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Title: Application of regression frameworks for presenting and interpreting cost effectiveness analysis of maternal and child health strategies
Author: Hounton, Sennen
ISNI:       0000 0004 2690 4710
Awarding Body: University of Aberdeen
Current Institution: University of Aberdeen
Date of Award: 2009
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The aim of this work is to explore the feasibility and comparative advantages of the use of regression methods (net-benefit approach) versus standard aggregate measures (incremental cost effectiveness ratio) for presenting and interpreting results of effectiveness and cost-effectiveness of maternal health strategies. Methods: Databases were existing datasets from a Demographic Surveillance Site and a prospective survey on costs borne by women and households for institutional delivery (to evaluate the community based health insurance scheme in Nouna, Burkina Faso), and from a real life evaluation (to asses the Skilled Care Initiative in Ouargaye, Burkina Faso). Results: Regression frameworks are feasible and more practical than traditional aggregate measures of maternal mortality ratio and incremental cost effectiveness ratio.  The approach has shown promise by overcoming the shortcomings of the use of aggregate measures by identifying differentials in outcomes by subgroups of populations and by providing useful information on the marginal cost-effectiveness of important covariates. Whilst regression frameworks provide straightforward interpretation and better clues for course of action, the application of the net-benefit approach also provides opportunities for enhancing data collection at Demographic Surveillance Site and national surveys.  Indeed, the application of this framework to maternal and child health requires a transformation of current surveys to allow for patient-level data on cost and on effectiveness measures and the use of various stated preference methods for eliciting the maximum contribution communities are willing to pay for extra gain of health outcome or for preventing an extra adverse outcome.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available