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Race and gender representation of hypertrophic cardiomyopathy or long QT syndrome cases in a South African research setting
SUMMARY: We researched hypertrophic cardiomyopathy (HCM) and long QT syndrome (LQTS) as models for studying the pathophysiology of arrhythmias and hypertrophy, and in the process we have had the opportunity to compare local disease profiles with global patterns. We trawled our database entries over...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Clinics Cardive Publishing
2007
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3975543/ https://www.ncbi.nlm.nih.gov/pubmed/17957320 |
Sumario: | SUMMARY: We researched hypertrophic cardiomyopathy (HCM) and long QT syndrome (LQTS) as models for studying the pathophysiology of arrhythmias and hypertrophy, and in the process we have had the opportunity to compare local disease profiles with global patterns. We trawled our database entries over the past 20 years to identify all cases of heart muscle and arrhythmic disease. Among these, we separated the index cases from the rest of their family members, segregating for the relevant heart disease, so that numbers were not biased by family size, and analysed the race and gender composition of the HCM and LQTS sectors. The majority of HCM index cases (n = 90, 51.1% of HCM index cases) were of mixed ancestry (MA), with white Caucasian ancestry following closely behind with 74 cases (42.0%); only a few black African (n = 9, 5.1%) or Indian/Asian (n = 3, 1.7%) cases were seen or referred. The LQTS index cases were almost exclusively white Caucasian (n = 36, 88% of LQTS index cases), with four cases (9.8%) of MA, one (2.4%) of Indian/Asian and none of black African descent. These race demographics did not fit the national demographics for South Africa as a whole. In contrast, in both groups, gender biases (slightly more male than female HCM cases, and a 0.4 ratio of males to females in LQTS) previously reported elsewhere appeared to be replicated in our database. Genetic bias is an unlikely explanation for the skewed demographics in our database; a more likely explanation relates to various missed opportunities to diagnose, missed diagnoses and misdiagnoses, as well as the real population drainage of our main referral centre in the context of a differentiated healthcare system. |
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