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SUPPLEMENTARY MATERIAL

1. ELABORATION OF SELECTED ISSUES

Why quantify dermal dose-response?

There are many reasons for quantifying dermal dose-response. First is recognition of the need for more sophisticated frameworks exemplified by the Key Events Dose-Response Framework (KEDRF) [1, 2]. Secondly despite numerous studies in the 1980s, pool outbreaks and closures continue and their frequency may be increasing [3-5]. Finally there are a range of specific risk issues and unknowns.

The need for better infection process conceptualization has been recognised in food risk assessment as evidenced by the KEDRF proposal [1, 6]. In this scheme Julien et al. (Figure 2 in [2]) propose a split of studies into: i) Intake/Exposure; ii) Biological Interaction/Process (adsorption); iii) Interaction/Process (transport/distribution/excretion); iv) Interaction/process (metabolism); v) target tissue Interaction or Process; and vi) Ultimate effect: to support data evaluation, focus research, strengthen decision-making and advance dose-response assessment. In KEDRF infection initiation comprises Steps i)-iii), correspond to the gaps we identified in dermal dose-response theory. This delineates them from ‘illness’ studies, corresponding to Steps iv)-vi), which appear better characterised as evidenced by diverse reviews [e.g. 7, 8-11] and Disability Adjusted Life Year quantification of illness [12].

A second driver is that despite numerous studies in the 1980s, pool outbreaks and closures continue and their frequency may even be increasing [compare 3-5, 13, 14]. The lack of dermal dose-response models for pools and other dermal exposure, contrasts with the progress in applying gastroenteritis risk models to pool management [15-17]. Mechanistic dermal dose-response models could also help better define the risks posed, and identify new, or improve existing, management strategies e.g. super-disinfection, contaminated water disposal.

The above knowledge gaps appear compounded by uncertainties in the available data. Epidemiology studies [e.g. 18] focus on disease incidence and pool disinfection but may neglect factors promoting infection e.g. head immersion frequency [15], skin ecology [19], bathing environments [20, 21]. The P. aeruginosa density thresholds which induce symptoms are poorly defined, and in some instances even suggest P. aeruginosa is not abundant [3, 22, 23]. Whether this reflects strain virulence differences [3], sampling delays [5, 24] or transient biofilm sloughing [22], is unclear. Most P. aeruginosa pool studies were undertaken in the 1980s when microbial taxonomy, ecology and analytical techniques were nascent. Even today comprehensive genomic surveys of pool isolates appear rare [20, 23] and isolate diversity data [3, 25] still reflect culturable species and isolation techniques [26-28].

Additional drivers for better dose-response characterization are the uncertainties and unknowns relating to: i) risks from bathing in contaminated potable water [29]; ii) the relative safety of closed versus natural, open air pool environments; iii) host and shedder factors including systemic co-morbidities (immunodeficiency, diabetes mellitus etc.), dermatitis, maceration, and ear canal trauma, anatomy and exostoses; iv) increasing use by aged and infirm individuals of hydrotherapy pools [30, 31]; v) occupational health and safety [32]; vi) the ecology of opportunistic waterborne pathogens e.g. the Viable But Non-Culturable (VBNC) state [27] and biofilms for which P. aeruginosa is a good model [33]; vii) whether pathogenic biotypes are environmental or human derived [34, 35] ; viii) the relationship between bacterial densities, and shedder and bather numbers [36-38]; ix) P. aeruginosa risks in natural recreational waters [39].

P. aeruginosa ecology and whether they are autochthonous, opportunistic or exogenous pathogens?

While pathogenic P. aeruginosa sources include contaminated water and soil [40-42], P. aeruginosa can also be a minor apparently commensal component of the normal ear microbiota whose growth is promoted by skin environment changes [43-46]. Alternatively autochthonous P. aeruginosa [26] may be transferred from elsewhere on a body. For example, P. aeruginosa appears to be common around finger nails, leading in extreme cases to the ‘Green’ nails condition [47, 48]. Though Gram-negative bacteria are only normally present in low numbers on the skin, the ear canal microbiota are unstable and high moisture promotes their growth in healthy individuals [19, 43, 45, 47]. P. aeruginosa has been detected from swabs of 2 to 8% of non-bather ear samples, 12% of throat samples from bathers [18, 49] and from 11 to 16% of adult bodies generally [50]. Even these numbers likely underestimate prevalence as the studies used culture techniques, which could have missed VBNC P. aeruginosa [27]. Finally P. aeruginosa AOE is a common complication of ear syringing [51] which can be expected to have used sterile water or saline. Together these observations raise the question how often are AOE and potentially Folliculitis, induced exogenously versus endogenously? If significant instances are due to endogenous P. aeruginosa a likely mechanism involved is quorum sensing [52-55].

Clinical Folliculitis lesion density

The possibility that Folliculitis pustule density, could correlate with water quality and exposure time raises the question of what lesion densities correspond to clinical Folliculitis? We found no such data. However, several outbreak reports included photographs and supporting information, indicating what is qualitatively considered Folliculitis. To provisionally quantify lesion density, lesion numbers on eight clear photographs typical of Folliculitis (Figure 3 in [56]) were counted (Table 1). As most images lacked magnification scales, lesion densities were estimated by measuring the equivalent area on an adult body and scaling each image based on patient age and average height for age values [57]. Minimum and geometric means (one significant figure) were 200 and 1000 follicular lesions.m-2 respectively. Though approximate, these estimates were of comparable magnitude and indicated that only a small proportion of the total number of follicles, reportedly 120000 to 300000 lesions.m-2 [58, 59], are typically infected. This is consistent with the hypothesis that Folliculitis is localized and involves a minority of available follicles.

Table 1. Folliculitis lesion density estimated from literature case studies

|Estimated Lesion Density* |Patient |Imaged Area |Comments |Source |

|104 .m-2 |Girl 11 y |Buttock papules |Indoor swimming pool, scale |Fig.1 [60] |

|(15 over 15 cm2) | | |unclear | |

|5x102 .m-2 |Boy est. 8 y |Lower mid, front papules |Shower/bath exposure. Image |Fig. 1 [29] |

|(30 over 600 cm2) | | |contrast possibly suboptimal.| |

|6x102 .m-2 | |Abdomen papules | |Fig. 1 [29] |

|(37 over 600 cm2) | | | | |

|1 x103 .m-2 | |Trunk |Good contrast, includes 8 |Figure 2 [29] |

|(35 over 320 cm2) | | |pustules | |

|4x103 .m-2 |Girl 9 y |Abdomen pustules |Hot tub |Fig. 1a [61] |

|(6 over 14 cm2) | | | | |

|6x102 .m-2 |Girl 8 y |Trunk |Whirlpool Spa |Fig. 1 [62] |

|(56 over 1000 cm2) | | | | |

|2x102 .m-2 |Girl 17 y |NA |Whirlpool Spa |Text data [62] |

|(10 over 600 cm2) | | | | |

|4x102 .m-2 |Adult male |Trunk papules |Home spa. |Fig. 1 [63] |

|(12 over 120 cm2) | | | | |

*Lesion density estimate based on adult body zone size adjusted for average patient age [57].

Traversing the epidermis

Away from the forehead the likelihood of an individual P. aeruginosa contacting a hair follicle directly from the void is low. In the case of the lower torso/upper leg, follicle coverage is only 0.19 to 0.33% [58] and we suggest this figure is also the likelihood of such direct contact. This raises the further question of how far cells would typically need to migrate horizontally from their point of contact prior to inducing a follicle infection if they did not impact a follicle directly?

Arms, torso and thighs/upper leg have median follicle densities of 17 to 32 cm-2 and orifice diameters of 80 to 130 µm [58]. To estimate an indicative distance of travel from impact point to follicle we used MS Excel to simulate n=10,000 random skin contacts and the distance of each to the nearest follicle assuming twenty five 100 µm diameter follicles were distributed either randomly (position simulated using a random number generator) or regularly (5 x 5 array with each follicle separated by 2 mm i.e. XY coordinates 0.1, 0.1 mm, 0.1, 0.3 mm etc.) within a 1 cm2 grid. Given randomly distributed (and potentially overlapping) follicles, the average, median, standard deviation and 5th percentile distances to the follicle boundaries were 1.0, 1.0, 0.6 and 0.13 mm respectively. For regularly distributed follicles the corresponding statistics were 0.7, 0.7, 0.3 and 0.12 mm to a follicle boundary. Consistent with measured follicle coverage, 0.22% of bacteria were estimated to impact follicles directly from the Void.

2. EXPANDED READING LIST

Introduction

[4, 14, 20, 23, 64]

Pools, infections and P. aeruginosa disease

[5, 40, 65-69].

Knowledge gaps and the need to quantify dermal dose-response

General: [20, 22, 23, 70]; KEDRF generally [1, 2, 6]; KEDRF ‘illness’ study, Steps iv)-vi), [7-12]; Why quantify dermal dose-response [3-5, 13-17, 27, 29-39]; Uncertainties [3, 5, 18, 20, 22, 23, 28, 71-77].

Dose-response theory and the epidermis

[6, 78-87].

Aetiology and dose-response

Water quality and infection likelihood

[3, 11, 18, 22, 23, 25, 39, 40, 67, 68, 88-91].

Experimentally induced skin infections

[19, 25, 40, 41, 43-45, 54, 56, 67, 92-94].

Autochthonous or exogenous pathogens?

[18, 19, 26, 27, 40-51].

A conceptual challenge for Folliculitis dose-response algorithms

[64, 69, 81, 95, 96].

Clinical Folliculitis lesion density

[29, 56-63].

The Ecology of Intimate Contact

Traversing ‘the Void’

General: [97, 98]; Diffusion and Dispersion [99-104]; Does motility help? [96, 99, 105-114].

Epidermal deposition and adhesion

[11, 33, 42, 98, 110, 113, 115-131].

Horizontal Dispersion

[131-140].

Ultrastructure and infection

[19, 39, 56, 58, 59, 89, 93, 141-144].

Adherence rates and follicle invasion efficiency

[58, 101, 105, 127, 132, 133, 142, 145, 146].

Hydrodynamic scouring and infection

[21, 100, 118, 126, 139, 141, 142, 147-155].

Quorum Sensing

[52-55, 156-158].

Time dependency of infection induction

[36, 37, 87, 96, 100, 103, 109, 128, 154, 159-163].

Towards dose-response models and frameworks for P. aeruginosa pool infections

Applicability of ‘Single-Hit’ theory

[64, 81, 82, 95].

Single-Hit theory caveats

[65, 82 , 96, 109].

‘Third Generation’ mechanistic modelling of Folliculitis

[1, 87, 98, 164].

Exogenously induced AOE

Folliculitis v. AOE aetiologies: [39, 41, 43, 45, 49, 56, 89, 92, 165]; Cerumen: [51, 67, 166-171].

A framework for exogenous Folliculitis and AOE

[2, 45, 172].

From tables

Algorithm references

[82-84, 100, 102, 103].

Outbreaks and surveys

[3, 22, 24, 30, 73, 143, 173-178]

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