Harnessing data science to control non-communicable diseases in Africa: a systematic review and gap analysis

Abstract

Background: Data science methods can provide novel and pragmatic approaches for preventing and controlling non-communicable diseases (NCDs) in Africa. This study highlights current efforts, opportunities, and challenges in leveraging data science methods to accelerate and advance the prevention and control of NCDs in Africa. Methods: We undertake a systematic review and gap analysis, as registered in PROSPERO (CRD42023406237). Results: Our findings suggest several data science methods have been used in research across the four leading NCDs in Africa. However, limited information exists on their application to improve disease surveillance, risk factor identification and characterization, prevention, treatment, drug discovery and rehabilitation. Machine learning outperforms traditional statistical methods in improving risk stratification in most studies (80.8%) designed for the prevention and control of NCDs. Notwithstanding, most (76.0%) data science techniques for NCDs prevention and control remain in the exploratory research phase, with limited clinical or public health application and minimal impact on the African population. There are critical gaps along the continuum of data generation, data quality, method development, and validation, which may be attributed to inadequate funding, capacity development, policy shortcomings, and infrastructure deficits. Considerable gaps exist in intra-African collaboration, data sharing, and replication, which hinder the cross-cultural replication and applicability of data science methods for NCDs prevention and control in Africa. Conclusions: Multi-sectoral interventions that promote interdisciplinary capacity building, investment, and knowledge linkages, taking into account indigenous epistemologies, are needed to harness the enormous potential of data science to accelerate the prevention and control of NCDs in Africa.