Does 2x2 airplane passenger contact tracing for infectious respiratory pathogens work? A systematic review of the evidence.

We critically appraised the literature regarding in-flight transmission of a range of respiratory infections to provide an evidence base for public health policies for contact tracing passengers, given the limited pathogen-specific data for SARS-CoV-2 currently available. Using PubMed, Web of Science, and other databases including preprints, we systematically reviewed evidence of in-flight transmission of infectious respiratory illnesses. A meta-analysis was conducted where total numbers of persons on board a specific flight was known, to calculate a pooled Attack Rate (AR) for a range of pathogens. The quality of the evidence provided was assessed using a bias assessment tool developed for in-flight transmission investigations of influenza which was modelled on the PRISMA statement and the Newcastle-Ottawa scale. We identified 103 publications detailing 165 flight investigations. Overall, 43.7% (72/165) of investigations provided evidence for in-flight transmission. H1N1 influenza A virus had the highest reported pooled attack rate per 100 persons (AR = 1.17), followed by SARS-CoV-2 (AR = 0.54) and SARS-CoV (AR = 0.32), Mycobacterium tuberculosis (TB, AR = 0.25), and measles virus (AR = 0.09). There was high heterogeneity in estimates between studies, except for TB. Of the 72 investigations that provided evidence for in-flight transmission, 27 investigations were assessed as having a high level of evidence, 23 as medium, and 22 as low. One third of the investigations that reported on proximity of cases showed transmission occurring beyond the 2x2 seating area. We suggest that for emerging pathogens, in the absence of pathogen-specific evidence, the 2x2 system should not be used for contact tracing. Instead, alternate contact tracing protocols and close contact definitions for enclosed areas, such as the same cabin on an aircraft or other forms of transport, should be considered as part of a whole of journey approach.

Melanoma incidence in Australian commercial pilots, 2011-2016.

OBJECTIVES: Occupational exposure to cosmic and ultraviolet radiation may increase airline pilots' risk of cutaneous melanoma. Meta-analyses of available data show a higher than average incidence of melanoma in airline pilots, but the most recent systematic review revealed that few contemporary data are available. Moreover, all relevant studies have been conducted in Northern Hemisphere populations. We therefore aimed to examine if Australian commercial pilots have a raised incidence of melanoma compared with the general population. METHODS: We examined all melanoma histologically diagnosed among Australian-licensed commercial pilots in the period 2011-2016 by manually reviewing de-identified data in the medical records system of the Australian Civil Aviation Safety Authority. We estimated age-specific incidence rates and compared these with corresponding population rates using standardised incidence ratios (SIRs) as measures of relative risk. Expected numbers were calculated by multiplying age- and calendar period-specific person-years (PYs) with corresponding rates from the entire Australian population; 95% CI were calculated assuming a Poisson distribution of the observed cases. RESULTS: In this cohort of Australian-licensed commercial pilots observed for 91 370 PYs, 114 developed a melanoma (51 invasive, 63 in situ). More than 50% of melanomas occurred on the trunk, and the predominant subtype was superficial spreading melanoma. The SIR for invasive melanoma was 1.20 (95% CI 0.89 to 1.55) and for melanoma in situ, 1.39 (95% CI 1.08 to 1.78). CONCLUSION: Australian-licensed commercial pilots have a modestly raised risk of in situ melanoma but no elevation of invasive melanoma compared with the general population.

Identifying Pilots with Parkinson's Disease.

BACKGROUND: In 2012 the Australian Institute of Health and Welfare produced a report titled 'Dementia in Australia.'(2) The report noted that the number of people with dementia in Australia would reach almost 400,000 by 2020. Australia is a jurisdiction which does not impose a mandatory retirement age for pilots. With an aging population it was hypothesized that conditions such as Parkinson's disease (PD) were likely to be seen more commonly by the Civil Aviation Safety Authority (CASA). It was decided that this was an appropriate time to retrospectively study the data held by CASA. METHODS: An interrogation of CASA databases was undertaken. Data was produced comparing percentage of Class 1 certificate holders over 60 yr of age against time. A cohort of pilots and controllers with PD was identified. The history of the cases was reviewed. RESULTS: The study confirms that the pilot population is aging in line with population trends. Over a period from 1992 to 2012, 22 cases of pilots and controllers with PD were identified. DISCUSSION: The study confirmed that PD will be of increased relevance over the next decade. Gaps between policy and practice managing past cases were identified. Updated guidelines have been published aiming to address the deficiencies identified in the study. Historically pilots and controllers have been able to maintain certification for an average of 3.75 yr. This information should be of benefit to clinicians, pilots, and controllers when considering occupation and treatment options.

Aeromedical decision making: from principles to practice.

While there is literature which describes aeromedical decision making (ADM), there is not much which describes a process identifying the steps to be taken in arriving at such decisions. A five-step algorithm is proposed to clarify the ADM process. The five steps are: 1) determine the likelihood of a clinically significant event from the health condition; 2) determine the likelihood of an undesirable aviation event from the health condition; 3) determine the acceptability of the combined risks (#1 and #2); 4) determine the risk level after clinical intervention for the health condition; and 5) determine the risk level after operational restrictions for the health condition. There are several factors which can affect the various steps in the algorithm, such as uncertainty, difficulty in generalization, power and "fit" of the studies, etc. Notwithstanding these issues, the algorithm serves a useful purpose in providing a pathway for ADM.