Minorities’ Diminished Returns of Educational Attainment on Hospitalization Risk: National Health Interview Survey (NHIS)

Abstract

Background.

As suggested by the Minorities’ Diminished Returns (MDRs) theory, educational attainment shows a weaker protective effect for racial and ethnic minority groups compared to non-Hispanic Whites. This pattern, however, is never shown for hospitalization risk.

Objectives.

This cross-sectional explored racial and ethnic variation in the association between educational attainment and hospitalization in the US.

Methods.

Data came from the National Health Interview Survey (NHIS 2015). The total sample was 28,959 American adults. Independent variables were educational attainment and hospitalization. The main outcome was hospitalization during the last 12 months. Age, gender, employment, marital status, region, obesity, and the number of cardiovascular conditions were covariates. Race and ethnicity were the effect modifiers. Logistic regression models were utilized to analyze the data.

Results.

From all participants, 16.2% were Black and 11.6 were Hispanic, with a mean age of 51 years old. Overall, higher education levels were associated with lower odds of hospitalization, independent of all confounders. Educational attainment showed significant interactions with race (OR =1.04, 95% CI = 1.01 – 1.08) and ethnicity (OR = 1.04, 95% CI =1.01 −1.07) on hospitalization, indicating smaller protective effects of educational attainment on hospitalization of Hispanics and Blacks than non-Hispanic Whites.

Conclusion.

The protective effect of educational attainment on population health is smaller for Blacks and Hispanics compared to non-Hispanic Whites. To prevent health disparities, there is a need to minimize diminished returns of educational attainment for racial and ethnic minorities. To do so, there is a need for innovative and bold economic, public, and social policies that do not limit themselves to equalizing socioeconomic status but also help minorities leverage their available resources and gain tangible outcomes.

Background

High socioeconomic status (SES) particularly educational attainment is protective against undesired health outcomes^1–3. Individuals with high educational attainment are less likely to develop cardiovascular diseases (CVDs), which is the main source of morbidity^1,4–10. Racial and ethnic inequalities in health are in part due to racial and ethnic inequalities in SES and educational attainment¹¹.

Populations, however, differ in the protective effects of SES indicators on health outcomes^12–15. The effects of SES indicators particularly educational attainment on CVDs are also shown to vary across populations^13,14. According to the Minorities’ Diminished Returns (MDRs)^16,17, SES indicators particularly educational attainment are less protective for racial and ethnic minority groups that the majority groups¹⁸. Most of this literature, however, has focused on the effects of SES on health outcomes for Blacks^18–20 and Hispanics^21,22, compared to Whites. For example, various studies have shown that SES indicators show larger effects on smoking²³ and drinking²¹ for non-Hispanic Whites than Hispanics and Blacks.

Very limited knowledge, however, exists on MDRs on the hospitalization risk. We are only aware of one study that has focused on MDRs on health care use. In a study, family SES better reduced unmet health needs for Whites than, Hispanics and non-Hispanic. Thus, it seems that the very same MDRs that are established on Health for Hispanics and Blacks^18–20 may also be relevant to health service use.

Aim

This study explored the racial and ethnic variation in the effects of educational attainment on the odds of hospitalization over the past 12 months among Americans in the US. We hypothesized that high educational attainment would be associated with lower hospitalization risk, however, this effect would be smaller for Hispanics and Blacks than non-Hispanic Whites.

Methods

Design and Settings

The National Health Interview Survey (NHIS) −2015 was used for this study. The NHIS is the primary source of information on the health and wellbeing of American adults. The sample is limited to the civilian noninstitutionalized population of the United States. The data collection is done by the National Center for Health Statistics (NCHS), CDC.

Data Collection

The U.S. Census Bureau acts as the data collection agent for the NHIS, under a contractual agreement between the two organizations. Interviews on the NHIS are face-to-face and occur in the participants’ households. This interview is sometimes followed and in rare occasions replaced with a telephone interview. The NHIS data collection is conducted continuously through each year. Data were collected on demographic factors, socioeconomic characteristics, health behaviors, mental health, physical health, as well as health care utilization.

Sample and Sampling

The NHIS sampling and sample design are described elsewhere. The NHIS applies a multi-stage sampling strategy. The stage 1 of the sample design was sampling 428 primary sampling units (PSUs) drawn from 1,900 geographically defined PSUs, with all the 50 US states and the District of Columbia have representative PSUs in the study. A PSU may be either a county, a small group of contiguous counties, or a metropolitan statistical area.

The NHIS draws samples from households and noninstitutional group quarters such as college dormitories. The NHIS has four main Cores that consist of: (1) the Household Composition section, (2) the Family Core, (3) the Sample Child Core, and (4) the Sample Adult Core.

Inclusion and exclusion criteria

Participants were eligible if they were adult (age 18 years or more), and US resident. Participants were not eligible if they were institutionalized in a correctional or health setting.

Measures

Predictor

Educational Attainment.

Educational attainment was a continuous variable ranging from 0 to 21. Participants were asked about the number of years of schooling. A higher score reflected a higher number of schooling.

Moderators

Race and Ethnicity.

All participants self-identified their race and ethnicity. Race was Blacks / African Americans = 1 and Whites = 0. Ethnicity was a dichotomous variable: Hispanics = 1, Non-Hispanics = 0.

Covariates

Demographic Factors.

Demographic data included age and gender. Age was continuous. Gender was dichotomous: males =1, females = 0.

Marital status.

A dichotomous variable was used for marital status: 1 married, 0 any other status.

Employment.

A single item was used to measure employment over the last week. This variable was a binary variable: employed =1, unemployed =0.

Region.

The region was a four-level categorical variable coded as below: (1) Northeast, (2) Midwest, (3) South, and (4) West. This variable was treated as a nominal variable with West as the reference group.

Obesity.

The current study defined obesity as the body mass index (BMI) of equal to, or larger than, 30 kg/m2.²⁴ This was based on collected data on individuals’ self-reported their weight and height.

Number of cardiovascular diseases (CVDs).

Number of CVDs was measured using the self-reported history of doctor-diagnosed CVDs. Participants reported if a doctor has told them that they have 1) diabetes, 2) hypertension, 3) high cholesterol, 4) coronary heart disease, 5) angina pectoris, 6) heart attack, 7) heart condition/disease, and 8) stroke. We calculated a sum CVD score, ranging from 0 to 8, with a higher score indicating more CVDs.

Dependent Variable

Hospitalization.

Participants were asked if they have been in a hospital overnight during the past 12 months. The answers were yes and no.

Statistical Analysis

We applied SPSS 23.0 (IBM Inc, NY, USA) to perform data analysis. For descriptive statistics, we used mean and proportion (frequencies). For multivariable analysis, we used two survey logistic regression models. In these models, we used educational attainment as the independent variable, hospitalization as the dependent variable, demographic factors, marital status, employment, region, obesity, and number of CVDs were covariates, and race and ethnicity were the focal moderators. Both logistic regression models were estimated in the pooled sample that included Blacks, Hispanics, and non-Hispanic Whites. Model 1 did not include race and ethnicity by educational attainment interaction terms. Model 2 included race and ethnicity by educational attainment interaction terms. Cox & Snell R Square and Nagelkerke R Square were used to compare the fit of Model 1 and Model 2. Adjusted Odds Ratios (ORs), 95% Confidence Intervals (CI), and p-values were reported.

Results

Descriptive Statistics

A total number of 28,959 individuals entered this analysis. Table 1 summarizes the descriptive statistics of the participants overall. On average, our participants were 50.91 years old. Most of our sample was White (83.8%) and non-Hispanic (88.4%). Most participants were female (55.3%). From all, 54.2% were employed, 43.4% were married, 34.1% were obese, and 10.1% were hospitalized during the past 12 months (Table 1).

Table 1.

Descriptive statistics of the participants overall.

	n	%
Race
White	24270	83.8
Black	4689	16.2
Ethnicity
Non-Hispanic	25609	88.4
Hispanic	3350	11.6
Gender
Female	16017	55.3
Male	12942	44.7
Married
Other	16397	56.6
Married	12562	43.4
Employed
Unemployed	13264	45.8
Employed	15695	54.2
Region
Northeast	4884	16.9
Midwest	6593	22.8
South	10633	36.7
West	6849	23.7
Obese (BMI > 30)
No	19074	65.9
Yes	9885	34.1
Hospitalized
No	26032	89.9
Yes	2927	10.1
	Mean	SD
Age (Years)	50.91	18.41
Education (Years)	15.19	3.14
Cardiovascular diseases (n)	1.00	1.31

Logistic Regressions

Table 2 presents the results of two pooled sample logistic regression models. Both models were statistically significant at the 0.001 level. Cox & Snell R Square for Model 1 and Model 2 were .057 and .058. Nagelkerke R Square were .120 and .121 for Model 1 and Model 2, respectively. These fit statistics suggest that Model 2 (with the interaction term) better fitted the data. Model 2 showed statistically better fit compared to Model 1. Model 1 only included the main effects. Model 2, however, also included two interaction terms between race and ethnicity with educational attainment. Model 1 showed that high educational attainment had a protective effect against hospitalization (OR = 0.99, 95% CI =0.97–1.00) above and beyond our covariates. Model 2 showed significant interactions between the effects of race (OR =1.04, 95% CI = 1.01 – 1.08) and ethnicity (OR = 1.04, 95% CI =1.01 −1.07) and educational attainment on hospitalization, suggesting that the inverse association between educational attainment ad odds of hospitalization is significantly smaller for Blacks and Hispanics than for non-Hispanic Whites (Table 2).

Table 2.

Association between educational attainment and hospitalization.

	b	SE	B	95% CI	p	b	SE	B	95% CI	p
				Model 1					Model 2
Race (Black)	0.00	0.06	1.00	0.90–1.12	.943	−0.60	0.25	0.55	0.33–0.90	.017
Ethnicity (Hispanic)	−0.07	0.07	0.93	0.81–1.08	.342	−0.59	0.23	0.55	0.35–0.87	.010
Gender (Male)	−0.23	0.04	0.80	0.73–0.87	< .001	−0.23	0.04	0.80	0.73–0.87	< .001
Age (Years)	0.00	0.00	1.00	0.99–1.00	.011	0.00	0.00	1.00	0.99–1.00	.013
Married	−0.13	0.04	0.88	0.81–0.96	.003	−0.12	0.04	0.88	0.81–0.96	.005
Employed (last week)	−0.88	0.05	0.42	0.38–0.46	< .001	−0.88	0.05	0.42	0.38–0.46	< .001
Region
Northeast	0.12	0.07	1.13	0.99–1.28	.080	0.12	0.07	1.12	0.99–1.28	.081
Midwest	0.14	0.06	1.15	1.02–1.30	.022	0.14	0.06	1.15	1.01–1.29	.030
South	0.07	0.06	1.07	0.95–1.20	.247	0.06	0.06	1.06	0.95–1.19	.292
Obesity (BMI > 30)	0.05	0.04	1.06	0.97–1.15	.217	0.05	0.04	1.05	0.97–1.15	.241
Cardiovascular diseases (n)	0.38	0.02	1.46	1.41–1.50	< .001	0.38	0.02	1.46	1.41–1.50	< .001
Educational Attainment (Years)	−0.01	0.01	0.99	0.97–1.00	.042	−0.03	0.01	0.97	0.95–0.99	< .001
Ethnicity (Hispanic) × Educational Attainment (Years)					< .001	0.04	0.02	1.04	1.01–1.08	.014
Race (Black) × Educational Attainment (Years)						0.04	0.02	1.04	1.01–1.07	.018
Constant	X1.89	0.14	0.15			−1.65	0.16	0.19		< .001

Discussion

Higher educational attainment was inversely associated with hospitalization in the pooled sample, however, this effect was smaller for Blacks and Hispanics than Non-Hispanic Whites.

We found that as educational attainment increases, people’s risk of hospitalization reduces. This is, however, less true for Hispanic and Black Americans than Non-Hispanic Whites. This is a replication of Minorities’ Diminished Returns (MDRs) of the educational attainment in terms of hospitalization.

Our finding is in line with the other observations that SES indicators particularly educational attainment generate less than expected health outcomes for marginalized than the mainstream groups. Similar patterns are shown for Blacks^25–28, Hispanics^21–23,29, and sexual minorities^30,31, meaning that minority status, regardless of its type, whether it is based on race³², ethnicity^21,22, or sexual orientation³⁰ reduces the health gains of SES resource³².

It is not only educational attainment³³, but also any SES that generates less health for Blacks and Hispanics than non-Hispanic Whites. Very same patterns are shown for income¹⁹, occupation³⁴, and marital status³⁵. The effects of education and income on chronic medical conditions such as CVDs¹⁹ such as asthma³⁶, obesity^18,37and ADHD²⁰ are also shown to be smaller for Hispanics and Blacks than non-Hispanic Whites. Finally, education and other SES indicators have a larger effect on reducing mental health problems such as depression^38–40, suicide⁴¹, and anxiety³⁵. Education also better reduces the risk of mortality for Blacks than Whites³⁴. None of these patterns were previously shown for Hispanics compare to non-Hispanics. The contribution of this study is to extend this literature to hospitalization as the outcome.

The systemic nature of the MDRs suggests that it is the society that differentially rewards and differently treats racial and ethnic groups^16,17. We argue that is probably the marginalization processes that reduce full participation in the society, and it is the social structure and function that does not allow non-White groups to fully leverage their human and economic resources and turn them to the highest level of tangible outcomes^16,17.

Future Research

There is a need to study the contextual, economic, and behavioral mediators of MDRs of educational attainment on the risk of hospitalization. One potential mechanism for this observation is through CVDs¹⁹ such as asthma³⁶, hypertension²⁹, obesity^18,37and depression^38–40, and suicidality⁴¹. Another mechanism that may increase the risk of hospitalization of high SES Blacks and Hispanics is high risk behaviors such as poor diet⁴², exercise²⁶, smoking^23,43, and drinking²¹. High SES Blacks and Hispanics are also exposed to a higher level of stress^40,44–46 and second-hand smoke exposure⁴⁷. We also need research on public and health policies that reduce the MDRs of educational attainment. Future research is needed for replication and validation of these findings in other contexts, settings, and age groups.

Limitations

Our study is with some methodological, conceptual, and statistical limitations. With a cross-sectional design, we do not draw any causal inferences between educational attainment and risk of hospitalization. The study could not include data on a wide range of potential covariates such as health care access and mental health. Other SES indicators such as wealth and childhood SES indicators should also be measured. All study variables were measured at the individual level. Structural factors such as the availability and density of health care services as well as area-level SES and racial and ethnic composition of the neighborhood were not included in this study. Despite these limitations, this is the first study on the relevance of MDRs to the hospitalization risk.

Conclusion

The protective effects of educational attainment on hospitalization seem to be smaller for Hispanics and Blacks compared to non-Hispanic Whites. To prevent racial and ethnic health disparities, policies and programs should go beyond equal access and focus on equal outcomes. For such a goal, we need policies that minimize diminished returns of SES for ethnic minorities. Innovative and bold economic, public, and social policies are needed to members of the racial and ethnic minorities to leverage their resources and gain tangible health outcomes. Researchers may also explore mediators and moderators of MDRs for Black and Hispanic people.

Ethics

All participants provided written consent and the ethics of the NHIS protocol was approved by the CDC Institutional Review Board (IRB). According to the NIH guideline and the decision tool regarding human subject involvement of secondary analyses of existing data, the current study was found to be “Not Human Subject Research”. The definition and also the decision tool which was applied is also available here: https://grants.nih.gov/policy/humansubjects/hs-decision.htm. According to the human research involvement in research, this study is found to be exempt from the IRB approval process by the CDU IRB.

Research Highlights.

What Is Already Known?

High educational attainment is known to protect individuals against the risk of hospitalization. This effect is mainly due to better health status of individuals with higher education compared to the individuals with lower educational levels.

What This Study Adds?

The protective effects of educational attainment on reducing the risk of hospitalization is not equal across racial and ethnic groups. In the United States, highly educated Blacks and Hispanics remain at high risk of hospitalization, probably because of structural racism.