Projection scenarios of body mass index (2013–2030) for Public Health Planning in Quebec

Lo, Ernest; Hamel, Denis; Jen, Yun; Lamontagne, Patricia; Martel, Sylvie; Steensma, Colin; Blouin, Chantal; Steele, Russell

doi:10.1186/1471-2458-14-996

Cited by 6 publications

(6 citation statements)

References 44 publications

(58 reference statements)

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“…A number of studies have attempted to examine past trends in obesity and make extrapolations to the future for the United States, [31][32][33] Canada, 34,35 Australia, 36 the United Kingdom, 37 European populations, 38 and globally. 39 However, the results presented here suggest that attempts to extrapolate from past data to possible future trends in obesity prevalence may not provide valid estimates.…”

Section: Discussionmentioning

confidence: 99%

Trends in Obesity Among Adults in the United States, 2005 to 2014

Flegal

Kruszon-Moran

Carroll

et al. 2016

JAMA

2,412

1,669

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DESIGN, SETTING, AND PARTICIPANTS Analysis of data obtained from the National Health and Nutrition Examination Survey (NHANES), a cross-sectional, nationally representative health examination survey of the US civilian noninstitutionalized population that includes measured weight and height. EXPOSURES Survey period. MAIN OUTCOMES AND MEASURES Prevalence of obesity (body mass index Ն30) and class 3 obesity (body mass index Ն40). RESULTS This report is based on data from 2638 adult men (mean age, 46.8 years) and 2817 women (mean age, 48.4 years) from the most recent 2 years (2013-2014) of NHANES and data from 21 013 participants in previous NHANES surveys from 2005 through 2012. For the years 2013-2014, the overall age-adjusted prevalence of obesity was 37.7% (95% CI, 35.8%-39.7%); among men, it was 35.0% (95% CI, 32.8%-37.3%); and among women, it was 40.4% (95% CI, 37.6%-43.3%). The corresponding prevalence of class 3 obesity overall was 7.7% (95% CI, 6.2%-9.3%); among men, it was 5.5% (95% CI, 4.0%-7.2%); and among women, it was 9.9% (95% CI, 7.5%-12.3%). Analyses of changes over the decade from 2005 through 2014, adjusted for age, race/Hispanic origin, smoking status, and education, showed significant increasing linear trends among women for overall obesity (P = .004) and for class 3 obesity (P = .01) but not among men (P = .30 for overall obesity; P = .14 for class 3 obesity). CONCLUSIONS AND RELEVANCE In this nationally representative survey of adults in the United States, the age-adjusted prevalence of obesity in 2013-2014 was 35.0% among men and 40.4% among women. The corresponding values for class 3 obesity were 5.5% for men and 9.9% for women. For women, the prevalence of overall obesity and of class 3 obesity showed significant linear trends for increase between 2005 and 2014; there were no significant trends for men. Other studies are needed to determine the reasons for these trends.

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Section: Discussionmentioning

confidence: 99%

Trends in Obesity Among Adults in the United States, 2005 to 2014

Flegal

Kruszon-Moran

Carroll

et al. 2016

JAMA

2,412

1,669

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“…However the Spearman correlations suggest possibilities for further exploration in terms of differences between regions rather than causal relationships. Third, we could not stratify by age due to small sample sizes, so age distributions are a possible confounder for the obesity projections, though a province-level projection study found that obesity trends were not driven by changes in population age structure, 3 and our …”

Section: Discussionmentioning

confidence: 99%

“…The weighted compositional regression approach was used, which fits and projects all BMI categories together, accounting for their multinomial nature as well as for heterogeneity in survey variance structure. 3 For the current study, only the linear scenario was retained, which assumes that future prevalence time trends will be an approximately linear extrapolation of historical trends. Table 1.…”

Section: Projection Analysesmentioning

confidence: 99%

“…[1][2][3] However substantial heterogeneity in population health, risk factors and environment is often observed regionally, potentially rendering inefficient any planning and target setting based on information at these high levels. 4,5 While most regional health jurisdictions have their own surveillance programs, there remains a lack of studies that collectively assess and compare interregional obesity levels and temporal trends within larger administrative territories.…”

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confidence: 99%

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Region-level obesity projections and an examination of its correlates in Quebec

Jahagirdar

2017

Can J Public Health

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OBJECTIVES: Regional public health policy-makers frequently adopt obesity programs and objectives that have been established at global, provincial/state or national levels. However, the presence of substantial inter-regional disparities could render this practice inefficient. Studies that collectively assess obesity prevalence, temporal trends and their heterogeneity at the region level are rare, though they could be used to support better regional surveillance and planning. To address this gap, our study projected obesity prevalence time series to 2023 for 16 health regions in Quebec. We also compared the extent to which yearly rates of increase (or slope) versus cross-sectional prevalence drove regional heterogeneity and correlated with obesity-related sociodemographic and behavioural characteristics.METHODS: Projections were done using weighted compositional regression to fit and extrapolate obesity prevalence time series . Heterogeneity in obesity prevalence as a function of time and obesity slope were characterized using standard deviation. The correlation of region-level obesity prevalence and slope with 14 area-level obesity-related characteristics was assessed.RESULTS: Obesity prevalence is projected to increase in all regions. Region-level heterogeneity in prevalence in 2012 (σ = 2.2%) is projected to increase to (σ = 3.1%) by 2023. The increase in prevalence heterogeneity appeared to be driven by region-level heterogeneity in slope (β = 0.22%-0.51%/year). Obesity-related characteristics were found to be more strongly correlated with slope than with prevalence.CONCLUSION: Large area obesity trends mask substantial and increasing region-level disparities. Obesity slope appears to drive region-level heterogeneity and correlate strongly with explanatory factors, and may represent a pertinent metric for public health monitoring. However substantial heterogeneity in population health, risk factors and environment is often observed regionally, potentially rendering inefficient any planning and target setting based on information at these high levels.4,5 While most regional health jurisdictions have their own surveillance programs, there remains a lack of studies that collectively assess and compare interregional obesity levels and temporal trends within larger administrative territories. This gap in information could mask important region-level disparities, which could in turn hinder the effectiveness of obesity programs at high levels or their implementation at the regional level. In addition, obesity surveillance and risk factor burden typically rely on cross-sectional prevalence rather than time trends. Prevalence measures provide "snapshots" of burden, i.e., the average proportion of a given population that is obese at a given time. These have formed the basis for monitoring and target setting in public health. For instance, the World Health Organization's (WHO) obesity-related target is to "halt the rise" in prevalence, while its recent report outlines prevalence estimates in hundreds of countri...

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“…Models 8, 9, and 10 assume that overweight/obesity prevalence will increase by 1.2 percent and 19.6 percent, respectively (Lo et al 2014). Model 8 assumes no changes in risk factors and that no interventions have occurred (that is, no government intervention and no increase in prevalence).…”

Section: Overweight and Obesity Increasing Prevalencementioning

confidence: 99%

Overweight and Obesity in Saudi Arabia: Consequences and Solutions

2022

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Some rights reserved 1 2 3 4 25 24 23 22 Books in this series are published to communicate the results of World Bank research, analysis, and operational experience with the least possible delay. The extent of language editing varies from book to book.This work is a product of the staff of The World Bank with external contributions. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy, completeness, or currency of the data included in this work and does not assume responsibility for any errors, omissions, or discrepancies in the information, or liability with respect to the use of or failure to use the information, methods, processes, or conclusions set forth. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries.Nothing herein shall constitute or be construed or considered to be a limitation upon or waiver of the privileges and immunities of The World Bank, all of which are specifically reserved.

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Projection scenarios of body mass index (2013–2030) for Public Health Planning in Quebec

Cited by 6 publications

References 44 publications

Trends in Obesity Among Adults in the United States, 2005 to 2014

Trends in Obesity Among Adults in the United States, 2005 to 2014

Region-level obesity projections and an examination of its correlates in Quebec

Overweight and Obesity in Saudi Arabia: Consequences and Solutions

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