The effect of anmHealth clinical decision-making support systemon neonatal mortality in a low resource setting: A cluster-randomized controlled trial

Abstract

Background: MHealth interventions promise to bridge gaps in clinical care but documentation of their effectiveness is limited. We evaluated the utilization and effect of an mhealth clinical decision-making support intervention that aimed to improve neonatal mortality in Ghana by providing access to emergency neonatal protocols for frontline health workers. Methods: In the Eastern Region of Ghana, sixteen districtswere randomized into two study arms (8 intervention and 8 control clusters) in a cluster-randomized controlled trial. Institutional neonatal mortality data were extracted from the District Health Information System-2 during an 18-month intervention period. We performed an intention-to-treat analysis and estimated the effect of the intervention on institutional neonatalmortality (primary outcomemeasure) using grouped binomial logistic regression with a random intercept per cluster. This trial is registered at ClinicalTrials.gov (NCT02468310) and Pan African Clinical Trials Registry (PACTR20151200109073). Findings: There were 65,831 institutional deliveries and 348 institutional neonatal deaths during the study period. Overall, 47∙3% of deliveries and 56∙9% of neonatal deaths occurred in the intervention arm. During the intervention period, neonatal deaths increased from 4∙5 to 6∙4 deaths and, from3∙9 to 4∙3 deaths per 1000 deliveries in the intervention armand control arm respectively. The odds of neonatal deathwas 2⋅09 (95% CI (1∙00;4∙38); p=0∙051) times higher in the intervention arm compared to the control arm (adjusted odds ratio). The correlation between the number of protocol requests and the number of deliveries per intervention cluster was 0∙71 (p = 0∙05). Interpretation: The higher risk of institutional neonatal death observed in intervention clustersmay be due to problems with birth and death registration, unmeasured and unadjusted confounding, and unintended use of the intervention. The findings underpin the need for careful and rigorous evaluation of mHealth intervention implementation and effects.