Abstract
In low-income countries, complex comorbidities and weak health systems confound disease diagnosis and treatment. Yet, data-driven approaches have not been applied to develop better diagnostic strategies or to tailor treatment delivery for individuals within rural poor communities. We observed symptoms/diseases reported within three months by 16 357 individuals aged 1+ years in 17 villages of Mayuge District, Uganda. Symptoms were mapped to the Human Phenotype Ontology. Comorbidity networks were constructed. An edge between two symptoms/diseases was generated if the relative risk greater than 1, ϕ correlation greater than 0, and local false discovery rate less than 0.05. We studied how network structure and flagship symptom profiles varied against biosocial factors. 88.05% of individuals (14 402/16 357) reported at least one symptom/disease. Young children and individuals in worse-off households—low socioeconomic status, poor water, sanitation, and hygiene, and poor medical care—had dense network structures with the highest comorbidity burden and/or were conducive to the onset of new comorbidities from existing flagship symptoms, such as fever. Flagship symptom profiles for fever revealed self-misdiagnoses of fever as malaria and sexually transmitted infections as a potentially missed cause of fever in individuals of reproductive age. Network analysis may inform the development of new diagnostic and treatment strategies for flagship symptoms used to characterize syndromes/diseases of global concern.
Highlights
Biological and social factors determine individual variation in the onset and progression of disease pathologies [1,2,3]
We showed that individuals who have low socioeconomic status and poor hygienic/sanitary behaviours, and who receive poor quality medical care display the greatest selfreported comorbidity burden with the highest density and variability of comorbid conditions
To our knowledge, no data-driven or network analyses of patient symptoms have been conducted in low-income countries
Summary
Biological and social factors determine individual variation in the onset and progression of disease pathologies [1,2,3]. Precision medicine seeks to incorporate such variation into the design of treatments for individuals [4,5]. The ultimate aim is to produce tailored clinical treatments for individuals or subsets of a population based on their genetic, environmental and lifestyle factors. Due to rapid advances in deoxyribonucleic acid sequencing, the availability of large genomic datasets, and electronic medical records (EMRs), the study of precision medicine has focused on genetic variation [6]. Genome-wide and phenome-wide studies investigate many, sometimes millions of genetic variants against phenotypic traits/outcomes and vice versa [6]. These data-driven methods reveal causal pathways of a disease and common genetic variants of seemingly unrelated phenotypic abnormalities [7]
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