Rose revisited: a "middle road" prevention strategy to reduce noncommunicable chronic disease risk.
Author(s) -
Wendy J. Brown,
Richard Hockey,
Annette Dobson
Publication year - 2007
Publication title -
pubmed
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.459
H-Index - 168
pISSN - 0042-9686
DOI - 10.2471/blt.07.041566
Subject(s) - medicine , overweight , obesity , body mass index , disease , incidence (geometry) , population , gerontology , demography , environmental health , risk factor , physics , sociology , optics
In light of worldwide concern about the obesity crisis and prevention of noncommunicable chronic disease, it is timely to revisit the principles advocated by Geoffrey Rose. (1) The essential tenet of his work is that while strategies that focus on high-risk individuals (for example, weight-loss clinics for obese people) may help these people reduce their risk of chronic disease, the impact on the total burden of disease at the population level may be disappointing. This is because numerous cases of risk-factor-related health problems may arise among the many people who are in the middle of the risk distribution. In contrast, by lowering the risk across the whole population, the numbers of attributable cases of disease are significantly reduced. (1) Although this principle is well documented for conditions like hypertension, which have a relatively direct or linear relationship with risk factors such as body mass index (BMI), it is unclear for conditions like diabetes, where incidence rises sharply among people who are in the overweight and obese categories of BMI. To explore this issue, we used data from eight years' follow-up of middle-aged women in the Australian Longitudinal Study on Women's Health (2) to estimate the reductions in incidence of hypertension and diabetes that would result if the BMI distribution were shifted to the left in various ways. The Australian Longitudinal Study of Women's Health Participants were randomly selected from the national Medicare health insurance database (which includes all permanent residents of Australia regardless of age, including immigrants and refugees) with intentional over-representation of women living in rural and remote areas. Further details of the recruitment methods and response rates have been described elsewhere. (2) The study collects self-reported data using mailed surveys at 2- to 3-year intervals from about 40 000 women living in all Australian states and territories. The surveys include questions about: health conditions, symptoms and diagnoses; use of health services; health-related quality of life, including measures of physical and mental health; social circumstances, including work and time use; demographic factors; and health behaviours. Informed consent was obtained from all participants in 1996, with ethical clearance by the University of Newcastle, Australia. This paper includes data from 13 716 women in who were aged 45-50 at the time of the first survey in 1996. The women were asked to report their height and weight at each survey. BMI was calculated as reported weight (kilograms) divided by the square of reported height (metres). (2) At each survey women were asked if they had been told by a doctor that they had any of a list of conditions, including hypertension and diabetes. At survey 1 (1996) they were asked if they had ever had a diagnosis of hypertension or diabetes. At surveys 2 (1998), 3 (2001) and 4 (2004) they were asked whether they had been diagnosed with each condition in the time period that had elapsed since the previous survey. Modelling different prevention approaches BMI at survey one is presented as a simple frequency distribution in Fig. 1. After excluding data from women who reported having hypertension (n = 2859) or diabetes (n = 395) at survey 1, incidences of hypertension and diabetes (1996-2004) were calculated and superimposed on the 1996 BMI distribution (Fig. 1). Mean BMI was 25.8 (standard deviation, 5.13) kg x [m.sup.-2]. Hypothetical reductions in the incidence of each condition were then modelled for: (1) a 1-unit reduction in BMI in the whole population (the whole-population strategy); (2) a 3-unit reduction in BMI for women in the top 20 percent of the BMI distribution (BMI > 29; the high-risk strategy); and (3) a 2-unit reduction in BMI for women in the top 50% of the BMI distribution (BMI [greater than or equal to] 24; the "middle road" strategy). These analyses were performed using SAS, version 9. …
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