Background

Life events are significant occurrences, often with far-reaching consequences. They take place with or necessitate changes in behavioral patterns (Filipp 1995; Holmes and Rahe 1967). Diverse forms of life events, such as social, physical and biological events, can be classified as normative or non-normative (Wrzus et al. 2013). Normative life events comprise puberty, marriage, relocation, parenthood, entering the labor market, retirement or the death of the parents. They are expected or of a high probability for most people at a certain age. In contrast, non-normative life events are often unexpected and occur at any time during the lifespan. Examples of non-normative life events are death of a spouse, divorce or unemployment. While non-normative life events have a moderate occurrence probability, normative life events are more likely (Filipp 1995). Thus, most studies focus on normative life events.

Life events and sociodemographic variables

Socioeconomic status was shown to have an impact on life events and other stressors (Lantz et al. 2005; Hobson et al. 1998). Higher financial stress was associated with moderate-to-severe functional limitations and poor-to-fair self-rated health. Differential exposure to stress and negative life events based on socioeconomic disparities may be one determinant for socioeconomic inequalities in health. Gender also determines the risk of exposure to stress (Landrine et al. 1995; Thomas et al. 2008). Gender has been found to influence the predictive role of stressful life events (SLEs) for the risk of major depressive episodes over a long-term period of 25 years (Assari and Lankarani 2016). The predictive role of SLEs for depression was stronger in men than women. However, these findings may be due to gender differences in the threshold of reporting stress, perceiving risks or use of coping strategies (Harris et al. 2006).

Relationship of life events with health-related variables

Health is defined as “a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity,” according to the World Health Organization (WHO). A meta-analysis by Kraaij, Arensman and Spinhoven (Kraaij et al. 2002) revealed that almost all negative life events appeared to have a modest but significant relationship with depression. For instance, the total numbers of negative life events and of daily hassles had the strongest relationships with depression. Furthermore, longitudinal data from a 10-year follow-up health survey were analyzed including life events as stressors and morbidity years of ten chronic conditions (Hsu 2011). Accordingly, loss of offspring increased the risk for impaired physical health, psychological health and life satisfaction. Being newly widowed was related to depressive symptoms, whereas being newly divorced increased the risk of poor social support. There is also longitudinal evidence for life events related to the development of anxiety disorders (Jonker et al. 2017). With reference to morbidity years, increased failure of successful aging was observed, especially after medical illness such as stroke (Kornerup et al. 2010), respiratory system disease (Lietzén et al. 2011), cancer (Lillberg et al. 2003), diabetes and visceral adiposity (Mooy et al. 2000). Chronic inflammatory diseases of the central nervous system such as multiple sclerosis have also been linked to SLEs (Mohr et al. 2004). There is evidence for gene-environment correlations with regard to SLEs and psychotic experiences (Shakoor et al. 2016). The nature and extent of the association between life event exposure and stress reactivity in young adults depended on the valence of the events together with the sex of the individual and their social network size (Phillips et al. 2005). To provide a theoretical framework, Schwarzer and Schulz (2003) proposed a model suggesting different mediating pathways with regard to the association of stressful life events and illness indicators. The potential mediators were health-compromising behaviors (e.g., smoking, alcohol consumption, physical inactivity), physiological changes and negative affect.

Assessment of life events

Originally, according to Holmes and Rahe, critical life events were supposed to be associated with stress experience regardless of the valence of a certain life event (Holmes and Rahe 1967). This means that important life changes, whether negative, such as the death of a close friend, or positive, such as marriage, all induce stress. This theoretical framework implies that life events require the individual to adapt and regain stability. The Social Readjustment Rating Scale [SRRS; (Holmes and Rahe 1967)] is a widely used checklist for the occurrence of stressful life events (SLEs) (Scully et al. 2000). The authors suggested scores reflecting the severity of the impact of the life event for each item, which refer to the past year. The scores called “life-changing units” range from 11 to 100. Stress is operationalized as a cumulative psychological construct and estimated from the summed frequency of life events. Higher sum scores correspond to higher stress levels. There are modified English versions of the SRRS (Hobson et al. 1998; Hobson and Delunas 2001; Miller and Rahe 1997); some are based on specific populations (Goldberg and Comstock 1980; Lüdtke et al. 2011). In summary, checklists are still the most popular assessment methods for stressful life events (Hobson and Delunas 2001; Cleary 1980) although interview methods have been introduced [e.g., (Paykel 1997; Wittchen et al. 1989)]. As criticized by some researchers (Hobson and Delunas 2001), the life event items contain both discrete events (e.g., sustained injury or disease) and possible symptoms or consequences of stress (e.g., change of sleep patterns). Additionally, critical life events vary in their valence, ranging from positive to negative, e.g., marriage and death of a spouse. Also, it is not clear whether and how operationalization of a stress score improves the predictive value of life events with regard to mental or general health (Cooper et al. 1985).

Objectives of the study

First, we aimed to present the frequency of stressful life events for a large representative sample of the German population. Thus, we wanted to provide data on the frequency of life events depending on age, sex and socioeconomic status. This allows for cross-national comparison and for the investigation of cultural differences. Second, we further wanted to determine which of the SLEs are of interest for epidemiological research in terms of psychological distress (depression and anxiety) and subjective health. Highly stressful life events (based on the SRRS guidelines) are assumed to have a negative impact on health resulting in higher distress (depression, anxiety) and lower self-reported general health. Finally, we tested whether weighted, as suggested by the original authors (Holmes and Rahe 1967), or unweighted SLE scores of life events predict distress and subjective health taking sociodemographic variables into account. We expected stronger covariations of SLE with indicators of psychological distress compared with the more abstract variable of subjective general health in terms of convergent and discriminant validity.

Methods

Baseline data of the Gutenberg Health Study (GHS) were used in the current study. The first wave provided cross-sectional data of N = 12,947 participants enrolled from April 2007 to April 2012. The GHS is a population-based, prospective, observational single-center cohort study in western mid-Germany (Rhein-Main region). The main objective of the GHS is prevention of cardiovascular, metabolic, ophthalmological and mental disorders in the general population of the region by improving disease prediction by considering lifestyle, psychosocial and environmental risk factors, protein patterns, genetic variants and subclinical disease. Previous studies based on the GHS dealing with psychosomatic issues have been published identifying risk factors of, for instance, mental (Tibubos et al. 2019a; Tibubos et al. 2019b) and somatic ill health (Beutel et al. 2019; Michal et al. 2014; Ernst et al. 2019). The study design and procedure have been described in detail elsewhere (Wild et al. 2012; Höhn et al. 2015).

Participants

Prior to enrollment, participants signed written informed consent. The study and its procedure were approved by the ethics committee of the Statutory Physician Board of the State of Rhineland-Palatinate and controlled by the local and federal data safety commissioners. The GHS sample was drawn randomly from the local registry of the city of Mainz and the district of Mainz-Bingen. The sample was stratified 1:1 for gender and residence and in equal strata for decades of age. Participants between the ages of 35 to 74 years were included. Individuals with insufficient knowledge of the German language, or physical or mental inability to participate, were excluded; 5.2% were excluded based on these exclusion criteria. The response rate (defined as the recruitment efficacy proportion, i.e., the number of persons participating in the baseline examination divided by the sum of the number of persons participating in the baseline examination plus those who refused and those who could not be contacted) was 55.5%. The sample is described in detail in the supplement material (Table S2a and Table Sb) separately for men and women. Mean age was 55.0 (SD = 11.1) years; 49.4% were female.

Measures and assessment

Data were collected during a 5-h examination in the study center. They included evaluations of classic cardiovascular risk factors, laboratory examinations, a computer-assisted personal interview and anthropometric measurements. In the computer-assisted personal interview, participants were asked questions about sociodemographic variables and completed questionnaires. All examinations were performed according to standard operating procedures by certified medical technical assistants.

The Social Readjustment Rating Scale (SRRS) (Holmes and Rahe 1967) is a widely used checklist for stressful life events (Scully et al. 2000; Hobson and Delunas 2001). It provides a standardized measure of the impact of a wide range of common stressors. The original version includes 43 items. An adapted German version with 36 items was administered in the GHS (Cronbach’s alpha = 0.81). Seven items were excluded because of partially overlapping content and time constraints. In line with the original scale, the GHS version of the SRRS used four response categories (0 = never occurred; 1 = occurred in the last year; 2 = occurred 2 to 5 years ago; 3 = occurred > 5 years ago). Response categories 2 and 3 were combined as “occurred > 1 year” in the descriptive statistics based on single SLEs stratified for sociodemographic variables (see supplement material Table S1, Table S2, Table S2a and Table S2b). Since weighted scores indicating the severity of stress for each life event were only available for SLE regarding the past year (Miller and Rahe 1997), we accordingly used only the categories 0 and 1 in multiple regression analyses with SLE sum scores as dependent variables to compare unweighted and weighted sum scores of the SRRS items. Death of the spouse or a close family member, divorce or marital separation, and jail terms were regarded as the most severe SLEs.

Depression was measured by the Patient Health Questionnaire (PHQ-9) (Kroenke and Spitzer 2002), which quantifies the frequency of being bothered by each of the nine diagnostic criteria of major depression over the past 2 weeks. The items were rated on a 4-point Likert scale, 0 = not at all to 3 = nearly every day. Responses are summed to create a score between 0 and 27 points.

Generalized anxiety (GAD) was identified by the GAD-2 screener derived from the anxiety module of the PHQ (Kroenke et al. 2007). Participants answered the two GAD items “Feeling nervous, anxious or on edge” and “Not being able to stop or control worrying” on a 4-point Likert scale, 0 = not at all to 3 = nearly every day.

General subjective health was measured by the item “How do you rate your health in general on a 5-point Likert scale ranging from 1 = “very good” to 5 = “bad”? (Brähler et al. 1979). To ease the interpretation, we recoded this variable so that a high score indicated better health.

Sociodemographic variables used in this study were: sex, age, socioeconomic status (SES), partnership and income. Sex and partnership were dichotomous variables. Age was measured as a continuous variable for descriptive statistics, and subsequent analysis of variance categories were calculated based on 10-year intervals. SES was defined according to Lampert and Kroll’s scores of SES with a range from 3 to 21: 3 indicates the lowest SES and 21 the highest. Subsequently, we generated three SES categories (low, moderate, high) based on Lampert et al.'s (Lampert and Kroll 2009) recommendation for descriptive analyses and analyses of variance. Following the Organisation for Economic Co-operation and Development [OECD (OECD 2011)], we estimated equivalence income by dividing net household income by the square root of the number of people living permanently in a household.

Statistical analysis

Descriptive statistics were calculated for each SRRS item. Group-specific statistics were provided, such as sex, age and socioeconomic status. To test group differences, univariate and multivariate analyses of variance were subsequently performed. Spearman correlation analyses (SLE at the item and aggregated level) and multiple hierarchical regression analyses (SLE at the aggregated level) with the enter method were used to investigate the association of stressful life events with health-related variables. We did not estimate missing data for inference statistical analyses because they can be systematic, for instance, because of sex (e.g., pregnancy), age (e.g., school-related questions, retirement) and other determinants. For multivariate analyses (i.e., variance and regression analyses), listwise deletion was used. Multiple regression models with SLE on the item level were not conducted because of the detrimental reduction of the sample size and lack of variance for many single SLE item predictors. In the first block of the regression analyses at the aggregated level, sociodemographic variables were controlled. The second block included the aggregated SLE sum score (unweighted or weighted). Due to the big baseline sample, p values should be treated with caution and are primarily given for descriptive reasons; they correspond to two-tailed tests. Statistical analyses were conducted using SPSS for Windows 23. Correlation and regression coefficients were interpreted based on Cohen (Cohen 1988).

Results

Descriptive statistics for all items of the SRRS are displayed in Table S1 (see supplement material) for all participants referring to the frequency of live events which occurred in the past year. Detailed data reporting frequencies with regard to all response categories [“never,” “occurred in the past year” and “> 1–5 years (lifetime)”] are presented as supplement material (Table S2 overall, Table S2a female, Table S2b male).

In the past year, having been on vacation was reported most frequently by a total of 68.6%, followed by personal injury or illness (23.7%), outstanding personal achievements (19.2%), changed health status of a family member (17.7%), change of personal habits (16.2%), conflicts with the boss (13.5%), change in financial state (12.8%), change in sleeping habits (12.4%), change in recreation and change in responsibilities at work (11.3% each), reconciliation with a partner (11.2%) and death of a close family member (9.6%), business readjustment, change to a different line of work and death of a close friend (8.8% each). Patterns were similar for women and men; however, men reported more than twice as many minor law violations (19.7%) as women (8.9%). Regarding lifetime incidences, the majority of participants reported life events such as marriage, pregnancy (women), death of a close family member, moving, and vocational and educational changes. Women reported the death of a spouse more frequently (9.6%) than men (1.4%). Table S2a and S2b shows the SLE items according to age and SES for females and males, respectively.

Multivariate analyses of variance with SLE at the item level as dependent variable revealed large main effects for sex (Wilks’ λ = 0.70, F(36, 8466) = 99.30, p < 0.001, ηp2 = 0.30) and age (Wilks’ λ = 0.42, F(108, 25,355) = 79.79, p < 0.001, ηp2 = 0.25). A moderate effect was found for SES (Wilks’ λ = 0.85, F(72, 16,932) = 20.26, p < 0.001, ηp2 = 0.08). Interaction effects of sex × age (Wilks’ λ = 0.96, F(108, 25,355) = 3.19, p < 0.001, ηp2 = 0.01), sex × SES (Wilks’ λ = 0.98, F(72, 16,932) = 2.88, p < 0.001, ηp2 = 0.01), age × SES (Wilks’ λ = 0.95, F(216, 50,205) = 2.18, p < 0.001, ηp2 = 0.01) were also significant, but with neglectable effect sizes. No significant effect was found for the triple interaction among the three independent variables (Wilks’ λ = 0.97, F(216, 50,205) = 1.16, p = 0.06, ηp2 = 0.01). Correlations between weighted and unweighted SLE sum scores were r = 0.96. Overall, almost identical findings were observed for unweighted scores because of the high correlation of weighted and unweighted scores. Therefore, only the results for weighted SLE sum scores are reported. As depicted in Fig. 1, the cumulated SLE increased with SES, but decreased with age in males and females. Compared with SLE analyses at item level, univariate analyses of variance with the aggregated SLE score (unweighted and weighted) revealed significant main effects with a rather small effect size for age (F(3,12,913) = 310.69, p < 0.001, ηp2 = 0.07) and SES (F(2, 12,913) = 44.08, p < 0.001, ηp2 = 0.01), but not for sex (F(1, 12,913) = 0.03, p = 0.88, ηp2 = 0.00). None of the interaction terms turned out to be significant.

Fig. 1
figure 1

Aggregated stressful life events (SLE) for men and women stratified by age and socioeconomic status (SES) in a German representative sample (N = 12,947)

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With regard to the association of mental and general subjective health with stressful life events, unweighted and weighted aggregated SLEs were significantly correlated with depression (r = 0.17 and 0.18, p < 0.001) and anxiety (r = 0.16 and 0.17, p < 0.001). There was only a very weak association of the unweighted SLE sum score with general subjective health (r = −0.04, p < 0.001), but none of the weighted SLE sum score (r = −0.01, n.s.). The differences of correlation coefficients referring to the unweighted SLE sum score were significant between general health and depression (z = 14.26, p < 0.001) or anxiety (z = 7.37, p < 0.001), respectively, indicating a larger effect on mental distress compared with general health. Table 1 depicts correlations between SLEs and depression, anxiety and general health. While SLEs such as personal injury or illness and sexual difficulties are similarly detrimental for general and mental health, change in responsibilities at work increases mental distress, but is also linked to better general health. Higher detrimental associations with mental health compard with general health were found for change in different areas such as living conditions, recreation, sleeping habits, personal habits, financial state, health status of a family member or line of work. Having trouble with the boss is primarily linked with mental distress, not with general health.

Table 1 Bivariate correlation of stressful life events (SLE) with mental health in a German representative sample (N = 12,947)
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Regression analyses showed that unweighted and weighted aggregated SLEs significantly predicted depression (β = 0.18 and 0.19, p < 0.001) and anxiety (β = 0.17 and 0.18, p < 0.001) and to a lesser degree lower subjective health (β = −0.09 and −0.10, p < 0.001) controlling for sociodemographic factors. With regard to mental health, especially female sex and lower income, and to a neglectable amount absent partnership, lower SES and lower age additionally predicted depression. A similar pattern was observed for anxiety, but age and SES did not reach significance. Referring to general health, especially lower age and higher SES, but also employment and income were significant sociodemographic factors. The explained variance for all models ranged from 6% to 7% (Table 2).

Table 2 Prediction of distress (depression, anxiety) and general health by stressful life events (SLE) at aggregated level controlling for sociodemographic variables in a German representative sample (N = 12,947)
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Discussion

Our study provided frequencies of SLEs (Holmes and Rahe 1967; Hobson and Delunas 2001; Miller and Rahe 1997) for a large sample from the German population. We reported associations of stressful life events in terms of Holmes and Rahe’s stress definition with mental health for a population-based German sample which had not been available to date. Comparisons with international data using the SRRS are now possible with a reliable data set from Germany allowing cross-cultural insights (Assari and Lankarani 2016). Based on the large and representative data set, we further provided reliable data without common selection bias for specific subpopulations, which is necessary for target-oriented public health services.

In the past year, having been on vacation as a normative life event was reported by the majority. Frequent health-related issues referring to the person were personal injury or illness or to the social network were changed health status of a family member, death of a close family member and death of a close friend. Changes of habits or health behaviors included: change of personal habits, change in recreation and change in sleeping habits. Vocational changes included trouble with the boss, change in financial state, change in responsibilities at work, change to a different line of work and business readjustment. Additional changes included personal achievements and reconciliation with the partner. Patterns were similar for women and men; however, men reported more than twice as many minor law violations as women and had more sexual problems than women.

Regarding lifetime incidences, the majority reported life events such as death of a close family member and normative events such as marriage, moving, and vocational and educational changes. Referring to reported single life events, there were some differences for men and women. In line with statistics of a higher mortality rate for men than women (Bundesamt 2016), for instance, women reported the death of a spouse more frequently than men. As was expected, women also reported a high lifetime frequency of pregnancy. Surprisingly, also men described pregnancy as a personal life event, albeit with a lower frequency for lifetime incidence, but to a similar amount for the past year. Thus, cognitive effects based on proximity in time and probably also on cohort effects seem to be relevant for this finding. Interestingly, the incidence of life events significantly decreased with age. A descriptive trend for higher SLE incidence rates was found for increasing social status: The youngest participants with the highest social status reported the highest sum of life events. A high SES, as evidenced by education, vocational status and income, is also likely to require flexibility and mobility. Adults from 35 to 44 years old are in an important phase of their family building and career advancement, so many SLEs can be expected.

At the bivariate level, many significant correlations of single SLEs with mental health were observed. Change of sleep habits and sexual difficulties can be taken as precursors or signs of depression. Conflicts with the boss, lack of personal achievement, and having an injury or disease are known predictors of mental distress (Kraaij et al. 2002); having spent Christmas alone can be taken as an indicator of social isolation. Conflicts with the boss, changes of personal finances and changed health status of a family member may indicate loss of security, a potential cause of anxiety and sexual difficulties. Both are more frequent in women. Conversely, general health has been associated with absence of personal illness, personal achievement, constant sleep patterns and vacationing along with younger age and higher SES.

Surprisingly, SLEs that are considered to be extremely stressful according to proposed scoring weights (death of the spouse or a close family member, divorce or marital separation, and jail term) were not linked to mental health at all or only weakly for both. An explanation for the lack of association between severe SLEs and mental health is mediating processes that are not included in our statistical models. For instance, Schwarzer and Schulz (2003) proposed the mediating role of health behavior and coping strategies to explain the link between SLEs and illness indicators. Another potential explanation is crucial moderating variables that have not been taken into account. Irrespective of different scoring suggestions of SLEs, further investigation of moderating variables for severe life events [e.g., for death of a spouse, see (Specht et al. 2011)] is required to understand these effects. Potential moderating variables are interindividual differences, especially psychological traits, as well as structural and social factors, such as generational and societal differences. The death of a beloved person after a long struggle with illness together with the psychological and financial burden is likely to result in a decrease of distress compared with a sudden unexpected loss. Also, the theory of SLEs proposed by Holmes and Rahe (1967), which is the theoretical foundation of the SRRS, assumes that valence of a life event is irrelevant. However, the subjective valence of an experienced life event has been found to vary interindividually (Cooper et al. 1985). Finally, a third potential explanation might be associated with the outcome variables used in our study. Depressive symptoms and anxiety represent internalizing disorders. Individuals who were former prison inmates were more likely to express mental distress in externalizing symptoms, e.g., anger disorders (Rohrmann et al. 2013).

Bivariate correlation and multiple regression analyses revealed the effect of SLE on depression, anxiety and general health controlling for sociodemographic variables. In line with other studies (Spinhoven et al. 2010; Vardaxi et al. 2018; Kessler 1997), aggregated SLEs in terms of SLE sum scores were significant and consistent positive predictors of both indicators of mental distress. Weaker or no effects were found between cumulated SLEs and general health. Our findings emphasize the larger impact of cumulated SLEs on mental health compared with general health representing a more abstract concept including mental, physical and social health. As assumed before, the manifestation of life event effects is supposed to be reflected predominantly in mental health before affecting overall health comprising many domains. Also, findings at the item level revealed stronger relations between mental health and most SRRS items. Only a few items such as those including somatic health content (e.g., personal illness and sleep patterns) showed a significant association with general heath. From a psychometric point of view, this result can be interpreted as validation for SLE scores in terms of convergent and discriminant validity. In our study, which refers to a large and representative sample, similar results were observed regardless of the use of weighted or unweighted sum scores. Thus, the scoring debate of life event checklists is not relevant for data interpretation of the current study compared with studies with small study populations and a higher likelihood of sample bias [cf. (Hobson et al. 1998)]. Self-reported general health revealed a stronger association with cumulated SLEs when sociodemographic variables were controlled. Overall, however, the proportion of explained variance of mental distress and general health was only 6% to 7%.

Positive bivariate relations of vacation and outstanding personality achievements were also observed in general health. Interestingly, a change in responsibilities at work was linked to better general health, but also to higher anxiety levels that might arise along with more novel and thus uncertain circumstances. In multivariate analyses, personal outstanding achievements were linked to lower depression and better general health. Overall, results were quite heterogeneous, not allowing the identification of SLE categories, such as normative vs. non-normative, health-related, social or vocational, that might affect mental health more than others. For epidemiological studies among adults, SLEs with low incidence rates such as a jail term or change in schools can be regarded as less relevant.

Large interindividual differences are inherent with regard to effects of life events on health-related outcomes (Schwarzer and Schulz 2003). Stress caused by a particular stressor varies from one person to another because of the variability of circumstances, appraisal processes and many other personality-related characteristics such as goals, values, coping strategies and resources. In summary, although the SRRS is well researched, the results are only a rough approximation at best. A scale development of life event checklists is beyond the scope of this report, but data derived from the current study provide helpful information to do so. To interpret the results as causal effects of SLEs on mental health, a clear distinction of the chronical occurrence of SLEs and symptoms of depression and anxiety is pivotal. An overlap of the chronical appearance of the independent and dependent variables has not been controlled in the current study design. For a better understanding of the underlying mechanisms, the observation of trajectories of health-related variables and single SLEs in specific populations (i.e., in terms of sex-sensitive analyses) and in defined domains (e.g., vocational, family, individual) taking the corresponding circumstances and the interindividual emotional valence of an SLE into account will be a first step.