Self-reported Prevalence Of Obesity Research Articles

Household availability of ultra-processed foods and obesity in nineteen European countries.

To assess household availability of NOVA food groups in nineteen European countries and to analyse the association between availability of ultra-processed foods and prevalence of obesity. Ecological, cross-sectional study. Europe. Estimates of ultra-processed foods calculated from national household budget surveys conducted between 1991 and 2008. Estimates of obesity prevalence obtained from national surveys undertaken near the budget survey time. Across the nineteen countries, median average household availability amounted to 33·9 % of total purchased dietary energy for unprocessed or minimally processed foods, 20·3 % for processed culinary ingredients, 19·6 % for processed foods and 26·4 % for ultra-processed foods. The average household availability of ultra-processed foods ranged from 10·2 % in Portugal and 13·4 % in Italy to 46·2 % in Germany and 50·4 % in the UK. A significant positive association was found between national household availability of ultra-processed foods and national prevalence of obesity among adults. After adjustment for national income, prevalence of physical inactivity, prevalence of smoking, measured or self-reported prevalence of obesity, and time lag between estimates on household food availability and obesity, each percentage point increase in the household availability of ultra-processed foods resulted in an increase of 0·25 percentage points in obesity prevalence. The study contributes to a growing literature showing that the consumption of ultra-processed foods is associated with an increased risk of diet-related non-communicable diseases. Its findings reinforce the need for public policies and actions that promote consumption of unprocessed or minimally processed foods and make ultra-processed foods less available and affordable.

Abstract P383: Do Geographic Patterns of Obesity Prevalence Correlate with Patterns of High Stroke Mortality?

Background: Self-reported data from the Behavioral Risk Factor Surveillance System (BRFSS) survey show higher prevalence of obesity in the south, particularly for the East South Central(ESC) states of LA, MS, and AL. Because obesity is associated with hypertension and diabetes, higher prevalence of obesity has been hypothesized as a primary contributor to the stroke belt, an area of high stroke mortality in the southeast. We assessed measured obesity from the National Health and Nutrition Examination Survey (NHANES) and REasons for Geographic and Racial Differences in Stroke (REGARDS), a national population-based cohort from 48 states, and compared geographic prevalence patterns across studies. Methods: Weighted obesity prevalence estimates (with 95% CIs) were calculated from NHANES (2003-2008; n = 6138), REGARDS (2003-2007; n = 30,183), and BRFSS (2003-2007; n = 67,742) in non-Hispanic black and white adults over 45 years of age in the census divisions. Obesity was defined as BMI >= 30 kg/m 2 . Results: There is a lack of concordance in the obesity prevalence (see Figure) between BRFSS and both REGARDS (top left) and NHANES (bottom right). In contrast, the comparison of REGARDS and NHANES (top right) shows good agreement in the estimated obesity prevalence for all divisions (including those containing the stroke belt states, shown in red). Conclusion: Our results indicate discordance in measured and self-reported obesity prevalence especially in the ESC division. The objectively measured obesity prevalence for both NHANES and REGARDS was high in the West North Central division including ND, SD, NE, KS, MN, IA, and MO. Factors other than obesity may contribute to the high prevalence of hypertension and diabetes, and high stroke mortality rates, in the stroke belt. Additionally, these data suggest that errors in self-reporting height and/or weight differ by region and may have distorted past estimates of obesity rates by region.

The Intestinal Microbiota and Obesity

Obesity has been and continues to be an epidemic in the United States. Obesity has been addressed in multiple health initiatives, including Healthy People 2010, with no state meeting the proposed goal of a prevalence of obesity < 15% of the adult population. In contrast, obesity rates have continued to increase, with the self-reported prevalence of obesity among adults increasing by 1.1% from 2007 to the present. Indeed, since 2009, 33 states reported obesity prevalences of 25% or more with only 1 state reporting prevalence < 20%. There have been multiple approaches for the treatment of obesity, including fad diets, incentive-based exercise programs, and gastric bypass surgery; none of which have been optimal. In a murine model, it was shown that the majority of the intestinal microbiome consists of two bacterial phyla, the Bacteroidetes and the Firmicutes, and that the relative abundance of these two phyla differs among lean and obese mice; the obese mouse had a higher proportion of Firmicutes to Bacteroidetes (50% greater) than the lean mouse. The same results were appreciated in obese humans compared to lean subjects. The postulated explanation for this finding is that Firmicutes produce more complete metabolism of a given energy source than do Bacteroidetes, thus promoting more efficient absorption of calories and subsequent weight gain. Researchers were able to demonstrate that colonizing germ-free mice with the intestinal microbiome from obese mice led to an increased total body fat in the recipient mice despite a lack of change in diet. The converse, that, colonizing germ-free obese mice with the intestinal microbiome of thin mice causing a decreased total body fat in the recipient mice, has not yet been done. Other possible mechanisms by which the intestinal microbiome affects host obesity include induction of low-grade inflammation with lipopolysaccharide, regulation of host genes responsible for energy expenditure and storage, and hormonal communication between the intestinal microbiome and the host. The following review discusses the microbiome-obesity relationship and proposed mechanisms by which the intestinal microbiota is hypothesized to influence weight gain.

Change in bias in self-reported body mass index in Australia between 1995 and 2008 and the evaluation of correction equations

BackgroundMany studies have documented the bias in body mass index (BMI) determined from self-reported data on height and weight, but few have examined the change in bias over time.MethodsUsing data from large, nationally-representative population health surveys, we examined change in bias in height and weight reporting among Australian adults between 1995 and 2008. Our study dataset included 9,635 men and women in 1995 and 9,141 in 2007-2008. We investigated the determinants of the bias and derived correction equations using 2007-2008 data, which can be applied when only self-reported anthropometric data are available.ResultsIn 1995, self-reported BMI (derived from height and weight) was 1.2 units (men) and 1.4 units (women) lower than measured BMI. In 2007-2008, there was still underreporting, but the amount had declined to 0.6 units (men) and 0.7 units (women) below measured BMI. The major determinants of reporting error in 2007-2008 were age, sex, measured BMI, and education of the respondent. Correction equations for height and weight derived from 2007-2008 data and applied to self-reported data were able to adjust for the bias and were accurate across all age and sex strata.ConclusionsThe diminishing reporting bias in BMI in Australia means that correction equations derived from 2007-2008 data may not be transferable to earlier self-reported data. Second, predictions of future overweight and obesity in Australia based on trends in self-reported information are likely to be inaccurate, as the change in reporting bias will affect the apparent increase in self-reported obesity prevalence.

Regression models to predict corrected weight, height and obesity prevalence from self-reported data: data from BRFSS 1999–2007

Surveys such as the Behavioral Risk Factor Surveillance System (BRFSS) collect only self-reported data on height and weight to estimate obesity prevalence rates. Because of biased self-reporting of height and weight, obesity prevalence rates reported by these surveys are too low. To develop regression models that can predict corrected height, weight and obesity prevalence from self-reported data, as well as to compare obesity prevalence rates based on self-reported and modeled data and test for trends in obesity prevalence by gender, age and race/ethnicity. Data from the National Health and Nutrition Examination Survey (NHANES) for the period 1999-2006 were used to develop regression models to predict corrected height, weight and obesity prevalence. Regression coefficients estimated from these models were used to predict corrected height, weight and obesity prevalence for BRFSS data for 1999-2007. Self-reported weights for males were higher by 0.1-0.2 kg and lower by about 1.25 kg than corrected weights for females. Underreporting of weights was lowest for Hispanics when compared with other race/ethnicities. In addition, underreporting of weight increased with an increase in body mass index. Self-reported heights for males were higher than corrected heights by about 2 cm, and for females, by about 1 cm. Overreporting of height increased with an increase in age. Self-reported obesity prevalence was 4.5-5.8% lower than corrected rates for males and by 4.4-5.1% for females. Underreporting of obesity prevalence increased with an increase in age. Obesity prevalence rates increased over time for each gender, race/ethnicity and age group for BRFSS data. Obesity prevalence calculated from self-reported data is too low and should be used with caution for health-care planning purposes. When it is not possible to have measured data, corrected heights and weights may be predicted by using models such as those presented by us from a relatively large data set that has both measured and self-reported data.

Self-reported prevalence of obesity among 20,329 adults from large territories of Greece, Serbia and Turkey

The aim of this study was to investigate the prevalence of overweight, obesity and body mass index (BMI) distribution in people living in large territories of Greece, Serbia and Turkey. A cross-sectional multi-national epidemiological survey was designed on the basis of self-reported information on height and weight using the formula kg/m2. Overall, 20,329 individuals, aged 20-68 yrs, 8,973 males and 11,356 females, participated in the study. Of these, 4,032 were selected from northern Greece, 10,069 from southwest Turkey and 6,228 from central Serbia. More specifically, participants from northern Greece were recruited from central Macedonia, from Southwest Turkey from the Kayseri region and from central Serbia from the greater Belgrade area. Our results showed that 12.4% of the population studied was obese and 31.3% overweight. The corresponding figures for participants from Greece were 19.9% and 35.4%, from Turkey 12.0% and 30.9% and from Serbia 8.2% and 29.4%, respectively. Overall, male participants had a higher mean BMI than females. However, Greek females were more obese in comparison with males. The opposite was true of the other two countries. Finally, in both males and females mean BMI tended to increase with age up to 50-59 years, and declined slightly thereafter. In conclusion, the overall prevalence of obesity and overweight in the Balkan peninsula does not differ from that reported in other European countries. However, BMI values between different countries of the Balkan peninsula are fairly variable, Greece leading in the prevalence of obesity with Serbia having the lowest prevalence of obesity. The data from this survey underline the need for health intervention aimed at preventing obesity, especially in Greece.