The accumulation of adverse life events across the life course and their association with mental health and well-being: exploring gender heterogeneity.

The accumulation of adverse life events across the life course and their association with mental health and well-being: exploring gender heterogeneity

Abstract

Background

Most individuals experience multiple adverse life events across their life course, accumulating into cumulative disadvantage with lasting consequences for mental health and well-being. Evidence suggests that women’s mental health and well-being is more vulnerable to the negative consequences of single adverse life events, due to gender differences in event perception, available resources, and preferred coping strategies. Until now it’s unclear whether these gendered patterns hold when looking at the accumulation of adverse events over a life course. The aim of this study is to examine if the accumulation of adverse life events is associated with depressive symptomatology, life satisfaction, and quality of life with a central focus on gender differences in these relationships.

Methods

A secondary data analysis was conducted using Wave 3 of the English Longitudinal Study of Ageing (ELSA). Accumulation of adverse life events was operationalized as the number of different adverse events experienced during the life course, covering 22 life events. Three accumulation scores were calculated: a total score (TCALE) including all adverse events and two separate scores for self-oriented (SOCALE) and other-oriented (OOCALE) adverse events. Associations with depressive symptomatology, life satisfaction, and quality of life were analyzed using logistic and linear regression models. Interaction terms between accumulation scores and gender were included to examine gender differences in these associations.

Results

Greater accumulation of adverse life events is associated with higher likelihood of depressive symptomatology, lower life satisfaction, and reduced quality of life. Gender moderates the relationships for TCALE, with women being more vulnerable to the accumulation of adverse life events. Although no consistent gender interactions could be found for SOCALE and OOCALE, non-significant trends, for OOCALE, aligned with the patterns observed for the total accumulation score.

Conclusion

Cumulative life course adversity is a significant risk factor for poorer mental health and well-being, with women showing heightened vulnerability.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12889-026-30076-7.

Keywords: Adverse life events, Cumulative adversity, Life course, Mental health, Well-being, Gender, Depression, Life satisfaction, Quality of life

Introduction

Most individuals experience multiple adverse life events throughout their lives [1, 2], ranging from personal losses and health challenges to major disruptions in social or occupational life. The majority of these events are linked to severe and long-lasting negative consequences for an individual’s mental health and well-being [e.g., 3–10]. Past research primarily focused on individual adverse life events and their impact on mental health and well-being. Although this approach allows the examination of event-specific burden, it risks decontextualizing adverse experiences and overlooking the complex interplay between multiple adversities across the life course [11]. Empirical evidence indicates that adverse life events rarely occur in isolation. They can trigger further adverse events [12–14], interact with each other [15, 16], and accumulate stress over a lifetime [1, 17–19], thereby amplifying their negative consequences for mental health and well-being. This study aims to advance our understanding of how multiple adversities collectively shape mental health and well-being across the life course and how these associations vary by gender.

One of the most common and serious mental health outcomes following an adverse life event is depression [20], which constitutes a major public health challenge [21]. As the most prevalent mental disorder worldwide [22], depression affects about one in ten individuals annually and one in five over a lifetime [23]. Its social consequences are severe, as this mental illness accounts for 5.6% of all years lived with disability [24] and imposes a substantial financial burden on healthcare systems [25].

Beyond depression, adverse life events have also been shown to be associated with poorer well-being outcomes, including life satisfaction and quality of life [26, 27]. These two measures of well-being are key markers of societal health [27, 28] as they are closely linked to various mental [27, 29–32] and physical health outcomes [28, 29, 33–35]. Because of their immense relevance for both individual and population health, measures of life satisfaction and quality of life are increasingly included in economic and political decision-making [36, 37].

Although depressive symptomatology, life satisfaction, and quality of life are related constructs of mental health and well-being, they capture distinct dimensions of individuals’ experiences. Depressive symptomatology primarily reflects affective and emotional distress [38], life satisfaction represents a person’s evaluation of his or her own life in relation to one’s own expectations and standards [39], and quality of life encompasses a broader assessment of an individual’s life and living conditions across different domains, including physical and social functioning, material and emotional well-being, and personal development (26). Examining all three outcomes therefore provides a comprehensive picture of the consequences of accumulated adverse life events for mental health and well-being.

Given their importance for individual and societal health, it is crucial for the public health sector to understand how depression, life satisfaction, and quality of life are shaped by the accumulation of adverse life events over the life course in order to design interventions that mitigate long-term mental health and well-being inequalities associated with cumulative adversity.

Although there is no universal definition of what characterizes an adverse life event, prior literature highlights several key characteristics of such events. Adverse life events are events that have the potential to significantly alter a person’s social world [40], require considerable adjustment in daily life or coping strategies [41], and are perceived as a threat to one’s own or close others’ life, physical, or mental well-being [42, 43]. Examples of adverse life events include the death of a loved one, loss of a home due to a natural disaster, or a divorce. It should be noted that some adverse events, such as being the victim of sexual assault, are largely exogenous, while others, for example, widowhood, are often accompanied by chronic stressors that precede the event, such as spousal caregiving, or follow it, such as financial strain. Adverse life events that involve chronic stressors have been shown to be particularly burdensome in the context of accumulated adversities, as chronic stress is not only associated with poorer mental health and well-being but also amplifies the burden associated with subsequent stressors [44].

Understanding the characteristics of adverse life events provides a foundation for examining how burdens caused by these events accumulate over a lifetime. In this study, accumulation is understood as the progressive aggregation of adverse life events during a person’s life. Life-course theory states that adversities are embedded in social and historical contexts and that earlier experiences as well as critical transitions shape vulnerability to subsequent stressors [43, 45, 46]. Building on this perspective, stress-proliferation and cumulative-inequality theory together describe how initial adversities can generate further stressors, which additionally negatively affect a person’s mental health and well-being, as well as socioeconomic and social resources, that are necessary to cope with further adverse events. This results in a self-reinforcing process of increasing disadvantage [16, 47], in which the accumulation of adverse life events is a dynamic process where adversities generate additional stressors and resource losses that interact with each other and shape long-term mental health and well-being.

Building on this accumulation and self-reinforcing process, studies have shown that the accumulation of adverse life events is associated with a higher probability of clinical depression or depressive symptomatology [1, 2, 18, 48–58], lower life satisfaction [1, 2, 6, 18, 53, 59], and reduced quality of life [18]. Despite broad empirical consensus, two studies examining older Israeli adults report divergent findings, observing no association between the accumulation of adverse life events and quality of life [60] and an association with depressive symptomatology only for one of two depression indicators examined [55]. One possible explanation for these contrasting findings is the exceptionally high lifetime exposure to collective stressors and trauma among older Israeli adults, including the Holocaust, large-scale migration, and repeated military conflicts [61, 62], which may shape how adverse events are experienced and processed [55]. Notably, the specific adverse events considered differ substantially across studies, reflecting variations in data sources and event selection.

Prior research on the consequences of the accumulation of adverse life events for mental health and well-being highlights the importance of distinguishing events based on their primary orientation into self- and other-oriented adverse life events [18, 55, 56, 60, 63]. This distinction is grounded in a change in the definition of traumatic events in the Diagnostic And Statistical Manual Of Mental Disorders (DSM-IV), published in 1994, which extended the concept of traumatic events to include events affecting close others [42], thereby acknowledging the interconnectedness of individuals’ lives and the ways in which experiences of close others can shape one’s own life course, mental health, and well-being [45]. Self-oriented adverse life events directly affect the individual (e.g., serious illness, divorce, or sexual assault), whereas other-oriented adverse life events primarily impact close others (e.g., death of a loved one, witnessing a fatal accident, or parental divorce). This distinction of adverse events, however, does not solely depend on who is primarily affected by the event but also on the coping role adopted. Other-oriented adverse events often place individuals in caregiving, supporting, or grieving roles, whereas self-oriented events mainly require individual emotion regulation and self-focused coping [55]. Prior studies suggest that the accumulation of self-oriented adverse life events leads to more intense and direct psychological distress than the accumulation of other-oriented adverse events [18, 55, 56, 60, 63]. This difference may be explained by other-oriented events often coming with more meaningful role-taking [64, 65], greater fostering of social interactions [66], and larger emotional distance to the adverse consequences [67], all together buffering their negative impact on mental health and well-being compared with self-oriented adverse events.

Across studies, the accumulation of self-oriented adverse events is consistently linked to a higher risk of depression [18, 55, 56, 60] as well as lower life satisfaction [18, 56] and quality of life [18, 56]. In contrast, evidence for the accumulation of other-oriented adverse events is less clear and somewhat conflicting. Studies in older Israeli populations showed mixed findings: one study found an association between accumulated other-oriented events and increased depressive symptoms but not lifetime depression diagnoses, alongside higher quality of life [60]; another study found no or even a negative association with depressive symptoms depending on the measurement instrument used [19]. When accounting for life stage, analyses in an Israeli population showed that other-oriented adverse events during childhood were linked to fewer depressive symptoms as well as higher life satisfaction and quality of life, while such events during adulthood showed largely non-significant associations with depression and well-being [56]. In contrast, evidence from an older English population linked accumulated other-oriented events to higher risk of experiencing depression as well as lower life satisfaction and decreased quality of life, regardless of the life stage the adversities occurred [18]. Overall, while the accumulation of self-oriented adverse events reliably predicts poorer mental health and reduced well-being, findings on accumulated other-oriented events remain heterogeneous and vary across context and outcome measures, highlighting the need for further research.

A large body of research suggests that the negative consequences of adverse life events on mental health and well-being are highly gendered, with women experiencing a greater burden than men for most events [20, 68–70].

Women consistently report perceiving adverse life events as more stressful, less controllable, and more disruptive than men, particularly when these events involve close others [69, 71, 72]. This heightened impact of other-oriented adversity could be explained by women’s stronger relational orientation [73, 74] and their greater tendency to define themselves through their close interpersonal relationships [75, 76], compared to men. As a result, adverse events affecting loved ones are particularly striking for women and may contribute more strongly to cumulative burden and long-term negative consequences for mental health and well-being.

Building on gendered patterns in the perception of adverse life events, differences in coping resources and strategies, which are crucial for managing adverse events, may further shape gendered consequences of accumulated adversity for mental health and well-being. Studies report that men tend to possess greater personal resources, such as internal locus of control, self-efficacy, or optimism [77–82], as well as material resources like income and wealth [83, 84]. Women, in contrast, typically maintain larger and more supportive social networks [85, 86]. Nevertheless, it has been shown that men are less reliant on their social network to cope with adverse life events than women due to the other resources available to them [87–89]. In addition to gender differences in available coping resources, gender also shapes preferred coping strategies. It has been shown that women tend to rely on emotion-oriented coping strategies such as venting, rumination, or seeking emotional support, while men are more likely to use problem-focused strategies, including planning and active problem solving [82, 90–93]. This is of great importance, since problem-focused strategies have been shown to offer greater protection against the long-term negative consequences of adverse life events on mental health and well-being [94, 95].

These individual resources and coping strategies are embedded in the structural conditions that individuals experience over the life course. Women, particularly in older generations, have historically had less access to educational opportunities [96, 97], more fragmented employment trajectories [97], and greater caregiving responsibilities [98] than men. Such structural constraints may further contribute to gender differences in the mental health and well-being consequences of adverse life events [43, 99].

Overall, these gender differences in perception, available resources, preferred coping strategies, and exposure to structural constraints provide a theoretical basis for expecting gender-specific effects in the relationship between the accumulation of adverse life events and depressive symptomatology, life satisfaction, and quality of life, with women being particularly vulnerable. This may be especially pronounced for the accumulation of other-oriented adverse events, which women tend to perceive as more stressful and disruptive than men, potentially intensifying gendered consequences.

Previous research on gender differences in the association between the accumulation of adverse life events over the life course with mental health and well-being is still limited, with only one study reporting greater vulnerability for women in terms of depressive symptomatology [52]. No study so far examined gender differences in the relationship between accumulated adverse life events and measures of well-being. In addition, the potential role of event orientation in shaping gendered effects has not yet been considered.

Building on the theoretical considerations and empirical findings presented above, this study examines how the accumulation of adverse life events across the life course is related to depressive symptomatology, life satisfaction, and quality of life, with a special focus on gender differences and event orientation. It is expected that the accumulation of adverse life events overall as well as separately for self- and other-oriented adverse events is associated with a greater likelihood of depressive symptomatology, lower life satisfaction, and reduced quality of life. In addition, gender is expected to moderate these relationships, with women being more vulnerable to the negative consequences of accumulated adversity across overall, self-oriented, and other-oriented life events.

This study contributes to the literature on the accumulation of adverse life events and their consequences on mental health and well-being in several ways. First, by examining a broad range of adverse events, it provides a complementary perspective, strengthening existing evidence on the association between cumulative exposure to adversity and mental health and well-being. Second, it clarifies the role of event orientation, particularly shedding light on the accumulation of other-oriented events, for which previous findings have been mixed. Third, it provides additional evidence for the role of gender in the association between cumulative adversity and depression and offers novel insights into gendered associations in the relationship between cumulative adversity and life satisfaction as well as quality of life. Finally, for the first time, this study examines how event orientation interacts with gender in the accumulation of adverse life events, providing a more nuanced understanding of how mental health and well-being are shaped by their combined effects.

Taken together, this study aims to advance our understanding of how the cumulative burden caused by adverse life events shapes men’s and women’s mental health and well-being. This can help to identify high-risk populations and inform the development of interventions and preventive strategies that take into account not only single adverse life events but also the broader life-course context in which the burdens of adverse experiences can accumulate.

Methods

Study sample

The current study utilizes data from the English Longitudinal Study of Ageing (ELSA), a nationally representative study of older adults aged 50 + and their partners living in private households in England [100, 101]. The analyses were restricted to data from Wave 3, conducted in 2006–2007, as this is the only wave including a life history interview together with a self-completion questionnaire, both of which were used to collect detailed retrospective information on a wide range of life events throughout an individual’s life course [102].

Within the life history interview, life events were recorded using a Life History Calendar, an interview-based method for collecting information about past life events. It has the form of a calendar that depicts different areas of life (e.g., living, work, family) over a timeline and includes socially important events. By entering personal events alongside predefined socially meaningful events, it is easier for respondents to recall and chronologically place events. This method improves the accuracy and completeness of retrospective data on life events. Whereas the life history interview was conducted face-to-face, the self-completion questionnaire was filled out alone on paper, giving respondents more time to think as well as greater privacy [102].

A total of 9,771 individuals participated in the ELSA survey in Wave 3. Only participants who completed both the life history interview and the self-completion questionnaire were retained in the sample (excluded = 3,189). Next, observations with missing information on any of the three outcomes (missing values = 956), at least one adverse life event (missing values = 547), or at least one confounder (missing values = 15) were stepwise removed. This results in a final analytical sample comprising 5,064 individuals.

As approximately 48% of the initial sample (n = 4,707 observations) were excluded from the analytical sample due to missing information, comparisons between included and excluded individuals were conducted across key characteristics used in subsequent analyses to assess potential selection bias. Results of this comparison are presented in Additional File 1.

Outcomes

Depressive symptomatology was assessed using the eight-item Center for Epidemiological Studies Depression Scale (CES-D), a widely used screening test for assessing depression based on the number of depressive symptoms [100, 103]. It should be noted that this measure does not directly assess clinical depression, as depressive symptomatology may occur without meeting diagnostic criteria [21]. Participants were asked to indicate (yes/no) whether they had experienced each of eight depressive symptoms for much of the time during the last week: (1) feeling depressed, (2) feeling that everything was an effort, (3) having restless sleep, (4) feeling lonely, (5) feeling sad, (6) being unable to get going, (7) not enjoying life, and (8) not feeling happy. Two items were initially positively worded (“enjoyed life” and “felt happy”) and, in the following, reverse-coded to ensure that all items were scaled in the same direction. Responses were then summed to create a total score ranging from 0 to 8. Depressive symptomatology was defined as the presence of four or more symptoms of depression, consistent with previous studies [104, 105]. This cut-off point has been found to produce comparable results to the ≥ 16 cut-off point for the well-validated 20-item CES-D scale [103, 106].

Life satisfaction was assessed using the Satisfaction with Life Scale [39], a commonly used five-item scale designed to measure life satisfaction. The scale consists of the following items: (1) “In most ways my life is close to my ideal”, (2) “The conditions of my life are excellent”, (3) “I am satisfied with my life”, (4) “So far I have got the important things I want in life”, and (5) “If I could live my life again, I would change almost nothing”. Responses were originally recorded on a 7-point Likert scale ranging from 1 (“strongly agree”) to 7 (“strongly disagree”). To facilitate interpretation, all item responses were shifted by subtracting one point, resulting in a scale ranging from 0 to (6). Item scores were summed to generate a total life satisfaction score, which was subsequently reverse-coded so that higher values indicated greater life satisfaction. The final score ranged from 0 to 30.

To assess quality of life, the Control, Autonomy, Self-realization, and Pleasure 19 scale (CASP-19 scale), a validated instrument for measuring quality of life in older adults, was used [107–109]. The CASP-19 scale consists of nineteen items across four different domains, which are shown in Table 1. The questions begin with the phrase, “Here is a list of statements that people have used to describe their lives or how they feel. How often do you feel like this?”. Responses were recorded on a four-point Likert scale ranging from 0 (“never”) to 3 (“often”). Six items (1, 2, 3, 6, 8, and 9) were negatively worded and therefore reverse-coded so that higher values consistently indicated better quality of life. Item responses were summed to create a total CASP-19 score ranging from 0 to 57, with higher scores reflecting higher quality of life [109].

Table 1Domains and items in the CASP-19

Domain Items

Control 1. My age prevents me from doing the things I would like to doa

2. I feel that what happens to me is out of my controla

3. I feel free to plan for the future

4. I feel left out of thingsa

Autonomy 5. I can do the things I want to do

6. Family responsibilities prevent me from doing the things I want to doa

7. I feel that I can please myself what I do

8. My health stops me from doing the things I want to doa

9. Shortage of money stops me from doing things I want to doa

Pleasure 10. I look forward to each day

11. I feel that my life has meaning

12. I enjoy the things that I do

13. I enjoy being in the company of others

14. On balance, I look back on my life with a sense of happiness

Self-realization 15. I feel full of energy these days

16. I choose to do things that I have never done before

17. I feel satisfied with the way my life has turned out

18. I feel that life is full of opportunities

19. I feel that the future looks good for me

a Negatively worded and for the analysis reverse coded

Accumulation of adverse life events

Selection of adverse life events was guided by the Cumulative Lifetime Adversity Measure (CLAM) [59], a frequently used instrument for capturing the accumulation of adverse life events across the lifespan [1, 59, 110, 111]. This measure is based on the “trauma section” of the Diagnostic Interview Schedule [112] and has been expanded to include additional stressful life events identified from primary care patient reports [113].

Although the CLAM already covers a broad spectrum of adverse life events, this list is still not exhaustive and therefore must be supplemented with selected additional events [110]. Subsequently, two further adverse events available in ELSA, first unexpected job loss and second separation from a long-term or cohabiting partner, were additionally included. This results in a total of 22 different adverse life events considered in the analyses. For three of these, only experiences up to age 16 were recorded.

As information on the frequency of occurrence for individual adverse life events was not available for most events, each event was coded as a dichotomous variable (0 = never experienced, 1 = ever experienced). Cumulative adverse life event exposure was therefore operationalized as the total number of different adverse life events experienced throughout an individual’s life course, thereby assuming that each type of adverse life event contributes equally to the cumulative exposure. This approach is widely used in prior research [1, 114] and resulted in a total cumulative adverse life event score (TCALE), which increases by 1 for every adverse life event experienced, ranging from 0 to 22.

Adverse life events were further categorized as self- or other-oriented following prior definitions and previous literature [18, 55, 56]. Of the 22 adverse life events considered, 12 were classified as self-oriented and 10 as other-oriented. This resulted in a self-oriented cumulative adverse life event score (SOCALE), ranging from 0 to 12, and an other-oriented cumulative adverse life event score (OOCALE), ranging from 0 to 10. All adverse life events included in the analyses, together with their orientation, are presented in Table 2.

Table 2Descriptive distribution of experienced adverse life events and accumulation scores, stratified by gender

Men(n = 2232) Women(n = 2832) p-valueb

Self-oriented adverse life eventsa

 Divorce 458 (20.5) 653 (23.1) 0.033

 Separation from a long-term or/and cohabiting partner 397 (17.8) 612 (21.6) 0.001

 Diagnosis of a serious physical disease 1248 (55.9) 1716 (60.6) 0.001

 Job loss due to company closing 585 (26.2) 523 (18.5) < 0.001

 Experienced a natural disaster 289 (12.9) 249 (8.8) < 0.001

 Suffered a life-threatening illness or serious accident 684 (30.6) 618 (21.8) < 0.001

 Victim of a serious physical attack or assault 152 (6.8) 127 (4.5) < 0.001

 Victim of sexual assault 62 (2.8) 243 (8.6) < 0.001

 Fired a weapon or came under fire during combat 245 (11.0) 16 (0.6) < 0.001

 Experienced severe financial hardship 391 (17.5) 577 (20.4) 0.011

 Physically abused by parents before age 16 67 (3.0) 99 (3.5) 0.368

 Experienced evacuation during war 249 (11.2) 234 (8.3) 0.001

Other-oriented adverse life eventsa

 Death of long-term partner/spouse 223 (10.0) 599 (21.2) < 0.001

 Death of child 118 (5.3) 181 (6.4) 0.111

 Death of father 261 (11.7) 322 (11.4) 0.754

 Death of mother 250 (11.2) 237 (8.4) 0.001

 Parental divorce before age 16 108 (4.8) 148 (5.2) 0.576

 Experienced violence between parents before age 16 382 (17.1) 619 (21.9) < 0.001

 Witnessed serious injury or death in war or military action 234 (10.5) 37 (1.3) < 0.001

 Witnessed a fatal or violent accident (not war) 487 (21.8) 206 (7.3) < 0.001

 Lost a very close friend or relative in war or military service 285 (12.8) 223 (7.9) < 0.001

 Close friend or relative was at risk of death or died due to illness or serious accident 407 (63.0) 1871 (66.1) 0.027

Accumulation scores

 TCALE 3.84 (2.21) 3.57 (1.99) < 0.001

 SOCALE 2.16 (1.50) 2.00 (1.48) < 0.001

 OOCALE 1.68 (1.27) 1.57 (1.09) 0.001

Absolute numbers (percentages) are presented for individual adverse life events; mean values (standard deviation) are presented for accumulation scores

a For single adverse life events, only participants who reported experiencing the event are shown. Non-experienced cases (coded as “never experienced”) are not presented

b p-values indicating gender differences from chi-square tests for single adverse life events or two-tailed t-tests for accumulation scores

Abbreviations: TCALE total cumulative adverse life event score, SOCALE  self-oriented cumulative adverse life event score, OOCALE  other-oriented cumulative adverse life event score

Covariates

To account for potential confounders in the association between the accumulation of adverse life events and mental health and well-being, several control variables were considered.

Selecting confounders in the context of the accumulation of adverse life events is challenging, as individual characteristics can serve multiple roles in the theoretical causal pathway. Two issues are of particular relevance. First, some variables may function both as confounders and as part of the exposure itself. For example, marital status is an important confounder when examining associations between adverse events such as unexpected job loss or a life-threatening illness and mental health and well-being [115, 116]. At the same time, marital status also includes the adverse life events of divorce or widowhood, which are considered in the accumulation scores and are independently associated with mental health and well-being [117–119]. Adjusting for such variables may therefore control for parts of the exposure, potentially leading to an underestimation of the effects.

Second, some variables may act as both confounders and mediators. Income, for instance, is a well-established confounder in the relationship between many adverse life events and mental health/well-being outcomes [57, 120]. At the same time, it is also heavily shaped by the accumulation of adverse events [121, 122]. In the ELSA data, income (as well as all other covariates) is surveyed at the time of the interview and therefore after the adverse events were experienced, making it primarily a product of cumulative adversity. Adjusting for such variables could block mediation pathways, resulting in an underestimation of the association.

Given these considerations, it was decided to adjust only for stable confounders that are relevant across all adverse life events and closely linked to the outcomes. These include gender (men vs. women), age (≤ 59, 60–69, 70–79, 80+), and place of birth (UK vs. outside of UK). In addition to these sociodemographic characteristics, the highest level of education attained was also considered, as education is typically established early in life and remains relatively stable in adulthood [123, 124]. Education was categorized into four levels (no qualification, low education, medium education, high education). The “no qualification” category includes individuals without a formal school leaving certificate. “Low education” covers basic schooling or foreign qualifications that cannot be clearly classified. “Medium education” includes secondary/high school diplomas, and “high education” comprises university or other post-secondary degrees.

Statistical analysis

Descriptive statistics of adverse life events were presented separately for men and women, with absolute frequencies and percentages reported for each individual event and mean values with standard deviations calculated for the three accumulation scores. Gender differences were tested using Pearson chi-square tests for individual events and two-tailed t-tests for the accumulation scores. Overall sample characteristics were summarized for the total sample and by gender, with frequencies and percentages for nominal and ordinal variables and means with standard deviations for continuous variables.

Regression models were used to examine the association between the three accumulation scores and the considered outcomes. Logistic regression models were applied for the dichotomous outcome depressive symptomatology, whereas linear regression models were used for the metric outcome’s life satisfaction and quality of life. All models were estimated with standard errors clustered at the household level to account for the potential dependence between respondents from the same household.

Some previous research suggests a potential quadratic association between the accumulation of adverse life events and mental health and well-being [54, 59, 110]. To examine this hypothesis, quadratic terms of the three accumulation scores were included in the regression models for all outcomes. Smoothing splines were additionally fitted for the three accumulation scores as a further check for non-linearity. As the quadratic terms were not statistically significant and the smoothing splines showed no substantial deviations from the linear pattern (with deviations only occurring at the upper end of the distribution where 10 or fewer observations were available) in any model (see Additional File 2), the linear terms were retained.

For each outcome, four regression models were estimated on the full sample, all adjusted for the confounders described above. Model 1 included TCALE, whereas Model 2 included SOCALE and OOCALE. To examine potential moderation effects of gender, Model 3 extended Model 1 and Model 4 extended Model 2 by adding interaction terms between the respective adverse life event score and gender.

To visualize the moderating effects of gender, marginal predicted probabilities (for depressive symptomatology) and marginal means (for life satisfaction and quality of life) were calculated and averaged across the observed distribution of all covariates. Figures display 84% confidence intervals to facilitate visual group comparisons, as non-overlapping 84% confidence intervals between two groups correspond approximately to a statistically significant difference at the 5% level [125, 126]. Based on the results of the interaction analyses and theoretical considerations, additional gender-stratified regression models were estimated, which are presented in Additional File 3.

A significance level α of 0.05 was applied to all statistical tests. The statistical analysis was performed using R version 4.5.1 [127].

Sensitivity analyses

To check the robustness of the results against modifications of the constructs and procedures used, four sensitivity analyses were conducted.

First, an alternative operationalization to the checklist-based measurement used to capture the accumulation of adverse life events was examined. In the main analysis, the accumulation scores were based on the number of different adverse events ever experienced, as 16 of the available 22 adverse life events were surveyed only in dichotomous form. For 6 events (divorce, death of a long-term partner/spouse, death of a child, separation from a long-term or cohabiting partner, diagnosis of a serious physical disease, and job loss due to company closure), information on frequency of occurrence across the life course was available. Because the repeated exposure to the same adverse event still negatively affects mental health and well-being [128–131], these six adverse life events were not dichotomized in this sensitivity analysis. Instead, the accumulation scores incorporated the number of times each of these adverse events occurred, whereas the other 16 events remained dichotomous. This allowed the accumulation scores to increase with each additional occurrence of the 6 events, making them theoretically open-ended.

The second sensitivity analysis addresses a raised criticism of using an unweighted sum score to measure the accumulation of adverse life events. Critics have argued that an unweighted sum score does not account for the different severity or impact of individual adverse life events [132]. To address this, general weights for each adverse event were assigned, derived from a study in which participants were asked to rate life events on a 0–100 scale according to their perceived stress and adjustment demands [133]. Using these weights, the accumulation of adverse life events is now conceptualized as the sum of the total stress experienced through adverse life events over the life course. Although these weights do not capture the individual-level perceived burden of the events, they provide an alternative perspective on the examined associations, accounting for the type and intensity of the events. The assigned weights are shown in Additional File 4.

Third, robustness to the inclusion of additional confounders into the regression models was tested. These include current employment status (in labor force, unemployed, retired), equivalized household income (calculated by dividing the household income of one individual by the square root of the number of household members) divided into tertiles (low, medium, high), partnership status (married, cohabiting, neither), number of children under the age of 18 living in the household, social participation, and social network size. Equivalized household income tertile cut-offs were determined using the full sample prior to excluding observations with missing values. Social participation was measured by the number of memberships (yes/no) in eight types of organizations, clubs, and societies, resulting in a sum score ranging from 0 to 8 [134, 135]. Social network size was calculated as the sum of close relationships with children, other family members, and friends, with each category top-coded at 10 to limit the influence of outliers, resulting in a total score from 0 to 30 [134, 136, 137]. All confounders were measured at Wave 3, with no information available on these characteristics at the time the adverse life events occurred. Individuals with missing data on the additional confounders were excluded, resulting in a sample of 4,183 instead of 5,064 participants for this sensitivity analysis.

The fourth sensitivity analysis considers alternative operationalizations of the examined outcomes. For depressive symptomatology, a more sensitive cut-off of ≥ 3 symptoms (instead of ≥ 4) was used, as in some other previous ELSA studies [18, 138]. Quality of life was measured in this sensitivity analysis using the CASP-12, a shortened version of the CASP-19 scale [139], which includes items 1, 2, 4, 5, 7, 9, 10, 11, 12, 15, 18, and 19, shown in Table 1 [108], resulting in a slightly modified operationalization of quality of life.

Overall, the main results were largely robust to variations in measurement, covariate inclusion, and outcome definitions. Some interaction terms between the accumulation scores and gender that were significant in the main analysis marginally lost significance in supplementary analyses, likely due to reduced statistical power, partial adjustment for exposure pathways, or increased measurement complexity. Yet the observed trends in gender differences remained consistent. Full descriptive statistics and regression results of the sensitivity analyses are presented in Additional File 5.

Results

Descriptive results

Table 3 shows the general characteristics of the study population stratified by gender and for the entire sample. The sample consists of 2232 men and 2832 women, with women making up the larger proportion of the study population (55.9%). Depressive symptomatology was observed in 8.8% of men and 13.9% of women. Mean life satisfaction scores were 20.37 among men and 20.04 among women, whereas the average quality of life score was 41.49 among men and 41.83 among women. Additional descriptive information on the life satisfaction score and the quality of life score, comprising their 25th percentile, median, and 75th percentile, is shown in Additional File 6.

Table 3General characteristics of the study population stratified by gender and overall

Men(n = 2232) Women(n = 2832) Overall(n = 5064) p-valuea

Depressive symptomatology < 0.001

 No 2035 (91.2) 2438 (86.1) 4473 (88.3)

 Yes 197 (8.8) 394 (13.9) 591 (11.7)

Life satisfaction score 0.069

 Mean (SD) 20.4 (6.3) 20.04 (6.47) 20.18 (6.4)

Quality of life score (CASP-19) 0.152

 Mean (SD) 41.5 (8.4) 41.8 (8.5) 41.68 (8.4)

Age 0.013

 ≤ 59 years 754 (33.8) 1079 (38.1) 1833 (36.2)

 60–69 years 740 (33.2) 866 (30.6) 1606 (31.7)

 70–79 years 544 (24.4) 640 (22.6) 1184 (23.4)

 80 + years 194 (8.7) 247 (8.7) 441 (8.7)

Birthplace 0.373

 UK 2115 (94.8) 2666 (94.1) 4781 (94.4)

 Outside of UK 117 (5.2) 166 (5.9) 283 (5.6)

Highest education < 0.001

 No qualification 424 (19.0) 761 (26.9) 1185 (23.4)

 Low education 602 (27.0) 1013 (35.8) 1615 (31.9)

 Middle education 641 (28.7) 602 (21.3) 1243 (24.5)

 High education 565 (25.3) 456 (16.1) 1021 (20.2)

Absolute numbers (percentages) are presented for nominal and ordinal variables; mean values (standard deviation) are presented for continuous variables

ap-values indicating gender differences from chi-square tests for categorical variables or two-tailed t-tests for continuous characteristics

Abbreviations: SD  standard deviation, CASP-19  Control, Autonomy, Self-realization, and Pleasure 19, UK  United Kingdom

Table 2 shows the descriptive distribution of individual adverse life events experienced, as well as the three accumulation scores, stratified by gender, along with the test results for gender differences. It can be seen that women reported more frequently that they experienced divorce (23.1% vs. 20.5% for men; p = 0.033), separation from a long-term and/or cohabiting partner (21.6% vs. 17.8% for men; p = 0.001), diagnosis of a serious physical disease (60.6% vs. 55.9% for men; p = 0.001), sexual assault (8.6% vs. 2.8% for men; p < 0.001), severe financial hardship (20.4% vs. 17.5% for men; p = 0.011), death of a long-term partner/spouse (21.2% vs. 10% in men; p < 0.001), violence between parents before age 16 (21.9% vs. 17.1% in men; p < 0.001), and that a close friend or relative was at risk of death or dying (66.1% vs. 63.0% in men; p = 0.027).

In contrast, men reported higher prevalence of job loss due to company closure (26.2% vs. 18.5% for women; p < 0.001), exposure to natural disasters (12.9% vs. 8.8% for women; p < 0.001), life-threatening illness or serious accident (30.6% vs. 21.8% for women; p < 0.001), being the victim of a serious physical attack or assault (6.8% vs. 4.5% for women; p < 0.001), having used a weapon or having combat experience (11.0% vs. 0.6% for women; p < 0.001), having to leave their home because of a war (11.2% vs. 8.3% for women; p = 0.001), experiencing the death of one’s mother (11.2% vs. 8.4% for women; p = 0.001), witnessing a serious injury or death in war or military action (10.5% vs. 1.3% for women; p < 0.001), witnessing a fatal or violent accident outside of war (21.8% vs. 7.3% for women; p < 0.001), and the loss of a very close friend or relative in war or military service (12.8% vs. 7.9% for women; p < 0.001).

No significant gender differences were observed in the lifetime experience of physical abuse by parents (3.0% for men vs. 3.5% for women; p = 0.368), parental divorce (4.8% for men vs. 5.2% for women; p = 0.576), the death of a child (5.3% for men vs. 6.4% for women; p = 0.111), and experiencing the death of one’s own father (11.7% of men vs. 11.4% of women; p = 0.754).

Descriptive analysis of the accumulation scores shows that men reported a higher total number of adverse life events than women (3.84 for men vs. 3.57 for women; p < 0.001), with this pattern remaining consistent when looking at self-oriented (2.16 for men vs. 2.00 for women; p < 0.001) and other-oriented adverse life events (1.68 for men vs. 1.57 for women; p = 0.001) separately.

Zero-order correlations between the three accumulation scores were calculated, showing correlations of r = 0.84 between TCALE and SOCALE, r = 0.72 between TCALE and OOCALE, and r = 0.23 between SOCALE and OOCALE. Additional descriptive information on the accumulation scores, comprising their 25th percentile, median, and 75th percentile, is provided in Additional File 6.

Regression results

Table 4 presents the results of the logistic regression models examining the association between the accumulation scores TCALE, SOCALE, and OOCALE with depressive symptomatology. Each additional adverse life event is associated with 30% higher odds of experiencing depressive symptomatology (OR = 1.30, 95% CI: [1.25; 1.35]; Model 1). When distinguishing by event orientation, each additional self-oriented event is associated with 37% higher odds of experiencing depressive symptomatology (OR = 1.37, 95% CI: [1.29; 1.45]; Model 2), while each additional other-oriented event is associated with 19% higher odds (OR = 1.19, 95% CI: [1.11; 1.29]; Model 2).

Table 4Logistic regression models predicting depressive symptomatology

Model 1 Model 2 Model 3 Model 4

TCALE 1.30 *** [1.25; 1.35] - 1.24 *** [1.16; 1.31] -

SOCALE - 1.37 *** [1.29; 1.45] - 1.30 *** [1.18; 1.43]

OOCALE - 1.19 *** [1.11; 1.29] - 1.16 * [1.03; 1.29]

TCALE x Gender - - 1.09 * [1.00; 1.18] -

SOCALE x Gender - - - 1.08 [0.96; 1.22]

OOCALE x Gender - - - 1.07 [0.92; 1.24]

Gender: Men (ref.) - - - -

 Women 1.68 *** [1.39; 2.03] 1.67 *** [1.38; 2.02] 1.15 [0.76; 1.74] 1.19 [0.79; 1.81]

Age: ≤ 59 years (ref.) - - - -

 60–69 years 0.87 [0.69; 1.08] 0.83 [0.66; 1.04] 0.87 [0.69; 1.08] 0.83 [0.66; 1.05]

 70–79 years 0.75 * [0.59; 0.95] 0.73 * [0.57; 0.93] 0.75 * [0.59; 0.96] 0.73 * [0.57; 0.94]

 80 + years 0.90 [0.65; 1.26] 0.93 [0.67; 1.29] 0.91 [0.66; 1.27] 0.94 [0.67; 1.30]

Birthplace: UK (ref.) - - - -

 Outside of UK 1.60 ** [1.13; 2.28] 1.65 ** [1.16; 2.35] 1.62 ** [1.14; 2.30] 1.66 ** [1.17; 2.36]

Highest education: No qualification (ref.) - - - -

 Low education 0.60 *** [0.48; 0.74] 0.60 *** [0.48; 0.75] 0.60 *** [0.48; 0.74] 0.60 *** [0.48; 0.75]

 Medium education 0.48 *** [0.37; 0.62] 0.48 *** [0.38; 0.62] 0.48 *** [0.37; 0.62] 0.48 *** [0.37; 0.62]

 High education 0.27 *** [0.20; 0.37] 0.27 *** [0.20; 0.37] 0.27 *** [0.19; 0.37] 0.27 *** [0.19; 0.37]

Intercept 0.06 *** [0.04; 0.09] 0.06 *** [0.05; 0.09] 0.08 *** [0.05; 0.12] 0.08 *** [0.05; 0.12]

Shown values are odds ratios, with 95% confidence intervals in brackets. Standard errors are clustered at the household level

Significance levels: < 0.05*, < 0.01**, < 0.001***

Abbreviations: ref. reference category, TCALE  total cumulative adverse life event score, SOCALE  self-oriented cumulative adverse life event score, OOCALE  other-oriented cumulative adverse life event score, UK  United Kingdom

The association between TCALE and depressive symptomatology is significantly moderated by gender (OR = 1.09, 95% CI: [1.00; 1.18]; Model 3), with women showing a steeper increase in predicted probability of depressive symptomatology than men as adverse life events accumulate (Fig. 1; Panel A1). No significant gender moderation is observed for SOCALE (OR = 1.08, 95% CI: [0.96; 1.22]; Model 4) and OOCALE (OR = 1.07, 95% CI: [0.92; 1.24]; Model 4). Nevertheless, the predicted probabilities presented in Fig. 1 (Panels A2 and A3) show non-significant trends, indicating that the accumulation of both self- and other-oriented adverse life events may be somewhat more strongly associated with depressive symptomatology in women compared to men.

Table 5 displays the regression results for the relationship between the three accumulation scores, TCALE, SOCALE, and OOCALE, with life satisfaction. Each additional adverse life event is associated with 0.75 scale points lower life satisfaction (β = -0.75, 95% CI: [-0.85; -0.66]; Model 1). When considering event orientation, each additional self-oriented event corresponds to 0.98 scale points lower life satisfaction (β = -0.98, 95% CI: [-1.11; -0.85]; Model 2), whereas each additional other-oriented event is associated with 0.42 scale points lower life satisfaction (β = -0.42, 95% CI: [-0.58; -0.26]; Model 2). Significant moderation effects of gender are observed for TCALE (β = -0.26, 95% CI: [-0.43; -0.09]; Model 3) and OOCALE (β = -0.36, 95% CI: [-0.66; -0.06]; Model 4), indicating that the negative association between adverse life event accumulation and life satisfaction is stronger in women. As shown in Fig. 1 (Panels B1 and B3), women report slightly higher life satisfaction at lower levels of TCALE and OOCALE, yet show a steeper gradient across higher levels of accumulated adversity compared to men. No significant gender moderation is found for SOCALE (β = -0.14, 95% CI: [-0.38; 0.10]; Model 4), although Fig. 1 (Panel B2) shows a comparable non-significant trend to the ones of TCALE and OOCALE.

Table 5Linear regression models predicting life satisfaction

Model 1 Model 2 Model 3 Model 4

TCALE -0.75 *** [-0.85; -0.66] - -0.62 *** [-0.75; -0.49] -

SOCALE - -0.98 *** [-1.11; -0.85] - -0.91 *** [-1.10; -0.72]

OOCALE - -0.42 *** [-0.58; -0.26] - -0.25 * [-0.47; -0.03]

TCALE x Gender - - -0.26 ** [-0.43; -0.09] -

SOCALE x Gender - - - -0.14 [-0.38; 0.10]

OOCALE x Gender - - - -0.36 * [-0.66; -0.06]

Gender: Men (ref.) - - - -

 Women -0.37 * [-0.70; -0.05] -0.37 * [-0.69; -0.05] 0.60 [-0.06; 1.25] 0.50 [-0.15; 1.16]

Age: ≤ 59 years (ref.) - - - -

 60–69 years 0.57 * [0.13; 1.02] 0.72 ** [0.27; 1.17] 0.58 * [0.13; 1.02] 0.72 ** [0.27; 1.17]

 70–79 years 1.58 *** [1.12; 2.05] 1.70 *** [1.24; 2.17] 1.57 *** [1.11; 2.03] 1.69 *** [1.23; 2.16]

 80 + years 1.28 *** [0.62; 1.95] 1.21 *** [0.56; 1.87] 1.26 *** [0.60; 1.92] 1.20 *** [0.55; 1.86]

Birthplace: UK (ref.) - - - -

 Outside of UK 0.22 [-0.53; 0.98] 0.13 [-0.62; 0.88] 0.21 [-0.55; 0.96] 0.12 [-0.63; 0.87]

Highest education: No qualification (ref.) - - - -

 Low education -0.17 [-0.63; 0.29] -0.20 [-0.66; 0.26] -0.18 [-0.64; 0.28] -0.21 [-0.67; 0.25]

 Medium education 0.38 [-0.13; 0.89] 0.31 [-0.20; 0.82] 0.38 [-0.13; 0.89] 0.30 [-0.21; 0.81]

 High education 0.87 ** [0.32; 1.42] 0.81 ** [0.26; 1.36] 0.89 ** [0.34; 1.43] 0.82 ** [0.27; 1.36]

Intercept 22.29 *** [21.68; 22.90] 22.18 *** [21.57; 22.79] 21.78 *** [21.10; 22.46] 21.75 *** [21.07; 22.42]

Shown values are beta coefficients, with 95% confidence intervals in brackets. Standard errors are clustered at the household level

Significance levels: < 0.05*, < 0.01**, < 0.001***

Abbreviations: ref. reference category, TCALE  total cumulative adverse life event score, SOCALE  self-oriented cumulative adverse life event score, OOCALE  other-oriented cumulative adverse life event score, UK  United Kingdom

Finally, Table 6 presents regression results for the association between the accumulation scores TCALE, SOCALE, and OOCALE, and quality of life. Each additional adverse life event is associated with 0.91 scale points lower quality of life (β = -0.91, 95% CI: [-1.03; -0.80]; Model 1). Distinguishing by event orientation of adverse life events, each additional self-oriented adverse life event is associated with 1.25 scale points lower quality of life (β = -1.25, 95% CI: [-1.41; -1.09]; Model 2), whereas each additional other-oriented adverse life event is associated with 0.41 scale points lower quality of life (β = -0.41, 95% CI: [-0.62; -0.20]; Model 2).

Table 6Linear regression models predicting quality of life

Model 1 Model 2 Model 3 Model 4

TCALE -0.91 *** [-1.03; -0.80] - -0.78 *** [-0.95; -0.61] -

SOCALE - -1.25 *** [-1.41; -1.09] - -1.18 *** [-1.43; -0.94]

OOCALE - -0.41 *** [-0.62; -0.20] - -0.25 [-0.54; 0.05]

TCALE x Gender - - -0.26 * [-0.49; -0.04] -

SOCALE x Gender - - - -0.12 [-0.44; 0.19]

OOCALE x Gender - - - -0.34 [-0.74; 0.06]

Gender: Men (ref.) - - - -

 Women 0.54 * [0.12; 0.97] 0.54 * [0.12; 0.97] 1.51 *** [0.64; 2.39] 1.36 ** [0.48; 2.24]

Age: ≤ 59 years (ref.) - - - -

 60–69 years 0.55 [-0.02; 1.12] 0.77 ** [0.20; 1.34] 0.55 [-0.02; 1.12] 0.77 ** [0.19; 1.34]

 70–79 years 0.21 [-0.41; 0.83] 0.39 [-0.23; 1.01] 0.19 [-0.43; 0.81] 0.38 [-0.24; 1.00]

 80 + years -2.14 *** [-3.03; -1.25] -2.25 *** [-3.14; -1.37] -2.17 *** [-3.06; -1.28] -2.26 *** [-3.15; -1.38]

Birthplace: UK (ref.) - - - -

 Outside of UK -0.61 [-1.58; 0.36] -0.75 [-1.72; 0.22] -0.63 [-1.60; 0.34] -0.76 [-1.73; 0.21]

Highest education: No qualification (ref.) - - - -

 Low education 1.43 *** [0.80; 2.06] 1.39 *** [0.77; 2.02] 1.43 *** [0.80; 2.05] 1.38 *** [0.76; 2.01]

 Medium education 2.70 *** [2.02; 3.39] 2.59 *** [1.91; 3.27] 2.70 *** [2.02; 3.39] 2.58 *** [1.90; 3.27]

 High education 3.86 *** [3.15; 4.57] 3.77 *** [3.06; 4.48] 3.88 *** [3.16; 4.59] 3.78 *** [3.06; 4.49]

Intercept 42.85 *** [42.04; 43.66] 42.69 *** [41.88; 43.50] 42.34 *** [41.43; 43.26] 42.29 *** [41.38; 43.20]

Shown values are beta coefficients, with 95% confidence intervals in brackets. Standard errors are clustered at the household level

Significance levels: < 0.05*, < 0.01**, < 0.001***

Abbreviations: ref. reference category, TCALE  total cumulative adverse life event score, SOCALE  self-oriented cumulative adverse life event score, OOCALE  other-oriented cumulative adverse life event score, UK  United Kingdom

A significant interaction term between TCALE and gender is observed (β = -0.26, 95% CI: [-0.49; -0.04]; Model 3), indicating that the negative association between cumulative adverse life events and quality of life is stronger in women. No significant gender differences are found for SOCALE (β = -0.12, 95% CI: [-0.44; 0.19]; Model 4) and OOCALE (β = -0.34, 95% CI: [-0.74; 0.06]; Model 4), although the interaction term for OOCALE only narrowly misses significance. As illustrated in Fig. 1 (Panel C3), women report higher quality of life at lower levels of OOCALE yet show a steeper gradient across higher levels of accumulated adversity compared to men.

Discussion

Summary of findings

This study examined whether the accumulation of adverse life events across the life course – overall and differentiated by event orientation – is associated with depressive symptomatology, life satisfaction, and quality of life, as well as potential gender differences in these relationships.

Results show that greater accumulation of adverse life events, overall and separately for self- and other-oriented adverse events, is linked to a higher risk of depressive symptomatology, lower life satisfaction, and poorer quality of life. Gender moderates several of these associations. Women appear to be more vulnerable to the overall accumulation of adverse life events with respect to all three outcomes, as well as to the accumulation of other-oriented adverse life events in relation to life satisfaction. For depressive symptomatology and quality of life, women likewise tend to show poorer outcomes as other-oriented events accumulate compared with men, although these gender differences do not reach statistical significance. This study found no clear evidence for gender differences in the associations between accumulated self-oriented adverse life events and any of the outcomes examined.

The observed relationship between the accumulation of adverse life events and poorer mental health and reduced well-being is consistent with prior research [1, 2, 18, 48–59]. This study contributes to the existing literature by confirming these associations while considering a wide range of adverse events. Results for self-oriented adverse life events also align with existing evidence showing that the accumulation of direct personal adversity is associated with poorer mental health and well-being [18, 55, 56, 60]. Importantly, the present findings also indicate that the accumulation of other-oriented adverse life events is related to a higher risk of depressive symptomatology, lower life satisfaction, and reduced quality of life, contrasting with previous studies reporting no association [55, 56, 60] or even beneficial effects of accumulated other-oriented adversity for mental health and well-being [56, 60].

One possible explanation for these divergent findings on the consequences of accumulated other-oriented adverse life events is contextual differences between study populations. Previous studies reporting contrasting findings were conducted among older Israeli adults [55, 56, 60]. This population is characterized by exceptionally high lifetime exposure to collective stressors and trauma, including the Holocaust, large-scale migration, and repeated military conflicts [61, 62]. Correspondingly, the prevalence of depression in this population is nearly twice as high as in Western countries [55], such as England. Such extensive exposure to severe and collective stressors may shape both the experience and psychological processing of adverse life events in ways that differ substantially from those in other national contexts [55]. Compared with previous work examining consequences of accumulated other-oriented adverse life events for mental health and well-being in England [18], the present study covers a broader spectrum of other-oriented adverse events and provides additional evidence that their accumulation is associated with poorer mental health and reduced well-being.

The central contribution of this study is the examination of gender differences in the relationship between the accumulation of adverse life events and mental health and well-being. Results indicate a greater vulnerability to overall adverse life event accumulation with respect to all outcomes, and thus across a wide range of well-being dimensions, among women compared to men. This finding is consistent with prior evidence suggesting heightened sensitivity in women to accumulated adverse life events in relation to depression [52]. The present findings extend this existing evidence by showing that these gendered patterns also apply to other facets of well-being, namely life satisfaction and quality of life. Importantly, by distinguishing between self- and other-oriented adverse life events, this study provides novel insight into how observed gendered patterns vary depending on the orientation of accumulated adverse events.

Regarding the accumulation of other-oriented adverse life events, gender differences were visible, reaching statistical significance for life satisfaction while narrowly missing significance for depressive symptomatology and quality of life. This indicates that gendered consequences of the accumulation of other-oriented adverse life events are particularly pronounced for subjective evaluations of one’s life circumstances. Given that associations clearly and consistently point in the same direction across outcomes, the non-significant results for accumulated other-oriented adverse life events may reflect insufficient statistical power to detect small but potentially meaningful gender differences rather than a true absence of variation by gender.

The lack of significant gender differences in the relationship between accumulated self-oriented adverse events and mental health and well-being may reflect the nature of these stressors. Self-oriented adverse events are primarily processed through personal coping resources [140] and are often perceived as less controllable than other-oriented adverse events [72, 141]. This could reduce the influence of gender-role expectations in the coping process. The relative uncontrollability of self-oriented events also enhances the effectiveness of emotion-focused coping strategies [142], which women tend to employ more than men [82, 90–93], potentially buffering the negative consequences of accumulated self-oriented events for women’s mental health and well-being and thereby attenuating observable gender differences for this event type.

Strengths, limitations, and further research directions

A major strength of this study is the broad range of adverse life events considered. However, information on some potentially important events, such as the death of a sibling, was not available, and other events, such as unexpected job loss, are only partially captured with ELSA data. Some of these unobserved adverse events may differ in their consequences for men’s and women’s mental health and well-being [72, 141]. As a result, the estimated associations and observed gender differences reflect the set of adverse life events covered in this study.

Due to limited information on event frequency for most adverse life events, accumulation was operationalized as the diversity of events experienced over the life course. While this checklist-based approach does not capture repeated exposure to adverse events, it nevertheless provides a widely used and transparent measure of cumulative adversity [1]. Furthermore, although relying on a unweighted sum score is the most commonly used approach for measuring the accumulation of adverse life events, it does not account for differences in the perceived severity of individual events nor for variations in their overall impact [132]. A strength of this study is that it addresses these two measurement-related concerns through two sensitivity analyses. First, frequency information for a subset of events was incorporated, and second, event weights derived from an external study [133] were applied. Both additional analyses yielded results largely consistent with the main analysis, supporting the robustness of the findings. It should be noted, however, that the available weights did not perfectly correspond to every adverse life event considered in this study, were not specifically developed for the outcomes examined, and do not account for the timing of events in the life course, therefore providing only an approximate indication of the relative burden for each event.

As with all retrospective collection of information, recall bias cannot be entirely ruled out. Despite the use of a life history calendar in ELSA, improving recall accuracy [102, 143], some memory limitations remain, particularly for events that occurred decades before the interview [140, 144]. Gender differences in recall and reporting of adversities and mental health outcomes may also affect the results of this study. Women tend to remember and ruminate more about past adverse events than men [145–147], while men tend to report fewer events due to social norms encouraging emotional restraint, self-control, and stoicism [69, 71, 148]. In addition, internalized gender norms and concerns about negative social perceptions may affect men’s willingness to disclose mental health difficulties [149], although some research has found no gender differences in the reporting of depressive symptoms [150, 151]. While these patterns could influence observed gender differences, the direction and magnitude of any resulting bias remain uncertain.

Gender was operationalized as a binary variable, consistent with ELSA coding. Yet gender is a multidimensional construct encompassing non-binary, trans, and gender-diverse identities [152]. Future research incorporating more inclusive measures of gender may provide more nuanced patterns.

Although temporal ordering is supported by the timing of events preceding outcome assessment, causal interpretations remain limited due to the cross-sectional study design [153]. In addition, information on participants’ characteristics prior to or at the time of each event, such as social engagement, socioeconomic status, and childhood socioeconomic circumstances, was not available, limiting adjustment for potential confounders [63, 154]. Yet, results proved to be largely robust to the inclusion of available additional confounding variables, supporting the robustness of the findings.

The data used in this study was collected in 2006/2007 and therefore reflects the social, healthcare, and gender norms of that period. Societal changes since then, including reduced stigma toward mental illness [155], shifts toward more egalitarian gender roles [156, 157], and improvements in social support and healthcare structures [158, 159], should be considered when interpreting the findings in a contemporary context. In addition, most participants experienced the Second World War and/or the post-war period, followed by considerable economic and social changes in the UK, which may have shaped coping strategies and responses to adversity [160]. While participants born after 1945 account for approximately 50% of the sample, most were born in the late 1940s and early 1950s and thus still grew up in the immediate post-war period. As those born sufficiently late to be largely unaffected by wartime and its direct aftermath represent only a very small fraction of the study sample, subgroup analyses for this group were not feasible.

In addition, findings of this study reflect the experiences of adults aged 50 and older and their partners living in private households in England, as represented in the ELSA study [100]. Their life circumstances have been shown to differ from those of younger age groups [93] and people living outside traditional private households [161]. It has been shown that coping behaviors change across the life course, with evidence indicating a convergence between men and women in the use of coping strategies as they age, with both increasingly relying on problem-focused coping [93]. Therefore, further research is needed to determine the extent to which these findings generalize to other cohorts, younger age groups, and different living arrangements.

The analysis sample included about 52% of the original Wave 3 ELSA participants, as many did not complete the life history interview or self-completion questionnaires or had missing information in important study variables. Differences between included and excluded participants (presented in Additional File 1) suggest potential selection effects, which should be taken into account when interpreting the findings of this research.

Future research should further examine the consequences of accumulating adverse life events for mental health and well-being, and especially associated gender differences, by extending analyses to populations of different ages, cultural contexts, and living arrangements (e.g., including individuals living in institutional settings such as nursing homes). Applying alternative approaches to capture the accumulation of adverse life events, such as taking into account repeated exposure, perceived burden, and different sets of adverse life events, may further advance the insights gained from this study.

Conclusion

The results of this study indicate that the accumulation of adverse life events across the life course, overall and separately for self- and other-oriented events, is associated with a higher risk of experiencing depressive symptomatology, lower life satisfaction, and reduced quality of life. The consistent associations across all three outcomes suggest that cumulative adversity represents a substantial source of strain across multiple dimensions of psychological well-being. Women’s mental health and well-being appear more vulnerable to cumulative adversity compared to men’s, particularly regarding overall accumulation and the accumulation of other-oriented adverse life events.

From a public health perspective, these findings underscore the importance of identifying populations with high cumulative adversity exposure, with particular attention to women who may be particularly affected by its long-term consequences. Gender-sensitive interventions, including targeted mental health support, promotion of coping resources, and strategies addressing both the overall burden and orientation of adverse events, may help mitigate long-term negative consequences of accumulated adversity for mental health and well-being.

Future research should build on these findings by examining gendered consequences of cumulative adversity for mental health and well-being with greater attention to frequency and perceived severity, as well as across different populations.

Supplementary Information

Acknowledgements

The author would like to thank Anna Oksuzyan for supervision during the research process and Chiara Baldan for helpful feedback on an earlier draft of this manuscript.

Abbreviations

CASP-19Control, Autonomy, Self-realization, and Pleasure 19

CASP-12Control, Autonomy, Self-realization, and Pleasure 12

CES-DCenter for Epidemiological Studies Depression Scale

CLAMCumulative Lifetime Adversity Measure

ELSAEnglish Longitudinal Study of Ageing

OOCALEOther-oriented cumulative adverse life event score

SOCALESelf-oriented cumulative adverse life event score

TCALETotal cumulative adverse life event score

Authors’ contributions

SH conceived the study, conducted the statistical analyses, interpreted the results, and drafted the manuscript.

Funding

Open Access funding enabled and organized by Projekt DEAL. The author received no specific funding for this work.

Data availability

English Longitudinal Study of Ageing (ELSA) data can be accessed by application via https://www.elsa-project.ac.uk/accessing-elsa-data.

Declarations

Ethics approval and consent to participate

Ethical approval for the third Wave of the English Longitudinal Study of Ageing (ELSA) was obtained from the London Multi-Centre Research Ethics Committee on 27th October 2005 (05/MRE02/63), in accordance with the Declaration of Helsinki. All participants provided written informed consent. The present study involved secondary analysis of anonymized data and required no additional ethical approval.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

Supplementary Materials

Data Availability Statement

English Longitudinal Study of Ageing (ELSA) data can be accessed by application via https://www.elsa-project.ac.uk/accessing-elsa-data.