POPULATION HEALTH STATUS MEASURE: A COMPARATIVE STUDY BETWEEN DALY AND MIMIC-HEALTH STATUS INDEX

Objective: Improving population health through planning and priority setting in health systems requires the development of practical population health status measures. The objective of this paper is to compare two different indexes for the assessment of the level of population health status: (1) DALY (Disability-Adjusted Life Years), a WHO (World Health Organisation) composite health index and (2) MIMIC-HSI (Multiple Indicators MultIple Causes Health Status Index). Methods: First, a review of concepts behind DALY and MIMIC-HSI is presented. Then the respective models are described along with the models specifications and their underlying assumptions. Validity and reliability of both indexes are compared. Finally using their positive characteristics a conceptual framework for the development of a new population health status measure, Multiple Indicators MultIple Causes – Burden of Disease Index (MIMIC-BDI) is presented. Results: The MIMIC-HSI assumes that health is not directly measurable and can be characterized by its causes (health determinants) and by its effects (health indicators). On the other hand, DALYs index quantifies population health status by a direct measure of health outcomes. The MIMIC model uses only traditional indicators such as life expectancy and does not assess non fatal health outcomes, while the DALY combines both fatal and non fatal health outcomes into a single value. Another major disadvantage of MIMIC-HSI is the lack of disease-related variables such as disease incidence. However, variables related to health systems functions such as health services supply have been introduced in the model, unlike the DALYs approach. The DALYs approach presents two methodological weaknesses: (1) the multiplication of disability duration by severity used for its computation has not been validated and (2) the valuation method used for the assessment of disability weight (i.e. disability severity) is hardly reliable and valid. Therefore, the results depend greatly on the severity scale chosen. In contrast, the MIMIC-HSI offers an interesting property of independence from the health level unit of measurement and origin point. This property enables interpretation of ratios of differences between MIMIC-HSI values. Both indexes are derived from static models, and do not express the dynamic relationship between health systems efficiency and population health level. Conclusion: MIMIC-HSI and DALYs index seem to be complementary for population health status measure, as each could benefit of the others strengths to overcome their respective weaknesses. The dependence of the DALYs index on the selected severity scale should be reduced by the specification of each disability weight as a single latent variable in a simultaneous equation model. Nevertheless, this valuation method still has to be validated and does not free the DALY from its non valid multiplicative specification. As regards to MIMIC-HSI, we propose to introduce non fatal health outcomes and disease-related variables in the model in order to enhance its validity and applicability in public health. Thus MIMIC-HSI could be transformed into a burden of disease index, MIMIC-BDI, which benefits from the DALY and the MIMIC-HSI respective strengths. A further study might investigate MIMIC-BDI reliability and validity. In addition, a dynamic transformation of the model should be considered.