1. Epidemiology and Global Health

Adverse childhood experiences and resilience among adult women: A population-based study

  1. Hilda Björk Daníelsdóttir  Is a corresponding author
  2. Thor Aspelund
  3. Edda Bjork Thordardottir
  4. Katja Fall
  5. Fang Fang
  6. Gunnar Tómasson
  7. Harpa Rúnarsdóttir
  8. Qian Yang
  9. Karmel W Choi
  10. Beatrice Kennedy
  11. Thorhildur Halldorsdottir
  12. Donghao Lu
  13. Huan Song
  14. Jóhanna Jakobsdóttir
  15. Arna Hauksdóttir
  16. Unnur Anna Valdimarsdóttir
  1. Centre of Public Health Sciences, Faculty of Medicine, University of Iceland, Iceland
  2. Department of Medical Epidemiology & Biostatistics, Karolinska Institutet, Sweden
  3. Clinical Epidemiology and Biostatistics, School of Medical Sciences, Örebro University, Sweden
  4. Institute of Environmental Medicine, Karolinska Institutet, Sweden
  5. Department of Psychiatry, Massachusetts General Hospital, United States
  6. Harvard T.H. Chan School of Public Health, United States
  7. Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Sweden
  8. Department of Psychology, Reykjavík University, Iceland
  9. West China Biomedical Big Data Center, West China Hospital, Sichuan University, China
https://doi.org/10.7554/eLife.71770
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Cite this article
  1. Hilda Björk Daníelsdóttir
  2. Thor Aspelund
  3. Edda Bjork Thordardottir
  4. Katja Fall
  5. Fang Fang
  6. Gunnar Tómasson
  7. Harpa Rúnarsdóttir
  8. Qian Yang
  9. Karmel W Choi
  10. Beatrice Kennedy
  11. Thorhildur Halldorsdottir
  12. Donghao Lu
  13. Huan Song
  14. Jóhanna Jakobsdóttir
  15. Arna Hauksdóttir
  16. Unnur Anna Valdimarsdóttir
(2022)
Adverse childhood experiences and resilience among adult women: A population-based study
eLife 11:e71770.
https://doi.org/10.7554/eLife.71770
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Abstract

Background:

Adverse childhood experiences (ACEs) have consistently been associated with elevated risk of multiple adverse health outcomes, yet their contribution to coping ability and psychiatric resilience in adulthood is unclear.

Methods:

Cross-sectional data were derived from the ongoing Stress-And-Gene-Analysis cohort, representing 30% of the Icelandic nationwide female population, 18–69 years. Participants in the current study were 26,198 women with data on 13 ACEs measured with the ACE-International Questionnaire. Self-reported coping ability was measured with the Connor-Davidson Resilience Scale and psychiatric resilience was operationalized as absence of psychiatric morbidity. Generalized linear regression assuming normal or Poisson distribution were used to assess the associations of ACEs with coping ability and psychiatric resilience controlling for multiple confounders.

Results:

Number of ACEs was inversely associated with adult resilience in a dose-dependent manner; every 1SD unit increase in ACE scores was associated with both lower levels of coping ability (β = −0.14; 95% CI-0.15,–0.13) and lower psychiatric resilience (β = −0.28; 95% CI-0.29,–0.27) in adulthood. Compared to women with 0 ACEs, women with ≥5 ACEs had 36% lower prevalence of high coping ability (PR = 0.64, 95% CI 0.59,0.70) and 58% lower prevalence of high psychiatric resilience (PR = 0.42; 95% CI 0.39,0.45). Specific ACEs including emotional neglect, bullying, sexual abuse and mental illness of household member were consistently associated with reduced adult resilience. We observed only slightly attenuated associations after controlling for adult socioeconomic factors and social support in adulthood.

Conclusions:

Cumulative ACE exposure is associated with lower adult resilience among women, independent of adult socioeconomic factors and social support, indicating that adult resilience may be largely determined in childhood.

Funding:

This work was supported by the European Research Council (Consolidator grant; UAV, grant number 726413), and the Icelandic Center for Research (Grant of excellence; UAV, grant number 163362-051). HBD was supported by a doctoral grant from the University of Iceland Research Fund.

Editor's evaluation

This study aims to investigate the impact of adverse experiences during childhood on adult psychological and psychiatric resilience. Leveraging the excellent data from an ongoing cohort study on Icelandic women, the authors showed that in the face of accumulated adverse childhood events the prevalence of resilience declines, which supports earlier studies suggesting that resilience is not invincibility. The study makes an important contribution to raising awareness of the adverse childhood experiences and their impact on resiliency.

https://doi.org/10.7554/eLife.71770.sa0

Introduction

Exposure to adverse childhood experiences (ACEs), including child abuse, neglect and growing up in dysfunctional households, is associated with elevated risk of a wide range of physical and mental health problems across the life course (Anda et al., 2006; Bellis et al., 2015; Hughes et al., 2017; Petruccelli et al., 2019). The results from a recent meta-analysis (Hughes et al., 2017) suggest that the adult health outcomes most clearly associated with ACEs include problematic alcohol consumption and substance use, violence, and mental illness. However, there is great variation in long-term outcomes of children exposed to ACEs, and many children remain healthy despite excessive ACE exposure. Importantly, it has been documented that a substantial proportion of individuals exposed to ACEs do not develop mental illness in adulthood (Green et al., 2010; Kessler et al., 2010), but instead exhibit resilience (Holmes et al., 2015; DuMont et al., 2007).

Resilience is generally conceptualized as maintained mental health or positive adaptation despite trauma exposure (Luthar et al., 2000; Kalisch et al., 2017; Choi et al., 2019; Rutter, 2006). And although the scientific investigation of resilience can be traced back to the 1970s (Luthar et al., 2015; Masten et al., 2008; Garmezy, 1974; Garmezy, 1990; Werner, 1989; Rutter, 1990), the complexity of the concept has to date contributed to varying definitions and measurement approaches (Southwick et al., 2014). Two common contemporary approaches to operationalize the concept among adults, define resilience as perceived coping ability reflecting individuals’ perceptions of their ability to cope effectively with stress and adversity (Campbell-Sills and Stein, 2007; Connor and Davidson, 2003), and as psychiatric resilience reflecting an empirically derived outcome, such as the absence of PTSD or other psychiatric disorders among individuals exposed to traumatic events (Nishimi et al., 2021; Sheerin et al., 2018). Indeed, it is important to note that psychiatric resilience is never directly measured as it consists of two separate components, trauma exposure and positive adaptation, and is therefore indirectly ascertained based on evidence of the two components (Luthar, 2006). The different resilience definitions (i.e. perceived coping ability and psychiatric resilience) are not mutually exclusive, but rather complementary and may capture different underlying dimensions of resilience (Choi et al., 2019; Sheerin et al., 2018; Fisher and Law, 2020).

Only a handful of previous studies have addressed the association between ACEs and adult resilience. Childhood maltreatment, variously defined, has been negatively associated with psychiatric resilience (Mersky and Topitzes, 2010; Topitzes et al., 2013; McGloin and Widom, 2001; Williams et al., 2006) and perceived coping ability (Campbell-Sills et al., 2009; Nishimi et al., 2020) in adulthood. However, most studies have focused solely on childhood maltreatment or included only a small number of ACEs. Therefore, to date, little is known about the association between cumulative ACE exposure and adult resilience, and whether specific ACE types are to a varying extent associated with resilience.

Leveraging a large nationwide study of Icelandic women, we aimed to investigate the association between the cumulative number of a broad spectrum of ACEs and two distinct measures of adult resilience, that is perceived coping ability and an outcome-based measure of low psychiatric morbidity.

Materials and methods

Study sample

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In this study, we utilized data from the ongoing Stress-And-Gene-Analysis (SAGA) cohort, a population-based study in Iceland on the impact of trauma on women’s health. All 18–69 year-old Icelandic speaking women residing in Iceland with an identifiable address or telephone number (n≈104,197), were invited to participate in the study from March 2018. Data collection continued until July 1st 2019, yielding a total of 30,403 participating women (30% of eligible women). The participants of the SAGA cohort represent the general Icelandic female population in terms of distribution of age, education level, geographical location, and monthly wages (Appendix 1—figure 1).

Since trauma exposure is intrinsic to psychiatric resilience, the analytic sample was restricted to women reporting a worst traumatic event at some point during their lifetime (see description of PCL-5 below). In addition, women who had more than 25% missing on perceived coping ability (n = 511), psychiatric resilience (n = 1040) and/or ACE-IQ (n = 93) were excluded, which resulted in a final study population of 26,198 women (Appendix 1—figure 2).

Measures

Adverse childhood experiences (ACEs)

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ACEs were measured with a modified version of the Adverse Childhood Experiences International Questionnaire (ACE-IQ) developed by the WHO (WHO, 2021). The instrument consists of 39 items assessing how often individuals were exposed to the following 13 ACEs during the first 18 years of their life: emotional neglect, physical neglect, emotional abuse, physical abuse, sexual abuse, domestic violence, living with a household member who abuses drugs and/or alcohol, living with a household member who is mentally ill or suicidal, incarceration of a household member, parental death or separation/divorce, being bullied, witnessing community violence, and exposure to war/collective violence. Response options varied between items and items were either answered on a 5-point scale ranging from 0 (never) to 4 (always), on a 4-point scale ranging from 0 (never) to 3 (many times) or answered dichotomously 0 (no) and 1 (yes). For an overview of included items and their response options see Appendix 1—table 1. The recommended frequency scoring system (WHO, 2021), which takes into account the level of exposure for each ACE, was used to generate three types of exposure variables: (1) a continuous ACE-IQ total score ranging from 0 to 13, reflecting the number of ACEs participants were exposed to; (2) the total score was categorized (0, 1, 2, 3–4 and ≥5 ACEs) based on the distribution of the sample; (3) binary variables for each individual ACE type (described above), coded as 0 (unexposed) and 1 (exposed).

Perceived coping ability

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Perceived coping ability was assessed with the 10-item version of the Connor-Davidson Resilience Scale (CD-RISC-10) (Campbell-Sills and Stein, 2007). The scale, which measures individuals’ perceptions of their ability to cope effectively with stress and adversity, such as the ability to adapt to change, achieving goals despite obstacles, and maintaining positivity in the face of stress, has demonstrated good reliability and validity (Campbell-Sills and Stein, 2007). All items were answered on a 5-point scale ranging from 0 (not true at all) to 4 (true nearly all the time). Items were summed to create a total score ranging from 0 to 40 with a higher score indicating higher levels of perceived coping ability. As there is no standardized cut-off for the CD-RISC, the total scores were divided into quintiles (for more details see the Appendix 1), and a binary variable was created where the highest quintile was used to define a high level of perceived coping ability (i.e. resilience, CD-RISC score ≥35).

Psychiatric resilience

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Consistent with previous literature (Choi et al., 2019; Nishimi et al., 2021; Stein et al., 2019) psychiatric resilience was defined as absence of or low psychiatric morbidity among women exposed to lifetime trauma, that is total sum of the inverse number of above-threshold symptom levels on PTSD, trauma-related sleep disturbances, binge drinking, depression, and anxiety.

The PTSD Checklist for DSM-5 (PCL-5) is a 20-item valid instrument that assesses symptoms of PTSD in the past month according to the DSM-5 (Weathers et al., 2013; Blevins et al., 2015; Wortmann et al., 2016; Bovin et al., 2016). Before answering the PCL-5, participants reported their lifetime exposure to potentially traumatic events assessed with the Life Events Checklist for DSM-5 (LEC-5). Participants were asked to determine the worst traumatic event they had experienced and answer the PCL-5 according to the worst selected trauma. A clinical cut-off score of ≥33 was used to indicate probable PTSD (Weathers et al., 2013). The Pittsburgh Sleep Quality Index Addendum for PTSD (PSQI-A) is a seven-item valid questionnaire designed to assess the frequency of disruptive nocturnal behaviors common in individuals with PTSD (Germain et al., 2005). A clinical cut-off score of ≥4 was used to discriminate between participants with and without trauma-related sleep disturbances (Germain et al., 2005). Binge drinking was defined as having six or more units of alcohol on a single occasion (one unit corresponds to a single measure of spirits) at least once a month during the last year (Bush et al., 1998). The widely used Patient Health Questionnaire (PHQ-9) (Kroenke et al., 2001) and Generalized Anxiety Disorder Scale (GAD-7) (Kroenke et al., 2010; Spitzer et al., 2006; Löwe et al., 2008) were used to measure the presence of depression and anxiety symptoms, respectively, during the past 2 weeks. The standard cut-off score of ≥10 was used, indicating clinically relevant depression and anxiety (Kroenke et al., 2001; Spitzer et al., 2006).

Binary variables were created indicating whether an individual met the clinical cut-off for symptoms of each disorder above (0 = no, 1 = yes). The psychiatric resilience phenotype was derived by summing together the binary variables and reversing the score which resulted in a total score ranging from 0 to 5 where higher scores indicate greater psychiatric resilience in these trauma-exposed women. In addition, we created a binary variable where endorsement of 0 on all measures indicated high psychiatric resilience.

Covariates

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We summarised variables with a conceptual rationale for being associated both with ACEs and perceived coping ability or psychiatric resilience in Appendix 1—figure 3. We considered as covariates, age (at responding) and childhood deprivation (potential confounders), as well as educational level, employment status, civil status and current monthly income at responding (potential mediators). Age was divided into five groups for descriptive purposes: 18–29 years, 30–39 years, 40–49 years, 50–59 years, and 60 years and older. The age covariate was used as a continuous variable in all models. Education was categorized as primary education, secondary education (high school or vocational education), tertiary education A (BSc or equivalent), and tertiary education B (MSc or above). Civil status was divided into married or in a relationship and single or widowed, and employment status was divided into employed (including being a student and being on parental leave) and retired or on disability or sick leave. Current monthly income was categorized into the following groups: low income (<$2527), low-medium income ($2528-$4212), medium income ($4213-$5897), medium-high income ($5898-$8424), and high income (>$8425; conversion rates according to Central Bank of Iceland, October 17, 2018). Childhood deprivation was assessed with the question: Was your family’s economic situation ever so bad that you suffered any deprivation as a consequence? For example, this could apply to deprivation of nutritious food and/or deprivation of warm clothes and appropriate footwear during the winter months, with response options ranging from 0 (never) to 4 (often). In addition, current perceived social support, measured with the Multidimensional Scale of Perceived Social Support (MSPSS) (Zimet et al., 1988), and perceived happiness, measured with a 10-point visual digital scale, were included in additional analyses (for further details, see the Appendix 1).

Multiple imputation

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The ACE-IQ scale and the five psychopathology scales (PHQ-9, GAD-7, PCL-5, PSQI-A and binge drinking) used to derive psychiatric resilience, had missing values which resulted in a reduced sample size (see Appendix 1—figure 4). We used multiple imputation (MI) to replace missing data with m = 20 rounds of imputations, using predictive mean matching (van Buuren, 2019). We imputed data for participants who responded to more than 75% of items on each scale and then calculated the total score for the scales. The subsequent analyses (described below) were conducted using the imputed dataset. For comparison, the main analyses were repeated in the original dataset with complete data.

Statistical analyses

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Descriptive characteristics were compared using Chi-square tests for categorical data and ANOVAs for continuous data.

Rank order correlations were used to determine (i) the correlation between perceived coping ability and psychiatric resilience, (ii) correlations between perceived coping ability and different measures of psychopathology (PHQ-9, GAD-7, PCL-5, PSQI-A, binge drinking) used to derive psychiatric resilience, and (iii) correlations between different ACE subtypes.

We used linear regression models assuming normally distributed errors and log-linear Poisson regression models with robust error variance to determine the associations between ACEs and perceived coping ability and psychiatric resilience, as continuous and binary outcomes (high perceived coping ability/ high psychiatric resilience), respectively. In all analyses, we adjusted for age and childhood deprivation (model 1) and then additionally for adult educational level, civil status, employment level and income (model 2).

We ran all models with ACE-IQ as a continuous predictor and as a categorical predictor where we compared resilience levels of unexposed women (0 ACEs) to resilience levels of those who had been exposed to increasing number of ACEs (1, 2, 3–4, and ≥5 ACEs). In addition, we carried out stratified analyses to assess whether the association between ACEs and resilience differed by levels of perceived social support (linear models). Furthermore, because parental separation/divorce is a common childhood experience, we carried out a sensitivity analysis excluding this item from the ACE-IQ score and re-ran the linear models. Finally, to preclude whether a particular response style influenced the observed associations between ACEs and resilience, we excluded women who scored low or high on happiness (10% top/bottom scores) and re-ran the linear models.

To determine the independent associations of specific types of ACEs with resilience, we ran analyses on perceived coping ability and psychiatric resilience for each of the 13 ACEs. We first examined each ACE type separately while adjusting for covariates, and then re-ran the analyses with all ACE subtypes entered simultaneously into the model, as ACEs frequently co-occur (Radford et al., 2013).

Standardized regression coefficients were reported from all linear regression analyses and prevalence ratios (PR) were reported from the Poisson regression analyses. All statistical analyses were performed using R (version 3.6.1).

Results

Characteristics of the sample

Descriptive statistics of the study population are presented in Table 1. Overall, 19.7% of participants reported no ACEs and 20.4% reported five or more ACEs. Middle aged women had on average higher scores on the ACE-IQ than younger or older women. Women with higher ACE scores were more often single or widowed, less educated, unemployed, had lower income and were more likely to report childhood deprivation and low perceived social support (Table 1).

Table 1
Descriptive characteristics of the study population by number of adverse childhood experiences (ACE-IQ) (n = 26,198).
Number of ACEsACE-IQ sum score
Total
N (%)		0 ACE
N (%)		1 ACE
N (%)		2 ACEs
N (%)		3–4 ACEsN (%)≥ 5 ACEsN (%)p-value global*Mean (SD)p-value global†
Total26,1985,149 (19.7)5,567 (21.3)4,491 (17.1)5,640 (21.5)5,351 (20.4)2.6 (2.4)
Age, mean (SD)44.0 (13.6)43.7 (13.9)44.7 (13.9)44.6 (13.6)44.1 (13.4)43.1 (12.8)< 0.001
Age groups
18–29 years4,881 (18.6)1,043 (20.3)993 (17.8)805 (17.9)1,045 (18.5)995 (18.6)< 0.0012.6 (2.5)< 0.001
30–39 years5,309 (20.3)1,074 (20.9)1,128 (20.3)864 (19.2)1,107 (19.6)1,136 (21.2)2.7 (2.4)
40–49 years5,923 (22.6)1,092 (21.2)1,167 (21.0)1,003 (22.3)1,301 (23.1)1,360 (25.4)2.8 (2.5)
50–59 years6,055 (23.1)1,091 (21.2)1,281 (23.0)1,088 (24.2)1,356 (24.0)1,239 (23.2)2.7 (2.4)
≥ 60 years4,030 (15.4)849 (16.5)998 (17.9)731 (16.3)831 (14.7)621 (11.6)2.3 (2.2)
Educational level
Primary education3,739 (14.3)442 (8.6)662 (11.9)602 (13.4)889 (15.8)1,144 (21.4)< 0.0013.4 (2.61)< 0.001
Secondary education8,013 (30.6)1,402 (27.2)1,658 (29.8)1,360 (30.3)1,744 (30.9)1,849 (34.6)2.8 (2.32)
Tertiary A (BSc or equivalent)8,359 (31.9)1,856 (36.0)1,872 (33.6)1,488 (33.1)1,782 (31.6)1,361 (25.4)2.3 (2.09)
Tertiary B (MSc or above)5,990 (22.9)1,437 (27.9)1,360 (24.4)1,022 (22.8)1,210 (21.5)961 (18.0)2.3 (2.08)
Unknown97 (0.4)12 (0.2)15 (0.3)19 (0.4)15 (0.3)36 (0.7)3.7 (2.59)
Civil status
Married/in a relationship19,750 (75.4)4,061 (78.9)4,309 (77.4)3,442 (76.6)4,173 (74.0)3,765 (70.4)< 0.0012.5 (2.3)< 0.001
Single/widowed6,314 (24.1)1,070 (20.8)1,241 (22.3)1,030 (22.9)1,432 (25.4)1,541 (28.8)2.9 (2.5)
Unknown134 (0.5)18 (0.3)17 (0.3)19 (0.4)35 (0.6)45 (0.8)3.6 (2.8)
Employment status
Employed/studying22,088 (84.3)4,639 (90.1)4,826 (86.7)3,888 (86.6)4,734 (83.9)4,001 (74.8)< 0.0012.5 (2.3)< 0.001
Retired/disability/sick leave3,941 (15.0)494 (9.6)718 (12.9)573 (12.8)854 (15.1)1,302 (24.3)3.5 (2.8)
Unknown169 (0.6)16 (0.3)23 (0.4)30 (0.7)52 (0.9)48 (0.9)3.5 (2.6)
Income
Low income7,723 (29.5)1,206 (23.4)1,532 (27.5)1,216 (27.1)1,750 (31.0)2019 (37.7)< 0.0013.0 (2.6)< 0.001
Low-medium income7,862 (30.0)1,478 (28.7)1,663 (29.9)1,406 (31.3)1,717 (30.4)1,598 (29.9)2.6 (2.4)
Medium income6,050 (23.1)1,352 (26.3)1,363 (24.5)1,081 (24.1)1,236 (21.9)1,018 (19.0)2.3 (2.2)
High-medium income2,636 (10.1)654 (12.7)567 (10.2)466 (10.4)534 (9.5)415 (7.8)2.2 (2.2)
High income929 (3.5)250 (4.9)226 (4.1)141 (3.1)196 (3.5)116 (2.2)2.0 (2.0)
Unknown998 (3.8)209 (4.1)216 (3.9)181 (4.0)207 (3.7)185 (3.5)2.5 (2.4)
Childhood deprivation
Never19,727 (75.3)4,843 (94.1)4,919 (88.4)3,628 (80.8)3,954 (70.1)2,383 (44.5)< 0.0012.0 (2.0)< 0.001
Rarely2,929 (11.2)218 (4.2)430 (7.7)516 (11.5)833 (14.8)932 (17.4)3.5 (2.4)
Sometimes2,320 (8.9)71 (1.4)193 (3.5)278 (6.2)627 (11.1)1,151 (21.5)4.6 (2.5)
Often1,169 (4.5)15 (0.3)21 (0.4)60 (1.3)206 (3.7)867 (16.2)6.3 (2.6)
Unknown53 (0.2)2 (0.0)4 (0.1)9 (0.2)20 (0.4)18 (0.3)3.8 (2.0)
Perceived social support
Low6,332 (24.2)747 (14.5)995 (17.9)972 (21.6)1,582 (28.0)2036 (38.0)< 0.0013.5 (2.7)< 0.001
Moderate12,831 (40.0)2,487 (48.3)2,833 (50.9)2,282 (50.8)2,762 (49.0)2,467 (46.1)2.5 (2.3)
High6,151 (23.5)1,790 (34.8)1,579 (28.4)1,102 (24.5)1,075 (19.1)605 (11.3)1.8 (1.9)
Unknown884 (3.4)125 (2.4)160 (2.9)135 (3.0)221 (3.9)243 (4.5)3.2 (2.5)
  1. *

    p-values were obtained by χ2 tests, except for mean age which was compared with an ANOVA.

  2. †

    p-values were obtained by ANOVA.

Bivariate associations

Both perceived coping ability and psychiatric resilience were consistently associated with older age, higher educational level, being in a relationship or married, being employed, higher income, lower childhood deprivation and higher perceived social support (Table 2). The two resilience measures were moderately correlated (rs = 0.47). Perceived coping ability was moderately negatively correlated with each of the measures used to derive the psychiatric resilience phenotype (i.e. symptoms of depression, anxiety, PTSD, and trauma-related sleep disturbances) but weakly correlated with binge drinking (Appendix 1—table 2). The 13 ACE subtypes were weakly to moderately correlated with each other (rs ranging from 0.06 to 0.46), with emotional abuse showing the strongest correlation with other ACEs (Appendix 1—table 3).

Table 2
Distribution of perceived coping ability (CD-RISC) and psychiatric resilience scores by sociodemographic characteristics.
Perceived coping abilityPsychiatric resilience
Mean (SD)p-value global*Mean (SD)p-value global*
Total28.0 (7.5)3.6 (1.5)
Age groups
18–29 years24.8 (7.9)< 0.0013.1 (1.6)< 0.001
30–39 years26.8 (7.5)3.5 (1.6)
40–49 years27.8 (7.4)3.7 (1.5)
50–59 years28.5 (7.2)3.8 (1.4)
≥ 60 years28.8 (6.9)4.0 (1.3)
Educational level
Primary education24.2 (8.3)< 0.0013.1 (1.6)< 0.001
Secondary education26.3 (7.6)3.4 (1.5)
Tertiary A (BSc or equivalent)28.0 (7.0)3.8 (1.4)
Tertiary B (MSc or above)29.8 (6.5)4.0 (1.3)
Unknown23.6 (8.1)3.1 (1.7)
Civil status
Married/in a relationship27.7 (7.4)< 0.0013.7 (1.4)< 0.001
Single/widowed26.3 (7.9)3.3 (1.6)
Unknown24.7 (8.5)2.7 (1.8)
Employment status
Employed/studying28.0 (7.2)< 0.0013.7 (1.4)< 0.001
Retired/disability/sick leave23.9 (8.5)3.0 (1.6)
Unknown23.3 (9.0)2.8 (1.6)
Income
Low income24.5 (8.0)< 0.0013.1 (1.6)< 0.001
Low-medium income27.0 (7.2)3.6 (1.5)
Medium income29.3 (6.5)4.0 (1.3)
High-medium income30.9 (6.1)4.1 (1.2)
High income31.9 (6.1)4.1 (1.2)
Unknown26.6 (7.9)3.7 (1.5)
Childhood deprivation
Never27.9 (7.3)< 0.0013.8 (1.4)< 0.001
Rarely26.4 (7.6)3.3 (1.6)
Sometimes25.4 (7.9)3.1 (1.6)
Often25.0 (8.4)2.6 (1.6)
Unknown24.0 (8.9)2.6 (1.6)
Perceived social support
Low24.5 (8.0)< 0.0013.1 (1.6)< 0.001
Moderate27.2 (7.1)3.7 (1.5)
High30.9 (6.4)4.1 (1.2)
Unknown24.7 (8.0)3.2 (1.6)
  1. *

    p-values were obtained by ANOVAs.

Associations between ACEs and resilience

Linear models revealed that exposure to one or more ACEs (relative to 0 ACEs) was associated with lower perceived coping ability and psychiatric resilience in a dose-dependent manner (Table 3); every 1 SD unit increase in ACE-IQ scores was associated with lower levels of perceived coping ability (β = −0.14; 95% CI −0.15,–0.13) and psychiatric resilience (β = −0.28; 95% CI −0.29,–0.27) in the fully adjusted model. Associations between ACEs and perceived coping ability and psychiatric resilience were observed across levels of social support but were slightly stronger among women with low social support (Appendix 1—table 4). Sensitivity analyses showed that the associations remained evident when excluding parental separation/divorce from the ACE-IQ score (Appendix 1—table 5), and when excluding women with top/bottom 10% happiness values (Appendix 1—table 6).

Table 3
Associations between the number of ACEs and perceived coping ability (CD-RISC) and psychiatric resilience (β and 95% CI)*.
Perceived coping abilityPsychiatric resilience
N (%)Model 1†Model 2 ‡Model 1†Model 2 ‡
ACE-IQ total score **26,198–0.19 (-0.20,–0.17)–0.14 (-0.15,–0.13)–0.32 (-0.34,–0.31)–0.28 (-0.29,–0.27)
Number of ACEs
0 ACE5,149 (19.7)0 (ref.)0 (ref.)0 (ref.)0 (ref.)
1 ACE5,567 (21.3)–0.07 (-0.08,–0.05)–0.05 (-0.07,–0.04)–0.09 (-0.10,–0.07)–0.07 (-0.09,–0.06)
2 ACE4,491 (17.1)–0.10 (-0.11,–0.08)–0.08 (-0.10,–0.07)–0.13 (-0.15,–0.12)–0.12 (-0.13,–0.10)
3–4 ACE5,640 (21.5)–0.16 (-0.17,–0.14)–0.13 (-0.14,–0.11)–0.22 (-0.23,–0.20)–0.19 (-0.21,–0.18)
≥ 5 ACEs5,351 (20.4)–0.22 (-0.24,–0.20)–0.16 (-0.18,–0.15)–0.36 (-0.37,–0.34)–0.31 (-0.33,–0.30)
  1. *

    Coefficients are standardized; **per 1 SD unit increase in ACE-IQ scores.

  2. †

    adjusted for age and childhood deprivation.

  3. ‡

    additionally adjusted for education level, civil status, employment status and income.

Poisson models revealed that compared to women with 0 ACEs, women with ≥5 ACEs had a lower prevalence of high perceived coping ability (PR = 0.64, 95% CI 0.59, 0.70), and high psychiatric resilience (PR = 0.42, 95% CI 0.39, 0.45) in the fully adjusted model (Table 4). Every unit increase in the ACE-IQ scores was associated with lower prevalence of high perceived coping ability (PR = 0.93, 95% CI 0.92, 0.94) and high psychiatric resilience (PR = 0.87, 95% CI 0.86, 0.87).

Table 4
Prevalence Ratios (with 95% CI) of high perceived coping ability (CD-RISC ≥35) and high psychiatric resilience (absence of psychiatric morbidity) in relation to the number of ACEs.
Perceived coping abilityPsychiatric resilience
N (%)Model 1†aModel 2 ‡Model 1†Model 2 ‡
ACE-IQ total score*26,1980.91 (0.90, 0.92)0.93 (0.92, 0.94)0.85 (0.84, 0.86)0.87 (0.86, 0.87)
Number of ACEs
0 ACE5,149 (19.7)1.00 (ref.)1.00 (ref.)1.00 (ref.)1.00 (ref.)
1 ACE5,567 (21.3)0.83 (0.77, 0.89)0.87 (0.81, 0.93)0.82 (0.79, 0.84)0.84 (0.81, 0.87)
2 ACE4,491 (17.1)0.71 (0.66, 0.77)0.75 (0.70, 0.81)0.73 (0.70, 0.76)0.75 (0.72, 0.78)
3–4 ACE5,640 (21.5)0.61 (0.56, 0.66)0.66 (0.61, 0.71)0.59 (0.56, 0.61)0.62 (0.60, 0.65)
≥ 5 ACEs5,351 (20.4)0.56 (0.51, 0.61)0.64 (0.59, 0.70)0.38 (0.35, 0.40)0.42 (0.39, 0.45)
  1. *

    per 1 SD unit increase in ACE-IQ scores.

  2. †

    adjusted for age and childhood deprivation.

  3. ‡

    additionally adjusted for education level, civil status, employment status and income.

All ACE subtypes were associated with lower levels of perceived coping ability and psychiatric resilience (Appendix 1—figure 5) as well as lower prevalence of high psychiatric resilience and high perceived coping ability except for physical neglect- and abuse, parental death or separation/divorce, incarceration of a household member, and community- and collective violence (Appendix 1—figure 6). After mutual adjustment for all ACE subtypes, we found that emotional neglect, being bullied, sexual abuse, and growing up with a mentally ill household member had consistent associations with both resilience measures, both in linear models (Figure 1) and Poisson models (Figure 2). Associations were also suggested for emotional abuse, domestic- and community violence with psychiatric resilience in the full model while associations for other ACEs were substantially attenuated (Figures 1 and 2).

Figure 1
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Associations between different types of ACEs and perceived coping ability (A) and psychiatric resilience (B) (β and 95% CI).

Models were corrected for age, childhood deprivation, educational level, civil status, employment status, income and mutually adjusted for other ACEs. *Coefficients are standardized.

Figure 2
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Prevalence Ratios (with 95% CI) of high perceived coping ability (A) and high psychiatric resilience (B) in relation to individual ACEs.

Models were corrected for age, childhood deprivation, educational level, civil status, employment status, income and mutually adjusted for other ACEs.

Overall, the results of the complete case analyses were similar to the results using multiple imputation (main analyses) in terms of both effect sizes and confidence intervals. See Appendix 1—tables 7 and 8 (number of ACEs), and Appendix 1—figures 7 and 8 (ACE subtypes) for the complete case analyses.

Discussion

In a large nationwide study of Icelandic women, a comprehensive measure of 13 adverse childhood experiences (ACEs) was negatively associated with two distinct measures of resilience in adulthood in a dose-dependent manner. Indeed, women who endorsed five or more ACEs had 36% lower prevalence of high perceived coping ability and 58% lower prevalence of high psychiatric resilience compared to women who endorsed 0 ACEs. In addition, specific ACEs including emotional neglect, bullying, sexual abuse and growing up with a mentally ill household member were strongly associated with lower adult resilience.

To our knowledge, the present study is the first to consider a wide range of ACEs in relation to adult resilience, and to address their association with two distinct resilience measures. Overall, our results are in line with previous studies examining the association between childhood maltreatment and adult resilience (Mersky and Topitzes, 2010; Topitzes et al., 2013; McGloin and Widom, 2001; Williams et al., 2006; Campbell-Sills et al., 2009; Nishimi et al., 2020). However, the existing evidence base is limited by relatively small sample sizes, consideration of only few ACEs (McGloin and Widom, 2001; Williams et al., 2006; Campbell-Sills et al., 2009; Nishimi et al., 2020), and/or the use of composite measures of childhood adversity (Mersky and Topitzes, 2010; Topitzes et al., 2013).

Our results indicating a dose-response relationship between the cumulative number of ACEs and lower adult resilience is consistent with a recent cross-sectional study by Nishimi et al., 2020 who observed a similar pattern between four ACEs (emotional abuse, physical abuse, sexual abuse, and witnessing domestic violence) and perceived coping ability, as measured with the CD-RISC. We have now extended these findings to psychiatric resilience as an outcome, and further demonstrated this pattern for a broad set of ACEs in a large nationwide study representing 30% of the Icelandic female population. Furthermore, our results suggest that all ACE subtypes are associated with perceived coping ability and psychiatric resilience. However, when mutually adjusting for other ACEs, only emotional neglect, sexual abuse, being bullied and growing up with a mentally ill household member were consistently associated with both resilience measures.

In line with previous literature (Nishimi et al., 2021; Sheerin et al., 2018), we found that self-assessed coping ability and outcome-based psychiatric resilience were only moderately correlated with each other, indicating these may reflect different patterns of adaptive functioning following adversity. Collectively, our research adds to the growing literature on the nature of resilience as a construct. The associations between ACEs and adult resilience were independent of age, childhood deprivation and, importantly, other adult socio-demographic factors (i.e. educational level, civil status, employment level and income), suggesting that ACEs affect resilience over and above these potential mediating variables. Indeed, one possible mechanism through which ACEs could influence resilience, are functional outcomes in adulthood (e.g. social, financial and/or educational functioning). Previous research has found that ACEs are associated with greater risk of poor educational and financial outcomes, as well as poor social functioning (Bellis et al., 2014; Copeland et al., 2018), both adult factors that have previously been associated with resilience (Nishimi et al., 2021; Campbell-Sills et al., 2009; Southwick et al., 2016). However, in the current study, effect sizes only diminished slightly when we additionally adjusted for adult socio-demographic factors, which indicates that adult characteristics such as education and employment level do not compensate for the deleterious impact of ACEs on adult resilience. This suggests that adult resilience may largely be determined in childhood and that situational factors in adulthood (e.g. high social support) only marginally buffer the association between ACEs and adult resilience.

The main strengths of the study include the population-based design and large sample size of the SAGA cohort, which represents the Icelandic adult female population in terms of distribution of age, residence, education and income. The wealth of measures in the SAGA cohort baseline assessments made it possible to derive two types of resilience measures and examine a wide range of ACEs. The wealth of relevant data also allowed us to adjust for childhood deprivation, an important confounder when examining ACEs and adult outcomes (Copeland et al., 2018; Arseneault et al., 2011; Bellis et al., 2017), which has not been taken into account in previous ACE-resilience studies. However, our study also has several limitations. First, the cross-sectional nature of our data does not allow us to make any inferences about the directionality of the studied associations. Second, we cannot rule out that the observed association between ACE growing up with a mentally ill household member and resilience, is due to a genetic predisposition for psychopathology rather than the experience itself. Future genetically informative studies will need to examine the extent to which this association is confounded by genetic factors. Although we adjusted for an array of important confounding factors, we cannot exclude the possibility that unmeasured or residual confounding may contribute to our results. In addition, ACEs were retrospectively reported and thus may be subject to recall bias. However, previous studies have shown acceptable validity for retrospective assessments of ACEs (Reuben et al., 2016; Hardt and Rutter, 2004; Widom and Shepard, 1996; Widom and Morris, 1997), although they may be influenced by current mental health status or response style (Reuben et al., 2016). Yet, the similar results obtained from our sensitivity analyses excluding individuals with extreme values on the happiness assessment, reduce concerns that our results are due to a particular response style. Finally, our results are based on an exclusively female sample, therefore, future studies should explore whether there are qualitative differences in how ACEs relate to adult resilience among men as well as among sexual and gender minorities.

In conclusion, in a large nationwide-representative female population, we observed a negative association between cumulative exposure of ACEs and two distinct measures of adult resilience. Specific ACEs, including exposure to emotional neglect and bullying, sexual abuse, and growing up with a mentally ill household member were consistently associated with lower adult resilience. If these findings are confirmed through prospective designs, there may be huge societal benefits of prevention strategies targeting the protection of children against traumatic occurrences and their consequences. Future research is needed to address how children exposed to ACEs can be supported to reduce risks of compromised adult resilience and health inequalities.

Appendix 1

Perceived coping ability

The CD-RISC total scores were divided into quintiles which resulted in 21.50% of the sample in the lowest quintile (raw CD-RISC scores = 0–21), 20.95% in the lower middle quintile (raw CD-RISC scores = 22–26), 20.63% in the middle quintile (raw CD-RISC scores = 27–30), 17.99% in the higher middle quintile (raw CD-RISC scores = 31–34) and 18.93% in the highest quintile (raw CD-RISC scores = 35–40).

Covariate assessment

Social support was assessed with the Multidimensional Scale of Perceived Social Support (MSPSS) (Zimet et al., 1988). The instrument consists of 12 items answered on a 7-point scale ranging from 0 (very strongly disagree) to 6 (very strongly agree). Items were summed to create a total score ranging from 0 to 72, with higher scores indicating higher levels of perceived social support. In addition, the total scores were divided into quartiles which resulted in 25.09% of the sample in the lowest quartile (raw MSPSS scores = 0–49), 25.52% of the sample in the low-middle quartile (raw MSPSS scores = 50–61), 24.92% of the sample in the high-middle quartile (raw MSPSS scores = 62–69) and 24.47% of the sample in the highest quartile (raw MSPSS scores = 70–72). A categorical variable was then created where the highest tertile was used to define a high level of social support, the two middle quartiles were merged and used together to define a moderate level of social support and the lowest quartile was used to define a low level of social support. Happiness was assessed with the question “In general, how would you rate your happiness?”, and participants rated their happiness with a slider ranging from 1 to 10.

Appendix 1—table 1
List of the 30 ACE-IQ items used to derive the 13 different ACEs and their response options.
ACE itemScoring*
Neglect 
Emotional neglect 
Did your parents/guardians understand your problems and worries?Always = 0, Most of the time = 1, Sometimes = 2, Rarely = 3, Never = 4
Did your parents/guardians really know what you were doing with your free time when you were not at school or work?Always = 0, Most of the time = 1, Sometimes = 2, Rarely = 3, Never = 4
Physical neglect 
How often did your parents/guardians not give you enough food even when they could easily have done so?Never = 0, Once = 1, A few times = 2, Many times = 3
Were your parents/guardians too drunk or intoxicated by drugs to take care of you?Never = 0, Once = 1, A few times = 2, Many times = 3
How often did your parents/guardians not send you to school even when it was available?Never = 0, Once = 1, A few times = 2, Many times = 3
Abuse 
Emotional abuse 
Did a parent, guardian or other household member yell, scream or swear at you, insult or humiliate you?Never = 0, Once = 1, A few times = 2, Many times = 3
Did a parent, guardian or other household member threaten to, or actually, abandon you or throw you out of the house?Never = 0, Once = 1, A few times = 2, Many times = 3
Physical abuse 
Did a parent, guardian or other household member spank, slap, kick, punch or beat you up?Never = 0, Once = 1, A few times = 2, Many times = 3
Did a parent, guardian or other household member hit or cut you with an object, such as a stick (or cane), bottle, club, knife, whip etc?Never = 0, Once = 1, A few times = 2, Many times = 3
Sexual abuse 
Did someone touch or fondle you in a sexual way when you did not want them to?Never = 0, Once = 1, A few times = 2, Many times = 3
Did someone make you touch their body in a sexual way when you did not want them to?Never = 0, Once = 1, A few times = 2, Many times = 3
Did someone attempt oral, anal, or vaginal intercourse with you when you did not want them to?Never = 0, Once = 1, A few times = 2, Many times = 3
Did someone actually have oral, anal, or vaginal intercourse with you when you did not want them to?Never = 0, Once = 1, A few times = 2, Many times = 3
Household dysfunction 
Domestic violence 
Did you see or hear a parent or household member in your home being yelled at, screamed at, sworn at, insulted or humiliated?Never = 0, Once = 1, A few times = 2, Many times = 3
Did you see or hear a parent or household member in your home being slapped, kicked, punched or beaten up?Never = 0, Once = 1, A few times = 2, Many times = 3
Did you see or hear a parent or household member in your home being hit or cut with an object, such as a stick (or cane), bottle, club, knife, whip etc.?Never = 0, Once = 1, A few times = 2, Many times = 3
Lost a parent / separation of parents 
Were your parents ever separated or divorced?No = 0, Yes = 1
Did your mother, father or guardian die?No = 0, Yes = 1
Mental illness in household 
Did you live with a household member who was depressed, mentally ill or suicidal?No = 0, Yes = 1
Household substance abuse 
Did you live with a household member who was a problem drinker or alcoholic, or misused street or prescription drugs?No = 0, Yes = 1
Incarcerated household member 
Did you live with a household member who was ever sent to jail or prison?No = 0, Yes = 1
Other violence 
Community violence 
Did you see or hear someone being beaten up in real life?Never = 0, Once = 1, A few times = 2, Many times = 3
Did you see or hear someone being stabbed or shot in real life?Never = 0, Once = 1, A few times = 2, Many times = 3
Did you see or hear someone being threatened with a knife or gun in real life?Never = 0, Once = 1, A few times = 2, Many times = 3
Collective violence 
During the first 18 years of your life, were you exposed to war/collective violence (e.g. from gangs or police)?†No = 0, Yes = 1
Were you forced to go and live in another place due to any of these events?Never = 0, Once = 1, A few times = 2, Many times = 3
Did you experience the deliberate destruction of your home due to any of these events?Never = 0, Once = 1, A few times = 2, Many times = 3
Were you beaten up by soldiers, police, militia, or gangs?Never = 0, Once = 1, A few times = 2, Many times = 3
Was a family member or friend killed or beaten up by soldiers, police, militia, or gangs?Never = 0, Once = 1, A few times = 2, Many times = 3
Bullying 
How often were you bullied?Never = 0, Once = 1, A few times = 2, Many times = 3
  1. *

    all items also had the option „can‘t/don‘t want to answer“.

  2. †

    this is a screening question, only participants that responded yes got the following four questions.

Appendix 1—table 2
Rank order correlations for perceived coping (CD-RISC) and different measures of psychopathology used to derive the psychiatric resilience phenotype (n = 26,198).
CD-RISCPHQ-9GAD-7PCL-5PSQI-ABinge drinking
CD-RISC1
PHQ-9–0.551
GAD-7–0.510.761
PCL-5–0.480.700.661
PSQI-A–0.410.620.620.631
Binge drinking–0.040.100.090.070.091
Appendix 1—table 3
Rank order correlations for ACE subtypes (n = 26,198).
Emotional abusePhysical abuseSexual abuseEmotional neglectPhysical neglectDomestic violenceLost a parent/ separationMental illness in householdHousehold substance abuseIncarcerated household memberCommunity violenceCollective violenceBullying
Emotional abuse1
Physical abuse0.461
Sexual abuse0.190.141
Emotional neglect0.360.230.221
Physical neglect0.290.180.140.271
Domestic violence0.510.310.180.340.301
Lost a parent/separation of parents0.160.090.120.190.190.211
Mental illness in household0.330.180.140.260.230.370.191
Household substance abuse0.200.110.160.240.290.360.260.311
Incarcerated household member0.140.110.090.120.180.220.140.180.251
Community violence0.200.200.100.130.140.180.080.120.110.111
Collective violence0.090.080.060.070.080.090.060.070.060.100.111
Bullying0.210.160.130.150.110.150.060.160.080.060.120.081
Appendix 1—table 4
Associations between the number of ACEs and perceived coping ability (CD-RISC) and psychiatric resilience stratified by social support (n = 25,314) (β and 95% CI).
Perceived coping abilityPsychiatric resilience
N (%)Low supportModerate supportHigh supportLow supportModerate supportHigh support
Number of ACEs*
0 ACE5,024 (19.8)0 (ref.)0 (ref.)0 (ref.)0 (ref.)0 (ref.)0 (ref.)
1 ACE5,407 (21.3)–0.06 (–0.10,–0.02)–0.05 (–0.06,–0.03)–0.02 (–0.05,–0.00)–0.10 (–0.14,–0.06)–0.07 (–0.09,–0.05)–0.05 (–0.07,–0.03)
2 ACE4,356 (17.2)–0.08 (–0.12,–0.05)–0.06 (–0.08,–0.04)–0.06 (–0.08,–0.03)–0.13 (–0.16,–0.09)–0.11 (–0.13,–0.09)–0.09 (–0.11,–0.07)
3–4 ACE5,419 (21.4)–0.11 (–0.14,–0.07)–0.09 (–0.11,–0.07)–0.08 (–0.11,–0.06)–0.22 (–0.25,–0.17)–0.16 (–0.18,–0.14)–0.13 (–0.16,–0.10)
≥ 5 ACEs5,108 (20.2)–0.14 (–0.17,–0.10)–0.10 (–0.12,–0.08)–0.08 (–0.11,–0.05)–0.32 (–0.36,–0.28)–0.27 (–0.29,–0.25)–0.21 (–0.24,–0.1)
  1. *

    Coefficients are standardized; adjusted for age, childhood deprivation, education level, civil status, employment status and income.

Appendix 1—table 5
Associations between the number of ACEs (excluding parental divorce/separation) and perceived coping ability (CD-RISC) and psychiatric resilience (β and 95% CI)*.
N (%)Perceived coping abilityPsychiatric resilience
Model 1aModel 2bModel 1aModel 2b
Number of ACEs*
0 ACE6,095 (23.3)0 (ref.)0 (ref.)0 (ref.)0 (ref.)
1 ACE6,201 (23.7)–0.08 (–0.09,–0.06)–0.06 (–0.08,–0.05)–0.10 (–0.11,–0.08)–0.08 (–0.10,–0.07)
2 ACE4,877 (18.6)–0.12 (–0.13,–0.10)–0.10 (–0.11,–0.08)–0.16 (–0.17,–0.14)–0.14 (–0.16,–0.13)
3–4 ACE5,582 (21.3)–0.18 (–0.19,–0.16)–0.14 (–0.16,–0.13)–0.25 (–0.27,–0.24)–0.23 (–0.24,–0.21)
≥ 5 ACEs3,443 (13.1)–0.20 (–0.21,–0.19)–0.15 (–0.15,–0.12)–0.33 (–0.35,–0.32)–0.29 (–0.30,–0.28)
  1. *

    Coefficients are standardized; aadjusted for age and childhood deprivation; badditionally adjusted for education level, civil status, employment status and income.

Appendix 1—table 6
Associations between the number of ACEs and perceived coping ability (CD-RISC) and psychiatric resilience excluding participants with ≈10% lowest and highest happiness values (raw scores 1–5 and 10) (n = 15,449) (β and 95% CI)*.
Perceived coping abilityPsychiatric resilience
N (%)Model 1aModel 2bModel 1aModel 2b
Number of ACEs*
0 ACE4,088 (20.38)0 (ref.)0 (ref.)0 (ref.)0 (ref.)
1 ACE4,384 (21.86)–0.05 (–0.07,–0.04)–0.04 (–0.06,–0.02)–0.09 (–0.10,–0.07)–0.08 (–0.09,–0.06)
2 ACE3,573 (17.82)–0.08 (–0.09,–0.06)–0.06 (–0.08,–0.05)–0.12 (–0.14,–0.11)–0.11 (–0.13,–0.10)
3–4 ACE4,341 (21.65)–0.12 (–0.14,–0.10)–0.10 (–0.12,–0.08)–0.19 (–0.20,–0.17)–0.17 (–0.19,–0.16)
≥ 5 ACEs3,669 (18.29)–0.15 (–0.17,–0.13)–0.12 (–0.13,–0.10)–0.31 (–0.33,–0.29)–0.28 (–0.30,–0.27)
  1. *

    Coefficients are standardized; aadjusted for age and childhood deprivation; badditionally adjusted for education level, civil status, employment status and income.

Appendix 1—table 7
Associations between the number of ACEs and perceived coping ability (CD-RISC) and psychiatric resilience (β and 95% CI)*, complete case analyses.
Perceived coping abilityPsychiatric resilience
N (%)Model 1aModel 2bModel 1aModel 2b
ACE-IQ total score*19,637–0.18 (-0.19,–0.16)–0.13 (-0.15,–0.12)–0.31 (-0.32,–0.30)–0.28 (-0.29,–0.26)
Number of ACEs
0 ACE4,377 (22.3)0 (ref.)0 (ref.)0 (ref.)0 (ref.)
1 ACE4,496 (22.9)–0.07 (-0.09,–0.05)–0.06 (-0.07,–0.04)–0.08 (-0.10,–0.06)–0.07 (-0.09,–0.05)
2 ACE3,437 (17.5)–0.10 (-0.11,–0.08)–0.08 (-0.10,–0.06)–0.13 (-0.14,–0.11)–0.11 (-0.13,–0.10)
3–4 ACE3,985 (20.3)–0.14 (-0.16,–0.13)–0.12 (-0.14,–0.10)–0.20 (-0.21,–0.18)–0.18 (-0.19,–0.16)
≥ 5 ACEs3,342 (17.0)–0.20 (-0.22,–0.18)–0.15 (-0.17,–0.14)–0.33 (-0.35,–0.31)–0.29 (-0.31,–0.27)
  1. *

    Coefficients are standardized; **per 1 SD unit increase in ACE-IQ scores; aadjusted for age and childhood deprivation; badditionally adjusted for education level, civil status, employment status and income.

Appendix 1—table 8
Prevalence Ratios (with 95% CI) of high perceived coping ability (CD-RISC ≥35) and high psychiatric resilience (absence of psychiatric morbidity) in relation to the number of ACEs, complete case analyses.
High perceived coping abilityHigh psychiatric resilience
N (%)Model 1*Model 2bModel 1*Model 2b
ACE-IQ total score*19,6370.92 (0.90, 0.93)0.94 (0.92, 0.95)0.86 (0.85, 0.87)0.87 (0.87, 0.88)
Number of ACEs
0 ACE4,377 (22.3)1.00 (ref.)1.00 (ref.)1.00 (ref.)1.00 (ref.)
1 ACE4,496 (22.9)0.83 (0.77, 0.89)0.87 (0.81, 0.93)0.84 (0.81, 0.87)0.86 (0.83, 0.89)
2 ACE3,437 (17.5)0.73 (0.67, 0.79)0.77 (0.71, 0.83)0.74 (0.71, 0.78)0.76 (0.73, 0.80)
3–4 ACE3,985 (20.3)0.66 (0.60, 0.71)0.70 (0.65, 0.76)0.62 (0.59, 0.65)0.65 (0.62, 0.68)
≥ 5 ACEs3,342 (17.0)0.59 (0.53, 0.65)0.67 (0.60, 0.74)0.41 (0.38, 0.44)0.44 (0.41, 0.48)
  1. *

    adjusted for age and childhood deprivation; badditionally adjusted for education level, civil status, employment status and income.

Appendix 1—figure 1
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Sociodemographic characteristics of SAGA participants vs. the general female population of Iceland (see further: https://www.afallasaga.is/nidurstodur).
Appendix 1—figure 2
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Flow-chart of the analytic sample.
Appendix 1—figure 3
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Proposed causal model with alternative pathways of how ACEs could influence resilience in adulthood.

Note: Boxes in green indicate potential confounders of the association between ACEs and adult resilience, whereas boxes in red indicate potential mediators of the association.

Appendix 1—figure 4
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Flow-chart of the analytic sample (complete case analysis).
Appendix 1—figure 5
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Associations between different types of ACEs and perceived coping ability (A) and psychiatric resilience (B) (β and 95% CI).

Models were corrected for age, childhood deprivation, education level, civil status, employment status and income. *Coefficients are standardized.

Appendix 1—figure 6
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Prevalence Ratios (with 95% CI) of high perceived coping ability (A) and high psychiatric resilience (B) in relation to individual ACEs.

Models were corrected for age, childhood deprivation, education level, civil status, employment status and income.

Appendix 1—figure 7
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Associations between different types of ACEs and perceived coping ability (A) and psychiatric resilience (B) (β and 95% CI), complete case analyses (n = 19,637).

Models were corrected for age, childhood deprivation, educational level, civil status, employment status, income and mutually adjusted for other ACEs. *Coefficients are standardized.

Appendix 1—figure 8
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Prevalence Ratios (with 95% CI) of high perceived coping ability (A) and high psychiatric resilience (B) in relation to individual ACEs, complete case analyses (n = 19,637).

Models were corrected for age, childhood deprivation, educational level, civil status, employment status, income and mutually adjusted for other ACEs.

Data availability

The data used in this study are compiled in the Stress-And-Gene-Analysis (SAGA) cohort. We cannot make the data publicly available because of Icelandic laws regarding data protection and the approval for the current study granted by the National Bioethics Committee (NBC) of Iceland. The SAGA cohort contains extremely sensitive data and all use of data is restricted to scientific purposes only subjected to approval of the NBC (email: vsn@vsn.is). Interested researchers can obtain access to deidentified data by submitting a proposal to the SAGA cohort data management board (email: afallasaga@hi.is) which assists with submitting an amendment to the NBC. The corresponding author of the present study submitted a research proposal to the SAGA cohort data management board / the NBC and got access only to deidentified data, that cannot be shared in any way.

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Article and author information

Author details

  1. Hilda Björk Daníelsdóttir

    1. Centre of Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavík, Iceland
    2. Department of Medical Epidemiology & Biostatistics, Karolinska Institutet, Stockholm, Sweden
    Contribution
    Conceptualization, Formal analysis, Methodology, Writing – original draft, Writing – review and editing
    For correspondence
    hilda.bjork.danielsdottir@ki.se
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4967-2495

  2. Thor Aspelund

    Centre of Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavík, Iceland
    Contribution
    Data curation, Formal analysis, Methodology, Supervision, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7998-5433

  3. Edda Bjork Thordardottir

    Centre of Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavík, Iceland
    Contribution
    Methodology, Project administration, Resources, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3775-9611

  4. Katja Fall

    1. Clinical Epidemiology and Biostatistics, School of Medical Sciences, Örebro University, Örebro, Sweden
    2. Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
    Contribution
    Writing – review and editing, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3649-2639

  5. Fang Fang

    Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
    Contribution
    Writing – review and editing, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3310-6456

  6. Gunnar Tómasson

    Centre of Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavík, Iceland
    Contribution
    Methodology, Project administration, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1797-7091

  7. Harpa Rúnarsdóttir

    Centre of Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavík, Iceland
    Contribution
    Project administration, Writing – review and editing
    Competing interests
    No competing interests declared

  8. Qian Yang

    Department of Medical Epidemiology & Biostatistics, Karolinska Institutet, Stockholm, Sweden
    Contribution
    Writing – review and editing, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6389-3975

  9. Karmel W Choi

    1. Department of Psychiatry, Massachusetts General Hospital, Boston, United States
    2. Harvard T.H. Chan School of Public Health, Boston, United States
    Contribution
    Writing – review and editing, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3914-2431

  10. Beatrice Kennedy

    Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
    Contribution
    Writing – review and editing, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0066-4814

  11. Thorhildur Halldorsdottir

    1. Centre of Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavík, Iceland
    2. Department of Psychology, Reykjavík University, Reykjavik, Iceland
    Contribution
    Methodology, Project administration, Resources, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0637-8912

  12. Donghao Lu

    1. Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
    2. Harvard T.H. Chan School of Public Health, Boston, United States
    Contribution
    Data curation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4186-8661

  13. Huan Song

    West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
    Contribution
    Methodology, Project administration, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3845-8079

  14. Jóhanna Jakobsdóttir

    Centre of Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavík, Iceland
    Contribution
    Data curation, Project administration, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8019-9683

  15. Arna Hauksdóttir

    Centre of Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavík, Iceland
    Contribution
    Methodology, Project administration, Resources, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4253-1059

  16. Unnur Anna Valdimarsdóttir

    1. Centre of Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavík, Iceland
    2. Department of Medical Epidemiology & Biostatistics, Karolinska Institutet, Stockholm, Sweden
    3. Harvard T.H. Chan School of Public Health, Boston, United States
    Contribution
    Conceptualization, Funding acquisition, Methodology, Resources, Supervision, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5382-946X

Funding

Icelandic Centre for Research (Doctoral grant)

  • Hilda Björk Daníelsdóttir

H2020 European Research Council (Consolidator grant grant number 726413)

  • Unnur Anna Valdimarsdóttir

Icelandic Centre for Research (Grant of excellence grant number 163362-051)

  • Unnur Anna Valdimarsdóttir

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

Funding This work was supported by the European Research Council (UAV, grant number 726413), and the Icelandic Center for Research (Grant of excellence; UAV, grant number 163362–051). HBD was supported by a doctoral grant from the University of Iceland Research Fund. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: The study was approved by the National Bioethics Committee (NBC number: 17-238) and all participants gave informed consent before participation.

Copyright

© 2022, Daníelsdóttir et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Hilda Björk Daníelsdóttir
  2. Thor Aspelund
  3. Edda Bjork Thordardottir
  4. Katja Fall
  5. Fang Fang
  6. Gunnar Tómasson
  7. Harpa Rúnarsdóttir
  8. Qian Yang
  9. Karmel W Choi
  10. Beatrice Kennedy
  11. Thorhildur Halldorsdottir
  12. Donghao Lu
  13. Huan Song
  14. Jóhanna Jakobsdóttir
  15. Arna Hauksdóttir
  16. Unnur Anna Valdimarsdóttir
(2022)
Adverse childhood experiences and resilience among adult women: A population-based study
eLife 11:e71770.
https://doi.org/10.7554/eLife.71770

Share this article

https://doi.org/10.7554/eLife.71770

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Research organism

Further reading

    1. Epidemiology and Global Health

    Lifestyles and their relative contribution to biological aging across multiple-organ systems: Change analysis from the China Multi-Ethnic Cohort study

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    Background:

    Biological aging exhibits heterogeneity across multi-organ systems. However, it remains unclear how is lifestyle associated with overall and organ-specific aging and which factors contribute most in Southwest China.

    Methods:

    This study involved 8396 participants who completed two surveys from the China Multi-Ethnic Cohort (CMEC) study. The healthy lifestyle index (HLI) was developed using five lifestyle factors: smoking, alcohol, diet, exercise, and sleep. The comprehensive and organ-specific biological ages (BAs) were calculated using the Klemera–Doubal method based on longitudinal clinical laboratory measurements, and validation were conducted to select BA reflecting related diseases. Fixed effects model was used to examine the associations between HLI or its components and the acceleration of validated BAs. We further evaluated the relative contribution of lifestyle components to comprehension and organ systems BAs using quantile G-computation.

    Results:

    About two-thirds of participants changed HLI scores between surveys. After validation, three organ-specific BAs (the cardiopulmonary, metabolic, and liver BAs) were identified as reflective of specific diseases and included in further analyses with the comprehensive BA. The health alterations in HLI showed a protective association with the acceleration of all BAs, with a mean shift of –0.19 (95% CI −0.34, –0.03) in the comprehensive BA acceleration. Diet and smoking were the major contributors to overall negative associations of five lifestyle factors, with the comprehensive BA and metabolic BA accounting for 24% and 55% respectively.

    Conclusions:

    Healthy lifestyle changes were inversely related to comprehensive and organ-specific biological aging in Southwest China, with diet and smoking contributing most to comprehensive and metabolic BA separately. Our findings highlight the potential of lifestyle interventions to decelerate aging and identify intervention targets to limit organ-specific aging in less-developed regions.

    Funding:

    This work was primarily supported by the National Natural Science Foundation of China (Grant No. 82273740) and Sichuan Science and Technology Program (Natural Science Foundation of Sichuan Province, Grant No. 2024NSFSC0552). The CMEC study was funded by the National Key Research and Development Program of China (Grant No. 2017YFC0907305, 2017YFC0907300). The sponsors had no role in the design, analysis, interpretation, or writing of this article.

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    Protection afforded by post-infection SARS-CoV-2 vaccine doses: A cohort study in Shanghai

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    Background:

    In many settings, a large fraction of the population has both been vaccinated against and infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Hence, quantifying the protection provided by post-infection vaccination has become critical for policy. We aimed to estimate the protective effect against SARS-CoV-2 reinfection of an additional vaccine dose after an initial Omicron variant infection.

    Methods:

    We report a retrospective, population-based cohort study performed in Shanghai, China, using electronic databases with information on SARS-CoV-2 infections and vaccination history. We compared reinfection incidence by post-infection vaccination status in individuals initially infected during the April–May 2022 Omicron variant surge in Shanghai and who had been vaccinated before that period. Cox models were fit to estimate adjusted hazard ratios (aHRs).

    Results:

    275,896 individuals were diagnosed with real-time polymerase chain reaction-confirmed SARS-CoV-2 infection in April–May 2022; 199,312/275,896 were included in analyses on the effect of a post-infection vaccine dose. Post-infection vaccination provided protection against reinfection (aHR 0.82; 95% confidence interval 0.79–0.85). For patients who had received one, two, or three vaccine doses before their first infection, hazard ratios for the post-infection vaccination effect were 0.84 (0.76–0.93), 0.87 (0.83–0.90), and 0.96 (0.74–1.23), respectively. Post-infection vaccination within 30 and 90 days before the second Omicron wave provided different degrees of protection (in aHR): 0.51 (0.44–0.58) and 0.67 (0.61–0.74), respectively. Moreover, for all vaccine types, but to different extents, a post-infection dose given to individuals who were fully vaccinated before first infection was protective.

    Conclusions:

    In previously vaccinated and infected individuals, an additional vaccine dose provided protection against Omicron variant reinfection. These observations will inform future policy decisions on COVID-19 vaccination in China and other countries.

    Funding:

    This study was funded the Key Discipline Program of Pudong New Area Health System (PWZxk2022-25), the Development and Application of Intelligent Epidemic Surveillance and AI Analysis System (21002411400), the Shanghai Public Health System Construction (GWVI-11.2-XD08), the Shanghai Health Commission Key Disciplines (GWVI-11.1-02), the Shanghai Health Commission Clinical Research Program (20214Y0020), the Shanghai Natural Science Foundation (22ZR1414600), and the Shanghai Young Health Talents Program (2022YQ076).

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    Serum metabolome indicators of early childhood development in the Brazilian National Survey on Child Nutrition (ENANI-2019)

    Marina Padilha, Victor Nahuel Keller ... Gilberto Kac
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    Background:

    The role of circulating metabolites on child development is understudied. We investigated associations between children’s serum metabolome and early childhood development (ECD).

    Methods:

    Untargeted metabolomics was performed on serum samples of 5004 children aged 6–59 months, a subset of participants from the Brazilian National Survey on Child Nutrition (ENANI-2019). ECD was assessed using the Survey of Well-being of Young Children’s milestones questionnaire. The graded response model was used to estimate developmental age. Developmental quotient (DQ) was calculated as the developmental age divided by chronological age. Partial least square regression selected metabolites with a variable importance projection ≥1. The interaction between significant metabolites and the child’s age was tested.

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    Twenty-eight top-ranked metabolites were included in linear regression models adjusted for the child’s nutritional status, diet quality, and infant age. Cresol sulfate (β=–0.07; adjusted-p <0.001), hippuric acid (β=–0.06; adjusted-p <0.001), phenylacetylglutamine (β=–0.06; adjusted-p <0.001), and trimethylamine-N-oxide (β=–0.05; adjusted-p=0.002) showed inverse associations with DQ. We observed opposite directions in the association of DQ for creatinine (for children aged –1 SD: β=–0.05; pP=0.01;+1 SD: β=0.05; p=0.02) and methylhistidine (–1 SD: β = - 0.04; p=0.04;+1 SD: β=0.04; p=0.03).

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    Supported by the Brazilian Ministry of Health and the Brazilian National Research Council.