DIFFERENCES IN HEALTHCARE EXPENDITURE RELATED TO BECOMING DIVORCED OR WIDOWED

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DIFFERENCES IN HEALTHCARE EXPENDITURE RELATED TO BECOMING DIVORCED OR WIDOWED

Master thesis by Iris Meulman

European Master in Health Economics and Management

Student number University of Oslo: 618979 Student number University of Bologna: 0900064244

University of Oslo

The Faculty of Medicine, Institute of Health and Society, Department of Health Management and Health Economics

Supervisor: Prof. Tron Anders Moger

A thesis submitted in fulfillment of the requirements for the degree of Master of Philosophy in Health Economics and Management

31-07-2020

Utrecht, the Netherlands

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Acknowledgements

Hereby I present to you my thesis ‘Differences in healthcare expenditure related to becoming divorced or widowed’ as a final part of the European Master in Health Economics and Management. I am very grateful that I had the opportunity to learn from four top universities in the field of health economics, the Erasmus University Rotterdam, University of Oslo, University of Bologna and Management Centre Innsbruck.

I would first like to thank my supervisor Tron Anders Moger of the department of Health Management and Health Economics at the University of Oslo for his support and advice regarding both the content and the organizational aspects of this thesis. Although we were never able to meet in person, Tron always kept an eye on the progress and provided the appropriate feedback when I needed it.

This master thesis would not be possible without the means and funding provided by the Dutch National Institute for Public Health and the Environment (RIVM) that made it possible for me to access the data. I would like to thank my colleagues, especially Ellen Uiters and Bette Loef, for welcoming me to the institute and advising me during the entire process. I learned a lot from and got inspired by our many discussions and brainstorm sessions.

Furthermore, I want to thank my boyfriend, Jorrit, for the eternal support and faith in me.

Together we succeeded in maintaining a fixed and structured rhythm during this special time which definitely contributed to this final result.

Last, but far from least, I wish to show my gratitude to my parents for their love, help and support throughout my entire life. Thanks to them, I have become the person I am today.

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Abstract

Background Despite the high prevalence and evident health effects of divorce and widowhood, little longitudinal research has examined the difference in healthcare expenditure related to becoming divorced and widowed.

Objective This study aims to investigate whether there is a difference in healthcare expenditure between individuals who became divorced or widowed and individuals who remained married.

Furthermore, this study investigates if there is a difference in healthcare expenditure for individuals who have been divorced or widowed more than four years ago and explores the role of remarriage.

Methods This study relies on longitudinal registration and claim microdata provided by Vektis and Statistics Netherlands. Overall, 2,121,706 Dutch individuals are included in the study who became divorced or widowed in 2013, were already divorced or widowed for more than 4 years in 2013 or were continuously married. Propensity score matching (PSM) followed by generalized linear models is used to estimate differences in yearly healthcare expenditure compared to individuals who remained married for five consecutive years. Analyses control for differences in sex, age, highest completed education, standardized disposable household income, healthcare expenditure and household composition before a change in marital status and are stratified by age (25-44 years, 45-64 years and 65-79 years).

Results Overall, individuals who became divorced or widowed have higher healthcare expenditure than individuals who remained married in the 4-5 years after the change in marital status. Individuals aged 45-64 and 65-79 who have been divorced or widowed more than 4 years ago have on average between 10% and 14% higher healthcare expenditure. Limited differences in healthcare expenditure are found for divorced and widowed who remarry within 4-5 years compared to individuals who remained married.

Conclusion Divorced and widowed generally have higher healthcare expenditure than individuals who remained married. This could potentially be explained by lower health status among divorced or widowed, the loss of informal care and social support, and/or the social role of the divorced in society. Furthermore, this study suggests that remarriage might buffer the relation between becoming divorced or widowed and healthcare expenditure, although further research is needed to confirm this suggestion.

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List of figures

Figure 1 – Potential mechanisms explaining the relationship between becoming divorced or widowed

and healthcare expenditure. ... 15

Figure 2 – Possible trajectories on the disruption in normal functioning after an event (Bonanno, 2004). ... 20

Figure 3 – The flow of changes in marital status in a life course perspective... 28

Figure 4 – Marital biography of individuals included in the study. ... 30

Figure 5 – Flowchart of individuals involved in the study. ... 35

Figure 6 – Mean healthcare expenditure with 95% CI for divorced and continuously married... 38

Figure 7 – Rate ratio of divorced compared to continuously married (control group) with 95% CI... 39

Figure 8 – Mean healthcare expenditure with 95% CI for widowed and continuously married ... 43

Figure 9 – Rate ratio of widowed compared to continuously married (control group) with 95% CI. . 44

Figure A1 – Example of assessment of covariate balance of matching-variables ... 69

Figure A2 – Example of jitter plot of propensity score before and after matching ... 69

List of tables

Table 1 – Literature overview of the association between divorce and different types of healthcare services. ... 5

Table 2 – Overview of the effect of widowhood on healthcare expenditure... 8

Table 3 – Variables used in the analysis of a change in marital status ... 25

Table 4 – Characteristics of the study population in 2013 before matching ... 34

Table 5 – Number of people before matching per research group and age group ... 36

Table 6 – Mean difference and rate ratio for divorced individuals per age group ... 40

Table 7 – Distribution of the mean difference in healthcare expenditure. ... 41

Table 8 – Decomposition of total healthcare expenditure by GP-care and mental healthcare. ... 41

Table 9 – Mean difference and rate ratio for widowed individuals per age group. ... 45

Table A1 – Example of characteristics before and after matching ... 70

Table A2 – Characteristics of the study population after matching for the age group 25-44 years ... 71

Table B1 – Descriptive statistics of healthcare expenditure 2013-2017 after matching for the age group 25-44 years ... 74

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List of abbreviations

CI Confidence interval

GML Generalized linear model

GP General practitioner

HCE Healthcare expenditure

HIA Health Insurance Act

MED Median

OOP Out-of-pocket

OR Odds ratio

PSM Propensity score matching

RR Rate ratio

SD Standard deviation

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1 Introduction

The growing use of healthcare and the increasing healthcare expenditure pose a huge burden on the healthcare system and affordability. Since 1972, total healthcare expenditure in the Netherlands has increased gradually from €6450 million to €106,152 million in 2019.

Likewise, the average individual healthcare expenditure in the Netherlands rose from €484 to

€6120 in the same period (Statistics Netherlands Statline, 2020). Much research has already been devoted to the identification of important drivers for this booming growth and the way scarce healthcare resources are being used. The increased use of healthcare services, the ageing population, lifestyle and the development of new and expensive medical technology are often named as main drivers (Hilderink & Verschuuren, 2018).

Since the introduction of the Social Readjustment Scale by Holmes and Rahe in 1967, much research has been devoted to the influence of life events on well-being and health outcomes (Cohen, Murphy, & Prather, 2019; Hatch & Dohrenwend, 2007; Holmes & Rahe, 1967;

Luhmann, Fassbender, Alcock, & Hähner, 2020). For example, exposure to non-life- threatening negative life events negatively influence happiness, life satisfaction (Wingen, de Jonge, & Arts, 2012), subjective well-being (Lucas, 2007; Luhmann, Hofmann, Eid, & Lucas, 2012) and increases the probability of developing depression, cardiovascular diseases and infectious diseases (Cohen et al., 2019; Kraaij, Arensman, & Spinhoven, 2002) although individual differences in the response to major life events exist (Bonanno, 2004; Mancini, Bonanno, & Clark, 2011). Considering the established link between an individual’s health and healthcare expenditure, it is reasonable to hypothesize that the exposure to negative life events may contribute to higher healthcare expenditure on an individual level.

However, relatively little is known about the effect of exposure to life events on healthcare utilization or healthcare expenditure. Three studies in the 1980’s find that negative life events and life-stress are not directly associated with healthcare utilization in terms of physician visits (Counte & Glandon, 1991; Krause, 1988), outpatient clinic visits (Pilisuk, Boylan, &

Acredolo, 1987), ER-visits and hospital stay (Counte & Glandon, 1991) while controlling for socio-demographical characteristics among 40+ aged and elderly. More specifically to mental healthcare, experiencing life events is seen to both increase and decrease mental health service use depending on the type of life event (both positive and negative), source and quantity of

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2 social support received and type of mental healthcare service used (Maulik, Eaton, &

Bradshaw, 2011). A positive direct association between increased life events and the volume of both mental and somatic healthcare use is found by Blazer (1980) after controlling for social support, although this only explains a very small part of individual variation in health-seeking behavior.

When introducing the Social Readjustment Scale, Holmes and Rahe (1967) indicated that, from the 43 identified life events, divorce and widowhood are two of the most stressful life events and require the most adjustment. To delineate the topic of this thesis, I will focus on two specific types of life events; becoming divorced and widowed, and their relationship with healthcare expenditure. Both life events are similar in that they imply a dissolution of marriage.

Hence, marital dissolution in the context of this thesis implies the end of marriage either through divorce or widowhood. In the Netherlands, around 1% of all marriages are dissolved yearly because of divorce and around 1.8% of marriages are dissolved yearly because of the death of a spouse (Statistics Netherlands Statline, 2019b). Although the percentage of marriages dissolved per year because of the death of a spouse has been approximately stable since 1950, the number of divorces has tripled since.

However, no longitudinal study investigating the effect of becoming divorced on healthcare utilization or healthcare expenditure is present. Longitudinal findings related to the effect of widowhood on healthcare expenditure are not consistent. Six longitudinal analyses indicate that the loss of a spouse increases healthcare utilization or healthcare expenditure (Goda, Shoven, & Slavov, 2013; Guldin, Jensen, Zachariae, & Vedsted, 2012; Ornstein et al., 2019;

Prigerson, Maciejewski, & Rosenheck, 2000; Rolden, Van Bodegom, & Westendorp, 2014;

Tseng, Petrie, Wang, Macduff, & Stephen, 2018). On the other hand, Van Boekel, Cloin, and Luijkx (2019) did not find any difference in healthcare expenditure one year after widowhood.

Paragraph 2.2 and 2.3 will discuss the relation between respectively divorce or widowhood and healthcare expenditure in more detail.

Because of the evident health effects, it is reasonable to hypothesize that negative life events may be related to healthcare expenditure. Divorce and widowhood specifically have been

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3 marked as the life events requiring the most adaptability (Holmes & Rahe, 1967). Given the high prevalence of divorce and widowhood, it is important and relevant to understand the consequences of a change in marital status and whether a change in marital status is a determinant for healthcare expenditure. Despite, the relationship between becoming divorce and healthcare expenditure is highly under-researched and inconsistent results are found for the relationship between widowhood and healthcare expenditure. This thesis, therefore, aims to investigate whether differences in healthcare expenditure can be related to a change in marital status (becoming widowed or divorced) for the Dutch population. The following research question has been posed to achieve this goal:

What is the difference in healthcare expenditure within the mandatory benefits package between individuals who experienced a change in marital status and individuals who did not experience a change in marital status in the Netherlands? More specifically, healthcare expenditure of:

- Individuals who became divorced or widowed and remained divorced or widowed compared to individuals who remained married

- Individuals who became divorced or widowed and remarried compared to individuals who remained married

- Individuals who have been divorced or widowed for more than 4 years compared to individuals who remained married

The second chapter provides an overview of the current literature regarding the consequences of becoming divorced or widowed. The literature overview functions as the scientific basis for the theoretical framework constructed in chapter 3. The theoretical framework will address the potential mechanisms explaining the relationship between a change in marital status on healthcare expenditure and introduces a model to evaluate the changes in healthcare expenditure over time. The fourth chapter describes the data and methodology used to study the consequence of a change in marital status. The results of the analyses are presented in chapter 5. This master thesis is completed with a summary and discussion of the results in chapter 6.

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2 Literature overview

Life events are defined as stressful experiences or transitions with the potential to cause a disruption of stability and continuity in daily life. The potential disruption due to life events can be expressed in many ways, for example in the disruption of normal functioning (Bonanno, 2004), deterioration of subjective well-being (Luhmann et al., 2012) or elevated health risks (Cohen et al., 2019). In data analytic research, life events are often classified in traumatic (life- threatening) or non-traumatic, and positive or negative events (Luhmann et al., 2020). In this context, becoming divorced or widowed can be viewed as negative non-traumatic life events.

Other life events in this category are, for example, accidents, moderate illness (activity limitation), illness of family member, death of other relatives or loved ones, contact with justice, increased number of conflicts, a victim of crime and abuse, financial shock, loss of employment, start/change of job, etc..

Divorce is the dissolution of marriage or registered partnership by other means than the death of the spouse. To my knowledge, no longitudinal study considered the effect of becoming divorced on healthcare expenditure. One longitudinal study investigated the effect of divorce on healthcare utilization (hospital care, psychiatrist, psychologist, medical doctor, clergyman or social worker) and has shown that depression and physical health were rated similar among divorced and non-divorced but women who became divorced consulted a psychiatrist more often (Prigerson, Maciejewski, & Rosenheck, 1999). No difference in the number of hospital days or doctor visits was found.

Table 1 summarizes the findings of cross-sectional studies on the association between divorce and specific types of healthcare services. Comparing results from different studies should be done with caution because there is variation in measures of health service consumption (volume, probability or cost), different confounding factors are taken into account and other aspects of the study design may vary. Findings regarding the use of somatic healthcare utilization are mostly inconsistent. Mental healthcare utilization outcomes are, for the largest part, more consistent and tend towards increased mental healthcare utilization among divorced irrespective of a correction for health measures.

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5 Table 1 – Literature overview of the association between divorce and different types of healthcare services. Confounding factors in between brackets in the column ‘controlled for’ indicate that the association is found with and without controlling for this variable.

Type of care and

measure Association divorced vs.

married Controlled for Reference

GP care

Volume No difference Age, sex (health) Kunst, Meerding, Varenik,

Polder, and Mackenbach (2007) Probability

Probability No difference

No difference Age, sex (education, severe illness)

Age, sex, socio-demographic, health status (chronic conditions) Wingen and Otten (2009) Joung, Van der Meer, and Mackenbach (1995) Probability

Probability Higher

Higher Age, sex (socio-demographics)

Age, sex-stratified, depressive symptoms, subjective health, heavy alcohol consumption (number of children in the household, number of adults in the household, informal care) (SES)

Joung et al. (1995) Corijn and Van Peer (2013)

Specialist

Volume Higher Age, sex (health) Kunst et al. (2007)

Probability

Probability No difference

No difference Age, sex (education, severe illness)

Age, sex (socio-demographics) (health) (chronic conditions) Wingen and Otten (2009) Joung et al. (1995) Use of medication

Volume No difference for women

only Bivariate Berk and Taylor (1984)

Volume No difference Age, sex, health Kunst et al. (2007)

Volume Higher Age, sex Kunst et al. (2007)

Probability

No difference

Age, sex-stratified depressive symptoms, subjective health, heavy alcohol consumption, number of children in the household, number of adults in the household, informal care (SES) Age, sex, socio-demographic, health status/chronic conditions

Corijn and Van Peer (2013)

Joung et al. (1995) Probability

Probability Higher

Higher Bivariate

Age, sex, socio-demographic, health status, chronic conditions Corijn and Van Peer (2013) Joung et al. (1995) Probability Higher for women only Age, sex-stratified, depressive symptoms, subjective health, heavy alcohol consumption Corijn and Van Peer (2013) Dental care

Volume No difference Age, sex Kunst et al. (2007)

Hospital admission

Volume Higher Age, sex (health) Kunst et al. (2007)

Probability Higher Age, sex (sociodemographic) (health status) (chronic conditions) Joung et al. (1995)

Probability No difference for women

only Bivariate Berk and Taylor (1984)

Probability of

readmission No difference Age, sex, primary payer, score of trauma triage (only among patient with orthopedic trauma) Konda, Gonzalez, Johnson, Friedlander, and Egol (2020)

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Cost No difference Age, sex, primary payer, score of trauma triage (only among patient with orthopedic trauma) Konda et al. (2020) Hospital length of stay

Volume No difference Age, sex, primary payer, score of trauma triage (only among patient with orthopedic trauma) Konda et al. (2020) Volume Higher during last year of

life Age, sex, cause of death, multimorbidity, ICU during the last stay, educational level, house or

flat owner, room category, nationality, language region Hedinger, Braun, Kaplan, Bopp, and Swiss National Cohort Study (2016)

Physiotherapy

Volume No difference Age, sex Kunst et al. (2007)

Probability No difference Age, sex (education, severe illness) Wingen and Otten (2009)

Mental healthcare Volume

Volume Higher

Higher Age, sex

Sex (mental well-being, physical well-being) (household characteristics, education, work status, age, first health support, urbanization)

Kunst et al. (2007)

Bracke, Colman, Symoens, and Van Praag (2010)

Probability

Probability Higher

Higher Bivariate

Age, number of (step)children, EHI, social support, education, employment status, depression, self-rated health, frequency of healthcare use

Corijn and Van Peer (2013) Colman, Symoens, and Bracke (2012)

Probability

Higher for women only Age, sex-stratified, depressive symptoms, subjective health, heavy alcohol consumption (number of children in the household, number of adults in the household, informal care) (SES) Age, number of (step)children, EHI, social support, education, employment status, depression, self-rated health

Corijn and Van Peer (2013)

Colman et al. (2012) Nursing home

Probability Higher Sex, year of birth, race, education (economic resources) Thomeer, Mudrazija, and Angel

(2015)

Probability Higher only for men Sex, year of birth, race/ethnicity, education, health, disability, economic resources, social ties Thomeer et al. (2015)

Probability > 3 months Higher Sex, year of birth, race, education Thomeer et al. (2015)

Probability > 3 months Higher only for men Sex, year of birth, race, education, economic resources (social ties) Thomeer et al. (2015) Home care

Volume Higher Sex, age Kunst et al. (2007)

Informal care

Volume Higher Age, sex Kunst et al. (2007)

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7 Overall, either no difference or higher healthcare utilization is observed for divorced individuals. This would suggest that the total healthcare expenditure of divorced might be higher compared to married individuals. The direction of these findings is in accord with Kunst et al. (2007) who estimated that the total healthcare expenditure of divorced is 57% higher than married individuals in the Netherlands. However, Cabrera-Alonso, Long, Bangalore, and Lescoe-Long (2003) concluded that healthcare expenditure for married than divorced elderly (aged 75+) in a managed care setting is significantly higher. They explain their findings by higher perceived consequences of illness, benefits of care and the presence of spousal trigger to use healthcare for married individuals.

Widowhood is the dissolution of marriage or registered partnership due to the death of the spouse. Table 2 summarizes the findings of longitudinal studies on the change in healthcare expenditure after widowhood. The study population of these studies comprise only elderly, except for Prigerson et al. (2000) who included individuals aged 25+. All studies, except for Van Boekel et al. (2019), have found elevated healthcare expenditure in the period following widowhood. Both Rolden et al. (2014) and Goda et al. (2013) attribute the higher healthcare expenditure to an increase in nursing-home and other long-term care expenses due to the loss of informal care. Results regarding sex-differences are mixed. Ornstein et al. (2019) find that bereavement only affects healthcare expenditure for women. This is inconsistent with Goda et al. (2013) and Van Boekel et al. (2019) who indicate that the effect of widowhood on healthcare expenditure or out-of-pocket expenditure does not differ by sex. The impact of bereavement on healthcare expenditure may also depend on the quality of marriage. Prigerson et al. (2000) find that healthcare expenditure of widowed is 59% higher 0-3 years after widowhood compared to individuals who remained married. They have shown that this difference is mainly driven by widowers who report their marriage as harmonious (125% increase in healthcare expenditure) while the increase in healthcare expenditure of individuals whose marital harmony was below the median score was only 17%. As said, Van Boekel et al. (2019) did not find any effect of spousal loss on healthcare costs after one year while they did find a significant effect on psychological well-being and graded quality of life. Likely, the exclusion of costs of non-urgent visits to the general practitioner (GP), medication and long-term care expenses by Van Boekel et al. (2019) is responsible for these contradictory findings.

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8 Table 2 – Overview of the effect of widowhood on healthcare expenditure found in other longitudinal studies. HCE = healthcare expenditure, OOP = out-of-pocket, GP = general practitioner.

Article Type of healthcare expenditure Change in healthcare expenditure after

widowhood Control group Follow-up period

Rolden et al. (2014) Medical and long-term care expenditure 48% higher Pre-widowhood 42 months

Goda et al. (2013) OOP-expenditure 24% higher Married individuals 8 years (average)

Ornstein et al. (2019) Total Medicare expenditure 18% higher for women only Non-bereaved 2 years

Prigerson et al. (2000) HCE for hospital care, nursing home, medical doctor

and psychiatric care 59% higher Married individuals Between 0-3 years

Van Boekel et al. (2019) HCE for hospital care, urgent GP visits, and home care No difference Married individuals 1 year

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9 Analyzing healthcare expenditure at multiple points in time allows identifying differences in healthcare expenditure relative to the time before or after widowhood. Three studies have described the change in healthcare expenditure after widowhood at multiple points in time with healthcare expenditure as the outcome variable (Goda et al., 2013; Ornstein et al., 2019; Rolden et al., 2014) and two studies with healthcare utilization as the outcome variable (Guldin et al., 2012; Tseng et al., 2018). Rolden et al. (2014) have observed stable healthcare expenditure in the 42-months (3,5 year) before the loss, a sudden increase in healthcare expenditure around the time of loss because of the loss of informal care, and stable but elevated healthcare expenditure in the 42-months after the loss. Goda et al. (2013) researched the out-of-pocket spending of individuals becoming widowed compared to married individuals in an approximately 8 years pre- and post-widowhood timeframe (time between follow-up measurements were inconsistent). They have not found a difference in healthcare expenditure for individuals who will become widowed compared to individuals who will stay married in the 8 years before potential widowhood. Increased healthcare expenditure for widowed is found between the moment of the loss of the spouse and approximately 4 years after widowhood.

This difference has become statistically insignificant between approximately 4 to 8 years after the loss, although this could be due to a reduction in statistical power in later years. Ornstein et al. (2019) have shown that healthcare expenditure is peaking 3-6 months after the death of the spouse. For females, they have not found pre-existing differences in healthcare expenditure between individuals who will become widowed and individuals who will remain widowed.

Higher healthcare expenditure is maintained after the peak during the two years after widowhood. For males, they have shown that individuals who will become widowed have lower healthcare expenditure 18 to 6 months before widowhood, have peaking healthcare expenditure 3-6 months before widowhood and the differences narrows again after widowhood. In their research to the probability of hospitalization and length of stay, Tseng et al. (2018) have not found any difference between married individuals and future widowed individuals pre-widowhood. The probability of hospitalization is 1% higher for widowed individuals 1 year after widowhood and 2% in the subsequent year. No difference after widowhood is found in the length of stay once admitted to the hospital. Guldin et al. (2012) performed a longitudinal analysis in a 2 years pre- and post-loss timeframe on the national adult population of Denmark using healthcare utilization as their outcome measure. Increased use of GP-care was found to start already 1 year before the loss of a spouse and peaked at the

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10 moment of the loss. Individuals who will become divorced were already almost twice as likely to be referred to a psychologist 2 years before the loss of their spouse and peaked around 6 months after widowhood. The probability of admission to a somatic or psychiatric hospital was higher for widowed individuals in the 24-months after widowhood and their stays were on average twice as long.

In conclusion, Guldin et al. (2012) is the only study who have found evidence for increased healthcare expenditure (GP-care and psychologist visits) already before widowhood (anticipatory effect). Concerning healthcare expenditure following widowhood, no difference in healthcare expenditure is found after one year by Van Boekel et al. (2019) while others find elevated healthcare expenditure up to 2 years (Guldin et al., 2012; Ornstein et al., 2019), 4 years (Goda et al., 2013; Rolden et al., 2014) and 7 years (Tseng et al., 2018).

The impact of a particular life event is not homogenous among individuals. Each individual, even with the same socioeconomic and demographic characteristics, can react differently to the same event. The degree and duration of the consequences of life events vary among individuals depending on their adaptability (Lucas, 2007). The difference in the appraisal of life events, such as divorce and widowhood, is caused by interdependence of personal factors (such as personality, commitment and beliefs) and situational factors (novelty, predictability, event uncertainty, temporal factors, ambiguity, and timing of the event) (Lazarus & Folkman, 1984;

M. Stroebe, Schut, & Stroebe, 2007). The appraisal of the event determines the coping strategy.

The coping theory forms a possible explanation for the interindividual variation in the reaction to life events and specifically a change in marital status.

Coping can be defined as the changing thoughts, acts and efforts that an individual uses to manage the external or internal demands of stressful situations (Folkman, 2001; Lazarus &

Folkman, 1984). In general, there are two ways of coping: problem-focused coping and emotion-focused coping. Problem-focused coping addresses the cause of the distress by attempting to interpret and explain the event to give meaning and sense to it. Emotion-focused coping addresses the emotional response to the event. Because coping requires effort, an individual needs resources to deal with the event. Resources can be in the form of physical resources (e.g. health and energy), psychological resources (e.g. pre-existing beliefs and

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11 commitment), competencies (e.g. social and problem-solving skills) and environmental resources (e.g. social support and material resources). Together, the appraisal of the event and the available resources determine the individual's reaction and actions to cope with the event (Lazarus & Folkman, 1984). The availability and capability to use resources for coping can buffer the effect of marital dissolution on healthcare expenditure. This can explain the different reactions observed among individuals experiencing the same life event.

According to Folkman (2001) and M. Stroebe et al. (2007), positive outcomes after bereavement are related to problem-focused coping, positive reappraisal and sensemaking because it gives a form of control and sheds positive lights on the situation. One way to give a positive twist to a negative life event is to view the event as a lesson learned. Contrarily, when an event is perceived as a negative turning point, it is associated with worse self-rated and psychological health (Sutin, Costa, Wethington, & Eaton, 2010). When the coping theory is applied to becoming divorced or widowed and the consequences in terms of healthcare expenditure, it can be hypothesized that there will be interindividual variation in this response even after controlling for (socio)demographic characteristics such as age, sex, education and income.

2.5.1 Role of remarriage after divorce or widowhood

Remarriage is the change in marital status from being divorced or widowed to being married.

Therefore, remarriage only takes into account individuals who are legally married or entered a registered partnership after divorce or widowhood. Healthcare expenditure of individuals who remarry after divorce or widowhood may be affected differently than the healthcare expenditure of individuals who do not remarry because remarriage can provide the benefits of marriage and having a spouse (Couch, Tamborini, & Reznik, 2015). For example, those who remarry after divorce or widowhood have generally better health than those who remain unmarried (Hughes & Waite, 2009). Couch et al. (2015) have shown that divorced who remarry show significant differences in the probability of work disabilities compared to individuals who remained divorced but show no significant differences compared to continuously married.

Compared to continuously married, individuals who remarry after divorce or widowhood show similar probabilities of long-term care admission (Thomeer et al., 2015) and men who remarry within 10 years show no higher mortality than first-time married (Berntsen & Kravdal, 2012).

Controversially, Hughes and Waite (2009) have found that remarried individuals have more chronic conditions, mobility limitations and depressive symptoms and have worse self-rated

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12 health than continuously married. Ornstein et al. (2019) also looked at the influence of remarriage in their sample, although there were only a limited number of remarriages, and suggest that remarriage most likely did not impact the effect of widowhood on healthcare expenditure. Overall, remarried individuals show better health outcomes than individuals who stay divorced or widowed but the findings related to the differences in health outcomes for remarried and continuously married are mixed. Therefore, remarriage may potentially moderate the effect of becoming divorced or widowed on healthcare expenditure. An alternative explanation is however provided by a selection effect where the probability of remarriage is affected by health status: divorced or widowed individuals with a good health status are more likely remarry (Joung, 1996).

2.5.2 Role of social support from friends and family

The findings regarding a buffering effect of social support in the relation between becoming divorced or widowed and healthcare expenditure are mixed. Several studies confirmed that a combination of exposure to life events and lower levels of social support is associated with more non-mental healthcare visits (Counte & Glandon, 1991; Krause, 1988; Pilisuk et al., 1987). However, scholars have more recently have indicated that the assumed buffering effect of social support may be non-existing for life events in general and widowhood specifically.

They argued that social support is directly related to healthcare utilization but do not find a buffering effect of life events (or specifically widowhood) on health or healthcare utilization (Anusic & Lucas, 2014; Maulik et al., 2011; M. Stroebe et al., 2007; W. Stroebe, Zech, Stroebe,

& Abakoumkin, 2005). To explain their finding, Anusic and Lucas (2014) reasoned that a higher level of social support before widowhood is an indication of happiness, and individuals with higher baseline happiness encounter the most negative impact of life events (Lucas, Clark, Georgellis, & Diener, 2003).

2.5.3 Role of marital quality

The effect of marital dissolution on health, healthcare utilization or healthcare expenditure may also be buffered by marital quality. Individuals involved in a discordant marriage may feel relieved after marital dissolution resulting in positive or less negative health effects (Prigerson et al., 1999; Williams, 2003). For example, separation after a discordant marriage may reduce the risk of hypertension (Prigerson et al., 1999). Similarly, individuals with harmonious marriage before dissolution have a greater negative impact on life satisfaction and depression rate (Williams, 2003) and have greater morbidity (Prigerson et al., 1999) compared to

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13 individuals who dissolved from low-quality marriage. Concerning healthcare expenditure, Prigerson et al. (2000) have indicated that the increase in healthcare expenditure is much larger for widowers who reported their marriage as harmonious compared to individuals whose marital harmony was below the median score (125% increase in healthcare expenditure versus 17% increase).

However, some studies questioned the moderating effect of marital quality. Kalmijn and Monden (2004) and (2006) have found limited evidence for an interaction effect of marital quality and health outcomes and argue that their research is superior to that of Prigerson et al.

(1999) and Williams (2003) in terms of sample size and study design. To explain their findings, Kalmijn and Monden (2004) and (2006) argued that marital problems may not end after marital dissolution and that individuals most sensitive to marital problems are more likely to dissolve a marriage. Also, although Prigerson et al. (1999) have found a buffering effect of marital harmony on health outcomes, no such buffering effect was found for healthcare utilization.

They suggested that not marital quality but financial considerations (low SES and absence of healthcare insurance) are important in explaining the difference in healthcare utilization among divorced and widowed.

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3 Theoretical framework

The theoretical framework constructed in this chapter provides guidance for studying the relationship between a change in marital status and healthcare expenditure. First, two theories studying healthcare utilization are discussed. These theories function as the backbone for the theoretical framework. Second, potential mechanisms through which becoming divorced or widowed are linked with healthcare expenditure are identified. Last, the framework introduced by Bonanno (2004) for studying changes in the disruption in daily functioning after a life-event over time is applied to the context of healthcare expenditure.

3.1.1 Andersen’s behavioral model of healthcare utilization

The decision to seek care depends on many factors. In his model, Andersen classifies determinants of healthcare utilization into three categories: predisposing factors, enabling characteristics and need factors (Andersen, 1995). The predisposing factors are individual characteristics such as demographics, social structure, and health beliefs. The enabling characteristics incorporate the ability and facilities of consuming healthcare, such as personal/familial resources, community resources, health insurance, health literacy and knowledge about available healthcare services. The need factor implies that there must be some form of personal or professional recognition of the health problem to seek care. Andersen indicated that the need factor is thought to be the most direct cause of healthcare consumption and the final decisive factor for the use of healthcare. Whereas predisposing factors and enabling characteristics are likely to influence the probability of seeking care.

3.1.2 Rosenstock’s health belief model

Rosenstock's health belief model describes the relationship between health beliefs and health- promoting behavior or health service utilization. It gives four parallel explanations influencing the decision to seek medical care, which are: perceived susceptibility, perceived seriousness, perceived benefits of taking action and perceived barriers of taking action. The model also argues that a cue or trigger is necessary to stimulate health-promoting behavior (Rosenstock, 1974). These constructs are likely to vary among individuals because individual characteristics (such as demographic, socio-psychological and structural variables) indirectly affect the perceived susceptibility, seriousness, benefits and barriers.

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15 Figure 1 – Potential mechanisms explaining the relationship between becoming divorced or widowed and healthcare expenditure. Signs in between brackets indicate the general expected direction of the mechanism. This does not imply that the suggested direction is applicable to all individuals or hold under all circumstances. The variables in Italic indicate how the concerning factors are expressed within this study.

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16 Based on the two theories and scientific literature, four potential mechanisms are identified that can explain the relationship between a change in marital status and healthcare expenditure. As well, potential confounding factors are identified. Each arrow in the theoretical framework I constructed in Figure 1 represents a potential mechanism or confounding factor.

3.2.1 Higher perceived benefits of taking care

In the context of Rosenstock’s health belief model, the perceived benefits of taking action (consuming healthcare) for married individuals will be higher because of higher responsibility and indirect consequences of impaired health on spouse and children. The loss of a spouse, either through divorce or widowhood, can take away the internal motivation to seek care. The direct arrow ‘higher perceived benefits’ in Figure 1 refers to this mechanism and implies that the loss of a spouse decreases healthcare expenditure.

3.2.2 Spousal encouragement to seek care

Interpersonal interaction with a spouse can encourage and trigger the use of healthcare when the spouse emphasizes the seriousness, stimulates to seek care, and assist in finding appropriate care (Joung et al., 1995; Wood, Avellar, & Goesling, 2009). In the context of Rosenstock’s health belief model, spousal encouragement can increase the perceived seriousness and may function as the cue or trigger needed to seek care. The loss of a spouse, either through divorce or widowhood, can take away this external encouragement. The direct arrow ‘no spousal encouragement’ in Figure 1 refers to this mechanism which hypothesizes that becoming divorced or widowed lowers the healthcare expenditure compared to individuals who remain married.

3.2.3 Different levels of health status

Another possible mechanism through which healthcare expenditure is related to becoming divorced or widowed is by different levels of health status. Poor health status often coincides with higher healthcare expenditure. According to Andersen’s behavioral model, the need factor is the decisive factor for seeking medical care (Andersen, 1995), leading to increased healthcare expenditure. The arrow ‘need for care’ in Figure 1 to this established mechanism linking lower health status with increased healthcare expenditure.

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17 The relation between becoming divorced or widowed and health is, however, more complex.

Cross-sectional studies have consistently reported a negative association between individuals who are (recently) divorced or widowed and different types of health measures such as mortality, morbidity and happiness (Bronselaer, Koker, & Van Peer, 2008; Joung, 1996, 2007;

Wingen, de Jonge, & Arts, 2010). Two main theories are used to explain the differences in health by marital status: a causal effect and a selection effect.

The causal effect argues that becoming divorced or widowed affects an individual’s health status (Joung, 1996, 2007; Monden & Uunk, 2013; M. Stroebe et al., 2007). Becoming divorced or widowed are stressful life-events. The stress associated with separation or loss of a spouse, either through divorce or widowhood, can cause deterioration of health (Lazarus & Folkman, 1984). For example, longitudinal studies have shown that individuals who became divorced or widowed have lower psychological and subjective well-being (Lucas, 2005, 2007), a higher probability of poor mental health (Wade & Pevalin, 2004) and a higher risk of mortality (Manzoli, Villari, M Pirone, & Boccia, 2007; Sbarra, Law, & Portley, 2011). However, one should be careful with generalizing average effects to all individuals, as becoming widowed or divorced may not negatively affect every individual’s health or may even improve health status (see paragraph 2.5) (Monden & Uunk, 2013). For example, individuals can also be relieved after divorce or widowhood (e.g. after dissolving stressful marriages or intensively providing informal care for the deceased spouse).

The selection theory argues that health status affects the probability of becoming divorced or widowed. Individuals with (prolonged) poor health status are more likely to become divorced or widowed (Joung, Van de Mheen, Stronks, Van Poppel, & Mackenbach, 1998; Monden &

Uunk, 2013; Sullivan & Fenelon, 2013). The probability of becoming divorced is increased by poor health status because it affects the gains of marriage (deterioration of economic position) and quality of marriage. The probability of becoming widowed is increased by poor health status because of marital selection and shared household characteristics such as age, SES of the household and unhealthy behavior. Longitudinal findings related to the presence of a selection effect in becoming divorced or widowed are ambiguous (Joung, 2007).

The causal effect and selection effect are not mutually exclusive: it is likely both effects simultaneously contribute to the health differences by marital status (Joung, 2007; Monden &

Uunk, 2013; Sbarra et al., 2011). This implies that, although part of the negative association

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18 can be explained by a selection effect, becoming divorced or widowed also negatively affect health outcomes. The arrows ‘causal effect’ and ‘selection effect’ in Figure 1 refer to these mechanisms linking a change in marital status to health.

3.2.4 Loss of informal care

A spouse or partner is likely to assist in simple (daily) tasks, such as washing, dressing, cleaning or cooking. An individual might not be able to carry out these tasks without a partner’s help.

Denton (1997) argued that there are five possible relations between the balance of formal and informal care utilization: a task specificity model of care, a substitution model, a supplementary model, a compensatory model and a complementary model. The complementary model, a combination of the compensatory and supplementary effect, indicate that individuals consume formal care when informal care is lacking (compensatory) or when the need for care is larger than the informal caregiver can provide (supplementary). Denton (1997) concluded that individuals using informal care are more likely to be married, live with others or have children.

As well, the use of informal care (measured in terms of the number of people in the household, being married, having children and years of education) is negatively associated with the use of formal care among elderly with functional disabilities. This has provided evidence for the complementary model predominated by a compensatory effect. The arrows ‘spousal home care’ and ‘compensative and supplementary effect’ in Figure 1 refers to this mechanism and hypothesizes that the loss of a spouse, either becoming divorced or widowed, increases healthcare expenditure through lower levels of informal care.

Rolden et al. (2014) have shown that this mechanism may play an important role in the relationship between becoming widowed and healthcare expenditure. They assigned their findings of increased healthcare expenditure after widowhood to the loss of informal care. In light of the health belief model, this would imply that the perceived marginal benefits of consuming formal care are larger in the absence of informal care (Van Houtven & Norton, 2004). In the perspective of Andersen’s behavioral model, this means that the use of informal care, compensating for formal care, is facilitated by individual and familial resources (enabling factors) such as having a spouse (Gaugler & Kane, 2001).

3.2.5 Confounding factors

The application of Andersen's behavioral model provides insights into the possible confounding factors in the relationship between marital status and healthcare utilization.

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19 Predisposing factors and enabling characteristics affect the probability of using healthcare services and might explain (part of) the variation of healthcare utilization among individuals (Joung, 1996). Marital status is categorized as a predisposing factor, but the model shows that other socio-demographical characteristics (such as age and sex), personal resources (such as income) and knowledge about available healthcare services (education) also affect the probability of seeking care (Andersen, 1995). Age, sex, income and educational attainment are confirmed to be associated with healthcare expenditure although this is partly explained by health inequalities (Kunst et al., 2007; National Institute for Public Health and the Environment, 2015). As well, age, sex, educational level and income are unevenly distributed among marital status (Joung, 1996; Statistics Netherlands Statline, 2019a). Therefore, age, sex, education and income are likely to affect the association between a change in marital status and healthcare expenditure and are identified as confounding factors. The confounding factors are represented in the model by the arrow labelled ‘confounding factors’ in Figure 1.

In this section, a theoretical framework for studying healthcare expenditure over time after a change in marital status is constructed. Because of the absence of theories relating a change in marital status and healthcare expenditure, this theoretical framework borrows from theories related to life events, normal functioning and subjective well-being. This framework is applied to the relationship between a change in marital status and healthcare expenditure. Bonanno (2004) identified four prototypical trajectories on the disruption in normal functioning after an event (interpersonal loss or traumatic events): resilience, chronic, delayed and recovery (Figure 2). The patterns are discussed in more detail in the next paragraphs.

3.3.1 Resilience

Resilience after a life event is the pattern of little disruption on the continuity in daily life. It reflects the ability to maintain a stable equilibrium (Bonanno, 2004). This does not mean that an individual does not encounter any stress, but the response is mild and have little to no influence on functioning. Regarding subjective well-being, Mancini et al. (2011) have shown that 72% of the individuals included in their study showed a pattern of resilience after divorce and 59% of the individuals showed a pattern of resilience after widowhood.

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20 Figure 2 – Possible trajectories on the disruption in normal functioning after an event (Bonanno, 2004).

3.3.2 Recovery

The pattern of recovery implicates moderate to severe disruptions in normal functioning at the moment of the event. This disruption fades away over time towards the initial way of functioning (‘getting back to normal’). Recovery requires adaptation to the event. Luhmann et al. (2012) have found evidence for a recovery pattern for subjective well-being after a life event. They have shown that exposure to life events influences the subjective well-being at the moment of the event (negatively in case of divorce and widowhood). In the period after the event, an adaptive or recovering pattern is observed where subjective well-being is moving in the direction of the initial level. Mancini et al. (2011) have found a pattern of recovery in subjective well-being in 9% of the individuals after divorce and 21% of the individuals after widowhood.

The set-point theory argues that individuals can react in such a way to life events that subjective well-being will fully get back to original levels. Lucas et al. (2003) tested the set-point theory on the levels of subjective well-being in the reaction to a change in marital status. They have found that, on average, individuals followed the pattern expected by the set-point theory: initial decrease of life-satisfaction at the time of widowhood and adaptation back to pre-widowhood levels in the subsequent years. However, Lucas et al. (2003) also have shown that there is

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21 variation in reaction among individuals, for example, individuals who showed long term decreases in life satisfaction which is more closely to the chronic pattern described by Bonanno (2004) in Figure 2 or people who ended up in a new equilibrium of life satisfaction.

When looking specifically to divorce, Lucas (2005) have shown that divorced individuals do adapt to a drop in life satisfaction, but did not reach baseline levels. Booth and Amato (1991) have found that the level of unhappiness and distress for divorced individuals is equal to married individuals 5 to 8 years after the divorce. This indicated that the level of unhappiness and distress is changed temporarily due to divorce, but can be restored completely and therefore provide evidence for the set-point theory. However, with the same dataset, Johnson and Wu (2002) have found persistent effects of divorce on the level of distress and refute the complete adaptation found by Booth and Amato (1991).

3.3.3 Delayed

The pattern of delayed effects of life events has derived from theoretical reasoning that the absence of effect at the moment of the event is due to the defensive mechanisms and hiding of symptoms. Eventually, one will suffer from the event by delayed reactions (Bonanno & Field, 2001). However, little evidence has been found for the pattern of delayed disruption of normality or depressive symptoms (Bonanno, 2004; Bonanno & Field, 2001; Bonanno, Westphal, & Mancini, 2011; Dautovich, Dzierzewski, & Gum, 2014). Mancini et al. (2011) have found that 21% of the individuals show a pattern of delayed effects after divorce, but no pattern of delayed effects was observed after widowhood. Lorenz, Wickrama, Conger, and Elder (2006) have shown no difference in physical illness among women shortly after divorce compared to individuals who remained married, but more physical illnesses 10 years later.

These findings suggest that a delayed negative effect of divorce might be possible among some individuals.

3.3.4 Chronic

The chronic pattern is described by long term effects of life events on normal functioning.

Without pre-event data, one should be careful with interpreting stable outcome levels. Outcome levels could be chronically low (low levels long before the event) indicating a selection effect or sharply decreased by the event and exhibiting long term effects indicating a causal effect (Bonanno, Wortman, Lehman, & Tweed, 2002; Mancini et al., 2011). Mancini et al. (2011) have indicated that 15% of the individuals show a pattern of chronically low subjective well-

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22 being which already existed before widowhood. No pattern of chronically lower subjective well-being as a result of becoming divorced or widowed is observed.

With the pattern of recovery, the effects at the moment of the event most of the time do not restore to original levels (Bonanno et al., 2002; Lucas, 2005, 2007). Although people do adapt up to a certain extent, some of the effects are longer-lasting and may result in a new equilibrium. This ‘mild’ form of chronic effects of life events may, therefore, be more common.

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4 Research Methods

In the Netherlands, the healthcare system is arranged in four health-related acts: the Health Insurance Act (HIA), the Long-Term Care Act, the Social Support Act and the Youth Act. The HIA accounts for approximately 60% of the Dutch healthcare budget and is based on regulated competition between the insured, insurance company and healthcare providers (Ministry of Public Health Welfare and Sport, 2016). Health insurance under the HIA is compulsory for all Dutch citizens, in the form of a mandatory benefits package, but they are free to choose among health insurance companies. For health insurance companies, they cannot reject individuals for health insurance and have a duty to provide care. Health insurance companies outsource the provision of care by selectively contracting healthcare providers. The competition on the insurance market and selective contracting of healthcare providers ensure high efficiency and quality of care. The specific content of the mandatory benefits package is determined by the government and legally defined. Broadly, the mandatory benefits package under the HIA covers medical care provided by a GP, medical specialists and obstetricians, district nursing, hospitalization, mental healthcare up to 3 years, medication, services provided by various types of therapists (e.g. physical therapists, remedial therapists, speech therapists and occupational therapists), nutritional care, medical aids, ambulance support and transport, and physiotherapy for people with chronic illnesses. The HIA is funded through monthly premiums from the insured to the insurance company, an income-dependent contribution paid via the employee, a deductible of €385 a year (except for GP care and maternity care) and co-payments for some types of care. In addition to the mandatory benefits package, individuals can voluntarily choose supplementary benefit packages for additional care. Each healthcare insurer can compile various supplementary benefit packages. Approximately 90% of the Dutch population has a supplementary benefits package (Ministry of Public Health Welfare and Sport, 2016).

Results were based on calculations using non-public microdata from Statistics Netherlands.

Statistics Netherlands functioned as a trusted third party, enabling the linkage between the datasets, while ensuring the privacy of the involved individuals, according to Dutch law (Statistics Netherlands Act 2003). To obtain access to the data, the University of Oslo was granted institutional authorization for the use of non-public microdata from Statistics

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24 Netherlands. Given these prerequisites, the project was approved by personvernombud at the University of Oslo.

In this thesis, healthcare expenditure comprises total healthcare expenditure for all types of care covered by the mandatory benefits package. Healthcare expenditure under other healthcare acts was not included because of data availability. The data of healthcare expenditure under the mandatory benefits package was obtained from Vektis (Dutch healthcare information centre) and provided by Statistics Netherlands. This database was based on claims submitted to Dutch health insurance companies (Statistics Netherlands, 2019). Social demographical characteristics were derived from the personal records database (age and sex) and the system of social-statistical files (highest completed education, standardized disposable household income, and household composition).

The variables used for studying the difference in healthcare expenditure after a change in marital status are listed in Table 3.

4.4.1 The outcome variable: healthcare expenditure

Yearly healthcare expenditure between 2013 and 2017 is the outcome of interest for these analyzes. Based on the literature and theoretical framework, the magnitude of the difference in healthcare expenditure is hypothesized to be dependent on the time since becoming divorced or widowed. Figure 2 shows the possible courses of (differences in) healthcare expenditure in the years following a change in marital status. Therefore, healthcare expenditure was analyzed for 5 consecutive years after divorce or widowhood. Additionally, differences in healthcare expenditure for individuals who are divorced or widowed for more than 4 years was observed for 5 consecutive years.

Total healthcare expenditure included all declared healthcare expenditure in the context of the Dutch mandatory benefits package and expenditures born by the patient because of (voluntary) deductibles per year. It did not include declared healthcare costs within supplementary benefit packages, healthcare expenditure covered under a different legal framework (youth and long- term care) and undeclared out-of-pocket expenditure.

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25 Table 3 – Variables used in the analysis of a change in marital status

Variable Type Units Label

Change in marital status Determinant Categorical Becoming divorced or widowed in 2013

Healthcare expenditure Outcome variable Continuous The total healthcare expenditure within the mandatory benefits package (health insurance) in 2013, 2014, 2015, 2016 or 2017.

Age Confounder Categorical In 5-years categories

Sex Confounder Binary Male/female

Standardized disposable

household income Confounder Categorical Standardized disposable household income quintile to which a person belongs. The lowest quintile represents the 20% lowest household incomes and highest quintile the 20% highest household incomes Highest completed education Confounder Categorical Highest completed education level classified in low (primary education or less), low-moderate (lower

vocational education or lower secondary education), moderate-high (intermediate vocational education or higher secondary education), and high (higher vocational education or university) educational level.

Healthcare expenditure in 2012

(as a proxy for health status) Confounder and

intermediate variable Continuous The total healthcare expenditure within the mandatory benefits package in 2012 Household composition

(as a proxy for the potential of informal care)

Covariate and

intermediate variable Categorical The number of people that are part of the household classified as single (1-person household), paired (2- persons household) or family (3 or more persons in the household)

DIFFERENCES IN HEALTHCARE EXPENDITURE RELATED TO BECOMING DIVORCED OR WIDOWED