The Milbank Memorial Fund quarterly最新文献

Context: Preventive medicine has historically favored reducing a risk factor by a small amount in the entire population rather than by a large amount in high-risk individuals. The use of multivariable risk prediction tools, however, may affect the relative merits of this strategy.

Methods: This study uses risk factor data from the National Health and Nutrition Examination Survey III to simulate a population of more than 100 million Americans aged thirty or older with no history of CV disease. Three strategies that could affect CV events, CV mortality, and quality-adjusted life years were examined: (1) a population-based strategy that treats all individuals with a low- or moderate-intensity intervention (in which the low-intensity intervention represents a public health campaign with no demonstrable adverse effects), (2) a targeted strategy that treats individuals in the top 25 percent based on a single risk factor (LDL), and (3) a risk-targeted strategy that treats individuals in the top 25 percent based on overall CV risk (as predicted by a multivariable prediction tool). The efficiency of each strategy was compared while varying the intervention's intensity and associated adverse effects, and the accuracy of the risk prediction tool.

Findings: The LDL-targeted strategy and the low-intensity population-based strategy were comparable for CV events prevented over five years (0.79 million and 0.75 million, respectively), as were the risk-targeted strategy and moderate-intensity population-based strategy (1.56 million and 1.87 million, respectively). The risk-targeted strategy, however, was more efficient than the moderate-intensity population-based strategy (number needed to treat [NNT] 19 vs. 62). Incorporating a small degree of treatment-related adverse effects greatly magnified the relative advantages of the risk-targeted approach over other strategies. Reducing the accuracy of the prediction tool only modestly decreased this greater efficiency.

Conclusions: A population-based prevention strategy can be an excellent option if an intervention has almost no adverse effects. But if the intervention has even a small degree of disutility, a targeted approach using multivariable risk prediction can prevent more morbidity and mortality while treating many fewer people.

Context: Geoffrey Rose's two principal approaches to public health intervention are (1) targeted strategies focusing on individuals at a personal increased risk of disease and (2) population-wide approaches focusing on the whole population. Beyond his discussion of the strengths and weaknesses of these approaches, there is no empiric work examining the conditions under which one of these approaches may be better than the other.

Methods: This article uses mathematical simulations to model the benefits and costs of the two approaches, varying the cut points for treatment, effect magnitudes, and costs of the interventions. These techniques then were applied to the specific example of an intervention on blood pressure to reduce cardiovascular disease.

Findings: In the general simulation (using an inverse logit risk curve), lower costs of intervention, treating people with risk factor values at or above where the slope on the risk curve is at its steepest (for targeted interventions), and interventions with larger effects on reducing the risk factor (for population-wide interventions) provided benefit/cost advantages. In the specific blood pressure intervention example, lower-cost population-wide interventions had better benefit/cost ratios, but some targeted treatments with lower cutoffs prevented more absolute cases of disease.

Conclusions: These simulations empirically evaluate some of Rose's original arguments. They can be replicated for particular interventions being considered and may be useful in helping public health decision makers assess potential intervention strategies.