Mr.
Keywords: Stated Choice experiment, Blocked Fractional Factorial Design, Confounding, Aliasing
TL;DR: Preferences of women for maternal healthcare services
Abstract: This study examined women’s preferences for the choice of place for delivery in the Upper East Region of Ghana. Data was collected from 200 respondents with diverse sociodemographic characteristics. The study identified several influential attributes that influenced women’s decision-making, including the availability of drugs and equipment, facility environment, provider attitude, distance to the health facility, and referrals at the health facility. Using a panel mixed logit model, the analysis revealed that all attributes, except for the cost of delivery services, significantly influenced women’s choices. The model demonstrated a good fit, and the coefficients for the attributes were statistically significant at a 95\% confidence level. Interestingly, the cost of delivery services did not play a significant role in women’s decision-making process. Furthermore, the study explored the interactions between sociodemographic variables and attributes, highlighting the impact of factors such as age, employment status, marital status, religion, education, and place of last delivery on women’s preferences. Among these variables, the availability of drugs and equipment emerged as the most influential attribute across different sociodemographic groups. The study underscores the importance of understanding women's preferences when developing interventions for maternal healthcare. It emphasizes the significance of attributes related to the availability of drugs and equipment, facility environment, and provider attitude. Policymakers are encouraged to take these factors into consideration in order to enhance the utilization of healthcare facilities, reduce maternal mortality rates, and improve overall maternal health outcomes. In summary, this study provides valuable insights into women’s preferences for the choice of place for delivery in the Upper East Region of Ghana. It emphasizes the need for high-quality, patient-centered care that aligns with women’s preferences in order to promote positive maternal health outcomes.
Submission Category: Machine learning algorithms
Submission Number: 22
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