Abstract
Supply chain strengthening (SCS) is a key component in the overall strategy of countries to move towards universal health coverage. Estimating the health benefit of investments in such health system strengthening (HSS) interventions has been challenging because these benefits are mediated through their impact on the delivery of a wide range of healthcare interventions, creating a problem of attribution. We overcome this challenge by simulating the impact of SCS within the Thanzi La Onse (TLO) model, an individual-based simulation of health care needs and service delivery for Malawi, drawing upon demographic, epidemiological and routine healthcare system data (on facilities, staff and consumables).
In this study, we combine the results of a previous inferential analysis on the factors associated with consumable availability at health facilities in Malawi with the TLO model to estimate the potential for health impact of SCS interventions in the country. We do this by first predicting the expected change in consumable availability by making a positive change to these factors using previously fitted multi-level regression models of consumable availability. We then run the TLO model with these improved consumable availability estimates. The difference in the DALYs accrued by the simulated population under the baseline availability of consumables and that under improved consumable availability estimates gives us the potential for health impact of SCS interventions which would influence these factors.
Countries regularly need to make decisions on allocating resources across a range of health interventions (including service delivery and HSS). Crucial to guide these decisions is a value-for-money (VfM) assessment comparing these interventions. Our analysis offers the first step in estimating the VfM of a sample of SCS interventions and can guide Malawi in its evaluation of alternative health sector investments.
In this study, we combine the results of a previous inferential analysis on the factors associated with consumable availability at health facilities in Malawi with the TLO model to estimate the potential for health impact of SCS interventions in the country. We do this by first predicting the expected change in consumable availability by making a positive change to these factors using previously fitted multi-level regression models of consumable availability. We then run the TLO model with these improved consumable availability estimates. The difference in the DALYs accrued by the simulated population under the baseline availability of consumables and that under improved consumable availability estimates gives us the potential for health impact of SCS interventions which would influence these factors.
Countries regularly need to make decisions on allocating resources across a range of health interventions (including service delivery and HSS). Crucial to guide these decisions is a value-for-money (VfM) assessment comparing these interventions. Our analysis offers the first step in estimating the VfM of a sample of SCS interventions and can guide Malawi in its evaluation of alternative health sector investments.
| Original language | English |
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| DOIs | |
| Publication status | E-pub ahead of print - 13 Nov 2024 |
| Event | European Health Economics Association (EuHEA) conference 2024 - University of Economics and Business Vienna, Vienna, Austria Duration: 30 Jun 2024 → 3 Jul 2024 https://euhea.eu/program_euhea_conference_2024.html |
Conference
| Conference | European Health Economics Association (EuHEA) conference 2024 |
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| Country/Territory | Austria |
| City | Vienna |
| Period | 30/06/24 → 3/07/24 |
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Bibliographical note
Presentation at ConferenceKeywords
- supply chain
- health system strengthening
- predictive data analytics
- resource allocation
- disease modeling