Connected diagnostics to improve accurate diagnosis, treatment, and conditional payment of malaria services in Kenya.

BACKGROUND: In sub-Saharan Africa, the material and human capacity to diagnose patients reporting with fever to healthcare providers is largely insufficient. Febrile patients are typically treated presumptively with antimalarials and/or antibiotics. Such over-prescription can lead to drug resistance and involves unnecessary costs to the health system. International funding for malaria is currently not sufficient to control malaria. Transition to domestic funding is challenged by UHC efforts and recent COVID-19 outbreak. Herewith we present a digital approach to improve efficiencies in diagnosis and treatment of malaria in endemic Kisumu, Kenya: Connected Diagnostics. The objective of this study is to evaluate the feasibility, user experience and clinical performance of this approach in Kisumu. METHODS: Our intervention was performed Oct 2017-Dec 2018 across five private providers in Kisumu. Patients were enrolled on M-TIBA platform, diagnostic test results digitized, and only positive patients were digitally entitled to malaria treatment. Data on socio-demographics, healthcare transactions and medical outcomes were analysed using standard descriptive quantitative statistics. Provider perspectives were gathered by 19 semi-structured interviews. RESULTS: In total 11,689 febrile patients were digitally tested through five private providers. Malaria positivity ranged from 7.4 to 30.2% between providers, significantly more amongst the poor (p < 0.05). Prescription of antimalarials was substantially aberrant from National Guidelines, with 28% over-prescription (4.6-63.3% per provider) and prescription of branded versus generic antimalarials differing amongst facilities and correlating with the socioeconomic status of clients. Challenges were encountered transitioning from microscopy to RDT. CONCLUSION: We provide full proof-of-concept of innovative Connected Diagnostics to use digitized malaria diagnostics to earmark digital entitlements for correct malaria treatment of patients. This approach has large cost-saving and quality improvement potential.

Seasonal changes in gene expression represent cell-type composition in whole blood.

Seasonal patterns in behavior and biological parameters are widespread. Here, we examined seasonal changes in whole blood gene expression profiles of 233 healthy subjects. Using weighted gene co-expression network analysis, we identified three co-expression modules showing circannual patterns. Enrichment analysis suggested that this signal stems primarily from red blood cells and blood platelets. Indeed, a large clinical database with 51 142 observations of blood cell counts over 3 years confirmed a corresponding seasonal pattern of counts of red blood cells, reticulocytes and platelets. We found no direct evidence that these changes are linked to genes known to be key players in regulating immune function or circadian rhythm. It is likely, however, that these seasonal changes in cell counts and gene expression profiles in whole blood represent biological and clinical relevant phenomena. Moreover, our findings highlight possible confounding factors relevant to the study of gene expression profiles in subjects collected at geographical locations with disparaging seasonality patterns.