Equity in public health spending in Ethiopia: a benefit incidence analysis

Reducing the equity gap in under-5 mortality through an innovative community health program in Ethiopia: an implementation research study

BACKGROUND

The Ethiopian government implemented a national community health program, the Health Extension Program (HEP), to provide community-based health services to address persisting access-related barriers to care using health extension workers (HEWs). We used implementation research to understand how Ethiopia leveraged the HEP to widely implement evidence-based interventions (EBIs) known to reduce under-5 mortality (U5M) and address health inequities.

METHODS

This study was part of a six-country case study series using implementation research to understand how countries implemented EBIs between 2000-2015. Our mixed-methods research was informed by a hybrid implementation science framework using desk review of published and gray literature, analysis of existing data sources, and 11 key informant interviews. We used implementation of pneumococcal conjugate vaccine (PCV-10) and integrated community case management (iCCM) to illustrate Ethiopia's ability to rapidly integrate interventions into existing systems at a national level through leveraging the HEP and other implementation strategies and contextual factors which influenced implementation outcomes.

RESULTS

Ethiopia implemented numerous EBIs known to address leading causes of U5M, leveraging the HEP as a platform for delivery to successfully introduce and scale new EBIs nationally. By 2014/15, estimated coverage of three doses of PCV-10 was at 76%, with high acceptability (nearly 100%) of vaccines in the community. Between 2000 and 2015, we found evidence of improved care-seeking; coverage of oral rehydration solution for treatment of diarrhea, a service included in iCCM, doubled over this period. HEWs made health services more accessible to rural and pastoralist communities, which account for over 80% of the population, with previously low access, a contextual factor that had been a barrier to high coverage of interventions.

CONCLUSIONS

Leveraging the HEP as a platform for service delivery allowed Ethiopia to successfully introduce and scale existing and new EBIs nationally, improving feasibility and reach of introduction and scale-up of interventions. Additional efforts are required to reduce the equity gap in coverage of EBIs including PCV-10 and iCCM among pastoralist and rural communities. As other countries continue to work towards reducing U5M, Ethiopia's experience provides important lessons in effectively delivering key EBIs in the presence of challenging contextual factors.

Nomogram to predict risk of neonatal mortality among preterm neonates admitted with sepsis at University of Gondar Comprehensive Specialized Hospital: risk prediction model development and validation

BACKGROUND

Mortality in premature neonates is a global public health problem. In developing countries, nearly 50% of preterm births ends with death. Sepsis is one of the major causes of death in preterm neonates. Risk prediction model for mortality in preterm septic neonates helps for directing the decision making process made by clinicians.

OBJECTIVE

We aimed to develop and validate nomogram for the prediction of neonatal mortality. Nomograms are tools which assist the clinical decision making process through early estimation of risks prompting early interventions.

METHODS

A three year retrospective follow up study was conducted at University of Gondar Comprehensive Specialized Hospital and a total of 603 preterm neonates with sepsis were included. Data was collected using KoboCollect and analyzed using STATA version 16 and R version 4.2.1. Lasso regression was used to select the most potent predictors and to minimize the problem of overfitting. Nomogram was developed using multivariable binary logistic regression analysis. Model performance was evaluated using discrimination and calibration. Internal model validation was done using bootstrapping. Net benefit of the nomogram was assessed through decision curve analysis (DCA) to assess the clinical relevance of the model.

RESULT

The nomogram was developed using nine predictors: gestational age, maternal history of premature rupture of membrane, hypoglycemia, respiratory distress syndrome, perinatal asphyxia, necrotizing enterocolitis, total bilirubin, platelet count and kangaroo-mother care. The model had discriminatory power of 96.7% (95% CI: 95.6, 97.9) and P-value of 0.165 in the calibration test before and after internal validation with brier score of 0.07. Based on the net benefit analysis the nomogram was found better than treat all and treat none conditions.

CONCLUSION

The developed nomogram can be used for individualized mortality risk prediction with excellent performance, better net benefit and have been found to be useful in clinical practice with contribution in preterm neonatal mortality reduction by giving better emphasis for those at high risk.