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      Voices for Healthy Kids: a multisectoral collaboration to accelerate policy changes that promote healthy weight for all children and adolescents in the United States

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          Emily Callahan and colleagues report how a multisectoral collaboration of more than 140 stakeholder organisations is advancing policy changes to improve food and physical environments in the United States to promote healthy weight for all children and adolescents

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          Prevalence of Obesity Among Youths by Household Income and Education Level of Head of Household — United States 2011–2014

          Obesity prevalence varies by income and education level, although patterns might differ among adults and youths ( 1 – 3 ). Previous analyses of national data showed that the prevalence of childhood obesity by income and education of household head varied across race/Hispanic origin groups ( 4 ). CDC analyzed 2011–2014 data from the National Health and Nutrition Examination Survey (NHANES) to obtain estimates of childhood obesity prevalence by household income (≤130%, >130% to ≤350%, and >350% of the federal poverty level [FPL]) and head of household education level (high school graduate or less, some college, and college graduate). During 2011–2014 the prevalence of obesity among U.S. youths (persons aged 2–19 years) was 17.0%, and was lower in the highest income group (10.9%) than in the other groups (19.9% and 18.9%) and also lower in the highest education group (9.6%) than in the other groups (18.3% and 21.6%). Continued progress is needed to reduce disparities, a goal of Healthy People 2020. The overall Healthy People 2020 target for childhood obesity prevalence is 130% to ≤350%, and >350% of FPL. The cut-off point for participation in the Supplemental Nutrition Assistance Program is 130% of FPL, and 350% provides relatively equal sample sizes for each income group. Education was defined using education level of head of household and was categorized as a high school graduate or less, some college, and college graduate. All estimates accounted for the complex survey design including examination sample weights. Confidence intervals for estimates were constructed using the Korn and Graubard method ( 7 ). Differences between groups were tested using a 2-sided univariate t statistic (p 130% to ≤350% 1,974 19.9 (16.8–23.3) 18.0 (12.6–24.6) 19.9 (15.5–25.0) 8.9 (4.9–14.6) 23.7 (19.4–28.5) >350% 1,256 10.9 (8.0–14.4)*,† 11.0 (7.3–15.7) 19.8 (12.2–29.4) 4.4 (1.9–8.4)*,§ 11.8 (7.5–17.4)*,† Females ≤130% 1,539 19.7 (17.4–22.1) 17.8 (13.3–23.1) 19.9 (15.7–24.6) 8.4 (2.6–19.1)¶ 22.5 (18.9–26.3) >130% to ≤350% 969 21.5 (16.9–26.8) 21.2 (13.0–31.6) 21.6 (16.3–27.6) 8.2 (2.4–19.0)¶ 22.7 (17.0–29.2) >350% 613 8.0 (5.0–12.0)*,† 7.2 (3.5–12.8)*,† 21.1 (9.6–37.2) 1.3 (0.1–4.8)¶ 13.8 (6.3–25.2) Males ≤130% 1,592 18.1 (15.5–21.0) 13.5 (9.2–18.7) 19.0 (15.7–22.6) 18.0 (10.1–28.6) 23.1 (18.0–28.9) >130% to ≤350% 1,005 18.4 (15.6–21.4) 15.0 (10.0–21.2) 18.1 (12.1–25.5) 9.5 (3.9–18.7)§ 24.6 (20.0–29.7) >350% 643 13.7 (9.5–18.8) 14.7 (9.2–21.9) 18.7 (12.1–26.9) 7.6 (2.8–16.0)*,§ 10.0 (4.8–17.9)*,† Education level of head of household Total High school graduate or less 3,254 21.6 (20.0–23.3) 19.6 (16.2–23.3) 21.1 (17.5–25.0) 13.2 (8.5–19.3) 24.2 (20.9–27.7) Some college 1,936 18.3 (15.4–21.5)** 17.6 (12.4–23.9) 19.7 (16.3–23.4) 12.0 (6.0–20.7) 19.9 (16.2–23.9) College graduate 1,464 9.6 (7.3–12.5)**,†† 8.5 (5.8–12.1)**,†† 15.4 (9.8–22.5) 5.5 (3.1–8.9)** 13.5 (6.9–22.8)** Females High school graduate or less 1,583 22.7 (20.7–24.9) 22.5 (17.5–28.1) 21.0 (16.0–26.7) 9.2 (4.4–16.5) 23.9 (20.1–28.0) Some college 938 18.3 (14.6–22.6)** 18.0 (11.8–25.7) 22.1 (17.4–27.4) 8.0 (1.3–23.7)¶ 17.3 (12.5–23.0)** College graduate 739 8.5 (5.5–12.4)**,†† 7.5 (3.9–12.8)**,†† 16.3 (10.2–24.1) 3.3 (0.7–9.2)¶ 14.0 (6.8–24.3)** Males High school graduate or less 1,671 20.6 (18.1–23.2) 16.9 (11.6–23.3) 21.1 (17.5–25.1) 16.9 (9.0–27.7) 24.4 (20.5–28.7) Some college 998 18.3 (14.7–22.4) 17.3 (11.0–25.3) 17.2 (13.4–21.6) 14.6 (6.7–26.4) 22.3 (15.9–29.8) College graduate 725 10.7 (7.6–14.7)**,†† 9.6 (5.5–15.2)** 14.5 (6.9–25.4) 7.9 (3.8–14.0) 12.9 (5.8–23.9) §,** Abbreviation: CI = confidence interval. * Significantly different from ≤130% of FPL, p 130% to ≤350% of FPL, p 40%. ** Significantly different from high school graduate or less, p 130% to ≤350% of FPL. † Quadratic trend (p<0.05) for males ≤130% of FPL. The figure above is a line graph showing trends in obesity prevalence among youths (persons aged 2–19 years), by household income, in the United States, from 1999–2002 through 2011–2014. Obesity prevalence among youths increased from 1999–2002 to 2011–2014 among females and males in households headed by persons with the least education (high school graduate or less) and among females in households headed by persons with some college education. There were no other significant trends. In addition, the difference in childhood obesity prevalence between the lowest and highest head of household education groups increased over time for females but not for males (Figure 2). FIGURE 2 Trends* in prevalence of obesity among youths (persons aged 2–19 years), by education level of head of household — National Health and Nutrition Examination Survey, United States, 1999–2002 through 2011–2014 * Linear trend (p<0.05) for females, high school graduate or less and some college, and males, high school graduate or less. The figure above is a line graph showing trends in prevalence of obesity among youths (persons aged 2–19 years), by education level of head of household, in the United States, from 1999–2002 through 2011–2014. Discussion During 2011–2014, the relationships between childhood obesity and income and childhood obesity and education of household head were complex, differing depending upon the subgroup of the population. The prevalence of obesity among youths living in households headed by college graduates was lower than that among those living in households headed by less educated persons for each race-Hispanic origin group. The same was not true for those living in the highest income group. Moreover, differences by income and education of household head are widening among females. Similar to results based on data from 2005 to 2008 ( 4 ), during 2011–2014 childhood obesity prevalence was lower among youths living in households in the highest income group. However, this was not the pattern seen in all subgroups. For example, obesity prevalence was lower in the highest income group compared with the other groups among non-Hispanic white females, but not among non-Hispanic black females, non-Hispanic white males, or non-Hispanic black males. Obesity prevalence decreased as head of household education increased in all subgroups examined. The prevalence of obesity was consistently lowest among children in households headed by college graduates, which differed from the pattern seen by income level. This difference in the relationship between obesity and income versus education has been observed in at least one other study ( 8 ). In addition, some relationships changed since 2005–2008. For example, there was a significant decreasing trend in obesity prevalence by income among non-Hispanic white males during 2005–2008 ( 4 ) but there were no differences during 2011–2014. This report also presents differences in childhood obesity prevalence by income and education among non-Hispanic Asian youths in the United States. It has been suggested that the cut-off point that typically defines obesity might underestimate associated health risks among Asian persons ( 9 ). The findings in this report are subject to at least one limitation. The sample size was small among some subgroups, such as non-Hispanic Asian females living in households with income above 350% of the FPL, where the prevalence of obesity is very low (1.3%) and the sample size is small (138). Additional years of data might provide more information about obesity prevalence by income, especially among non-Hispanic Asian youths. Trends in childhood obesity prevalence by income and education level of head of household indicate that disparities have existed at least since NHANES III, 1988–1994 ( 10 ). These differences have widened since 1999–2002 among females but not among males, where differences in obesity prevalence by income and education of the head of household have remained relatively constant from 1999–2002 to 2011–2014. These findings demonstrate that lower levels of income are not universally associated with childhood obesity. The association is complex and differs by sex, race, and Hispanic origin, and possibly over time. Differences by education are more consistent across subgroups than differences by income. More progress is needed to reduce disparities in childhood obesity prevalence, an important Healthy People 2020 objective. Summary What is already known about this topic? Studies have suggested that childhood obesity prevalence varies by income and education, although patterns might differ between adults and youths. What is added by this report? Analysis of data from the 2011–2014 National Health and Nutrition Examination Survey (NHANES) demonstrates that childhood obesity prevalence patterns among persons aged 2–19 years by household income are less consistent by race and Hispanic origin than are the patterns by level of education attained by the head of household. Moreover, the differences in childhood obesity prevalence by income and education of household head are widening among females while differences among males have remained relatively constant over time. What are the implications for public health practice? NHANES will continue to be an important source of data for monitoring disparities in childhood obesity. These data will help track the Healthy People 2020 objective of reducing disparities and might inform obesity prevention programs at the federal, state, and local levels.
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            The politics of obesity: a current assessment and look ahead.

            The continuing rise in obesity rates across the United States has proved impervious to clinical treatment or public health exhortation, necessitating policy responses. Nearly a decade's worth of political debates may be hardening into an obesity issue regime, comprising established sets of cognitive frames, stakeholders, and policy options. This article is a survey of reports on recently published studies. Much of the political discussion regarding obesity is centered on two "frames," personal-responsibility and environmental, yielding very different sets of policy responses. While policy efforts at the federal level have resulted in little action to date, state and/or local solutions such as calorie menu labeling and the expansion of regulations to reduce unhealthy foods at school may have more impact. Obesity politics is evolving toward a relatively stable state of equilibrium, which could make comprehensive reforms to limit rising obesity rates less feasible. Therefore, to achieve meaningful change, rapid-response research identifying a set of promising reforms, combined with concerted lobbying action, will be necessary.
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              United States of America: health system review.

              This analysis of the United States health system reviews the developments in organization and governance, health financing, health-care provision, health reforms and health system performance. The US health system has both considerable strengths and notable weaknesses. It has a large and well-trained health workforce, a wide range of high-quality medical specialists as well as secondary and tertiary institutions, a robust health sector research program and, for selected services, among the best medical outcomes in the world. But it also suffers from incomplete coverage of its citizenry, health expenditure levels per person far exceeding all other countries, poor measures on many objective and subjective measures of quality and outcomes, an unequal distribution of resources and outcomes across the country and among different population groups, and lagging efforts to introduce health information technology. It is difficult to determine the extent to which deficiencies are health-system related, though it seems that at least some of the problems are a result of poor access to care. Because of the adoption of the Affordable Care Act in 2010, the United States is facing a period of enormous potential change. Improving coverage is a central aim, envisaged through subsidies for the uninsured to purchase private insurance, expanded eligibility for Medicaid (in some states) and greater protection for insured persons. Furthermore, primary care and public health receive increased funding, and quality and expenditures are addressed through a range of measures. Whether the ACA will indeed be effective in addressing the challenges identified above can only be determined over time. World Health Organization 2013 (acting as the host organization for, and secretariat of, the European Observatory on Health Systems and Policies).
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                Author and article information

                Contributors
                Role: consultant
                Role: national partnerships manager
                Role: global advocacy manager
                Role: consultant
                Role: vice president
                Role: senior programme officer
                Journal
                BMJ
                BMJ
                BMJ-UK
                bmj
                The BMJ
                BMJ Publishing Group Ltd.
                0959-8138
                1756-1833
                2018
                08 December 2018
                : 363
                : k4763
                Affiliations
                [1 ]EAC Health and Nutrition LLC, Washington DC, USA
                [2 ]American Heart Association, Washington DC, USA
                [3 ]EquiACT, Lyon, France
                [4 ]Robert Wood Johnson Foundation, New Jersey, USA
                Author notes
                Correspondence to M Hollander Marla.hollander@ 123456heart.org
                Article
                cale47393
                10.1136/bmj.k4763
                6282719
                30530628
                652bd8bc-7905-4085-bc9c-3f3c78678959
                Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions

                This is an Open Access article distributed under the terms of the Creative Commons Attribution IGO License ( https://creativecommons.org/licenses/by-nc/3.0/igo/), which permits use, distribution, and reproduction for non-commercial purposes in any medium, provided the original work is properly cited.

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