Lower Obesity Rate during Residence at High Altitude among a Military Population with Frequent Migration: A Quasi Experimental Model for Investigating Spatial Causation
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{"title"=>"Lower obesity rate during residence at high altitude among a military population with frequent migration: A quasi experimental model for investigating spatial causation", "type"=>"journal", "authors"=>[{"first_name"=>"Jameson D.", "last_name"=>"Voss", "scopus_author_id"=>"55567519200"}, {"first_name"=>"David B.", "last_name"=>"Allison", "scopus_author_id"=>"7202902345"}, {"first_name"=>"Bryant J.", "last_name"=>"Webber", "scopus_author_id"=>"56602427600"}, {"first_name"=>"Jean L.", "last_name"=>"Otto", "scopus_author_id"=>"35269258400"}, {"first_name"=>"Leslie L.", "last_name"=>"Clark", "scopus_author_id"=>"55208968100"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"607990639", "issn"=>"19326203", "doi"=>"10.1371/journal.pone.0093493", "scopus"=>"2-s2.0-84957111371", "pmid"=>"24740173", "sgr"=>"84957111371"}, "id"=>"2a781e80-2d4c-3346-95b2-bc6601b0476e", "abstract"=>"We sought to evaluate whether residence at high altitude is associated with the development of obesity among those at increased risk of becoming obese. Obesity, a leading global health priority, is often refractory to care. A potentially novel intervention is hypoxia, which has demonstrated positive long-term metabolic effects in rats. Whether or not high altitude residence confers benefit in humans, however, remains unknown. Using a quasi-experimental, retrospective study design, we observed all outpatient medical encounters for overweight active component enlisted service members in the U.S. Army or Air Force from January 2006 to December 2012 who were stationed in the United States. We compared high altitude (>1.96 kilometers above sea level) duty assignment with low altitude (<0.98 kilometers). The outcome of interest was obesity related ICD-9 codes (278.00-01, V85.3x-V85.54) by Cox regression. We found service members had a lower hazard ratio (HR) of incident obesity diagnosis if stationed at high altitude as compared to low altitude (HR 0.59, 95% confidence interval [CI] 0.54-0.65; p<0.001). Using geographic distribution of obesity prevalence among civilians throughout the U.S. as a covariate (as measured by the Centers for Disease Control and Prevention and the REGARDS study) also predicted obesity onset among service members. In conclusion, high altitude residence predicts lower rates of new obesity diagnoses among overweight service members in the U.S. Army and Air Force. Future studies should assign exposure using randomization, clarify the mechanism(s) of this relationship, and assess the net balance of harms and benefits of high altitude on obesity prevention.", "link"=>"http://www.mendeley.com/research/lower-obesity-rate-during-residence-high-altitude-among-military-population-frequent-migration-quasi", "reader_count"=>29, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>2, "Researcher"=>5, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>7, "Student > Master"=>6, "Other"=>2, "Student > Bachelor"=>2, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>2, "Researcher"=>5, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>7, "Student > Master"=>6, "Other"=>2, "Student > Bachelor"=>2, "Professor"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>3, "Engineering"=>2, "Biochemistry, Genetics and Molecular Biology"=>3, "Nursing and Health Professions"=>2, "Medicine and Dentistry"=>9, "Agricultural and Biological Sciences"=>2, "Neuroscience"=>1, "Arts and Humanities"=>1, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Sports and Recreations"=>3, "Psychology"=>1, "Computer Science"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>9}, "Neuroscience"=>{"Neuroscience"=>1}, "Sports and Recreations"=>{"Sports and Recreations"=>3}, "Psychology"=>{"Psychology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>2}, "Computer Science"=>{"Computer Science"=>1}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>2}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>3}, "Unspecified"=>{"Unspecified"=>3}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}, "Arts and Humanities"=>{"Arts and Humanities"=>1}}, "reader_count_by_country"=>{"Belgium"=>1, "United Kingdom"=>1, "Mexico"=>1, "France"=>1}, "group_count"=>0}

Scopus | Further Information

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  • {"files"=>["https://ndownloader.figshare.com/files/1466487"], "description"=>"<p>Lines represent altitude categorization based on natural break at 1.96</p>", "links"=>[], "tags"=>["Biochemistry", "metabolism", "Oxygen metabolism", "Bioenergetics", "nutrition", "physiology", "Physiological parameters", "Body weight", "obesity", "epidemiology", "Disease surveillance", "Spatial epidemiology", "Public and occupational health", "Global health", "research design", "Clinical research design", "Retrospective studies", "observed"], "article_id"=>1001012, "categories"=>["Biological Sciences"], "users"=>["Jameson D. Voss", "David B. Allison", "Bryant J. Webber", "Jean L. Otto", "Leslie L. Clark"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0093493.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Histogram_of_Observed_Density_by_Altitude_/1001012", "title"=>"Histogram of Observed Density by Altitude.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-16 02:55:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/1466488"], "description"=>"<p>Cumulative Hazard of Obesity Diagnosis based on Cox Proportional Hazards Model Adjusted for Enlistment BMI, Sex, Race, Occupation, Time in Service, Branch of Service, Housing Allowance, and Age. The Red Curve is High Altitude and the Blue Curve is Low Altitude.</p>", "links"=>[], "tags"=>["Biochemistry", "metabolism", "Oxygen metabolism", "Bioenergetics", "nutrition", "physiology", "Physiological parameters", "Body weight", "obesity", "epidemiology", "Disease surveillance", "Spatial epidemiology", "Public and occupational health", "Global health", "research design", "Clinical research design", "Retrospective studies"], "article_id"=>1001013, "categories"=>["Biological Sciences"], "users"=>["Jameson D. Voss", "David B. Allison", "Bryant J. Webber", "Jean L. Otto", "Leslie L. Clark"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0093493.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Cumulative_Hazard_Function_/1001013", "title"=>"Cumulative Hazard Function.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-16 02:55:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/1466490"], "description"=>"<p>The x-axis represents the natural log of analysis time in days.</p>", "links"=>[], "tags"=>["Biochemistry", "metabolism", "Oxygen metabolism", "Bioenergetics", "nutrition", "physiology", "Physiological parameters", "Body weight", "obesity", "epidemiology", "Disease surveillance", "Spatial epidemiology", "Public and occupational health", "Global health", "research design", "Clinical research design", "Retrospective studies", "goodness", "proportional", "hazards"], "article_id"=>1001015, "categories"=>["Biological Sciences"], "users"=>["Jameson D. Voss", "David B. Allison", "Bryant J. Webber", "Jean L. Otto", "Leslie L. Clark"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0093493.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Log_Log_Plot_for_Goodness_of_Fit_with_Proportional_Hazards_Assumption_/1001015", "title"=>"Log-Log Plot for Goodness of Fit with Proportional Hazards Assumption.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-16 02:55:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/1466496"], "description"=>"<p>*P-values based on χ square test of homogeneity for Sex, Service Branch, Housing Allowance, Occupation, and Race/Ethnicity and are based on unequal variance t-test for Age, Time in Service, and BMI. Statistical tests were not weighted for observation time.</p>", "links"=>[], "tags"=>["Biochemistry", "metabolism", "Oxygen metabolism", "Bioenergetics", "nutrition", "physiology", "Physiological parameters", "Body weight", "obesity", "epidemiology", "Disease surveillance", "Spatial epidemiology", "Public and occupational health", "Global health", "research design", "Clinical research design", "Retrospective studies", "overweight", "members"], "article_id"=>1001016, "categories"=>["Biological Sciences"], "users"=>["Jameson D. Voss", "David B. Allison", "Bryant J. Webber", "Jean L. Otto", "Leslie L. Clark"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0093493.t001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Demographic_and_Military_Characteristics_among_Overweight_Active_Component_Army_and_Air_Force_Service_Members_by_Altitude_2006_8211_2012_/1001016", "title"=>"Demographic and Military Characteristics among Overweight Active Component Army and Air Force Service Members by Altitude, 2006–2012.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-04-16 02:55:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/1466497"], "description"=>"<p>Hazard Ratios by Fully Adjusted Cox Model.</p>", "links"=>[], "tags"=>["Biochemistry", "metabolism", "Oxygen metabolism", "Bioenergetics", "nutrition", "physiology", "Physiological parameters", "Body weight", "obesity", "epidemiology", "Disease surveillance", "Spatial epidemiology", "Public and occupational health", "Global health", "research design", "Clinical research design", "Retrospective studies", "ratios", "adjusted", "cox"], "article_id"=>1001017, "categories"=>["Biological Sciences"], "users"=>["Jameson D. Voss", "David B. Allison", "Bryant J. Webber", "Jean L. Otto", "Leslie L. Clark"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0093493.t002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Hazard_Ratios_by_Fully_Adjusted_Cox_Model_/1001017", "title"=>"Hazard Ratios by Fully Adjusted Cox Model.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-04-16 02:55:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/1466498"], "description"=>"<p>*Model 1 adjusted for all variables in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0093493#pone-0093493-t001\" target=\"_blank\">Tables 1</a>–<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0093493#pone-0093493-t002\" target=\"_blank\">2</a> aside from altitude category and housing allowance (branch of service, time in service, age, enlistment BMI, sex, Race/Ethnicity, and job category).</p>†<p>Model 2 was Model 1 plus housing allowance and altitude category.</p>‡<p>Combined refers to a single model including all three measures of civilian obesity prevalence.</p>", "links"=>[], "tags"=>["Biochemistry", "metabolism", "Oxygen metabolism", "Bioenergetics", "nutrition", "physiology", "Physiological parameters", "Body weight", "obesity", "epidemiology", "Disease surveillance", "Spatial epidemiology", "Public and occupational health", "Global health", "research design", "Clinical research design", "Retrospective studies", "incidence", "measures"], "article_id"=>1001018, "categories"=>["Biological Sciences"], "users"=>["Jameson D. Voss", "David B. Allison", "Bryant J. Webber", "Jean L. Otto", "Leslie L. Clark"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0093493.t003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Hazard_of_Obesity_Incidence_Based_on_Different_Measures_of_Civilian_Obesity_/1001018", "title"=>"Hazard of Obesity Incidence Based on Different Measures of Civilian Obesity.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-04-16 02:55:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/1466499"], "description"=>"<p>Adjusted for time in service, age, enlistment BMI, sex, Race/Ethnicity, housing allowance, and job category. Adjusted for branch of service if occupation is in both Army and Air Force.</p>", "links"=>[], "tags"=>["Biochemistry", "metabolism", "Oxygen metabolism", "Bioenergetics", "nutrition", "physiology", "Physiological parameters", "Body weight", "obesity", "epidemiology", "Disease surveillance", "Spatial epidemiology", "Public and occupational health", "Global health", "research design", "Clinical research design", "Retrospective studies", "altitude", "stratified"], "article_id"=>1001019, "categories"=>["Biological Sciences"], "users"=>["Jameson D. Voss", "David B. Allison", "Bryant J. Webber", "Jean L. Otto", "Leslie L. Clark"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0093493.t004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Hazard_of_Obesity_Diagnosis_at_High_Altitude_vs_Low_when_stratified_by_Occupation_/1001019", "title"=>"Hazard of Obesity Diagnosis at High Altitude vs. Low when stratified by Occupation.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-04-16 02:55:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/1466500"], "description"=>"<p>*Model 1 adjusted for all variables in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0093493#pone-0093493-t001\" target=\"_blank\">Tables 1</a>–<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0093493#pone-0093493-t002\" target=\"_blank\">2</a> aside from altitude category (branch of service, time in service, age, enlistment BMI, sex, housing allowance, Race/Ethnicity, and job category).</p>†<p>Model 2 also adjusted for self-reported current smoking among civilians (as reported in 2011 BRFSS).</p>‡<p>New England not used as the referent group as it had the smallest sample size.</p><p>**The average altitude of 3 digit zip code areas throughout the Mountain census region varied from 0.44 km to 2.87 km (highest residence of a service member was 2.34 km) and a majority of person time observed in this region was from members living at <1.96 km.</p>", "links"=>[], "tags"=>["Biochemistry", "metabolism", "Oxygen metabolism", "Bioenergetics", "nutrition", "physiology", "Physiological parameters", "Body weight", "obesity", "epidemiology", "Disease surveillance", "Spatial epidemiology", "Public and occupational health", "Global health", "research design", "Clinical research design", "Retrospective studies"], "article_id"=>1001020, "categories"=>["Biological Sciences"], "users"=>["Jameson D. Voss", "David B. Allison", "Bryant J. Webber", "Jean L. Otto", "Leslie L. Clark"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0093493.t005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Regional_Variation_in_Obesity_Hazard_and_Relationship_with_Civilian_Obesity_/1001020", "title"=>"Regional Variation in Obesity Hazard and Relationship with Civilian Obesity.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-04-16 02:55:42"}

PMC Usage Stats | Further Information

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Relative Metric

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