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Impact of adverse childhood experiences (ACEs) on adult alcohol consumption behaviors
Child Abuse & Neglect xxx (xxxx) xxx–xxx
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Child Abuse & Neglect journal homepage: www.elsevier.com/locate/chiabuneg
Impact of adverse childhood experiences (ACEs) on adult alcohol consumption behaviors ⁎
Elaine Loudermilka, , Kevin Loudermilkb, Julie Obenauera, Megan A. Quinna a
Department of Biostatistics and Epidemiology, College of Public Health, East Tennessee State University, P.O. Box 70623, Johnson City, 37614, TN, United States Internal Medicine Department, Joint Base San Antonio - Lackland Air Force Base, 59th MDW, 959 MDOS, TX, 78236, United States
b
A R T IC LE I N F O
ABS TRA CT
Keywords: Adverse childhood experiences Alcohol consumption
Background: Long term negative physical and mental health problems occur from the lack of appropriate interventions targeting the adult population who experienced adverse childhood experiences (ACEs) and partake in risky alcohol consumption behaviors. Objective: This study aimed to identify the risk for alcohol consumption behaviors, specifically binge drinking (BD) and any drinking (AD), among adults with a history of adverse childhood experiences (ACEs). Methods: Behavioral Risk Factor Surveillance System (BRFSS) 2011–2012 data were used. Descriptive statistics were completed followed by simple and multiple logistic regression to determine the strength of association between ACEs and alcohol consumption, controlling for sociodemographic factors. Results: The final adjusted sample size was 69,793. Adults who experienced household abuse were 30% more likely to BD (Odds Ratio (OR): 1.30, 95% Confidence Interval (CI): 1.20–1.41) and 21% more likely for AD (OR: 1.21, 95% CI: 1.14–1.28) in the past month. Males were over two times more likely to BD (OR: 2.12, 95% CI: 1.96–2.29) and 60% more likely for AD (OR: 1.60, 95% CI: 1.51–1.69) in the past month compared to females. Individuals who completed some college were at higher risk of BD (OR: 1.51, 95% CI: 1.26–1.82), whereas those who graduated college were nearly two and a half times more likely to report AD in the past month (OR: 2.27, 95% CI: 1.99–2.59) compared to individuals with less than high school education. Conclusion: Adults who experienced household abuse, are male, or possess at least some college education are at increased risk for BD and AD.
1. Introduction Alcoholism alongsidechild abuse and neglect have been found to have a strong association established by previous studies. Specifically, adverse childhood experiences (ACEs) involve abuse and household dysfunction resulting in physical, social, and psychological consequences that can cause addictive risk behaviors in adults. In 2014, approximately 702,000 children were confirmed by child protective services as victims of ACEs. Adverse effects from this trauma may include, but are not limited to, improper brain development, impaired cognition, anxiety, blindness, smoking, alcoholism, and drug abuse (CDC, 2016). If left uncontrolled, excessive alcohol consumption alone can cause or exacerbate numerous health conditions, including hypertension, gastroesophageal ⁎
Corresponding author. E-mail addresses: [email protected] (E. Loudermilk), [email protected] (K. Loudermilk), [email protected] (J. Obenauer), [email protected] (M.A. Quinn). https://doi.org/10.1016/j.chiabu.2018.08.006 Received 2 May 2018; Received in revised form 20 July 2018; Accepted 13 August 2018 0145-2134/ © 2018 Elsevier Ltd. All rights reserved.
Please cite this article as: Loudermilk, E., Child Abuse & Neglect, https://doi.org/10.1016/j.chiabu.2018.08.006
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reflux disease, and sleep disturbances. More severe consequences include pancreatitis, cirrhosis, cardiomyopathy, Wernicke-Korsakoff syndrome, and gastrointestinal malignancies (Tetrault & O’Connor, 2016; Wilkins, 2008). Furthermore, ACEs are associated with increased risk for several health complications, such as increased risk of heart and lung disease, various cancers, sexually transmitted infections (STIs), obesity, and poor health-related quality of life (Korotana et al., 2016). In 2001 and 2002, the National Epidemiologic Survey collected data from 43,000 participants analyzing the relationship between ACEs and the development of lifetime alcohol dependence. The study found an increased risk of lifetime alcohol dependence in children who experienced two or more ACEs compared to those children who experienced one or no ACEs (Pilowsky et al., 2009). A study conducted in 2012 determined that ACEs and psychological distress impacted the risk for self-reported alcohol problems among men and women (Strine et al., 2012). An association has been determined between the risk of alcoholism and depression in adults who suffered from ACEs. Adults with a higher ACE score possessed a higher risk of adult alcoholism (Anda et al., 2002). The combined effects of ACEs and alcohol consumption behaviors may contribute and complicate the involvement of unhealthy alcohol consumption behaviors, such as binge drinking and Alcohol Use Disorder (AUD), into adulthood. The most recent study to date examining ACEs and alcohol misuse investigated the relationship between ACEs, gender differences, and substance misuse among adults in South Carolina. Variables of interest included self-reported binge drinking and heavy drinking to assess alcohol abuse; nearly all ACEs and an ACE score of greater than four resulted in greater odds for binge and heavy drinking (Crouch et al., 2018). These studies indicate ACEs play a pivotal role in childhood development and thus greatly influence mental and physical well-being into adulthood. Our study sought to establish an association among adults with ACEs and alcohol consumption behaviors using data obtained from the 2011 and 2012 Behavioral Risk Factor Surveillance System (BRFSS). Most ACE studies have determined an association between alcohol and ACEs but specifically focused on child sexual abuse, household substance abuse, and other specific ACEs. This study is among the first to focus on ACE abuse, household dysfunction categories and alcohol consumption using a nationally representative sample. This study builds upon the current literature regarding the two categories of ACEs, abuse and dysfunction, and their impact on adult alcohol consumption behaviors in the U.S.
2. Methods 2.1. Data collection Data from the 2011–2012 Behavioral Risk Factor Surveillance System (BRFSS) were collected via a cross-sectional survey conducted by the Centers for Disease Control and Prevention (CDC) (CDC, 2017c). This questionnaire included an optional ACE module consisting of eleven questions categorized abuse and household dysfunction (CDC, 2017c). Nine states responded to the ACE questionnaire for these years: Tennessee, Wisconsin, North Carolina, Minnesota, Montana, Vermont, Washington, and Iowa (CDC, 2017c). While more recent data from specific states that collected the ACE module exist, those must be requested from individual states and are not included in the available datasets for public download on the CDC’s website.
2.1.1. Variables BRFSS data for alcohol consumption behaviors consisted of few variables: average drinks, number of drinks, binge drinking occasions, and maximum drinks in the past thirty days. The current study focused specifically on two variables, a calculated variable for any drinking (AD) and binge drinking (BD). These variables were chosen to provide a better understanding between potential problem drinking behaviors, BD, versus daily drinking behaviors that may not lead to serious health consequences, AD. Participants self-reported for having alcohol consumption for AD or BD in the last 30 days as “yes” or “no.” BD had a limit of five drinks or more in two hours for both men and women per National Institutes of Health (NIH) guidelines (NIH, 2017; National Institute of Health (NIH), 2017). AD was defined as having an alcoholic beverage in the past month. ACEs were hypothesized to predict unsafe alcohol consumption behaviors. Thus, household dysfunction (yes/no) and abuse (yes/ no) were defined as the independent variables. ACE variables were taken from the CDC’s ACE module 11-item questionnaire. Abuse was specific to the ACE variables within the BRFSS questionnaire which consisted of experiencing physical, verbal, and/or sexual abuse before the age of 18. Household dysfunction was specific to the ACE variables within the BRFSS questionnaire as well, which consisted of growing up with an incarcerated parent, a parent who abused substances, witnessing intimate partner violence in the home, parental divorce or had parents who were mentally ill. Alcohol related behavioral outcomes were analyzed in the presence of ACEs and categorized into household dysfunction and abuse. Household dysfunction regarded those who were raised by parents who abused alcohol, abused drugs, went to prison, were divorced, or were mentally ill. Any individual ever having experienced verbal, emotional, physical, or sexual abuse before the age of 18 was coded as 1 for “yes” and 0 for “no” for abuse. The same numerical value was assigned for individuals who experienced household dysfunction. Age groups were categorized into six groups: 18–24, 25–34, 35–44, 45–54, 55–64, and 65 and older. Marital status included currently married, divorced, widowed, separated, and never married. Race included White, Black, Asian, or other. Educational attainment was described as having an education of less than high school, graduated high school, some college, and graduated college. Socioeconomic status was measured using annual income levels divided into five categories: less than $15,000, $15,000 to $25,000, $25,000 to $35,000, $35,000 to $50,000, and greater than $50,000. Employment status was not included to avoid the potential for collinearity with the income and education variables. 2
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Table 1 Descriptive statistics for Alcohol Consumption, Household Abuse, Household Dysfunction, and Sociodemographic variables. Demographic Category
Weighted % N = 34,047,602
Unweighted % (N = 84,255)
Binge Drinking Any Drinking Abuse Dysfunction Age 18–24 25-34 35–44 45–54 55–64 65+ Missing Sex Male Female Missing Married Currently Married Divorced/Widowed/Separated Never Married Missing Race White Black Hispanic Other Missing School Less than high school Graduated high school Some college Graduated college Missing Income < $15,000 $15,000- < $25,000 $25,000- < $35,000 $35,000- < $50,000 > $50,000 Missing
17.31 52.28 36.02 44.02
13.44 52.29 39.79 46.29
12.82 16.79 16.62 18.83 16.03 18.24 0.67
4.78 9.67 12.39 18.38 22.75 31.10 0.92
48.82 51.18 0.00
40.90 59.10 0.00
52.99 19.36 23.36 0.43
54.32 28.52 14.04 3.13
79.85 9.38 5.27 0.47 0.79
85.90 5.86 3.14 4.22 0.88
12.59 30.20 32.17 24.69 0.35
7.64 29.31 28.50 34.20 0.35
8.88 16.26 10.91 13.17 35.48 15.29
8.94 15.77 11.28 13.87 35.76 14.38
2.1.2. Statistical analysis The unweighted sample size was 84,255 (Table 1). After weighting the data, the sample size was 34,047,602 (Table 1). The final adjusted sample size was 69,793 (Tables 2 and 3). Analyses were conducted in STATA 12. BD, AD, and ACE data were analyzed using descriptive statistics and logistic regression. Frequencies were reported for only those who experienced ACEs in the US. Simple (SLR) and multiple logistic regression (MLR) analyses were conducted to assess the relationship between ACEs and associated risks for BD and AD. Odds ratios (ORs) and corresponding 95% confidence intervals (CIs) for variables were included in the final model and reported in Tables 2 and 3. Confounding presence was determined by controlling for sociodemographic factors. Covariates included age, sex, income, race, education, and marital status, and these were controlled in MLR. Variables from the SLR with a p-value of > 0.2 were left out of MLR. Variables with p-values of < 0.2 and those found to be significant with a p-level of < 0.05 from the SLR were left in MLR.
3. Results Nearly half of the sample experienced household dysfunction before the age of 18 (44.02%) while only about one-third (36.02%) had experienced abuse. Over half the sample (52.28%) encompassed adults who reported AD, whereas adults who reported BD included less than one-fourth of the sample (17.31%). Approximately half of the sample was female (51.18%) and currently married (52.99%). The majority of the population were White (79.85%), with some college education (32.17%), and an income of greater than $50,000 annually (35.48%), as shown in Table 1.
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Table 2 Multiple logistic regression investigating the relationship of adverse childhood experiences (ACEs) and binge drinking (BD), controlling for sociodemographic factors. N = 69,793. Demographic Category *
Abuse Age 18–24 25-34 35–44 45–54 55–64 65+ Sex Male vs Female Married Currently Married Widowed/Divorced/Separated Never Married Race White Black Hispanic Other School Less than high school Graduated high school Some college Graduated college Income < $15,000 $15,000- < $25,000 $25,000- < $35,000 $35,000- < $50,000 > $50,000
Odds Ratio
SE
Z
P-value
95% CI
1.30
0.05
6.68
< 0.001
1.20
1.41
Reference 0.96 0.69 0.49 0.30 0.12
0.08 0.06 0.04 0.03 0.01
−0.57 −4.35 −8.57 −14.29 −22.46
0.57 < 0.001 < 0.001 < 0.001 < 0.001
0.82 0.59 0.41 0.25 0.10
1.12 0.82 0.57 0.35 0.14
2.12
0.08
19.15
< 0.001
1.96
2.29
Reference 1.43 1.62
0.08 0.09
6.65 8.42
< 0.001 < 0.001
1.29 1.45
1.59 1.81
Reference 0.53 0.89 0.48
0.05 0.09 0.05
−7.29 −1.14 −6.55
< 0.001 0.25 < 0.001
0.45 0.72 0.39
0.63 1.09 0.60
Reference 1.36 1.51 1.32
0.13 0.14 0.13
3.23 4.40 2.90
0.001 < 0.001 0.004
1.13 1.26 1.10
1.64 1.82 1.60
Reference 1.30 1.46 1.39 1.64
0.11 0.13 0.12 0.13
3.08 4.21 3.87 6.09
0.002 < 0.001 < 0.001 < 0.001
1.10 1.22 1.18 1.40
1.53 1.74 1.65 1.92
* Dysfunction was not included in the final model (p > 0.20).
3.1. The relationship between adverse childhood experiences, binge drinking, and sociodemographic factors The results displayed in Table 2 represent MLR analysis for the relationship between ACEs, BD, and sociodemographic factors. Those who experienced household abuse were 30% more likely to BD (OR: 1.30, 95% CI: 1.20–1.41, P < 0.0001). Household dysfunction was left out of the final model due to very low association and insignificant p-value (P > 0.20). Age was found to be protective against BD behaviors. Adults aged 35–44 were 29% less likely to BD compared to those aged 18–24 (OR: 0.69, 95% CI: 0.59-0.82, P < 0.0001). Adults aged 45–54 were 49% less likely to BD compared to those aged 18–24 (OR: 0.49, 95% CI: 0.41-0.57, P < 0.0001). Adults aged 55–64 were 70% less likely to BD compared to those aged 18–24 (OR: 0.30, 95% CI: 0.25-0.35, P < 0.0001). Finally, adults aged 65 and older were 88% less likely to BD compared to those aged 18–24 (OR: 0.12, 95% CI: 0.100.14, P < 0.0001). Males were found to be two times more likely to BD compared to females (OR: 2.12, 95% CI: 1.96–2.29, P < 0.0001). Adults widowed, divorced or separated were 43% more likely to BD compared to those currently married (OR: 1.43, 95% CI: 1.29–1.59, P < 0.0001). Adults never married were 62% more likely to BD compared to those currently married (OR: 1.62, 95% CI: 1.45–1.81, P < 0.0001). When examining the association between race and BD, Black race was found protective against BD by 47% compared to White (OR: 0.53, 95% CI: 0.45-0.63, P < 0.0001). Hispanic race was not found to be significant with a p-value of greater than 0.2. However, Other races were protective against BD by 52% (OR: 0.48, 95% CI: 0.39-0.60, P < 0.0001). The majority of the sample, which consisted of individuals with some college education, were found to be 51% more likely to BD than those with less than a high school education. Those who graduated high school and graduated college were approximately 30% more likely to BD (ORs: 1.36 and 1.32 respectively, P < 0.01) compared to those with less than a high school education. A notable difference was seen among incomes. Those with incomes between $15,000 and $24,999 annually were 36% more likely to BD compared to those with less than $15,000 income annually (OR: 1.36, 95% CI: 1.10–1.53, P = 0.0002). Incomes between $25,000 and $34,999 were found to increase risk for BD by nearly 50% compared to those making less than $15,000 annually (OR: 1.46, 95% CI: 1.22–1.74, P < 0.0001). Those with an income between $35,000 and $49,999 were 39% more likely to BD compared to those with an income of less than $15,000 annually (OR: 1.39, 95% CI: 1.18–1.65, P < 0.0001). Those with an income of greater than $50,000 were 64% more likely to BD compared to those making less than $15,000 annually (see Table 2). The dynamic between income and education should be noted. As education level increases, so does income. Therefore, there may be some interaction between education and income, however interaction was not tested for. It can also be seen from the results that as 4
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Table 3 Multiple Logistic Regression investigating the association between adverse childhood experiences (ACEs) and any drinking, controlling for sociodemographic factors. N = 69,793. Demographic Category *
Abuse Age 18–24 25-34 35–44 45–54 55–64 65+ Sex Male vs Female Married Currently Married Widowed/Divorced/Separated Never Married Race White Black Hispanic Other School Less than high school Graduated high school Some college Graduated college Income < $15,000 $15,000- < $25,000 $25,000- < $35,000 $35,000- < $50,000 > $50,000
Odds Ratio
SE
Z
P-value
95% CI
1.21
0.04
6.20
< 0.001
1.14
Reference 1.25 0.95 0.91 0.70 0.58
0.10 0.08 0.07 0.05 0.04
2.83 −0.59 −1.24 −4.68 −7.08
0.01 0.55 0.22 < 0.001 < 0.001
1.07 0.82 0.78 0.60 0.50
1.45 1.11 1.06 0.81 0.68
1.60
0.05
16.13
< 0.001
1.51
1.69
Reference 1.11 1.24
0.04 0.06
2.92 4.22
< 0.001 < 0.001
1.04 1.12
1.19 1.37
Reference 0.69 0.82 0.58
0.04 0.07 0.05
−6.66 −2.29 −6.76
< 0.001 0.02 < 0.001
0.61 0.69 0.50
0.77 0.97 0.68
Reference 1.30 1.70 2.27
0.08 0.11 0.15
4.03 8.09 12.23
< 0.001 < 0.001 < 0.001
1.14 1.49 1.99
1.47 1.93 2.59
Reference 1.35 1.74 1.97 2.76
0.08 0.11 0.12 0.17
5.09 8.91 10.95 16.85
< 0.001 < 0.001 < 0.001 < 0.001
1.20 1.54 1.74 2.45
1.51 1.97 2.22 3.11
1.28
* Dysfunction was not included in the final model due to insignificance (p > 0.20).
income and education level increased, so did BD behaviors.
3.2. The relationship between adverse childhood experiences, any drinking, and sociodemographic factors Results for MLR for the relationship between ACEs, AD and sociodemographic factors, as seen in Table 3, show abuse was likely to increase AD by 21% (OR: 1.21, 95% CI: 1.14–1.28, P < 0.0001). Abuse was likely to increase the likelihood for AD by 21% (OR: 1.21, 95% CI: 1.14–1.28, P < 0.0001). Household dysfunction was again left out of the final model due to insignificance and weak association with AD (p > 0.20). Adults aged 25-24 were 25% more likely to AD compared to those aged 18–24 (OR: 1.25, 95% CI: 1.07–1.45, P = 0.01). There was no significant association for adults between the ages of 35–44 and 45–54 with AD. However, age becomes significantly protective for those aged 55–64 by 30% (OR: 1.30, 95% CI: 0.60-0.81, P < 0.0001) and for those aged 65 and older by 42% (OR: 0.58, 95% CI: 0.50-0.68, P < 0.0001). Males were 60% more likely for AD in the past month compared to females (OR: 1.60, 95% CI: 0.51-0.69, P < 0.0001). Adults widowed, divorced or separated were only 11% more likely for AD compared to those currently married (OR: 1.11, 95% CI: 1.04–1.19, P < 0.0001). Adults never married were 24% more likely for AD compared to those currently married (OR: 1.24, 95% CI: 1.12–1.37, P < 0.0001). Blacks were 31% less likely for AD compared to Whites (OR: 0.69, 95% CI: 0.61-0.77, P < 0.001). Hispanics were 18% less likely for AD compared to Whites (OR: 0.82, 95% CI: 0.69-0.97, P = 0.02). Other races were 42% less likely for AD compared to Whites (OR: 0.58, 95% CI: 0.50-0.68, P < 0.0001). As education increased, so did the risk for AD within the past month. Adults who graduated high school were 30% more likely for AD compared to those with less than high school education (OR: 1.30, 95% CI: 1.14–1.47, P < 0.0001). Adults with some college were 70% more likely for AD compared to adults with less than high school education (OR: 1.70, 95% CI: 1.49–1.93, P < 0.0001). Adults who graduated college were over two times more likely for AD compared to adults with less than high school education (OR: 2.27, 95% CI: 1.99–2.59, P < 0.0001). A positive relationship was also seen with income and AD. Adults with an annual income of between $15,000 and $24,999 were 35% more likely for AD compared to those with less than $15,000 annual income (OR: 1.35, 95% CI: 1.20–1.51, P < 0.0001). Those with an annual income of $25,000 to $34,999 were 74% more likely for AD compared to those with an annual income of less than $15,000 (OR: 1.74, 95% CI: 1.54–1.97, P < 0.0001). Those with an annual income between $35,000 and $49,999 were nearly two times more likely for AD compared to those with an annual income of less than $15,000 (OR: 1.97, 95% CI: 1.74–2.22, P < 0.0001). Lastly, those with an annual income of greater than $50,000 were nearly three times more likely for AD compared to those with an income of less than $15,000 (OR: 2.76, 95% CI: 2.45–3.11, P < 0.0001) (Table 3). 5
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4. Discussion The goal of the current study was to establish a relationship between alcohol consumption behaviors among adults who had experienced childhood abuse and household dysfunction. Our study extends the literature by commenting on specific alcohol consumption behaviors found to be significantly associated with childhood abuse. Overall, our findings established that abuse during childhood contributed to an increased risk for AD and BD among adults. Our findings were consistent with a previous study conducted in 2002, which found a graded relationship between the number of ACEs reported and the risk of alcoholism, regardless of whether the respondent grew up with parents who abused alcohol (Anda et al., 2002). Using data collected from the 2011 and 2012 BRFSS, this study showed an increased risk of BD and AD among adults who experienced abuse as a child (OR 1.30 and 1.21 respectively, as shown in Table 2 and 3 respectively). Household dysfunction was not found to be significant in either association with BD or AD. Males were at a two-fold higher risk of BD compared to females when controlling for childhood abuse and sociodemographic factors (OR 2.12).This is concurrent with earlier findings that males are more likely to BD than females (CDC, 2017b). A recent study conducted by Lee and Chen, found that adults who experienced child abuse were 39% more likely to BD compared to adults who had not experienced child abuse (Lee & Chen, 2017). Moreover, they found females to be 55% less like to BD compared to males, which is consistent with our findings that males are more likely to BD (Lee & Chen, 2017). An additional study conducted by Fang and McNeil, established a similar relationship between males and BD compared to females and BD (Fang & McNeil, 2017). This study differs from ours because it used an ACE score to determine the likelihood of complications. The study found that males who had a score of 3 or more were nearly 1.7 times more likely to BD. Females were not found to have a significant association between ACE score and BD (Fang & McNeil, 2017). Our study indicates a significantly stronger relationship between males and BD compared to Fang and McNeil’s study (Fang & McNeil, 2017). One reason our study analysis did not use an ACE score was to provide further detail into the relationship between abuse and household dysfunction alongside AD and BD. Our study also differs because we controlled for sociodemographic factors in our logistic regression models while determining the impact household dysfunction and childhood abuse had on AD and BD (Fang & McNeil, 2017). High school graduates and adults with higher incomes, specifically those with incomes greater than $50,000 annually, were found to be at higher risk for BD (OR: 1.64 and > 1.30) compared with individuals without a high school degree (Table 2). An even stronger association was found showing a nearly three-fold increase in likelihood for AD (OR: 2.76, P < 0.001) with those possessing higher incomes alone (Table 3). These two factors, education and income, follow one another because income often increases alongside education level. Thus, some degree of confounding bias may exist. Future analyses are needed to explore potential interaction between these variables. Likewise, further investigation is needed to determine if other commonly abused substances, such as opioids, narcotics, marijuana, could impact the strength of association between risky alcohol consumption among adults with ACEs. 4.1. Strengths and limitations Exposure to ACEs, particularly abuse, increases the likelihood for drinking behaviors. A true causal relationship between exposure to ACEs and the effect on adults through increased alcohol consumption cannot be established due to limitations inherent to crosssectional studies. Though, we can assume some degree of temporality, because ACEs occurred among adults before the age of 18. This research is nationally representative due to its large sample size and since data were obtained from a national database, BRFSS. The potential for recall bias is present given that data were collected through a questionnaire. BD, AD and exposure to ACEs were selfreported; those individuals may not remember every detail correctly or may not want to report their experiences. Social desirability bias may also have been present as individuals may have only wanted to answer in a way they deemed favorable by society. This ACE study draws attention to a critical need for public health interventions. Public health personnel and other professionals must collaborate to educate and prevent further childhood abuse and household dysfunction. Through these efforts, ACEs and their long-term mental, physical, and socioeconomic burdens may be avoided. Additional investigation is needed through the use of primary data collection and additional epidemiologic studies (case-control or cohort studies) to determine if substance abuse of a different variety could determine the level of association between ACEs and alcohol consumption. 4.2. Future implications The ACE questionnaire can be used in addition to substance use and misuse screenings in a clinical setting. The most appropriate screenings may involve pediatric clinics. This would potentially allow for primary prevention through provider-patient discussions. Conversely, these screenings may expose children currently at risk or currently experiencing ACEs in addition to providing them access to trauma-informed care, which focuses on policies, procedures, approaches, and specific interventions for families (Child and Adolescent Health Measurement Initiative (CAHMI), 2018). A small feasibility study involving a family medicine setting discussed the use of screening to aid in allowing physicians to have a more complete sense of the social determinants impacting early life trauma while incorporating appropriate treatments that may be needed (Glowa et al., 2016). A second study piloted using a 7-item ACE tool screened for ACEs to identify specific early child health outcomes from different risk levels (Marie-Mitchell & O’Connor, 2013). Findings were consistent with ACE literature in that accumulation of ACEs also impacted child behaviors (Marie-Mitchell & O’Connor, 2013). Lastly, a third study of interest discussed an important issue regarding ACE studies and screenings, whether to assess ACEs on a population level or target high risk groups (Bethell et al., 2017). Bethell and colleagues determined that several 6
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variations of ACE questionnaires existed. However, all were important in guiding further research in using ACEs as an education piece for families (Bethell et al., 2017). Our current study hopes to provide further insight into the complex relationship between alcohol consumption behaviors and the abuse and household dysfunction categories of ACEs to aid in developing future preventative or treatment methods. Funding No funding obtained for this project. Conflict of interest None declared. Disclaimer The view(s) expressed herein are those of the author(s) and do not reflect the official policy or position of Brooke Army Medical Center, the U.S. Army Medical Department, the U.S. Army Office of the Surgeon General, the Department of the Army, the Department of the Air Force and Department of Defense or the U.S. Government. References Anda, R. F., Whitfield, C. L., Felitti, V. J., Chapman, D., Edwards, V. J., Dube, S. R., ... Williamson, D. F. (2002). Adverse childhood experienes, alcoholic parents, and later risk of alcoholism and depression. Psychiatric Services, 53(8), 1001–1009. Bethell, C. D., Carle, A., Hudziak, J., Gombojav, N., Powers, K., Wade, R., ... Braveman, P. (2017). Methods to assess adverse childhood experiences of children and families: Toward approaches to promote child well-being in policy and practice. Frameworks and Measurement, 17(7s), S51–S66. CDC (2016). Violence prevention: Child abuse and neglect consequences. Retrieved 11 29, 2017, from Centers for Disease Control and Prevention. CDC (2017b). Alcohol and public health: Fact sheets binge drinking. Retrieved 11 29, 2017, from Centers for Disease Control and Preventionhttps://www.cdc.gov/ alcohol/fact-sheets/binge-drinking.htm. CDC (2017c). Annual survey data: Behavioral risk factor surveillance system. Retrieved 11 29, 2017, from Centers for Disease Control and Preventionhttps://www.cdc. gov/brfss/annual_data/annual_data.htm. Child and Adolescent Health Measurement Initiative (CAHMI) (2018). ACEs Resource packet: Adverse childhood experiences (ACEs) basics. Retrieved 07 11, 2018http:// childhealthdata.org/docs/default-source/cahmi/aces-resource-packet_all-pages_12_06-16112336f3c0266255aab2ff00001023b1.pdf?sfvrsn=2. Crouch, E., Radcliff, E., Strompolis, M., & Wilson, A. (2018). Adverse childhood experiences (ACEs) and alcohol abuse among South Carolina adults. Substance Use & Misuse, 53(7), 1212–1220. https://doi.org/10.1080/10826084.2017.1400568. Fang, L., & McNeil, S. (2017). Is there a relationship between adverse childhood experiences and problem drinking behaviors? Findings from a population-based sample. The Royal Society of Public Health, 150, 34–42. https://doi.org/10.1016/j.puhe.2017.05.005. Glowa, P. T., Olson, A. L., & Johnson, D. J. (2016). Screening for adverse childhood experiences in a family medicine setting: A feasibility study. Journal of the American Board of Family Medicine, 25(3), 303–307. https://doi.org/10.3122/jabfm.2016.03.150310. Korotana, L. M., Dobson, K. S., Pusch, D., & Josephson, T. (2016). A review of primary care interventions to improve health outcomes in adult survivors of adverse childhood experiences. Clinical Psychology Review, 46, 59–90. https://doi.org/10.1016/j.cpr.2016.04.007. Lee, R. D., & Chen, J. (2017). Adverse childhood experienes, mental health, and excessive alcohol use: Examination of race/ethnicity and sex differences. Child Abuse & Neglect, 69, 40–48. https://doi.org/10.1016/j.chiabu.2017.04.004. Marie-Mitchell, A., & O’Connor, T. G. (2013). Adverse childhood experiences: Translating knowledge into identification of children at risk for poor outcomes. Academic Pediatrics, 13(1), 14–19. National Institute of Health (NIH) (2017). Alcohol use disorder. Retrieved 11 29, 2017, from National Institutes of Health: National Institute on Alcohol Abuse and Alcoholismhttps://www.niaaa.nih.gov/alcohol-health/overview-alcohol-consumption/alcohol-use-disorders. NIH (2017). Drinking levels defined. Retrieved 11 29, 2017, from National Institutes of Health: National Institute on Alcohol Abuse and Alcoholismhttps://www.niaaa. nih.gov/alcohol-health/overview-alcohol-consumption/moderate-binge-drinking. Pilowsky, D. J., Keyes, K. M., & Hasin, D. S. (2009). Adverse childhood events and lifetime alcohol dependence. American Journal of Public Health, 99(2), 258–263. Strine, T. W., Dube, S. R., Edwards, V. J., Prehn, A. W., Rasmussen, S., Wagenfeld, M., ... Croft, J. B. (2012). Associations between adverse childhood experiences, psychological distress, and adult alcohol problems. American Journal of Health Behavior, 36(3), 408–423. https://doi.org/10.5993/AJHB.36.3.11. Tetrault, J. M., & O’Connor, P. G. (2016). Risky drinking and alcohol use disorder: Epidemiology, pathogenesis, clinical manifestations, course, assessment, and diagnosis. UpToDate.. Wilkins, L. W. (2008). Professional guide to diseases. Philadelphia: Wolters Kluwer Health/Lippincott Williams & Wilkins.
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Contents lists available at ScienceDirect
Child Abuse & Neglect journal homepage: www.elsevier.com/locate/chiabuneg
Impact of adverse childhood experiences (ACEs) on adult alcohol consumption behaviors ⁎
Elaine Loudermilka, , Kevin Loudermilkb, Julie Obenauera, Megan A. Quinna a
Department of Biostatistics and Epidemiology, College of Public Health, East Tennessee State University, P.O. Box 70623, Johnson City, 37614, TN, United States Internal Medicine Department, Joint Base San Antonio - Lackland Air Force Base, 59th MDW, 959 MDOS, TX, 78236, United States
b
A R T IC LE I N F O
ABS TRA CT
Keywords: Adverse childhood experiences Alcohol consumption
Background: Long term negative physical and mental health problems occur from the lack of appropriate interventions targeting the adult population who experienced adverse childhood experiences (ACEs) and partake in risky alcohol consumption behaviors. Objective: This study aimed to identify the risk for alcohol consumption behaviors, specifically binge drinking (BD) and any drinking (AD), among adults with a history of adverse childhood experiences (ACEs). Methods: Behavioral Risk Factor Surveillance System (BRFSS) 2011–2012 data were used. Descriptive statistics were completed followed by simple and multiple logistic regression to determine the strength of association between ACEs and alcohol consumption, controlling for sociodemographic factors. Results: The final adjusted sample size was 69,793. Adults who experienced household abuse were 30% more likely to BD (Odds Ratio (OR): 1.30, 95% Confidence Interval (CI): 1.20–1.41) and 21% more likely for AD (OR: 1.21, 95% CI: 1.14–1.28) in the past month. Males were over two times more likely to BD (OR: 2.12, 95% CI: 1.96–2.29) and 60% more likely for AD (OR: 1.60, 95% CI: 1.51–1.69) in the past month compared to females. Individuals who completed some college were at higher risk of BD (OR: 1.51, 95% CI: 1.26–1.82), whereas those who graduated college were nearly two and a half times more likely to report AD in the past month (OR: 2.27, 95% CI: 1.99–2.59) compared to individuals with less than high school education. Conclusion: Adults who experienced household abuse, are male, or possess at least some college education are at increased risk for BD and AD.
1. Introduction Alcoholism alongsidechild abuse and neglect have been found to have a strong association established by previous studies. Specifically, adverse childhood experiences (ACEs) involve abuse and household dysfunction resulting in physical, social, and psychological consequences that can cause addictive risk behaviors in adults. In 2014, approximately 702,000 children were confirmed by child protective services as victims of ACEs. Adverse effects from this trauma may include, but are not limited to, improper brain development, impaired cognition, anxiety, blindness, smoking, alcoholism, and drug abuse (CDC, 2016). If left uncontrolled, excessive alcohol consumption alone can cause or exacerbate numerous health conditions, including hypertension, gastroesophageal ⁎
Corresponding author. E-mail addresses: [email protected] (E. Loudermilk), [email protected] (K. Loudermilk), [email protected] (J. Obenauer), [email protected] (M.A. Quinn). https://doi.org/10.1016/j.chiabu.2018.08.006 Received 2 May 2018; Received in revised form 20 July 2018; Accepted 13 August 2018 0145-2134/ © 2018 Elsevier Ltd. All rights reserved.
Please cite this article as: Loudermilk, E., Child Abuse & Neglect, https://doi.org/10.1016/j.chiabu.2018.08.006
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reflux disease, and sleep disturbances. More severe consequences include pancreatitis, cirrhosis, cardiomyopathy, Wernicke-Korsakoff syndrome, and gastrointestinal malignancies (Tetrault & O’Connor, 2016; Wilkins, 2008). Furthermore, ACEs are associated with increased risk for several health complications, such as increased risk of heart and lung disease, various cancers, sexually transmitted infections (STIs), obesity, and poor health-related quality of life (Korotana et al., 2016). In 2001 and 2002, the National Epidemiologic Survey collected data from 43,000 participants analyzing the relationship between ACEs and the development of lifetime alcohol dependence. The study found an increased risk of lifetime alcohol dependence in children who experienced two or more ACEs compared to those children who experienced one or no ACEs (Pilowsky et al., 2009). A study conducted in 2012 determined that ACEs and psychological distress impacted the risk for self-reported alcohol problems among men and women (Strine et al., 2012). An association has been determined between the risk of alcoholism and depression in adults who suffered from ACEs. Adults with a higher ACE score possessed a higher risk of adult alcoholism (Anda et al., 2002). The combined effects of ACEs and alcohol consumption behaviors may contribute and complicate the involvement of unhealthy alcohol consumption behaviors, such as binge drinking and Alcohol Use Disorder (AUD), into adulthood. The most recent study to date examining ACEs and alcohol misuse investigated the relationship between ACEs, gender differences, and substance misuse among adults in South Carolina. Variables of interest included self-reported binge drinking and heavy drinking to assess alcohol abuse; nearly all ACEs and an ACE score of greater than four resulted in greater odds for binge and heavy drinking (Crouch et al., 2018). These studies indicate ACEs play a pivotal role in childhood development and thus greatly influence mental and physical well-being into adulthood. Our study sought to establish an association among adults with ACEs and alcohol consumption behaviors using data obtained from the 2011 and 2012 Behavioral Risk Factor Surveillance System (BRFSS). Most ACE studies have determined an association between alcohol and ACEs but specifically focused on child sexual abuse, household substance abuse, and other specific ACEs. This study is among the first to focus on ACE abuse, household dysfunction categories and alcohol consumption using a nationally representative sample. This study builds upon the current literature regarding the two categories of ACEs, abuse and dysfunction, and their impact on adult alcohol consumption behaviors in the U.S.
2. Methods 2.1. Data collection Data from the 2011–2012 Behavioral Risk Factor Surveillance System (BRFSS) were collected via a cross-sectional survey conducted by the Centers for Disease Control and Prevention (CDC) (CDC, 2017c). This questionnaire included an optional ACE module consisting of eleven questions categorized abuse and household dysfunction (CDC, 2017c). Nine states responded to the ACE questionnaire for these years: Tennessee, Wisconsin, North Carolina, Minnesota, Montana, Vermont, Washington, and Iowa (CDC, 2017c). While more recent data from specific states that collected the ACE module exist, those must be requested from individual states and are not included in the available datasets for public download on the CDC’s website.
2.1.1. Variables BRFSS data for alcohol consumption behaviors consisted of few variables: average drinks, number of drinks, binge drinking occasions, and maximum drinks in the past thirty days. The current study focused specifically on two variables, a calculated variable for any drinking (AD) and binge drinking (BD). These variables were chosen to provide a better understanding between potential problem drinking behaviors, BD, versus daily drinking behaviors that may not lead to serious health consequences, AD. Participants self-reported for having alcohol consumption for AD or BD in the last 30 days as “yes” or “no.” BD had a limit of five drinks or more in two hours for both men and women per National Institutes of Health (NIH) guidelines (NIH, 2017; National Institute of Health (NIH), 2017). AD was defined as having an alcoholic beverage in the past month. ACEs were hypothesized to predict unsafe alcohol consumption behaviors. Thus, household dysfunction (yes/no) and abuse (yes/ no) were defined as the independent variables. ACE variables were taken from the CDC’s ACE module 11-item questionnaire. Abuse was specific to the ACE variables within the BRFSS questionnaire which consisted of experiencing physical, verbal, and/or sexual abuse before the age of 18. Household dysfunction was specific to the ACE variables within the BRFSS questionnaire as well, which consisted of growing up with an incarcerated parent, a parent who abused substances, witnessing intimate partner violence in the home, parental divorce or had parents who were mentally ill. Alcohol related behavioral outcomes were analyzed in the presence of ACEs and categorized into household dysfunction and abuse. Household dysfunction regarded those who were raised by parents who abused alcohol, abused drugs, went to prison, were divorced, or were mentally ill. Any individual ever having experienced verbal, emotional, physical, or sexual abuse before the age of 18 was coded as 1 for “yes” and 0 for “no” for abuse. The same numerical value was assigned for individuals who experienced household dysfunction. Age groups were categorized into six groups: 18–24, 25–34, 35–44, 45–54, 55–64, and 65 and older. Marital status included currently married, divorced, widowed, separated, and never married. Race included White, Black, Asian, or other. Educational attainment was described as having an education of less than high school, graduated high school, some college, and graduated college. Socioeconomic status was measured using annual income levels divided into five categories: less than $15,000, $15,000 to $25,000, $25,000 to $35,000, $35,000 to $50,000, and greater than $50,000. Employment status was not included to avoid the potential for collinearity with the income and education variables. 2
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Table 1 Descriptive statistics for Alcohol Consumption, Household Abuse, Household Dysfunction, and Sociodemographic variables. Demographic Category
Weighted % N = 34,047,602
Unweighted % (N = 84,255)
Binge Drinking Any Drinking Abuse Dysfunction Age 18–24 25-34 35–44 45–54 55–64 65+ Missing Sex Male Female Missing Married Currently Married Divorced/Widowed/Separated Never Married Missing Race White Black Hispanic Other Missing School Less than high school Graduated high school Some college Graduated college Missing Income < $15,000 $15,000- < $25,000 $25,000- < $35,000 $35,000- < $50,000 > $50,000 Missing
17.31 52.28 36.02 44.02
13.44 52.29 39.79 46.29
12.82 16.79 16.62 18.83 16.03 18.24 0.67
4.78 9.67 12.39 18.38 22.75 31.10 0.92
48.82 51.18 0.00
40.90 59.10 0.00
52.99 19.36 23.36 0.43
54.32 28.52 14.04 3.13
79.85 9.38 5.27 0.47 0.79
85.90 5.86 3.14 4.22 0.88
12.59 30.20 32.17 24.69 0.35
7.64 29.31 28.50 34.20 0.35
8.88 16.26 10.91 13.17 35.48 15.29
8.94 15.77 11.28 13.87 35.76 14.38
2.1.2. Statistical analysis The unweighted sample size was 84,255 (Table 1). After weighting the data, the sample size was 34,047,602 (Table 1). The final adjusted sample size was 69,793 (Tables 2 and 3). Analyses were conducted in STATA 12. BD, AD, and ACE data were analyzed using descriptive statistics and logistic regression. Frequencies were reported for only those who experienced ACEs in the US. Simple (SLR) and multiple logistic regression (MLR) analyses were conducted to assess the relationship between ACEs and associated risks for BD and AD. Odds ratios (ORs) and corresponding 95% confidence intervals (CIs) for variables were included in the final model and reported in Tables 2 and 3. Confounding presence was determined by controlling for sociodemographic factors. Covariates included age, sex, income, race, education, and marital status, and these were controlled in MLR. Variables from the SLR with a p-value of > 0.2 were left out of MLR. Variables with p-values of < 0.2 and those found to be significant with a p-level of < 0.05 from the SLR were left in MLR.
3. Results Nearly half of the sample experienced household dysfunction before the age of 18 (44.02%) while only about one-third (36.02%) had experienced abuse. Over half the sample (52.28%) encompassed adults who reported AD, whereas adults who reported BD included less than one-fourth of the sample (17.31%). Approximately half of the sample was female (51.18%) and currently married (52.99%). The majority of the population were White (79.85%), with some college education (32.17%), and an income of greater than $50,000 annually (35.48%), as shown in Table 1.
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Table 2 Multiple logistic regression investigating the relationship of adverse childhood experiences (ACEs) and binge drinking (BD), controlling for sociodemographic factors. N = 69,793. Demographic Category *
Abuse Age 18–24 25-34 35–44 45–54 55–64 65+ Sex Male vs Female Married Currently Married Widowed/Divorced/Separated Never Married Race White Black Hispanic Other School Less than high school Graduated high school Some college Graduated college Income < $15,000 $15,000- < $25,000 $25,000- < $35,000 $35,000- < $50,000 > $50,000
Odds Ratio
SE
Z
P-value
95% CI
1.30
0.05
6.68
< 0.001
1.20
1.41
Reference 0.96 0.69 0.49 0.30 0.12
0.08 0.06 0.04 0.03 0.01
−0.57 −4.35 −8.57 −14.29 −22.46
0.57 < 0.001 < 0.001 < 0.001 < 0.001
0.82 0.59 0.41 0.25 0.10
1.12 0.82 0.57 0.35 0.14
2.12
0.08
19.15
< 0.001
1.96
2.29
Reference 1.43 1.62
0.08 0.09
6.65 8.42
< 0.001 < 0.001
1.29 1.45
1.59 1.81
Reference 0.53 0.89 0.48
0.05 0.09 0.05
−7.29 −1.14 −6.55
< 0.001 0.25 < 0.001
0.45 0.72 0.39
0.63 1.09 0.60
Reference 1.36 1.51 1.32
0.13 0.14 0.13
3.23 4.40 2.90
0.001 < 0.001 0.004
1.13 1.26 1.10
1.64 1.82 1.60
Reference 1.30 1.46 1.39 1.64
0.11 0.13 0.12 0.13
3.08 4.21 3.87 6.09
0.002 < 0.001 < 0.001 < 0.001
1.10 1.22 1.18 1.40
1.53 1.74 1.65 1.92
* Dysfunction was not included in the final model (p > 0.20).
3.1. The relationship between adverse childhood experiences, binge drinking, and sociodemographic factors The results displayed in Table 2 represent MLR analysis for the relationship between ACEs, BD, and sociodemographic factors. Those who experienced household abuse were 30% more likely to BD (OR: 1.30, 95% CI: 1.20–1.41, P < 0.0001). Household dysfunction was left out of the final model due to very low association and insignificant p-value (P > 0.20). Age was found to be protective against BD behaviors. Adults aged 35–44 were 29% less likely to BD compared to those aged 18–24 (OR: 0.69, 95% CI: 0.59-0.82, P < 0.0001). Adults aged 45–54 were 49% less likely to BD compared to those aged 18–24 (OR: 0.49, 95% CI: 0.41-0.57, P < 0.0001). Adults aged 55–64 were 70% less likely to BD compared to those aged 18–24 (OR: 0.30, 95% CI: 0.25-0.35, P < 0.0001). Finally, adults aged 65 and older were 88% less likely to BD compared to those aged 18–24 (OR: 0.12, 95% CI: 0.100.14, P < 0.0001). Males were found to be two times more likely to BD compared to females (OR: 2.12, 95% CI: 1.96–2.29, P < 0.0001). Adults widowed, divorced or separated were 43% more likely to BD compared to those currently married (OR: 1.43, 95% CI: 1.29–1.59, P < 0.0001). Adults never married were 62% more likely to BD compared to those currently married (OR: 1.62, 95% CI: 1.45–1.81, P < 0.0001). When examining the association between race and BD, Black race was found protective against BD by 47% compared to White (OR: 0.53, 95% CI: 0.45-0.63, P < 0.0001). Hispanic race was not found to be significant with a p-value of greater than 0.2. However, Other races were protective against BD by 52% (OR: 0.48, 95% CI: 0.39-0.60, P < 0.0001). The majority of the sample, which consisted of individuals with some college education, were found to be 51% more likely to BD than those with less than a high school education. Those who graduated high school and graduated college were approximately 30% more likely to BD (ORs: 1.36 and 1.32 respectively, P < 0.01) compared to those with less than a high school education. A notable difference was seen among incomes. Those with incomes between $15,000 and $24,999 annually were 36% more likely to BD compared to those with less than $15,000 income annually (OR: 1.36, 95% CI: 1.10–1.53, P = 0.0002). Incomes between $25,000 and $34,999 were found to increase risk for BD by nearly 50% compared to those making less than $15,000 annually (OR: 1.46, 95% CI: 1.22–1.74, P < 0.0001). Those with an income between $35,000 and $49,999 were 39% more likely to BD compared to those with an income of less than $15,000 annually (OR: 1.39, 95% CI: 1.18–1.65, P < 0.0001). Those with an income of greater than $50,000 were 64% more likely to BD compared to those making less than $15,000 annually (see Table 2). The dynamic between income and education should be noted. As education level increases, so does income. Therefore, there may be some interaction between education and income, however interaction was not tested for. It can also be seen from the results that as 4
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Table 3 Multiple Logistic Regression investigating the association between adverse childhood experiences (ACEs) and any drinking, controlling for sociodemographic factors. N = 69,793. Demographic Category *
Abuse Age 18–24 25-34 35–44 45–54 55–64 65+ Sex Male vs Female Married Currently Married Widowed/Divorced/Separated Never Married Race White Black Hispanic Other School Less than high school Graduated high school Some college Graduated college Income < $15,000 $15,000- < $25,000 $25,000- < $35,000 $35,000- < $50,000 > $50,000
Odds Ratio
SE
Z
P-value
95% CI
1.21
0.04
6.20
< 0.001
1.14
Reference 1.25 0.95 0.91 0.70 0.58
0.10 0.08 0.07 0.05 0.04
2.83 −0.59 −1.24 −4.68 −7.08
0.01 0.55 0.22 < 0.001 < 0.001
1.07 0.82 0.78 0.60 0.50
1.45 1.11 1.06 0.81 0.68
1.60
0.05
16.13
< 0.001
1.51
1.69
Reference 1.11 1.24
0.04 0.06
2.92 4.22
< 0.001 < 0.001
1.04 1.12
1.19 1.37
Reference 0.69 0.82 0.58
0.04 0.07 0.05
−6.66 −2.29 −6.76
< 0.001 0.02 < 0.001
0.61 0.69 0.50
0.77 0.97 0.68
Reference 1.30 1.70 2.27
0.08 0.11 0.15
4.03 8.09 12.23
< 0.001 < 0.001 < 0.001
1.14 1.49 1.99
1.47 1.93 2.59
Reference 1.35 1.74 1.97 2.76
0.08 0.11 0.12 0.17
5.09 8.91 10.95 16.85
< 0.001 < 0.001 < 0.001 < 0.001
1.20 1.54 1.74 2.45
1.51 1.97 2.22 3.11
1.28
* Dysfunction was not included in the final model due to insignificance (p > 0.20).
income and education level increased, so did BD behaviors.
3.2. The relationship between adverse childhood experiences, any drinking, and sociodemographic factors Results for MLR for the relationship between ACEs, AD and sociodemographic factors, as seen in Table 3, show abuse was likely to increase AD by 21% (OR: 1.21, 95% CI: 1.14–1.28, P < 0.0001). Abuse was likely to increase the likelihood for AD by 21% (OR: 1.21, 95% CI: 1.14–1.28, P < 0.0001). Household dysfunction was again left out of the final model due to insignificance and weak association with AD (p > 0.20). Adults aged 25-24 were 25% more likely to AD compared to those aged 18–24 (OR: 1.25, 95% CI: 1.07–1.45, P = 0.01). There was no significant association for adults between the ages of 35–44 and 45–54 with AD. However, age becomes significantly protective for those aged 55–64 by 30% (OR: 1.30, 95% CI: 0.60-0.81, P < 0.0001) and for those aged 65 and older by 42% (OR: 0.58, 95% CI: 0.50-0.68, P < 0.0001). Males were 60% more likely for AD in the past month compared to females (OR: 1.60, 95% CI: 0.51-0.69, P < 0.0001). Adults widowed, divorced or separated were only 11% more likely for AD compared to those currently married (OR: 1.11, 95% CI: 1.04–1.19, P < 0.0001). Adults never married were 24% more likely for AD compared to those currently married (OR: 1.24, 95% CI: 1.12–1.37, P < 0.0001). Blacks were 31% less likely for AD compared to Whites (OR: 0.69, 95% CI: 0.61-0.77, P < 0.001). Hispanics were 18% less likely for AD compared to Whites (OR: 0.82, 95% CI: 0.69-0.97, P = 0.02). Other races were 42% less likely for AD compared to Whites (OR: 0.58, 95% CI: 0.50-0.68, P < 0.0001). As education increased, so did the risk for AD within the past month. Adults who graduated high school were 30% more likely for AD compared to those with less than high school education (OR: 1.30, 95% CI: 1.14–1.47, P < 0.0001). Adults with some college were 70% more likely for AD compared to adults with less than high school education (OR: 1.70, 95% CI: 1.49–1.93, P < 0.0001). Adults who graduated college were over two times more likely for AD compared to adults with less than high school education (OR: 2.27, 95% CI: 1.99–2.59, P < 0.0001). A positive relationship was also seen with income and AD. Adults with an annual income of between $15,000 and $24,999 were 35% more likely for AD compared to those with less than $15,000 annual income (OR: 1.35, 95% CI: 1.20–1.51, P < 0.0001). Those with an annual income of $25,000 to $34,999 were 74% more likely for AD compared to those with an annual income of less than $15,000 (OR: 1.74, 95% CI: 1.54–1.97, P < 0.0001). Those with an annual income between $35,000 and $49,999 were nearly two times more likely for AD compared to those with an annual income of less than $15,000 (OR: 1.97, 95% CI: 1.74–2.22, P < 0.0001). Lastly, those with an annual income of greater than $50,000 were nearly three times more likely for AD compared to those with an income of less than $15,000 (OR: 2.76, 95% CI: 2.45–3.11, P < 0.0001) (Table 3). 5
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4. Discussion The goal of the current study was to establish a relationship between alcohol consumption behaviors among adults who had experienced childhood abuse and household dysfunction. Our study extends the literature by commenting on specific alcohol consumption behaviors found to be significantly associated with childhood abuse. Overall, our findings established that abuse during childhood contributed to an increased risk for AD and BD among adults. Our findings were consistent with a previous study conducted in 2002, which found a graded relationship between the number of ACEs reported and the risk of alcoholism, regardless of whether the respondent grew up with parents who abused alcohol (Anda et al., 2002). Using data collected from the 2011 and 2012 BRFSS, this study showed an increased risk of BD and AD among adults who experienced abuse as a child (OR 1.30 and 1.21 respectively, as shown in Table 2 and 3 respectively). Household dysfunction was not found to be significant in either association with BD or AD. Males were at a two-fold higher risk of BD compared to females when controlling for childhood abuse and sociodemographic factors (OR 2.12).This is concurrent with earlier findings that males are more likely to BD than females (CDC, 2017b). A recent study conducted by Lee and Chen, found that adults who experienced child abuse were 39% more likely to BD compared to adults who had not experienced child abuse (Lee & Chen, 2017). Moreover, they found females to be 55% less like to BD compared to males, which is consistent with our findings that males are more likely to BD (Lee & Chen, 2017). An additional study conducted by Fang and McNeil, established a similar relationship between males and BD compared to females and BD (Fang & McNeil, 2017). This study differs from ours because it used an ACE score to determine the likelihood of complications. The study found that males who had a score of 3 or more were nearly 1.7 times more likely to BD. Females were not found to have a significant association between ACE score and BD (Fang & McNeil, 2017). Our study indicates a significantly stronger relationship between males and BD compared to Fang and McNeil’s study (Fang & McNeil, 2017). One reason our study analysis did not use an ACE score was to provide further detail into the relationship between abuse and household dysfunction alongside AD and BD. Our study also differs because we controlled for sociodemographic factors in our logistic regression models while determining the impact household dysfunction and childhood abuse had on AD and BD (Fang & McNeil, 2017). High school graduates and adults with higher incomes, specifically those with incomes greater than $50,000 annually, were found to be at higher risk for BD (OR: 1.64 and > 1.30) compared with individuals without a high school degree (Table 2). An even stronger association was found showing a nearly three-fold increase in likelihood for AD (OR: 2.76, P < 0.001) with those possessing higher incomes alone (Table 3). These two factors, education and income, follow one another because income often increases alongside education level. Thus, some degree of confounding bias may exist. Future analyses are needed to explore potential interaction between these variables. Likewise, further investigation is needed to determine if other commonly abused substances, such as opioids, narcotics, marijuana, could impact the strength of association between risky alcohol consumption among adults with ACEs. 4.1. Strengths and limitations Exposure to ACEs, particularly abuse, increases the likelihood for drinking behaviors. A true causal relationship between exposure to ACEs and the effect on adults through increased alcohol consumption cannot be established due to limitations inherent to crosssectional studies. Though, we can assume some degree of temporality, because ACEs occurred among adults before the age of 18. This research is nationally representative due to its large sample size and since data were obtained from a national database, BRFSS. The potential for recall bias is present given that data were collected through a questionnaire. BD, AD and exposure to ACEs were selfreported; those individuals may not remember every detail correctly or may not want to report their experiences. Social desirability bias may also have been present as individuals may have only wanted to answer in a way they deemed favorable by society. This ACE study draws attention to a critical need for public health interventions. Public health personnel and other professionals must collaborate to educate and prevent further childhood abuse and household dysfunction. Through these efforts, ACEs and their long-term mental, physical, and socioeconomic burdens may be avoided. Additional investigation is needed through the use of primary data collection and additional epidemiologic studies (case-control or cohort studies) to determine if substance abuse of a different variety could determine the level of association between ACEs and alcohol consumption. 4.2. Future implications The ACE questionnaire can be used in addition to substance use and misuse screenings in a clinical setting. The most appropriate screenings may involve pediatric clinics. This would potentially allow for primary prevention through provider-patient discussions. Conversely, these screenings may expose children currently at risk or currently experiencing ACEs in addition to providing them access to trauma-informed care, which focuses on policies, procedures, approaches, and specific interventions for families (Child and Adolescent Health Measurement Initiative (CAHMI), 2018). A small feasibility study involving a family medicine setting discussed the use of screening to aid in allowing physicians to have a more complete sense of the social determinants impacting early life trauma while incorporating appropriate treatments that may be needed (Glowa et al., 2016). A second study piloted using a 7-item ACE tool screened for ACEs to identify specific early child health outcomes from different risk levels (Marie-Mitchell & O’Connor, 2013). Findings were consistent with ACE literature in that accumulation of ACEs also impacted child behaviors (Marie-Mitchell & O’Connor, 2013). Lastly, a third study of interest discussed an important issue regarding ACE studies and screenings, whether to assess ACEs on a population level or target high risk groups (Bethell et al., 2017). Bethell and colleagues determined that several 6
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variations of ACE questionnaires existed. However, all were important in guiding further research in using ACEs as an education piece for families (Bethell et al., 2017). Our current study hopes to provide further insight into the complex relationship between alcohol consumption behaviors and the abuse and household dysfunction categories of ACEs to aid in developing future preventative or treatment methods. Funding No funding obtained for this project. Conflict of interest None declared. Disclaimer The view(s) expressed herein are those of the author(s) and do not reflect the official policy or position of Brooke Army Medical Center, the U.S. Army Medical Department, the U.S. Army Office of the Surgeon General, the Department of the Army, the Department of the Air Force and Department of Defense or the U.S. Government. References Anda, R. F., Whitfield, C. L., Felitti, V. J., Chapman, D., Edwards, V. J., Dube, S. R., ... Williamson, D. F. (2002). Adverse childhood experienes, alcoholic parents, and later risk of alcoholism and depression. Psychiatric Services, 53(8), 1001–1009. Bethell, C. D., Carle, A., Hudziak, J., Gombojav, N., Powers, K., Wade, R., ... Braveman, P. (2017). Methods to assess adverse childhood experiences of children and families: Toward approaches to promote child well-being in policy and practice. Frameworks and Measurement, 17(7s), S51–S66. CDC (2016). Violence prevention: Child abuse and neglect consequences. Retrieved 11 29, 2017, from Centers for Disease Control and Prevention. CDC (2017b). Alcohol and public health: Fact sheets binge drinking. Retrieved 11 29, 2017, from Centers for Disease Control and Preventionhttps://www.cdc.gov/ alcohol/fact-sheets/binge-drinking.htm. CDC (2017c). Annual survey data: Behavioral risk factor surveillance system. Retrieved 11 29, 2017, from Centers for Disease Control and Preventionhttps://www.cdc. gov/brfss/annual_data/annual_data.htm. Child and Adolescent Health Measurement Initiative (CAHMI) (2018). ACEs Resource packet: Adverse childhood experiences (ACEs) basics. Retrieved 07 11, 2018http:// childhealthdata.org/docs/default-source/cahmi/aces-resource-packet_all-pages_12_06-16112336f3c0266255aab2ff00001023b1.pdf?sfvrsn=2. Crouch, E., Radcliff, E., Strompolis, M., & Wilson, A. (2018). Adverse childhood experiences (ACEs) and alcohol abuse among South Carolina adults. Substance Use & Misuse, 53(7), 1212–1220. https://doi.org/10.1080/10826084.2017.1400568. Fang, L., & McNeil, S. (2017). Is there a relationship between adverse childhood experiences and problem drinking behaviors? Findings from a population-based sample. The Royal Society of Public Health, 150, 34–42. https://doi.org/10.1016/j.puhe.2017.05.005. Glowa, P. T., Olson, A. L., & Johnson, D. J. (2016). Screening for adverse childhood experiences in a family medicine setting: A feasibility study. Journal of the American Board of Family Medicine, 25(3), 303–307. https://doi.org/10.3122/jabfm.2016.03.150310. Korotana, L. M., Dobson, K. S., Pusch, D., & Josephson, T. (2016). A review of primary care interventions to improve health outcomes in adult survivors of adverse childhood experiences. Clinical Psychology Review, 46, 59–90. https://doi.org/10.1016/j.cpr.2016.04.007. Lee, R. D., & Chen, J. (2017). Adverse childhood experienes, mental health, and excessive alcohol use: Examination of race/ethnicity and sex differences. Child Abuse & Neglect, 69, 40–48. https://doi.org/10.1016/j.chiabu.2017.04.004. Marie-Mitchell, A., & O’Connor, T. G. (2013). Adverse childhood experiences: Translating knowledge into identification of children at risk for poor outcomes. Academic Pediatrics, 13(1), 14–19. National Institute of Health (NIH) (2017). Alcohol use disorder. Retrieved 11 29, 2017, from National Institutes of Health: National Institute on Alcohol Abuse and Alcoholismhttps://www.niaaa.nih.gov/alcohol-health/overview-alcohol-consumption/alcohol-use-disorders. NIH (2017). Drinking levels defined. Retrieved 11 29, 2017, from National Institutes of Health: National Institute on Alcohol Abuse and Alcoholismhttps://www.niaaa. nih.gov/alcohol-health/overview-alcohol-consumption/moderate-binge-drinking. Pilowsky, D. J., Keyes, K. M., & Hasin, D. S. (2009). Adverse childhood events and lifetime alcohol dependence. American Journal of Public Health, 99(2), 258–263. Strine, T. W., Dube, S. R., Edwards, V. J., Prehn, A. W., Rasmussen, S., Wagenfeld, M., ... Croft, J. B. (2012). Associations between adverse childhood experiences, psychological distress, and adult alcohol problems. American Journal of Health Behavior, 36(3), 408–423. https://doi.org/10.5993/AJHB.36.3.11. Tetrault, J. M., & O’Connor, P. G. (2016). Risky drinking and alcohol use disorder: Epidemiology, pathogenesis, clinical manifestations, course, assessment, and diagnosis. UpToDate.. Wilkins, L. W. (2008). Professional guide to diseases. Philadelphia: Wolters Kluwer Health/Lippincott Williams & Wilkins.
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