Commentary: Making risk factors more cost-effective predictors of disease

This commentary will apply Michael Marmot’s 1 call for theories that make more accurate predictions to risk factors, which provide the etiological model for most chronic diseases. The use of risk factor research in health care will be compared with that in the life insurance industry, which developed the concept. Two proposals will be made to improve the predictive accuracy and cost-effectiveness of risk factors in health care. Marmot uses theories of the causes of coronary heart disease to examine models of causation in epidemiology and public health. He shows that science does not proceed in a straightforward inductive chain from facts to causal hypotheses and theories. Nor can hypotheses be refuted by simply producing facts that contradict them. Consequently, he suggests that a useful theory is one that produces relationships which accurately predict health outcomes. Accurate predictions, however, should not be the only objective of health care models, especially since predictions tend to be less accurate in chronic diseases than in most infectious diseases. A problem of equal urgency is cost-effectiveness. Too much health research fails to incorporate cost-effectiveness in its design or analyses and consequently too much health care produces only a small amount of benefit for the societal expenditures involved. Cost-effectiveness is a key issue because of the steep increases in health care expenditures that have afflicted all advanced countries. In the USA, health care expenditures have reached one-sixth of the gross domestic product and are climbing steadily. Individual states spend up to one-quarter of their budgets on medical care for the poor. Increased public and private expenditures for health care have reduced the standard of living in many ways, including less spending for education, social welfare, income security, the environment, infrastructure and public safety.