MétaCan
Menu
Retour à la cohorte
Enregistrement W2290062430 · doi:10.1093/jtm/tav006

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

2016· letter· en· W2290062430 sur OpenAlexaffabout
Rudy Zimmer

Notice bibliographique

RevueJournal of Travel Medicine · 2016
Typeletter
Langueen
DomaineMedicine
ThématiqueTravel-related health issues
Établissements canadiensUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésMedicineElevation (ballistics)

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,025
score de la tête « metaresearch » (Gemma)0,116
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,027
Score d'incertitude au seuil0,133

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0250,116
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0040,002
Bibliométrie0,0060,004
Études des sciences et des technologies0,0040,008
Communication savante0,0190,021
Science ouverte0,0040,006
Intégrité de la recherche0,0160,018
Charge utile insuffisante (le modèle a refusé de juger)0,0270,014

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,153
Tête enseignante GPT0,407
Écart entre enseignants0,254 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2016
Routes d'admission2
Résumé présentoui

Explorer davantage

Même revueJournal of Travel MedicineMême sujetTravel-related health issuesTravaux en français237 207