The effect of an mhealth clinical decision-making support system on neonatal mortality in Ghana

Abstract

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 districts were 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 neonatal mortality (primary outcome measure) using grouped binomial logistic regression with a random intercept per cluster. This trial is registered at ClinicalTrials.gov (NCT02468310). Results 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, from 3·9 to 4·3 deaths per 1,000 deliveries in the intervention arm and control arm respectively. The odds of neonatal death was non-significantly higher in the intervention arm compared to the control arm (odds ratio 2·10; 95% CI (0·77;5·77); p = 0·15). The correlation between the number of protocol requests and the number of deliveries per intervention cluster was 0·71 (p = 0·05). Conclusions Non-significant higher risk of neonatal death observed in intervention clusters may 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. Key messages Supposedly effective interventions must be evaluated in context before they are scaled-up. Mechanisms influencing outcomes in context must be considered in the design and evaluation of interventions.