Digital Data Linkage and Scheduling to Track Pregnancy With or Without Community Data Use to Increase Antenatal Clinic Uptake in Western Kenya.

Antenatal Clinic Uptake, Electronic Community Health Information Systems, Health Systems, Pregnancy

The investigators propose to increase ANC uptake through a health systems strengthening approach that links digital data platforms and trains community Work Improvement Teams (WITs) to use these data to identify problems and come up with local solutions. Our short name C-it DU-it (pronounced see-it; do-it) is an acronym intended to convey 'seeing' linked data (C-it) and 'doing' or acting on the data (DU-it). The trial design is a 2-arm, cluster-randomised controlled superiority trial in Homa Bay County to determine the efficacy of 'C-it DU-it' intervention (data use arm) to increase ANC contacts when compared to the 'C-it' enhanced standard of care (control arm).

Facility and community health data is being rapidly digitised using multiple parallel systems across the 47 devolved counties in Kenya, but data do not link. Setting up community-based antenatal care (ANC) to complement facility-based ANC and data systems that link these platforms is essential to support Kenya in adopting WHO's ambitious target of 8 ANC contacts. As of February 2023, national scale up of the national electronic community health information systems (eCHIS) for standard of care is ongoing, and there are increased efforts to scale-up use of the nationally approved Kenya Electronic Medical Records (KenyaEMR) Maternal and Child Health Module (MNH) to capture ANC, delivery and postnatal (PNC) data at health facilities. Data between eCHIS and Kenya EMR do not link. There are plans within the Community Health Division at national level to link eCHIS to facility EMRs, but this has yet to be developed. The investigators propose to increase ANC uptake through a health systems strengthening approach that links digital data platforms and trains community Work Improvement Teams (WITs) to use these data to identify problems and come up with local solutions. The short name C-it DU-it (pronounced see-it; do-it) is an acronym intended to convey 'seeing' linked data (C-it) and 'doing' or acting on the data (DU-it). The overarching research question the investigators will seek to answer is what is the effect of 'C-it DU it' on community health systems strengthening and what is required for effective transfer and scale-up? The investigators will use mixed methods implementation research to evaluate this in 4 counties in Western Kenya (Homa Bay, Migori, Kisumu, Kakamega) over a period of four years. The proposed methods include: (a) Realist evaluation to generate, empirically test and refine a transferrable programme theory to understand the causal relationship between context, participant response and outcomes; (b) A 2-arm, cluster-randomised controlled superiority trial in Homa Bay County to determine the efficacy of 'C-it DU-it' intervention (data use arm) to increase ANC contacts when compared to the 'C-it' enhanced standard of care (control arm); (c) Health economic evaluation and equity analysis to compare costs and catastrophic health expenditure of women accessing and engaging with ANC care and determine costs and cost-effectiveness of C-it Du-it from a health systems perspective; and (d) Qualitative interviews will assess transferability and iterative scale-up of C-it DU-it across the three remaining counties using toolkits developed in Homa Bay. This protocol describes the pragmatic cluster randomised trial and health economic evaluation. The realist evaluation and scale up will be addressed in a separate sister protocol.

Kenya