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Registration Number 160128822

Family Name Zhang First Name Lisong

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2

The relationship between marital status and health

among elder: evidence from the English Longitudinal

Study of Ageing (ELSA)

A study submitted in partial fulfilment

of the requirements for the degree of

Msc Information Management

at

THE UNIVERSITY OF SHEFFIELD

by

LISONG ZHANG

September 2017

3

Abstract

Background

The previous studies suggested that marital status is associated with health outcomes. In general, married

people are healthier than unmarried people.

Aims

This research aimed to examine the association between marital status and health of old people; to test

whether the marital status is a risk factor for health. In addition, how the impact of marital status changes over

time will be tested by longitudinal analysis.

Methods

This study is conducted by using data from the English Longitudinal Study of Ageing(ELSA). The Chi-square

test was used to test whether marital status is statistically associated with health outcomes. Bivariate logistic

regression and multivariate logistic regression models are used to test the risk factors for older people’s health.

Results

The results showed that being married are strong risk factors for long-standing illness, mobility difficulties,

ADL and IADL difficulties, and depression. The longitudinal analysis successfully showed the impact of

marital status on health were becoming significant over time. In addition, social participation and exercising

are also strong risk factors for health. In most cases, people had a certain degree of social participation and

exercise have better health condition than people did not.

Conclusions

This study had concluded that marital status does have the impact on health outcomes, especially for the

mental health condition and physical function. As people grow older, being married is becoming more and

more important for their health. Further study required to explore the health outcomes and covariates to gain a

more comprehensive view of the impact of marital status on health.

Acknowledgements

I would like to thank Dr. Laura Sbaffi for her help during my study. Thanks to her support and patience, I can

manage to finish my dissertation properly.

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Contents

Abstract .......................................................................................................................................................................................................... 3

Acknowledgements ..................................................................................................................................................................................... 3

Chapter 1: Introduction ............................................................................................................................................................................... 7

1.1 The context of the study ..................................................................................................................................................................... 7

1.1.2 Definition of health ........................................................................................................................................................... 8

1.2 Research aims and objectives ........................................................................................................................................................... 9

Chapter 2: Literature review .................................................................................................................................................................... 10

2.1 Introduction .......................................................................................................................................................................................... 10

2.2 Context ......................................................................................................................................................................................... 10

2.2.1 Ageing society ......................................................................................................................................................................... 10

2.2.2 The association between health and social relationships ............................................................................................... 10

2.3 The association between marital status and health ...................................................................................................................... 11

2.3.1 Married people are healthier than unmarried people ...................................................................................................... 11

2.3.2 The Impact of Marriage on old people’s health ............................................................................................................... 12

2.3.3 The impact of marriage on disability, mortality, and morbidity .................................................................................. 12

2.3.4 The reason why marriage benefits health .......................................................................................................................... 13

2.3.5 Selection and protection hypothesis ................................................................................................................................... 14

2.3.5.1 Selection hypothesis. ................................................................................................................................................. 14

2.3.5.2 Protection hypothesis ................................................................................................................................................ 14

2.3.6 Variations in unmarried groups ........................................................................................................................................... 16

2.3.7 Gender difference ................................................................................................................................................................... 16

2.4 An Overview of the English Longitudinal Study of Ageing (ELSA) .................................................................................... 17

2.4.1 Health predictors in ELSA ................................................................................................................................................... 17

2.4.2 Health behaviours and marital status ................................................................................................................................. 17

2.4.3 The associations between health and physical activities ............................................................................................... 18

2.4.4 Factors that are associated with health outcomes ............................................................................................................ 18

Chapter 3: Methodology ........................................................................................................................................................................... 18

3.1 Introduction .......................................................................................................................................................................................... 18

3.2 Research approach ............................................................................................................................................................................ 18

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3.3 The Methodology of the English Longitudinal Study of Ageing(ELSA) .............................................................................. 19

3.3.1 Sample design ......................................................................................................................................................................... 19

3.3.2.1 marital status ............................................................................................................................................................... 19

3.4 Statistical analysis ............................................................................................................................................................................... 22

3.4.1 Descriptive statistics .............................................................................................................................................................. 22

3.4.2 Chi-squared test ...................................................................................................................................................................... 22

3.4.3 Logistic regression ................................................................................................................................................................. 22

3.5 Ethical aspects...................................................................................................................................................................................... 23

Chapter 4: Results ..................................................................................................................................................................................... 24

4.1 Introduction .......................................................................................................................................................................................... 24

4.2 Descriptive Statistics .......................................................................................................................................................................... 24

4.2.1 Group 1 ..................................................................................................................................................................................... 24

4.2.1.1 Gender distribution .................................................................................................................................................... 24

4.2.1.2 Age Distribution ......................................................................................................................................................... 24

4.2.1.3 Marital status distribution ........................................................................................................................................ 25

4.2.1.5 Health outcomes: ....................................................................................................................................................... 27

4.2.2 Descriptive Statistics Results in group 2 and group 3 .................................................................................................... 33

4.2.2.1 Age distribution in group 2 and group 3 ............................................................................................................... 33

4.2.2.2 Gender Distribution in Group 2 and Group 3 ...................................................................................................... 33

4.2.2.3 Marital status distribution in group 2 and group 3 ............................................................................................. 34

4.2.2.4 Health Outcomes Distribution in Group 2 and Group 3 ................................................................................... 34

4.3 Chi-squared test results .................................................................................................................................................................... 36

4.3.1 Chi-squared test results in group 1 ..................................................................................................................................... 36

4.3.1.1. Relationships between marital status and health outcomes ............................................................................. 36

4.4 Logistic Regression Results .............................................................................................................................................................. 41

4.4.1 Bivariate Logistic regression results .................................................................................................................................. 41

4.4.1.1 marital status and health outcomes ........................................................................................................................ 41

4.4.1.2 gender and health outcomes .................................................................................................................................... 44

4.4.1.3 age and health outcomes .......................................................................................................................................... 46

4.4.1.4 qualification and health outcomes .......................................................................................................................... 49

4.4.1.5 social participation and health outcomes .............................................................................................................. 50

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4.4.1.6 Social-economic class ............................................................................................................................................... 52

4.4.1.7 drinking, smoking and exercising .......................................................................................................................... 53

4.4.2 Multivariate model ................................................................................................................................................................. 58

4.4.2.1 self-reported general health ..................................................................................................................................... 58

4.4.2.2 Cardiovascular diseases ............................................................................................................................................ 60

4.4.2.3 Long-standing illness ................................................................................................................................................ 71

4.4.2.4 ADL and IADL difficulties ..................................................................................................................................... 72

4.4.2.5 Mobility difficulties ................................................................................................................................................. 74

4.4.2.6 Depression ................................................................................................................................................................... 75

4.4.3 Longitudinal analysis ............................................................................................................................................................ 76

Chapter 5: Discussion ............................................................................................................................................................................... 79

5.1 Introduction .......................................................................................................................................................................................... 79

5.2 Cross-sectional analysis ..................................................................................................................................................................... 79

5.2.1 marital status and health outcomes ..................................................................................................................................... 79

5.2.2 The association between other variables and health outcomes .................................................................................... 81

5.3 Longitudinal analysis ......................................................................................................................................................................... 83

Chapter 6: Conclusion............................................................................................................................................................................... 84

Reference ..................................................................................................................................................................................................... 86

Appendix ...................................................................................................................................................................................................... 99

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Chapter 1: Introduction

1.1 The context of the study

1.1.1 Global ageing

With the development of medical technologies, improved sanitation, improved living standards, and declining

child mortality (Jin et al., 2015), there is a possibility of the ageing process becoming modifiable to ensure

that people lead longer lives without suffering from severe diseases (Christensen, Doblhammer & Vaupel,

2009).

Nowadays, it is widely known that the slowed ageing has become an irreversible trend in developed countries

(Baldassar et al., 2017). In 2013, the United Nations estimated that the number of ageing people would grow

to 2 billion people by 2050 (Ortman, Velkoff & Hogan,2014).

It has been two centuries since the life expectancy started to increase in developed countries (Christensen,

Doblhammer& Vaupel, 2009). According to data from the World Health Organization (WHO), the global life

expectancy is 71.4 years, an increase of five years since 2000. Europe has the highest life expectancy at 76.8

years on average. Although the increased life expectancy is likely to have a positive impact on human lives,

new challenges are emerging (Jin et al., 2015).

Since the elderly people perform differently compared to their younger counterparts in many aspects

(Grady,2012), it is important for policy makers to come up with ways of accommodating the needs of the

ageing people (Ezeh, Bongaarts, & Mberu, 2012).

Andrew (2000) pointed out that the increasing life expectancy poses significant challenges to global health

and economic status. Since ageing is a degenerative process, some diseases are more likely to appear during

old age. Christensen, Dobhammer and Vaupel (2009) pointed out that the prevalence of diseases among old

people has increased over time. Older people suffer from infectious diseases due to the declining immune

system (Weinberger et al. 2008). Therefore, it is important to research factors that are associated with health

conditions.

Social relationships, however, has been identified as an important factor that influences one’s health status

(Uchino, Cacioppo & Kiecolt-Glaser, 1996). Social isolation and living rearrangement are more likely to

8

occur during the old age (Korinek et al.,2011). As one of the most significant social relationships for most

adults (Kiecolt-Glase & Newton, 2001), marriage should be the focus of related research.

1.1.2 Definition of health

The proper definition of health is crucial for the purpose of management and implementation of policies (Karr,

1999).

The current WHO definition of health was formulated in 1948. According to WHO, health is “a state of

complete physical, mental, and social well-being,” in which the absence of disease should not be the only

indicator. However, there is a lot of criticism regarding this definition. One of the critics focus on the word

“complete”. Smith (2008) argued that most people would be considered unhealthy if the WHO definition was

to be followed. Larson (1999) indicated that the results of improved health should be more relevant to clients

and not just to health professionals.

Saylor (2004) summarised the western definition of health into three aspects: well-being, health promotion,

and multiple dimension of health. Some studies define well-being as a phycological state in which people can

lead happy and optimistic lives without worrying too much (Carlson, 2003).

In fact, the factors that determine health, especially for the elderly people are complex (Andrew, 2001).

Disability is the most used indicator of old people’s health trend (Parker & Thorslund, 2007). Stenholm et al.

(2014) indicated that there is a relationship between difficulties im physical functioning and a number of

diseases that the elderly people suffer with regard to both physical mobility and several diseases as two

important indicators of health. Luo et al. (2012) categorised health in three aspects: self-rated health,

depressive symptoms, and functional limitations. In summary, there is a need to have a multidimensional

definition of health.

However, few study has explored the association between marital status and a comprehensive set of health

outcomes among the older people. The same is the case for the changes in the impact of marriage on health

over time.

The study will be conducted by using data set from English Longitudinal Study of Ageing (ELSA), which is a

representative survey to study how people’s lives change over years. The sample includes people who are over

50 years old and live in private households in England. The survey involves multiple aspects, including health,

economic, and social circumstances (Steptoe et al., 2012).

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The association between multiple health outcomes and marital status will be examined. Also, the relationship

between health outcomes and other demographic factors will be tested. Furthermore, the change in the impact

of marital status will also be explored through longitudinal analysis.

1.2 Research aims and objectives

This study is mainly aimed at studying the association between marital status and health outcomes among the

older adults.

The objectives are as listed below:

Examine whether marital status is associated with health.

Examine what health outcomes are most likely to influenced by marital status

Examine whether being married is advantageous in term of health

Examine the association between other factors and health.

Explore how the impact of marital status changes cross time.

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Chapter 2: Literature review

2.1 Introduction

In the previous chapter, the introduction and research context have been presented. This chapter gives a

detailed description of the literature related to the relationship between marital status and health condition.

The findings that are summarised from this literature review will also be discussed. Also, the studies that were

conducted using the ELSA data set will be presented to give a more honest insight into this study.

2.2 Context

2.2.1 Ageing society

In an ageing society, people always wonder how to stay healthy as they get old (Haveman et al., 2003). Like

other developed countries, the life expectancy in England is increasing (Leon,2011). The effects of having an

ageing population are becoming the subject of endless debates especially the how many years will be spent

with disability (Steel et al., 2002). Such debates contribute to the concept of healthy ageing.

Healthy ageing, however, is normally discussed by explaining the general health condition followed by the

health status in relation to ageing (Haveman et al., 2003). As a multidimensional concept, it does not only

refer to the absence of disease and illness but also takes physical disability, cognitive function, and social

functioning into account (Rowe & Kahn, 1997).

Landefeld, Winker and Chernof (2009) indicated that the concept of healthy ageing plays an important part in

reducing the costs of health care the in the context of the elderly population. In this case, it is necessary to

explore the factors contribute to healthy ageing.

2.2.2 The association between health and social relationships

House, Landis, and Umberson (1988) indicate that it took a long time for scientists to notice the association

between health and social relationships. In 1897, Durkheim found out that the less socially integrated people

commit suicide more than the socially integrated people, and this was the very first empirical sociology

finding. In 1979, for the first time, Berman and Syme ruled out other possible explanations and confirmed the

association between social integration and mortality rate. Since then, studies have drawn consistent

conclusions regarding this topic (Uchino, 2006).

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Like other health risk factors such as unhealthy diet or excessive alcohol consumption, study shows that social

isolation is a risk factor for health as well (Holt-Lunstad, Smith & Layton, 2010). The association between

social relationships and health as well as mortality risk is as significant as smoking and obesity to health

(House, Landis & Umberson, 1988).

Franks et al. (2004) indicate that there was research to prove that social integration could influence an

individual’s health by influencing healthy behaviours and that the absence and presence of social partners have

different impacts on healthy behaviours. Since the marriage is one of the most important forms of social

relationships, the absence and presence of a spouse should be treated as a factor that influences health

outcomes.

Although different forms of social ties have been proven to be beneficial for health, marriage may be the most

important one (Lugaila, 1998). As an important type of social relationships, marriage is central for most adults

(Kiecolt-Glase & Newton, 2001). Therefore, the impact that marriage brings can be not be ignored.

2.3 The association between marital status and health

2.3.1 Married people are healthier than unmarried people

Many studies show that being married is more advantageous regarding health. For instance, Verbrugge (1979)

indicated that married people were the healthiest group in the United States. They enjoyed the lowest rates of

disability and tended to spend quite a short time in hospitals. By examining the data from the U.S.

Longitudinal Study of Ageing, Goldman et al. (1995) pointed out that the health outcomes and survival

outcomes are associated with marital status.

There are a large number of studies that focus on the association between marriage and health. Many of them

come up with the conclusions that married people have a higher level of health than those who are not married

(Goldman, Korenman, & Weinstein, 1995; Parker-Pope, 2010; Mata, Frank & Hertwig, 2015; Kiecolt-Glaser,

Gouin, & Hantsoo, 2010; Fu & Noguchi, 2016). Marital status is associated with several health outcomes, one

of which is self-rated health (SRH). Rohrer et al. (2008) indicated that married people have better self-rated

health than their unmarried counterparts.

Compared with cross-sectional analysis, longitudinal analysis can draw more persuasive conclusions. The

same person can be traced through years, and early health data can be treated as a constant (Fitzmaurice, Laird,

& Ware, 2012). After conducting a longitudinal analysis, Wilson and Oswald (2005) concluded that marriage

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benefits people both physically and mentally because people who are married live longer, healthier, and

happier lives. Also, the advantage of marriage tends to exist over time (Liu & Umberson, 2008).

2.3.2 The Impact of Marriage on old people’s health

Since social changes are more likely to occur at an old age, including the rising divorce rate and changing

patterns of re-partnering, there is an increasing diversity in marital status among old people in the UK and

other areas (Robards et al., 2012).

Studies show that marital status and living arrangement are related to the health outcomes of old people

(Grundy, 2010; Moustgaard, & Martikainen, 2009). The combined effect of marital status and living

arrangements was pointed out by Wang et al. (2003). They examined a cross-sectional study in China and

found out that married elderly adults living with their children have the best functional status (such as ADLs),

while the unmarried counterparts have the worst.

Bookwala (2005) concluded from a sample of people aged above 50 years that uncaring and unhelpful spousal

behaviours have bad influences on their physical health. Therefore, when discussing the health of old people,

marriage is a crucial factor.

2.3.3 The impact of marriage on disability, mortality, and morbidity

Disability refers to a “restriction in a person’s ability to perform normal activities of daily living”(Verbrugge &

Jette, 1994). It is strongly associated with individual well-being (Gill et al.,2001). Therefore, good information

on disability and all functioning levels are important for policy responses to the ageing population (Steel et al.,

2002).

Guranik and Ferrucci (2003) defined disability as limitations in performing social roles and tasks. Considering

the close relationship between social relationship and disability, there is literature to suggest that a low-level

of social integration means a higher rate of disability (Avlund et al., 2004). For both genders, being married or

cohabiting has been proven to contribute to protecting people from disability (Mor et al.,1989).

For old people, disability is indicated by difficulties in carrying out daily activities (Millan-Calenti et al.,

2010). According to WHO, the basic consensus for assessing daily difficulties are activities of daily living

(ADL) and instrumental activities of daily living(IADL).

Marriage has a protective effect on both mortality and morbidity (Sengupta & Agree, 2002). Married people

are at a lower risk of mortality than people with any other marital status (Johnson et al. 2000). This conclusion,

13

however, is universal to some extent. In a meta-analysis of older people by Manzoli et al. (2007), a similar

conclusion is drawn and does not vary based on such demographic factors as gender and location.

Both men and women benefit from marriage. Married people are less likely to engage in risky activities. Also,

the gains from marriage can be influenced by the quality of marriage and prior beliefs (Wilson & Oswald,

2005).

2.3.4 The reason why marriage benefits health

Zheng and Thomas (2013) argue that the impact of marriage can be demonstrated in several pathways,

including providing information and sources related to health promotion (Cohen, 2004), buffering effect

(Schwerdtfeger & Friedrich-Mai, 2009), and promoting healthy behaviours directly (Berkman & Glass，

2000). There are several assumptions about how marriage promotes health, including economic support,

social support, and cohabitation (Ross, Mirowsky, & Goldsteen,1990).

Regarding pathways through which one’s marital status influences health, physiological ways are most

preferred. Through people’s cognitions, emotions, healthy behaviours, and coping behaviours, they can

influence people’s health both directly and indirectly (Kiecolt-Glaser & Newton, 2001). Both stress and

support in marital relationships have an impact on one’s health condition.

Notice that support and stress are mentioned at the same time. In the stress/social support hypothesis that

Burman and Margolin (1992) proposed, marital factor was identified as a main source of stress. Unhappy

marriage does more harm than being unmarried (Slatcher, 2010). Despite all the benefits that marriage brings

to health, marriage stress can also be a major factor for deteriorating physical health (Robles & Kiecolt-Glaser,

2003). Moreover, according to the findings of a national longitudinal study, the adverse effect of marital strain

exists over time, especially among older people. Despite the gender difference in other aspects, from life

course perspective, marital quality affects the health of men and women in a similar way (Umberson et al.,

2006). Studies show that the quality of marriage makes a big difference in the protective effect of marriage

(Williams, 2003). However, the level of health may influence the protective effect of marriage on morality if

one’s health problem is not severe (Zheng & Thomas, 2013).

Various literature focuses on the impacts of social exchange on health-related behaviours. There are two main

forms of social exchanges: social support and social control (Frank et al., 2014). Antonucci, Lansford, and

Akiyama (2001) indicated that intimate relationships play an important role as a source of support throughout

adulthood. There is a link between social support and lower rates of morbidity and mortality (Uchino,

Cacioppo, & Kiecolt,1996). To some extent, the level of social support can be a predictor of cardiovascular

diseases (Bowen et al., 2014).

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In the context of marriage and other partnered relationships, support is considered to be “responsiveness to a

loved one’s needs” as well as the acts that relate to caring (Cutrona, 1996). In terms of health-promoting

support, studies focus on the buffering effects of marriage, meaning that the negative effects of health

problems or stressful circumstance can be offset by marriage (Gremore et al., 2011; Uchino, Uno, &

HoltLunstad, 1999).

2.3.5 Selection and protection hypothesis

Demographic literature indicates that there are two main reasons for the differences between the married and

the unmarried groups: selection and protection hypothesis (Wilson, 2012). The selection hypothesis

emphasises that healthy individuals are more likely to be chosen as marriage partners than those who are

fragile (Horn et al.,2013). On the other hand, protection hypothesis focuses on the protective benefits that

marriage offers (Rendall et al.,2011). Married people are less involved in the health-hazard activities, which

make them healthier than unmarried people (Schone & Weinick, 1998).

2.3.5.1 Selection hypothesis.

Regarding evolutionary principles, better health does bring marriage; the selection effects are more complex

than that. Wilson and Oswald (2005) mentioned that selection hypothesis cannot be simply explained as a

correlation if panels are not long enough. By controlling other variables, there are inconsistent conclusions.

If marriage benefits one’s health, unhealthy people tend to look for spouses to improve their health which

forms “adverse selection effect” (Lilard & Panis, 1996).

Joung et al. (1998) assume that the selection hypothesis occurs when people choose spouses in an ‘assortative

mating’ way. Healthy people choose healthy spouses while the less healthy people choose less healthy spouses.

In this case, unhealthy couples are more likely to experience widowhood, leading to a false conclusion that

widowhood causes poor health. In conclusion, the evidence to support the selection hypothesis is quite

limited (Stutzer & Frey, 2006).

2.3.5.2 Protection hypothesis

Protection hypothesis can be described in several aspects:

Economic resources

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To a large extent, the protective benefit of marriage in health equals economic support, especially for health

economic resource that is embedded in marriage (Stimpson, Wilson, & Peek, 2012). Married people are more

likely have access to sufficient economic resources (Waite & Lehrer, 2003). After controlling other variables,

married people earn much more than single people (Chu & Lee, 2001). They have higher incomes

(Greenwood, et al.,2014), accumulate more wealth (Lupton & Smith, 2003) and have a lower rate of poverty

(Sawhill & Thomas, 2002). It is obvious that two people who live together can gain from economies of scale

(Voena, 2015). With more disposable income, married people can afford higher standards of living, which

may be beneficial to their mental and physical health. Duflo (2012) also posits that women are more likely to

be affected by economic resource than men. The loss of economic resources has a negative impact on

women’s health and mortality.

Mental health

Some studies suggest that married people have better mental status. Diener et al. (2000) argue that married

people tend to report higher life satisfaction, lower negative emotion, and higher positive emotion than

divorced people. Also, married people lead happier lives than unmarried people. However, this difference is

not significant among old people (Taylor, Funk, & Craighill, 2006)

Soulsby and Bennett (2015) highlighted that marriage itself is not the key to delayed or decreased onset of

depression. Rather, it is the social support embedded in marriage that promotes mental health.

Health behaviour

Health behaviours are determinants of health and well-being. Health-damaging behaviours contribute to

increased disability and death rates (Watt et al., 2014). According to the World Health Organisation (2009),

bad health behaviours can be risk factors for death and disability, and they include alcohol abuse, sedentary

lifestyle, and poor diet.

Literature suggests that marriage reduces the health-hazard behaviours. Duncan et al. (2006) point out that the

possible mechanism behind this includes monitoring the spouses’ behaviours and the social norm that one

should clean up acts when they enter marriage.

Mortality

There are many research literature to suggest that protection hypothesis has a lot to do with the mortality rate.

Previous studies show that married people are less likely to commit suicide due to the protective effect, while

widowed people are very prone to committing suicide (Kachur et al., 1995).

16

Marriage can provide social and emotional stability, which unmarried people cannot get access to. Also, it

offers social and community integration, thus reducing social isolation (Kposowa, Breault & Singh, 1995).

However, after the death of a spouse, the mortality rate of the survivor shoots up (Stahl et al.,2016).

2.3.6 Variations in unmarried groups

According to Wilson and Oswald (2005), there are some distinctions between married and non-married

partnership groups.

Even for the cohabitation, which is the closest relationship to marriage, differences exist. In the western

society, the number of unmarried people living together is growing. Brown and Booth (1996) point out that

people who are in cohabiting relationships have lower quality relationships and lower socioeconomic status

than married people (Rindfuss & VandenHeuvel, 1990).

Evidence shows that marriage is negatively associated with substance abuse, but cohabitation is not (Mullan,

Harris, Lee, & DeLeone, 2010). Studies that focus on the links between marriage and health behaviours show

that people change when they first get into marriage. Since the social norms that are associated with

cohabitation of are different from that of marriage, cohabitation is less consistent with health behaviours

(Duncan et al., 2006).

With regard to health, there are also variations in the unmarried group. In general, widowed people have the

worst health conditions. A person’s experience can be complex. Considering the diversity of personal

experience, the married people cannot be treated as a homogenous group (Robards et al., 2012).

Although widowhood, as well as divorce, negatively affect women's health (Lillard &Waite, 1995), things

could be more serious in the case of widowhood. Although widowers can get emotional support from their

adult children or other family members, the support from a spouse is irreplaceable. Widowers have few

emotional ties and experience more social isolation, which is harmful to their health (Bookwala, Marshall, &

Manning, 2014).

Also, gender differences emerge. Williams and Umberson (2004) posit that marriage transitions can be more

harmful to men than women among the elderly. Considering that women are usually the caregivers, men tend

to be more negatively affected by widowhood (Hu & Goldman, 1990).

2.3.7 Gender difference

After going through 48 relevant articles, Manfredini et al. (2017) concluded that most of them indicated that

single men are the most likely group of people to have cardiovascular diseases. William and Umberson (2004)

regarded entering marriage as a "complex balance of rewards and strains". Despite the benefits of marriage,

the adjustment process could be stressful. Considering that women perform more household chores before

marriage, they are more prepared for the stressful roles they may perform in future life than men (Lennon &

17

Rosenfield,1994). This may be the reason for the gender disparities regarding health outcomes during

marriage transitions.

Generally speaking, widowed men are more likely to be disabled than married men (Goleman et al., 1995).

Demographic research shows that married people have better health and mortality outcomes, especially

among men (Robards et al., 2012). However, some research findings suggest that older women are more

likely to experience functional disabilities as well as poor health than their male counterparts (Chatterji et

al.,2015).

For women who are not employed, the impact of marital status on health is particularly significant (Waldron,

Hughes, & Brooks, 1996). Jobless women who are unmarried suffer particularly from poor health and other

interacting disadvantages. However, men and women have different health risk factors (Sengupta & Agree,

2002), which may explain the gender disparities in terms of health and disability.

2.4 An Overview of the English Longitudinal Study of Ageing (ELSA)

As the first longitudinal study in the UK that covers a multidisciplinary topic, numerous studies have been

conducted using the ELSA data set (Steptoe et al., 2012). Some of them focus on exploring the risk factors of

health outcomes, but none of them studies the relationship between health outcomes and marital status. Also,

none of them uses wave 7 data to study the related topics. However, some studies have shed light on the

choice of health outcomes and covariates in ELSA data sets. Related studies are listed below:

2.4.1 Health predictors in ELSA

To identify the symptoms that lead to early retirement, Rice et al. (2010) included general health, diagnosed

diseases, mobility, pain and depression in their study. By using logistic regression analysis, depression and

impaired mobility appeared to be two specific symptoms that make older employees quit their jobs.

In a study that examines the relationship between health literacy and participation in colorectal cancer,

Kobayashi, Wardle, and von Wagner (2014) identify having limiting long-standing illness, having depressive

symptoms, six activities of daily life (ADL), self-reported general health, and having cancer as the health

variables.

2.4.2 Health behaviours and marital status

Jackson, Steptoe, and Wardle (2015) confirmed the consistency between individual’s health behaviour and

their partners’ health behaviours. Their sample consisted of 3722 couples who participated in ELSA’s wave 1

to wave 6. The health behaviours included in this study were smoking, physical inactivity, and obesity. They

18

used logistic regression analysis and found that both men and women are influenced by their partner’ change

in health behaviours. People will be more likely to make positive change to their health behaviours if they

have the support of their partners.

2.4.3 The associations between health and physical activities

Hamer, Lavoie, and Bacon (2014) conducted an 8-year follow-up research on the relationship between

physical activities and healthy ageing. The sample was made up of 3454 disease-free men and women aged

63.7±8.9 years at the baseline. Healthy ageing was defined as participants surviving without chronic diseases,

depression, as well as physical and cognitive impairment. They found that people who were more engaged in

physical activities in their later life were more likely to age healthily.

2.4.4 Factors that are associated with health outcomes

Factors that are associated with health outcomes were also examined. As an important indicator of health, the

instrumental activity of daily living (IADL) was studied (d'Orsi et al., 2014). By doing a longitudinal analysis

with a sample of 8154 individuals from ELSA wave1, d'Orsi et al. (2014) found that improved quality of life,

regular physical activities, and good self-rated memory were independently associated with IADL recovery,

while low socioeconomic position was an important indicator of IADLs.

Chapter 3: Methodology

3.1 Introduction

This chapter will introduce the methodological approaches that have been used in this study as well as the

reasons why they are suitable for this research. A detailed description of the variables chosen for this study

will be presented. Furthermore, statistical methods that are used in the data analysis section will also be

presented.

3.2 Research approach

According to Creswell (2013), quantitative research focuses on examining the relationship between variables

through statistical analysis while focusing on understanding the meaning of groups or individuals that indicate

social problem. To analyse causal relationship between variables, randomisation has to be ensured. Therefore,

19

the sample size for quantitative approach must be representative enough to make sure that statistical methods

can generate meaningful results (Sale, Lohfeld, & Brazil, 2002).

The English Longitudinal Study of Ageing (ELSA) is a longitudinal study that is based on self-completion

questionnaires, and the answers were measured in numerical forms. Therefore, quantitative research should be

suitable for analysing the data from ELSA.

3.3 The Methodology of the English Longitudinal Study of Ageing(ELSA)

According to ELSA’s official website (https://www.elsa-project.ac.uk/), the study primarily aims is to gather

multidisciplinary data from a sample of people who are over 50 years old in England. The data collected

covers health, diseases, well-being, financial circumstance, social participation, and social networks.

Two data collection methods are used: face-to-face interview, which is conducted using computer-assisted

personal interview (CAPI), followed by a self-completion questionnaire completed by pen and paper (PAPI).

A nurse visit takes place every four years, during which the biomarkers of respondent are assessed by

qualified nurses.

3.3.1 Sample design

The initial sample of ELSA is drawn from the respondents who took part in the Health Survey for England

(HSE) in 1998, 1999, and 2000. There were 11, 391 respondents in wave 1 who meet the ELSA criteria

(Steptoe et al.,2012). According to ELSA official website(https://www.elsa-project.ac.uk/), there were four

sample refreshments that took place in wave 3, wave 4, wave 6, and wave 7 respectively to maintain the size

and representativeness of the study.

For cross-sectional analysis, the latest available wave 7 data set was chosen and recorded as group 1. To

exclude the interference, only the core members that participated from wave 1 to wave 7 were chosen in the

longitudinal analysis. The results from wave 1 was recorded as group 2, and the results from wave 7 was

recorded as group 3.

3.3.2 Chosen variables

3.3.2.1 marital status

In ELSA wave 1, marital status is categorised into: single, married once, married twice, legally separated,

divorced, and widowed. However, in wave 7, civil partnership was included. To achieve a degree of

20

consistency, the marital statuses in the three groups were categorised into: single, married, separated, divorced,

and widowed.

3.3.2.2 Health outcomes

This study adapted Huber et al.’s (2011) theory. They divided the measurement of health into three domains,

namely physical health, mental health, and social health.

Physical health is measured as the ability to cope with physiological stress. If the physical health fails,

diseases will appear. Therefore, the indicator of physical health is the incidences of illness. Mental health

refers to the ability to keep and improve subjective well-being. The assessment of depression symptom will be

used.

3.3.2.2.1 Physical health

Self-reported general health

Although self-reported health is subjective, and different individuals can have different perceptions about their

health status, studies have indicated that self-reported health can reflect several objective health outcomes.

Wu et al. (2013) argued that SRH can measure global health status in a general population, which makes SRH

outstanding among other indicators. Schnittker and Bacak (2014) pointed out that self-rated health can be a

strong predictor of mortality.

In large cohort studies, SRH always serves as an indicator of general health because it is a reliable predictor of

mortality, morbidity, and physical functioning (Grol-Prokopczyk et al., 2011).

In ELSA, SRH was collected during the interview. Participants were asked the question, ‘Would you say your

health is ….’ to rate their general health on a five-point Likert scale (1=excellent, 2=very good, 3=good, 4=fair,

5=poor) (O'Doherty et al., 2017). In Chi-squared test and logistic regression model, self-reported health will

be recoded into binary, with “poor health” as 1 and all the rest as 0.

Diagnosed diseases and long-standing illness

In ELSA interviews, respondents were asked whether a doctor confirm that they have any cardiovascular

diseases (CVD) or chronic conditions. In this study, nine conditions were included, namely: high blood

pressure, high cholesterol, stroke, angina, congestive heart failure, heart attack, heart murmur, diabetes, and

abnormal heart rhythm.

21

Respondents were asked whether they have long-term illnesses.

Functional impairment

While studying the relationship between marital quality and health, Robles et al. (2014) included functional

impairment as one of the health measurements. In this study, the measurement functional impairments

include activities of daily living(ADL), instrumental activities of daily living(IADL) and mobility problems

However, there is some inconsistency between wave 1 and wave 7. The functional impairment variables will

not be included in longitudinal analysis.

3.3.3.2.2 Mental health

CES-D

The Centre for Epidemiologic Studies Depression Scale (CES-D) is a 20-items measurement to assess

depressive symptoms (Radloff,1977).

In ELSA, CES-D is an 8-items test. Participants were asked eight questions about whether they have had such

depressive symptoms during the past week.The final score ranges from 0 to 8. According to Zivin et al. (2010),

individuals who reported more than three symptoms will be considered to have significant depressive

symptoms. Therefore, the final score is recoded into binary variables: scores ranging from 0 to 3 were

assigned “1”, which means participants did not show depression symptoms, while scores ranging from 4 to 8

were assigned “0”.

3.3.3.2.3 Other variables

Socioeconomic status (SES), social participation and qualification

In the ELSA data set, the key socioeconomic variables are based on the Registrar General Socio-Economic

Classification (NS-SEC) (Steptoe et al., 2012). This study adopts the five-category classification which

includes “managerial and professional, intermediate, small employers and own-account workers, lower

supervisory and technical and semi-routine and routine staff ”. (Donkin, Yuan, & Toson, 2002).

22

The social participation will be measured by whether the respondent is a member of any organisation. Also,

the education level will be simply measured by whether the respondent qualifies.

Health behaviours

In this study, health behaviours will include smoking, alcohol consumption, and exercising regularly.

3.4 Statistical analysis

3.4.1 Descriptive statistics

Before conducting inferential statistics, descriptive statistics will be used to describing the basic

characteristics of the sample (Pallant, 2013).

All the variables related to this study will be described by descriptive statistics. Since the variables were all

recorded into categories, frequency distribution analysis will be conducted. The frequency and the percentage

will be displayed in tables.

3.4.2 Chi-squared test

According to Pallent (2013), Chi-square test conducted is to examine the relationship between two categorical

variables. The null hypothesis of Chi-squared test is: there is no associations between two categorical

variables. By comparing the observed frequency and the expected frequency under the null hypothesis, the χ²

value and p-value will be calculated. If the p-value is less than 0.01 at the same time, the null hypothesis

should be rejected (Altman, 1991, p. 246).

Since ELSA is an extensive survey with many participants, the expected cell count is far more than 20 and

variables are categorical. Therefore, the chi-square test is appropriate for this study.

The chi-squared test will be conducted to analyse the relationship between health outcomes and marital status,

as well as with other demographic factors. The results will be used in identifying the significant associated

factors. The relevant health outcome variables will be recoded into binary with the missing values taken out.

3.4.3 Logistic regression

By using Chi-squared test, the degree of associations will also be assessed. When the dependent variable is

binary and independent variables are categorical variables, logistic regression can be used. The reference

category should be assigned (the last one by a default setting in SPSS). The risk factors that are related to poor

23

self-reported health, the incidence of cardiovascular diseases, and mental health will be explored using these

logistic regression results.

For this particular study, there are three logistic regression models. The main one serves to explore the

relationship between marital status and health outcomes when other covariates are included. The other two

will be conducted to compare the impact of marital status changes with time using group 2 and group 3 data.

Also, sub models will be divided: sub model 1 includes marital status, age and gender; sub model 2

includes social participation, NSE-D, and education; while sub model 3 includes health behaviours.

3.5 Ethical aspects

This study is a no risk study. All data that is related to this study is secondary data gained from U.K. Data

Services (https://www.ukdataservice.ac.uk/). NHS Research Ethics Committees granted the ethical approval

of ELSA. Further information can be accessed from http://www.nres.nhs.uk/.

The data included in English Longitudinal Study of Ageing is absolutely anonymised, without any identifying

data. The data is stored in my personal computer, and I also have a copy on my hard drive. The ethic approval

letter is attached in the Appendix.

http://www.nres.nhs.uk/

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Chapter 4: Results

4.1 Introduction

As discussed in chapter three, this chapter will give detailed description of the statistical results.

This chapter will be divided into three sections, including the descriptive statistics carried out for all related

variables in this study; the bivariate analysis which includes chi-squared test for independence to examine the

relationship between categorical variables; multivariate analysis that includes the logistic regression to test the

risk factors that are related to the health conditions of old people.

4.2 Descriptive Statistics

4.2.1 Group 1

4.2.1.1 Gender distribution

The table below shows that female participants (55.2%) in ELSA wave 7 outnumbered their male counterparts

(44.8%).

Table 1 gender difference in group 1

Frequency Valid Percent

Male 4249 44.8

Female 5242 55.2

Total 9491 100.0

4.2.1.2 Age Distribution

Table 2 shows the age distribution of 9489 respondents aged over 50 in wave 7.

The age is categorised into 5 groups. In the ELSA dataset, the definitive ages of participants aged over 90

were not recorded to avoid disclosure. People who were between 60 to 69 years old consisted of the largest

25

age group, accounting for 38.2 % of total participants. The proportion of people aged 70-79 and 50-59 are

26.5% of 21.9% respectively. There were only 193 individuals who were older than 90.

Table 2: age distribution in group 1

Age Frequency Valid Percent

50-60 2081 21.9

60-70 3629 38.2

70-80 2515 26.5

80-90 1071 11.3

90+ 193 2.0

Total 9489 100.0

4.2.1.3 Marital status distribution

Table 3 shows that 66.4% of people were married in group 1. The second largest group are widowers,

accounting for 14.8% of total respondents. Divorced people and single people ranked the third and fourth

places. While, those who were separated were the smallest proportion (1.3%).

Table 3: Marital Status distribution in group 1


single 593 6.2

married 6300 66.4

separated 122 1.3

divorced 1071 11.3

widowed 1403 14.8

Total 9489 100.0

4.2.1.4 Socio-economic factors

4.2.1.4.1 NS-CES classification

It can be seen from the table 4 that the largest proportion of respondents are the managerial and professional

26

class (33.3%). Interestingly, the second largest group is the lowest occupation class group, with 28.5% of

people from semi-routine and routine class. The figures for intermediate occupations, small employers or own

account workers are similar, 12.8% and 11.1% respectively.

Table 4 NS-CES distribution in group 1

NS-CES classification


managerial and professional 3157 35.9

intermediate occupations 1213 13.8

small employers and own account workers 1053 12.0

lower supervisory and technical occupations 783 8.9

semi-routine and routine 2507 28.5

other 82 .9

Total 8795 100.0

Missing 694

Total 9489

4.2.1.4.2 Social participation

Table 5 shows that only 23.6% of respondents reported that they were not a member of any organization.

However, there are 1811 missing values.

Table 5: Social integration in group 1

Whether the respondent is a

member of any organization?


No 5437 70.8

Yes 2241 29.2

Total 7678 100.0

Missing System 1811

Total 9489

4.2.1.4.3 Qualification attainment

27

Table 6 indicated that people who had gained qualifications consisted of 71.5% of total participants.

Table 6: qualification attainment in group 1

Whether the

respondent had

qualifications?


yes 6584 71.5

no 2625 28.5

Total 9209 100.0

Missing System 280

Total 9489 100.0

4.2.1.5 Health outcomes:

4.2.1.5.1 Self-reported health

It can be seen from table 7 that people who responded “good” are the largest group, accounting for 31% of

respondents in total. There were 2588 and 1044 individuals who thought their health was “very good” and

“excellent” respectively. Only 674 (7.5%) individuals regarded their health condition as poor.

Table 7: self-reported general health in group 1

Frequency Percent

self-reported general

health

Excellent 1044 11.7%

Very good 2588 29.0%

Good 2940 31.0%

Fair 1670 18.7%

poor 674 7.5%

Total 8916

Missing 573

Total 9489

4.2.1.5.2 Cardiovascular diseases

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Table 8 shows that high blood pressure is the most prevalent cardiovascular disease in group 1, with 31.7% of

respondents reporting it. High cholesterol has the second largest number, 28.7%. Diabetes, was the third

prevalent, only 9.2 % of people responded. In summary, the three dominant diseases are high blood pressure,

high cholesterol and diabetes.

Table 8 : Cardiovascular diseases distribution in group 1


High blood pressure no 6482 68.3

yes 3007 31.7

Total 9489 100.0

High cholesterol No 6770 71.3

Yes 2719 28.7

Total 9489 100.0

Stroke No 9176 96.7

Yes 313 3.3

Total 9489 100.0

Abnormal heart rhythm No 8895 93.7

Yes 594 6.3

Total 9489 100.0

Angina No 9425 99.3

Yes 64 .7

Total 9489 100.0

Congestive heart failure No 9458 99.7

Yes 31 .3

Total 9489 100.0

Diabetes No 8613 90.8

29

Yes 876 9.2

Total 9489 100.0

Heart attack No 9086 95.8

Yes 403 4.2

Total 9489 100.0

Heart murmur No 9215 97.1

Yes 274 2.9

Total 9489 100.0

4.2.1.5.3 Long-standing illness

Table 9 shows that there more than half of people (55%) have long-standing illness.

Table 9: whether have long-standing illness in group 1

Frequency Valid percent

No 4274 45.0

Yes 5214 55.0

Total 9488 100.0

Missing value 1

9489

4.2.1.5.6 physical function

According to ELSA data set, there are ten mobility difficulties involved. Respondents were asked “whether

you have none of these mobility problems?”. The table 10 below indicates that the proportion of people who

reported difficulties (52.2%) or did not (47.8%) are similar, with slightly more people reported difficulties.

In ELSA data set, there are six activities of daily life(ADL), including dressing, walking, showering, using

toilet and getting in and out of bed. And there are seven instrumental activities of daily life (IADL), including

making telephone calls, shopping, preparing meals, reading maps, communicating, taking medications and

30

recognising in the case of physical danger. In terms of assessment, respondents were asked “whether have

none of listed difficulties ?”

Table 11 shows that 25.1% of people who responded “no”, which means that most of people did not have

difficulties in ADL and IADLs.

Table 10: whether have difficulties in mobility


Yes 4949 52.2

No 4538 47.8

Total 9487 100.0

Missing 2

9489

Table 11: whether have difficulties in ADL&IADLs?


Yes 2381 25.1

No 7106 74.9

Total 9487 100.0

Missing 2

9489

4.2.1.5.5 Mental health

People who have significant depression symptoms accounted for 25.3% of total respondents which is three

times fewer people than that who were not depressed.

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Table 12: mental health in group 1


Depressed 2223 25.3

Not depressed 6570 74.7

Total 8793 100.0

Missing value 696

Total 9489

4.2.1.6 Health behaviours

Smoking

Table 13 displays how the respondents answered the question: whether smokes cigarettes at all nowadays?

Most people did not smoke at all (53.5%).

Table 13: whether smokes cigarettes at all nowadays?


no 5078 82.7

yes 1064 17.3

Total 6142 100.0

Missing 3347

Total 9489

Exercising

Table 14 shows how individual’s responding to the question: Did you go to walk or exercise yesterday?

Together, there were 7963 respondents who answer the question. Of all participants, 4693 individuals did

exercise the day before they were interviewed which accounted for 58.9%. While, 41.1% of them said they

had not.

32

Table 14: Did you go to walk or exercise yesterday?


No 3270 41.1

Yes 4693 58.9

Total 7963 100.0

Missing 1526

Total 9489

Drinking

Table 15 below displays the alcohol intake results. In total, 6833 individuals responded to the question:

Whether respondent had an alcoholic drink in the seven days ending yesterday? 73.8% of them had consumed

an alcoholic drink in the seven-day periods.

Table 15: Whether had an alcoholic drink in the seven days ending yesterday?


No 1793 26.2

Yes 5040 73.8

Total 6833 100.0

Missing 2656

Total 9489

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4.2.2 Descriptive Statistics Results in group 2 and group 3

4.2.2.1 Age distribution in group 2 and group 3

In group 1, respondents aged 50 to 59 years old have reached their 60s, accounting for 38% of total

respondents. After 6 waves interviews, people aged 70 to 79 years old consisted of the largest proportion of

total respondents (38.6%). There was a significant rise in the 80 to 89 age group, increasing from 2.2% to

19.6%.

Table 16 age distribution in group 2 and group 3

Group 2 (wave7) Group 3(wave 1)

Frequency Percent Frequency Percent

50-59 2342 47.9

60-69 1704 34.8 1858 38.0

70-79 740 15.1 1888 38.6

80-89 107 2.2 957 19.6

90+ 1 .0 191 3.9

Total 4894 100.0 4894 100.0

4.2.2.2 Gender Distribution in Group 2 and Group 3

It can be seen from table 17 that more than half of respondents are female, accounting for 56.5% of total

participants. In group 2 and group 3, female respondents were slightly more than that of group1.

Table 17: gender distribution in group 2 and group 3


Female 2767 56.5

Male 2127 43.5

Total 4894 100.0

34

4.2.2.3 Marital status distribution in group 2 and group 3

Table 18 below shows that there are two noticeable differences in marital status of group 2 and group 3. The

number of married people decreased by more than 10%, from 71.5% in group 3 to 61.3%. However, widowed

people increased by more than 11%, from 11.9% in group 2 to 23% in group3. 500 respondents who were

married in wave1 were no longer married in wave 7. Except for married people, the proportion of single,

divorced and separated people also declined. In summary, many people became widowed over time.

Table 18: marital status distribution in group 2 and group 3

Group 2 (wave 1) Group 3 (wave 7)

Frequency Frequency Valid Percent Valid Percent

single 236 4.8 220 4.5

married 3501 71.5 3001 61.3

separated 58 1.2 45 .9

divorced 516 10.5 500 10.2

widowed 582 11.9 1128 23.0

Missing 1 .0

Total 4894 100.0 4894 100.0

4.2.2.4 Health Outcomes Distribution in Group 2 and Group 3

Self-reported health

A significant difference can be identified in table 19 that the rates of “excellent” health have declined, from

18% in group 2 to 8.4 % in group 3. In addition, there is a slight decrease in “very good” health. On the

contrary, the proportion of the other four marital status have grown. It can be explained that people tend to

lower their self-assessment of health over time. However, there were only 2438 participants in group 2 who

responded, so this conclusion may be questionable.

35

Table 19: self-reported general health in group 2 and group 3

Group 2 Group 3

Frequency Valid Percent Frequency Valid Percent

Excellent 440 18.0 389 8.4

Very good 776 31.8 1319 28.4

Good 741 30.4 1564 33.7

Fair 368 15.1 1018 21.9

poor 113 4.6 355 7.6

Total 2438 100.0 4645 100.0

Missing System 2456 249

Total 4894 4894

Cardiovascular Diseases and long-standing illness

It can be seen from table 20 that the percentages of all diseases have increased. The figure for congestive

heart failure and heart murmur were zero in group 2, but there were increases in group 3. Among these

diseases, the proportion of diabetes and strokes increased significantly. Compared with 4.8% of people who

had a confirmed diabetes diagnosis, 11.1% of respondents reported that they have diabetes. The proportion of

stroke doubled, from 2.3% to 5%. It is noticeable that the percent of long-standing illness has increased from

54.1% to 60.7%.

Table 20: cardiovascular diseases and long-standing illness in group 2 and group 3

Group 2 Group 3

yes No yes No

High blood pressure 1586(32.4%) 3308(67.6%) 1936(39.6%) 2958(60.4%)

Angina 330(6.7%) 4564(93.3%)

Heart attack 187(3.8%) 4707(96.2%) 301(6.2%) 4593(93.8%)

Congestive heart failure 0(0%) 4894(100%) 16(0.3%) 4878(99.7%)

Heart murmur 0(0%) 4894(100%) 188(3.8%) 4706(96.2%)

Abnormal heart rhythm 251(5.1%) 4643(94.9%) 393(8%) 4501(92%)

Diabetes 235(4.8%) 4659(95.2%) 545(11.1%) 4369(88.9%)

Stroke 112(2.3%) 4782(97.7%) 243(5%) 4651(95%)

Long-standing illness 1332(54.1%) 1129(45.9%) 2971(60.7%) 1923(39.3%)

Mental health

Table 19 indicates that respondents that reported more than three symptoms only accounted for about 25% in

both groups. Although the number of depressed individuals has declined, the proportion of depressed and non-

36

depressed people are similar in two groups.

Table 21: whether the respondent had depressive symptoms in group 2 and group 3?

Group 2(wave1) Group 3(wave 7)

Frequency Valid Percent Frequency Valid Percent

Yes 1242 25.9% 1228 26.8%

no 3555 74.1% 3356 73.2%

Missing system 97 310

Total 4894 4894

4.3 Chi-squared test results

4.3.1 Chi-squared test results in group 1

4.3.1.1. Relationships between marital status and health outcomes

4.3.1.1.1 Relationships between marital status and self-reported general health

According to table 22 below, the p value is less than 0.001, marital status is associated with poor self-reported

health. It can be seen that married group has the smallest proportion of people (6.2%) who reported poor

health, compared with the group of separated people who have the largest (11.2%). Also, the figures for

widowed and divorced were much higher than the married group, reaching 10.3% and 10% respectively in

this study (χ2=24.882, df=4, p<0.001)

Table 22：Relationships between marital status and self-reported general health

Poor self-reported health

Marital status Yes No Total

single 57 522 579 p<0.001

9.8% 90.2% 100.0% df=4

married 363 5492 5855 χ2=24.882

6.2% 93.8% 100.0%

separated 13 103 116

11.2% 88.8% 100.0%

divorced 104 935 1039

10.0% 90.0% 100.0%

widowed 1190 137 1327

89.7% 10.3% 100%

Total 8242 674 8916

92.4% 7.6% 100.0%

37

4.3.1.1.2 Relationships between marital status and cardiovascular diseases

According to the p value, marital status is associated with high blood pressure, high cholesterol, strokes,

abnormal heart rhythm diabetes, heart attacks, heart murmurs, long-standing illnesses, ADL&IADL, mobility

and depression.

Interestingly, the married group does not represent the smallest proportion of people with confirmed high

blood diagnosis. Only 70.2 % of them answered “no”. It is the separated group with the lowest rate (18.9%).

The widowed group has a significantly high percent of high blood pressure (43.8%), compared with the

divorced group (31%) and single group (27%) in this study (χ2=84.470, df=4, p<0.001).

.

As with high blood pressure, people who were separated reported the lowest rate (24%) of high cholesterol,

followed by single people (26%) married people (28%) and divorced people (28.3%). The widowed has the

largest proportion (34%) of people with high cholesterol in this study. There were only 3.3% of total

respondents who confirmed the stroke diagnosis. The figure for separated is the lowest (1.6%), widowed

people have the highest (6.3%). In addition, married group has 2.8% and single has 2.4% (χ2=36.757, df=4,

p<0.001).

In terms of diabetes, the married group is not fortunate, with 8.6% of people that have a confirmed diagnosis,

and only 0.1% less than divorce group. However, separated people have the best outcomes, with 6.5% of

people reported as not having a diabetes diagnosis. Widowers have the highest rate (13.3%).

Compared with other groups, divorced people have a significantly high rate of heart attacks (7.3%) in this

study (χ2=38.141, df=4. p<0.001). Single people have the lowest rate of heart attacks (2%), followed by

married (3.7%), divorced (4.4%) and separated (4.9%). In this case, divorced people performed better than

separated people.

Table 23: Relationships between marital status and cardiovascular diseases

Marital

status

single married separated divorced widowed 6482 p<0.001

High blood

pressure

No 433 4422 99 739 789 68.3% df=4

73.0% 70.2% 81.1% 69.0% 56.2% 3007 χ2=84.470

Yes 160 1878 23 332 614 31.7%

27.0% 29.8% 18.9% 31.0% 43.8% 9489

Total 593 6300 122 1071 1403 100.0%

38

100.0% 100.0% 100.0% 100.0% 100.0%

High cholesterol .No 439 4542 93 768 928 6770 p<0.001

74.0% 72.1% 76.2% 71.7% 66.1% 71.3% df=4

Yes 154 1758 29 303 475 2719 χ2=17.105

26.0% 27.9% 23.8% 28.3% 33.9% 28.7%

Total 593 6300 122 1071 1403 9489

100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

stoke 579 6126 120 1036 1315 9176 p<0.001

97.6% 97.2% 98.4% 96.7% 93.7% 96.7% df=4

14 174 2 35 88 313 χ2=36.757

2.4% 2.8% 1.6% 3.3% 6.3% 3.3%

593 6300 122 1071 1403 9489

100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

Abnormal heart

rhythm

NO 561 5931 118 1012 1273 8895 p<0.001

94.6% 94.1% 96.7% 94.5% 90.7% 93.7% dF=4

YES 32 369 4 59 130 594 χ2=15.265

5.4% 5.9% 3.3% 5.5% 9.3% 6.3%

593 6300 122 1071 1403 9489

TOTAL 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

Angina

NO 590 6260 120 1065 1390 9425 p=0.285

99.5% 99.4% 98.4% 99.4% 99.1% 99.3% df=4

YES 3 40 2 6 13 64 χ2=1.142

0.5% 0.6% 1.6% 0.6% 0.9% 0.7%

593 6300 122 1071 1403 9489

100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

Congestive heart

failure

No 591 6282 121 1068 1396 9458 p=0.707

99.7% 99.7% 99.2% 99.7% 99.5% 99.7% df=4

Yes 2 18 1 3 7 31 χ2=1.013

0.3% 0.3% 0.8% 0.3% 0.5% 0.3%

593 6300 122 1071 1403 9489

100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

diabetes No 548 5757 114 978 1216 8613 p<0.001

92.4% 91.4% 93.4% 91.3% 86.7% 90.8% df=4

χ2=23.850

yes 45 543 8 93 187 876

7.6% 8.6% 6.6% 8.7% 13.3% 9.2%

593 6300 122 1071 1403 9489

100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

Heart attack No 581 6065 116 1024 1300 9086 p<0.001

98.0% 96.3% 95.1% 95.6% 92.7% 95.8% df=4

Yes 12 235 6 47 103 403 χ2=38.141

39

2.0% 3.7% 4.9% 4.4% 7.3% 4.2%

593 6300 122 1071 1403 9489

100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

Heart murmur No 577 6136 119 1042 1341 9215 p=0.002

97.3% 97.4% 97.5% 97.3% 95.6% 97.1% df=4

Yes 16 164 3 29 62 274 χ2=9.555

2.7% 2.6% 2.5% 2.7% 4.4% 2.9%

Total 593 6300 122 1071 1403 9489

100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

4.3.1.1.3 Relationships between marital status and long-standing illness

In terms of incidence of long-standing illness, the advantage of marriage is quite obvious. 51.3% of married

people have long-standing illness, compared with 67% in the widowed group. Among singles, separated and

divorced people, singles reported the smallest proportion of long-standing illness (54.3%), while the figure for

divorced people reached 60% in this study (χ2=118.968, df=4, p<0.001).

Table 24: Relationships between marital status and long-standing illness

Whether have Long-standing

illness?

marital status

single married separated divorced widowed Total p<0.001

No 271 3065 54 428 456 4274 df=4

45.7% 48.7% 44.3% 40.0% 32.5% 45.0% χ2=118.968

Yes 322 3234 68 643 947 5214

54.3% 51.3% 55.7% 60.0% 67.5% 55.0%

Total 593 6299 122 1071 1403 9488

100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

4.3.1.1.4 Relationships between marital status and ADLs and IADLs difficulties

It can be seen from table 25 that married people are the least likely group to report difficulties in ADLs and

IADL (20.9%). Single group ranks the second (21%). Divorced and separated have a similar figure, with

around 72% of them reporting no impairments in ADL and IADL. Widowed people performed worse in terms

of ADL and IADL, with 43.6% of them reported difficulties in this study (χ2=283.505, df=4. p<0.001).

40

Table 25: Relationships between marital status and ADLs and IADLs difficulties

Whether have difficulties in ADL and IADLs?

marital status

single married separated divorced widowed Total

No 125 1313 34 297 612 2381 df=4

21.1% 20.8% 27.9% 27.7% 43.6% 25.1% χ2=283.505

Yes 468 4985 88 774 791 7106 p<0.001

78.9% 79.2% 72.1% 72.3% 56.4% 74.9%

Total 593 6298 122 1071 1403 9487

100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

4.3.1.1.5 Relationships between marital status and mobility problems

Married people reported the lowest rates (47.3%) of mobility difficulties. Widowed people were the most

vulnerable group in terms of mobility, with 72% of individuals reporting difficulties. The figures for singles,

separated and divorced did not (χ2=250.890, df=4, p<0.001).

Table 26: Relationships between marital status and mobility problems

Whether have difficulties in mobility?

marital status

single married separated divorced widowed Total

No 292 2984 63 598 1012 4949 df=4

49.2% 47.4% 51.6% 55.8% 72.1% 52.2% χ2=250.890

Yes 301 3314 59 473 391 4538 p<0.001

50.8% 52.6% 48.4% 44.2% 27.9% 47.8%

Total 593 6298 122 1071 1403 9487

100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

4.3.1.1.6 Relationships between marital status and depression

Marital status is associated with depression (χ2=237.862, df=4, p<0.001). It can be seen from table 27 that

married people reported the lowest proportion of depression (20.1%), while widowers reported the highest

(40.4%).

41

Table 27: Relationships between marital status and depression

Whether the respondent have depressive symptoms? Total

marital status

single married separated divorced widowed

Yes 147 1163 44 340 529 2223 df=4

26.2% 20.1% 39.3% 33.2% 40.4% 25.3% χ2=237.862

No 415 4622 68 685 780 6570 p<0.001

73.8% 79.9% 60.7% 66.8% 59.6% 74.7%

Total 562 5785 112 1025 1309 8793

100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

4.4 Logistic Regression Results

In the last chapter, the association between marital status and health outcomes have been examined. Other

factors that significantly associated with health outcomes have also been introduced. In this chapter, the risk

factor for health will be explored using logistic regression.

In order to test if marital status is independently associated with health outcome variables, other related

variables should be taken account. In multivariate logistic regression models, all the variables that associated

with outcome variables will be included simultaneously. Before the conducting multivariate logistic

regression mode, bivariate logistic regression models will be used to test if the association still exist between

variables in the logistic regression.

4.4.1 Bivariate Logistic regression results

4.4.1.1 marital status and health outcomes

It can be seen that people who were married have 0.574 of risk to report poor health, compared with the

widowed ones (risk=1.00). Except for angina and congestive heart failure, marital status is significant for

other cardiovascular diseases. Separated people have the lowest risk of having high blood pressure, high

cholesterol, stroke, abnormal heart rhythm and diabetes. Married people have the lowest risk of having

mobility difficulties, ADL and IADLs difficulties, long-standing illness and depression.

42

Table 28: bivariate logistic regression model for marital status and health outcomes

Health outcomes p-value OR 95% C.I.for OR

Lower Upper

Poor Self-reported health

Marital

status

p<0.001

Single .750 .948 .685 1.314

Married .000 .574 .467 .706

Separated .765 1.096 .600 2.004

Divorced .802 .966 .738 1.264

Widowed . 1

High blood pressure

Marital

Status

p<0.001

Single p<0.001 .475 .385 .586

Married p<0.001 .546 .485 .614

Separated p<0.001 .299 .187 .476

Divorced p<0.001 .577 .489 .682

Widowed . 1

High cholesterol

Marital

Status

p<0.001

Single .001 .685 .553 .849

Married .000 .756 .668 .856

Separated .024 .609 .396 .938

Divorced .003 .771 .648 .916

Widowed . 1

Stroke

Marital

Status

. p<0.001

Single p<0.001 .361 .204 .640

Married p<0.001 .424 .326 .552

Separated .054 .249 .061 1.024

Divorced .001 .505 .338 .753

Widowed . 1

Abnormal heart rhythm

Marital

Status

p<0.001

Single .004 .559 .375 .833

Married p<0.001 .609 .494 .751

43

Separated .033 .332 .121 .914

Divorced .001 .571 .415 .785

Widowed . 1

Angina

Marital

Status

.482

Single .343 .544 .154 1.915

Married .235 .683 .364 1.281

Separated .450 1.782 .397 7.989

Divorced .306 .602 .228 1.590

Widowed . 1

Congestive heart failure

Marital

Status

.650

Single .624 .675 .140 3.258

Married .210 .571 .238 1.371

Separated .642 1.648 .201 13.507

Divorced .402 .560 .145 2.171

Widowed . 1

Diabetes

Marital

Status

p<0.001

Single p<0.001 .534 .380 .751

Married p<0.001 .613 .514 .732

Separated .036 .456 .219 .950

Divorced p<0.001 .618 .476 .804

Widowed . 1

Heart attack

Marital

Status

p<0.001

Single p<0.001 .261 .142 .478

Married p<0.001 .489 .385 .621

Separated .322 .653 .280 1.519

Divorced .003 .579 .406 .826

Widowed . 1

Heart murmur

Marital

Status

.009

Single .073 .600 .343 1.048

Married p<0.001 .578 .429 .779

Separated .311 .545 .169 1.763

Divorced .026 .602 .384 .942

44

Widowed . 1

No ADL and IADL difficulties

Marital

Status

p<0.001

Single p<0.001 2.897 2.316 3.623

Married p<0.001 2.937 2.601 3.318

Separated .001 2.003 1.330 3.016

Divorced p<0.001 2.016 1.700 2.391

Widowed . 1

No mobility difficulties

Marital

Status

p<0.001

Single p<0.001 2.668 2.187 3.255

Married p<0.001 2.874 2.532 3.263

Separated p<0.001 2.424 1.668 3.522

Divorced p<0.001 2.047 1.731 2.421

Widowed . 1

Long-standing illness

Marital

Status

p<0.001

Single p<0.001 .572 .470 .696

Married p<0.001 .508 .450 .574

Separated .009 .606 .417 .882

Divorced p<0.001 .723 .613 .854

Widowed . 1

Not depressed

Marital

Status

p<0.001

Single p<0.001 1.915 1.539 2.381

Married p<0.001 2.695 2.372 3.063

Separated .816 1.048 .706 1.556

Divorced p<0.001 1.366 1.152 1.620

Widowed 1

4.4.1.2 gender and health outcomes

Gender is not associated with self-reported general health. Compared with male ones, female one only had

56% of chance of not feeling depressed. As for cardiovascular diseases, female ones are more likely to have

heart murmur (OR=1.357). For abnormal heart rhythm, diabetes, heart attack and heart murmur, male

individuals have higher risks. Also, female respondents are less likely not to have mobility (OR=0.619) and

ADL/ IADLs problems(OR=0.806).

45

Table 29: bivariate logistic regression model for gender and health outcomes

Health outcomes p-value OR 95% C.I.for OR

Lower Upper

Poor Self-reported general health Gender

Male 1

Female .780 .978 .835 1.145

High blood pressure Gender

Male 1

Female .008 .888 .815 .969

High cholesterol

Gender

Male 1

Female .131 .933 .854 1.021

Stroke

Gender

Male 1

Female .108 .831 .664 1.042


Gender

Male 1

Female .004 .782 .662 .923

Angina

Male 1

Female .111 .669 .409 1.097


Gender

Male 1

Female .069 .511 .248 1.053

Diabetes

Gender

Male 1

Female p<0.001 .675 .587 .776

Heart attack

Gender

Male 1

Female p<0.001 .388 .314 .479

Heart murmur

Gender

46

Male 1

Female . p<0.001 1.357 1.059 1.738


Gender

Male 1

Female p<0.001 .806 .734 .886

No mobility difficulties Gender

Male 1

Female p<0.001 .619 .571 .672

Long-standing illness Gender

Male 1

Female p<0.001 1.018 .938 1.104

Not depressed Gender

Male 1

Female p<0.001 .565 .511 .625

4.4.1.3 age and health outcomes

Interestingly, people who were aged between 60 to 69 are most unlikely to feel depressed. People who aged

between 70 to 79 have the highest risk of high cholesterol (OR=2.629). However, 80 to 89 years old

individuals are most likely to have high blood pressure(OR=1.420) and diabetes (1.970). For heart murmur,

long-standing illness, depression, ADLs and IADLs and depression, people who younger than 90 years old

have significant reduced risk. In the case of ADL/IADL and mobility, the risks increases as the age get older.

Table 30: bivariate logistic regression model for age and health outcomes

p value OR 95% C.I.for OR

Lower Upper

Health outcomes

Poor Self-reported general health Age p<0.001

50-59 .948 .978 .465 1.729

60-69 .744 .896 .615 2.299

70-79 .606 1.189 .948 3.621

80-89 .071 1.853 .948 3.621

90+ 1

High blood pressure Age p<0.001

50-59 p<0.001 .321 .235 .439

60-69 .002 .627 .465 .845

47

70-79 .519 1.104 .818 1.490

80-89 .028 1.420 1.038 1.942

90+ 1

High cholesterol

Age p<0.001

50-59 .361 .834 .565 1.231

60-69 p<0.001 1.989 1.364 2.900

70-79 p<0.001 2.629 1.799 3.841

80-89 p<0.001 2.593 1.754 3.833

90+ 1

Stroke

Age p<0.001

50-59 p<0.001 .102 .052 .202

60-69 p<0.001 .195 .111 .345

70-79 .051 .583 .339 1.003

80-89 .993 1.003 .575 1.748

90+ 1


Age p<0.001

50-59 p<0.001 .248 .146 .422

60-69 .001 .446 .274 .726

70-79 .335 .788 .486 1.279

80-89 .528 1.174 .713 1.933

90+ 1

Angina Age .123

50-59 .995 16469010.128 .000 .

60-69 .996 6705304.938 .000 .

70-79 .995 12950152.062 .000 .

80-89 .996 12158280.110 .000 .

90+ 1


Age .034

50-59 .092 .092 .006 1.482

60-69 .420 .424 .053 3.409

70-79 .891 1.152 .151 8.768

80-89 .942 1.082 .130 9.035

90+ 1

Diabetes

Age p<0.001

50-59 .168 .666 .373 1.188

60-69 .532 1.194 .685 2.083

70-79 .044 1.771 1.015 3.091

80-89 .020 1.970 1.112 3.490

90+ 1

48

Heart attack

Age p<0.001

50-59 p<0.001 .121 .061 .239

60-69 .001 .374 .214 .655

70-79 .154 .668 .383 1.163

80-89 .139 1.526 .872 2.670

90+ 1

Heart murmur

Age p<0.001

50-59 .001 .341 .177 .655

60-69 .001 .362 .194 .674

70-79 .040 .521 .279 .972

80-89 .270 .692 .360 1.331

90+ 1


Age p<0.001

50-59 p<0.001 15.763 11.164 22.257

60-69 p<0.001 12.927 9.249 18.068

70-79 p<0.001 7.496 5.359 10.486

80-89 p<0.001 3.000 2.123 4.238

90+ 1

No mobility difficulties Age p<0.001

50-59 p<0.001 15.129 9.442 24.241

60-69 p<0.001 10.232 6.411 16.330

70-79 p<0.001 5.685 3.554 9.094

80-89 p<0.001 2.379 1.464 3.865

90+ 1

Long-standing illness Age p<0.001

50-59 p<0.001 .246 .176 .345

60-69 p<0.001 .360 .258 .502

70-79 p<0.001 .534 .382 .747

80-89 .088 .737 .519 1.047

90+ 1

Not depressed Age p<0.001

50-59 p<0.001 2.334 1.645 3.311

60-69 p<0.001 2.909 2.062 4.105

70-79 p<0.001 2.112 1.494 2.985

80-89 .045 1.444 1.009 2.068

90+ 1

.

49

4.4.1.4 qualification and health outcomes

Except for heart murmur, angina and congestive heart failure, qualification is significant. People with

qualification have reduced risk of diseases, ADL/IADL and mobility problems and being depressed.

Table 31: bivariate logistic regression model for qualification and health outcomes

p-value OR 95% C.I.for OR

Lower Upper

Health outcomes Whether have qualification?

Poor Self-reported general health

Yes p<0.001 2.499 2.125 2.939

No 1

High blood pressure

Yes .000 1.546 1.407 1.699

No 1

High cholesterol

Yes .000 1.412 1.282 1.556

No 1

Stroke

Yes .000 1.869 1.485 2.352

No 1


Yes .760 1.029 .857 1.236

No 1

Angina

Yes .770 1.084 .632 1.860

No 1


Yes .739 .872 .390 1.952

No 1

Diabetes

Yes .000 1.664 1.441 1.923

50

No 1

Heart attack

Yes .000 1.727 1.407 2.120

No 1

Heart murmur

Yes .597 1.074 .826 1.396

No 1


Yes .000 .385 .349 .425

No 1

No mobility difficulties

Yes .000 .424 .385 .466

No 1

Long-standing illness

Yes .000 1.820 1.657 1.999

No 1

Not depressed

Yes .000 .489 .441 .543

No 1

4.4.1.5 social participation and health outcomes

Except for abnormal heart, angina, congestive heart failure which social participation is not significant, being

a member of organisations does reduce the risk of poor health.

Table 32: bivariate logistic regression model for social participation and health outcomes

p-value OR 95% C.I. OR

Lower Upper

51

Poor Self-

reported general

health


member of an organization?

Yes .000 2.383 1.988 2.857

No 1

High blood

pressure



Yes .071 .909 .819 1.008

No 1

High cholesterol



Yes .005 .859 .773 .954

No 1

Stroke



Yes .001 .637 .489 .831

No 1

Abnormal heart

rhythm



Yes .546 1.064 .871 1.300

No 1

Angina Whether the respondent is a


Yes .943 .979 .545 1.759

No 1

Congestive heart

failure



Yes .543 .778 .346 1.748

No 1

Diabetes



Yes .000 .734 .624 .864

No 1

Heart attack



Yes .000 .655 .519 .826

52

No 1

Heart murmur



Yes .518 1.100 .824 1.470

No 1

No ADL and

IADL difficulties



Yes .000 1.601 1.430 1.793

No 1

No mobility

difficulties



Yes .000 1.564 1.416 1.728

No 1

Long-standing

illness



Yes .000 .764 .692 .844

No 1

Not depressed Whether the respondent is a


Yes .000 1.745 1.561 1.951

No 1

4.4.1.6 Social-economic class

Interestingly, social-economic class is not significant with any health outcomes(p>0.05). So the table for it is

not included.

53

4.4.1.7 drinking, smoking and exercising

Not smoking people have reduced risk in self-reported health, high blood pressure, abnormal heart rhythm and

depression. People who did not smoke are also more likely not to have ADL and IADL difficulties. Not

exercising people have higher risk of high blood pressure, diabetes, heart attack, long-standing illness. Also,

people who did not reported they exercised the day before the interview have less chance of not having ADL

and IADL difficulties. It is worth to notice that people who reported that they have been drinking for the last

seven days have better health outcomes than people they did not.

Table 33: bivariate logistic regression model for smoking and health outcomes


Lower Upper

Health outcomes Smoking

Poor Self-reported general

health

Yes 1

No p<0.001 .496 .405 .608

High blood pressure

Yes 1

No p<0.001 1.412 1.216 1.639

High cholesterol

Yes 1

No .731 1.026 .887 1.187

Stroke

Smoking

Yes 1

No .837 .965 .684 1.361


Smoking

Yes 1

No p<0.001 1.839 1.310 2.579

Angina

yes 1

54

no .473 .774 .384 1.561


Smoking

Yes 1

No .484 1.539 .460 5.151

Diabetes

Smoking

Yes 1

No .251 1.145 .908 1.444

Heart attack

Smoking

Yes 1

No .118 1.297 .936 1.796

Heart murmur

Smoking

Yes 1

No .712 1.079 .721 1.615

No ADL and IADL

difficulties

Smoking

Yes 1

No .013 1.199 1.039 1.385

No mobility difficulties Smoking

Yes 1

No .259 1.080 .945 1.234

Long-standing illness Smoking

Yes 1

No .085 .888 .776 1.017

Not depressed Smoking

Yes 1

No p<0.001 1.715 1.483 1.985

55

Table 34: bivariate logistic regression model for exercising and health outcomes


Lower Upper

Health outcomes

Poor Self-reported general

health

Exercising

Yes 1

No p<0.001 .299 .248 .361

High blood pressure Exercising

Yes 1

No p<0.001 .774 .704 .850

High cholesterol

Exercising

Yes 1

No p<0.001 .842 .765 .927

Stroke

Exercising

Yes 1

No .006 .705 .550 .904


Exercising

Yes 1

No p<0.001 .676 .566 .808

Angina Exercising

yes 1

no .059 .602 .355 1.019


Exercising

Yes 1

No .564 .803 .382 1.691

Diabetes

Exercising

Yes 1

No p<0.001 .587 .505 .683

Heart attack Exercising

56

Yes 1

No p<0.001 .652 .524 .812

Heart murmur

Exercising

Yes 1

No .134 .822 .637 1.062

No ADL and IADL

difficulties

Exercising

Yes 1

No .013 1.199 1.039 1.385

No mobility difficulties Exercising

Yes 1

No .259 1.080 .945 1.234

Long-standing illness Exercising

Yes 1

No p<0.001 .620 .566 .679

Not depressed Exercising

Yes 1

No p<0.001 1.755 1.581 1.948

Table 35 bivariate logistic regression model for drinking and health outcomes


Lower Upper

Poor self-reported general

health

Drinking

Yes 1

No .000 2.444 1.972 3.029

High blood pressure Drinking

Yes 1

No .000 1.328 1.186 1.486

57

High cholesterol

Drinking

Yes 1

No .002 1.204 1.073 1.351

Stroke

Drinking

Yes 1

No .001 1.635 1.215 2.199


Drinking

Yes 1

No .881 1.017 .816 1.266

Angina Drinking

Yes 1

No .039 1.920 1.034 3.564


Drinking

Yes 1

No .090 2.114 .889 5.025

Diabetes

Drinking

Yes 1

No .000 1.772 1.480 2.122

Heart attack

Drinking

Yes 1

No .048 1.309 1.002 1.710

Heart murmur

Drinking

Yes 1

No .004 1.565 1.156 2.119

No ADL and IADL

difficulties

Drinking

Yes 1

58

No .000 .524 .462 .595

No mobility difficulties Drinking

Yes 1

No .000 .552 .495 .616

Long-standing illness Drinking

Yes 1

No .000 1.584 1.420 1.767

Not depressed Drinking

Yes 1

No .000 .468 .413 .529

4.4.2 Multivariate model

4.4.2.1 self-reported general health

In model 1, marital status is significant for self-reported health. Married people have 0.680 of risk to report

poor health. In model 2, the marital status remained significant after adapted qualification and social

participation variables. Compared with widowed people, married people only have 0.693 chance of having

poor self-rated health. In model 3, marital status is no longer significant.

Table 36: multivariate logistic regression model for poor self-reported general health

variables categories Model 1 Model 2 Model 3

95%CI 95%CI 95%CI

p

value

OR lower higher p value OR lower higher p value OR lower higher

Age .000 .007 .420

50-59 .746 1.120 .565 2.221 .255 1.848 .641 5.322 .998 101982472.025 .000 .

60-69 .908 1.040 .531 2.039 .306 1.719 .609 4.851 .997 114230310.125 .000 .

70-79 .364 1.363 .699 2.659 .160 2.096 .746 5.888 .997 149454229.555 .000 .

80-89 .040 2.025 1.034 3.969 .042 2.934 1.038 8.292 .997 141607160.139 .000 .

90+ 1 1 1

Gender Male 1 1 1

59

female .261 .910 .773 1.072 .046 .817 .669 .996 .152 .812 .611 1.080

Marital

status

.000 p<0.001 .204

Single .421 1.154 .814 1.635 .442 1.194 .760 1.878 .358 1.355 .709 2.590

Married .001 .680 .540 .858 .079 .775 .584 1.030 .410 .832 .537 1.289

divorced .299 1.387 .748 2.572 .318 1.495 .679 3.291 .824 .843 .187 3.801

Separated .222 1.199 .896 1.604 .082 1.371 .961 1.956 .501 1.202 .703 2.057

Widowed 1 1 1

Qualification Yes p<0.001 .469 .382 .577 .002 .623 .463 .839

No 1 1

Social

participation

Yes p<0.001 .495 .406 .603 .005 .663 .498 .884

No 1 1

NS-SEC

classification

.900 .187

NS-SEC

classification

Managerial

and

professional

.980 1.012 .391 2.619 .561 1.556 .350 6.915

intermediate .898 .938 .355 2.477 .412 1.881 .415 8.514

Small

employers/

own

account

worker

.862 .917 .345 2.438 .526 1.636 .358 7.469

Lower

supervisory

and

technical

.928 .955 .356 2.563 .744 1.293 .277 6.028

Semi-

routine

.771 .868 .334 2.255 .911 1.090 .243 4.882

Other 1 1

Smoke Yes 1

No .012 .650 .464 .910

Drink

alcohol

yes 1

No p<0.001 1.869 1.400 2.494

Exercise? Yes 1

No p<0.001 3.177 2.376 4.247

60

4.4.2.2 Cardiovascular diseases

4.4.2.2.1 High blood pressure

In model 1, the p-values of marital status, age and gender are less than 0.05 which means that all of them are

risk factors. In model 3, marital status is not significant after included all the variables simultaneously.

However, other variables remained significant. Not having qualifications, not exercising and smoking are the

risk factors for high blood pressure. Notice that people that drank still less likely to have high blood

pressure(risk=1.00), relative to people they did not drank (risk=1.334).

Table 37: multivariate logistic regression model for high blood pressure


95%CI 95%CI 95%CI

p value OR lower higher p value OR lower higher p value OR lower higher

Age p<0.001 p<0.001 p<0.001

50-59 p<0.001 .385 .278 .532 p<0.001 .310 .196 .489 .002 .352 .184 .671

60-69 .048 .731 .536 .997 p<0.001 .452 .291 .701 .065 .559 .301 1.038

70-79 .160 1.247 .917 1.697 .292 .790 .510 1.224 .877 .952 .514 1.765

80-89 .010 1.515 1.104 2.077 .853 1.043 .666 1.633 .895 1.044 .554 1.964

90+ 1 1 1

Gender Male 1 1 1

female .002 .863 .787 .946 p<0.001 .786 .708 .873 p<0.001 .746 .645 .862

Marital status .000 p<0.001 p<0.001

Single .011 .747 .597 .935 .442 1.194 .760 1.878 .002 .352 .184 .671

Married p<0.001 .754 .659 .862 .079 .775 .584 1.030 .065 .559 .301 1.038

divorced .004 .498 .308 .804 .318 1.495 .679 3.291 .877 .952 .514 1.765

Separated .119 .866 .723 1.038 .082 1.371 .961 1.956 .895 1.044 .554 1.964

Widowed 1 1 1

Qualification Yes p<0.001 .469 .382 .577 .027 .831 .706 .979

No 1 1

Social

participation

Yes p<0.001 .495 .406 .603 .401 .935 .799 1.094

No 1 1

NS-SEC

classification

.900 .097

NS-SEC

classification

Managerial and

professional

.980 1.012 .391 2.619 .541 1.277 .583 2.795

intermediate .898 .938 .355 2.477 .416 1.391 .628 3.082

61

Small

employers/ own

account worker

.862 .917 .345 2.438 .654 1.201 .540 2.670

Lower

supervisory and

technical

.928 .955 .356 2.563 .152 1.801 .805 4.029

Semi-routine .771 .868 .334 2.255 .420 1.381 .630 3.027

Other 1 1

Smoke Yes 1

No .014 1.307 1.057 1.616

Drink alcohol yes 1

No p<0.001 1.352 1.150 1.590

Exercise? Yes 1

No p<0.001 1.313 1.139 1.513

4.4.2.2.2 High cholesterol

In model 1, marital status is not significant. However, in model 2, age, qualification and social participation

remained significant. People who aged between 80 to 89 years have the highest risk of high cholesterol

(risk=2.040). Qualification and social participation were still risk factors for high cholesterol, with reduced

risks of 0.779 and 0.827 respectively. In model 3, age, qualification, social participation, exercising are

significant.

Table 38: multivariate logistic regression model for high cholesterol


95%CI 95%CI 95%CI

p

value

OR lower higher p

value

OR lower higher p

value

OR lower higher

Age .000 .000 .000

50-59 .607 .900 .603 1.344 .414 .806 .481 1.352 .177 1.950 .740 5.140

60-69 .000 2.131 1.447 3.138 .065 1.602 .972 2.642 .002 4.374 1.693 11.299

70-79 .000 2.779 1.890 4.085 .005 2.045 1.242 3.367 .001 5.319 2.062 13.720

80-89 .000 2.672 1.804 3.957 .003 2.141 1.288 3.556 .000 5.491 2.108 14.303

90+ 1 1 1

Gender Male 1 1 1

female .264 .948 .864 1.041 .007 .866 .779 .961 .023 .846 .732 .977

Marital status .460 .703 .828

Single .468 .919 .732 1.155 .472 .909 .699 1.180 .296 .820 .565 1.189

Married .075 .882 .768 1.013 .256 .913 .781 1.068 .493 .925 .740 1.156

62

divorced .475 .850 .545 1.327 .257 .733 .428 1.254 .471 .755 .351 1.623

Separated .515 .940 .781 1.132 .328 .900 .728 1.112 .460 .895 .668 1.200

Widowed 1 1 1

Qualification Yes .000 .779 .692 .877 .089 .867 .735 1.022

No 1 1

Social

participation

Yes .001 .827 .738 .927 .051 .856 .732 1.001

No 1 1

NS-SEC

classification

.110 .006

NS-SEC

classification

Managerial and

professional

.161 .689 .409 1.160 .015 .407 .197 .840

intermediate .267 .740 .435 1.259 .053 .483 .231 1.010

Small employers/

own account worker

.246 .729 .427 1.243 .034 .448 .214 .941

Lower supervisory

and technical

.676 .892 .520 1.528 .217 .624 .296 1.319

Semi-routine .271 .746 .442 1.257 .049 .482 .233 .997

Other 1 1

Smoke Yes 1

No .923 .990 .805 1.217

Drink alcohol yes 1

No .082 1.156 .982 1.362

Exercise? Yes 1

No .013 1.198 1.038 1.381

4.4.2.2.3 stroke

In model I, marital status is not significant. People who were at early old ages show significant reduced risk of

stroke. Model 2 and model 3 showed that marital status was no longer significant.

Table 39: multivariate logistic regression model for stroke


95%CI 95%CI 95%CI

p value OR lower higher p

value

OR lower higher p

value

OR lower higher

Age p<0.001 .000 .000

63

50-59 .000 .118 .058 .240 .000 .111 .044 .277 .007 .144 .035 .596

60-69 .000 .219 .120 .398 .000 .169 .077 .369 .011 .191 .053 .685

70-79 .119 .640 .365 1.122 .072 .506 .241 1.062 .400 .588 .171 2.023

80-89 .848 1.056 .603 1.852 .728 .876 .416 1.845 .656 1.324 .385 4.553

90+ 1 1 1

Gender Male 1 1 1

female .032 .771 .608 .978 .100 .792 .600 1.046 .032 .651 .439 .964


Single .367 .761 .420 1.379 .129 .533 .237 1.201 .854 1.101 .393 3.086

Married .068 .756 .561 1.020 .290 .828 .583 1.175 .418 1.253 .726 2.161

divorced .493 .606 .145 2.535 .411 .431 .058 3.208 .722 1.456 .184 11.556

Separated .764 1.068 .696 1.638 .837 1.055 .635 1.753 .042 2.071 1.026 4.181

Widowed 1 1 1

Qualification Yes .511 .906 .676 1.215 .754 .937 .622 1.411

No 1 1

Social

participation

Yes .001 .619 .467 .820 .393 .840 .563 1.253

No 1 1

NS-SEC

classification

.928 .994

NS-SEC

classification

Managerial and professional .865 1.134 .266 4.830 .746 1.403 .180 10.952

intermediate .694 1.343 .309 5.831 .751 1.402 .174 11.286

Small employers/ own

account worker

.890 1.110 .251 4.907 .704 1.500 .186 12.125

Lower supervisory and

technical

.887 1.115 .250 4.975 .641 1.648 .202 13.425

Semi-routine .727 1.294 .304 5.517 .751 1.394 .179 10.884

Other 1 1

Smoke Yes 1

No .271 .739 .432 1.265

Drink alcohol yes 1

No .433 1.181 .779 1.790

Exercise? Yes 1

No .287 1.221 .846 1.764

4.4.2.2.4 Abnormal heart rhythm

Model 3 indicates that gender, marital status, smoking, exercising are risk factors for abnormal heart rhythm.

Single people were least likely to have abnormal heart, with 0.374 of risk compared with widowed ones.

Table 40: multivariate logistic regression model for abnormal heart rhythm


95%CI 95%CI 95%CI

64

p

value

OR lower higher p

value

OR lower higher p

value

OR lower higher

Age .000 .000 .000

50-59 .000 .270 .155 .470 .000 .215 .106 .437 .001 .207 .079 .542

60-69 .004 .473 .284 .786 .001 .337 .176 .647 .010 .331 .143 .770

70-79 .444 .823 .500 1.355 .191 .652 .344 1.237 .197 .581 .255 1.326

80-89 .481 1.198 .725 1.980 .994 1.003 .524 1.917 .377 .684 .294 1.591

90+ 1 1 1

Gender Male 1 1 1

female .003 .766 .644 .911 .003 .748 .617 .908 .018 .709 .533 .943


Single .609 .896 .589 1.363 .291 .766 .467 1.257 .020 .374 .163 .855

Married .240 .868 .685 1.099 .410 .895 .688 1.165 .002 .562 .390 .809

divorced .313 .590 .212 1.643 .695 .812 .287 2.300 .626 .693 .158 3.029

Separated .581 .909 .647 1.276 .226 .783 .526 1.163 .064 .598 .347 1.031

Widowed 1 1 1

Qualification Yes .082 1.218 .975 1.521 .402 1.148 .832 1.583

No 1 1

Social

participation

Yes .689 .958 .775 1.183 .375 1.153 .842 1.580

No 1 1

NS-SEC

classification

.665 .981

NS-SEC

classification

Managerial and

professional

.900 .942 .368 2.408 .933 1.066 .244 4.651

intermediate .547 .744 .284 1.949 .977 .978 .218 4.383

Small employers/

own account worker

.834 .902 .344 2.366 .882 1.120 .250 5.025

Lower supervisory

and technical

.944 1.035 .393 2.728 .869 1.136 .250 5.163

Semi-routine .883 .931 .363 2.388 .971 .973 .222 4.263

Other 1 1

Smoke Yes 1

No .011 1.993 1.169 3.397

Drink alcohol yes 1

No .857 .971 .703 1.341

Exercise? Yes 1

No .001 1.575 1.202 2.064

65

4.4.2.2.5 angina

For angina, only gender remained significant in three models. After including all the variables, females are

less likely to have angina than males (OR=0.352, 95% CI 0.137, 0.902).

Table 41: multivariate logistic regression model for angina


95%CI 95%CI 95%CI

p

value

OR lower higher p

value

OR lower higher p

value

OR lower higher

Age .079 .079 .087 .087 .044 .044

50-59 .995 26305209.679 .000 .995 .997 24752941.497 .000 .997 .998 58360261.498 .000 .998

60-69 .996 9901001.230 .000 .996 .997 7277325.597 .000 .997 .998 7125007.850 .000 .998

70-79 .995 17260699.074 .000 .995 .997 18525397.362 .000 .997 .998 26355921.114 .000 .998

80-89 .995 13885877.873 .000 .995 .997 16268767.293 .000 .997 .998 15549506.983 .000 .998

90+ 1 1 1

Gender Male 1 1 1

female .034 .575 .344 .959 .035 .517 .280 .955 .030 .352 .137 .902

Marital

status

.212 .198

Single .121 .350 .092 1.321 .313 .377 .137 .179 .019 1.730

Married .054 .497 .244 1.012 .227 .477 .077 1.838 .019 .261 .085 .802

divorced .764 1.269 .269 5.995 .067 1.092 .216 1.053 .998 .000 .000 .

Separated .148 .467 .166 1.310 .935 .337 .133 8.982 .103 .236 .042 1.339

Widowed 1 1 .088 1.285 1

1

Qualification Yes .825 .925 .463 1.848 .364 1.696 .542 5.299

No 1 1

Social

participation

Yes .876 .950 .496 1.818 .493 1.408 .529 3.751

No 1 1

NS-SEC

classification

.702

NS-SEC

classification

Managerial

and

professional

.344 .344 .998 9252516.773 .000

intermediate .997 12113535.150 .000 .997 .998 9612887.753 .000

Small

employers/

own account

worker

.997 7432876.696 .000 .997 .998 5879112.393 .000

Lower

supervisory

and technical

.997 10450822.410 .000 .997 .998 16480276.115 .000

Semi-routine .997 15616424.208 .000 .997 .998 5147549.736 .000

Other 1 1

Smoke Yes 1

No .645 1.354 .372 4.926

66

Drink

alcohol

yes 1

No .199 1.810 .733 4.469

Exercise? Yes 1

No .147 1.872 .802 4.368

4.4.2.2.6 Congestive heart failure

For congestive heart failure, all the variables are not significant.

Table 42: multivariate logistic regression model for congestive heart failure


95%CI 95%CI 95%CI

p

value

OR lower higher p

value

OR lower higher p

value

OR lower higher

Age .046 .024 .639

50-59 .114 .101 .006 1.741 .997 898507.013 .000 . 1.000 1.086 .000

60-69 .478 .457 .052 3.977 .997 2436827.068 .000 . .998 5043406.613 .000

70-79 .841 1.238 .155 9.912 .997 9519987.694 .000 . .998 11187916.829 .000

80-89 .906 1.137 .134 9.635 .997 7500585.339 .000 . .998 3610558.676 .000

90+ 1 1 1

Gender Male 1 1 1

female .062 .489 .230 1.038 .129 .527 .231 1.204 .102 .358 .105 1.224


Single .936 1.069 .208 5.495 .545 1.729 .293 10.186 .774 .705 .065 7.649

Married .517 .726 .276 1.912 .939 .955 .295 3.096 .234 .418 .099 1.757

divorced .305 3.096 .358 26.790 .134 5.692 .587 55.225 .998 .000 .000 .

Separated .856 .877 .212 3.626 .729 1.317 .277 6.253 .758 .740 .109 5.022

Widowed 1 1 1

Qualification Yes .639 1.245 .499 3.106 .568 .715 .226 2.262

No 1 1

Social

participation

Yes .393 .694 .300 1.606 .772 1.200 .350 4.116

No 1 1

NS-SEC

classification

.172 .291

NS-SEC

classification

Managerial and

professional

.997 5736578.091 .000 . .998 4108901.366 .000

intermediate .997 5155764.930 .000 . .998 7747160.832 .000

Small employers/

own account

.997 10882796.202 .000 . .998 15254108.251 .000

67

worker

Lower

supervisory and

technical

.997 3922525.045 .000 . 1.000 .953 .000

Semi-routine .998 1258491.632 .000 . .998 2454791.445 .000

Other 1 1

Smoke Yes 1

No .267 .462 .119 1.803

Drink alcohol yes 1

No .725 1.244 .368 4.211

Exercise? Yes 1

No .942 .959 .312 2.946

4.4.2.2.7 diabetes

In model 1, gender, marital status and age are all significant. Single people have the lowest risk of diabetes

(0.614), relative to divorced people (risk=1). In addition, being married and divorced also have lower risks.

People who aged 70 to 79 and 80-89 are 2 times more likely to have diabetes than people aged 90 and over.

The odds ratio for female ones is 0.635, indicating that male ones have higher risk. In model 2, social

participation and qualification turned out to have reduced risk (OR=0.746, OR=0.691). Model 3 indicates that

marital status, age, exercising and drinking are the risk factors for diabetes. Divorced people have the lowest

risk of having diabetes, with odds ratio of 0.574, followed by married people (OR=0.680). Not exercising

increase the chance of diabetes to 171.8%. However, not drinking also increase the risk to 194.8%.

Table 43: multivariate logistic regression model for diabetes


95%CI 95%CI 95%CI


Age p<0.001 p<0.001 .002

50-59 .688 .885 .487 1.607 .460 1.393 .578 3.354 .823 1.155 .327 4.082

60-69 .163 1.502 .848 2.663 .176 1.803 .767 4.241 .165 2.361 .702 7.936

70-79 .010 2.116 1.200 3.730 .054 2.305 .984 5.399 .109 2.689 .803 9.002

80-89 .009 2.157 1.213 3.834 .027 2.630 1.114 6.210 .217 2.168 .634 7.414

90+ 1 1 1

Gender Male 1 1 1

female .p<0.001 .635 .549 .734 p<0.001 .574 .486 .677 p<0.001 .486 .381 .620


Single .008 .614 .429 .879 .116 .723 .482 1.083 .568 .853 .494 1.472

68

Married .p<0.001 .641 .525 .783 .004 .711 .563 .896 .008 .636 .455 .891

divorced .153 .581 .276 1.223 .284 .623 .262 1.480 .123 .204 .027 1.539

Separated .019 .715 .540 .946 .014 .657 .471 .917 .004 .483 .296 .788

Widowed 1 1 1

Qualification Yes p<0.001 .679 .568 .812 .662 .943 .725 1.226

No 1 1

Social

participation

Yes .002 .760 .639 .903 .607 1.069 .830 1.376

No 1 1

NS-SEC

classification

.172 .226

NS-SEC

classification

Managerial and

professional

.385 .701 1.271 .375 4.312

intermediate .824 1.103 .465 2.615 .851 1.127 .325 3.911

Small

employers/ own

account worker

.832 1.100 .457 2.647 .861 1.118 .321 3.898

Lower

supervisory and

technical

.966 1.020 .421 2.470 .489 1.555 .445 5.428

Semi-routine .445 1.412 .583 3.421 .941 .955 .280 3.260

Other 1 1

Smoke Yes 1

No .593 1.097 .781 1.539

Drink alcohol yes 1

No p<0.001 1.948 1.523 2.491

Exercise? Yes 1

No p<0.001 1.718 1.369 2.156

4.4.2.2.8 Heart Attack

In model 1, single people have the lowest risk of heart attack(OR=0.400), while the figure for married people

is 0.632, relative to widowed people. Model 3 indicated that gender, exercising are the two risk factors for

heart attack. Females were much less likely to have heart attack(OR=0.273) than males. Not exercising

increase the chance of diabetes to 163.9%.

69

Table 44: multivariate logistic regression model for heart attack


95%CI 95%CI 95%CI


Age p<0.001 p<0.001 p<0.001

50-59 p<0.001 .149 .073 .304 p<0.001 .143 .055 .367 .344 .462 .093 2.283

60-69 .004 .417 .231 .754 .013 .364 .164 .806 .905 .914 .207 4.035

70-79 .287 .731 .411 1.302 .396 .714 .328 1.554 .495 1.670 .383 7.278

80-89 .095 1.626 .919 2.878 .403 1.395 .640 3.038 .236 2.444 .557 10.721

90+ 1 1 1

Gender Male 1 1 1

female p<0.001 .338 .270 .423 p<0.001 .322 .248 .418 p<0.001 .273 .188 .399

Marital status p<0.001 .030 .226

Single .004 .400 .213 .749 .028 .441 .212 .916 .075 .370 .124 1.107

Married .001 .632 .480 .833 .048 .720 .520 .997 .428 .828 .519 1.322

divorced .534 1.320 .550 3.170 .303 1.679 .626 4.502 .776 .741 .094 5.835

Separated .994 .999 .678 1.470 .924 .978 .616 1.553 .541 1.215 .651 2.269

Widowed 1 1 1

Qualification Yes .043 .765 .590 .991 .201 .796 .561 1.129

No 1 1

Social

participation

Yes p<0.001 .631 .491 .809 .178 .793 .566 1.111

No 1 1

NS-SEC

classification

.231 .077

NS-SEC

classification

Managerial and

professional

.656 .783 .267 2.297 .766 .794 .175 3.612

intermediate .837 .892 .298 2.668 .781 1.243 .269 5.748

Small

employers/ own

account worker

.730 .823 .271 2.494 .997 1.003 .214 4.695

Lower

supervisory and

technical

.939 1.044 .345 3.159 .659 1.415 .303 6.604

Semi-routine .408 .633 .214 1.869 .680 .726 .159 3.320

Other 1 1

Smoke Yes 1

No .349 1.279 .764 2.142

Drink alcohol yes 1

No .823 1.043 .719 1.514

Exercise? Yes 1

No .002 1.639 1.199 2.239

70

4.4.2.2.9 Heart Murmur

In model 1, marital status is not significant. The odds ratio for women is 1.308 which means females are more

likely have heart murmur condition. Also, people who aged 50-59 and 60-69 have reduced risk of heart

murmur (OR=0.393, OR=0.422). In model 3, only age remained to be significant. People who aged 50 to 79

years old are significantly less to have heart murmur.

Table 45: multivariate logistic regression model for heart murmur


95%CI 95%CI 95%CI

p

value

OR lower higher p value OR lower higher p

value

OR lower higher

Age .005 .004 .002

50-59 .009 .393 .196 .789 .002 .276 .122 .624 .002 .174 .058 .524

60-69 .010 .422 .218 .817 p<0.001 .251 .117 .542 .001 .173 .064 .468

70-79 .110 .590 .309 1.126 .004 .331 .156 .702 .002 .217 .082 .577

80-89 .372 .741 .383 1.431 .041 .447 .207 .968 .094 .431 .161 1.154

90+ 1 1 1

Gender Male 1 1 1

female .039 1.308 1.013 1.689 .019 1.410 1.059 1.876 .240 1.279 .848 1.929


Single .680 .884 .491 1.590 .236 .650 .318 1.325 .809 .895 .363 2.204

Married .252 .819 .581 1.153 .205 .785 .539 1.141 .213 .710 .415 1.217

divorced .706 .795 .242 2.614 .596 .676 .159 2.874 .734 .700 .090 5.466

Separated .509 .851 .526 1.375 .398 .794 .465 1.356 .385 .709 .326 1.540

Widowed 1 1 1


No 1 1

Social participation Yes p<0.001 .631 .491 .809 .440 1.195 .761 1.878

No 1 1

NS-SEC

classification

.788 .587

NS-SEC

classification

Managerial and professional .458 2.129 .290 15.632 .955 .943 .123 7.244

intermediate .486 2.045 .273 15.334 .886 .858 .106 6.921

Small employers/ own account

worker

.439 2.218 .295 16.700 .948 .932 .115 7.575

Lower supervisory and technical .454 2.171 .285 16.530 .851 1.223 .151 9.935

Semi-routine .353 2.573 .350 18.897 .759 1.375 .180 10.487

Other 1 1

Smoke Yes 1

No .457 .808 .461 1.417

Drink alcohol yes 1

No .057 1.508 .988 2.303

Exercise? Yes 1

No .796 1.054 .709 1.566

71

4.4.2.3 Long-standing illness

In model 1, age is not significant. Married people 71.1% of chances to have a long-standing illness, relative to

widowed people (risk=1). However, all the other categories are no longer significant. In model 2, after taking

qualification and social participation into account, the 50 to 59 age group appeared to be significant, people at

that age group have the 0.473 risk of having a long-standing illness compared with people who aged 90 years

old. Married people have 77.9% chance of having a long-standing illness, relative to those who were widowed.

People with qualifications and social have reduced risks of long-standing illness, with the odds ratios of 0.719

and 0.800 respectively. Model 3 indicates that marital status, qualification, drinking and exercising are risk

factors for long-standing illness. The odds ratio for married people is 0.730 which means married people are

73.0% less likely to have a long-standing illness. Qualification and exercising made people are less likely to

have long-standing illness. Interestingly, people who drank have the reduced risk of long-standing illness

(OR=1.434).

Table 48: multivariate logistic regression model for long-standing illness


95%CI 95%CI 95%CI


Age p<0.001 p<0.001 p<0.001

50-59 p<0.001 .292 .206 .414 .002 .473 .295 .760 .385 .752 .395 1.430

60-69 p<0.001 .425 .302 .600 .077 .659 .415 1.047 .520 1.228 .657 2.295

70-79 .006 .617 .438 .870 .646 .897 .565 1.425 .113 1.659 .888 3.100

80-89 .230 .805 .565 1.147 .589 1.140 .709 1.834 .051 1.895 .996 3.607

90+ 1 1 1

Gender Male 1 1 1

female .604 .978 .898 1.064 .744 1.017 .921 1.122 .940 .995 .867 1.141

Marital status p<0.001 .000 .011

Single .212 .875 .710 1.079 .769 .964 .752 1.234 .336 .843 .595 1.194

Married p<0.001 .711 .620 .815 .002 .779 .667 .911 .006 .730 .584 .912

divorced .885 .972 .662 1.428 .118 .685 .426 1.101 .184 .629 .318 1.246

Separated .472 1.067 .894 1.275 .506 1.072 .873 1.317 .750 .954 .716 1.272

Widowed 1 1 1

Qualification Yes p<0.001 .719 .640 .808 .069 .861 .733 1.012

No 1 1

Social

participation

p<0.001 .800 .718 .892 .506 .950 .818 1.104

72

NS-SEC

classification

.040 .686

NS-SEC

classification

Managerial and

professional

.987 .761 1.121 .537 2.336

intermediate .622 .879 .526 1.469 .670 1.177 .557 2.484

Small

employers/ own

account worker

.750 .918 .544 1.550 .611 1.215 .574 2.572

Lower

supervisory and

technical

.715 .907 .536 1.534 .905 .955 .447 2.038

Semi-routine .716 .906 .532 1.542 .749 1.127 .540 2.354

Other 1 1

total

Smoke Yes 1

No .111 .853 .702 1.037

Drink alcohol yes 1

No p<0.001 1.486 1.267 1.742

Exercise? Yes 1

No p<0.001 1.655 1.442 1.898

4.4.2.4 ADL and IADL difficulties

In model 1, single and married individuals are advantageous in terms of ADL and IADL, with 142% and

1.539 chances of not having IADL and IADL difficulties. Female ones were less likely to have difficulties in

IADL and IADL. It can be seen that younger ones are more likely to do ADLs and IADLs without any

difficulties. After adding qualification and social participation in model 2, marital status remained significant.

Single and married people have about 1.423 times more likely to do ADLs and IADLs without difficulties.

Also, qualification and social participation help people to gain advantages in terms of ADLs and IADLs.

Model 3 indicates that marital status, age, qualification, smoking, drinking and exercising are risk factors for

ADL and IADL. People who are married have 1.377 chance of not having difficulties in ADLs and IADLs.

Not smoking people are 1.284 times more likely to conduct ADLs and IADLs without difficulties. Not

drinking and not exercising make people less likely to have ADL and IADLs problems(OR=0.703,

OR=0.526).

73

Table 46: multivariate logistic regression model for ADL and IADLs difficulties


95%CI 95%CI 95%CI

p

value

OR lower higher p value OR lower higher p

value

OR lower higher

Age .005 .004 .002

50-59 .009 .393 .196 .789 .002 .276 .122 .624 .002 .174 .058 .524

60-69 .010 .422 .218 .817 p<0.001 .251 .117 .542 .001 .173 .064 .468

70-79 .110 .590 .309 1.126 .004 .331 .156 .702 .002 .217 .082 .577

80-89 .372 .741 .383 1.431 .041 .447 .207 .968 .094 .431 .161 1.154

90+ 1 1 1

Gender Male 1 1 1

female .039 1.308 1.013 1.689 .019 1.410 1.059 1.876 .240 1.279 .848 1.929


Single .680 .884 .491 1.590 .236 .650 .318 1.325 .809 .895 .363 2.204

Married .252 .819 .581 1.153 .205 .785 .539 1.141 .213 .710 .415 1.217

divorced .706 .795 .242 2.614 .596 .676 .159 2.874 .734 .700 .090 5.466

Separated .509 .851 .526 1.375 .398 .794 .465 1.356 .385 .709 .326 1.540

Widowed 1 1 1


No 1 1

Social

participation

Yes p<0.001 .631 .491 .809 .440 1.195 .761 1.878

No 1 1

NS-SEC

classification

.788 .587

NS-SEC

classification

Managerial and

professional

.458 2.129 .290 15.632 .955 .943 .123 7.244

intermediate .486 2.045 .273 15.334 .886 .858 .106 6.921

Small employers/

own account

worker

.439 2.218 .295 16.700 .948 .932 .115 7.575

Lower supervisory

and technical

.454 2.171 .285 16.530 .851 1.223 .151 9.935

Semi-routine .353 2.573 .350 18.897 .759 1.375 .180 10.487

Other 1 1

Smoke Yes 1

No .457 .808 .461 1.417

Drink alcohol yes 1

No .057 1.508 .988 2.303

Exercise? Yes 1

No .796 1.054 .709 1.566

74

4.4.2.5 Mobility difficulties

In model 1, married people have 140% chance of not having difficulties in mobility. The odds ratio for the

female is 0.619 which means female ones are less likely have mobility problem than their male counterparts.

As one gets older, the chance of not having difficulties in mobility decreases. In model 2, marital status

remained significant. The odd's ratio for married people is 1.228. Model 3 indicates that gender, age,

qualification, social participation, exercising and drinking are risk factors for mobility. Female ones have

59.9% chance of not having difficulties in mobility. People who do not exercise or not drinking have 55.0%

and 69.1% chance of not having mobility problems. Qualification and social participation have increased the

chance of not having mobility problems, with odds ratios of 1.423 and 1.326.

Table 47: multivariate logistic regression model for mobility problems


95%CI 95%CI 95%CI


Age p<0.001 p<0.001 p<0.001

50-59 p<0.001 13.135 8.111 21.272 p<0.001 10.303 5.327 19.927 p<0.001 5.462 2.498 11.942

60-69 p<0.001 8.534 5.298 13.747 p<0.001 7.085 3.693 13.594 .001 3.724 1.730 8.017

70-79 p<0.001 4.825 2.996 7.771 p<0.001 4.260 2.221 8.172 .048 2.169 1.007 4.670

80-89 .002 2.128 1.304 3.471 .053 1.927 .991 3.748 .603 1.232 .561 2.709

90+ 1 1 1

Gender Male 1 1 1

female p<0.001 .619 .567 .676 p<0.001 .589 .532 .652 p<0.001 .599 .521 .689

Marital

status

p<0.001 .005 .656

Single .272 1.129 .909 1.403 .601 1.071 .829 1.384 .307 1.205 .842 1.725

Married p<0.001 1.399 1.214 1.613 .010 1.240 1.054 1.460 .161 1.179 .936 1.484

divorced .851 1.038 .703 1.533 .923 1.024 .629 1.669 .728 1.132 .562 2.282

Separated .661 .960 .799 1.153 .803 .973 .787 1.204 .597 1.082 .807 1.451

Widowed 1 1 1

Qualification Yes p<0.001 1.569 1.391 1.769 p<0.001 1.423 1.206 1.680

No 1 1

Social

participation

Yes p<0.001 1.566 1.400 1.753 p<0.001 1.326 1.137 1.547

75

No 1 1

NS-SEC

classification

.040 .409

NS-SEC

classification

Managerial

and

professional

.267 .056 .472 .218 1.018

intermediate .045 .576 .336 .988 .072 .488 .223 1.066

Small

employers/

own account

worker

.112 .641 .370 1.110 .105 .522 .238 1.145

Lower

supervisory

and technical

.088 .619 .356 1.074 .135 .546 .247 1.207

Semi-routine .136 .654 .375 1.143 .084 .507 .235 1.096

Other 1 1

Smoke Yes 1

No .936 1.008 .827 1.230

Drink

alcohol

yes 1

No p<0.001 .691 .588 .813

Exercise? Yes 1

No p<0.001 .550 .478 .633

4.4.2.6 Depression

In model 1, marital status remained significant after adding age and gender. People who were married are two

times more likely not to be depressed, compared with widowed ones. In model 2, married people still have the

highest chance of not being depressed (OR=1.996). People who aged 60 to 69 have 175.5% chance of not

being depressed. Female ones are less likely to avoid depression(OR=0.647). Qualification and social

participation make people more likely not be depressed, with odds ratios of 1.602 and 1.577 respectively.

Model 3 indicated that marital status, gender, qualification, smoking, drinking were risk factors for depression.

Married people are almost 2 times more likely not to be depressed. Odds ratios for female ones, qualification,

not smoking and not drinking are 0.616, 1.428, 1.274 and 0.616.

Table 49: multivariate logistic regression model for depression


95%CI 95%CI 95%CI


76

Age p<0.001 p<0.001 .017

50-59 .012 1.608 1.111 2.328 .140 1.441 .886 2.344 .538 1.249 .616 2.534

60-69 p<0.001 1.966 1.368 2.824 .020 1.755 1.094 2.814 .259 1.484 .748 2.943

70-79 .020 1.533 1.070 2.196 .174 1.384 .866 2.213 .706 1.140 .577 2.251

80-89 .315 1.207 .836 1.744 .725 1.090 .675 1.760 .900 .957 .477 1.919

90+ 1 1 1

Gender Male 1 1 1

female p<0.001 .636 .573 .705 p<0.001 .647 .573 .731 p<0.001 .656 .555 .776

Marital status p<0.001 p<0.001 p<0.001

Single .002 1.453 1.152 1.832 .001 1.595 1.199 2.123 .131 1.356 .913 2.014

Married p<0.001 2.065 1.788 2.384 .000 2.007 1.702 2.368 p<0.001 1.964 1.544 2.498

divorced .387 .836 .557 1.255 .415 .813 .495 1.337 .995 1.002 .476 2.108

Separated .416 1.080 .898 1.299 .223 1.145 .921 1.422 .326 1.167 .858 1.588

Widowed 1 1 1

Qualification Yes p<0.001 1.602 1.408 1.823 p<0.001 1.461 1.217 1.755

No 1 1

Social

participation

Yes p<0.001 1.577 1.393 1.785 .074 1.175 .984 1.403

No 1 1

NS-SEC

classification

.040 .911

NS-SEC

classification

Managerial and

professional

.980 .510 1.309 .587 2.921

intermediate .448 1.250 .703 2.222 .683 1.185 .523 2.686

Small

employers/ own

account worker

.446 1.257 .699 2.262 .650 1.210 .531 2.754

Lower

supervisory and

technical

.526 1.210 .670 2.185 .756 1.141 .497 2.621

Semi-routine .535 1.208 .665 2.197 .639 1.212 .543 2.707

Other 1 1

Smoke Yes 1

No .032 1.274 1.021 1.590

Drink alcohol yes 1

No p<0.001 .616 .517 .736

Exercise? Yes 1

No p<0.001 .646 .549 .760

4.4.3 Longitudinal analysis

77

It can be seen that marital status is becoming significant in more health outcomes as time goes by. In group 2

(wave 1 data), marital status only remained significant (p<0.05) for depression, heart attack, angina, high

blood pressure. While, marital status stayed significant in most cases in group 3, except for abnormal heart

rhythm, angina, congestive heart failure and heart murmur.

Also, being married are becoming important in keeping good health. In group 2, married people have the

lowest risks of high blood pressure(OR=0.676). While, single ones have the lowest risk in more health

outcomes, including heart attack(OR=0.338), angina(OR=0.410) and not being depressed(OR=2.454). In

group 3, married people are the least likely group to have poor self-reported health(OR=0.564),

stroke(OR=0.414), diabetes(OR=0.726) and long-standing illness (OR=0.600). They have increased chance of

not being depressed (OR= 2.662).

There are also noticeable changes in the value of odds ratio. Marital status is significant for high blood

pressure in both groups. Married people had 66.1% of chance to have high blood pressure in group 2. While

the figure for group 3 is 70.1%. In terms of not being depressed which being married have the highest

chances in both groups, the advantage of being married was getting significant as the odds ratios is 2.454 in

group 2 and 2.662 in group 3. But there is also similarity among these two group: widowed people have the

worst health outcomes means that the disadvantage of being widowed had not changed during time.

Table 50: longitudinal analysis

Group 2 Group 3

p-value OR 95% C.I.for OR p-value OR 95% C.I.for OR

Poor self-

reported

health

Lower Upper Lower Upper

Marital

status

.084 p<0.001

Single .161 2.100 .744 5.926 .184 .691 .400 1.193

Married .225 1.580 .755 3.309 p<0.001 .564 .439 .724

Separated .232 2.654 .535 13.155 .095 1.964 .888 4.340

Divorced .013 2.900 1.246 6.749 .520 .888 .618 1.276

widowed 1 1

Long-

standing

illness

Marital

status

.221 p<0.001

Single .314 1.246 .812 1.911 .001 .619 .461 .833

Married .290 .880 .695 1.115 p<0.001 .600 .519 .694

Separated .313 1.570 .654 3.769 .353 .747 .404 1.383

78

Divorced .869 .974 .709 1.337 .301 .888 .710 1.112

widowed 1 1

Angina

Marital

status

.002 .863

Single .008 .410 .212 .793 .995 .000 .000 .

Married p<0.001 .571 .424 .767 .332 .666 .294 1.512

Separated .198 .457 .139 1.506 .998 .000 .000 .

Divorced .006 .536 .342 .840 .375 .499 .107 2.319

widowed 1 1

Abnormal

heart rhythm

Marital

status

.800 .292

Single .359 .713 .346 1.468 .517 .842 .500 1.417

Married .410 .853 .585 1.245 .029 .765 .602 .974

Separated .456 .576 .135 2.460 .878 .921 .324 2.622

Divorced .875 .960 .576 1.599 .199 .777 .528 1.142

widowed 1 1

Diabetes

Marital

status

.803 .042

Single .281 .646 .292 1.430 .106 .672 .416 1.088

Married .803 .951 .638 1.416 .002 .726 .590 .894

Separated .572 .657 .153 2.823 .637 .797 .310 2.050

Divorced .591 .858 .492 1.498 .330 .853 .619 1.175

widowed 1 1

High blood

pressure

Marital

status

p<0.001 p<0.001

Single .016 .676 .492 .929 .047 .741 .552 .996

Married p<0.001 .661 .552 .790 p<0.001 .701 .610 .805

Separated .116 .628 .351 1.121 .013 .428 .219 .838

Divorced .002 .679 .531 .870 .032 .792 .639 .980

widowed 1 1

Congestive

heart failure

Marital

status

.602 .433

Single .664 .615 .068 5.530 .633 1.469 .303 7.120

Married .101 .372 .114 1.213 .354 .643 .252 1.637

Separated .998 .000 .000 . .232 3.640 .438 30.226

Divorced .507 .562 .103 3.082 .961 .967 .249 3.754

widowed 1 1

Murmur Marital

status

.172 .138

Single .802 .910 .433 1.910 .123 .481 .190 1.219

Married .171 .742 .485 1.137 .035 .691 .490 .975

Separated .321 .361 .048 2.703 .525 1.478 .443 4.926

Divorced .551 1.179 .686 2.029 .192 .684 .387 1.210

79

widowed 1 1

Stroke

Marital

status

.109 .002

Single .034 .114 .015 .850 .028 .414 .189 .907

Married .037 .593 .363 .969 p<0.001 .570 .429 .758

Separated .463 .469 .062 3.549 .998 .000 .000 .

Divorced .301 .690 .342 1.393 .039 .607 .378 .976

widowed 1 1

Depression Marital

status p<0.001 p<0.001

Single p<0.001 2.363 1.666 3.350 p<0.001 2.325 1.647 3.284

Married p<0.001 2.454 2.040 2.951 p<0.001 2.662 2.283 3.105

Separated .306 1.344 .763 2.368 .713 1.127 .597 2.126

Divorced .002 1.475 1.150 1.892 .002 1.426 1.137 1.789

widowed 1 1

Chapter 5: Discussion

5.1 Introduction

In this chapter, the results from Chi-square test, bivariate logistic regression model and multivariate logistic

regression models will be discussed. This chapter will be divided into two sections: cross-sectional analysis

and longitudinal analysis.

5.2 Cross-sectional analysis

5.2.1 marital status and health outcomes

In general, being married does benefit older people’s health in some cases, which is supported by Goldman,

Korenman and Weistein (1995).

Previous studies indicated that married people are happier and healthier than unmarried ones (Waite &

Gallagher, 2002) which is in consistent with the Chi-square test results in this study. In the Chi-square test

section, married people have the smallest proportion in poor self-reported health, long-standing illness, and

depression.

80

However, for self-reported health, this may be because married people tend to overestimate their health.

Zheng and Thomas (2013) pointed out several possible reasons for this over estimation. The social

relationship embedded in marriage makes people feel that they can always get help when they have health

problems. Also, married people are regarded as a type of “advantaged group” which have higher thresholds for

poor health. In addition, married people tend to engage in better health behaviours which makes them feel

optimistic towards the condition of their health.

Marital status is significantly associated with less depressive symptoms and being married makes people less

likely to report depression which is in consistent with the study of Luppa et al. (2012).

Also, the percentages of ADL, IADL and mobility difficulties are the lowest among married people in Chi-

square test. The results from the bivariate logistic regression model suggest that the married group is the least

likely group to have ADL and IADL difficulties. Even in multivariate logistic regression, the results are

similar.

There are many findings that suggest that married people tend to receive more help than unmarried people so

they can recover from the ADL and IADL problems (Neuman & Werner, 2016). However, this study explored

this in a different direction. The results of the bivariate and multivariate logistic regression models indicate

that being married reduce the risk of function impairment which means being married would reduce the

incidences. According to Bratti and Mendola (2014), ADL is less likely to be subjective than other health

outcome variables which means that the impacts of marriage on ADL or IADL are objective to some extent.

In Chi-square test, marital status is associated with high blood pressure, high cholesterol, strokes, abnormal

heart rhythm, diabetes, heart attacks and heart murmurs. In the bivariate logistic regression models, being

married only have the lowest risk of heart murmur (OR=0.578) which is not consistent with the current study.

According to Capistrant et al. (2012), caregiving from a spouse can significantly predicted cardiovascular

incidence which means that at least married people should have lower risks than unmarried people. However,

variables that relate to caregiving were not included in this study, future studies are required.

The associations between other marital status and health outcomes

The results of the bivariate logistic regression models are in consistent with that Chi-square test. Married are

least likely to have mobility, ADL and IADLs difficulties, long-standing illness, depression and poor self-

reported health, relative to widowed people. Compared with other marital status groups, widowed people

have the worst physical and mental health outcomes in Chi-square test and logistic regression which is in

consistent with some previous research. For example, Manzoli et al. (2007) suggested that widowhood is

81

linked with an increase of in the mortality of the elderly. Schoenborn (2004) indicates that widowhood

increase the risk of functional impairment.

Stroebe, Schut and Stroebe (2007) pointed out that bereavement makes people more likely to suffer from

physical and mental health problems. Their findings are based on recent widowhood. The reduction of social

participation after bereavement is the main reason for health deterioration (Bennett,1998). However, the

duration of widowhood is not covered in this study.

But for separated or single people, this is not always the case. The results from this study is in agreement with

the study of Robards et al. (2012) which indicates that there are variations existing in the unmarried group.

In Chi-square test, single people reported the smallest proportion of heart attacks. In the bivariate logistic

regression model, separated people are the least likely group to have high blood pressure, high cholesterol,

stroke, abnormal heart rhythm and diabetes. After including age, gender, social participation, qualifications,

social-economic class and health behaviours, separated people still have the lowest risk of all the diseases that

mentioned above. Few study specifically focus on the health of separated people.

Previous studies had pointed out several possible reasons. One of them involves the equalities in sociocultural

factors (Artazcoz et al.,2011). However, the exception of separated and single people may be merely based on

the statistical results which are not meaningful. Further research is needed in this area.

5.2.2 The association between other variables and health outcomes

Age

Interestingly, people aged 60 to 69 years old have the lowest risk of depression. According to Jorm et al.

(2005), incidences of depression decreases in the 40-44 to 60-64 age groups. They also pointed out that bad

social relationships from work may be the reason for it. According to the retirement age in the UK, people

who were aged between 50 to 59 years old may still be working. While, people aged 60 to 69 years old just

begin their retirement life.

In the case of ADL/IADL and mobility, the risks increase as people get older which is in consistent with the

finding of Webber, Porter and Menec (2010).

Gender

In Chi-squared test and the bivariate logistic regression model, females are more likely to have physical

function problems. Millan-Calenti et al. (2010) indicates that the female gender is a strong predicator of

82

disability that leads to dependence. Also, Choi and Wodarski (1996) point out that female ones are more

likely to have ADLs and IADLs difficulties.

Except for heart murmur, females are less likely to have cardiovascular diseases which is in consistent with

females performing better in terms of subjective assessment of health (Crimmins, Kim & Solé-Auró, 2010).

Qualification

The results from Chi-square test, logistic regression models suggest that older people with qualifications have

better health outcomes than people who did not which is in agreement with Kye et al. (2014)’s findings. In the

multivariate logistic regression model, the odds ratios for qualifications decrease when all the variables had

been included which means that the degree that qualifications can predict health outcomes decreased.

Specifically, qualification is no longer significant for long-standing illnesses, heart attacks, diabetes and high

cholesterol. And not significant for heart murmurs, congestive heart murmur, angina, abnormal heart rhythm

and stroke.

Social participation

In the multivariate logistic regression models, social participation only remained significant in self-reported

general health, ADL, IADL difficulties and mobility difficulties when all the variables are taken into account.

This conclusion, however, is in consistent with Cornwell and Waite (2009)’s finding which suggested that the

social support does make people feel optimistic about their health. There are studies that suggest that social

participation is linked to mobility problems. Rosso et al. (2013) point out that low levels of any forms of

social participation is associated with low mobility.

NS-SEC classification

In Chi-squared test and bivariate logistic regression models, NS-SEC classification is not significant. In the

multivariate logistic regression models, NS-SEC is significant in high cholesterol (p=0.006) in model 3.

People who were in the managerial and professional class have the lowest risk of having high cholesterol

(OR=0.407). While people of lower class have higher risk, with odds ratios for small employers and semi-

routine at 0.448 and 0.482 respectively. However, the results are inconsistent with the finding of Chandola

and Jenkinson (2000) suggesting that the NS-SEC shows differences in health.

Health behaviours

83

Smoking is independently associated with most cardiovascular diseases mentioned in this study which is in

agreement with Chen and Boreham (2002)’s finding that indicates smoking is a strong risk factor for

cardiovascular diseases. However, the most mentioned cardiovascular diseases that is linked to smoking in

many studies is atherosclerosis (Messner & Bernhard,2014) which was not covered in this study.

Exercising is the most likely risk factors for health outcomes. Although in this study the variable is based on

the question that “whether walked or exercised yesterday”, the results indicated that doing exercise or not does

have an impact on health which is in consistent with the finding of Pendo and Dahn (2005). Hamer,

Stamatakis and Steptoe (2009) suggest that the physical activity is associated with a low risk of psychological

distress which is also reflected in the results of this study.

The results of drinking were interesting. Except for mobility problems, people who drank during the last

week had better health outcomes, especially for cardiovascular diseases. However, previous study suggests

that drinking or even high level of alcohol consumption have a cardioprotective effect (Britton & McKee,

2000) which may be the reason for that.

5.3 Longitudinal analysis

Longitudinal analysis shows that marital status is becoming more and more associated with health across time

which is in agreement with Waite and Lehrer (2003)’s findings which suggest that marriage have far-reaching

positive effects on people’s health.

In addition, being married is becoming more and more important for health from a longitudinal perspective.

As a risk factor for health, marriage’s impact on health changes in accordance with the changing odds ratios.

For example, with depression, in group 2 the odds ratio is 2.454, while in group 3 it is 2.662 which is

consistent with the study of Wilcox et al. (2003). Their study suggested that women who remain married show

stability in terms of mental health. However, the odds ratio for high blood pressure is 0.661 in group 2 and

0.701 in group 3 which means that marriage did not continue to be advantageous. This may be due to the fact

that high blood pressure is more of an age-related disease (Odden et al.,2012).

The results from the Chi-square test and bivariate logistic regression suggested that older people are more

likely to have high blood pressure which may partly explain the difference. Also, there were about 500 of

individuals who used to be married in group 2 and became widowed in group 3.

http://cn.bing.com/dict/search?q=atherosclerosis&FORM=BDVSP6&mkt=zh-cn

84

However, there are also studies that indicate that the cohort change does not interfere with the study results.

On the contrary, the change of health which is imposed by the change of marital status distribution actually

indicates the importance of marital status on health. Iwashyna and Christakis (2003) suggest that the impact of

marital status on health is becoming important because more people are becoming widowed over time.

Chapter 6: Conclusion

The main aim of this study is to examine the association between marital status and health outcomes. The data

used is from The English Longitudinal Study of Ageing (ELSA) wave 1 and wave 7. There are three groups of

data. Group 1 data is used in cross-sectional analysis in which the association between marital status and

health outcomes are investigated in Chi-square tests and Logistic regression. The results show that being

married are strong risk factors for long-standing illness, mobility, ADL and IADL difficulties, and depression.

The longitudinal analysis successfully how the associations between marital status and health change over

time. In group 2, the health variables associated with marital status is much less than that of group 3 which

means that marital status becomes significant. More importantly, the positive effect of marriage had been

reflected. In group 2, singles were more advantageous than married ones, while that is not the case in group 3.

However, among these health variables, high blood pressure and depression are two variables that stayed

associated with marital status. In which married people performed the best. This suggests that unmarried

people should pay attention to their blood pressure and mental health.

In addition, social participation and exercise are also strong risk factors for health. In most cases, people who

had a certain degree of social participation and exercise were better in health condition than people that did

not.

85

There are many limitations in this study. Some of them are due to the characteristics of the ELSA data set

itself. Since the ELSA interviews had been changed over time, there are inconsistencies that exist between

wave 1 and wave 7 which make some variables appear differently in group 2 and group 3. For example, there

were many people with a confirmed angina diagnosis in wav1 and marital status and other variables were

associated with it. While, in group 7, there were only a few people with reported angina and the there is no

associations between it and other independent variables. Also, in some variables, missing data accounted for a

large proportion. Apart from the limitations that embedded in the ELSA data set, this study itself also has

limitations.

In ELSA, marital status has categories that described the times of getting married. Considering the time

limitation and scope limitation of this study, some details had been neglected.

Except for marital status, the choice of other variables also had limitations. Some of the more meaningful

variables had too many missing values which may have impacted on the results.

Because of the multidimensional definition of health, there are several health outcome variables. Among these

dependent variables, some variables are associated which may interfere with the final results of this study. For

example, study shows that the existence of chronic physical diseases increases the risk of depression

(Moussavi et al.,2007).

Also, the independent variables in this study need to be expanded. Lum and Lighfoot (2005) suggests that

volunteering activities significantly improved self-reported health, mental health and physical function. The

ELSA data set includes a list of variables that describe the volunteering activities in more detail. While, this

study chose a general social participation variable due to the time constraints.

Further study could be carried out to analyses why separated people have better outcomes in terms of

cardiovascular diseases. Considering the NS-SEC classification is not significant for most health outcomes,

study that focus on the association between NS-SEC classification and health may not be necessary for ELSA

data. The health outcomes that were chosen for this study need to be explored. As mentioned above, health is

a multidimensional definition. Also, the factor sets that were chosen in multivariate logistic regression models

also need to be expanded.

In this study, people who have drinking habits have better health which is not in consistent with common

sense. If researchers want to find some evidence that supports the view that “drinking is hazardous to health”,

the ELSA data set may need variables that depict whether the respondent has an alcohol abuse condition.

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the original dissertation is better assured if the Department and/or Library can fulfill such requests by sending

a copy. The Department may also make your dissertation available via its web pages.

In certain cases where confidentiality of information is concerned, if either the author or the supervisor so

requests, the Department will withhold the dissertation from loan or consultation for the period specified

below. Where no such restriction is in force, the Department may also deposit the Dissertation in the

University of Sheffield Library.

To be completed by the Author – Select (a) or (b) by placing a tick in the appropriate box

If you are willing to give permission for the Information School to make your dissertation available in these

ways, please complete the following:

✓

(a) Subject to the General Regulation on Intellectual Property, I, the author, agree to this

dissertation being made immediately available through the Department and/or University Library

for consultation, and for the Department and/or Library to reproduce this dissertation in whole or

part in order to supply single copies for the purpose of research or private study

(b) Subject to the General Regulation on Intellectual Property, I, the author, request that this

dissertation be withheld from loan, consultation or reproduction for a period of [ ] years from the

date of its submission. Subsequent to this period, I agree to this dissertation being made

available through the Department and/or University Library for consultation, and for the

Department and/or Library to reproduce this dissertation in whole or part in order to supply single

copies for the purpose of research or private study

Name Lisong Zhang

Department Information School

Signed Lisong Zhang Date 31/08/2017

To be completed by the Supervisor – Select (a) or (b) by placing a tick in the appropriate box

(a) I, the supervisor, agree to this dissertation being made immediately available through the

Department and/or University Library for loan or consultation, subject to any special restrictions

(*) agreed with external organisations as part of a collaborative project.

*Special

restrictions

(b) I, the supervisor, request that this dissertation be withheld from loan, consultation or

reproduction for a period of [ ] years from the date of its submission. Subsequent to this period,

I, agree to this dissertation being made available through the Department and/or University

Library for loan or consultation, subject to any special restrictions (*) agreed with external

organisations as part of a collaborative project

Name

Department

Signed Date

THIS SHEET MUST BE SUBMITTED WITH DISSERTATIONS IN ACCORDANCE WITH DEPARTMENTAL

REQUIREMENTS.

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