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Adverse childhood experiences were associated with more frailty events.
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Adverse childhood experiences were associated with widening of the frailty trajectory.
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Women were more sensitive than men to socioeconomic deprivation and high levels of adversity in childhood.
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Women were not more sensitive to childhood intrafamilial aggression than men.
Abstract
Background
The determinants of gender differences in frailty remain unknown but may include social factors. International attention is increasingly focusing on the adverse childhood experiences of women. This study therefore examined the gender-specific association of adverse childhood experiences with frailty index level and trajectory.
Methods
This population-based study used data from the China Health and Retirement Longitudinal Study, with a nationally representative sample of people aged 45 years or more. The frailty index was based on 41 health measurements, and 18 types of adverse childhood experiences were examined. Weighted ordered logistic models were used with additive interaction.
Results
Compared with those exposed to one or no adverse events, exposure to two or three childhood adverse experiences was associated with a 44 % (95%CI: 1.16–1.80) increase in the likelihood of frail status for women, but not significantly associated with the likelihood of frail status for men. Additionally, for men and women, experiencing four or more adversities was associated with a 69 % (95%CI: 1.36–2.09) and a 138 % (95%CI: 1.93–2.94) increase in the likelihood of frail status, respectively. A similar association was found between accumulative scores for adverse childhood experiences and trajectory of the frailty index (men vs. women: OR of exposure to two or three adversities: 1.17 (95%CI: 0.84–1.64) vs. 1.26 (95%CI: 1.02–1.56); OR of exposure to four or more adversities: 1.70 (95%CI: 1.24–2.34) vs. 2.12 (95%CI: 1.70–2.63)). The greatest increase in risk of frailty was observed among men and women experiencing a high level of adversity, followed by socioeconomic deprivation and intrafamilial aggression. There was a significant additive interaction between women and childhood socioeconomic deprivation or a high level of adversity. The risk of being frail or having a rapidly increasing frailty index trajectory for women with a high level of adversity was approximately 4.34 (95%CI: 3.36–5.59) and 4·07 (95%CI: 3·34–4.96) times higher than that for men with a low level of adversity. However, gender differences were not found in the effects of childhood intrafamilial aggression.
Conclusions
Men and women routinely experienced adult frailty as a result of adverse childhood events. The biological interaction between women and adverse childhood experiences was evident, with women's frailty being more sensitive to childhood socioeconomic deprivation and a high level of adversity. The findings have important implications for reducing the risk of frailty by mitigating early life stress, especially among women.
Frailty is a hallmark of vulnerability in older adults predisposed to health risks, posing as a major threat to healthy aging. The negative consequences of frailty extend beyond the individual. Frailty is costly in terms of health care utilization as well and considered as a significant burden on families and health care system [
]. Mitigating life-long risk factors of frailty thus is of great prominent to healthy aging. Frailty is a complex status resulting from a combination of personal characteristics (e.g., gender) and social determinants. Clinical literature over the past few decades has identified clear gender differences in frailty, with women almost always having higher frailty prevalence and risk. Reasons for gender differences in frailty risk remain unknown but may include social and behavioural factors [
]. Adverse childhood experiences (ACEs) refer to various potentially stressful experiences that can affect children while growing up, such as abuse, neglect, and household dysfunction [
Life course health consequences and associated annual costs of adverse childhood experiences across Europe and North America: a systematic review and meta-analysis.
]. Evidence from biomedical studies suggests that ACEs-related stressors could be associated with various types of chronic activation, potentially result in a greater risk of developing age-related diseases [
]. Thus, theorized as a cumulative stressor, ACEs are profound determinants of frailty, and the association with frailty varies by gender.
An increasing number of empirical studies have identified associations between ACEs and various age-related health risks (including frailty) throughout the life course across different countries. Specifically, health measures ranged from physical health (including general health, morbidity and mortality of cancer, cardiovascular disease, respiratory diseases, cancer, and diabetes) to mental health (including depression and cognitive impairment) at a certain time in middle and older-age [
Adverse childhood experiences and subsequent chronic diseases among middle-aged or older adults in China and associations with demographic and socioeconomic characteristics.
]. The most widely used ACE scale, that is, the Centers for Disease Control and Prevention (CDC)–Kaiser Permanente ACE Study, includes 10 items generated based on a sample of primarily white and educated individuals [
]. Recently, an additional set of expanded ACEs (e.g., low-quality neighbour) are increasingly reported to be associated with life-course health outcomes [
]. According to the association between 18 types of ACEs and frailty Index (FI), a study highlights the necessity of expanded ACEs in China, but leaving open the question of gender-specific effects [
Nevertheless, several studies have identified gender differences in the associations between adverse childhood experiences and subsequent adulthood outcomes, but with mixed results. Compared to men, women were disproportionately and negatively affected by cumulative ACEs in terms of prevalence of cancer, opioid use disorder, psychopathology, and well-being in Western societies [
]. Yuan et al. also found that Chinese women are more sensitive to number of ACEs in terms of activities of daily life disability in middle and older-age [
Gender-specific effects of adverse childhood experiences on incidence of activities of daily life disability in middle-age and elderly chinese population.
Adverse childhood experiences and subsequent chronic diseases among middle-aged or older adults in China and associations with demographic and socioeconomic characteristics.
]. Inconsistent effects by gender between studies may be due to the number of ACEs used as predicting indicators in most studies, without considering the characteristics for different types of ACEs. There could be great heterogeneity in the characteristics of ACEs among participants even they experienced the same number of ACEs. Meanwhile, previous studies have documented that the effects of different types of adversities are heterogeneous. For example, physical abuse, sexual abuse, emotional abuse, and family dynamic were associated with higher odds of cancer for women, but only emotional abuse was associated with the outcome for men in United States [
]. Thus, gender-specific effects of ACEs may vary by the included ACEs.
To fill these gaps, we collected information on 18 ACEs from middle-aged Chinese adults, and estimated gender-specific effects of number of ACEs on FI level and trajectory. According to the cumulative disadvantage theory, the starting point may impact health trajectory [
]. Health risks could gradually accumulate in the later stages of development, worsening the situation. Thus, the dynamic effects of ACEs were considered in the studies by estimating the effects of ACEs on FI trajectories. Furthermore, cluster analysis was conducted to identify types of ACEs exposure with similar characteristics, then the effects of different types of ACEs on FI were assessed. Gender-specific effects of types of ACEs were compared with the same sampled population, which allowed us to recognize the most impactful ACEs for men and women in Chinese context. Cluster analysis also helps to reduce the potential collinearity issue between individual ACEs. Our study may have important policy implications for designing gender-specific interventions to mitigate life-long risk factors of FI.
2. Methods
2.1 Data source
This study was conducted using the China Health and Retirement Longitudinal Study (CHARLS) data, which is a nationally representative sample of people 45 years and older. A more detailed description of the study design and sampling procedure can be found in the cohort profile of CHARLS [
]. The first CHARLS nationwide data were collected in 2011, covering an extensive range of information, such as demographic characteristics, socioeconomic status, and health status. The CHARLS cohort was followed up every 2 years. There were 13,567 respondents participating in the four waves. The CHARLS life history survey, conducted in 2014, retrospectively collected the life history information of all live respondents in the previous waves. We successfully conducted 1:1 matching of 12,748 respondents who had completed CHARLS 2011–2018 and life history surveys with demographic information. After exclusion of 1029 individuals with missing information on ACE components, and 141 participants without any data on adulthood health status, 11,568 participants with complete data were included (Fig. 1). Since the missing rate of ACE components (approximately 9 %) exceeds 5 %, as robust check, we conducted multiple imputation (MI) for the sample that had completed the FI (n = 12,597) using 10 imputed data sets with the multiple imputation method by chained equations (MICE). MICE has emerged in the statistical literature as one principled method of addressing missing data. MICE creates multiple imputations, as opposed to single imputations, accounting for the statistical uncertainty in the imputations. In addition, the approach is very flexible and can handle variables of varying types [
]. In the study, the MI process was conditional on FI, age, gender, residence, marital status, being a heavy drinker, and education. Log and logit transformations were used to address non-normality (e.g., ACE components) in the MI process.
We extracted 18 ACEs from CHARLS, including intrafamilial aggression and neglect, family dynamics, loss or threat of loss within the family, socioeconomic deprivation, and neighborhood quality (Supplemental Table 1) [
Life course health consequences and associated annual costs of adverse childhood experiences across Europe and North America: a systematic review and meta-analysis.
]. ACEs were conceptualized as a cumulative score based on the total number of ACEs experienced and as cluster groups of individual ACE types. Responses to each item were dichotomized and summed to generate a cumulative ACE score for each participant, ranging from 0 to 18. Participants were categorized into 3 groups based on the mean cumulative ACE scores: ≤1, >1 to ≤3, or >3. Based on the cluster analysis, we furthermore categorized participants into four groups: low adversity, socioeconomic deprivation, intrafamilial aggression, and high adversity (Supplemental Text 1).
2.3 Frailty index
We selected 41 indicators to calculate the FI (Supplemental Table 2). Each deficit was categorized or mapped into the 0·00–1·00 interval, with 0·00 indicating the absence of a deficit and 1·00 indicating the maximal expression of the deficit. The FI was calculated for each respondent as the number of deficits present in a person divided by the total number of deficits (41). Following previous literature, we did not assign weights to individual indicators [
]. The FI is a continuous variable, with a higher value indicating a worse, frailer status. Based on FI in 2011, 2013, 2015 and 2018, the mean FI for each respondent was calculated in the study period, and was categorized as robust (FI ≤ 0·10), prefrail (>0·10 to <0·25), and frail (FI ≥0·25). Then, using group-based trajectory modeling, the FI trajectories for all respondents was classified into 2 groups, namely, stable at robust and prefrail, rapidly increasing to frail, reflecting the temporal variation in FI from 2011 to 2018 (Supplemental Text 2).
2.4 Statistical analysis
Weighted ordered logistic models were constructed to examine ACEs association with FI categories and its trajectory at a later age, and stratified analyses were conducted for gender. Demographic characteristics, adulthood socioeconomic status, and health behaviours were selected and controlled for Stepwise Regression procedure. Adulthood socioeconomic status was measured by educational attainment (a category variable: illiterate, elementary school, junior high school, and high school or above) and urban residence. Demographic variables included gender, marital status (unmarried included ever being single, divorced, or separated in the studied period), and age. Being a heavy drinker and current smoker were adjusted for health behaviours. Education, urban residence, age, gender, and heavy drinker were finally adjusted in our models. Since individuals with continued participation over years might be different from those who leave the cohort, attrition could lead to potential bias. The sample attrition adjustment method was applied to obtain the weight of our longitudinal data, and weighted regression models with robust variance estimates were derived from generalized estimating equations.
Furthermore, the additive interaction between gender and ACEs in association with FI was assessed by examining whether the estimated joint effect (ie, relative risk) of the two exposures (combining gender and ACEs into a comprehensive variable) was greater than the sum of the individual effect estimates for gender and ACEs. This form of interaction provides an indication of the presence of biological interaction between risk factors. Using weighted ordered logistic models, we estimated the relative excess risk (RERI) because of interaction and its 95 % confidence interval (CI) to test for statistical significance of additive interactions. Two-tailed P < .05 indicated statistical significance. Odds ratios (ORs) and 95 % CIs were reported. STATA, version 15 (StataCorp LLC), was used for all calculations.
3. Results
3.1 Descriptive statistics
Among the 11,568 respondents (mean [SD] age, 57·95 [9·16] years; 6230 women [53·86 %]), approximately 79 % of respondents were stable at robust or prefrail status, while 21 % of them rapidly rising to frail status (Table 1). Frail status was more common among women than men, partly due to a higher prevalence of mobility impairments in women (Supplemental Fig. 1). The prevalence of each ACE ranged from 0·9 % (parental separation or divorce) to 35 % (Adverse emotional neglect) among the convention versions of ACEs, and 51 % (Low parental education) in 18 ACEs (Supplemental Fig. 2). The prevalence of low adversity, intrafamilial aggression, socioeconomic deprivation, and high adversity were 34 %, 19 %, 24 %, and 23 %, respectively (Table 1). Women reported fewer ACEs, yet a higher prevalence of childhood hunger than men (33 % vs. 26 %) (Supplemental Fig. 2).
Table 1Participant descriptive statistics.
Characteristic
No. (%)
Total sample (N = 11,568)
Women (N = 6230)
Men (N = 5338)
FI categories
Robust (frailty index ≤0·10)
1725 (14.91)
685 (11.00)
1040 (19.48)
Prefrail (>0·10 to <0·25)
7974 (68.93)
4315 (69.26)
3659 (68.55)
Frail (frailty index ≥0·25)
1869 (16.16)
1230 (19.74)
639 (11.97)
FI trajectory
Stable at robust or prefrail
9095 (78.62)
4594 (73.74)
4501 (84.32)
Rapidly rising to frail
2473 (21.38)
1636 (26.26)
837 (15.68)
Number of ACEs
ACEs≤1
1757 (15.19)
1022 (16.40)
735 (13.77)
1 < ACEs≤3
5199 (44.94)
2882 (46.26)
2317 (43.41)
ACEs>3
4612 (39.87)
2326 (37.34)
2286 (42.83)
ACE types
Low adversities
3985 (34.45)
2268 (36.40)
1717 (32.17)
Intrafamilial aggression
2192 (18.95)
1172 (18.81)
1020 (19.11)
Socioeconomic deprivation
2769 (23.94)
1417 (22.74)
1352 (25.33)
High adversities
2622 (22.67)
1373 (22.04)
1249 (23.4)
Age in 2011, mean (SD), y
57.95 (9.16)
57.38 (9.44)
58.61 (8.77)
Ever being unmarried from 2011 to 2018
2634 (22.77)
1728 (27.74)
906 (16.97)
Majority residence: urban area
2247 (19.42)
1244 (19.97)
1003 (18.79)
Education
Illiterate
3162 (28.2)
1784 (29.11)
1477 (27.57)
Elementary school
2129 (18.4)
1171 (18.8)
958 (17.95)
Junior high school
2544 (21.99)
1331 (21.36)
1213 (22.72)
High school or above
3634 (31.41)
1944 (31.20)
1690 (31.66)
Ever being heavy drinker
2921 (25.25)
1282 (20.58)
1639 (30.7)
FI: frailty index; ACEs: adverse childhood experiences; SD: standard deviation.
Associations between ACEs and FI by gender are presented in Table 2. Compared to experiencing none or one adversity, exposure to four or more ACEs was associated with a 69 % (95%CI: 1.36–2.09) and 138 % (95%CI: 1.93–2.94) increase in the likelihood of being in frail status for men and women. In addition, exposure to two or three ACEs were related to a 44 % (95%CI: 1.16–1.80) increase in the likelihood of being in frail status for women. Similar association was found between accumulative scores of adverse childhood experiences and trajectory of frailty index. The odds for being in trajectory of rapidly rising to frail was increased by 70 % (95%CI: 1.24–2.34) and 112 % (95%CI: 1.70–2.63) due to experiencing four or more ACEs for men and women.
Table 2Association between cumulative ACEs and FI by gender.
FI level
FI Trajectory
Men
Women
Men
Women
OR (95%CI)
OR (95%CI)
OR (95%CI)
OR (95%CI)
Accumulative ACEs (reference group: ACE ≤ 1)
1 < ACEs≤3
1.20
1.44
1.17
1.26
(0.98–1.49)
(1.16–1.80)
(0.84–1.64)
(1.02–1.56)
ACE>3
1.69
2.38
1.70
2.12
(1.36–2.09)
(1.93–2.94)
(1.24–2.34)
(1.70–2.63)
ACE types (reference group: low adversity)
Intrafamilial aggression
1.29
1.35
1.29
1.30
(1.05–1.58)
(1.10–1.65)
(0.96–1.73)
(1.05–1.62)
Socioeconomic deprivation
1.52
1.65
1.61
1.68
(1.26–1.82)
(1.37–1.99)
(1.24–2.10)
(1.39–2.04)
High adversities
1.68
2.21
1.66
2.21
(1.39–2.04)
(1.80–2.70)
(1.24–2.12)
(1.82–2.69)
Education, urban residence, age, gender, and heavy drinker were adjusted in our models.
ORs and 95 % CIs were reported for weighted ordered logistic models. The sample attrition adjustment method was applied to obtain the weight of the longitudinal data.
Table 2 also shows the association between ACEs types and FI by Gender. In individuals of the high-adversity group, the odds of being in frailty status was the highest for men and women, respectively (men: 1.68 (95%CI: 1.39–2.04); Women: 2.21 (95%CI: 1.80–2.70)). The effect estimates of intrafamilial aggression and socioeconomic deprivation were 1.52 (95%CI: 1.26–1·82) and 1.29 (95%CI: 1.05–1.58) for men, and the effects estimates were for women 1.65 (95%CI: 1.37–1.99) and 1.35 (95%CI: 1.10–1.65). We also found a similar association between ACEs types and FI trajectory. Men and women with high adversity were more likely to be in a high and rapidly rising trajectory by 66 % (95%CI: 1.24–2.12) and 121 % (95%CI: 1.82–2.69). Followed by socioeconomic deprivation, the odd ratios corresponding to men and women were 1.61 (95%CI: 1.24–2.10) and 1·68 (95%CI: 1.39–2.04). Women exposure to childhood intrafamilial aggression was 30 % (95 % CI: 1.05–1.62) more likely to being in a high and rapidly rising trajectory. FI trajectory was not significantly affected by intrafamilial aggression for men.
3.3 The joint effects of gender and ACEs on FI
Risk of frailty according to gender and accumulative ACEs was documented in Fig. 2. The greatest increase in risk of frailty was observed among women experiencing four or more ACEs (the estimates on FI level: OR: 3.87, 95 % CI: 3.12–4.80; the estimates on FI trajectory: OR: 3.99, 95 % CI: 2.90–5.51). RERI for additive interaction between women and accumulative ACEs is shown in Table 3, which aims to reveal the presence of biological interaction between women and number of ACEs. We observed a positive additive interaction for FI level, and the RERI because of interaction of women and exposure to four or more ACEs was 1·51(95%CI: 1.39–2.52) for FI level. However, RERI for additive interaction between women and accumulative ACEs were insignificant for FI trajectory, indicating insignificant gender differences in the association between number of ACEs and FI trajectory.
Fig. 2Frailty index according to gender and ACEs types.
Education, urban residence, age, gender, and heavy drinker were adjusted in our models.
ORs and 95 % CIs were reported for weighted ordered logistic/logistic models. The sample attrition adjustment method was applied to obtain the weight of the longitudinal data
Table 3RERI and 95 % CI for additive interaction between ACEs and gender.
Gender
Number of ACEs
ACEs types
1 < Number of ACEs≤3
Number of ACEs>3
Intrafamilial aggression
Socioeconomic deprivation
High adversities
RERI and 95 % CI for additive interaction between ACEs and gender on FI level
Women
0.51
1.51
0.32
0.71
1.55
(−0.21–1.23)
(1.39–2.52)
(−0.31–0.96)
(0.02–1.41)
(0.62–2.47)
RERI and 95 % CI for additive interaction between ACEs and gender on FI trajectory
Women
0.31
1.52
0.37
0.84
1.79
(−0.74–1.36)
(−0.02–3.06)
(−0.52–1.26)
(−0.17–1.86)
(0.49–3.09)
Using weighted ordered logistic/logistic models, we estimated the relative excess risk because of interaction and its 95 % confidence interval (CI) to test for statistical significance of additive interactions.
Education, urban residence, age, gender, and heavy drinker were adjusted in our models.
Afterward, we estimated the joint effects of gender and ACEs types on FI level and trajectory (Fig. 3). Similarly, women in the high adversities group of ACEs reported the highest OR compared to those in other cluster groups of ACEs, including men in the high adversities group. The risk of being frail or having a rapidly increasing to frail trajectory for women with a high adversities was approximately 4.34 (95%CI: 3.36–5.59)- and 4·07 (95%CI: 3·34–4.96)-fold increases than men with a low adversities (Fig. 3). The correspond RERI was 1.55 (95%CI: 0·62–2·47) and 1.79(95%CI: 0.49–3.09). We also observe addictive interaction between women and socioeconomic deprivation for FI level (RERI: 0.71; 95%CI: 0.02–1.41), but not between women and intrafamilial aggression (Table 3).
Fig. 3Frailty index according to gender and ACEs types.
Education, urban residence, age, gender, and heavy drinker were adjusted in our models.
ORs and 95 % CIs were reported for weighted ordered logistic/logistic models. The sample attrition adjustment method was applied to obtain the weight of the longitudinal data.
As sensitive analyses, subsample analyses by their birth year were performed. We grouped the participants according to their birth year by decade. We also constructed a subsample according to their experiences to war (1937–1949) or the great famine (1959–1962) before 15 years old. Subsample analyses obtained the consistent results (Supplemental Tables 3–5). In addition, results using imputed data found similar results (Supplemental Tables 5 and 6).
4. Discussion and conclusions
In this national-representative cohort study, we extracted 18 ACEs and estimated the association of number and types of ACE with FI levels and trajectories. Men and women were routinely victims of ACEs. The effects of ACEs uncovered here were long-term, mainly with >30- and 50-year lags. However, gender difference in the effects of adversities has been found. Women were more sensitive to socioeconomic deprivation and high adversity, while gender differences were not documented in the association between childhood intrafamilial aggression and FI.
4.1 Association between ACEs and FI levels and trajectories
Men and women with a higher cumulative ACE score were more likely to being frail in their middle and old age, suggesting the effects of ACEs can be accumulative. This was consistent with the observed associations of increased ACEs with a rising trend in the prevalence of multimorbidity and chronic diseases, depression, and cognitive impairments among middle-aged and elderly individuals [
Adverse childhood experiences and subsequent chronic diseases among middle-aged or older adults in China and associations with demographic and socioeconomic characteristics.
]. The initial disadvantage accumulates over time, resulting in further disadvantages. Experiencing multiple ACEs was associated with widening of the FI disparities over time, which makes preventative interventions targeting ACEs particularly important.
4.2 The presence of biological interaction between gender and ACEs
Our study found the presence of biological interaction between gender and ACEs, with women's frailty being more sensitive to childhood socioeconomic deprivation and high adversity. Our results were in line with previous literature in terms of physical health problems and well-being [
Gender-specific effects of adverse childhood experiences on incidence of activities of daily life disability in middle-age and elderly chinese population.
]. ACEs are noxious stimuli that, when experienced, may increase the use of negative coping that are different between boys and girls. For example, boys who experience adversity are more likely to experience emotions conducive to delinquency and crime such as anger and frustration; whereas, girls are more likely to have internalized responses including depression, and anxiety which are more conducive to avoidance or self-destructive behaviours including substance use. These gender differences in negative coping strategies may transform to divergence in the incentive of adopting unhealthy behaviours and disease [
Gender-specific effects of adverse childhood experiences on incidence of activities of daily life disability in middle-age and elderly chinese population.
]. This biological interaction has important public health implications, which helps to identify women with cumulative ACEs or socioeconomic deprivation in childhood who are more likely to benefit from interventions.
Nevertheless, we didn't find the biological interaction between gender and intrafamilial aggression in China. Chinese parents believe in tough love and they usually exert strict physical discipline to motivate their children to achieve more academically, socially, and morally [
]. As such, parental physical maltreatment is more than often mixed with tough love. As sons are best qualified to carry the family name, parents may exert stricter physical discipline on their sons, but unfairly allocate their scarce time, energy, and resources by investing more in sons [
]. Thus, we may not observe gender differences in the effects of intrafamilial aggression in China. We are not aware of previous studies that have assessed biological interactions between women and the ACEs in relation to life-course health, but we cannot clarify the mechanisms of the gender differences in the ACEs association with FI.
4.3 Limitations
First, because of data limitations, the indicators to measure childhood status did not include some common ACEs (e.g., sex abuse), and cannot measure the strength and length of ACEs, which may have a different effect on FI. Second, ACEs measurements are retrospective self-evaluations. We have no way to correct the bias for childhood status. Nonetheless, the reliability of self-retrospective childhood status is supported by comparing responses to common questions in the CHARLS follow-up data and life history survey [
]. Third, the study is limited by sample selection, as people exposed to multiple childhood adversities might never be included in surveys (e.g., due to premature death). Objective data from electronic health registries might provide an opportunity to correct the bias and should be examined in future studies. Four, FI is a widely-used indicator to measure the overall health risks. However, there may be great heterogeneity in the characteristics of frailty among participants even they had the same FI level. Thus, future studies may need to further account for the characteristics of frailty while estimating the effects of ACEs.
4.4 Policy implications
An integrated health policy is highly recommended to mitigate frailty syndrome, focusing on those growing up with adverse experiences. International attention is increasingly focusing on prevention of ACEs against children, often emphasizing protection of girls [
]. Although women were more sensitive to some ACEs, both women and men were negatively influenced by multiple ACEs. Thus, interventions should consider both women and men. An additional set of ACEs were found associated with FI, thus, the most widely used ACE scales may underestimate the harm of ACEs, and we may want to expand ACEs. Since the effect size from these ACEs to adulthood health differed, more precise measures should be tailored according to specific ACEs. In China, we may want to focus on exposure to socioeconomic deprivation and high adversity in terms of FI for middle-aged and elderly individuals, and especially among women.
Contributors
Qing Wang is the sole author of the article and no other person made a substantial contribution to it.
Funding
This study was supported by Shandong University future plan funding of young scholars.
Ethical approval
Written inform consent was obtained from all participants. The institutional review board at Peking University approved the CHARLS study (IRB00001052-11015).
Provenance and peer review
This article was not commissioned and was externally peer reviewed.
Life course health consequences and associated annual costs of adverse childhood experiences across Europe and North America: a systematic review and meta-analysis.
Adverse childhood experiences and subsequent chronic diseases among middle-aged or older adults in China and associations with demographic and socioeconomic characteristics.
Gender-specific effects of adverse childhood experiences on incidence of activities of daily life disability in middle-age and elderly chinese population.
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