Abstract
BackgroundReduction in neonatal deaths has been a major challenge globally. To prevent neonatal deaths, improvements in newborn care have been promoted worldwide. The World Health Organization Western Pacific Regional Office has been promoting the Early Essential Newborn Care (EENC), a package of specific simple and cost-effective interventions, in their region. However, mere introduction of EENC cannot reduce neonatal deaths unless quality of care is ensured. In Lao PDR, the government introduced self-managed continuous monitoring as a sustainable way to improve the quality of care described in the EENC.MethodsA clustered randomized controlled trial was designed to compare the effectiveness of self-managed continuous monitoring with external supervisory visits to monitor health workers’ satisfactory EENC performance and their knowledge and skills related to the EENC in Lao PDR. Determinants of EENC performance will be measured with a structured questionnaire developed based on the Theory of Planned Behaviour, which predicts future behaviour. During self-managed continuous monitoring activities, health workers in each district hospital will conduct periodical peer reviews and feedback sessions.Fifteen district hospitals will be randomly allocated into the self-managed continuous monitoring (intervention) and the supervision (control) groups. Fifteen health workers routinely involved in maternity and newborn care including physicians, midwives and other health staff will be recruited from each hospital (effect size 0.6, intra-cluster correlation coefficient 0.06, 5% alpha error and 80% power). We will compare the change in the mean score of the determinants before and one year after randomisation between the two groups. We will also compare the retention of knowledge and skills related to the EENC between the two groups. The expected enrolment period is July 20th, 2017 to July 20th, 2018.DiscussionThis is the first cluster randomized trial to evaluate a self-managed continuous monitoring system for quality maintenance of newborn care in a resource-limited country. This research is conducted in collaboration with the Ministry of Health and international organizations; therefore, if effective, this intervention would be applied in larger areas of the country and the region.Trial registrationThis trial was registered at UMIN-CTR on 15th of June, 2017. Registration number is UMIN000027794.
Highlights
Reduction in neonatal deaths has been a major challenge globally
Study objectives This study aims to test whether in Lao Lao People’s Democratic Republic (PDR), a resourcelimited country, self-managed continuous monitoring of activities for the Essential Newborn Care (EENC) conducted by health workers involved in maternity and newborn care in district hospitals improves health workers’ behaviour as well as retention of knowledge and skills after the EENC coaching
Maintaining quality of newborn care is essential to prevent neonatal deaths; supervision of medical professionals imposes a heavy burden on the health system in resource-limited countries
Summary
Reduction in neonatal deaths has been a major challenge globally. Improvements in newborn care have been promoted worldwide. The World Health Organization Western Pacific Regional Office has been promoting the Early Essential Newborn Care (EENC), a package of specific simple and costeffective interventions, in their region. Mere introduction of EENC cannot reduce neonatal deaths unless quality of care is ensured. In the Western Pacific Region, more than 70% of child deaths are due to neonatal complications, and one neonate dies every two minutes [3, 4]. Given that complications in the first month of life have become the leading cause of death in children under five, actions to improve neonatal health have become tremendously important to achieve Sustainable Development Goal 3 in 2030 [5]. Simple methods can be applied to prevent neonatal deaths and complications due to preventable factors within the first 24 h of birth, during which about half of all neonatal deaths occur [3, 6, 7]
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