This research has often been bifurcated: Some scholars have focused on racial and ethnic differences, whereas others have been primarily interested in the social gradient i. Separation of these two areas of research is artificial and unproductive. Interactions among race or ethnicity, socioeconomic status SES , and health are complex, and researchers' understanding of racial differences in health status and health trajectories is likely to depend on their ability to explicate these linkages see, for example, Williams, The potential interplay between ethnicity and SES comes to the forefront in analyses of a pattern of health termed the Hispanic paradox.
Despite the unfavorable socioeconomic profile of U. This mortality advantage is most apparent among middle-aged and elderly Hispanics; for example, the ratio of death rates of Hispanics to non-Hispanic Whites exceeds 1.
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There are large variations in mortality within the Hispanic population, with foreign-born and certain regional subgroups—most notably Mexicans, Central Americans and South Americans—most likely to experience this mortality advantage Hummer et al. In the presence of controls for SES, the mortality advantage of persons of Mexican origin, the largest Hispanic group in the United States, widens notably and the disadvantage of Puerto Ricans largely disappears Hummer et al. Despite unabated interest among researchers in social inequalities in health, and continued efforts to understand the Hispanic paradox, social scientists know surprisingly little about social gradients in health among Hispanics.
The outcome that has received the most attention in health disparity research is mortality, both because it poses fewer measurement problems and because the Hispanic paradox is most apparent with regard to survival.
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Many studies of racial and ethnic inequalities in mortality include measures of SES most frequently education, income, or occupational status in statistical models as control variables. However, because researchers typically assume SES to have constant effects across ethnic groups, these findings provide little information about the nature of SES disparities in survival within the Hispanic population Hummer et al.
There have been several exceptions, however. Unfortunately, none of these analyses provided statistical comparisons of the SES differentials across ethnic categories. Estimates of social disparities with regard to health outcomes other than survival for the Hispanic population are also scarce.
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Based on data for African Americans, Whites, and Hispanics reported in Pamuk, Makuc, Heck, Reuben, and Lochner , Williams argued that SES differences in health within the Hispanic population and within the other two groups were large and more substantial than the differences across racial categories. This assertion contrasts sharply with recent findings from three U. In this study, education levels were only weakly related, or were not related at all, to several health behaviors smoking and drinking and health outcomes obesity, work-related limitations, and depressive symptoms for U.
Hispanic adults overall as well as for the subgroup of persons of Mexican origin.http://resaedu.com/includes/gykaxety/
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These shallow gradients for Hispanics differed significantly from the steeper ones characterizing non-Hispanic Whites, challenging the presumption that SES inequalities in health are universal and suggesting a second paradoxical health pattern for the Hispanic population. In the present analysis, we attempted to broaden the field's understanding of social inequalities in the United States by assessing mortality differences by education and income within the Hispanic population. Our goal was twofold. The first was to evaluate whether the relatively flat education gradients in health-related measures identified by Goldman and colleagues extended to mortality.
The second was to ascertain whether and how differences in social disparities in mortality between Hispanics and non-Hispanic Whites were related to the Hispanic paradox. Although we examined mortality patterns across the adult life cycle, we focused on the older ages, because the vast majority of deaths in the U. Extrapolation of findings regarding the magnitude of the social gradient from the measures of health reported by Goldman and colleagues to survival is problematic, because mortality risks reflect long-term exposure to a broad range of behaviors, acute and chronic health conditions, and access and use of health care.
There are at least two reasons to speculate that SES differentials in mortality for Hispanics will be more prominent than those identified by Goldman and colleagues. The second is the fact that many Hispanics, particularly those of low SES, are without health insurance and hence are less likely than others to use health services, have a regular place of care, or obtain adequate treatment.
Therefore, we hypothesized that mortality among Hispanic adults would vary significantly by education and income, albeit less than it would among non-Hispanic Whites. We based this study on data from the to waves of the NHIS with linked mortality data through NHIS is a nationally representative cross-sectional survey of the noninstitutionalized population of the 50 states and the District of Columbia; researchers have collected data annually since Information is obtained from adult respondents who are physically capable of providing data for all household members.
The matching methodology uses a score constructed on the basis of 12 criteria, which combines several personal identifiers such as social security number, first name, last name, and father's surname. Potential matches between the National Death Index and the NHIS records classify into one of five mutually exclusive categories based on number and type of items matched. Ascertainment of the vital status of individuals who fall into classes that do not fulfill the requirements to be considered either true or false matches is made based on cutoff scores calculated from two independent calibration samples NCHS, Biases in the matching procedure usually result from missing social security numbers, incorrect recording of ethnic names, emigration of foreign-born individuals, and changing surnames among women.
Of the , persons aged 18 and older interviewed in the to surveys, there were 30, presumed deaths from through To avoid duplicates, we excluded another 7, Hispanics from a supplemental sample in , most of whom were interviewed in As described below, we restricted the present study to Hispanics and non-Hispanic native-born Whites aged 25 and older. Among the persons in the sample satisfying these criteria, , remained after the exclusion of those with missing data on explanatory variables; this figure included , survivors and 22, deaths.
We defined all variables as of the NHIS interview date and, except for age, assumed them to remain constant throughout the follow-up period. Because this assumption is untenable for educational attainment among young adults, we restricted the analysis to persons aged 25 and older. Information on race and ethnicity was based on self-identification: Researchers first asked respondents about their racial background and then if their national origin or ancestry could be described by any of eight Hispanic origin categories.
Respondents who indicated any of these Hispanic designations were considered to be Hispanic. Additional information for the construction of nativity status was obtained from a question pertaining to number of years lived in the United States. Our analysis focused on two groups: Hispanics and native-born non-Hispanic Whites hereafter, Whites. In later stages of the analysis, we considered two subdivisions of the Hispanic group: a U. We followed convention by classifying Puerto Ricans born outside the 50 states or the District of Columbia as foreign born.
The sample size of deaths was not sufficiently large to permit further subdivision of these variables or joint classification of nativity and nationality. SES comprises educational attainment and family income. Educational attainment was classified into five categories: 0 to 8 years omitted category , 9 to 11 years, 12 years, 13 to 15 years, and 16 or more years. With regard to income, Because of the large ranges within these categories, we imputed values for income based on data from the Current Population Surveys for the years to For each of the 5 years of the NHIS, we assigned respondents the mean family income of persons of the same age, gender, ethnic group, and income category who were interviewed in the Current Population Survey of the same year.
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We then reclassified this imputed income variable into quartiles based on the combined sample of Hispanics and Whites. We used Poisson regression models to estimate the number of deaths during the follow-up period as a function of person-years of exposure, SES, ethnicity, age, and gender. Each respondent's exposure began at the date of interview and terminated at the time of death or the end of , whichever came first. Age was a continuous variable. We estimated two sets of models, one that included education levels and the second that replaced education levels with income quartiles.
Within each of these two sets, we fit three models that included different specifications of the Hispanic variable. The first model included Hispanics as a single group; the second distinguished between U. Thus, we estimated a total of six Poisson models. The models included interaction terms as well as main effects of the explanatory variables. Because our objective was to explore differences in the SES gradient by ethnicity, all models included interaction terms between SES education or income and the relevant Hispanic variable s.
In exploratory analyses, we tested for the inclusion of all remaining two-way interaction terms involving age, gender, SES, and ethnicity, and the three-way interaction term among age, SES, and ethnicity. The three-way interaction term was not significant for either education or income. After excluding this term, we retained those two-way interaction terms with p values less than.
The interaction terms between age and ethnicity and between age and SES were significant in all models, so we included them in both the education and income models. The interaction terms between age and gender and between gender and SES were significant in the income but not the education models, and we thus included them in only the income models. Because of the many interaction terms included in each of the models, it was not possible to present the coefficients here; however, they are available upon request.
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Instead, we present predicted death rates by education and by income. In order to examine the nature of the SES differentials over the life cycle, we calculated the predicted death rates separately for three ages—30, 50, and 70—selected to represent young, middle, and older ages. For a given model, we obtained the predicted death rates by considering all combinations of values for the categories of gender, ethnicity, SES, and the three selected ages; setting the interaction terms accordingly; and using the coefficients of the model to obtain the predicted number of deaths for the specified person-years of exposure.
We summarized the magnitude of the education differentials within each ethnic group by calculating the ratio of the death rate for persons with 9 to 11 years of schooling to that for persons with 13 to 15 years of schooling; we avoided the extreme categories because of the relatively small number of Hispanics in the highest education level and Whites in the lowest. With regard to income, we present the ratio of the death rate associated with the bottom quartile to that for the top quartile.
Note that because of the inclusion of interaction terms between gender and SES in the income but not the education models, the summary measure of education differentials was identical for men and women but the measure of income differentials for men consistently exceeded that for women. Backlund and colleagues speculated that these patterns may reflect differences between men and women in their exposure to environmental hazards according to income level, in their psychosocial reactions to particular income levels, in the permanency of income, or in the distribution of death by cause.
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In order to provide further insights into ethnic differences in mortality gradients in the middle and older ages, we show the predicted death rates for men by level of education at ages 50 and 70; patterns for women were very similar to those for men. Table 1 presents the numbers of deaths and person-years of exposure for each of the explanatory variables. The estimates underscore the relatively small number of deaths for some Hispanic subgroups that limited our ability to consider further classifications of ethnicity or interactions between nativity status and ancestry.
The only two exceptions were the education coefficients for Other Hispanics and the income coefficients for Puerto Ricans, presumably because of the small sizes of these two groups. The SES differentials decreased steadily with age, so that, by age 70, the differences in death rates by education were negligible for Hispanic men and women except Puerto Ricans , and those by income were small for women in all ethnic groups. Researchers have attributed such decreasing social inequalities in health with age to several factors, including government transfers to older persons, biologically driven frailty that dominates socioeconomic factors, and selective mortality Backlund et al.
Although mortality varied significantly by SES for most of the ethnic groups considered here, the SES patterns varied among groups. As shown in Figure 1 and Table 2 , the differences in mortality by education were generally smaller for Hispanic groups than for Whites. One notable exception occurred for Puerto Ricans, for whom the education gradients in mortality were generally as steep as those for Whites.
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