Cohort profile: measuring adverse pregnancy and newborn congenital outcomes (MANGO) study in Kenya.

PURPOSE

Pharmacovigilance (PV) systems to assess the safety of antiretroviral treatment used periconception and during pregnancy are lacking in low-resource settings with high HIV burdens, and strategies to guide their implementation are limited. We implemented the Measuring Adverse Pregnancy and Newborn Congenital Outcomes (MANGO) study in Kenya to address these gaps.

PARTICIPANTS

In MANGO, we ascertained delivery outcomes for pregnant women living with HIV (WLH) and not living with HIV (WNLH) enrolled in care at Moi Teaching and Referral Hospital (MTRH) through two cohorts: C1, a prospective cohort of 1:1 matched WLH and WNLH attending antenatal clinic; and C2, a cross-sectional cohort of all deliveries, including among those who did not attend antenatal clinic at MTRH.

FINDINGS TO DATE

24 205 deliveries were recorded from October 2020 to September 2023 (853 in C1 and 23 352 in C2). Median maternal age was 32 years, 4.5% were WLH and 2.6% of deliveries were stillbirths. Among liveborn infants, 17.2% were preterm (<37 weeks), and 15.1% were low birth weight (<2.5 kg). Prevalence of ≥1 major congenital abnormality was 73.9/10 000 births (47.7 in C1 and 76.1 in C2). Assessing implementation barriers/facilitators, lack of national PV policy was a barrier overcome through establishing partnerships with the Kenya Ministry of Health. The facility's size and complexity were barriers to newborn surface exam coverage overcome through staff training and cocreation of a standardised form for newborn surface exam documentation. High staff turnover was addressed by involving head nurses to champion implementation and incentivising staff participation with medical education credits. Use of audit/feedback cycles and focusing on PV as a way to improve care quality facilitated PV institutionalisation at MTRH.

FUTURE PLANS

The MANGO model is a multifaceted strategy with replicative potential in other settings. Research is needed to understand the model's opportunities for implementation in other settings.