from 2011 to 2014 (ages 2 to 5, ages 6 to 11, and ages 12 to 19) had the highest rates of obesity (15.6–25%), while Asians had the lowest (5.2–9.8%) (Figure 4.2) [11]. Children who are overweight or obese for an extended period are more likely to become overweight or obese adults, who are then more susceptible to heart disease, high blood pressure, and type 2 diabetes. Researchers have found associations between pre‐adulthood obesity and higher risk in adulthood of certain types of cancer. Such findings have prompted calls for more research, including analyses that are more robust across the lifespan [23].
Figure 4.2 Children and adolescents with obesity, overall (2–19 years of age; top, left), and in (following, left to right), generally, preschool, elementary, and secondary school age groups.
Source: US Centers for Disease Control and Prevention [11].
Though annual costs related to childhood obesity are estimated to be $14 billion, annual costs related to adult obesity rise to 10 times that or more ($147–$210 billion). Stigmatizing people with obesity, though a prejudice, is not generally recognized as one and remains largely acceptable in the young and old. Despite its adverse effects on its targets and the generation of health disparities, such prejudice further contributes to chronic burden and medical attention deterrence [24]. Though rapid increases have ended and rates have leveled off, indicators suggest in certain parts of the country disparities are widening because most reductions in obesity rates have occurred in students who are White and from families of higher than average socioeconomic position [14].
We address behavior and other health determinants, including socioeconomics, cultural environment, physical environment, biological and genetic influences, and healthcare, because we value good health for all. By recognizing disparities, we seek to create effective interventions that take into account these influences and to identify and enact policies that support the correction of disparities and the wise use of resources. That is not to suggest that these influences are static. They are dynamic, changing across the lifespan and across generations, and their influence waxes and wanes according to such factors as education, vulnerabilities, environmental stability, and structural drivers (power, income, and other resources) at individual, local, national, and global levels.
4.3 Relevant Metrics and Research Methods
4.3.1 Relevant Metrics
Table 4.1 shows the most common metrics relevant to health disparities in behavioral health determinants and specific examples [9, 25]. Investigations will continue to be undertaken in a wide array of designs and methods, but interdisciplinary teams and approaches, new methodologies, new technology, and multilevel applications of methodologies will take these in new directions.
Table 4.1 Types of disparity measurements and examples from behavioral determinants of health.
Sources: Types of disparity measurements adapted from Appendix 11 of the US Department of Health and Human Services [9]. Data from Example 1 are from the Global Health Data Exchange: US Health Map, and data for Examples 2 and 3 are from Data 2020, a resource linked to Healthy People 2020.
| Indicator | Grouping | Method of comparison | ||
|---|---|---|---|---|
| Measurement of health disparities | ||||
| Health outcomes (e.g., life expectancy, mortality, chronic disease rates)Healthcare access/quality of careSocial/environmental/ behavioral risk factors (e.g., neighborhood characteristics, discrimination) | Social grouping by level of disadvantage or advantage (racial/ethnic groups, income groups by poverty level, level of educational attainment) | Comparison of rates on single indicator for two groups (usually top and bottom), absolute difference between rates for two groups, other methods (e.g., slope and relative index of inequality) | ||
| Example 1: Disparities in health outcomes | ||||
| Life expectancy at birth by US state, both sexes, 2014 | Estimated lowest and highest life expectancy by state, 2014 (years) | Absolute difference | Ratio (Mississippi/Hawaii) | |
| Mississippi | Hawaii | (years) | (%) | |
| 74.91 | 81.15 | −6.24 | 92.3 | |
| Example 2: Disparities in healthcare | ||||
| Change in percentage making visit to clinic including tobacco screening, 2009–2011 | Percentage of men and women, 18 years and older, 2009–2011 | Absolute difference | Ratio | |
| NH Black | NH White | (Percentage points) | (NH Black/White) | |
| −14.8 | −0.1 | −14.7 | 148 | |
| Example 3: Disparities in social, environmental, and behavioral risk factors | ||||
| Children, 3–11 years of age, exposed to secondhand smoke | Percentage exposed by family poverty status | Absolute difference | Ratio | |
| <100% poverty threshold | ≥500% poverty threshold | (Percentage points) | (<100%/≥500%) | |
| 62.1 | 14.1 | 48.0 | 4.4 | |
Abbreviation: NH, non‐Hispanic.
Certain characteristics have long had a place in quantitative research on behavioral determinants of health and health disparities, including, for example, race or ethnicity, gender, education, and socioeconomic position. Place of residence can be an indicator of socioeconomic position, or it may reveal disparities resulting from a remote, rural location, such as identification as a medically underserved area or one without public transportation, broadband service, or other amenities common in cities. All these characteristics contribute to limited access to and utilization of healthcare or health information.
Place‐based approaches to improving population health outcomes have a lengthy history and are gaining momentum [10]. Among them are multilevel strategies that may involve working with nonprofit organizations or local, state, or national governments, performing epidemiological surveys, evaluating physical environments