The association of education gap among spouses and health-related quality of life: a population-based retrospective study

doi:10.21203/rs.3.rs-3201111/v1

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Research Article

The association of education gap among spouses and health-related quality of life: a population-based retrospective study

https://doi.org/10.21203/rs.3.rs-3201111/v1

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Purpose

The purpose of this study is to explore the relationship between marital education gap and the health-related quality of life (HRQOL) of husband and wife, and further examine the differences in this relationship at different levels of HRQOL

Methods

Firstly, univariate analysis was used to preliminarily examine the distribution differences of various variables in HRQOL. Secondly, linear regression was applied to analyze the relationship between marital education gap and the HRQOL of husband and wife. Finally, quantile regression was utilized to examine the differences of this relationship at different levels of HRQOL.

Results

Overall, the education gap between spouses has a positive impact on the HRQOL of both husband and wife, but special attention should also be paid to the situation where the wife's health level is much higher than that of the husband, which has no significant impact on the HRQOL of both husband and wife. The study also found that the improvement effect of education gap is mainly reflected in the lower level of HRQOL.

Conclusions

Research provides new insights into improving HRQOL from the perspective of educational disparities. Some measures can be further taken to enhance this positive impact. It is also important to pay attention to the adverse effects on each other when the wife's education level is much higher than that of the husband.

Education gap

Spouse

Health-related quality of life

Health-related quality of life (HRQOL) has gradually become a public health concern. As the biomedical model has shifted to the bio-psycho-social medical model, indicators that can measure an individual's overall health status have become more widely used in current medical model[1]. Some studies have assessed the impact of medical interventions on overall health improvement in patients, while others have focused on their potential social impact factors[2].

Several studies have found that an individual's education level is associated with HRQOL. Kamal et al. noted that oral HRQOL is lower in older populations with lower levels of education[3]. Meanwhile, recent research also suggests that a spouse's education level has a significant impact on an individual's health[4]. This suggests that the relationship between education level and HRQOL from the perspective of spouses (not limited to individuals) deserves further discussion.

Differences in educational attainment between spouses may be related to HRQOL. Previous study has generally shown that marriages in which the wife has more education than the husband are 27 to 38 percent more likely to break up[5]. Similarly, a husband with a higher education level than his wife is not conducive to marital stability. Divorce is less likely when at least one spouse has higher education[6]. This indicates that differences in education levels between couples may further affect HRQOL by affecting the stability of marital relationships.

In addition, the impact of different types of educational differences on HRQOL may vary. There are differences in the educational content received during the primary, secondary, and higher education stages. Receiving education at different stages has different effects on personal cognitive level, employment, and personality development[7]. Hence, the different types of educational differences between couples may potentially affect their work and life. Therefore, attention should also be paid to the impact of different types of educational level differences on HRQOL.

The impact of differences in education levels may also vary at different stages of HRQOL. HRQOL is a complex and multidimensional concept that often indicates significant health differences among individuals at different stages[8]. Research has shown that there are significant differences in HRQOL among patients at different stages of the disease[9], and the role of potential factors varies with changes in HRQOL[10, 11]. This suggests that attention should be paid to the heterogeneity of HRQOL in research.

Furthermore, demography and socioeconomic status (SES) variables also affect HRQOL. In a longitudinal study, Elias found that the HRQOL of people with very low birth weight decreased significantly between the ages of 20 and 32[12]. A study from Shanxi, China found that the HRQOL of urban populations is significantly higher than that of rural areas[13]. It was found that multimorbidity was strongly associated with a decline in HRQOL[14]. In addition, some studies have also revealed that work status and household income levels significantly affect HRQOL[15, 16]. It suggests that these factors need to be considered and controlled in this study.

The purpose of this study is: (1) analyze the relationship between differences in education levels between spouses and the HRQOL of husbands and wives; (2) explore the relationship between different forms of educational level differences and HRQOL between couples; (3) investigated the relationship between differences in education levels and varying degrees of HRQOL.

This study provides some novel insights into improving HRQOL between couples from the perspective of educational differences. First, the study focuses on the family environment of individuals from a couple's perspective, which provides evidence for improving HRQOL. Second, the study focused on differences in education levels between couples, providing new insights into the relationship between education levels and HRQOL. Finally, the study suggests new strategies for improving the HRQOL with respect to marital education differences.

Data sources

The data used in this study is from the China Health and Retirement Longitudinal Study (CHARLS), targeting middle-aged and elderly people (aged 45 and above) in China. CHARLS is an ongoing large-scale interdisciplinary survey project organized by the National Development Research Institute of Peking University, jointly implemented by the China Social Sciences Survey Center of Peking University and the Youth League Committee of Peking University. The survey is conducted every two or three years. Currently, four waves of surveys have been conducted (wave 1 in 2011, wave 2 in 2013, wave 3 in 2015, wave 4 in 2018).

CHARLS is sampled at the county and rural household levels in four stages. Specifically, in county and rural sampling, CHARLS uses probabilities proportional to size sampling (PPS). Based on the population of each district and county in 2009, and using regional, urban-rural, and GDP as stratified indicators, 150 counties (districts) were randomly selected nationwide. Based on the permanent population of each village or community in 2009, three villages (communities) were randomly selected from 150 counties (districts). Finally, 450 villages (communities) were obtained. A total of 80 households were randomly selected from the information list of all households in each village (community).

The study used data from the fourth wave survey in 2018. Since there was little change in educational attainment between couples over the four waves of the survey, longitudinal analysis may cause collinearity problems. Hence, the study used the latest wave of survey data. Figure 1 reflects the sample screening process. Finally, a total of 7874 couples (15748 respondents) were included in the study.

Figure 1 Flow chart of sample selection process

Variables and definitions

Education gap between spouses

First, according to the simplified version of the 1997 International Classification of Education Standards (ISCED-97), the internationally unified level of education is divided into as follows: 1. Less than lower secondary education (LLSE), 2. Upper secondary & vocational training (USVE), and 3. Tertiary education (TE). Second, the study uses the above classification of individual education level to classify the education gap between couples. Due to the large number of categories, studies were conducted separately on husbands and wives, and in both groups, studies were conducted on husbands with higher education levels than wives and husbands with lower education levels than wives.

HRQOL for couples

EQ-5D-3L was used to evaluate the HRQOL between couples. Items that reflect the respondent's ability to move, take care of themselves, daily activities, pain/discomfort, and anxiety were used to measure the dimensions of HRQOL. Based on the analysis of the assigned values of HRQOL in China[17], the study obtained the HRQOL scores for couples.

Control variables

Socioeconomic status, living location, chronic disease status and lifestyle of respondents were included in the study for control. The occupational status of all respondents was classified as whether they were still employed or engaged in work-related activities. Household expenditure per capita was used to measure the financial situation of respondents' families, and the quintile was used to indicate their economic level. In addition, smoking and drinking were included in the study and classified according to the lifestyle of smoking or drinking in the year prior to the survey. Table 1 provides the detailed encoding of each variable.

Table 1 Coding of variables included in the analysis

Statistical analysis

First, descriptive statistical analysis was used to initially analyze the basic information of the respondents, where analysis of variance was used to examine the characteristic differences of the respondents in different HRQOL. Second, linear regression was used to analyze the relationship between differences in educational level and types of couples and HRQOL. Finally, quantile regression was used to analyze the association between different levels of HRQOL and differences in marital education levels.

STATA software 13 was used to execute the descriptive analysis, linear regression and quantile regression, with statistical significance at p < 0.05.

Figure 2 shows the results of a univariate analysis of the HRQOL and education gap between couples. In terms of HRQOL for husbands, there was no significant difference between husband TE vs wife LLSE when the husband had a higher education level than the wife, compared to no education gap. Meanwhile, the husband USVE vs wife LLSE group was not significantly different from the husband TE vs wife LLSE group. Significant differences were shown between the other groups. Similar results were also found when husbands had lower education levels than their wives.

As for the HRQOL of wives, when the wife's education level was higher than that of the husband, there was no significant difference between the wife TE vs husband LLSE group and the no gap group. The following groups also showed the same results: the wife TE vs husband LLSE group and the wife USVE vs husband LLSE group, the wife TE vs husband LLSE group and the wife TE vs husband USVE group. When the education level of the wife is lower than that of the husband, it is important to note that there are also significant differences between the wife LLSE vs husband TE group and the wife USVE vs husband TE group.

Figure 2 Univariate analysis of educational gap between couples and HRQOL

Table 2 shows differences in the distribution of HRQOL for husbands and wives for other control variables. Respondents who were younger, no multimorbidity, working, and had higher levels of household wealth (both husband and wife) had better HRQOL. As for husbands, respondents who live in cities, do not drink alcohol, or smoke scored higher. Regarding wives, respondents who lived in rural areas, drank alcohol, and did not smoke scored higher. This implies that the control variables included in the study were appropriate.

Table 2 Univariate analysis of other independent variables and HRQOL

Models 1-3 in Table 3 show the analysis results of husband's HRQOL. Firstly, Model 1 indicates that husbands who have an educational gap with their wives (whether higher or lower) score higher. Model 2 indicates that husbands with higher education levels than their wives have higher HRQOL compared to those without an education gap. Model 3 indicates that there is no significant difference in the HRQOL between husband LLSE vs wife TE compared to the absence of education gap.

Models 4-6 show the analysis results of wife's HRQOL, and the results also indicate that there is no significant difference in HRQOL between wife TE vs husband LLSE compared to no education gap. Comparisons between other groups found a significant positive relationship between the education gap in couples and the wife's HRQOL. In summary, the educational gap between couples could significantly improve their HRQOL, except for the case of husband LLSE vs wife TE.

An interesting discovery is also worth further analysis. Research has found that smoking has different effects on the HRQOL of husbands and wives, respectively. Research shows that there is no significant association between husband smoking and HRQOL, while wife smoking has lower HRQOL scores.

Table 3 Regression analysis results of marital education gap and health-related quality of life

Figure 3 shows the results of the quantile regression analysis. For the husband's HRQOL, when the husband's education level is higher than the wife's, the improvement level of the education gap on the husband's HRQOL is higher in the low quantile of HRQOL. When the education level of the husband is lower than that of the wife, the downward trend is not obvious. The Quantile regression of wife's HRQOL also showed a similar trend.

Figure 3(a) Quantile regression results of education gap and husband's HRQOL

Figure 3(b) Quantile regression results of education gap and Wife's HRQOL

Overall, the study found that differences in education levels between couples had a significant effect on improving HRQOL for both partners, except for wives TE vs husbands LLSE. In addition, the study also found that the positive effect of education gap is mainly reflected in the lower level of HRQOL. Such findings may be related to factors such as the couple's work status and life roles.

The role of educational disparities between couples in improving each other's quality of life may be related to the diversity and complementarity of their lives. The level of education often affects the type of work, so couples with certain educational differences are more likely to engage in different professions[18, 19], which may increase the diversity of daily life between couples, thereby improving their HRQOL. In addition, engaging in different jobs can often coordinate and bear the burden of family responsibilities[20], such as avoiding overlapping work hours[21]. Finally, research has also pointed out that educational disparities are more conducive to the coordination and distribution of household chores[21]. These may be factors that promote quality of life due to educational disparities.

The insignificant effect of wife TE and husband LLSE on each other's HRQOL may be due to the influence of traditional Chinese couple roles. The study pointed out that under the influence of traditional Chinese culture, husbands need to bear more external responsibilities such as family income and life decisions[22, 23], while women need to deal with more internal affairs[24, 25]. However, when the education level of the wife is much higher than that of the husband, the traditional roles of husband and wife may be reversed, which may make it difficult for the husband and wife to adapt to the social relations inside and outside the family, and therefore may mitigate the diversity and complementarity of life brought by the increased education gap above. This may be the factor that the promoting effect of the education gap disappears when the wife's education level is much higher than that of the husband.

The results of Quantile regression show that the different effects of education gap may be related to the complexity of HRQOL. HRQOL is a comprehensive indicator that involves the physical and psychological well-being of respondents[26]. Improving at a higher level often requires the combined efforts of individuals[27, 28], family environments[29, 30], and external public services[4, 31], which may explain that the results of this study are mainly reflected at lower levels in terms of HRQOL. Nevertheless, this study still found a preliminary improvement effect of education gap on lower levels of HRQOL which is a novel finding.

Finally, the study also found that smoking has different effects on the HRQOL of husbands and wives, which may be related to current social norms. In the current social behavior, men's smoking is generally considered as a common behavior, while women's smoking may be considered as an abnormal social phenomenon[32, 33], and this social norm may bring some problems to women who smoke.

Limitations

This study is characterized by several limitations. Firstly, the respondents involved in the study were mainly those in the family survey, and some respondents who were hospitalized due to serious illness and were elderly in institutions were not included in the study. Due to the poorer HRQOL of these respondents, this actually indicates the robustness of the research results. Secondly, the study used data from the most recent period that best reflects the reality, as it found that the education situation of respondents in longitudinal surveys rarely changed. Further research can be conducted in the future, provided that data for longitudinal comparison is available.

The research results suggest that attention needs to be paid to the improvement effect of education gap on the HRQOL of both spouses, although this is mainly reflected in the lower level of HRQOL. In addition, it is also necessary to pay attention to the discomfort caused to both spouses when the wife's education level is much higher than that of the husband.

Author contributions

RC conceived the idea and wrote the manuscript. CY provided suggestions for the discussion of the manuscript and critically revised the paper. The author(s) read and approved the final manuscript.

Funding

The research was supported by Suzhou University Research Launch Funding.

Data availability

Our dataset is available on http://charl s.pku.edu.cn/zh-CN/page/data/harmonized charl s.

Code availability

The code used and/or analysed during the current study are available from the corresponding author on reasonable request.

Conflict of interest:

All authors declare that there is no conflict of interest.

Ethical approval

Ethical approval for data collection in CHARLS was obtained from the Biomedical Ethics Review Committee of Peking University (IRB00001052–11015). Consent to participate All interviewees gave written informed consent before recruitment to the study.

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Table 1

Coding of variables included in the analysis
Variables	Codes
HRQOL	Continuous variable
Education gap	Base model	Specific model
		Husband's HRQOL- Husband's education is higher than wife's	Husband's HRQOL- Husband's education is lower than wife's	Wife's HRQOL- Wife's education is higher than husband's	Husband's HRQOL- Wife's education is lower than husband's
	0 = No Gap 1 = yes	0 = No Gap 1 = Husband USVE vs wife LLSE 2 = Husband TE vs wife LLSE 3 = Husband TE vs wife USVE	0 = No Gap 1 = Husband LLSE vs wife USVE 2 = Husband LLSE vs wife TE 3 = Husband LLSE vs wife USVE	0 = No Gap 1 = Wife USVE vs husband LLSE 2 = Wife TE vs husband LLSE 3 = Wife TE vs husband USVE	0 = No Gap 1 = Wife LLSE vs husband USVE 2 = Wife LLSE vs husband TE 3 = Wife USVE vs husband TE
Age	0 = ≤ 50; 1 = 51–60; 2 = 61–70; 3 = ≥ 71
Residential location	0 = Rural; 1 = Urban
Multimorbidity	0 = No; 1 = Yes
Drink	0 = No; 1 = Yes
Smoke	0 = No; 1 = Yes
Expenditure	Quintile

Table 2

Univariate analysis of other independent variables and HRQOL
Variables	HRQOL
Variables	Husband P		Wife P
Age		< 0.001		< 0.001
<50	0.826(0.005)		0.770(0.005)
51–60	0.787(0.003)		0.717(0.004)
61–70	0.727(0.004)		0.650(0.004)
> 70	0.637(0.006)		0.581(0.007)
Residential location		< 0.001		< 0.001
Rural	0.732(0.003)		0.723(0.003)
Urban	0.768(0.004)		0.668(0.668)
Multimorbidity		< 0.001		< 0.001
Yes	0.688(0.003)		0.627(0.003)
No	0.813(0.003)		0.770(0.003)
Drink		< 0.001		< 0.001
Yes	0.705(0.004)		0.722(0.003)
No	0.777(0.003)		0.685(0.006)
Smoke		< 0.001		< 0.001
Yes	0.762(0.003)		0.647(0.011)
No	0.730(0.003)		0.692(0.002)
Work		< 0.001		< 0.001
Yes	0.780(0.002)		0.718(0.003)
No	0.650(0.005)		0.643(0.004)
Expenditure		< 0.001		< 0.001
1	0.717(0.005)		0.656(0.005)
2	0.737(0.005)		0.683(0.005)
3	0.762(0.005)		0.694(0.005)
4	0.739(0.005)		0.689(0.005)
5	0.776(0.005)		0.727(0.005)

Table 3

Regression analysis results of marital education gap and health-related quality of life
Variables	Husband			Wife
Variables	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
Education gap (ref: No)	0.04*** (0.029,0.051)			0.036*** (0.025,0.047)
1	0.04*** (0.029,0.051)	0.041***	0.028**	0.036*** (0.025,0.047)	0.038***	0.03***
		(0.027,0.054)	(0.007, 0.050)		(0.016,0.060)	(0.017, 0.044)
2		0.049**	-0.029		-0.05	0.042**
		(0.011,0.088)	(-0.128, 0.070)		(-.0151, -0.050)	(0.003, 0.082)
3		0.062***	0.082**		0.068**	0.094***
		(0.020,0.104)	(0.016, 0.147)		(0.003, 0.133)	(0.051, 0.137)
Age (ref: <50)
51–60	-0.019***	-0.021***	-0.018***	-0.024***	-0.027***	-0.022***
	(-0.032, -0.006)	(-0.034, -0.008)	(-0.032, -0.005)	(-0.035, -0.012)	(-0.039, -0.014)	(-0.034, -0.010)
61–70	-0.041***	-0.043***	-0.043***	-0.062***	-0.066***	-0.062***
	(-0.054. -0.028)	(-0.057, -0.030)	(-0.057, -0.029)	(-0.074, -0.050)	(-0.079, -0.053)	(-0.075, -0.050)
> 70	-0.093***	-0.096***	-0.096***	-0.112***	-0.117***	-0.111***
	(-0.109, 0.077)	(-0.112, -0.080)	(-0.112, -0.079)	(-0.128, -0.096)	(-0.134, -0.100)	(-0.127, -0.095)
Location (ref: Rural)	-0.047***	-0.048***	-0.048***	-0.055***	-0.056***	-0.055***
	(-0.055, -0.038)	(-0.057, -0.040)	(-0.057, -0.039)	(-0.064, -0.047)	(-0.065, -0.046)	(-0.064, -0.046)
Multimorbidity (ref: No)	-0.097***	-0.096***	-0.098***	-0.117***	-0.114***	-0.117***
Yes	(-0.105, -0.088)	(-0.105, -0.088)	(-0.107, -0.089)	(-0.125, -0.108)	(-0.124, -0.105)	(-0.126, -0.108)
Drink (ref: No)	0.032***	0.033***	0.033***	0.02***	0.02***	0.021***
Yes	(0.023,0.040)	(0.024,0.041)	(0.024, 0.042)	(0.008,0.032)	(0.008, 0.033)	(0.009, 0.033)
Smoke (ref: No)	0.002	0.003	-0.001	-0.023**	-0.019*	-0.021**
Yes	(-0.006,0.010)	(-0.006, 0.011)	(-0.010, 0.008)	(-0.043, -0.003)	(-0.040, 0.002)	(-0.041, -0.001)
Work (ref: No)	0.091***	0.094***	0.092***	0.051***	0.054***	0.051***
Yes	(0.080,0.101)	(0.084, 0.105)	(0.081, 0.103)	(0.042, 0.060)	(0.044, 0.064)	(0.041, 0.060)
Expenditure (ref: lowest)
2	0.006	0.005	0.006	0.016**	0.018***	0.015**
	(-0.006,0.019)	(-0.008, 0.018)	(-0.008, 0.019)	(0.003, 0.028)	(0.005, 0.032)	(0.002, 0.028)
3	0.027***	0.026***	0.031***	0.018***	0.022***	0.016**
	(0.014,0.040)	(0.013, 0.039)	(0.017, 0.044)	(0.005, 0.031)	(0.008, 0.036)	(0.003, 0.030)
4	0.013**	0.011*	0.011*	0.019***	0.023***	0.015**
	(0.001,0.026)	(-0.002, 0.024)	(-0.002, 0.025)	(0.006, 0.032)	(0.009, 0.037)	(0.002, 0.029)
5	0.033***	0.033***	0.032***	0.038***	0.04***	0.035***
	(0.020,0.046)	(0.019, 0.046)	(0.018, 0.046)	(0.025, 0.051)	(0.025, 0.054)	(0.021, 0.048)
_cons	0.754	0.755	0.756	0.773***	0.77	0.774
Observations	7874	7542	6869	7871	6869	7537
R-squared	0.211	0.216	0.208	0.202	0.2	0.199

No competing interests reported.

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The association of education gap among spouses and health-related quality of life: a population-based retrospective study

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Abstract

Purpose

Methods

Results

Conclusions

Figures

Introduction

Methods

Data sources

Variables and definitions

Education gap between spouses

HRQOL for couples

Control variables

Statistical analysis

Results

Discussions

Conclusions

Declarations

References

Tables

Additional Declarations

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