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Record W2290062430 · doi:10.1093/jtm/tav006

Higher learning: what we can learn from research conducted above 2500 m of elevation

2016· letter· en· W2290062430 on OpenAlexaffabout
Rudy Zimmer

Bibliographic record

VenueJournal of Travel Medicine · 2016
Typeletter
Languageen
FieldMedicine
TopicTravel-related health issues
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineElevation (ballistics)

Abstract

fetched live from OpenAlex

This Editorial refers to the article by Chan et al. (10.1093/jtm/tav008) Current travel medicine literature seems to rely heavily on lower quality cross-sectional observational studies1 and descriptive case reports and case series2 for a large part of our body of knowledge. However, the field of travel medicine provides a unique opportunity for conducting good quality research using longitudinal observational (i.e. case control,3 prospective and retrospective cohort4), quasi-experimental5 or experimental study designs.6 Unlike many exposures that lead to various disease or health outcomes in other areas of medicine (e.g. cardiovascular risk factors leading to disease outcomes that take decades to occur), the ecology of travel medicine is naturally designed with a pre-, peri- and post-travel phase occurring over a relatively short period of time (e.g. within weeks, months or a few years). Figure 1 illustrates the various designs that can be used to study various research questions affecting travel medicine practice. Main research designs to study travel medicine with examples in the study of acute HAI Certainly, time and funding are key barriers to publishing applied research for many regular full-time and busy practitioners.7 However, it is not clear why we continue to submit poor quality and underpowered studies8 in greater quantities compared with better quality larger longitudinal studies of travel medicine (Steffen et al.9 speak of such study weakness regarding vaccine preventable diseases). After all, we have that natural advantage of the pre-, peri- and post-exposure sequential periods occurring over a manageable duration that reduces the risk of various selection and information biases such as attrition in cohort studies or recall bias in case control studies.10 There is no good reason for travel medicine to avoid better quality studies; including randomized clinical trials (RCTs) using low-cost measuring tools (e.g. questionnaires, common blood tests) to assess preventive interventions such as risk-reduction counselling, immunizations and chemo prophylaxis. One exception to this trend appears to be the study of acute high-altitude illnesses (HAI) such as acute mountain sickness (AMS), high-altitude cerebral edema (HACE), high-altitude pulmonary edema (HAPE) and high altitude sleep disorder.11 For example in this issue of the Journal of Travel Medicine, Chang-Wei et al.12 have conducted a prospective cohort study with no attrition and controlled for background non-AMS symptoms. This required researchers to travel with subjects, and to be on-site measuring AMS and other signs and symptoms in real time at various stages of the climb up and down from a hypoxic environment. Over several decades, researchers studying HAI incidence, pathophysiology, prevention or treatment have developed low cost but validated and reliable measurement tools such as the Lake Louise Scoring System (LLS) for AMS for adults, teens and children.13,14 Moreover, consistent approaches using good quality RCTs have also assisted in useful evidence-based HAI preventive or treatment interventions for travellers.15 Of the 33 articles specifically on HAI that were published in the Journal of Travel Medicine over the past 20 years (1995–2014) with reference to study design, the majority of the studies was conducted in the travel environment or peri-travel period (24 papers in total), as well as one study in a simulated high altitude environment16 rather than being conducted solely during the pre- or post-travel periods. Even the six peri-travel HAI prospective (e.g., Oliver et al.17) or retrospective18 cohort studies are almost as prevalent as the seven peri-travel descriptive case reports or case series (e.g., Salazar et al.19) or the nine peri-travel descriptive or analytical cross-sectional studies, including one article with two historical observation periods that were 15 years apart.20 The trend of HAI studies over the past 20 years also appears to be that of publishing fewer observational ‘snap shot’ studies and more longitudinal studies. This seems to be in opposition to the stable trend in other topic areas such as travel-related risk behaviour, which is dominated by pre-travel cross-sectional KAP (knowledge–attitude–practice) designs that focus on intentions (e.g., Van Herck et al.21) or information recall (e.g., McGuinness et al.22) rather than observations of actual behaviour in the field (e.g., Ozdemir et al.23). There are also a few peri-travel HAI studies in this journal with experimental designs, including one RCT24 and one non-randomized quasi-experimental trial.25 Finally, there is also one systematic review on the effectiveness of acetazolamide in the prevention of AMS26 based on the meta-analysis of seventeen peri-travel RCTs previously published in various journals. Overall, the majority of studies on HAI published in this journal was conducted in the field rather than before or after travel. Why is this so? The likely answer lies with the underlying features of acute HAI that make it necessary for researchers to properly assess HAI during travel rather beforehand or afterward. First, hypobaric hypoxia within a natural travel environment can only occur at elevations usually >2500 m (∼8200 feet) above sea level. Some individuals adapt quickly and others do not.27 To consider constitutional symptoms such as headaches, nausea, fatigue or anorexia as HAI,13 the traveller needs to be currently at high altitude as a pre-requisite unlike many other travel-related conditions that may not be as geographically restricted. Hence, the exposure leading to HAI is easily determined by itinerary and localized to specific regions accessible to travellers above 2500 m sea level such as those destinations described in the review by Netzer et al.11 Second, the disease outcome is usually reversible if a traveller descends immediately or remains at the same elevation with or without adjunctive treatment.11, 27 Even among serious cases of HAPE and HACE where death is a real possibility, many successfully treated individuals are left with little clinical sequelae on returned to normobaric atmosphere below 2500 m (e.g., Basnyat28). There are also no consistent diagnostic features that remain for days or weeks, especially following milder AMS. Thus, it is virtually impossible to objectively confirm many cases in the post-travel period. If one wants to accurately monitor the incidence of HAI among a cohort of travellers, then researchers need to monitor cases within the high-altitude environment during travel. This was the approach of Chang-Wei et al.12 to validate students’ symptoms with direct observation in the field on Jade Mountain in Taiwan. Third, self-assessment of HAI by travellers using validated and standardized low-technology tools such as the LLS may still be unreliable among persons with serious neurologically impairment caused by the same condition that these same individuals are trying to measure. A drunken individual is not reliable in measuring his or her impairment while intoxicated. Observation by an independent acclimatized researcher ensures accuracy in measuring HAI syndromes, since there are often no established diagnostic tests unique to these conditions or practical to employ on-site. For example, oxygen saturation levels taken by a pulse oximeter may not correlate with one’s risk of HAI.29 These features of HAI may be the reason that this group of medical conditions was one of the first travel-related health issues studied peri-travel in commonly encountered high-altitude destinations such as the Mount Everest region (Nepal)30 and Mount Kilimanjaro (Tanzania)/Mount Kenya (Kenya).31 Finally, the occurrence of AMS is common enough for a travelling physician to observe on a regular basis. No amount of second-hand description is more informative to a travel health provider than directly observing and addressing various HAI cases in the field during one’s own travels. Personally, I have had to assist several overt cases of AMS, HACE and HAPE. In 1990, my first clinical experience was to carry a comatose Nepali porter down >500 m from the top of Thorong La (5400 m) on the Annapurna Circuit heading towards Muktinath, at which point the porter regained consciousness and was able to descend further on his own legs. High-altitude hypoxia clearly affects different individuals with varying outcomes from the extremes of no symptoms to life-threatening conditions (HACE and HAPE). This is easily recognized by any travel health provider while visiting these high-altitude locations (e.g., Welch et al.32), and easily enumerated as cases with minimal examination or interventions by researchers on-site rather than after the fact. In conclusion, the approach taken by many researchers to study HAI in-country may provide lessons that can be translated to other travel-related health topics crying out for better study within the travel environment rather than focusing predominantly on collecting data during the pre- and post-travel periods. This might include formulating better research questions, creating better quality study designs, monitoring and measuring subjects in the peri-travel phase as well as addressing the ethical and resource issues required to work in a foreign country. Prospective cohort studies such as that conducted by Chang-Wei et al.12 also offer an illustration of how some aspects of travel-related problems can be addressed in our own backyards. The field of travel medicine is naturally designed to be studied longitudinally through the pre-, peri- and post-travel phases of exposure to the travel environment in a foreign destination. Let’s use that design advantage to create better quality research in travel medicine. Conflict of interest: None declared.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.025
metaresearch head score (Gemma)0.116
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.027
Threshold uncertainty score0.133

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.116
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0060.004
Science and technology studies0.0040.008
Scholarly communication0.0190.021
Open science0.0040.006
Research integrity0.0160.018
Insufficient payload (model declined to judge)0.0270.014

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.153
GPT teacher head0.407
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2016
Admission routes2
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