Skip to main content

The relevant research of adverse childhood experiences and “risky drinking” in children of alcoholics in China



To determine whether adverse childhood experiences (ACEs) of children of alcoholics (COA) in male were associated with their current “risky drinking”.


This case–control study used the Alcohol Use Disorder Identification Test (AUDIT, cutoff is 7) to divide the participants into two groups, a “risky drinking” group (N = 53) and a "non-risky drinking” group (N = 97). Demographic data, Adverse Childhood Experiences-International Questionnaire (ACE-IQ), the Hamilton Anxiety Rating Scale (HAMA), the Hamilton Depression Rating Scale (HAMD) and the Mini-International Neuropsychiatric Interview (MINI) were used for assessment. The specific relationships between ACEs and “risky drinking” were explored.


Respondents ranged in age from 29.70 ± 6.72 years; 74.5% were females; 94.7% were of Han nationality; 56.7% had a level of education above high school; 12% had no formal or stable job. There was difference in attitude to self-drinking between two groups (P < 0.001). The “risky drinking” group was more likely to have experienced a major depressive episode (P < 0.05), nonalcohol psychoactive substance use disorder (P < 0.01) and bulimia nervosa (P < 0.05), and they also experienced more physical abuse (P < 0.05), community violence (P < 0.001) and collective violence (P < 0.01). In a single factor logistic regression, physical abuse, community violence and collective violence were associated with a two to 11- fold increase in “risky drinking” in the adult COA, and in multiple factor logistic regression, community violence showed a graded relationship with “risky drinking”.


The childhood adverse experiences contribute to “risky drinking” in COA. This finding in the Chinese context have significant implications for prevention not only in China but in other cultures. There must be greater awareness of the role of ACEs in the perpetuation of alcoholism.

Peer Review reports


Alcohol dependence was marked by compulsive drinking, withdrawal symptoms and increased tolerance to alcohol [1]. The prevalence of this mental disorder in Europe and the United States was about 3.5–13.5% [2] and 3–3.8% in China [3]. Thus, there were 7.8 million children, were affected by parents who suffered alcohol dependence living with them in the U.S. [4]. There lacked data of children of parents with alcohol dependence (COPAD) in China, but according to the prevalence of alcohol use disorder and the national population [3, 5], the number would not be less than that in western countries.

Although the number of offspring with alcoholic parents had not been counted worldwide, limited research had shown that alcohol dependence had an intergenerational impact with psychological and social implications. The offspring of those with alcoholism were at risk of experiencing the negative effects of parental alcohol dependence [6]. Compared with the children of those without alcoholism, these children had a higher vulnerability to developing “risky drinking” [7] and other negative social and mental outcomes, including mood problems, suicide, school dropout, marital discord, and work and social relationship problems [7,8,9]. “Risky drinking”, which was defined as consuming ≥ 5 standard drinks on a single occasion at least monthly [10], might lead to later heavy drinking and had been associated with a 60% increased risk of developing alcoholism, in which complicated genetic factors may play a role [11, 12]. Higher rates of alcoholism had been found in the offspring of an alcoholic twin than in the children of the nonalcoholic twin [13], indicating that when genetic factors were excluded the development of alcoholism in COA might mainly be due to environmental factors. In a Vietnamese twin study, the baseline rates of alcohol use problems were higher in children of alcoholic parents than in those of the nonalcoholic parents [13]. A Swedish retrospective cohort study showed that children of parents with alcohol use disorders had increased alcohol use problems [14]. Other studies found that parental alcohol dependence impaired family functioning and increased the risk of violence and physical abuse, criminal behavior and parental separation [15]. Given that the children of alcoholics who engaged in risky drinking were the most susceptible group to developing more serious mental health problems, including alcohol dependence, it was necessary to explore how the known adverse childhood experiences impacted later mental health problems [6].

ACEs included emotional abuse, physical abuse, sexual abuse, emotional neglect, physical neglect, and family dysfunction (domestic violence, parental drug use, divorce and incarceration) [16]. In a meta-analysis of Chinese children, 27% suffered physical abuse, 20% suffered emotional abuse, 9% suffered sexual abuse, and 26% suffered neglect [16, 17]. and higher ACEs scores were associated with worse psychological functioning, such as anxiety and depression, and externalizing symptoms, such as impulsive and aggressive behavior [18]. Limited research focused on Chinese populations had shown that greater ACEs exposure was associated with alcohol abuse [16, 19]. Meanwhile, among Chinese general medical students, a higher prevalence of “risky drinking” existed among people with parental alcoholism [20]. Students who had experienced an ACEs had a two to four fold increase in “risky drinking” compared to those who experienced no ACE [20]. Studies had indicated that COA had a higher risk of risky drinking, but the necessity of comparing the environmental factors still existed when analyzing the risk factors for alcohol use problems among COA.

Existing studies examining COA came mostly from Western countries, which had quite a different cultural background than Asian countries [21]. It was important that sources of bias and confounders, such as country, ethnicity, cultural background, were strictly considered and controlled. Meanwhile, we needed to exclude maternal drinking from the present study to decrease a confounding variable—parental gender—as the incidence of alcohol dependence was significantly higher in men than women [2]. In addition, other studies found that female alcoholism had less alcohol consumption and were less likely to have behavioral problems associated with heavy drinking [22]. Women tended to develop alcohol dependence at a later age [22]. Therefore, the effect of maternal drinking on children was relatively small. To our knowledge, this is the first study in China focusing on COA seeking an association with ACEs. Thus, we can better understand the relationship between adverse childhood experiences and current risky drinking among COA in male in Chinese culture. The study had implications for other Asian population. This study hypothesized that the “risky drinking” group would have suffered more ACEs than the comparison group.

Materials and methods


In this case–control study, participants were enrolled via an advertisement and screened from August 2020 to February 2021 at Peking University Sixth Hospital.

The inclusion criteria for those in the risky drinking group included the following:

  1. a.

    biological father could be diagnosed as alcohol dependence based on ICD-10 (International Classification of Diseases, tenth revision) criteria;

  2. b.

    participant was aged 18 to 45;

  3. c.

    participant had an AUDIT score ≥ 7 (This indicates that participants were at higher risk for alcohol dependence).

The exclusion criteria for those in the risky drinking group included the following:

  1. a.

    severe physical or neurological disease;

  2. b.

    history of loss of consciousness or learning disability;

  3. c.

    mother drank during pregnancy or could be diagnosed with maternal alcohol use disorder based on ICD-10 criteria;

  4. d.

    either parent was diagnosed with schizophrenia, bipolar disorder or dementia.

The inclusion criteria for those in the non-risky drinking group differed from the risky group was that AUDIT score < 7. The exclusion criteria for those in the non-risky drinking group were the same as for the risky drinking group.

A total of 213 participants were enrolled after the first screening, A total of 161 persons provided informed consent in writing, and the remaining persons did via WeChat. Eighty-four participants were not in Beijing and were interviewed via video phone, and 77 persons came to Peking University Sixth Hospital and were interviewed face to face. The protocol took one hour to complete. Finally, 150 participants met all inclusion criteria and did not meet the exclusion criteria.

Study design

The two-dimensional code of the advertisement was sent to discharged inpatients, follow-up groups, or posted in the outpatient department. There was a three-phase screening process. One attending psychiatrist, trained to perform the evaluation, interviewed the participants and completed all questionnaires. Simple feedback was given to the participants.

Six questionnaires were used in the interview.

  1. a.

    Demographic form recorded data including sex, age, race, education, occupation, income, marriage, attitudes toward their father's drinking, attitudes toward self-drinking and knowledge of self-help groups (see Table 1).

  2. b.

    The Alcohol Use Disorder Identification Test (AUDIT) was used to divide the participants into the “risky drinking group” and “non-risky drinking group” using a cutoff of 7, which had been tested in China and found to be the best score to identify “risky drinking” in the Chinese population. Cronbach’s alpha was 0.782, and the item-level content validity index was 0.83 [23].

  3. c.

    A Chinese version of the ACE adapted from World Health Organization (WHO) ACE-IQ [16], was used and included 29 items and 13 classifications (emotional neglect; physical neglect; emotional abuse; physical abuse; sexual abuse; alcohol and/or drug abuser in the household; living with someone chronically depressed, mentally ill, institutionalized or suicidal; living with incarcerated household member; one or no parents; parental separation or divorce; family violence; bullying; community violence; and collective violence). The participants were asked to choose the frequency of the ACE items, from “never” to “many times” to account for the level of exposure. Regarding internal consistency, Cronbach’s alpha was 0.83, and all the subscales showed good test–retest reliability, with intraclass correlations ranging between 0.78 and 0.90 [16].

  4. d.

    The Hamilton Anxiety Rating Scale (HAMA) was used to rate anxiety with a cutoff score of 14 to divide the different classes. The validity index for the total score was 0.93, and the validity correlation index of the subscales was 0.83–1 (P < 0.01). The reliability index was 0.36 [24].

  5. e.

    The 17-item Hamilton Depression Rating Scale (HAMD-17) was used to rate the degree of depression with a cutoff score of 17 used to divide the participants into depressed and nondepressed groups. The validity index of the total score was 0.99, and the validity correlation index of the subscales was 0.78–0.98 (P < 0.01). The reliability index was 0.92 [25].

  6. f.

    The Mini-International Neuropsychiatric Interview (MINI) was used to screen for mental disorders [26]. The concurrent validity within interviewers and a retest were 0.94 (P < 0.01) and 0.97 (P < 0.01)respectively, and the criterion validity was between 0.764 and 0.880 [27].

Table 1 Adverse childhood experience between “risky drinking” and “non-risky” drinking groups among adult children of alcoholics

The study was approved by the Institutional Review Board of Peking University Sixth Hospital (No. 202046). The Clinical Report Form contained an introduction to the study and informed consent, which stated that the participants joined the study voluntarily. Confidentially was assured. The participants who were not in Beijing verbally agreed to the informed consent by WeChat. No payment was given to the participants for joining this study. All the authors gave their consent for publication.

Statistical analysis

SPSS 26.0 was used to perform the statistical analysis. We compared the variables of demographic data, ACE-IQ, HAMA, HAMD-17, MINI, AUDIT between two groups. Nonparametric tests were used to compare the only continuous variable of age. Chi-square analyses and Z analysis were used to compare the remaining variables. The simple factor binary logistic regression compared risky drinking (AUDIT ≥ 7) as a dependent variable and adverse childhood experience and other variables that showed significant differences between the two groups as independent variables. In a multifactor binary logistic regression, we took all independent variables as covariates. In both regression analyses, we calculated the adjusted odds ratios (OR) and 95% confidence intervals (95% CI) for the associations between risky drinking and adverse childhood experiences. In all analyses, P-values below 0.05 were considered statistically significant.


Study participant characteristics

The mean age of the 150 participants in the total study was 29.70 with an SD of ± 6.722 years (range, 18 to 45 years). A total of 74.5% were females (n = 112), and 94.7% were of Han nationality (n = 142). Seven percent graduated from middle school (n = 11) or technical school, and 56.7% graduated or were currently in college or university (n = 85). There were 12% with no formal education and stable job (n = 18), and 43.3% earned less than 5000 RMB per month (n = 65). A total of 66.7% were single or divorced (n = 100) (see Table 1). The diagnosis of 140 (93.3%) of the participants' fathers was made by the interviewer based on the history that the offspring provided, and only 10 fathers were diagnosed by psychiatric hospitals.

Features compared between two groups

The “risky drinking” group had fewer females than the “non-risky drinking” group (54.7% vs. 85.6%, χ2 = 17.244, P < 0.05). There was statistical difference in attitude to self-drinking between two groups (χ2 = 24.382, P < 0.001). The “risky drinking” group was more likely to enjoy drinking, while the “non-risky drinking” group was more likely to hate drinking. More “risky-drinking” people had suffered a current major depressive episode (37.7% vs. 21.6%, Z = 4.465, P < 0.05), nonalcohol psychoactive substance use disorder (18.9% vs. 4.1%, Z = 8.805, P < 0.01), and bulimia nervosa (20.8% vs. 6.2%, Z = 7.239, P < 0.05). More people in the “risky drinking” group experienced physical abuse (75.5% vs. 54.6%, Z = 6.313, P < 0.05), community violence (94.3% vs. 59.8%, Z = 20.289, P < 0.001 = and collective violence (18.9% vs. 4.1%, Z = 7.149, P < 0.01). All other ACEs and mental disorders screened by MINI showed no significant differences. Comparing the number of ACEs showed that both groups suffered more than one kind of ACE, with most of them having suffered 4 or more ACEs; the risky drinking group suffered more ACEs, but the difference was not statistically significant (see Table 1).

Comparing the frequency of items on the ACE, more COA in the “risky drinking” group suffered physical abuse regardless of the frequency (χ2 = 8.659, χ2 = 7.934, P < 0.05), and more COA in the same group suffered community violence with frequencies of “a few times” and “many times” (χ2 = 17.310, P = 0.001). The item “Did you experience the deliberate destruction of your home due to any of these events?” showed a difference between the two groups (χ2 = 7.521, P < 0.05), because a few people in the risky drinking group suffered the experience one or more times, but no participant in the non-risky drinking group had this kind of experience. There was no significant difference between the two groups in emotional neglect, physical neglect, domestic violence, emotional abuse, sexual abuse or bullying (see Table 2).

Table 2 Specific ACE items compared between different groups

Association of risky drinking and adverse childhood experiences

We chose the variables that showed a significant difference between the two groups to perform a single factor logistic regression, which were HAMD scores ≥ 14 (OR = 2.488, P < 0.05), current major depressive episode (OR = 2.193, P < 0.05), nonalcohol psychoactive substance use disorder (OR = 5.407, P < 0.01), bulimia nervosa (OR = 3.972, P < 0.05), physical abuse (OR = 2.554, P < 0.05), community violence (OR = 11.207, P < 0.01) and collective violence (OR = 5.407, P < 0.01). Adverse childhood experiences showed a graded relationship with “risky drinking” in adult COA. Depression, nonalcohol psychoactive substance use and bulimia nervosa increased the risk of “risky drinking” two to five fold, while physical abuse, community violence and collective violence increased the risk of “risky drinking” two to 11- fold in adults with COA (see Table 3).

Table 3 Single factor logistic regression: relationship of potential factors and risky drinking

After controlling for the covariates of sex, attitude toward self-drinking, HAMD score, current depression, nonalcoholic psychoactive substance use and bulimia nervosa, which also impacted “risky drinking”, people who experienced community violence were 14 times more likely to have a “risky drinking” problem (see Table 4).

Table 4 Multifactor logistic regression of relationship between ACE and risky drinking


The major finding of this study was that the entire sample suffered at least one ACE, and that the COA with “risky drinking” had a broader range of patterns of adverse childhood experiences and a higher frequency than the non-risky drinking group, including physical abuse, community violence and collective violence. These factors had a graded relationship with “risky drinking” in the COA. Physical abuse, community violence and collective violence showed a two to 11- fold increase in “risky drinking” in the adult COA before controlling for the covariate of comorbidities. Community violence was still associated with a 14- fold increase after controlling for the covariates. In addition, we found that “risky drinking” group enjoyed self-drinking more, “non-risky drinking” group hated self-drinking more.

In this study, we found that all of the COA reported at least one ACE, but there was no significant difference in the number of ACEs between two groups, which could indicate that there was a strong association between alcohol-abusing parents and ACEs in children. One of the reason was that living with intoxicated parents probably led to children facing dysfunctional parenting, emotional and physical neglect and/or abuse, which will affect parent-children bonding and their feeling of safety [21, 28,29,30]. In addition, alcohol-using parents appeared to pass on drinking patterns through being a negative role model for responding to life difficulties or conflicts [31, 32]. Therefore, COA might learn maladaptive responses in their school and social relationships, witch was more strongly related to the content nature of ACEs, rather than the number of ACEs. Children might also pursue self-medication to release their negative emotions, such as fear, shame, phobia, anxiety and/or depression. Self-medication included the use of psychoactive substances and other risky behaviors [33]. Additionally, as they lived in unsafe surroundings, more physical abuse or community and collective violence would be experienced or witnessed by the COA [31, 32].

It was shown in this study that in the “risky drinking” group, community violence, domestic violence, emotional abuse, and physical abuse were the top four adverse experiences, and in the “non-risky drinking group”, domestic violence, emotional abuse, community violence and physical neglect had the highest prevalence, which was similar to findings in Hong Kong [16]; however, these data contrasted with the results from the US and Canada where there appeared to be higher exposure to household dysfunction (including neglect and abuse) rather than violence [34,35,36]. The high level of domestic violence toward both family members and COA reflected a feature of a different culture, such as rigid gender roles, endorsement of physical punishment and absolute parental authority [16, 37,38,39]. Community violence showed differences, and this factor might be added to the other ACEs that already influenced the health of COA. Community violence reflected a harsher living environment, and this finding was in accordance with the results of another study that showed the positive relationship between a disadvantaged community and risky drinking [40]. Part of the condition described as community violence and collective violence, such as “being threatened with a gun in real life, deliberate destruction of your home or having been beaten up by soldiers, police, militia, or gangs”, was not frequent in Chinese society; therefore, the understanding of community violence was different in Western and Eastern countries. The finding that the rest of the ACEs, excluding community violence, showed no significant impact on “risky drinking” might be due to the high rate of ACEs in both groups, and COA who showed “risky drinking” experienced more adverse childhood experiences than the general population.

In this experiment, we found that there were significant differences in attitude to self-drinking between the two groups, in addition to the significant differences in ACEs, Other studies had shown that ACEs could affect the acquired changes in brain structure and function, personality development, and interpersonal relationships of individuals [41]. In a study on adolescent drinking attitudes, they found that lack of parental presence was a risk factor for alcohol consumption among adolescents [42]. The possible reason was that adolescents were closely connected with their parents. According to social learning theory, adolescents were willing to learn behaviors from those around them [43]. Therefore, when individuals suffer more ACEs, they prefered to deal with the problem through drinking, leading to difference in attitude to self-drinking between two groups.

Regarding the comorbidities, the “risky drinking” group was more likely to suffer current depressive episodes, non-alcohol psychoactive substance use disorder and bulimia nervosa than the “non-risky drinking” group. It was difficult to determine the chronological sequence of “risky drinking” and other mental health problems because of the cross-sectional design of this study. This result was in accordance with a meta-analysis, which showed that externalizing problems in some studies and depression tended to be positively associated with alcohol use, but there was no clear association between alcohol problems and anxiety [44]. A potential explanation was that people who had a tendency toward behavioral disinhibition were more likely to be involved in restricted actions, especially those who had experienced adverse and high-risk living environments [45,46,47]. Another mechanism was related to the presence of an internalizing pathway, which was also known as “self-medication” or “tension reduction” [48], as previously elaborated. COA often lacked adaptive social skills when facing difficulties; consequently, they were more likely to have mental health problems, including “risky drinking” and depression [30, 49]. In a study conducted by our team in 2011, it was shown that most alcoholics who had a comorbidity of social anxiety declared that they had social anxiety before drinking and that drinking decreased the anxiety symptoms [50]. Comorbidities could interact with risky drinking or be a negative outcome of ACEs. In cross-sectional studies, comorbidities should be controlled as covariates in the logistic regression model.

In this study, through self-selection, most of the respondents were young females who were highly educated, had a stable job and had a relatively high income. This seemed counterintuitive and made the findings even more striking than if the group was marginal regarding their education, employment and income. It might be that females were more likely to be aware of fathers’ drinking problems and had a curiosity to know more about what is wrong with their father. In the “risky drinking” group, there were more males. One possible reason was that sons of alcoholics were more sensitive to the euphoric and stimulatory effects of alcohol [12]. They initiated repeated drinking to avoid negative hedonic effects [12]. Thus, sons of alcoholics were likely to develop alcohol use problems.

This study also had the following deficiencies. Although a structured interviewing tool was used to assess the participants, we gathered the history of the probands mainly through the reports of the COA. The exclusion of maternal alcoholics might have risked the introduction of biases to the findings, but as there was a significant discrepancy between the prevalence of male and female alcohol dependence in China (6.6% vs 0.2%) [51], it might not be a major problem for this study in the context of Eastern culture. There existed selection bias in that the participants in this study generally had a high education and stable employment, which were potentially protective factors, and they had a high percentage of ACEs and comorbidities, which might be risk factors. In the future, studies should expand the study population to be more representative of the general public. The ACE measure was used worldwide, but it was still limited regarding the collection of duration, frequency and onset of adverse experiences. As it was a case–control study, we could make the assumption of a causal relationship between risky drinking in COA and ACEs, but if we wanted to clarify the cause and result, future, larger sample cohorts with strict control of confounds should be developed.

The findings of this study are meant to help clinicians focus more on the family of COA and, for the first time in our country, provide data and a theoretical basis for the needed healthcare, psychological support and societal understanding. Schools and medical professionals need to perform more evaluations of the negative childhood experiences of COA and provide interventions for COA. The implications of this study are particularly strong for Asian cultures where awareness and prevention efforts lag.


COA engaging in “risky drinking” experienced more childhood adversities, such as physical abuse and environmental violence, and had more comorbidities, such as depression and bulimia nervosa. On the basis of the high prevalence of ACEs among the whole sample of COA, even after controlling for the covariates, one pattern of ACEs still had an apparent association with risky drinking. In China, this study was one of very few studies that have focused on COA who were at high risk of developing drinking and other mental health problems. In this case–control study, we aimed to understand the impact of factors influencing the lives of COA and explored possible targets for intervention and prevention. Raising awareness of the association between alcoholism and ACEs has significant implications not only in China but in other Asian cultures. It is essential for the prevention of the intergenerational “transmission” of a propensity to pursue “risky drinking” and alcoholism.

Availability of data and materials

The datasets used and analysed during the current study available from the corresponding author on reasonable request.


  1. Saunders JB, Aasland OG, Amundsen A, Grant M. Alcohol consumption and related problems among primary health care patients: WHO collaborative project on early detection of persons with harmful alcohol consumption–I. Addiction. 1993;88(3):349–62.

    Article  CAS  Google Scholar 

  2. Esser MB, Hedden SL, Kanny D, Brewer RD, Gfroerer JC, Naimi TS. Prevalence of alcohol dependence among US adult drinkers, 2009–2011. Prev Chronic Dis. 2014;11:E206.

    Article  Google Scholar 

  3. Phillips MR, Cheng HG, Li X, et al. Prevalence, correlates, comorbidity, and age of onset of alcohol use disorders in adult males from five provinces in China. Drug Alcohol Depend. 2017;173:170–7.

    Article  Google Scholar 

  4. Lipari RN, Van Horn SL. Children Living with Parents Who Have a Substance Use Disorder The CBHSQ Report. Rockville: Substance Abuse and Mental Health Services Administration (US); 2013. p. 1–7.

    Google Scholar 

  5. Zhonghua S, Wei H, Hongxian C. Alcohol Patterns, Alcohol Consumption and Alcohol-Related Problems in Five Areas in China: 3. Problems Related to Alcohol Use in General Population Collaborate Group for 2nd Survey on Alcohol Drinking in Five Areas in China. Chin Ment Health J. 2003;8:544–6.

    Google Scholar 

  6. Pisinger VS, Bloomfield K, Tolstrup JS. Perceived parental alcohol problems, internalizing problems and impaired parent - child relationships among 71 988 young people in Denmark. Addiction. 2016;111(11):1966–74.

    Article  Google Scholar 

  7. Brown-Rice KA, Scholl JL, Fercho KA, et al. Neural and psychological characteristics of college students with alcoholic parents differ depending on current alcohol use. Prog Neuropsychopharmacol Biol Psychiatry. 2017;81:284–96.

    Article  Google Scholar 

  8. Kashubeck S. Adult Children of Alcoholics and Psychological Distress. J Couns Dev. 2014;72(5):538–43.

    Article  Google Scholar 

  9. Wiers RW, Gunning WB, Sergeant JA. Do young children of alcoholics hold more positive or negative alcohol-related expectancies than controls? Alcohol Clin Exp Res. 1998;22(8):1855–63.

    Article  CAS  Google Scholar 

  10. Gore FM, Bloem PJ, Patton GC, et al. Global burden of disease in young people aged 10–24 years: a systematic analysis. Lancet London. 2011;377(9783):2093–102.

    Article  Google Scholar 

  11. Prescott CA, Kendler KS. Age at first drink and risk for alcoholism: a noncausal association. Alcohol Clin Exp Res. 1999;23(1):101–7.

    CAS  Google Scholar 

  12. Zimmermann US, Mick I, Laucht M, et al. Offspring of parents with an alcohol use disorder prefer higher levels of brain alcohol exposure in experiments involving computer-assisted self-infusion of ethanol (CASE). Psychopharmacology. 2009;202(4):689–97.

    Article  CAS  Google Scholar 

  13. Slutske WS, D’Onofrio BM, Turkheimer E, et al. Searching for an environmental effect of parental alcoholism on offspring alcohol use disorder: a genetically informed study of children of alcoholics. J Abnorm Psychol. 2008;117(3):534–51.

    Article  Google Scholar 

  14. Long EC, Lonn SL, Sundquist J, Sundquist K, Kendler KS. The role of parent and offspring sex on risk for externalizing psychopathology in offspring with parental alcohol use disorder: a national Swedish study. Soc Psychiatry Psychiatr Epidemiol. 2018;53(12):1381–9.

    Article  CAS  Google Scholar 

  15. Wlodarczyk O, Schwarze M, Rumpf HJ, Metzner F, Pawils S. Protective mental health factors in children of parents with alcohol and drug use disorders: a systematic review. PLoS One. 2017;12(6):e0179140.

    Article  Google Scholar 

  16. Ho GWK, Chan ACY, Chien WT, Bressington DT, Karatzias T. Examining patterns of adversity in Chinese young adults using the Adverse Childhood Experiences-International Questionnaire (ACE-IQ). Child Abuse Negl. 2019;88:179–88.

    Article  Google Scholar 

  17. Fang X, Fry DA, Ji K, Finkelhor D, Chen J, Lannen P, et al. The burden of child maltreatment in China: a systematic review. Bull World Health Organ. 2015;93(3):176–85C.

  18. Lei Z, Jiao F, Ywa B, et al. The patterns of adverse childhood experiences among Chinese children: Four-year longitudinal associations with psychopathological symptoms. J Psychiatr Res. 2020;122:1–8.

    Article  Google Scholar 

  19. Liu Z, Yang Y, Shi Z, Liu J, Wang Y. The risk of male adult alcohol dependence: The role of the adverse childhood experiences and ecological executive function. Compr Psychiatry. 2016;68:129–33.

    Article  Google Scholar 

  20. Xiao Q, Dong MX, Yao J, Li WX, Ye DQ. Parental alcoholism, adverse childhood experiences, and later risk of personal alcohol abuse among Chinese medical students. Biomed Environ Sci. 2008;21(5):411–9.

    Article  Google Scholar 

  21. Rossow I, Keating P, Felix L, McCambridge J. Does parental drinking influence children’s drinking? A systematic review of prospective cohort studies. Addiction. 2016;111(2):204–17.

    Article  Google Scholar 

  22. Erol A, Karpyak VM. Sex and gender-related differences in alcohol use and its consequences: Contemporary knowledge and future research considerations. Drug Alcohol Depend. 2015;156:1–13.

    Article  Google Scholar 

  23. Bing L, Yucun S, Boquan Z, Xiaohua Z, Xiaoguang W. The Test of AUDIT in China. Chin Ment Health J. 2003;17(001):1–3.

    Google Scholar 

  24. Hamilton M. The assessment of anxiety states by rating. Br J Med Psychol. 1959;32(1):50–5.

    Article  CAS  Google Scholar 

  25. Hamilton M. A rating scale for depression. J Neurol Neurosurg Psychiatry. 1960;23:56–62.

    Article  CAS  Google Scholar 

  26. Sheehan DV, Lecrubier Y, Sheehan KH, et al. The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J Clin Psychiatry. 1998;59 Suppl 20:22–33 quiz (34-57).

    CAS  Google Scholar 

  27. Tianmei S, Liang S, Weimin D, et al. Evaluation of the Reliability and Validity of Chinese Version of the Mini-International Neuropsychiatric Interview in Patients with Mental Disorders. Chin Ment Health J. 2009;023(0z1):30–6.

    Google Scholar 

  28. Manning V, Best DW, Faulkner N, Titherington E. New estimates of the number of children living with substance misusing parents: results from UK national household surveys. BMC Public Health. 2009;9:377.

    Article  Google Scholar 

  29. Niccols A, Milligan K, Smith A, Sword W, Thabane L, Henderson J. Integrated programs for mothers with substance abuse issues and their children: a systematic review of studies reporting on child outcomes. Child Abuse Negl. 2012;36(4):308–22.

    Article  Google Scholar 

  30. Edwards EP, Eiden RD, Colder C, Leonard KE. The development of aggression in 18 to 48 month old children of alcoholic parents. J Abnorm Child Psychol. 2006;34(3):409–23.

    Article  Google Scholar 

  31. Barker ED, Maughan B. Differentiating early-onset persistent versus childhood-limited conduct problem youth. Am J Psychiatry. 2009;166(8):900–8.

    Article  Google Scholar 

  32. Gage SH, Hickman M, Heron J, et al. Associations of cannabis and cigarette use with psychotic experiences at age 18: findings from the Avon Longitudinal Study of Parents and Children. Psychol Med. 2014;44(16):3435–44.

    Article  CAS  Google Scholar 

  33. Turner S, Mota N, Bolton J, Sareen J. Self-medication with alcohol or drugs for mood and anxiety disorders: A narrative review of the epidemiological literature. Depress Anxiety. 2018;35(9):851–60.

    Article  Google Scholar 

  34. Cavanaugh CE, Petras H, Martins SS. Gender-specific profiles of adverse childhood experiences, past year mental and substance use disorders, and their associations among a national sample of adults in the United States. Soc Psychiatry Psychiatr Epidemiol. 2015;50(8):1257–66.

    Article  Google Scholar 

  35. Roos LE, Afifi TO, Martin CG, Pietrzak RH, Tsai J, Sareen J. Linking typologies of childhood adversity to adult incarceration: Findings from a nationally representative sample. Am J Orthopsychiatry. 2016;86(5):584–93.

    Article  Google Scholar 

  36. Shin SH, McDonald SE, Conley D. Patterns of adverse childhood experiences and substance use among young adults: A latent class analysis. Addict Behav. 2018;78:187–92.

    Article  Google Scholar 

  37. Chan KL. Children exposed to child maltreatment and intimate partner violence: a study of co-occurrence among Hong Kong Chinese families. Child Abuse Negl. 2011;35(7):532–42.

    Article  Google Scholar 

  38. Ho GW, Gross DA. Differentiating physical discipline from abuse: Q findings from Chinese American mothers and pediatric nurses. Child Abuse Negl. 2015;43:83–94.

    Article  Google Scholar 

  39. Zhai F, Gao Q. Child maltreatment among Asian Americans: characteristics and explanatory framework. Child Maltreat. 2009;14(2):207–24.

    Article  Google Scholar 

  40. Swahn MH, Bossarte RM. Assessing and quantifying high risk: comparing risky behaviors by youth in an urban, disadvantaged community with nationally representative youth. Public Health Rep Mar-Apr. 2009;124(2):224–33.

    Article  Google Scholar 

  41. Windle M, Spear LP, Fuligni AJ, et al. Transitions into underage and problem drinking: developmental processes and mechanisms between 10 and 15 years of age. Pediatrics. 2008;121 Suppl 4(Suppl 4):S273-289.

    Article  Google Scholar 

  42. Kask K, Markina A, Podana Z. The Effect of Family Factors on Intense Alcohol Use among European Adolescents: A Multilevel Analysis. Psychiatry J. 2013;2013:250215.

    Article  Google Scholar 

  43. Smoktunowicz E, Cieslak R, Demerouti E. Interrole conflict and self-efficacy to manage work and family demands mediate the relationships of job and family demands with stress in the job and family domains. Anxiety Stress Coping. 2017;30(5):485–97.

    Article  Google Scholar 

  44. Ning K, Gondek D, Patalay P, Ploubidis GB. The association between early life mental health and alcohol use behaviours in adulthood: A systematic review. PLoS One. 2020;15(2):e0228667.

    Article  CAS  Google Scholar 

  45. Iacono WG, Malone SM. Developmental Endophenotypes: Indexing Genetic Risk for Substance Abuse with the P300 Brain Event-Related Potential. Child Dev Perspect. 2011;5(4):239–47.

    Article  Google Scholar 

  46. Zucker RA, Heitzeg MM, Nigg JT. Parsing the Undercontrol/Disinhibition Pathway to Substance Use Disorders: A Multilevel Developmental Problem. Child Dev Perspect. 2011;5(4):248–55.

    Article  Google Scholar 

  47. Hussong AM, Curran PJ, Chassin L. Pathways of risk for accelerated heavy alcohol use among adolescent children of alcoholic parents. J Abnorm Child Psychol. 1998;26(6):453–66.

    Article  CAS  Google Scholar 

  48. Hussong AM, Jones DJ, Stein GL, Baucom DH, Boeding S. An internalizing pathway to alcohol use and disorder. Psychol Addict Behav. 2011;25(3):390–404.

    Article  Google Scholar 

  49. Ramchandani P, Psychogiou L. Paternal psychiatric disorders and children’s psychosocial development. Lancet. 2009;374(9690):646–53.

    Article  Google Scholar 

  50. Yujia Q, Bing L, Zhong L. The rate of social anxiety symptoms among alcoholics: A hospital-based survey. Chin Ment Health J. 2013;27(002):132–5.

    Google Scholar 

  51. Zhigang W, Zhonghua S, Wei H. Demographic characteristics and related factors of alcohol dependence in five areas in China. Chin J Behav Med Sci. 2004;13(1):52–4.

    Google Scholar 

Download references


We appreciate all those doctors who helped in disseminating the advertisement of our study and who collected data. In addition, we wish to thank all the participants who overcame the difficulty of distance and time to complete the time-consuming interviewing via video phone or in our hospital during the pandemic of COVID-19. I wish to acknowledge the guidance and support of my thesis adviser, Prof. Huali Wang and Prof. Bing Li.


This study was supported by Self-exploration Project of National Clinical Research Center for Mental Disorders (No. NCRC2020M10) and National Science and Technology Major Project for IND (Investigational New Drug) (No.2018ZX09201-014). The funding was not involved in the design, implementation, data analysis, and publication process.

Author information

Authors and Affiliations



Xin Yu did the administration of the project and revising. Yujia Qiu and Guangqiang Sun conducted the investigation and data collection, statistical analysis, and writing original manuscript. Tingfang Wu, Chengbing Huang, Mingchao Yu, Yan Guo, Zhongqing Sui and Xihua Zhu contributed to the data collection. All authors had final responsibility for the submission and all of them read and approved the final version of the manuscript. Yujia Qiu, Guangqiang Sun and Xin Yu had primary access to the data used in the analysis.

Corresponding author

Correspondence to Yujia Qiu.

Ethics declarations

Ethics approval and consent to participate

The study was approved by the Institutional Review Board of Peking University Sixth Hospital (No. 202046). Meanwhile, the informed consent by the WeChat method is approved by the Institutional Review Board of Peking University Sixth Hospital (No. 202046). The Clinical Report Form contained an introduction to the study and informed consent, which stated that the participants joined the study voluntarily. Confidentially was assured. The participants who were not in Beijing verbally agreed to the informed consent by WeChat. No payment was given to the participants for joining this study. All methods were carried out in accordance with relevant guidelines and regulations.

Consent for publication

All the authors gave their consent for publication.

Competing interests

None of the authors has a conflict of interest.

Additional information

Publisher’s Note

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

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sun, G., Wu, T., Huang, C. et al. The relevant research of adverse childhood experiences and “risky drinking” in children of alcoholics in China. BMC Psychiatry 23, 34 (2023).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: