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.
Journal article
Journal of the Royal Society, Interface
10/2018
15
Department of Pathology, University of Cambridge, Tennis Court Road, Cambridge CB2 1QP, UK gjc36@cam.ac.uk.
Humans, Communicable Diseases, Hygiene, Cluster Analysis, Sanitation, Comorbidity, Water Supply, Developing Countries, Socioeconomic Factors, Adolescent, Adult, Middle Aged, Child, Child, Preschool, Infant, Rural Population, Delivery of Health Care, Uganda, Female, Male, Young Adult, Global Health