Multimodal, open-source big data analysis in asthma: A novel approach to inform public health programming

Abstract

Asthma is a chronic respiratory disease affecting over 358 million people worldwide; for this reason analysing big data on asthma from different countries could give a more detailed picture of current disease burden. We aim to investigate the correlations between asthma and key socio-demographic parameters from different world databases. We found a direct correlation with the gross domestic product (GDP) per capita and its nominal counterpart, with wealthiest countries seen to have the highest prevalence of asthma, as also confirmed by a similar correlation with the human development index (HDI). A positive correlation was also seen between asthma prevalence and a number of socio-cultural data being representative of a good life quality index and prevalent in more developed and wealthier countries. Concerning medical data, an inverse relationship was seen between asthma prevalence and helminthiasis. Those data indicate a higher prevalence for asthma in more developed countries, where socio-economic status is higher and also the access to medical care is more ubiquitous. The approach used in our study highlighted the role of medical literacy and access to healthcare facilities in the correct diagnosis of asthma and vice versa. Our data appear to be suitable in terms of a health programming approach because of the high burden of disease worldwide.

Keywords

• Asthma
• Big data
• Socio economic status
• Health programming
• Epidemiology
• Real data