Methoden vergelijken

	Invaliditeitsgewogen levensjaren (DALY)	Markov-ketenmodel in de gezondheidseconomie
Vakgebied	Gezondheidseconomie	Gezondheidseconomie
Familie	Process / pipeline	Process / pipeline
Jaar van ontstaan	1990	1983
Grondlegger	Christopher J. L. Murray and Alan D. Lopez (World Health Organization / World Bank)	Beck & Pauker (medical decision analysis, Massachusetts General Hospital)
Type	Method	Method
Oorspronkelijke bron	Murray, C. J., Lopez, A. D., & Jamison, D. T. (1994). The Global Burden of Disease in 1990: Summary Results, Sensitivity Analysis, and Future Directions. In C. J. Murray & A. D. Lopez (Eds.), Global Burden of Disease and Injury. Cambridge: Harvard University Press.	Beck, J. R., & Pauker, S. G. (1983). The Markov Process in Medical Prognosis. Medical Decision Making, 3(4), 419-458.
Aliassen	DALY, global disease burden metric, burden of disease	Markov model, state transition model, cohort simulation
Verwant	5	5
Samenvatting	A DALY quantifies disease burden as the sum of years of life lost to premature death and years lived with disability. Developed by the World Health Organization and World Bank in 1990 as part of the Global Burden of Disease (GBD) study, DALYs enable epidemiologists and public health planners to compare disease burden across populations, identify health priorities, and evaluate intervention impact. One DALY = one lost year of 'healthy' life; DALYs averted measure progress toward health goals.	A Markov model is a decision-analytic tool that simulates disease progression through defined health states over time, calculating cumulative costs and quality-adjusted life years (QALYs) to enable cost-effectiveness analysis. Developed by Beck and Pauker in 1983, Markov models are now the standard framework for projecting long-term outcomes of health interventions, especially chronic diseases where patients transition between clinical states (treatment response, disease progression, remission, death). Used by health technology assessment bodies and pharmaceutical companies to predict intervention value beyond trial duration.