Socioeconomic inequality of obesity in the United States: do gender, age, and ethnicity matter?

ARTICLE IN PRESS

Social Science & Medicine 58 (2004) 1171–1180

Socioeconomic inequality of obesity in the United States: do gender, age, and ethnicity matter? Qi Zhanga,*, Youfa Wangb b

a Department of Medicine, MC2007, University of Chicago, 5841 S. Maryland Avenue, Chicago, IL 60637, USA Department of Human Nutrition and Division of Epidemiology and Biostatistics, University of Illinois at Chicago, 1919 West Taylor Street, Chicago, IL 60612-7256, USA

Abstract This study introduces the concentration index (CI) to assess socioeconomic inequality in the distribution of obesity among American adults aged 18–60 years old. The CI provides a summary measure of socioeconomic inequality, and enabled comparisons across gender, age, and ethnicity. Data from the National Health and Nutrition Examination Survey III, 1988–1994 (NHANES III) were used. The degree of socioeconomic inequality in obesity varied considerably across gender, age, and ethnic groups. Among women, we found a stronger, inverse association between socioeconomic status (SES) and obesity compared with men, as well as greater socioeconomic inequality among middle-aged adults (41–49) compared to other age groups. Consistent with previous studies, we found remarkable ethnic differences in the relationship between SES and obesity. Although the extant literature documented a higher prevalence of obesity among minorities than in whites, our results presented a lower socioeconomic inequality in obesity within minority groups. Our analyses suggested that gender, age, and ethnicity could be important factors on socioeconomic inequality in obesity. r 2003 Elsevier Ltd. All rights reserved. Keywords: Obesity; Body mass index; Inequality in health; Socioeconomic status; Concentration index; USA

Introduction Obesity has become a global epidemic, and the prevalence of obesity continues to increase in both developed and developing countries (Wang, Monteiro, & Popkin, 2002; WHO, 1998). Obesity increases the risk of a number of diseases and health conditions, including cardiovascular disease, hypertension, Type II diabetes, and certain types of cancer (Bray, Bouchard, & James, 1998; WHO, 1998). In the United States, over 60% of the adult population is overweight or obese, and obesity is currently the second leading cause of preventable disease and death, next to smoking (US HHSD, 2001). *Corresponding author. Tel.: +1-773-702-4565; fax: +1773-834-2238. E-mail address: [email protected] (Q. Zhang).

The magnitude of obesity as a major public burden is reflected in its total direct and indirect cost, which was estimated at 120 billion dollars in 2000 (US HHSD, 2001). Effective interventions to control or manage obesity will necessarily hinge on understanding the complex processes that determine body composition, including excess adiposity, in the population. These processes involve the interactions of numerous factors, including biological characteristics such as genetic predisposition, social, cultural, environmental, and behavioral factors (Bray et al., 1998; WHO, 1998). For example, socioeconomic status (SES) has been demonstrated to influence individual’s energy intake and energy expenditure, and as a result, to affect body fat storage (Sundquist & Johansson, 1998). Further evidence can be found in the repeated findings of disproportionally high rates of obesity among minority and low-income groups

0277-9536/03/$ - see front matter r 2003 Elsevier Ltd. All rights reserved. doi:10.1016/S0277-9536(03)00288-0

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(Dreeben, 2001; Flegal, Carroll, Kuczmarski, & Johnson, 1998). In the biomedical field, linear and logistic regression analyses are the classical approaches to studying the association between SES and obesity. Usually, odds ratios (OR) or beta coefficients are reported to indicate the magnitude and direction of the association (Sobal & Stunkard, 1989; Wang, 2001). These methods are straightforward, but suffer from several limitations. First, although linear regression analysis can help examine whether there is an association between SES and obesity, it is not powerful enough to measure the disparity quantitatively, i.e., to tell how severe the inequality is. Second, comparing inequality across studies or over time using traditional regression analysis is difficult, since the validity of regression analysis is based on the assumption of multi-normality and independence between study variables over time (Zeger, Liang, & Albert, 1988). Third, from a statistical perspective, linear regression analysis assesses the relationship between the outcome and explanatory variables on average but ignores the possibility that the effect of explanatory variables may vary across the distribution. To illustrate, suppose SES is positively related to BMI at one tail of the distribution of SES, while SES is negatively related to BMI at the other tail of the distribution. Simply regressing BMI on SES may result in a finding of zero mean effect of SES on BMI. To solve similar problems, economists have developed summary indices such as the Gini coefficient and the concentration index to quantitatively measure the degree of income-related inequality. A variety of techniques based on these indices have since been developed to address the limitations of linear regression (Lambert, 1993). These indices and methods are used extensively in economics and have proven useful in studying issues like tax progressivity (Kakwani, 1977; Plotnick, 1981; Jenkins, 1988). More recently, economists have exploited the intuitive analogy between taxes and poor health outcomes as burdens that may be unevenly apportioned to members of different SES groups, and have applied techniques of analyzing income inequality to analyze inequality in general health status (Kunst, Geurts, & van den Berg, 1995; Wilkinson, 1996; van Doorslaer et al., 1997). However, only a few studies have focused on SES inequality of specific diseases (see Pamuk, 1988; Sturm & Gresenz, 2002). To our knowledge, no published study has quantitatively measured the degree of SES inequality of obesity. The primary goal of this study is to apply a summary index, the concentration index, to assess the degree of inequality in the distribution of obesity across SES using a national representative survey data set. We also investigate whether there are differences in the inequality across sex, age, and ethnic groups. Our study yields

potentially important implications for medical scientists engaged in studying the relationship between SES and obesity, social scientists interested in the effects of health inequality with respect to disease, and policy makers committed to reduce health disparities across socioeconomic groups.

Methodology The concentration index as a measure of health inequality A few summary indices of health inequality have been reported in the literature, including the Gini coefficient, the index of dissimilarity, the index of inequality, the relative index of inequality and the concentration index (see Wagstaff, Paci, & van Doorslaer, 1991 for a review). Of these summary indices, Wagstaff et al. (1991) argued that the concentration index (CI) is the most appropriate measure of health inequality, since it meets the three basic requirements of a health inequality index, namely, ‘‘y(i) that it reflects the socioeconomic dimension to inequalities in health; (ii) that it reflects the experiences of the entire population; and (iii) that it is sensitive to changes in the distribution of the population across socioeconomic groupsy’’ Moreover, the CI lends itself to graphical representation that is more easily and intuitively interpreted (see van Doorslaer et al., 1997 for more details). Therefore, we used the CI to measure the degree of socioeconomic inequality in obesity within the US population. To illustrate the theory behind the CI, Fig. 1 plots the cumulative proportion of the population, ranked by 100% R

90%

Cumul. Share of Obesity

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L'(x)

E 50%

C L(x) P 10%

0

30%

50%

70%

100%

Cumul. Share of Pop. Ranked by Income Fig. 1. Concentration curves of obesity. Note: L(x) represents progressive concentration curves; L0 (x) represents regressive concentration curves.

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income, from the poorest to the richest (0–100% of the total income in the US), against the cumulative proportion within the obese population, from lowest to highest (0–100% of the obese members within the population in the US). The resulting curve is referred to as the obesity concentration curve L(x), where x represents cumulative share of population. In a special case, the concentration curve coincides with the diagonal. In Fig. 1, at point E, 50% of the people ranked by income account for 50% of the obese individuals in the population. Since every point of the concentration curve lies on the diagonal, the obesity ‘‘burden’’ is equally distributed across income levels. The diagonal is also known as the ‘‘egalitarian line’’. If the concentration curve is concave with respect to the egalitarian line, and all points are below the diagonal, then the obesity ‘‘burden’’ is concentrated more heavily among the wealthy. This is illustrated by the curve L(x) in Fig. 1. At point P on the curve L(x), the bottom 30% of the population ranked by income account for only 10% of the cases of obesity. In the economics literature, concentration curves like L(x) are referred to as ‘‘progressive curves’’ i.e., the wealthy bear a greater burden than the poor. If the concentration curve is convex with respect to the egalitarian line, and all points lie above the diagonal, then the obesity ‘‘burden’’ is concentrated more heavily among the poor. This is illustrated by the curve L0 (x). At point R, the bottom 70% of the population with respect to income account for 90% of the cases of obesity. Those curves like L0 (x) are known as ‘‘regressive curves,’’ i.e., the poor bear a disproportionate share of the burden. The distance between the concentration curve and the diagonal shows the degree of inequality characterizing the distribution of obesity across socioeconomic status: the greater the distance, the greater the degree of inequality characterizing the distribution. Mathematically, the CI is defined as twice the area between the concentration curve and the diagonal: Z 1 CI ¼ 1  2 LðxÞ dx: ð1Þ 0

The value of the CI theoretically ranges from 1 to +1. The CI is zero when the concentration curve coincides the diagonal, which indicates that there is no socioeconomic inequality in the distribution of obesity. If the curve lies above the diagonal, the CI has a negative value, meaning that obesity ‘‘favors’’ lower SES groups (i.e. there is a negative association between SES and obesity). If the concentration curve lies below the egalitarian line, the index has a positive value, indicating that obesity ‘‘favors’’ higher SES groups (i.e. there is a positive association between SES and obesity). Statistical inference: CIs are empirically derived from sample data, and it is of interest to test whether these indices are statistically significantly different from zero.

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To conduct these tests, we adopted inference methods developed by Kakwani, Wagstaff, and van Doorslaer (1997).1 Confounding variables: Clearly, many other factors in addition to SES influence obesity, so there are obvious limitations in employing the two-dimensional concentration index to measure SES inequality. Previous studies suggest that demographic factors, such as gender, age and ethnicity, may also influence the prevalence of obesity (Sundquist & Johansson, 1998; Kumanyika, 1987, 1999; Winkleby, Kraemer, Ahn & Varady, 1998; Winkleby, Robinson, Sundquist, & Kraemer, 1999). However, because SES may also be correlated with gender, age, and ethnicity, it is necessary to control these variables when studying the SES inequality of obesity. Two approaches can be adopted. The first approach is to conduct analyses stratified by these demographic variables. The second one is the standardization procedure proposed by Kakwani et al. (1997).2 We applied both methods but found small differences between them. Thus, to simplify the presentation simple, only the results with stratification were presented. In summary, we first calculated the CI for the overall population. Then, we stratified the population by gender, age, and ethnicity to estimate the inequality in each specific demographic group. Finally, we compared the inequalities between these groups.

Data and study variables Data We used data collected from the National Health and Nutrition Examination Survey III (NHANES III, 1988– 1994), which is a cross-sectional representative sample of the US civilian, non-institutionalized population aged 2 months and older. NHANES III contains data for a sample of 33,994 individuals. Data on weight and height were collected for each individual in the full mobile examination center through direct physical examinations. Based on self-reported race and ethnicity, subjects were classified into non-Hispanic white, non-Hispanic 1 Mathematically, the formula to estimate the standard error (SE) of confidence index equation (21) in KWD’s paper: ( " #)1=2 K 1 X 2 2 a  ð1 þ CÞ ; where sðCÞ # ¼ N i¼1 i

Mi ð2Ri  1  CÞ þ 2  bi1  bi ; m P in which bi ¼ ð1=mÞ it¼1 mt wt ; s2i is the variance in the ith group. 2 Details of the standardization procedure are in Appendix.

ai ¼

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Table 1 Sample sizes and sociodemographic characteristics of American adults aged 18–65 years, The Third National Health and Nutritional Examination Survey (NHANES III), 1988–1994 Sex/age

All

Non-Hispanice White

Non-Hispanic Black

Mexican American

Other

Male 18–29 30–39 40–49 50–65 Total

1763 1376 1134 897 5170

449 439 410 440 1738

560 461 340 232 1593

674 425 340 187 1626

80 51 44 38 213

Female 18–29 30–39 40–49 50–65 Total

1863 1657 1248 994 5762

525 564 451 489 2029

637 578 410 258 1883

612 452 323 194 1581

89 63 64 53 269

black, Mexican American, and ‘‘other’’ ethnic groups. Because NHANES III oversampled blacks and Mexican American, sampling weights were used in our analyses to generate national representative estimates. Detailed descriptions of the sample design, interviewing procedures, and physical examinations conducted were published elsewhere (CDC, 1996). In the present study, we focused on 10,932 adults aged 18–60 years old who had complete data on measures of anthropometric and sociodemographic characteristics.3 Pregnant women were excluded. Table 1 lists the sample sizes by gender, age, and ethnicity. Measures Obesity We calculated body mass index (BMI) for each individual. [BMI= weight (kg)/height (m2)]. We used the definition of obesity recommended by the World Health Organization (WHO), in which individuals with BMIX25 are overweight, and individuals with BMIX30 are obese (WHO, 1998). Note that these two categories are not mutually exclusive.

are most commonly used to measure SES, each has its own advantages and limitations (Williams & Collins, 1995). We used income as our measure of SES. In addition to being a measure of material resources, income is also highly correlated with other dimensions of SES such as education and occupational prestige. Moreover, educational attainment and occupational status tend to be stable among adults, and would mask substantial socioeconomic variation in BMI. The income measure used in this study was family income per capita over the last 12 months. Gender, age, race and ethnicity Three demographic variables, namely gender, age, and race/ethnicity are considered. We divided subjects into four age groups: 18–29, 30–39, 40–49, and 50–60, and four race/ethnic groups: non-Hispanic white, nonHispanic black, Mexican American, and ‘other’. Because of the heterogeneity and small sample size of the ‘other’ group, we included only whites, blacks, and Mexican American in our analyses of ethnic differences.

Results SES A specific SES variable is necessary for calculating the CI of socioeconomic inequality. SES is a complex construct whereby individuals are ranked relative to one another in a society on the basis of characteristics including income, education, occupation, residence, and family background. SES is multidimensional, and cannot be easily reduced to a single component. Although income, education, and occupational status 3

1050 (8.8%) out of 11,982 observations were dropped due to missing value on income, sex, ethnicity, age, and/or BMI. Of the 1050 observations dropped, 1034 lacked data on income.

Overall socioeconomic inequality of obesity and overweight The CIs of obesity and overweight are presented in Table 2. CIs in row 1 are measures of the socioeconomic inequality in obesity and overweight for the whole study population. The CI of obesity (BMIX30) was –0.055 and was statistically significant (Po0.05), indicating that socioeconomic inequality favors higher SES groups. In other words, SES was negatively related to obesity. The CI of overweight (BMIX25) was –0.007, which was not significantly different from 0. This suggested that

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Table 2 Concentration indices (CI) of obesity and overweight by sex and age Sex

Age

N

Obesity

Overweight

CI

95% Confidence interval

Z

CI

95% Confidence interval

Z

Men and Women

18–60

10932

0.055

(0.066, 0.043)

9.266

0.007

(0.017, 0.003)

1.291

Men

18–60 18–29 30–39 40–49 50–60

5170 1763 1376 1134 897

0.015 0.063 0.057 0.126 0.001

(0.031, (0.095, (0.087, (0.160, (0.037,

0.002) 0.030) 0.027) 0.092) 0.039)

1.725 3.763 3.725 7.224 0.065

0.023 0.017 0.006 0.034 0.026

( 0.009, 0.037) (0.044, 0.009) (0.032, 0.020) (0.064,0.004) (0.008, 0.061)

3.145 1.266 0.444 2.253 1.495

Women

18–60 19–29 30–39 40–49 50–60

5762 1863 1657 1248 994

0.082 0.130 0.080 0.198 0.086

(0.098, (0.162, (0.110, (0.231, (0.123,

0.066) 0.097) 0.051) 0.164) 0.048)

9.910 7.800 5.334 11.649 4.473

0.048 0.102 0.103 0.100 0.024

(0.063,0.034) (0.130,0.073) (0.130,0.075) (0.130,0.069) (0.058, 0.010)

6.429 7.022 7.323 6.442 1.361

 The concentration index was significantly different from zero, po0.05.

there was no socioeconomic inequality in the distribution of overweight status. Our analyses indicated that the definitions of obesity based on different BMI levels might influence findings regarding the association between SES and obesity. Socioeconomic inequality by gender The CIs for men and women aged 18–60 are presented in rows 2 and 7 in Table 2. The CIs of obesity in men and in women were negative, which indicates a negative association between SES and obesity within both sexes. Men’s CI of obesity was not statistically significant. Women had a larger CI of obesity than men, and their CI was statistically significant (Po0.05). These results showed that among men, the obesity ‘‘burden’’ appeared almost equally distributed across SES groups, whereas among women, there were strong disparities across SES. Regarding overweight, the CI for men was positive, but the CI for women was negative. Both were statistically significant from zero. The striking reverse association between SES and overweight for men versus women indicates an important gender difference. Men of high SES were significantly more likely to be overweight than men of low SES. Women of high SES were significantly less likely to be overweight than women of low SES. Socioeconomic inequality by age Our analysis shows that the relationship between SES and obesity was affected by age. CIs of obesity by age groups are reported in rows 3–6 and 8–11 in Table 2. For women, the CI of obesity was negative and was

statistically significant in all age groups. Similarly, the CI of overweight in women was negative and statistically significant, except for those aged 50–60. For men, the CI of obesity was less than zero in all age groups except for age group 50–60. The CI of overweight in men was not statistically significant from zero in all age groups except for age group 40–49. Our results revealed that socioeconomic inequality in obesity existed in all age groups in women and in some age groups in men, but that the degree of inequality varied across age groups. The severest inequality in obesity was found within the 40–49 age range in both sexes. Interestingly, the CI of obesity in the oldest was dramatically lower compared to those of the immediately preceding age group. Socioeconomic inequality by ethnicity Ethnicity–gender-specific CIs were presented in Table 3, which showed remarkable variations in patterns of socioeconomic inequality in obesity across ethnic groups in the US. Among men, we found a positive association between SES and obesity for blacks and Mexican American, but found a negative association for white men. Among women, the CI of obesity was significant only for white females, where it was significantly negative. We consistently found a negative association between SES and obesity in whites regardless of gender, whereas gender differences exist in the black and Mexican American populations. The CIs of obesity and overweight in each gender– age–ethnic group were depicted graphically in Figs. 2 and 3, respectively. The most striking findings were for young adults aged under 30. Among whites, we found

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Table 3 Concentration indices (CI) of obesity and overweight by sex and ethnicity Sex

Obesity/overweight

Ethnicity

N

CI

95% Confidence interval

Z

Men

Obesity

White Black Mexican American White Black Mexican American

1738 1593 1626 1738 1593 1626

0.047 0.061 0.092 0.007 0.082 0.047

(0.066, 0.027) ( 0.012, 0.109) ( 0.025, 0.158) (0.010, 0.024) ( 0.039, 0.125) (0.010, 0.104)

4.77 2.446 2.699 0.838 3.755 1.616

White Black Mexican American White Black Mexican American

2029 1883 1581 2029 1883 1581

0.055 0.022 0.004 0.015 0.001 0.031

(0.074,0.035) (0.065, 0.021) (0.064, 0.071) (0.032, 0.002) (0.039, 0.042) (0.094, 0.033)

5.591 0.997 0.114 1.682 0.063 0.948

Overweight

Women

Obesity

Overweight

 The concentration index was significantly different from zero, po0.05.

Fig. 2. Concentration indices of obesity by gender, age and ethnicity.

Fig. 3. Concentration indices of overweight by gender, age and ethnicity.

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that a strong negative association between SES and obesity/overweight existed for both young women and men. Among blacks, we found a weaker negative relationship between SES and obesity/overweight for women, but a strong and positive relationship for men. As for the magnitude of inequality by ethnicity, it appeared that the greatest inequality in the distribution of obesity and SES occurred among whites, followed by Mexican American and blacks. In other age groups, the gender-specific ethnic differences in the socioeconomic inequality were more complicated with the exception of one universal finding, the negative association between SES and obesity among whites regardless of age and gender. Among women, the ethnic disparity in CIs was small in the 30–49 age group. The relationship between SES and obesity was positive among older Mexican American women aged over 50, while it was negative in black and white older women. For men, a positive relationship between SES and obesity existed in most minority groups. We noted that although the degree of socioeconomic inequality was nearly equal across ethnical groups for men, the direction of inequality among white men was in the opposite direction for blacks and Mexican Americans.

Discussion To our knowledge, no previous studies have quantitatively measured socioeconomic inequality in the distribution of obesity in the US. using the concentration index. Our analyses are among the first to examine the socioeconomic inequalities across gender, age, and ethnic groups by using a national sample. We found that the SES inequality of obesity varied dramatically across demographic groups and there were several interesting patterns. First, large ethnic differences exist in the association between SES and obesity as well as in the magnitude of inequality. Although numerous studies have shown that minority groups are more vulnerable to obesity than whites in the US (Winkleby 1998, 1999; Burke et al., 1992; Must, Gortmaker, & Dietz, 1994; Paeratakul, Lovejoy, Ryan, & Bray, 2002), our results indicate that minority groups do not necessarily have a higher SES inequality. For some demographic groups, the socioeconomic inequality in obesity among whites was more severe than that among minority groups (e.g., whites aged 40–49). This implies that SES factors could have a strong impact on obesity in whites than in minorities. Second, we found a consistent reverse association between SES and obesity among both white women and men, and in all the white gender-age groups. In contrast, for blacks and Mexican Americans, the association between SES and obesity varies by gender and age. A positive relationship between SES and

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obesity exists in minority men. Third, we found a strong inverse association between SES and obesity among white females especially in the young age group, but not among black and Mexican American females. Many factors may contribute to the ethnic disparities in obesity. For example, some evidence support the hypothesis that energy metabolism is not the same across ethnic groups, which might have contributed to the varying prevalence of obesity among different ethnic groups (see Holmes, et al., 1998; Weyer, Snitker, Bogardus, Ravussin, 1999; Kumanyika, 1999). On the other hand, as suggested by Winkleby (1997), ethnic disparities might also be explained partially by the confounding factors related to SES, such as education and occupations. Nevertheless, in studies that controlled for education, racial and ethnic differences persisted (see Winkleby, 1997). Overall our findings suggest that the focus on SES factors may need to be varied across ethnic groups in the prevention and management of obesity, because different ethnic groups may respond differently to intervention programs or policies targeting obesity tailored to socioeconomic factors. For example, the US internal revenue service provides partial relief in tax return to obesity patients (IRS, 2002). This policy may help reduce obesity rates more effectively in white women than in minority women if reducing income inequality could help reduce health inequality. Moreover, there are important gender differences in the direction of the association between SES and obesity and the magnitude of socioeconomic inequality. In general, we observed a stronger SES inequality of obesity in women than in men. There was prevailing inverse association among females, while the association was positive in some male minority groups. As pointed out by Sobal and Stunkard (1989), SES affected the body weight status of men and women differently, particularly in developed nations. In these societies, men and women could have strikingly different attitudes towards body weight status and have different practice for controlling body weight. SES influences people’s access to foods, exercise facilities and health care service as well as affecting their living and working environments, which might influence the association between SES and obesity. Wardle et al. (1999) found that lowSES men were more likely to have physically demanding occupations, which reduced the risk of obesity, than high-SES men. Previous studies consistently show that in most Western societies, women (and the society in general) hold a more negative attitude towards obesity than men (see Cahnman, 1968; Delong, 1980; Wardle & Griffith, 2001). It is arguable that the negative public attitude towards obesity could be detrimental to both women and men. However, it is important to ask whether the individuals are concerned about the public negative attitude towards obesity. In most Western

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societies, a reality is that many women are heavily influenced by the public negative view towards obesity. Thus, women are more likely to invest their resources to pursue a thinner ideal body than men, which helps explain the gender difference in the SES inequality of obesity (Garn, 1986; Gordon, 1990). Another interesting finding of our study is that among men, the CIs of obesity were negative, but the CIs of overweight were positive. These contradictory findings in the relationship between SES and degrees of obesity coincide with findings of many previous studies in men. Sobal and Stunkard (1989) reviewed 27 studies conducted in American men, among which 12 studies (44%) showed a positive relationship, 12 studies (44%) showed an inverse relationship, and 3 studies (11%) indicated no clear relationship. Several more recent studies show similar inconsistent patterns (Leigh, Fries, & Hubert, 1992; Kuskowska-Wolk & Bergstrom, 1993; Mailard et al., 1999). The discrepancies in findings may be due to differences in the definitions of obesity, study samples, SES variables, analysis methods as well as the data quality across studies (Wardle, Waller, & Jarvis, 2002; Winkleby, 1997). Our analyses suggest that the differences in the definitions of obesity may be an important source of variation in published findings. One challenge for studying the impact of SES on health outcomes, as pointed out by Krieger (1993), Williams and Collins (1995), and Kaufman, Cooper, and McGee (1997), is that SES measures may not be commensurate between different ethnic groups, particularly between whites and minority populations. If one were to rely exclusively on education as a measure of SES, considerable heterogeneity in income levels would be masked, since blacks may earn lower incomes compared to their white counterparts. If one were to rely exclusively on income as a measure of SES, systematic differences in patterns of residence may mask significant material differences if blacks and whites sort to areas with different costs of living, or conditions of living. Thus, age, sex, and ethnicity confound the relationship between obesity and SES in multiple ways at multiple levels. Few studies in the literature on SES and obesity literature have statistically investigated the complicated confounding effect of ethnicity on the association between SES and obesity. The CI approach can partially eliminate this problem by focusing on the relative ranking within ethnic groups rather than the absolute amount of SES. Finally, worth of noting, the CI approach provides marginal advantages over the classical regression technique. The theoretical bounds of CI, 1 and 1, naturally construct the limit on the magnitude of inequality. If the concentration curve is more distant from the diagonal or the CI approaches to 1 or –1 from zero, the more severe inequality exists. For example, van Doorslaer et al. (1997) studied the income-related

inequalities in self-assessed health status in developed countries. They found that the US had the largest health inequality that generated a CI of 0.1360. Since the absolute value of the CI indicates the magnitude of the inequality, we suggest that the socioeconomic inequality in obesity is not as severe as for self-assessed health status in the US. Because the values of the concentration curve and CI are not affected by differences in units, these methods facilitate examining cross-national differences and intertemporal changes in inequality. Thus, CI is an appropriate summary index to measure the inequality quantitatively.

Conclusions We introduced the concentration curve and concentration index as an alternative approach to study the socioeconomic disparity in obesity. The concentration index provides a quantitative summary measure of socioeconomic inequality in obesity. It not only tells the direction of the association between SES and obesity, but also indicates how severe the inequality is. This method could be a useful tool in future research that compares the socioeconomic inequality in obesity over time and across populations. We found that the socioeconomic inequality existed in most gender and age groups. The relationship between SES and obesity was complex across gender and age, but we observed several important patterns: first, minority groups do not necessarily have a higher SES inequality in obesity than whites, although minorities are more vulnerable to obesity; second, a reverse association between SES and obesity existed among both white women and men, especially in young white women. No consistent association existed across genders among minorities. No significant inverse association was found among young black women; third, generally there was a stronger SES inequality of obesity in women than in men. In conclusion, there is considerable socioeconomic inequality in obesity among the US population. The inequality patterns vary substantially across gender, age and ethnic groups. Further research is needed to fully understand the mechanisms that cause the socioeconomic inequality in obesity, which is important for developing effective policies and intervention programs to fight the obesity epidemic.

Acknowledgements We would like to thank Dr. Jeanette W. Chung, Ms. Lisa Johns for her comments and assistance on editing to improve the manuscript. We appreciate Dr. John

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Formby, Dr. John Bishop, and two reviewers’ invaluable comments.

Appendix. Standardization procedure Kakwani et al. (1997) created the standardization procedure by averaging the degree of illness in the same socioeconomic group with weights. Specifically, the degree of illness can be a scale of a person’s health status, or a 0–1 variable. Then, in that socioeconomic group, each person’s individual degree of illness is replaced by the weighted average degree of illness of that group. If we use the obesity index as the degree of illness (0 for non-obesity and 1 for obesity), the formula for standardization is: Mt ¼

t X ni mi ; Nt i¼1

ðA:1Þ

where ni is the number of individuals in the ith demographic group of the t income group, Nt is the total number of individuals in the t income group, mi is the average degree of obesity in the ith demographic group, and Mt is the weighted average degree of obesity in the t income group. As shown by Kakwani et al. (1997), the concentration index can then be calculated as follows: C ¼ 1 þ

k 2 X Mt Rt ; Nm t¼1

ðA:2Þ

where N is the total number of individuals in the population, m is the mean degree of obesity in the population, and Rt is thePrelative rank of the t income t1 group, in which Rt ¼ i¼1 wi þ 0:5wt ; where wt is the weight of tth income group.

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