Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2022 Dec 1.
Published in final edited form as: SSM Ment Health. 2022 Jan 29;2:100062. doi: 10.1016/j.ssmmh.2022.100062

Adverse childhood experiences, alcohol consumption, and the modifying role of social participation: population-based study of adults in southwestern Uganda

Scholastic Ashaba 1, Bernard Kakuhikire 1, Charles Baguma 1, Emily N Satinsky 2,3, Jessica M Perkins 4, Justin D Rasmussen 5, Christine E Cooper-Vince 6, Phionah Ahereza 1, Patrick Gumisiriza 1, Justus Kananura 1, David R Bangsberg 1,7, Alexander C Tsai 1,2,8,9
PMCID: PMC9023342  NIHMSID: NIHMS1797444  PMID: 35463801

Abstract

Background

Adverse childhood experiences (ACEs) include multiple forms of child maltreatment, including abuse and neglect, as well as other forms of household dysfunction. Studies from Uganda have revealed a high prevalence of child abuse, as well as one of the highest levels of alcohol consumption in Africa. Few population-based studies from Africa have estimated associations between ACEs and adult alcohol use, or assessed the potential buffering effects of social participation.

Methods

This cross-sectional, population-based study was conducted in a rural parish in southwestern Uganda between 2016 and 2018. We assessed self-reported ACEs using a modified version of the Adverse Childhood Experiences – International Questionnaire (ACE-IQ) scale. We measured heavy alcohol consumption using a 3-item scale previously validated in this population. We measured social participation using a 10-item scale eliciting participants’ membership and participation in different community groups over the past two months. We fitted multivariable Poisson regression models to estimate the associations between ACEs and heavy alcohol consumption, and to assess for the potential buffering effects of social participation.

Results

We estimated statistically significant associations between the total ACE score and heavy alcohol consumption (adjusted relative risk [ARR] per ACE=1.17; 95% CI, 1.09–1.25; P ≤0.001). Social participation had a statistically significant moderating effect on the association between total ACE score and heavy alcohol consumption (P=0.047 for interaction): the estimated association between total ACE score and heavy alcohol consumption among study participants who did not participate in a community group was larger, with a narrower confidence interval (ARR=1.21 per ACE; 95% CI, 1.11–1.33; P<0.001), while the estimated association among study participants who did participate in a community group was smaller and less precisely estimated (ARR=1.12 per ACE; 95% CI, 1.02–1.24; P=0.02).

Conclusions

Our findings demonstrate an association between ACEs and heavy alcohol consumption behavior among adults in rural Uganda. The adverse effects of ACEs were buffered in part by social participation. To prevent or reduce harmful alcohol use behaviors among adults, it is important to address the chronic stress caused by ACEs.

Keywords: adverse childhood experiences, heavy alcohol consumption, social participation, Uganda

1. Introduction

Adverse childhood experiences (ACEs) range from multiple forms of child maltreatment, including abuse and neglect, to other forms of household dysfunction (Dube et al., 2002; Felitti et al., 1998; WHO, 2018). A survey of children aged 8–18-years-old in Uganda indicated that 98% of study participants had experienced physical and/or emotional abuse, while 76% reported sexual abuse (Naker, 2014). Another survey from Uganda, Kenya, and Ethiopia found that 94% of young women aged 18–24-years-old experienced physical violence during childhood, including being punched, kicked, or beaten with an object (Stavropoulos, 2006). Furthermore, there is a widely held belief in many countries across sub-Saharan Africa that physical punishment instills discipline, improves behavior, and regulates emotions (Tomasello, 2007); thus, corporal punishment, spanking, and other forms of harsh/coercive discipline are common (Boydell et al., 2017; Clarke et al., 2016).

In addition to a high prevalence of ACEs, Uganda is also documented to have one of the highest levels of alcohol consumption in Africa. With an annual per capita rate of alcohol consumption of 23.7 liters, 5.8% of the Ugandan population over age 15 are affected by alcohol use disorder (AUD) (Tumwesigye et al., 2009; World Health Organization, 2015). A community survey among 3,956 adults in Uganda indicated that 27% of the population reported heavy alcohol consumption, with 10% meeting DSM-5 criteria for AUD (Kabwama et al., 2016). Heavy alcohol consumption has also been linked to domestic violence (Jouriles et al., 2008; Tumwesigye et al., 2012) and separation or divorce, both of which are classified as ACEs for children exposed to these experiences (Devaney, 2004, 2009; McGavock & Spratt, 2017).

ACEs have been associated with a wide range of mental health problems in adulthood, including problematic substance and alcohol use (Anda et al., 2002; Dube et al., 2002; Dube et al., 2003; Dube et al., 2006; Strine et al., 2012), depression and suicidal behaviors (Brodsky & Stanley, 2008; Chapman et al., 2004; Molnar et al., 2001; Mwachofi et al., 2020; Satinsky et al., 2021), health risk behaviors (Ramiro et al., 2010), and negative coping behaviors (Schilling et al., 2007, 2008). Heavy alcohol consumption and AUD have been documented more frequently among adolescents and adults with a history of ACEs than among those in the general population (Chatterjee et al., 2016; Chatterjee et al., 2018; Dube et al., 2006; Fang & McNeil, 2017; Felitti et al., 2019; Jung et al., 2020; Kiburi et al., 2018; Rothman et al., 2008). Further, among people with substance use disorder, ACEs negatively affect treatment outcomes: people with a history of ACEs are less likely to fully recover from symptoms of substance use disorder compared with people who have not had these experiences (Douglas et al., 2010; Kabiru et al., 2010; Pirard et al., 2005).

Previous research has suggested there may be a buffering effect of social support against the negative mental health outcomes associated with ACEs among adolescents and adults (Ashaba et al., 2021; Taylor & Aspinwall, 1996; Uchino, 2004). The putative mechanisms vary. Social support enhances people’s motivation to remain abstinent (Stevens et al., 2015) by improving their positive coping behaviors under situations of stress (Humphreys et al., 1999). Social participation (i.e., through involvement in social groups) is associated with an increased sense of belonging, security, and purpose; these, in turn, motivate individuals to focus on their health (Berkman & Glass, 2000). Furthermore, social groups provide acceptance and companionship, and have the potential to promote self-esteem and psychological wellbeing (Bathish et al., 2017; Berkman, 1985; Haslam et al., 2005; Jetten et al., 2015; Underwood, 2000). On the other hand, social groups can potentially also promote negative behaviors among adolescents, such as cigarette smoking, either through the enforcement of negative norms and demand for conformity (Boissevain, 1974; Simmel, 1964) or through downward leveling norms (Philippe, 1995; Suarez-Orozco, 1987). Thus in certain cases the harms imposed by negative social capital can outweigh the benefits to health and well-being (Dingle et al., 2015).

Previous studies on the relationship between ACEs and heavy alcohol use have largely been conducted in high-income countries and among adolescents (Dube et al., 2006; Fang & McNeil, 2017; Jung et al., 2020; Kabiru et al., 2010; Kiburi et al., 2018). Although studies in Uganda have explored alcohol use behavior and treatment outcomes among people with AUD (Kabwama et al., 2016; Kalema et al., 2020), no studies have documented the impacts of ACEs on adult alcohol consumption, nor have any studies examined the potential buffering effects of social participation. To address this gap in the literature, we conducted a cross-sectional, population-based study of adults in rural Uganda to investigate ACEs, heavy alcohol use, and the potentially modifying role of social participation.

2. Methods

2.1. Study setting and population:

This cross sectional, population-based study was conducted in Nyakabare Parish, Rwampara District, a rural region of southwestern Uganda (Takada et al., 2019). Nyakabare Parish is located about 20km from Mbarara Town and is comprised of 8 villages, largely rural. The economy is driven by subsistence farming, animal husbandry, and small scale trading; food and water insecurity have been widely documented in this area (Mushavi et al., 2020; A. C. Tsai et al., 2011).

2.2. Sampling procedure and data collection:

We first conducted an initial population census to enumerate all eligible adults across the 758 households in the parish. Study eligibility was limited to adults aged 18 years and older (and emancipated minors aged 16 to 18 years) who reported having a stable residence in the parish. There were 1,795 people who were eligible for the survey wave, and 1,630 people participated (90.8%). The eligible people who did not participate were either not found at home or refused. An additional 71 adults who had been identified in the parish census were found to be ineligible due to various reasons (e.g. they had died by the time of the survey; they had left the parish before they could participate; or they had cognitive impairment, behavioral problems, neurological damage, acute intoxication, and/or communication impairments that would affect their ability to provide informed consent and/or participate in the study).

Data were collected between December 13, 2016 and June 6, 2018 by trained research assistants. These research assistants visited all eligible adults in the parish and formally requested participation. Individuals who expressed a willingness to participate were asked to provide written informed consent. Following the consent process, interviews were conducted in a private location in the participant’s home or in a nearby location, based on the participant’s preference. Data were collected using the Computer Assisted Survey Information Collection (CASIC) Builder™ software program. All instruments were written in English, translated into Runyankore, and back translated into English in an iterative process to confirm translation fidelity. Interviews were conducted in Runyankore, the local language.

2.3. Measures:

ACEs were assessed using a modified version of the Adverse Childhood Experiences – International Questionnaire (ACE-IQ). The ACE-IQ was developed by the World Health Organization and Centers for Disease Control (CDC) for use across different cultures and was designed to assess different forms of abuse, neglect, and family dysfunction (World Health Organization, 2018). It has been validated for use among adolescents and adults in South Africa, Malawi, and Nigeria (Kazeem, 2015; Kidman et al., 2019; Quinn et al., 2018).

The ACE-IQ was modified for use in this setting, and included 16 items regarding exposure to adverse experiences during the participant’s first 18 years of life (Satinsky et al., 2021). The first set of questions asked participants about experiences of verbal, physical, and sexual abuse (threatened or enacted), perpetrated by a parent or other adult in the household. These were followed by questions exploring different aspects of household dysfunction including: witnessing violence or threats of violence towards their mother or grandmother; parental divorce or separation; exposure to a family member who had a mental illness or who used alcohol and/or other drugs on a regular basis; and/or exposure to a family member who was incarcerated. Participants were also asked about insecure access to food and water. These 16 items represented 9 unique domains of ACEs, with any experience in a given domain scored as 1 and no experience in a given domain scored as 0. We then calculated a cumulative ACE score, which had a range of 0–9.

Alcohol consumption behaviors were assessed with a series of questions. First, participants were asked to report how often they consumed alcohol in the past 12 months: never, once a month or less, 2–4 times per month, 2–3 times per week, or 4 or more times per week. Among participants who reported any alcohol use, three additional yes/no questions were used to elicit heavy alcohol consumption behaviors: 1) whether in the past 12 months they had taken 6 or more drinks in a single morning, afternoon, or night; 2) whether in the past 30 days they had experienced drunkenness or intoxication on 3 or more days; and 3) whether in the past 30 days they had spent more than 25,000 Ugandan shillings on any kind of alcohol for themselves. Following Fatch et al. (Fatch et al., 2013) participants who endorsed one or more of the three items were classified as having engaged in heavy alcohol consumption. Due to challenges in quantifying total alcohol consumption in a setting where nonstandard drink sizes are commonly consumed (Papas et al., 2010), we opted for the locally validated measure by Fatch et al.(Fatch et al., 2013) over the more widely used Alcohol Use Disorders Identification Test (AUDIT) (Saunders et al., 1993).

Social participation was measured using a 10-item scale that elicited participants’ membership and/or participation in different community groups during the past two months. These community groups were readily available and accessible to members of the community and included: HIV groups, gardening groups, vocation groups, the local council, the water committee, village health teams, National Agriculture Advisory Services groups, religious groups, women’s groups, and savings and credit cooperative organizations (SACCOs). For this analysis, we created a binary variable equal to 1 for participation in any community group.

Loneliness was measured using the 3-item University of California Los Angeles (UCLA) Loneliness Scale (Hughes et al., 2004). Each question is scored on a 3-point Likert type scale, generating a total score with a range of 3–9. The Hopkins Symptom Checklist for Depression (HSCLD-15) was used to assess depression symptom severity. The HSCLD-15 has been modified and validated for use in Uganda (Ashaba et al., 2018; Bolton, 2001). Each symptom is scored on a 4-point Likert type scale, and the total scale score is computed by taking the average across the items. A threshold of >1.75 is commonly used as a screening threshold for probable depression (Hesbacher et al., 1980). Water insecurity was measured using the 8-item Household Water Access Insecurity Survey (HWIAS), a scale that was developed for use in the Ugandan context (Alexander C Tsai, Kakuhikire, et al., 2016). Each item is scored on a 4-point Likert type, with a total score range of 0–24. Food insecurity was measured using the nine-item Household Food Insecurity Access Scale (HFIAS) (Swindale & Bilinsky, 2006; Alexander C Tsai et al., 2012). Each item is scored on a 4-point Likert type-scale, with a scoring algorithm (Coates et al., 2007) categorizing respondents as food secure, mildly food insecure, moderately food secure, and severely food insecure.

2.4. Ethical considerations

This study received ethical approval from the Mbarara University of Science and Technology Research Ethics Committee, the Partners Human Research Committee, and the Uganda National Council for Science and Technology.

2.5. Data analysis

After dropping participants with missing information on any of the variables, the data set consisted of 1586 participants for analysis. Variables missing information included age (24 participants), probable depression (4 participants), food insecurity (9 participants), water insecurity (3 participants), binge drinking and drunkenness (4 participants), and excessive spending on alcohol (5 participants). We summarized sociodemographic characteristics using proportions and means/standard deviations. To estimate the associations between ACEs and alcohol consumption behavior we fitted to the data multiple Poisson regression models with robust estimates of variance. As shown by (Zou, 2004), the estimated incidence rate ratios can be interpreted as relative risk ratios. We fitted a single Poisson regression model to estimate an association between total ACE score (continuous variable, out of 9) and heavy alcohol consumption (Fatch et al., 2013) while adjusting for age, marital status, level of education, food insecurity (HFIAS), water insecurity (HWIAS), self-reported HIV status, probable depression (HSCLD-15), loneliness (UCLA Loneliness Scale), and social participation. These covariates were included in the regression model because of their potentially confounding effects as shown in previously published work (Chilton et al., 2015; Goodman et al., 2017; Hernandez et al., 2014; Meinck et al., 2019; Roy et al., 2019; Satinsky et al., 2021; Wong et al., 2019) or because they have been shown to correlate strongly with the outcome (Cambron et al., 2018; Canham et al., 2016; Kim et al., 2008; McKay et al., 2017). Estimates were also stratified by sex. We estimated associations between total ACE score and individual heavy alcohol consumption behaviors by fitting separate Poisson regression models for the 3 outcomes of binge drinking, drunkenness, and excessive spending on alcohol, adjusting for the same covariates listed above.

To determine the robustness of the estimated associations we performed an e-value analysis using methods proposed by (VanderWeele & Ding, 2017). The e-value describes the minimum strength of association, on the risk ratio scale, between the supposed confounder and the exposure, and between the supposed confounder and the outcome, that would be needed to explain away the observed association. A large e-value suggests that potential confounding would need to be very strong in order to sufficiently explain away the observed association (Blum et al., 2020).

We also sought to determine whether social participation modified the relationship between ACEs and heavy alcohol consumption. This analysis was motivated by previous research findings that have demonstrated that social support enhances motivation to quit alcohol use (Stevens et al., 2015) and can serve as a buffer against psychosocial stressors (Ashaba et al., 2021; Alexander C Tsai et al., 2012; Alexander C Tsai, Tomlinson, et al., 2016). Effect modification was assessed by including a main effect for social participation, a main effect for exposure to ACEs, and a product term to test for the interaction between total ACE score and social participation. These multivariable regression models were also adjusted for the covariates listed above. Stratified estimates were examined to aid in exposition of the product terms. All analyses were conducted in Stata version 16 (StataCorp LP, College Station, Texas) and adjusted for clustering at the village level.

3. Results

More than half of the participants were women (56% [n=882]) (Table 1). The mean age was 39.7 years (standard deviation [SD]=15.9) for men and 40.2 years (SD=17.3) for women. Overall, the prevalence of heavy alcohol consumption was 12% (n=193) and was greater among men vs. women (25% vs. 2%; P<0.001). In terms of the individual alcohol consumption behaviors, the prevalence of drunkenness was 8% (n=132), the prevalence of binge drinking was 6% (n=94), and the prevalence of excessive spending on alcohol was 5% (n=83). The mean ACE score was 3.44 (SD=2.21) among men and 3.30 (SD=2.18) among women. A comparable percentage of women and men reported participation in a community group (44% vs. 46%).

Table 1:

Characteristics of the sample, stratified by sex (n=1586)

Men (n=704) Women (n=882) Total (n=1586)
n Mean/% SD n Mean/% SD n %
Age (years) 39.7 15.9 40.2 17.3
Marital status
 Married/cohabitating 461 65% 522 59% 983 62%
 Separated, divorced, widow 56 8% 215 24% 271 17%
 Single, never married 188 27% 147 17% 335 21%
HIV status
 Positive 58 8.2% 106 12% 164 10%
 Negative or Unknown 646 91.8% 776 88% 1422 90%
Education level
 No formal education 40 6% 150 17% 190 12%
 Some primary (P1-P6) 180 26% 252 29% 432 27%
 Completed primary (P7-P8) 204 29% 188 21% 392 25%
 Secondary, vocational, or university 280 39% 292 33% 572 36%
Food insecurity
 Food secure 253 36% 263 30% 516 33%
 Mildly food insecure 105 15% 100 11% 205 13%
 Moderately food insecure 245 35% 364 41% 609 38%
 Severely food insecure 101 14% 155 18% 256 16%
Water insecurity
 Water secure 367 52% 415 47% 782 49%
 Mildly water insecure 73 10% 114 13% 187 12%
 Moderately water insecure 146 21% 199 23% 345 22%
 Severely water insecure 118 17% 154 17% 272 17%
Total ACEs (out of 9) 3.44 2.21 3.30 2.18
Ever experienced ACE
 Yes 631 90% 794 90% 1425 90%
 No 73 10% 88 10% 161 10%
Probable depression (HSCLD>1.75)
 Yes 89 13% 232 26% 321 20%
 No 615 87% 650 74% 1265 80%
Social participation
 Yes 323 46% 387 44% 710 45%
 No 381 54% 495 56% 876 55%
Loneliness (UCLA Loneliness Scale≥6)
 Yes 70 10% 133 15% 203 13%
 No 634 90% 749 85% 1383 87%
Binge drinking
 Yes 88 13% 6 0.7% 94 6%
 No 616 88% 876 99.3% 1492 94%
Drunkenness
 Yes 117 17% 15 1.7% 132 8%
 No 587 83% 867 98.3% 1454 92%
Excessive spending on alcohol
 Yes 78 11% 5 0.6% 83 5%
 No 626 89% 877 99.4% 1503 95%
Heavy alcohol consumption
 Yes 176 25% 17 2% 193 12%
 No 528 75% 865 98% 1393 88%
Open in a new tab
*

ACE, adverse childhood experience; HSCLD, Hopkins Symptom Checklist for Depression; SD, standard deviation; UCLA, University of California at Los Angeles

We estimated a statistically significant association between total ACE score and heavy alcohol consumption (adjusted risk ratio [ARR] per ACE=1.17; 95% CI, 1.08–1.25; P<0.001) (Table 2). This estimated association was large in magnitude: at the 25th percentile of ACEs (2), the predicted probability of heavy alcohol consumption was 9.5%, while at the 75th percentile of ACEs (5), the predicted probability of heavy alcohol consumption was 15.3%. Thus, an interquartile difference in the total ACEs score was associated with a 5.8 percent point difference in the predicted probability of heavy alcohol consumption, or a 47.5% difference relative to the baseline prevalence. The Pearson goodness-of-fit chi-squared test statistic was 1382.2 (P=0.99).

Table 2:

Association between total ACE score and heavy alcohol consumption (n=1586)

Unadjusted RR (95% CI)
P-value		 Adjusted RR (95% CI)
P-value
Total ACE score (per ACE) 1.13 (1.07–1.190) <0.001 1.17 (1.08–1.25) <0.001
Age (per year) 1.00 (0.99–1.00) 0.66 1.01 (1.00–1.02) 0.005
Married 1.08 (0.84–1.38) 0.54 0.96 (0.75–1.22) 0.75
HIV-positive 0.73 (0.41–1.31) 0.29 0.67 (0.39–1.15) 0.15
Education level
 No formal education Ref Ref
 Some primary 2.72 (1.74–4.25) <0.001 3.03 (1.76–5.20) <0.001
 Completed primary 2.71 (1.43–5.15) 0.002 3.13 (1.46–6.69) 0.003
 Secondary, vocational, or university 2.15 (1.44–3.22) <0.001 2.56 (1.52–4.32) <0.001
Food insecurity
 Food secure Ref Ref
 Mildly food insecure 0.76 (0.57–1.03) 0.08 0.79 (0.56–1.13) 0.19
 Moderately food insecure 0.79 (0.55–1.16) 0.24 0.77 (0.54–1.10) 0.15
 Severely food insecure 1.11 (0.82–1.51) 0.50 1.12 (0.75–1.67) 0.57
Water insecurity
 Water secure
 Mildly water insecure 0.61 (0.34–1.07) 0.08 0.60 (0.35–1.03) 0.06
 Moderately water insecure 0.97 (0.76–1.22) 0.79 0.94 (0.78–1.14) 0.54
 Severely water insecure 0.86 (0.48–1.55) 0.63 0.78 (0.39–1.57) 0.49
Probable depression 0.97 (0.60–1.41) 0.85 0.71 (0.48–1.05) 0.08
Social participation 0.95 (0.71–1.30) 0.74 0.90 (0.68–1.20) 0.48
Loneliness 0.88 (0.54–1.40) 0.58 0.87 (0.53–1.45) 0.60
Open in a new tab
*

ACE, adverse childhood experience; CI, confidence interval; RR, relative risk.

Estimates in column 1 (“unadjusted”) are derived from 10 Poisson regression models in which the row variable is the only covariate in the model. Estimates in column 2 (“adjusted”) are derived from a single multivariable Poisson regression model in which all of the row variables are included simultaneously.

We also estimated statistically significant associations between the total ACE score and individual heavy alcohol consumption behaviors among study participants. The estimated associations between the total ACE score and these individual variables were largely consistent with each other for binge drinking (ARR=1.21 per ACE; 95% CI, 1.06–1.37; P=0.003), drunkenness (ARR=1.20 per ACE; 95% CI, 1.12–1.29; P≤0.001), and excessive spending on alcohol (ARR=1.23 per ACE; 95% CI, 1.12–1.34; P≤0.001) (Appendix A). The Pearson goodness-of-fit chi-squared test statistics ranged in value from 1389.1–1570.4 (P-values ranged from 0.55–0.99). When we stratified by sex, the association between total ACE score and heavy alcohol consumption was stronger among women (ARR=1.19 per ACE; 95% CI, 1.06–1.34, P=0.004) than among men (ARR=1.10 per ACE; 95% CI, 1.03–1.18; P=0.004) (Appendix B).

When we included a main effect for social participation in the regression model for heavy alcohol consumption, along with product terms to test for an interaction between social participation and total ACE score, social participation moderated the effect of total ACE score on heavy alcohol consumption. The coefficient on the product term was statistically significant (P=0.047 for interaction). The estimated association among people who did not participate in a community group was larger in magnitude with a narrower confidence interval and more strongly significant (ARR=1.21 per ACE; 95% CI 1.11–1.33; P<0.001) compared with the estimated association among people who did participate in a community group (ARR=1.12 per ACE; CI 1.02–1.24; P=0.02) (Appendix C). As is depicted in Figure 1, the effect appears to be driven largely by a differential association at the upper end of the distribution of ACEs, i.e. the predicted probability of heavy alcohol consumption is largely similar for people with a low number of ACEs whereas the predicted probability of heavy alcohol consumption among people reporting 9 ACEs is approximately twice as large among people who report no social participation compared with people who participate in one or more groups.

Figure 1.

Figure 1.

Open in a new tab

The modifying effect of social participation on the association between total ACE score and alcohol consumption

We also examined effect modification for each of the individual alcohol consumption behaviors. The differences in the estimated association between total ACE score and the individual alcohol consumption behaviors, stratified by social participation, varied according to the specific behavior, but generally followed the same pattern: the estimated association with the total ACE score was larger among study participants who did not participate in a community group (ARRs ranged from 1.22–1.31; P-values ranged from <0.001 to 0.004) than the estimated association with the total ACE score among study participants who did participate in a community group (ARRs ranged from 1.13–1.20; P-values ranged from <0.001 to 0.15).

The e-value analysis yielded e-values ranging from 1.62–1.76, depending on the outcome. This represents the strength of association, on the risk ratio scale, that an unmeasured confounder would need to have with both alcohol consumption and total ACEs in order to explain away the reported estimates.

Discussion

In this population-based study of adult men and women, we estimated statistically significant associations between ACEs and heavy alcohol consumption. Participation in community groups moderated the effect of ACEs on heavy alcohol consumption, suggesting that social participation exerted a buffering effect against this long-term stressor. While other studies from sub-Saharan Africa have estimated associations between ACEs and alcohol use among women and adolescents (Bhengu et al., 2019; Goodman et al., 2017; Kabiru et al., 2010), none have yet estimated associations between ACEs and alcohol use in a population-based sample of both men and women or assessed the modifying role of social participation.

The high prevalence of ACEs is consistent with other research from Uganda (Koenig et al., 2004; Naker, 2014; Stavropoulos, 2006), where reports indicate that most children are subjected to physical abuse as a form of discipline (Boydell et al., 2017; Clarke et al., 2016; Tomasello, 2007). In addition, domestic violence is common in Uganda, and many children are exposed through either observation or experience (Koenig et al., 2003; Ogland et al., 2014). For example, studies have estimated a prevalence of intimate partner violence against women between 54% and 56% (Karamagi et al., 2006; Uganda Bureau of Statistics, 2016). Furthermore, exposure to domestic violence increases the risk of exposure to other childhood adversities (McGavock & Spratt, 2017), with most children who are exposed to domestic violence also being victims of maltreatment themselves (Hamby et al., 2010, 2016; Jouriles et al., 2008).

The finding of heavy alcohol consumption behaviors among our study participants is also reflective of previous studies from low and middle-income countries (Emslie et al., 2009; Hao et al., 2004; Helzer & Canino, 1992; Kabwama et al., 2016). This finding is also in keeping with cultural expectations and gender roles in Uganda, whereby alcohol use among men is associated with masculinity and social independence, with no expectations for domestic responsibilities (Kafuko & Bukuluki, 2008; Wolff et al., 2006). However, alcohol use among women is associated with defiant behavior, contrary to local feminine ideals (Kafuko & Bukuluki, 2008; Wolff et al., 2006). In some African societies, women who engage in alcohol use may be subject to punishment and social restraint (Bryceson, 2002).

The robust associations between ACEs and heavy alcohol consumption in our study have been demonstrated in both low and high-income countries (Fang & McNeil, 2017; Felitti et al., 2019; Jung et al., 2020). Previous studies have shown that ACEs are associated with unhealthy alcohol use behaviors during adulthood, including binge drinking and drunkenness, as well as clinically diagnosed AUDs (Crouch et al., 2018; Fang & McNeil, 2017; Kiburi et al., 2018; Loudermilk et al., 2018). Higher cumulative ACE scores are associated with higher risk for these problems (Anda et al., 2002; Loudermilk et al., 2018; Pilowsky et al., 2009; Strine et al., 2012). Moreover, the cycle of alcohol consumption exposes more children to trauma due to the fact that most children grow up in families where at least one family member uses alcohol regularly (Tumwesigye & Kasirye, 2005). Our findings also support research showing that people who have experienced traumatic events may adopt negative coping mechanisms, including substance misuse, particularly when other support systems are lacking (Larkin et al., 2014).

It has been reported that adult women with a history of ACEs are at a higher risk of AUD and other mental health problems compared with men (Briere et al., 2010; Kendler et al., 2004; McLaughlin et al., 2010). Our findings showed a statistically significant association between total ACEs and heavy alcohol consumption among both men and women, although the estimated association was slightly stronger among women (Currie et al., 2020; Frankenberger et al., 2015). It is possible that ACEs among women more strongly amplify emotional reactivity and emotion regulation and more greatly impair responses to reward mechanisms that drive binge drinking behavior to alleviate negative emotions (Brenhouse et al., 2013; Dennison et al., 2019; McLaughlin et al., 2019). However some previous research has also documented a higher risk of binge drinking among men with a history of ACEs (Kappel et al., 2021; Loudermilk et al., 2018).

One of the primary findings to emerge from this study was that, consistent with conceptual models of buffering and social support, social participation moderated the relationship between total ACE score and heavy alcohol consumption. This is in line with previous research showing that social support modifies harmful behavior and helps people focus on their health by supporting the adoption of positive behaviors (Taylor & Aspinwall, 1996; Uchino, 2004). Social support has a protective effect against traumatic experiences (Ashaba et al., 2021; Jonzon & Lindblad, 2006; Stevens et al., 2015), and social support networks have been reported to influence or prevent alcohol consumption behaviors (Perreira & Sloan, 2001). A higher number of supportive relationships has been associated with reduced levels of alcohol consumption (Booth et al., 1992; Perreira & Sloan, 2001; Zywiak et al., 2002). The protective nature of social support networks and reduced alcohol consumption relates to how much an individual is invested in the social network (Longabaugh et al., 2010; Zywiak et al., 2002), especially if the group promotes self-esteem and psychological wellbeing (Bathish et al., 2017; Jetten et al., 2015). This is further reinforced by the quality of group memberships and social connectedness that contribute towards improved quality of life (Sani, 2012).

Our study has limitations which must be considered when interpreting these results. First, self-reported ACEs elicited during adulthood may be limited by recall bias. Inconsistences in reporting ACEs among adults have been reported in previous research (Baldwin et al., 2019). Second, the study was conducted in a single rural parish in southwestern Uganda. Since Uganda has multiple tribes with differing cultural practices, the findings may not generalize to other adults in the country, or other countries in sub-Saharan Africa. Third, this was a cross-sectional study, limiting our ability to establish the causal pathway between ACEs and adult alcohol consumption behavior. Fourth, we did not use a standard instrument, like the AUDIT, to measure alcohol consumption behavior, because consumption of alcohol beverages of nonstandard volumes and varying concentrations is common in this rural area of Uganda, which limits the application of standard instruments. While our measure of heavy alcohol consumption was validated in the local setting (Fatch et al., 2013), measurement error is nonetheless possible. The direction of bias is unpredictable; we have no basis for speculating whether overestimation or underestimation is more likely. If the measurement error is non-differential, then this would tend to bias our estimates of the association between ACEs and alcohol consumption toward the null. Moreover, the findings are in agreement with findings of other studies among adults indicating an association between ACEs and alcohol use behaviors (Kiburi et al., 2018; Strine et al., 2012). Lastly, although the ACE-IQ was modified for the local context, some of the items could have resulted in underreporting. For example, the questions on physical abuse only elicited experiences that took place within the household, potentially excluding experiences of corporal punishment that may have taken place in schools (Boydell et al., 2017; Clarke et al., 2016; Devries et al., 2014). On this note, due to cultural norms, some participants may not have viewed spanking or corporal punishment as forms of physical abuse.

Conclusion

Our findings reinforce the pervasiveness of ACEs in this context, and demonstrate an association between ACEs and heavy alcohol consumption behavior. These findings are relevant given that Uganda has a high prevalence of both ACEs and alcohol consumption (Ferreira-Borges et al., 2016; Kabwama et al., 2016; World Health Organization, 2019). In addition to allocating resources to address problematic alcohol use among adults, there is also a need to address the chronic stress resulting from ACEs to prevent or reduce harmful alcohol consumption behaviors. The finding that social integration partly moderated the relationship between ACEs and heavy alcohol consumption suggests a role for social support programs to be disseminated within the community. Such programs would aim to foster social connectedness and improve self-esteem in order to prevent harmful alcohol use in rural settings in Uganda.

Supplementary Material

Appendix_Tables

Funding Sources:

This work was supported by Friends for a Healthy Uganda and U.S. National Institutes of Health [R01MH113494]. Views expressed in this manuscript are solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Footnotes

Declarations of Interest: None

References

  1. Anda RF, Whitfield CL, Felitti VJ, Chapman D, Edwards VJ, Dube SR, & Williamson DF (2002). Adverse childhood experiences, alcoholic parents, and later risk of alcoholism and depression. Psychiatric services, 53(8), 1001–1009. [DOI] [PubMed] [Google Scholar]
  2. Ashaba S, Cooper-Vince C, Maling S, Satinsky EN, Baguma C, Akena D, Nansera D, Bajunirwe F, & Tsai AC (2021). Childhood trauma, major depressive disorder, suicidality and the modifying role of social support among adolescents living with HIV in rural Uganda. Journal of Affective Disorders Reports, 100094. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Ashaba S, Kakuhikire B, Vořechovská D, Perkins JM, Cooper-Vince CE, Maling S, Bangsberg DR, & Tsai AC (2018). Reliability, validity, and factor structure of the Hopkins Symptom Checklist-25: population-based study of persons living with HIV in rural Uganda. AIDS and Behavior, 22(5), 1467–1474. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Baldwin JR, Reuben A, Newbury JB, & Danese A (2019). Agreement between prospective and retrospective measures of childhood maltreatment: a systematic review and meta-analysis. JAMA psychiatry, 76(6), 584–593. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Bathish R, Best D, Savic M, Beckwith M, Mackenzie J, & Lubman DI (2017). “Is it me or should my friends take the credit?” The role of social networks and social identity in recovery from addiction. Journal of Applied Social Psychology, 47(1), 35–46. [Google Scholar]
  6. Berkman LF (1985). The relationship of social networks and social support to morbidity and mortality. [Google Scholar]
  7. Berkman LF, & Glass T (2000). Social integration, social networks, social support, and health. Social epidemiology, 1, 137–173. [Google Scholar]
  8. Bhengu BS, Tomita A, Mashaphu S, & Paruk S (2019). The role of adverse childhood experiences on perinatal substance use behaviour in KwaZulu-Natal Province, South Africa. AIDS and Behavior, 1–10. [DOI] [PubMed] [Google Scholar]
  9. Blum MR, Tan YJ, & Ioannidis J (2020). Use of E-values for addressing confounding in observational studies—an empirical assessment of the literature. International journal of epidemiology. [DOI] [PubMed] [Google Scholar]
  10. Boissevain J (1974). Friends of friends: Networks, manipulators and coalitions. Oxford: Blackwell. [Google Scholar]
  11. Bolton P (2001). Cross-cultural validity and reliability testing of a standard psychiatric assessment instrument without a gold standard. The Journal of nervous and mental disease, 189(4), 238–242. [DOI] [PubMed] [Google Scholar]
  12. Booth BM, Russell DW, Soucek S, & Laughlin PR (1992). Social support and outcome of alcoholism treatment: An exploratory analysis. The American journal of drug and alcohol abuse, 18(1), 87–101. [DOI] [PubMed] [Google Scholar]
  13. Boydell N, Nalukenge W, Siu G, Seeley J, & Wight D (2017). How mothers in poverty explain their use of corporal punishment: a qualitative study in Kampala, Uganda. The European journal of development research, 29(5), 999–1016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Brenhouse HC, Lukkes JL, & Andersen SL (2013). Early life adversity alters the developmental profiles of addiction-related prefrontal cortex circuitry. Brain sciences, 3(1), 143–158. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Briere J, Hodges M, & Godbout N (2010). Traumatic stress, affect dysregulation, and dysfunctional avoidance: A structural equation model. Journal of traumatic Stress, 23(6), 767–774. [DOI] [PubMed] [Google Scholar]
  16. Brodsky BS, & Stanley B (2008). Adverse childhood experiences and suicidal behavior. Psychiatric Clinics of North America, 31(2), 223–235. [DOI] [PubMed] [Google Scholar]
  17. Bryceson DF (2002). Alcohol in Africa: mixing business, pleasure, and politics. [Google Scholar]
  18. Cambron C, Kosterman R, Catalano RF, Guttmannova K, & Hawkins JD (2018). Neighborhood, family, and peer factors associated with early adolescent smoking and alcohol use. Journal of youth and adolescence, 47(2), 369–382. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Canham SL, Mauro PM, Kaufmann CN, & Sixsmith A (2016). Association of alcohol use and loneliness frequency among middle-aged and older adult drinkers. Journal of aging and health, 28(2), 267–284. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Chapman DP, Whitfield CL, Felitti VJ, Dube SR, Edwards VJ, & Anda RF (2004). Adverse childhood experiences and the risk of depressive disorders in adulthood. Journal of Affective Disorders, 82(2), 217–225. [DOI] [PubMed] [Google Scholar]
  21. Chatterjee D, McMorris B, Gower A, & Eisenberg M (2016). Experience of abuse, household dysfunction, and early use of alcohol and marijuana among Minnesota youth: the moderating role of internal assets. Journal of Adolescent Health, 58(2), S12–S13. [Google Scholar]
  22. Chatterjee D, McMorris B, Gower AL, Forster M, Borowsky IW, & Eisenberg ME (2018). Adverse childhood experiences and early initiation of marijuana and alcohol use: The potential moderating effects of internal assets. Substance Use & Misuse, 53(10), 1624–1632. [DOI] [PubMed] [Google Scholar]
  23. Chilton M, Knowles M, Rabinowich J, & Arnold KT (2015). The relationship between childhood adversity and food insecurity:’It’s like a bird nesting in your head’. Public health nutrition, 18(14), 2643–2653. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Clarke K, Patalay P, Allen E, Knight L, Naker D, & Devries K (2016). Patterns and predictors of violence against children in Uganda: a latent class analysis. BMJ open, 6(5), e010443. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Coates J, Swindale A, & Bilinsky P (2007). Household Food Insecurity Access Scale (HFIAS) for measurement of food access: indicator guide: version 3. [Google Scholar]
  26. 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. [DOI] [PubMed] [Google Scholar]
  27. Currie CL, Sanders JL, Swanepoel L-M, & Davies CM (2020). Maternal adverse childhood experiences are associated with binge drinking during pregnancy in a dose-dependent pattern: findings from the All Our Families cohort. Child abuse & neglect, 101, 104348. [DOI] [PubMed] [Google Scholar]
  28. Dennison MJ, Rosen ML, Sambrook KA, Jenness JL, Sheridan MA, & McLaughlin KA (2019). Differential associations of distinct forms of childhood adversity with neurobehavioral measures of reward processing: A developmental pathway to depression. Child development, 90(1), e96–e113. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Devaney J (2004). Relating outcomes to objectives in child protection. Child & Family Social Work, 9(1), 27–38. [Google Scholar]
  30. Devaney J (2009). Chronic child abuse: The characteristics and careers of children caught in the child protection system. British journal of social work, 39(1), 24–45. [Google Scholar]
  31. Devries KM, Child JC, Allen E, Walakira E, Parkes J, & Naker D (2014). School violence, mental health, and educational performance in Uganda. Pediatrics, 133(1), e129–e137. [DOI] [PubMed] [Google Scholar]
  32. Dingle GA, Stark C, Cruwys T, & Best D (2015). Breaking good: Breaking ties with social groups may be good for recovery from substance misuse. British journal of social psychology, 54(2), 236–254. [DOI] [PubMed] [Google Scholar]
  33. Douglas KR, Chan G, Gelernter J, Arias AJ, Anton RF, Weiss RD, Brady K, Poling J, Farrer L, & Kranzler HR (2010). Adverse childhood events as risk factors for substance dependence: partial mediation by mood and anxiety disorders. Addictive behaviors, 35(1), 7–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Dube SR, Anda RF, Felitti VJ, Edwards VJ, & Croft JB (2002). Adverse childhood experiences and personal alcohol abuse as an adult. Addictive behaviors, 27(5), 713–725. [DOI] [PubMed] [Google Scholar]
  35. Dube SR, Felitti VJ, Dong M, Chapman DP, Giles WH, & Anda RF (2003). Childhood abuse, neglect, and household dysfunction and the risk of illicit drug use: the adverse childhood experiences study. Pediatrics, 111(3), 564–572. [DOI] [PubMed] [Google Scholar]
  36. Dube SR, Miller JW, Brown DW, Giles WH, Felitti VJ, Dong M, & Anda RF (2006). Adverse childhood experiences and the association with ever using alcohol and initiating alcohol use during adolescence. Journal of Adolescent Health, 38(4), 444. e441–444. e410. [DOI] [PubMed] [Google Scholar]
  37. Emslie C, Lewars H, Batty GD, & Hunt K (2009). Are there gender differences in levels of heavy, binge and problem drinking? Evidence from three generations in the west of Scotland. Public health, 123(1), 12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Fang L, & McNeil S (2017). Is there a relationship between adverse childhood experiences and problem drinking behaviors? Findings from a population-based sample. Public health, 150, 34–42. [DOI] [PubMed] [Google Scholar]
  39. Fatch R, Bellows B, Bagenda F, Mulogo E, Weiser S, & Hahn JA (2013). Alcohol consumption as a barrier to prior HIV testing in a population-based study in rural Uganda. AIDS and Behavior, 17(5), 1713–1723. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Felitti VJ, Anda RF, Nordenberg D, Williamson DF, Spitz AM, Edwards V, Koss MP, & Marks JS (2019). Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults: The adverse childhood experiences (ACE) study. American journal of preventive medicine, 56(6), 774–786. [DOI] [PubMed] [Google Scholar]
  41. Felitti VJ, Anda RF, Nordenberg D, Williamson DF, Spitz AM, Edwards V, & Marks JS (1998). Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults: The Adverse Childhood Experiences (ACE) Study. American Journal of Preventive Medicine, 14(4), 245–258. [DOI] [PubMed] [Google Scholar]
  42. Ferreira-Borges C, Rehm J, Dias S, Babor T, & Parry CD (2016). The impact of alcohol consumption on African people in 2012: an analysis of burden of disease. Tropical Medicine & International Health, 21(1), 52–60. [DOI] [PubMed] [Google Scholar]
  43. Frankenberger DJ, Clements-Nolle K, & Yang W (2015). The association between adverse childhood experiences and alcohol use during pregnancy in a representative sample of adult women. Women’s health issues, 25(6), 688–695. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Goodman ML, Grouls A, Chen CX, Keiser PH, & Gitari S (2017). Adverse childhood experiences predict alcohol consumption patterns among Kenyan mothers. Substance use & misuse, 52(5), 632–638. [DOI] [PubMed] [Google Scholar]
  45. Hamby S, Finkelhor D, Turner H, & Ormrod R (2010). The overlap of witnessing partner violence with child maltreatment and other victimizations in a nationally representative survey of youth. Child abuse & neglect, 34(10), 734–741. [DOI] [PubMed] [Google Scholar]
  46. Hamby S, Finkelhor D, Turner H, & Ormrod R (2016). Children’s Exposure to Intimate Partner Violence and Other Family Violence (2011). [Google Scholar]
  47. Hao W, Su Z, Liu B, Zhang K, Yang H, Chen S, Biao M, & Cui C (2004). Drinking and drinking patterns and health status in the general population of five areas of China. Alcohol and alcoholism, 39(1), 43–52. [DOI] [PubMed] [Google Scholar]
  48. Haslam SA, O’Brien A, Jetten J, Vormedal K, & Penna S (2005). Taking the strain: Social identity, social support, and the experience of stress. British journal of social psychology, 44(3), 355–370. [DOI] [PubMed] [Google Scholar]
  49. Helzer JE, & Canino GJ (1992). Alcoholism in North America, Europe, and Asia. Oxford University Press, USA. [Google Scholar]
  50. Hernandez DC, Marshall A, & Mineo C (2014). Maternal depression mediates the association between intimate partner violence and food insecurity. Journal of women’s health, 23(1), 29–37. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Hesbacher PT, Rickels K, Morris RJ, Newman H, & Rosenfeld H (1980). Psychiatric illness in family practice. The Journal of clinical psychiatry. [PubMed] [Google Scholar]
  52. Hughes ME, Waite LJ, Hawkley LC, & Cacioppo JT (2004). A short scale for measuring loneliness in large surveys: Results from two population-based studies. Research on aging, 26(6), 655–672. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Humphreys K, Mankowski ES, Moos RH, & Finney JW (1999). Do enhanced friendship networks and active coping mediate the effect of self-help groups on substance abuse? Annals of Behavioral Medicine, 21(1), 54. [DOI] [PubMed] [Google Scholar]
  54. Jetten J, Branscombe NR, Haslam SA, Haslam C, Cruwys T, Jones JM, Cui L, Dingle G, Liu J, & Murphy S (2015). Having a lot of a good thing: Multiple important group memberships as a source of self-esteem. PloS one, 10(5), e0124609. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Jonzon E, & Lindblad F (2006). Risk factors and protective factors in relation to subjective health among adult female victims of child sexual abuse. Child abuse & neglect, 30(2), 127–143. [DOI] [PubMed] [Google Scholar]
  56. Jouriles EN, McDonald R, Slep AMS, Heyman RE, & Garrido E (2008). Child abuse in the context of domestic violence: Prevalence, explanations, and practice implications. Violence and victims, 23(2), 221–235. [DOI] [PubMed] [Google Scholar]
  57. Jung J, Rosoff DB, Muench C, Luo A, Longley M, Lee J, Charlet K, & Lohoff FW (2020). Adverse Childhood Experiences are Associated with High-Intensity Binge Drinking Behavior in Adulthood and Mediated by Psychiatric Disorders. Alcohol and alcoholism, 55(2), 204–214. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Kabiru CW, Beguy D, Crichton J, & Ezeh AC (2010). Self-reported drunkenness among adolescents in four sub-Saharan African countries: associations with adverse childhood experiences. Child and Adolescent Psychiatry and Mental Health, 4(1), 1–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Kabwama SN, Ndyanabangi S, Mutungi G, Wesonga R, Bahendeka SK, & Guwatudde D (2016). Alcohol use among adults in Uganda: findings from the countrywide non-communicable diseases risk factor cross-sectional survey. Global health action, 9(1), 31302. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Kafuko A, & Bukuluki P (2008). Qualitative research in Uganda on Knowledge, attitudes and practices concerning alcohol. Health Communication Partnership (HCP), Afford Project, Young Empowered and Healthy (YEAH). [Google Scholar]
  61. Kalema D, Van Damme L, Vindevogel S, Derluyn I, Meulewaeter F, & Vanderplasschen W (2020). Predictors of Early Recovery after Treatment for Alcohol use Disorders in Uganda. Alcoholism Treatment Quarterly, 38(2), 148–164. [Google Scholar]
  62. Kappel RH, Livingston MD, Patel SN, Villaveces A, & Massetti GM (2021). Prevalence of adverse childhood experiences (ACEs) and associated health risks and risk behaviors among young women and men in Honduras. Child abuse & neglect, 115, 104993. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Karamagi CA, Tumwine JK, Tylleskar T, & Heggenhougen K (2006). Intimate partner violence against women in eastern Uganda: implications for HIV prevention. BMC public health, 6(1), 1–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Kazeem OT (2015). A validation of the adverse childhood experiences scale in Nigeria. Research on Humanities and Social Sciences, 5(11), 18–23. [Google Scholar]
  65. Kendler KS, Kuhn JW, & Prescott CA (2004). Childhood sexual abuse, stressful life events and risk for major depression in women. Psychological medicine, 34(8), 1475. [DOI] [PubMed] [Google Scholar]
  66. Kiburi SK, Molebatsi K, Obondo A, & Kuria MW (2018). Adverse childhood experiences among patients with substance use disorders at a referral psychiatric hospital in Kenya. BMC psychiatry, 18(1), 1–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Kidman R, Smith D, Piccolo LR, & Kohler H-P (2019). Psychometric evaluation of the Adverse Childhood Experience International Questionnaire (ACE-IQ) in Malawian adolescents. Child Abuse & Neglect, 92, 139–145. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Kim JH, Lee S, Chow J, Lau J, Tsang A, Choi J, & Griffiths SM (2008). Prevalence and the factors associated with binge drinking, alcohol abuse, and alcohol dependence: a population-based study of Chinese adults in Hong Kong. Alcohol & Alcoholism, 43(3), 360–370. [DOI] [PubMed] [Google Scholar]
  69. Koenig MA, Lutalo T, Zhao F, Nalugoda F, Wabwire-Mangen F, Kiwanuka N, Wagman J, Serwadda D, Wawer M, & Gray R (2003). Domestic violence in rural Uganda: evidence from a community-based study. Bulletin of the world health organization, 81, 53–60. [PMC free article] [PubMed] [Google Scholar]
  70. Koenig MA, Zablotska I, Lutalo T, Nalugoda F, Wagman J, & Gray R (2004). Coerced first intercourse and reproductive health among adolescent women in Rakai, Uganda. International family planning perspectives, 156–163. [DOI] [PubMed] [Google Scholar]
  71. Larkin H, Felitti VJ, & Anda RF (2014). Social work and adverse childhood experiences research: Implications for practice and health policy. Social work in public health, 29(1), 1–16. [DOI] [PubMed] [Google Scholar]
  72. Longabaugh R, Wirtz PW, Zywiak WH, & O’malley SS (2010). Network support as a prognostic indicator of drinking outcomes: The COMBINE study. Journal of Studies on Alcohol and Drugs, 71(6), 837–846. [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. Loudermilk E, Loudermilk K, Obenauer J, & Quinn MA (2018). Impact of adverse childhood experiences (ACEs) on adult alcohol consumption behaviors. Child abuse & neglect, 86, 368–374. [DOI] [PubMed] [Google Scholar]
  74. McGavock L, & Spratt T (2017). Children exposed to domestic violence: Using adverse childhood experience scores to inform service response. British journal of social work, 47(4), 1128–1146. [Google Scholar]
  75. McKay MT, Konowalczyk S, Andretta JR, & Cole JC (2017). The direct and indirect effect of loneliness on the development of adolescent alcohol use in the United Kingdom. Addictive behaviors reports, 6, 65–70. [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. McLaughlin KA, Conron KJ, Koenen KC, & Gilman SE (2010). Childhood adversity, adult stressful life events, and risk of past-year psychiatric disorder: a test of the stress sensitization hypothesis in a population-based sample of adults. Psychological medicine, 40(10), 1647. [DOI] [PMC free article] [PubMed] [Google Scholar]
  77. McLaughlin KA, DeCross SN, Jovanovic T, & Tottenham N (2019). Mechanisms linking childhood adversity with psychopathology: Learning as an intervention target. Behaviour research and therapy, 118, 101–109. [DOI] [PMC free article] [PubMed] [Google Scholar]
  78. Meinck F, Orkin F, & Cluver L (2019). Does free schooling affect pathways from adverse childhood experiences via mental health distress to HIV risk among adolescent girls in South Africa: a longitudinal moderated pathway model. Journal of the International AIDS Society, 22(3), e25262. [DOI] [PMC free article] [PubMed] [Google Scholar]
  79. Molnar BE, Buka SL, & Kessler RC (2001). Child sexual abuse and subsequent psychopathology: results from the National Comorbidity Survey. American Journal of Public Health, 91(5), 753. [DOI] [PMC free article] [PubMed] [Google Scholar]
  80. Mushavi RC, Burns BF, Kakuhikire B, Owembabazi M, Vořechovská D, McDonough AQ, Cooper-Vince CE, Baguma C, Rasmussen JD, & Bangsberg DR (2020). “When you have no water, it means you have no peace”: a mixed-methods, whole-population study of water insecurity and depression in rural Uganda. Social science & medicine, 245, 112561. [DOI] [PMC free article] [PubMed] [Google Scholar]
  81. Mwachofi A, Imai S, & Bell RA (2020). Adverse childhood experiences and mental health in adulthood: evidence from North Carolina. Journal of Affective Disorders, 267, 251–257. [DOI] [PubMed] [Google Scholar]
  82. Naker D (2014). Violence against children: The voices of Ugandan children and adults. Raising Voices and Save the Children in Uganda. [Google Scholar]
  83. Ogland EG, Xu X, Bartkowski JP, & Ogland CP (2014). Intimate partner violence against married women in Uganda. Journal of family violence, 29(8), 869–879. [Google Scholar]
  84. Papas RK, Sidle JE, Wamalwa ES, Okumu TO, Bryant KL, Goulet JL, Maisto SA, Braithwaite RS, & Justice AC (2010). Estimating alcohol content of traditional brew in Western Kenya using culturally relevant methods: the case for cost over volume. AIDS and Behavior, 14(4), 836–844. [DOI] [PMC free article] [PubMed] [Google Scholar]
  85. Perreira KM, & Sloan FA (2001). Life events and alcohol consumption among mature adults: a longitudinal analysis. Journal of Studies on Alcohol, 62(4), 501–508. [DOI] [PubMed] [Google Scholar]
  86. Philippe B (1995). In search of respect: Selling crack in El Barrio. Cambridge, University press. [Google Scholar]
  87. Pilowsky DJ, Keyes KM, & Hasin DS (2009). Adverse childhood events and lifetime alcohol dependence. American journal of public health, 99(2), 258–263. [DOI] [PMC free article] [PubMed] [Google Scholar]
  88. Pirard S, Sharon E, Kang SK, Angarita GA, & Gastfriend DR (2005). Prevalence of physical and sexual abuse among substance abuse patients and impact on treatment outcomes. Drug and alcohol dependence, 78(1), 57–64. [DOI] [PubMed] [Google Scholar]
  89. Quinn M, Caldara G, Collins K, Owens H, Ozodiegwu I, Loudermilk E, & Stinson JD (2018). Methods for understanding childhood trauma: modifying the adverse childhood experiences international questionnaire for cultural competency. International journal of public health, 63(1), 149–151. [DOI] [PubMed] [Google Scholar]
  90. Ramiro LS, Madrid BJ, & Brown DW (2010). Adverse childhood experiences (ACE) and health-risk behaviors among adults in a developing country setting. Child Abuse & Neglect, 34(11), 842–855. [DOI] [PubMed] [Google Scholar]
  91. Rothman EF, Edwards EM, Heeren T, & Hingson RW (2008). Adverse childhood experiences predict earlier age of drinking onset: results from a representative US sample of current or former drinkers. Pediatrics, 122(2), e298–304. 10.1542/peds.2007-3412 [DOI] [PubMed] [Google Scholar]
  92. Roy M, Bouldin E, Bennett M, & Hege A (2019). Adult food security and the relationship with Adverse Childhood Experiences among residents of Appalachian North Carolina. Journal of Appalachian Health, 1(3), 17–26. [DOI] [PMC free article] [PubMed] [Google Scholar]
  93. Sani F (2012). Group identification, social relationships, and health. The social cure: Identity, health and well-being, 21–37. [Google Scholar]
  94. Satinsky EN, Kakuhikire B, Baguma C, Rasmussen JD, Ashaba S, Cooper-Vince CE, Perkins JM, Kiconco A, Namara EB, & Bangsberg DR (2021). Adverse childhood experiences, adult depression, and suicidal ideation in rural Uganda: A cross-sectional, population-based study. PLoS medicine, 18(5), e1003642. [DOI] [PMC free article] [PubMed] [Google Scholar]
  95. Saunders JB, Aasland OG, Babor TF, De la Fuente JR, & Grant M (1993). Development of the alcohol use disorders identification test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption-II. Addiction, 88(6), 791–804. [DOI] [PubMed] [Google Scholar]
  96. Schilling EA, Aseltine RH, & Gore S (2007). Adverse childhood experiences and mental health in young adults: a longitudinal survey. BMC Public Health, 7(1), 30. [DOI] [PMC free article] [PubMed] [Google Scholar]
  97. Schilling EA, Aseltine RH, & Gore S (2008). The impact of cumulative childhood adversity on young adult mental health: Measures, models, and interpretations. Social Science & Medicine, 66(5), 1140–1151. [DOI] [PMC free article] [PubMed] [Google Scholar]
  98. Simmel G (1964). The mental life of the metropolis (Pp. 409–424). The Sociology of Georg Simmel. New York: Free Press. [Google Scholar]
  99. Stavropoulos J (2006). Violence against girls in Africa: A retrospective survey in Ethiopia, Kenya and Uganda. [Google Scholar]
  100. Stevens E, Jason LA, Ram D, & Light J (2015). Investigating social support and network relationships in substance use disorder recovery. Substance abuse, 36(4), 396–399. [DOI] [PMC free article] [PubMed] [Google Scholar]
  101. Strine TW, Dube SR, Edwards VJ, Prehn AW, Rasmussen S, Wagenfeld M, Dhingra S, & Croft JB (2012). Associations between adverse childhood experiences, psychological distress, and adult alcohol problems. American journal of health behavior, 36(3), 408–423. [DOI] [PubMed] [Google Scholar]
  102. Suarez-Orozco MM (1987). Towards a psychosocial understanding of Hispanic adaptation to American schooling. Success or failure, 156–168. [Google Scholar]
  103. Swindale A, & Bilinsky P (2006). Development of a universally applicable household food insecurity measurement tool: process, current status, and outstanding issues. The Journal of nutrition, 136(5), 1449S–1452S. [DOI] [PubMed] [Google Scholar]
  104. Takada S, Nyakato V, Nishi A, O’Malley AJ, Kakuhikire B, Perkins JM, Bangsberg DR, Christakis NA, & Tsai AC (2019). The social network context of HIV stigma: Population-based, sociocentric network study in rural Uganda. Social Science & Medicine, 233, 229–236. [DOI] [PMC free article] [PubMed] [Google Scholar]
  105. Taylor SE, & Aspinwall LG (1996). Mediating and moderating processes in psychosocial stress: appraisal, coping, resistance, and vulnerability. [Google Scholar]
  106. Tomasello M (2007). Cooperation and communication in the 2nd year of life. Child Development Perspectives, 1(1), 8–12. [Google Scholar]
  107. Tsai AC, Bangsberg DR, Emenyonu N, Senkungu JK, Martin JN, & Weiser SD (2011). The social context of food insecurity among persons living with HIV/AIDS in rural Uganda. Social Science and Medicine, 73(12), 1717–1724. [DOI] [PMC free article] [PubMed] [Google Scholar]
  108. Tsai AC, Bangsberg DR, Frongillo EA, Hunt PW, Muzoora C, Martin JN, & Weiser SD (2012). Food insecurity, depression and the modifying role of social support among people living with HIV/AIDS in rural Uganda. Social Science & Medicine, 74(12). [DOI] [PMC free article] [PubMed] [Google Scholar]
  109. Tsai AC, Kakuhikire B, Mushavi R, Vořechovská D, Perkins JM, McDonough AQ, & Bangsberg DR (2016). Population-based study of intra-household gender differences in water insecurity: reliability and validity of a survey instrument for use in rural Uganda. Journal of water and health, 14(2), 280–292. [DOI] [PMC free article] [PubMed] [Google Scholar]
  110. Tsai AC, Tomlinson M, Comulada WS, & Rotheram-Borus MJ (2016). Food insufficiency, depression, and the modifying role of social support: evidence from a population-based, prospective cohort of pregnant women in peri-urban South Africa. Social science & medicine, 151, 69–77. [DOI] [PMC free article] [PubMed] [Google Scholar]
  111. Tumwesigye NM, & Kasirye R (2005). Gender and the major consequences of alcohol consumption in Uganda. Alcohol, gender and drinking problems, 189. [Google Scholar]
  112. Tumwesigye NM, Kasirye R, & Nansubuga E (2009). Is social interaction associated with alcohol consumption in Uganda? Drug Alcohol Depend, 103(1–2), 9–15. [DOI] [PubMed] [Google Scholar]
  113. Tumwesigye NM, Kyomuhendo GB, Greenfield TK, & Wanyenze RK (2012). Problem drinking and physical intimate partner violence against women: evidence from a national survey in Uganda. BMC public health, 12(1), 1–11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  114. Uchino BN (2004). Social support and physical health: Understanding the health consequences of relationships. Yale University Press. [Google Scholar]
  115. Uganda Bureau of Statistics. (2016). Uganda Demographic and Health survey ( [Google Scholar]
  116. Underwood P (2000). Social support: the promise and the reality In Handbook of stress, coping and health. Implications for nursing research, theory and practice.[sl]: Virginia Hill Rice Editor. Sage Publications. [Google Scholar]
  117. VanderWeele TJ, & Ding P (2017). Sensitivity analysis in observational research: introducing the E-value. Annals of internal medicine, 167(4), 268–274. [DOI] [PubMed] [Google Scholar]
  118. WHO. (2018). Adverse childhood experiences international questionnaire. Adverse childhood experiences international questionnaire (ACE-IQ). [Google Scholar]
  119. Wolff B, Busza J, Bufumbo L, & Whitworth J (2006). Women who fall by the roadside: gender, sexual risk and alcohol in rural Uganda. Addiction, 101(9), 1277–1284. [DOI] [PubMed] [Google Scholar]
  120. Wong AE, Dirghangi SR, & Hart SR (2019). Self-concept clarity mediates the effects of adverse childhood experiences on adult suicide behavior, depression, loneliness, perceived stress, and life distress. Self and Identity, 18(3), 247–266. [Google Scholar]
  121. World Health Organization. (2015). Global status report on alcohol and health—2014. Geneva, Switzerland: World Health Organization; 2014. Google Scholar. [Google Scholar]
  122. World Health Organization. (2018). Adverse childhood experiences international questionnaire. Adverse childhood experiences international questionnaire (ACE-IQ). [Google Scholar]
  123. World Health Organization. (2019). Global status report on alcohol and health 2018. World Health Organization. [Google Scholar]
  124. Zou G (2004). A modified poisson regression approach to prospective studies with binary data. American journal of epidemiology, 159(7), 702–706. [DOI] [PubMed] [Google Scholar]
  125. Zywiak WH, Longabaugh R, & Wirtz PW (2002). Decomposing the relationships between pretreatment social network characteristics and alcohol treatment outcome. Journal of Studies on Alcohol, 63(1), 114–121. [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Appendix_Tables
NIHMS1797444-supplement-Appendix_Tables.docx (25.9KB, docx)

RESOURCES