Ethiopian health-decision makers likely to use data-driven predictive models in their future work to decide on cervical cancer interventions

We discuss what Ethiopian decision-makers require from predictive mathematical models, particularly compartmental transmission models, to improve decisions for cervical cancer interventions. We used deliberative interviews, a novel interview style, to co-create knowledge.
Published in Healthcare & Nursing
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Background

Cervical cancer still burdens a significant part of the world. LMICs are particularly affected by this disease, and African settings still struggle with many lives lost due to this preventable illness. Ethiopia takes its share of this burden, which primarily originates from high-risk human papillomavirus infections provoking epithelial cells of the cervix to degenerate and subsequently produce cancer. To better understand where and how these HPV infections spread, we can use compartmental HPV transmission models based on ordinary differential equations. At first, to develop efficient models, we saw the need to ask health decision-makers what information they would need from such a model to make improved decisions.  

Methods with obstacles

To better understand needs, wishes, and perceptions of Ethiopian health decision-makers, we employed a novel interview style, the deliberative interview. This interview style aims at leading a conversation between interviewer and interviewee, where both parties may pose questions, learn from each other, and jointly co-create knowledge. After cautiously planning our study, we finally traveled to Addis Ababa to take samples. On-site, things did not turn out to be as easy as we thought. Bureaucracy almost brought us down, and to finally be able to conduct interviews we had to overcome some serious obstacles. This was only possible thanks to our great collaborators in Ethiopia, who facilitated our joint work enormously. Back at home, we drew on thematic analysis to analyze the data. We deductively and inductively generated codes and summarized them in a codebook.  

Results

Health decision-makers who decide on health interventions to prevent cervical cancer base their work on national and international data sources. Particularly helpful for them are the Demographic & Health Survey (DHS) implemented by the Central Statistical Agency (CSA) of Ethiopia, which is a survey of representative samples on the national and regional level, as well as the District Health Information System 2 (DHIS2), which gathers local data managed by the Federal Ministry of Health (FMOH). Health decision-makers recognize the lack of data in Ethiopia yet highly value the local data sources. In Ethiopia, decision-makers are already using, or will use in the future, data-driven forecasting models to guide their work. Moreover, the deliberative interviews we used proved useful in our Ethiopian context when learning from health decision-makers. In the paper, we discuss what decision-makers require from such predictive models. We discover that finding clusters or groups of people with the human papillomavirus infection is crucial to our participants in making health decisions. Also, decision-makers are interested in knowing how cost-effective potential future cervical cancer interventions will be. In essence, they want to know if the money they have spent on fighting cervical cancer will be worthwhile in the long run. Furthermore, tight collaboration with decision-makers is wished for while developing such models, and cultural aspects of the different Ethiopian regions are considered crucial. In conclusion, Ethiopian health decision-makers might increasingly leverage the power of data-driven predictive models tailored to the Ethiopian context.

Happy End

We are thrilled with this great intercultural effort. Only through the brilliant cooperation between our Ethiopian and German researchers, in which each played a key role, could this project be brought to success. In the future, the results of this research project will enable the development of Ethiopian transmission models to gain knowledge about local HPV dynamics to contribute to the battle against the cervical cancer burden. 

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