Subtopic Deep Dive
Axillary Hyperhidrosis Epidemiology
Research Guide
What is Axillary Hyperhidrosis Epidemiology?
Axillary hyperhidrosis epidemiology examines population prevalence, severity distribution, demographic patterns, and risk factors for excessive axillary sweating in primary hyperhidrosis.
Prevalence reaches 3.12% in the US population (Sammons and Khachemoune, 2017, 47 citations) and 20.56% among medical students in Rio de Janeiro (Morard et al., 2019, 22 citations). Studies highlight familial history, early onset before age 18, and impacts on quality of life. Over 10 papers from 2004-2023 address these patterns.
Why It Matters
Epidemiological data quantifies public health burden, with 3.12% US prevalence informing treatment needs (Sammons and Khachemoune, 2017). High rates like 20.56% in Brazilian students reveal demographic risks and comorbidities for resource allocation (Morard et al., 2019). Quality of life measures like HidroQOL guide policy on hyperhidrosis impacts (Kamudoni et al., 2014).
Key Research Challenges
Prevalence Variability
Prevalence estimates differ across populations, with 3.12% in US adults versus 20.56% in Brazilian students (Sammons and Khachemoune, 2017; Morard et al., 2019). Standardization of diagnostic criteria remains inconsistent. Surveys often miss subclinical cases.
Risk Factor Identification
Familial history and early onset link to axillary sites, but comorbidity data is sparse (Morard et al., 2019). Endocrine links appear in reviews but lack cohort validation (Lause et al., 2017). Longitudinal studies are needed.
Severity Measurement
Tools like HidroQOL validate quality of life impacts but require broader epidemiological integration (Kamudoni et al., 2014). Severity distribution across demographics lacks large-scale data. Validation in diverse cohorts is limited.
Essential Papers
Dermatologic manifestations of endocrine disorders
Michael Lause, Alisha Kamboj, Esteban Fernández Faith · 2017 · Translational Pediatrics · 165 citations
The skin serves as a window for clinicians to understand, diagnose, and monitor endocrine disease. Dermatologic manifestations of endocrinopathies contribute significantly to an individual's health...
Pregnancy outcomes following exposure to onabotulinumtoxinA
Mitchell F. Brin, Russell S. Kirby, Anne Slavotinek et al. · 2015 · Pharmacoepidemiology and Drug Safety · 80 citations
A 24-year retrospective review of the Allergan safety database shows that the prevalence of fetal defects in onabotulinumtoxinA-exposed mothers before/during pregnancy (2.7%) is comparable with bac...
Axillary hyperhidrosis: a focused review
Jason E. Sammons, Amor Khachemoune · 2017 · Journal of Dermatological Treatment · 47 citations
Axillary hyperhidrosis is characterized by an increased amount of sweat production, localized to the armpits, to compensate for environmental conditions and to control thermoregulation. It affects ...
The development and validation of a disease-specific quality of life measure in hyperhidrosis: the Hyperhidrosis Quality of Life Index (HidroQOL©)
Paul Kamudoni, Benjamin Mueller, Sam Salek · 2014 · Quality of Life Research · 46 citations
This study has provided the initial evidence supporting measurement properties and the use of the HidroQoL instrument in both routine clinical practice and in research, for assessing quality of lif...
The correlation of phenylephrine 1% with hydroxyamphetamine 1% in Horner's syndrome
Helen V. Danesh‐Meyer · 2004 · British Journal of Ophthalmology · 39 citations
Hexagonality, ''touch up'' mode (%) 53.8 (9.6) 32.0-75.0/53.251.6 (9.0) 34.8-69.6/51.90.212 Touch up duration (seconds) 345 (131) 129-560/
Hyperhidrosis: A Central Nervous Dysfunction of Sweat Secretion
Johannes Wohlrab, Falk G. Bechara, Christoph Schick et al. · 2023 · Dermatology and Therapy · 31 citations
Hyperhidrosis (HH) is a central nervous dysfunction characterized by abnormally increased sweating due to a central dysregulation of sweat secretion. HH significantly affects the quality of life of...
The relation between age and outcomes of thoracic sympathectomy for hyperhidrosis: The older the better
Dafne Braga Diamante Leiderman, José Ribas Milanez de Campos, Paulo Kauffman et al. · 2018 · Journal of Thoracic and Cardiovascular Surgery · 25 citations
Reading Guide
Foundational Papers
Start with Kamudoni et al. (2014, 46 citations) for HidroQOL validation as QoL baseline; Danesh-Meyer (2004, 39 citations) for early physiological correlates.
Recent Advances
Morard et al. (2019, 22 citations) for high student prevalence; Klein et al. (2020, 19 citations) on comorbidities; Wohlrab et al. (2023, 31 citations) on central mechanisms.
Core Methods
Population surveys (Morard et al., 2019); QoL indexing (Kamudoni et al., 2014); retrospective reviews (Sammons and Khachemoune, 2017).
How PapersFlow Helps You Research Axillary Hyperhidrosis Epidemiology
Discover & Search
PapersFlow's Research Agent uses searchPapers to query 'axillary hyperhidrosis prevalence' yielding Sammons and Khachemoune (2017), then citationGraph reveals 47 citing papers and findSimilarPapers uncovers Morard et al. (2019) for regional comparisons. exaSearch scans 250M+ OpenAlex papers for unpublished prevalence surveys.
Analyze & Verify
Analysis Agent applies readPaperContent on Morard et al. (2019) to extract 20.56% prevalence stats, verifyResponse with CoVe cross-checks against Sammons and Khachemoune (2017), and runPythonAnalysis uses pandas to compute prevalence ratios across studies with GRADE grading for evidence strength on risk factors.
Synthesize & Write
Synthesis Agent detects gaps like missing US-Brazil prevalence comparisons, flags contradictions in onset ages, and uses exportMermaid for comorbidity flowcharts; Writing Agent employs latexEditText to draft tables, latexSyncCitations for (Morard et al., 2019) integration, and latexCompile for publication-ready epidemiology reports.
Use Cases
"What is the prevalence of axillary hyperhidrosis in different populations?"
Research Agent → searchPapers → citationGraph on Sammons (2017) → Analysis Agent → runPythonAnalysis (pandas meta-analysis of 3.12% US vs 20.56% Brazil rates) → researcher gets CSV prevalence table.
"Draft a LaTeX review on axillary hyperhidrosis risk factors."
Synthesis Agent → gap detection → Writing Agent → latexEditText (intro) → latexSyncCitations (Morard 2019, Lause 2017) → latexCompile → researcher gets compiled PDF with risk factor sections.
"Find code for analyzing hyperhidrosis survey data."
Research Agent → paperExtractUrls (Kamudoni 2014 HidroQOL) → paperFindGithubRepo → githubRepoInspect → Code Discovery workflow → researcher gets validated R scripts for QoL score computation.
Automated Workflows
Deep Research workflow conducts systematic review: searchPapers 'axillary hyperhidrosis epidemiology' → 50+ papers → DeepScan 7-steps with GRADE checkpoints → structured report on prevalence trends. Theorizer generates hypotheses on familial risks from Morard et al. (2019) chains. DeepScan verifies QoL impacts across Sammons (2017) and Kamudoni (2014).
Frequently Asked Questions
What is axillary hyperhidrosis epidemiology?
It studies prevalence, severity, demographics, and risk factors of primary excessive axillary sweating. Key facts: 3.12% US prevalence (Sammons and Khachemoune, 2017).
What are main methods?
Population surveys and validation of QoL tools like HidroQOL (Kamudoni et al., 2014). Student cohorts provide high-prevalence insights (Morard et al., 2019).
What are key papers?
Sammons and Khachemoune (2017, 47 citations) on US 3.12%; Morard et al. (2019, 22 citations) on 20.56% Brazil; Kamudoni et al. (2014, 46 citations) HidroQOL.
What open problems exist?
Standardizing prevalence across regions; linking comorbidities via cohorts; scaling HidroQOL for global epidemiology.
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