Modeling of medical technology supply chains via partnership in Zanzibar, Tanzania
In recent years, mathematical modelling and predictive analytics have become invaluable in informing public health interventions when large scale controlled trials are not logistically feasible. Successful models of systems accurately predict the outcome of changing one or more variables in a system. This allows mathematical models to be a powerful, quantitative tool in understanding where a limited change in the funding, supplies, or other intervention in the system may have the greatest impact over a number of years. Models must be built upon quality data and employ the minimum, most necessary, clearly stated assumptions. Moreover, they should be continually revised to account for changes in variables and unexpected events.
Our group is developing a mathematical model of medical technology and therapeutic supply chains from manufacturer to consumer in Zanzibar, Tanzania. We will collect input data directly through our close partnership with local medical professionals that are both involved in the system and directly impacted by it. Not only does this partnership allow for direct sourcing of data, it also allows for a higher rate of revision of the model to fit the changing supply chain by being able to identify current event factors that are difficult to gather through literature reviews – providing an advantage over models developed from secondary data sources.
The data for the model is obtained and processed from surveys completed by medical professionals in Zanzibar. The responses to surveys will determine the variables impacting quality of access to medical technology both to healthcare centers from manufacturers as well as to patients from healthcare centers. A healthcare center’s access to medical technology is identified by factors associated with the main manufacturers of medical technology, how frequently supplies arrives, how it is stored, and the entities that control acquisition of medical technology. Conversely, the patient’s access to medical technology is identified by the maximum price they are able to pay for the medical technology in question, their ability to access healthcare centers, and their perceptions of new medical technology, among other factors. The predictive model developed uses these factors as variables and functions based on a dynamic multi-loop system consisting of factors affecting inflow and outflow of medical technology from healthcare centers, factors affecting the decision to use medical technology by healthcare professionals and patients, and the factors affecting sourcing and transport of medical technologies to Zanzibar. The model may be used to identify potential targets in the system to improve the efficiency and efficacy of the supply chain towards wider access to life saving treatment.
References
1. Garnett, Geoffrey, Simon Cousens, Timothy Hallett, Richard Steketee, and Neff Walker. “Mathematical models in the evaluation of health programmes.” The Lancet 378, no. 9790 (2011): 515-25
2. Weinstein, Milton C., Bernie O’Brien, John Hornberger, Joseph Jackson, Magnus Johannesson, Chris McCab, and Bryan R. Luce. “Principles of Good Practice for Decision Analytic Modeling in Health-Care Evaluation: Report of the ISPOR Task Force on Good Research Practices—Modeling Studies.” Value in Health 6, no. 1 (2003)