MULTI-CRITERIA DECISION ANALYSIS IN HEALTH INTERVENTION PRIORITY SETTING FOR UNIVERSAL HEALTH COVERAGE IN KENYA: AN EXPLORATORY STUDY
Author(s)
Melvin Obadha, DPhil1, Audrey Mumbi, MPH1, Rebecca G Njuguna, MSc1, Beryl Opiyo, RN2, Euphresia Shitote, RN3, Nancy Ombaso, RN4, Stacey Orangi, PhD1, Peter Nguhiu, MSc5, Gloria Ngaiza, MD, DPhil6, Cecile Juma, BSc7, Monica Kay, MA7, Mary Wawira, BASc8, Esteban Garcia-Gallo, PhD9, Hezron Omollo, MD10, Nancy Njeru Mucogo, MSc11, Edwine Barasa, PhD1;
1KEMRI-Wellcome Trust Research Programme, Health Economics Research Unit, Nairobi, Kenya, 2University of Nairobi, Nairobi, Kenya, 3Kenya Medical Research Institute, Kisumu, Kenya, 4Independent, Nairobi, Kenya, 5Hecta Consulting, Nairobi, Kenya, 6University of Oxford, Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, Oxford, United Kingdom, 7Busara, Nairobi, Kenya, 8Yale University, New Haven, CT, USA, 9University of Oxford, ISARIC, Pandemic Sciences Institute, Oxford, United Kingdom, 10Nairobi City County, Nairobi, Kenya, 11Ministry of Health Kenya, NAIROBI, Kenya
1KEMRI-Wellcome Trust Research Programme, Health Economics Research Unit, Nairobi, Kenya, 2University of Nairobi, Nairobi, Kenya, 3Kenya Medical Research Institute, Kisumu, Kenya, 4Independent, Nairobi, Kenya, 5Hecta Consulting, Nairobi, Kenya, 6University of Oxford, Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, Oxford, United Kingdom, 7Busara, Nairobi, Kenya, 8Yale University, New Haven, CT, USA, 9University of Oxford, ISARIC, Pandemic Sciences Institute, Oxford, United Kingdom, 10Nairobi City County, Nairobi, Kenya, 11Ministry of Health Kenya, NAIROBI, Kenya
OBJECTIVES: Kenya has made UHC a top priority. Therefore, we explored the use of quantitative Multi-Criteria Decision Analysis (MCDA) to set priorities for health interventions for UHC. We used the example of the Kenyan UHC benefit package.
METHODS: Quantitative MCDA was conducted using discrete choice modelling as the underlying method for scoring and weighting. Alternatives were five health interventions for the prevention of HIV/AIDs, Malaria and Tuberculosis i.e., PrEP, Voluntary medical male circumcision, Intermittent malaria prevention during pregnancy, Intermittent malaria prevention in infancy, and TB preventive therapy (Isoniazide). We measured performance of health interventions using six priority setting criteria i.e., burden of disease, congruence with existing priorities, cost of intervention, effectiveness of intervention, equity, and health systems capacity. We used data from an earlier conducted discrete choice survey with 312 participants (academics, patients, public, payers, & providers) to score alternatives and weight criteria. Probability of selection was used to compute the aggregate value score, and data were presented on a composite league table with unit cost estimates alongside. A further cost per value metric was computed and used in ranking.
RESULTS: Intermittent presumptive treatment during pregnancy was valued highly (0.981 95% CI 0.974 - 0.989) followed by PrEP (0.970, 95% CI 0.959 - 0.982), and TB preventive therapy (Isoniazide) (0.970, 95% CI 0.959 - 0.982). When cost of intervention was considered, Intermittent presumptive treatment during pregnancy was still ranked first with the lowest cost per value ($1.02, 95% CI $1.01 - $1.03). It was followed by Intermittent malaria prevention in infancy ($1.14, 95% CI $1.13 - $1.16) and Voluntary medical male circumcision ($79.35, 95% CI $78.41 - $80.30).
CONCLUSIONS: MCDA incorporates the use of multiple criteria at the same time and allows tradeoffs between criteria. In this method different stakeholder groups can be included in the process such as policymakers, patients and the public.
METHODS: Quantitative MCDA was conducted using discrete choice modelling as the underlying method for scoring and weighting. Alternatives were five health interventions for the prevention of HIV/AIDs, Malaria and Tuberculosis i.e., PrEP, Voluntary medical male circumcision, Intermittent malaria prevention during pregnancy, Intermittent malaria prevention in infancy, and TB preventive therapy (Isoniazide). We measured performance of health interventions using six priority setting criteria i.e., burden of disease, congruence with existing priorities, cost of intervention, effectiveness of intervention, equity, and health systems capacity. We used data from an earlier conducted discrete choice survey with 312 participants (academics, patients, public, payers, & providers) to score alternatives and weight criteria. Probability of selection was used to compute the aggregate value score, and data were presented on a composite league table with unit cost estimates alongside. A further cost per value metric was computed and used in ranking.
RESULTS: Intermittent presumptive treatment during pregnancy was valued highly (0.981 95% CI 0.974 - 0.989) followed by PrEP (0.970, 95% CI 0.959 - 0.982), and TB preventive therapy (Isoniazide) (0.970, 95% CI 0.959 - 0.982). When cost of intervention was considered, Intermittent presumptive treatment during pregnancy was still ranked first with the lowest cost per value ($1.02, 95% CI $1.01 - $1.03). It was followed by Intermittent malaria prevention in infancy ($1.14, 95% CI $1.13 - $1.16) and Voluntary medical male circumcision ($79.35, 95% CI $78.41 - $80.30).
CONCLUSIONS: MCDA incorporates the use of multiple criteria at the same time and allows tradeoffs between criteria. In this method different stakeholder groups can be included in the process such as policymakers, patients and the public.
Conference/Value in Health Info
2026-05, ISPOR 2026, Philadelphia, PA, USA
Value in Health, Volume 29, Issue S6
Code
HTA3
Topic
Health Technology Assessment
Topic Subcategory
Decision & Deliberative Processes
Disease
SDC: Infectious Disease (non-vaccine)