Population Versus High-risk Interventions for Obesity
Year: 2009
Type: article
Abstract: To the Editor: In 1985 Geoffrey Rose demonstrated that preventive strategies enacting population-wide shifts in the distribution of risk factors would most affect the population burden of disease.1 Using plasma cholesterol and blood pressure as examples, Rose demonstrated that a small leftward shift in the population distribution could decrease morbidity and mortality more than a large decrease in risk in the high-risk individuals. This approach has become a fundamental tenet of prevention. Obesity provides a more complex situation. The increased risk conferred by cholesterol or blood pressure increases progressively across the population distribution, but the risk conferred by body weight is J-shaped, affecting mainly the upper and lower ends of the weight distribution. Thus, a population approach that shifts the body mass index to lower levels may harm those at the lowest end of the distribution. Furthermore, the increasing prevalence of obesity in developed countries is a result of an increased skewing of the distribution of body weight rather than a simple uniform shift.2–4 The unequal shifting of BMI across the population and the J-shaped relationship between body weight and morbidity/mortality suggest that high-risk strategies may be the most appropriate obesity-reduction response. We analyzed 4 theoretical responses to obesity-reduction strategies (Table). Scenario 1 is a population approach that reduces all levels of the distribution by 1.5 BMI units (as observed in Cuba during its 1989–2000 economic crisis6). Scenario 2 is a high-risk approach that reduces the weight of the obese individuals by 5 kg (the weight loss that may be expected by lifestyle and dietary interventions7). Scenario 3 is a more targeted high-risk approach that reduces the weight of the severely obese individuals by 50 kg (a figure achievable by surgical intervention8). Scenario 4 is a hypothetical population approach that acts unequally across the range of body weight such that it de-skews the distribution, achieving the 1980 distribution of BMI.TABLE: Population Impact of Theoretical Obesity-reduction StrategiesA uniform shirt of the BMI distribution comes close to achieving the 1980 distribution (Scenario 1). However based on the association between body weight and mortality, Scenario 1 is not the most effective strategy, preventing a similar number of premature deaths as one of the high-risk strategies (Scenario 2). Scenario 3 is relatively ineffective for the population as a whole. Scenario 4 achieves an approximation of the 1980 BMI distribution (by definition), and prevents the most premature deaths. The scenarios demonstrate that both high-risk and population approaches can be relatively effective. High-risk strategies may be more appropriate until we are certain we are not creating harm at the lower end of the distribution by using population strategies. Population approaches have the potential to be most effective and perhaps cost-effective in the long-term. They are, as Rose explained, the ultimate answer to strongly environmentally determined health problems. Furthermore, weight loss from interventions targeting high-risk groups is generally not maintained (weight loss through bariatric surgery being an exception9).10 The most appropriate approach for reducing obesity is less clear than for some other prevention areas. Our results suggest a combination of high-risk and population-based approaches is needed. A population approach that de-skewed the population's body weight—reducing most the weight of the heaviest people and leaving largely unchanged the weight of the lightest people—would be particularly safe and effective, but it is not clear how to achieve such a change. We need to monitor changes over time in the body weight distribution and its response to interventions. We also need to better understand the benefits and risks associated with these changes, to successfully reverse the potential harm associated with the obesity epidemic. ACKNOWLEDGMENTS We thank Luc Bonneux for reviewing the manuscript. Sources of financial support: Helen L. Walls was supported by a National Health and Medical Research Council and National Heart Foundation PhD scholarship, and a National Health and Medical Research Council grant (No. 465130). Anna Peeters was supported by a VicHealth fellowship. Helen L. Walls John J. McNeil Anna Peeters Department of Epidemiology and Preventive Medicine, Monash University, Alfred Hospital, Victoria, Australia, [email protected]
Source: Epidemiology
Authors: Helen L. Walls, John J. McNeil, Anna Peeters
Institutions: The Alfred Hospital, Alfred Health, Monash University
Language: English
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W1968057862