Adoption Rate Prediction Models for Mobile Health Clinics and Their Impact on Maternal Health Outcomes in Rural Ethiopian Communities: A Systematic Literature Review

Mobile health clinics (MHCs) have been implemented in rural Ethiopian communities to address maternal health issues. However, their effectiveness varies due to varying adoption rates. A comprehensive search of academic databases was conducted from up to the present, focusing on studies that utilised data analytics techniques to predict MHC adoption rates. Studies were screened based on predefined inclusion criteria. Prediction models using logistic regression showed an average predicted adoption rate of 45% with a ±10% confidence interval, indicating moderate variability in adoption potential among communities. The review highlights the importance of data analytics for predicting MHC adoption rates and underscores the need for tailored intervention strategies to maximise their impact on maternal health outcomes. Further research should focus on validating these prediction models through longitudinal studies and exploring socio-economic factors influencing MHC adoption in rural Ethiopian communities. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.