kate schlag, berkeley school of public health fall 2014...

Kate Schlag, Berkeley School of Public Health

Fall 2014

BMI and Socioeconomic Status in Adults: United States, 2005-2006

The purpose of this fact sheet is to analyze the relationship between BMI and socioeconomic status among adults using data from NHANES 2005-2006 and to discuss these findings within the context of recent epidemiological studies.

Snapshot

Epidemiology

The prevalence of obesity in adults has doubled in the past three decades, reaching a prevalence of 35%

Implications

Increases in the prevalence of obesity result in numerous chronic health problems and impose significant costs on the health care system. Future policies and interventions should target disparities that exist between SES gradients.

Introduction In the past three decades, the prevalence of obesity (defined as a BMI equal to or over 30) in adults has doubled, reaching a prevalence of 34.9%. However, recent reports suggest that while this rapid growth has plateaued in groups of high socioeconomic status (SES), it continues to climb among groups of lower SES.1 While there are many factors that play a role in the development of obesity, including both social and physical determinants of health, epidemiologic data has shown that a correlation between SES and obesity exists.2,3,4 Specifically, previous research has found that lower SES, an aggregate of economic, social, and work status, is associated with higher rates of obesity; causal mechanisms are shown in Table 4.5,6 In addition to the health consequences that can result from obesity, including heart disease, stroke, type 2 diabetes, certain cancers, and reproductive health complications, 7 obesity and its associated diseases are incurring larger and larger costs on America’s healthcare system, reaching $190.2 billion, or 21% of annual medical spending. 8 Obesity and its related diseases are imposing a burden not only on the future health of Americans, but also on taxpayers and federal budgets.

Problem Definition

Recent studies suggest that the widening gap in socioeconomic status may affect obesity rates

11.5 million Americans are considered poor and live in low-income areas that are over one mile away from a grocery store that sells healthy food. Without access to transportation, these low SES groups turn to low-cost, energy-dense food to feed their families.4

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Kate Schlag, Berkeley School of Public Health Fall 2014

Methods This analysis relies on data from the 2005-2006 NHANES dataset, a nationally representative program of cross-sectional studies that describes the nutritional and health status of adults and children. It includes demographic, socioeconomic, dietary, and health-related data; medical, dental, and physiological measurements; as well as laboratory data. 9 For this analysis, which focused on the adult subpopulation, all ages below age 18 were dropped, removing 5,236 observations for a total of 4,081 observations. The exposure, Family Poverty Income Ratio (Family PIR), is a continuous variable that is a ratio of family income to poverty threshold, as defined by the Department of Health and Human Services’ guidelines. It includes values between 0 and 4.99 and one code to represent all PIRs above 5. For the purpose of this analysis, observations above or equal to five were dropped (n=1,155) in order to focus on populations that are not indisputably wealthy (>500% of PIR). In this analysis, family PIR is used as an indicator of SES. Family PIR was recoded into five categorical values, such that 0 includes all values between 0 and .99 and indicates an income between 0 and 99% of the poverty line; 1 includes all values between 1 and 1.99 and indicates an incomes between 100 and 199% of the poverty line, etc. The outcome, BMI, is a continuous variable with a range of 14.65 to 130.21 kg/m2. Because the value 130.21 is an outlier, it was dropped, giving the dataset a maximum value of 76.07 kg/m2. As past analyses have shown, BMI is right skewed.10 A Bartlett’s test for equal variances determined that the five groups of poverty income ratio have unequal variances (𝛸2 = 24.2005, p = 0.000). As such, a Kruskal-Wallis H-Test was conducted to determine if BMI was significantly different for five groups of family PIR: 0-99%, 100-199%, 200-299%, 300-399%, and 300-499%. Covariables were not included in the analysis, but previous studies have found that gender, race/ethnicity, age, education level, physical activity level, and social support differ among income groups and play a mediating role in BMI. 5,11,12,13

Table 1. Selected Demographics of Population

Table 2. Descriptive Statistics of BMI and PIR Category

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Results Selected demographics and descriptive statistics of

the study population are presented in Table 1 and 2. The mean BMI was 28.49 kg/m2 (95% CI 28.42 - 28.84); 67.59% of the sample was overweight. The mean family PIR was 1.59, indicating that the mean family PIR lies at 159% of the federal poverty line. Frequency distributions of family PIR and BMI are presented in Figure 1 and 2.

Several studies show that there are no statistically significant differences in BMI according to income. One such study analyzed data from NHANES from 1971 to 2006 and found no difference in the rates of overweight (BMI > 25) between poor (individuals whose incomes are greater than 130% of the poverty line) and non-poor categories. However, when the same data analyzed rates of obesity (BMI ≥ 30) according to income, there is a statistically significant relationship, with the poor having 5.1-6.5% higher rates of obesity.14 These results are important when considering policy implications, as it may be more cost- and time-effective to focus resources on obese, as opposed to overweight, populations. Other studies suggest that gender plays a mediating role in the relationship between BMI and SES, whereby differences exist in female populations, but not in male populations.15,16

Discussion

Table 3. Results from Kruskal-Wallis H-Test

The Kruskal-Wallis H-Test found that there is not a statistically significant difference in BMI between the five groups (𝛸2 = 4.474(4), p = 0.3457). However, although it is not statistically significant, a bar graph (Figure 3) reveals that there is a slight downward trend in BMI as family PIR category increases, with the exception of the lowest category of family PIR (0-99% of the poverty threshold).

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While no statistically significant association between SES, as indicated by PIR, and BMI was found in this analysis, other studies and literature reviews have found that a negative correlation exists. In order to develop effective policy measures--both in targeting nutritional intake of lower SES individuals and in reducing the prevalence of overweight and obesity--it is important to further explore the causal mechanisms between SES and obesity to determine where funding and interventions are best distributed.

The majority of epidemiologic and public health studies have found consistent negative correlations between BMI and income.15,17,18,19 These studies are also consistent with literature indicating that lower SES is causally related to disparities in health behavior and status, including overweight and obesity.18,20,21 Even when controlling for body composition by also analyzing fat composition, lower incomes are statistically significantly associated with excess weight due to fat.22 As the outcome variable, BMI lends this analysis both strengths and limitations. While using BMI as a categorical, or even dichotomous, variable to measure the prevalence of overweight fails to reveal information about the distribution of BMI, defining BMI as a continuous variable maintains this data. In addition, for NHANES, BMI is measured by trained professionals (instead of self-report), making it more reliable. However, BMI is not necessarily the best measure of overweight and obesity, as it does not take into account muscle mass. As the exposure variable, family PIR also adds both strengths and limitations. As a ratio of family income to poverty threshold, it takes into account both the size of the family and the ages of the members of the family, 23 thus measuring how financially stable a household and its members are as compared to the poverty line. Other variables, like annual household income, do not take into account these variables and would inaccurately portray SES. This analysis may lack validity in that all observations for family PIR greater than 5, or 500% of the poverty line, were excluded. The true population includes families with a PIR greater than 5, and it is possible that including these observations would have shown a more drastic difference in the relationship between BMI and PIR, indicating a greater disparity in BMI among the poor and the wealthy. In addition, this analysis used family PIR as an indicator for SES; however, SES tends to also include other components, like education level and occupation status. While epidemiologic studies may reveal correlations, a correlation does not imply causation. While most of the research on BMI and SES focus on the impact of SES on BMI, it is possible that the effect is bidirectional. Several studies have found that a higher BMI may lead to discrimination and bias that ultimately lead to lower wages; one study found that obese women earned 12% less than non-obese women. 24 A recent longitudinal study found that males who are obese as teenagers may earn up to 18% less than their normal-weight peers.25 Other studies have found that obese individuals were denied health benefits (pre-ACA) because of their weight or were fired or pressured to resign because of their weight, further widening the socioeconomic gap in health status. 26

Conclusions

Table 4. Suggested Mechanisms for the Causal Link Between BMI and SES

Discussion, continued

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References

kate schlag, berkeley school of public health fall 2014...

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