BACKGROUND: Acute mountain sickness (AMS) is a potentially lethal condition caused by acute hypoxia after ascending to altitudes higher than 2500 m in a short time. The main symptom of AMS is headache. Numerous risk factors of AMS have been examined, including gender, obesity, ascent rate, age and individual susceptibility. In previous studies, age was considered a predisposing factor for AMS. However, different opinions have been raised in recent years. To clarify the association between AMS and age, we conducted this meta-analysis.
METHODS: We obtained observational studies that explored risk factors for AMS by searching PubMed, Embase, China National Knowledge Internet (CNKI), the Wanfang database and CQVIP for articles published before March 2017. The studies included were required to provide the mean age and its standard deviation for subjects with and without AMS, the maximum altitude attained and the mode of ascent. The Lake Louse Score (LLS) or the Chinese AMS score (CAS) was used to judge the severity of AMS symptoms and incidence. Studies were pooled for the analysis by using a random effects model in RevMan 5.0. Meta-regression and subgroup analyses were conducted to identify sources of heterogeneity using Stata 14.2 and RevMan 5.0.
RESULTS: In total, 17 studies were included, and the overall number of subjects with and without AMS was 1810 and 3014, respectively. The age ranged from 10 to 76 years. Analysis of the 17 included studies showed that age was not associated with AMS (mean difference (MD) = 0.10; 95% CI: -0.38-0.58; P = 0.69).
CONCLUSION: This meta-analysis suggests that there is no association between age and the risk of AMS. Race, age, and ascent mode are common sources of heterogeneity, which may provide an analytical orientation for future heterogeneity analyses.
Aims. Previous epidemiological investigations of the relationship between smoking and acute mountain sickness (AMS) risk yielded inconsistent findings. Therefore, a meta-analysis of observational studies was performed to determine whether smoking is related to the development of AMS. Methods. Searches were performed on PubMed, Scopus, Embase, and Web of Science for relevant studies that were published before November 2016 reporting smoking prevalence and AMS. Two evaluators independently selected studies, extracted data, and assessed study quality. The pooled relative risks (RRs) and 95% confidence intervals (CIs) were obtained using random-effects models. Subgroup analyses were performed according to the type of participant, altitude, and study design. Results. A total of 11 observational studies involving 7,106 participants, 2,408 of which had AMS, were eligible for inclusion in this meta-analysis. The summary RR for AMS comparing smokers to nonsmokers was 1.02 (95% CI: 0.83 to 1.26). Specific analyses for altitude, type of participant, and study design yielded similar results. There was significant heterogeneity for all studies (Q=37.43; P<0.001; I2=73%, 95% CI: 51% to 85%). No publication bias was observed (Egger’s test: P=0.548, Begg’s test: P=0.418). Conclusions. The meta-analysis indicates that no difference was found in AMS risk with regard to smoking status.
AIM: Studies of the potential association between cigarette smoking and acute mountain sickness (AMS) have reached contradictory conclusions. Our aim was to perform a meta-analysis of studies across a range of populations to ascertain better the true relationship between cigarette smoking and AMS. MATERIALS AND METHODS: We used the PRISMA protocol to identify and screen eligible studies of smoking and AMS. Databases including Pubmed and Google Scholar were searched, using the terms 'smoking' and 'acute mountain sickness.' We conducted a meta-analysis of the selected studies in order to evaluate causal inference, evaluate potential biases, and investigate possible sources of heterogeneity across studies. RESULTS: We identified 3907 publications, of which 29 were eligible for inclusion by reporting smoking status and AMS. Of these, eight publications were excluded because they were duplicative or were lacking quantitative data. The 21 studies analyzed included 16 566 subjects. These fell into two groups: occupational/military (n = 8) or volunteers/trekkers/mixed (n = 13). Study heterogeneity was high (X² = 55.5, P < .001). Smoking was not statistically associated with increased risk of AMS: pooled OR = 0.88 (95% CI = 0.74–1.05). Stratification yielded similar risk estimates among the occupational/military studies versus all others and studies at relatively higher and lower altitudes. CONCLUSIONS: Overall, smoking was not statistically significantly associated with AMS: there is no consistent effect of cigarette smoking acting as either a protective factor against or a risk factor for AMS. Implications: This is the first quantitative assessment of published studies on smoking and AMS, which shows smoking to be neither a risk, nor protective. Studies specifically focusing on smoking as a risk factor, should guide further research on this issue. Although all smokers should be strongly advised to quit, studies on risk factors for AMS focusing on other exposures could shed light on the full range of risks for AMS. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
BACKGROUND: People rapidly ascending to high altitudes (>2500 m) may suffer from acute mountain sickness (AMS). The association between smoking and AMS risk remains unclear. Therefore, we performed a meta-analysis to evaluate the association between smoking and AMS risk.
METHODS: The association between smoking and AMS risk was determined according to predefined criteria established by our team. Meta-analysis was conducted according to the PRISMA guidelines. We included all relevant studies listed in the PubMed and Embase databases as of September 2015 in this meta-analysis and performed systemic searches using the terms "smoking", "acute mountain sickness" and "risk factor". The included studies were required to provide clear explanations regarding their definitions of smoking, the final altitudes reached by their participants and the diagnostic criteria used to diagnose AMS. Odds ratios (ORs) were used to evaluate the association between smoking and AMS risk across the studies, and the Q statistic was used to test OR heterogeneity, which was considered significant when P < 0.05. We also computed 95% confidence intervals (CIs). Data extracted from the articles were analyzed with Review Manager 5.3 (Cochrane Collaboration, Oxford, UK).
RESULTS: We used seven case-control studies including 694 smoking patients and 1986 non-smoking controls to analyze the association between smoking and AMS risk. We observed a significant association between AMS and smoking (OR = 0.71, 95% CI 0.52-0.96, P = 0.03).
CONCLUSIONS: We determined that smoking may protect against AMS development. However, we do not advise smoking to prevent AMS. More studies are necessary to confirm the role of smoking in AMS risk.
Acute mountain sickness (AMS) is a potentially lethal condition caused by acute hypoxia after ascending to altitudes higher than 2500 m in a short time. The main symptom of AMS is headache. Numerous risk factors of AMS have been examined, including gender, obesity, ascent rate, age and individual susceptibility. In previous studies, age was considered a predisposing factor for AMS. However, different opinions have been raised in recent years. To clarify the association between AMS and age, we conducted this meta-analysis.
METHODS:
We obtained observational studies that explored risk factors for AMS by searching PubMed, Embase, China National Knowledge Internet (CNKI), the Wanfang database and CQVIP for articles published before March 2017. The studies included were required to provide the mean age and its standard deviation for subjects with and without AMS, the maximum altitude attained and the mode of ascent. The Lake Louse Score (LLS) or the Chinese AMS score (CAS) was used to judge the severity of AMS symptoms and incidence. Studies were pooled for the analysis by using a random effects model in RevMan 5.0. Meta-regression and subgroup analyses were conducted to identify sources of heterogeneity using Stata 14.2 and RevMan 5.0.
RESULTS:
In total, 17 studies were included, and the overall number of subjects with and without AMS was 1810 and 3014, respectively. The age ranged from 10 to 76 years. Analysis of the 17 included studies showed that age was not associated with AMS (mean difference (MD) = 0.10; 95% CI.: -0.38-0.58; P = 0.69).
CONCLUSION:
This meta-analysis suggests that there is no association between age and the risk of AMS. Race, age, and ascent mode are common sources of heterogeneity, which may provide an analytical orientation for future heterogeneity analyses.
