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Addressing Structural Racism, Bias, and Health Communication as Foundational Drivers of Obesity Proceedings of a Workshop Series (2022) / Chapter Skim
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10 Leveraging Data for Systems Change: Connecting Obesity and Its Underlying Determinants
Pages 79-88

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From page 79...
... (Lee) • Trends of decreasing death rates from cardiometabolic dis eases have flattened in the United States, driven presum ably by prolonged exposure to the rising U.S.
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From page 80...
... workshop began with a session focused on driving systems change by leveraging data that connect obesity with its underlying determinants. Carlos Crespo, professor at Oregon Health and Science University and Portland State University School of Public Health and vice provost for undergraduate training in biomedical research at Portland State University, moderated the session.
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From page 81...
... Lee maintained that open sharing of data about the factors and processes that affect obesity at various scales and levels -- genetics, physiology, individual behavior, social environments, physical environments, and societal forces such as policies -- would help paint a more complete picture and reveal major forces driving the outcomes observed. In his fifth example, Lee explained the value of understanding the variations among different locations.
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From page 82...
... Addressing the obesity epidemic effectively, he argued, will require multiple multiscale, layered, and integrated policies and interventions that address the multifaceted contributors to obesity. Ninth, Lee urged the use of new, innovative technologies to understand and address complex systems, asking, what if society were to rely on old technologies to understand weather patterns?
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From page 83...
... This issue can be approached at the population level by examining death rates from cardiometabolic diseases, he explained, which are the greatest contributor to poor trends in U.S. life expectancy.
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From page 84...
... . According to Lee, the convergence was due primarily to faster rates of reduction in deaths from ischemic heart disease in the United States versus peer countries, while the divergence was driven by U.S.
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From page 85...
... The difference in mortality risk is believed to be an outgrowth of differences in individuals' metabolic health profiles, he continued, with low mortality risk reflecting such indicators as greater fitness and normal insulin and blood sugar levels, and higher mortality risk reflecting such indicators as chronic illness, sarcopenia, lower fitness, and inflammation. These observations spurred a new era of proposed indices, Masters noted, that use measures of body size, shape, mass, and adiposity distribution to assess metabolic health and mortality risk more accurately.
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From page 86...
... Current efforts appear to be shifting away from BMI and toward new metrics, he observed, but he believes it is worth considering the payoff for pursuing new metrics compared with that for pursuing new questions and designs with which to explore associations. PANEL AND AUDIENCE DISCUSSION Following their presentations, Lee and Masters answered questions about barriers to implementing obesity solutions; obesity, life expectancy, and mortality risk; and the relationship between health care spending and life expectancy.
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From page 87...
... obesogenic environment affects mortality risk. Relationship between Health Care Spending and Life Expectancy Crespo asked Lee to comment on why the United States spends more money on health care than peer countries do yet has inferior life expectancy outcomes.
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Key Terms

  • life expectancy
  • death rates
  • normal weight
  • care spending
  • obesity prevention

  • weight category
  • peer countries
  • cardiometabolic diseases
  • metabolic health
  • lee urged

  • lee continued
  • obesity category
  • data science
  • bmi states
  • disease mortality

  • disease outcomes
  • body mass
  • obesity solutions
  • masters added
  • implementing obesity

  • integrate data
  • patient care
  • connect obesity
  • masters observed
  • risk reflecting

  • bmi levels
  • specific disease
  • cardiometabolic disease
  • lee suggested
  • single measures


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