordance correlation coefficient to evaluate reproducibility.
Biometrics.
1989; 45(1): 255–268. PubMed Abstract
| Publisher Full Text
24.
Loudon RG, Roberts RM:
Droplet expulsion from the respiratory tract.
Am Rev Respir Dis.
1967; 95(3): 435–442. PubMed Abstract
25.
Heil M, Hazel AL, Smith JA, et al.:
The mechanics of airway closure.
Respir Physiol Neurobiol.
2008; 163(1–3): 214–221. PubMed Abstract
| Publisher Full Text
26.
Almstrand AC, Bake B, Ljungstrom E:
Effect of airway opening on production of exhaled particles.
J Appl Physiol.
2010; 108(3): 584–588. PubMed Abstract
| Publisher Full Text
27.
Duguid JP:
The numbers and the sites of origin of the droplets expelled during expiratory activities.
Edinb Med J.
1945; 52: 385–401. PubMed Abstract
28.
Killingley B, Enstone J, Booy R, et al.:
Potential role of human challenge studies for investigation of influenza transmission.
Lancet Infect Dis.
2011; 11(11): 879–886. PubMed Abstract
| Publisher Full Text
1
Centre of Infectious Disease Epidemiology, Research Department of Infection and Population Health, University College London, London, UK 2
Pulmatrix Inc., Lexington, 02421, USA 3
Maryland Institute of Applied Environmental Health, University of Maryland School of Public Health, College Park, 20742, USA 4
Research Department of Epidemiology and Public Health, University College London, London, WC1E 6BT, UK
WD and MB are current employees of Pulmatrix, Inc. which supplied the Exhalair equipment used in the study. Neither have any non-financial competing interests to declare. The remaining authors have no competing interests to declare.
The work was funded by a UCL discretionary account.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Wurie F, Le Polain de Waroux O, Brande M et al. Characteristics of exhaled particle production in healthy volunteers: possible implications for infectious disease transmission [version 1; peer review: 2 approved] F1000Research 2013, 2:14 (https://doi.org/10.12688/f1000research.2-14.v1)
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
track
receive updates on this article
Track an article to receive email alerts on any updates to this article.
Share
Open Peer Review
Current Reviewer Status:
?
Key to Reviewer Statuses
VIEWHIDE
ApprovedThe paper is scientifically sound in its current form and only minor, if any, improvements are suggested
Approved with reservations
A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.
Not approvedFundamental flaws in the paper seriously undermine the findings and conclusions
Hill AT. Reviewer Report For: Characteristics of exhaled particle production in healthy volunteers: possible implications for infectious disease transmission [version 1; peer review: 2 approved]. F1000Research 2013, 2:14 (https://doi.org/10.5256/f1000research.1198.r752)
I would recommend the authors alter the statement that 'high particle production was associated with older age'- the multivariable analysis showed that it was in the group aged 40-49 that was statistically significant but
... Continue reading
A well written article with interesting findings
I would recommend the authors alter the statement that 'high particle production was associated with older age'- the multivariable analysis showed that it was in the group aged 40-49 that was statistically significant but not in the group aged 50+
In the multivariable analysis it specifies that it took account of the medical history- did this take account of the treatments patients had e.g. inhaled corticosteroids etc?
The authors state that 'measurements repeated within a session were relatively stable with good agreement between particle counts (concordance coefficient rho ranging from 0.3 to 0.65, p<0.01)'. Although I agree this is statistically significant there is still a lot of intrinsic variability and this would merit further discussion in the Discussion section.
Competing Interests: No competing interests were disclosed.
I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.
Hill AT. Reviewer Report For: Characteristics of exhaled particle production in healthy volunteers: possible implications for infectious disease transmission [version 1; peer review: 2 approved]. F1000Research 2013, 2:14 (https://doi.org/10.5256/f1000research.1198.r752)
This is a nice paper that is generally of high quality and using appropriate methodology. It is a contribution to the literature in terms of its relative size compared to many studies; and it adds new knowledge on the subject
... Continue reading
This is a nice paper that is generally of high quality and using appropriate methodology. It is a contribution to the literature in terms of its relative size compared to many studies; and it adds new knowledge on the subject of particle emission in health adults. The data are clearly constrained by a lack of data on children and the elderly; this issue is fully acknowledged but one or two statements might be seen as overly bold given these shortcomings. Some of the raw observations require greater explanation (for example why particle emission would be higher at each end of the range of humidity and indoor temperature (are U-shaped curves already recognised? If not the data are more puzzling). There could be improved clarity and separation of the results of univariate and multivariable analyses. But overall, a nice paper that adds something.
Competing Interests: No competing interests were disclosed.
I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.
Alongside their report, reviewers assign a status to the article:
Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested
Approved with reservations -
A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.
Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions
Adjust parameters to alter display
View on desktop for interactive features
Includes Interactive Elements
View on desktop for interactive features
Competing Interests Policy
Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list:
Examples of 'Non-Financial Competing Interests'
Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper.
You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors.
You are a close professional associate of any of the authors (e.g. scientific mentor, recent student).
You work at the same institute as any of the authors.
You hope/expect to benefit (e.g. favour or employment) as a result of your submission.
You are an Editor for the journal in which the article is published.
Examples of 'Financial Competing Interests'
You expect to receive, or in the past 4 years have received, any of the following from any commercial organisation that may gain financially from your submission: a salary, fees, funding, reimbursements.
You expect to receive, or in the past 4 years have received, shared grant support or other funding with any of the authors.
You hold, or are currently applying for, any patents or significant stocks/shares relating to the subject matter of the paper you are commenting on.
Stay Updated
Sign up for content alerts and receive a weekly or monthly email with all newly published articles
Comments on this article Comments (0)