Mustafa Vora, DY Patil University, Navi Mumbai
Emerging market economies, low and middle-income countries face parallel challenges of high premature mortality due to communicable as well as non-communicable diseases (CDs and NCDs). On the other hand, developing countries face severe resource constraints. It is important to understand the evolution of these diseases in the short to medium term and the involvement of tradeoffs in decreasing the burden of one disease versus another.
Academic and non-academic institutions have put forward the disease, country-specific mortality, and life expectancy forecasts. The translation of changes in health outcomes into currency is very useful as it allows one to compare the changes in life expectancy against policy or intervention costs. It also allows policymakers to combine such monetary estimates with information on changes in other welfare outcomes, across and beyond health sectors. It helps in calculating the country’s full income which is useful for health policymakers to increase investments in improving social health. Thus the development of an analytical framework took place, this framework quantifies in monetary terms, changes annual age-sex mortality due to various NCD and CDs.
A recent study to develop the analytical framework:
Disease-specific IEMV (income equivalent monetary value) was estimated and its applications were presented using publicly available estimates on mortality rates, population, and income across low, lower-middle, and upper-middle-income countries (LIC, LMIC, UMIC) between the years 2017 to 2030. The LICs had a gross national income per capita lower than $1005. Whereas the LMICs and UMICs fall in the range between $1006 to $3995 and $3995 to $12,235 respectively. Eight disease categories in these countries were classified by the Global Burden of Disease Study. Three steps are involved in the IEMV estimation.
Step 1 was the estimation of changes in mortality risks of high or low-performance routes and base case routes. The age-sex mortality rate for a particular disease in a particular country was determined at a particular time of the year. Three mortality trajectories were determined. Base-case, high performance, and low-performance trajectories. This was followed by estimating the differences in the mortality risks between the three trajectories.
Step 2 was estimating the IEMV of changes in the mortality risk. The monetary value associated with standardized mortality units is called the value of a standardized mortality unit (VSMU). To estimate VSMU for each disease category, the estimation of VSL for each country in each year was done. VSL is the willingness to pay for a small reduction in the risk of mortality. The change in mortality risk between the three trajectories was multiplied by the respective VSMU to calculate the IEMV.
Step 3 was the final step. It was a sensitivity analysis. The reports were summarised by taking an aggregate of the age group year across all countries.
Results and analysis of the study:
Regarding the NCDs, the absolute monetary value associated with changing mortality risk was the highest for cardiovascular diseases in the older age group. A connection was established between changing mortality rate from base case to high-performance trajectory and high monetary value for CDs and also in the younger age group. Two key results emerge from the study. The value of curbing both NCD and CD-specific mortality is very high for low and middle-income countries, although the age groups differ. Countries could experience major hitch if NCD control is not scaled up. This method of estimating the IEMV can be useful for policymakers in providing critical input into estimating the value for money in curbing NCDs and CDs.
Covid-19 is having a devastating effect on individuals affected by NCDs because of which the economic burden of disease framework is important. The framework of this study may help in pointing out the high value for money interventions for optimized disease control.
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Reference:
- Khadka, Aayush, and Stéphane Verguet. “The Economic Value of Changing Mortality Risk in Low- and Middle-Income Countries: A Systematic Breakdown by Cause of Death.” BMC Medicine, vol. 19, no. 1, July 2021, p. 156. BioMed Central, https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-021-02029-x
- Mathers, Colin, et al. “Global and Regional Causes of Death: Patterns and Trends, 2000–15.” Disease Control Priorities: Improving Health and Reducing Poverty, edited by Dean T. Jamison et al., 3rd ed., The International Bank for Reconstruction and Development / The World Bank, 2017. PubMed, http://www.ncbi.nlm.nih.gov/books/NBK525280/.
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