Abstract
Background and aims Systemic autoimmune diseases (SAD) are characterised by a wide spectrum of demographic patterns with respect to the ethnic differences, age at diagnosis and especially gender distribution. Studying the distribution of these diseases across geographic regions using a big data-driven approach may help obtain a more “high-definition resolution” of these complex diseases.
Methods We explored the potential of the Google search engine to collect and merge 133 SLE cohorts (>100 patients) reported in the Pubmed library. The country indicators are subclassified into 20 specific topics.Statistically-significant correlations were further corrected according to the Lasso statistical model (LC).
Results We found statistical correlations in the following areas: Education, Environment, Infrastructure, Economy and Growth, Health, Private sector, Public sector and Social Protection and Labour. A higher F:M ratio was found in countries who had a higher frequency of women in tertiary education/academic staff ,female legislators, higher% of CO2 emissions from electricity/heat, higher% of terrestrial and marine protected areas and of taxes. In contrast, a lower F:M ratio was found in countries who had a higher frequency of women in unemployment and countries with a higher out-of-pocket health expenditure for private healthcare
Conclusions There is a clear trend of association between the percentage of women diagnosed with SLE and some indicators of development of each country. The gap between women and men diagnosed with SLE is wider in countries with the highest frequencies of women working and women with high study degrees, and those countries with more taxes and a higher percentage of protected geographical areas.
