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Published in final edited form as: Child Abuse Negl. 2024 Jun 12;154:106895. doi: 10.1016/j.chiabu.2024.106895

Prevalence and Early-Life Predictors of Adverse Childhood Experiences: Longitudinal Insights from a Low-Income Country

Rachel Kidman a, Etienne Breton b, Jere Behrman c, Yang (Tingting) Rui b, Hans-Peter Kohler d
PMCID: PMC11382283  NIHMSID: NIHMS2020873  PMID: 38870710

Abstract

Background:

A sizeable literature shows that adverse childhood experiences (ACEs) are associated with poor health outcomes in later life. However, most studies on the prevalence and predictors of ACEs have been carried out in high-income countries using cross-sectional approaches.

Objective:

The present study explores the prevalence and predictors of ACEs in Malawi, a low-income country, using prospective longitudinal data collected on adolescents.

Participants:

We use data on 1,375 adolescents and their biological mothers from the Malawi Longitudinal Study of Families and Health (MLSFH). ACEs were reported by adolescents over two survey waves, in 2017–18 and 2021. Predictors were reported by mothers in 2008 and 2010.

Methods:

Multivariate ordinary least square and logistic regression analyses of ACEs exposure reported by adolescents on indicators of family arrangements and resources.

Results:

Adolescents report having been exposed to nearly seven ACEs on average. Among indicators of family arrangements and resources, the only significant predictors of cumulative ACEs exposure are polygyny (linked to parental absence) and mother’s SF-12 mental health score (linked to physical abuse and witnessing domestic violence).

Conclusions:

ACEs are much more prevalent in the low-income country under study than in middle- and high-income countries surveyed in prior research. Despite adversity being widespread, most indicators of family arrangements and resources highlighted in prior studies are not associated with adolescents’ cumulative ACEs exposure in this context. Mothers’ mental health in childhood nevertheless emerges as a significant predictor of adolescents’ self-reported ACEs. These findings inform efforts aimed at preventing ACEs in high-adversity contexts.

1. Introduction

Adverse childhood experiences (ACEs) are potentially traumatic experiences including abuse and neglect, family dysfunction, and peer and community violence. A sizeable literature shows dose-response relationships between exposure to ACEs and poor later-life health outcomes (Hughes et al., 2017; Kalmakis & Chandler, 2015; Petruccelli et al., 2019), making ACEs prevention a critical public-health concern. However, most studies on the prevalence and predictors of ACEs have been carried out in high-income countries (HICs). Studies show that the prevalence of ACEs often differs between HICs and low- and middle-income countries (LMICs) (Moody et al., 2018; Stoltenborgh et al., 2015). Owing to differences in the economic and sociocultural context of adversities, it also remains unclear whether predictors of ACEs differ between HICs and LMICs.

While there is a large literature on the risk factors of child maltreatment, the literature on the predictors of ACEs, which encompass a broader category of adverse experiences than only child maltreatment as conventionally defined, is much sparser (Stith et al., 2009; White et al., 2015). Studies on child maltreatment specifically highlight a broad set of risk factors operating at multiple levels of analysis (individuals, families and communities) including (but not limited to) poverty, mother’s low age at birth and poor mental health, parental stress, neighborhood drug use, more siblings, in addition to important geographical variations (Brown et al., 1998; Sidebotham et al., 2006; Stith et al., 2009; Thornberry et al., 2014; Walsh et al., 2019).

ACEs may share many of these determinants (Bussemakers et al., 2019; Crouch et al., 2018; Manyema & Richter, 2019; Martins et al., 2022; A. L. G. Soares et al., 2016). For example, prior studies underscore associations between socio-economic circumstances in childhood and adversity-related outcomes (Misiak et al., 2022; Walsh et al., 2019). However, ACEs include a broader array of experiences than maltreatment alone. Such experiences – including household dysfunction and peer violence - may have distinct predictors. Mothers’ health condition, for example, plays a critical role in shaping the nurturing environment of a child’s early years (Beeber et al., 2008; Bennett et al., 2015; Eisenhower et al., 2013; Kabiry et al., 2015; Wong et al., 2016), and may be a key predictor of household dysfunction. Understanding what factors most strongly predict ACEs is essential, given their potential far-reaching impact on a child’s development.

Existing studies on ACEs rely heavily on cross-sectional approaches. This is especially relevant insofar as child maltreatment is mostly measured by external sources such as court and child protection services records, whereas ACEs are measured using retrospective self-reports. Cross-sectional approaches to analyzing ACEs increases the risk that omitted variables simultaneously bias the recall of ACEs, their predictors and their hypothesized outcomes (Breton et al., 2022; Danese, 2020). To our knowledge, there are only two longitudinal studies on the predictors of ACEs in LMICs: one on Brazil (A. L. G. Soares et al., 2016) and the other on South Africa (Manyema & Richter, 2019). More longitudinal research is needed to address the methodological challenges associated with cross-sectional designs, especially in LMICs. Accordingly, the present study explores the prevalence and predictors of ACEs in rural Malawi, a low-income country, using prospective longitudinal data collected over a period of 13 years on a cohort of adolescents and their parents.

2. Methods

We use data from the Malawi Longitudinal Study of Families and Health (MLSFH) (Kohler et al., 2015), which provides a sample of three districts in central, southern and northern Malawi. Comparisons of the MLSFH study population with the rural samples of the Malawi Demographic and Health Survey (DHS) and Integrated Household Survey (HIS3) has demonstrated a close resemblance in characteristics (Kohler et al., 2020). Malawi ranks 169 out of 184 countries and territories on the Human Development Index (Economy, 2021), about half the population lives in poverty (Malawi, 2021), and approximately 9% of adults are living with HIV (Ministry of Health (MOH), 2022).

The full sample of adolescents are household members (mostly children) of original MLSFH respondents. 2,061 adolescents ages 10–16 were interviewed at survey baseline (2017–18) and 1,878 were reinterviewed at follow-up (2021). More than 60% of adolescent respondents had at-least one sibling interviewed. Our sample includes 1,375 adolescents interviewed at baseline and follow-up and who were residing with their biological mothers during the 2008–10 MLSFH wave. Data on predictors come from interviews with the adolescents’ mothers during the 2008–10 wave. Data on ACEs come from the two waves of a prospective cohort study of adolescents. Adults and adolescents over 18 provided informed consent; adolescents under 18 years old provided written assent after guardian consent. All participants were interviewed face-to-face in their preferred local language, with precautions taken to ensure privacy. More information on the sampling strategy, attrition and data collection can be found on the MLSFH-ACE cohort profile (Kidman et al., 2023).

2.1. Measures

ACEs were measured by adolescent self-reports on the ACE-International Questionnaire (ACE-IQ), an instrument developed by the World Health Organization and validated in Malawi (Kidman et al., 2019). The ACE-IQ elicits retrospective self-reports over 13 binary domains of adversity (see Figure 1). Adolescents were deemed to have experienced an ACE if they reported exposure at either survey baseline or follow-up. We summed exposure to these 13 domains in a cumulative score. We analyzed this score and each domain of adversity separately.

Figure 1 –

Figure 1 –

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Lifetime Prevalence of ACEs by Domain

Predictors were drawn from surveys completed by biological mothers in 2008 and 2010 (when children were aged 2–8). Family arrangement indicators include whether respondents ever resided with a grandparent during this period, whether mothers were ever in polygynous marriages or not currently married, numbers of siblings, and mothers’ ages at birth (dichotomized as under 20 versus 20+). Family resources indicators include household wealth indices (standardized count of durable assets owned), mothers’ average Short Form Survey 12-item (SF-12) physical and mental health scores, mothers’ self-reported schooling level, and mothers’ HIV status. The SF-12 is a common instrument measuring mental and physical health and has been validated in Malawi (Ohrnberger et al., 2020). Standardized SF-12 scores were used in regression analyses.

2.2. Analytic Plan

We estimated multivariate ordinary-least-squares (OLS) and logistic regressions of respondents’ ACEs on family-arrangements and family-resources indicators. Standard errors were clustered by biological mothers. Approximately 20% of respondents had missing values for indicators of maternal marital instabilities and HIV statuses. Up to 8% of respondents had missing data on other predictors of interest. Multiple imputations were used (results are robust to using listwise deletions).

3. Results

3.1. Prevalence of ACEs

Figure 1 highlights the high prevalence of adversities among adolescents in our sample. On average, respondents reported 6.74 ACEs. Nearly 90% reported ≥4 ACEs, a threshold used to denote high exposure in prior studies (Meehan et al., 2022). More than 95% reported experiences of community violence and emotional neglect, and more than 80% declared that they had had experiences of physical abuse, emotional abuse and/or witnessing domestic violence in their household.

3.2. Predictors of ACEs

Table 1 shows OLS regression estimates of cumulative ACEs scores on family-arrangements and family-resources indicators in childhood. Differences in coefficients between the adjusted and full (combined) models highlight only slight confounding and mediation among included variables. Most family-arrangements and family-resources indicators in childhood are not significantly associated with adolescents’ cumulative ACEs. Polygyny in early childhood is linked to a slightly higher cumulative ACEs score in adolescence. Further analyses in Table 2 reveal that a main pathway of this association is through fathers’ absence in adolescence.

Table 1 –

Descriptive Statistics and Multivariate Regression Analyses Showing the Associations between Potential Predictors and Cumulative ACEs

Predictors: Family Arrangements and Resources Variable Type Range Mean/Proportion Adjusted Models OR [95% CI] Combined Models OR [95% CI]
Grandparent Lives in Household dichotomous 0,1 0.07 −0.32 [−0.74, 0.10] −0.38 [−0.83, 0.06]
Mother in Polygynous Union dichotomous 0,1 0.31 0.32** [0.08, 0.56] 0.29* [0.04, 0.53]
Number of Siblings continuous 0 – 9 4.53 (SD=1.95) −0.01 [−0.06, 0.05] 0.02 [−0.04, 0.08]
Mother Not Continuously Married dichotomous 0,1 0.15 0.34* [0.03, 0.64] 0.24 [−0.11, 0.60]
Mother’s Age at Birth <20 dichotomous 0,1 0.12 0.16 [−0.14, 0.47] 0.17 [−0.16, 0.50]
Wealth Index continuous −4 – 9 0.05 (SD=1.78) −0.06 [−0.13, 0.01] −0.06 [−0.13, 0.01]
Mother’s Schooling: Less than Primary dichotomous 0,1 0.18 0 [Ref.] 0 [Ref.]
Mother’s Schooling: Primary dichotomous 0,1 0.72 0.11 [−0.17, 0.39] 0.14 [−0.15, 0.44]
Mother’s Schooling: More than Primary dichotomous 0,1 0.1 0.15 [−0.33, 0.63] 0.26 [−0.26, 0.77]
Mother’s SF12-Physicala continuous 0–100 52.64 (SD=5.64) 0.03 [−0.10, 0.17] 0.03 [−0.11, 0.17]
Mother’s SF12-Mentala continuous 0–100 52.89 (SD=8.08) −0.14* [−0.25, −0.03] −0.12* [−0.24, −0.01]
Mother is HIV Positive dichotomous 0,1 0.05 0.29 [−0.14, 0.72] 0.13 [−0.33, 0.58]
N 1,375 1,375 1,375
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Notes: Asterisks indicate statistical significance (**p<0.01 *p<0.05).

a

Mothers’ SF-12 physical and mental scores are standardized for the multivariate regression analyses. Results reported are coefficients with 95% confidence intervals in brackets. Each cell in the “Adjusted” column represents a separate regression model of the cumulative ACEs score on the predictor of interest controlling for respondents’ age, gender and region of residence. Cells in the column “Combined” are taken from a single regression model of the cumulative ACEs score on all predictors of interest controlling for respondents’ age, gender and region of residence. In all models, standard errors are clustered by biological mother.

Table 2 -.

Multivariate Regression Analyses Showing the Associations between Potential Predictors and Each Type of ACE Odds Ratios [95% CI]

Adverse Childhood Experiences
Predictors Phys. Abuse Emotion. Abuse Sexual Abuse Phys. Neglect Emotion. Neglect Drug Use Mental Illness Domes. Violence Bullied Absent Parent Incar. H. Mem Collect. Violence
Grandparent Lives in Household 1.06 [0.55, 2.06] 0.73 [0.41, 1.31] 0.76 [0.37, 1.59] 1.29 [0.82, 2.01] 0.29** [0.13, 0.67] 0.80 [0.49, 1.30] 0.77 [0.39, 1.52] 1.00 [0.53, 1.91] 0.85 [0.54, 1.34] 1.25 [0.77, 2.03] 0.25*** [0.12, 0.55] 0.59 [0.27, 1.25]
Mother in Polygynous Union 1.15 [0.80, 1.64] 1.25 [0.84, 1.84] 1.07 [0.74, 1.54] 1.21 [0.93, 1.58] 0.96 [0.45, 2.04] 0.97 [0.74, 1.27] 0.93 [0.62, 1.38] 1.49 [0.99, 2.24] 1.30 [0.98, 1.73] 1.52* [1.10, 2.10] 1.37 [0.99, 1.88] 1.44* [1.02, 2.03]
Number of Siblings 0.96 [0.88, 1.05] 0.94 [0.85, 1.04] 1.05 [0.95, 1.15] 1.03 [0.97, 1.11] 0.88 [0.72, 1.07] 1.00 [0.93, 1.08] 1.08 [0.98, 1.19] 0.99 [0.89, 1.09] 1.05 [0.98, 1.13] 0.85*** [0.78, 0.92] 1.03 [0.94, 1.13] 0.98 [0.90, 1.08]
Mother Not Continuously Married 1.17 [0.71, 1.92] 1.27 [0.71, 2.26] 1.10 [0.61, 1.96] 1.20 [0.80, 1.82] 1.82 [0.66, 5.05] 1.07 [0.73, 1.58] 0.77 [0.41, 1.43] 1.26 [0.68, 2.32] 1.18 [0.79, 1.78] 3.21*** [1.89, 5.47] 1.22 [0.80, 1.85] 1.43 [0.90, 2.27]
Mother’s Age at Birth <20 0.95 [0.57, 1.59] 1.48 [0.76, 2.87] 1.18 [0.70, 1.98] 0.79 [0.55, 1.14] 0.97 [0.25, 3.70] 1.03 [0.71, 1.51] 1.30 [0.76, 2.23] 1.41 [0.76, 2.62] 1.01 [0.68, 1.49] 1.06 [0.68, 1.67] 1.45 [0.93, 2.25] 1.24 [0.78, 1.96]
Wealth Index 1.06 [0.96, 1.16] 0.92 [0.83, 1.02] 1.05 [0.95, 1.15] 0.89** [0.82, 0.96] 0.83* [0.70, 0.98] 0.92* [0.85, 0.99] 0.96 [0.86, 1.08] 1.01 [0.91, 1.12] 0.99 [0.92, 1.07] 0.96 [0.88, 1.05] 1.00 [0.91, 1.10] 0.97 [0.89, 1.06]
Mother’s Schooling: Less than Primary 1.00 [Ref.] 1.00 [Ref.] 1.00 [Ref.] 1.00 [Ref.] 1.00 [Ref.] 1.00 [Ref.] 1.00 [Ref.] 1.00 [Ref.] 1.00 [Ref.] 1.00 [Ref.] 1.00 [Ref.] 1.00 [Ref.]
Mother’s Schooling: Primary 0.68 [0.45, 1.04] 0.76 [0.42, 1.36] 1.49 [0.92, 2.41] 1.09 [0.79, 1.52] 0.93 [0.32, 2.68] 1.42 [0.99, 2.02] 1.51 [0.88, 2.59] 0.88 [0.53, 1.46] 0.86 [0.60, 1.24] 1.48 [1.00, 2.19] 1.37 [0.90, 2.09] 1.28 [0.79, 2.10]
Mother’s Schooling: More than Primary 0.79 [0.40, 1.58] 0.71 [0.31, 1.63] 0.77 [0.33, 1.80] 1.30 [0.76, 2.23] 0.45 [0.11, 1.79] 1.91* [1.11, 3.29] 1.31 [0.55, 3.11] 1.16 [0.52, 2.61] 0.93 [0.53, 1.66] 1.26 [0.68, 2.35] 1.87 [1.00, 3.49] 1.19 [0.55, 2.57]
Mother’s SF12-Physical Score a 1.02 [0.85, 1.24] 1.04 [0.84, 1.29] 1.02 [0.85, 1.23] 0.97 [0.84, 1.13] 0.93 [0.67, 1.30] 1.02 [0.87, 1.21] 0.94 [0.77, 1.15] 1.07 [0.85, 1.35] 0.95 [0.82, 1.12] 1.17 [0.97, 1.40] 1.13 [0.93, 1.37] 1.11 [0.89, 1.38]
Mother’s SF12-Mental Score a 0.81* [0.69, 0.97] 0.82 [0.68, 1.01] 0.92 [0.77, 1.10] 1.01 [0.89, 1.15] 1.45** [1.11, 1.89] 0.89 [0.78, 1.02] 1.09 [0.90, 1.32] 0.75** [0.60, 0.93] 0.95 [0.83, 1.08] 0.91 [0.78, 1.07] 0.89 [0.76, 1.03] 0.99 [0.83, 1.17]
Mother is HIV Positive 1.18 [0.55, 2.54] 1.21 [0.49, 3.00] 0.74 [0.31, 1.77] 1.07 [0.59, 1.95] 0.38 [0.13, 1.15] 0.99 [0.60, 1.64] 1.41 [0.62, 3.20] 1.37 [0.54, 3.48] 0.90 [0.51, 1.61] 1.55 [0.72, 3.33] 1.47 [0.83, 2.61] 1.22 [0.59, 2.51]
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Notes: Bold font and asterisks indicate statistical significance (*** p<0.001 **p<0.01 *p<0.05).

a

Mothers’ SF-12 physical and mental scores and standardized for the multivariate regression analyses. Each column represents a multivariate logistic regression model of the selected binary domain of ACEs on all predictors of interest, also controlling for respondents’ age, gender and region of residence. In all models, standard errors are clustered by biological mother. Results are odds ratios. Sample size for all models is N=1,375. Community violence is omitted because virtually all respondents reported that they experienced it.

Maternal mental health is significantly and negatively associated with the cumulative ACE score. Further analyses on Table 2 reveal that mothers’ mental health scores are negatively associated with adolescents’ self-reports of physical abuse and witnessing domestic violence in their households. Contrary to this, mothers’ mental health is positively associated with the likelihood of having experienced emotional neglect, although less than 3% of respondents reported not having experienced emotional neglect. While household wealth is not associated with cumulative ACEs exposure, it is negatively associated with the likelihood of experiencing physical and emotional neglect, and living with someone with substance-abuse problems.

4. Discussion

In the high-adversity context of rural Malawi, we find that, despite ACEs being widespread, several indicators of family arrangements and resources highlighted in prior studies are not significant predictors of cumulative exposure to ACEs. More specifically, we find no evidence that mothers’ physical health, schooling, age at birth <20 years old, marital instabilities and sibship sizes are significantly related to adolescents’ cumulative ACEs score. Household wealth does not predict cumulative ACEs exposure but is significantly associated with specific domains of adversity. This appears to operate, at least partially, through paternal absence as divorce rates are higher in polygynous unions (Reniers, 2003).

Against this backdrop, a noteworthy finding is that mothers’ mental health during respondents’ early childhood is significantly associated with adolescents’ self-reported ACEs. This finding is especially compelling insofar as our indicator of mental health is not a refined indicator of psychopathology, suggesting that even sub-clinical levels of mental illness among mothers may affect the likelihood of ACEs. There is a sizeable literature on the impact of mothers’ mental health on their children’s health and development in early childhood (Gelaye et al., 2016; Parsons et al., 2012; Stein et al., 2014). We document that maternal mental health in childhood has potentially lasting impacts on adolescents’ subjective assessment of their own adversities (Raposa et al., 2014).

4.1. Limitations

The current study’s main limitation pertains to the self-reported nature of the ACEs. A growing literature highlights problems with inaccuracies in ACEs self-reports. These mostly pertain to false negatives, as many respondents forget or do not disclose ACEs recorded during their childhood by external sources (Baldwin et al., 2019). Prior research also documents inconsistencies in self-reports over time (Colman et al., 2016); in the present cohort study, some adolescents reported fewer lifetime ACEs at follow-up than at baseline (Breton et al., 2022). To minimize false negatives and such inconsistencies, our indicators of ACEs combine self-reports from both baseline and follow-up in single measures denoting exposure at either wave. Other key limitations of the present study include cohort attrition and missing data on predictors of interest. Finally, predictors may vary by local context and thus findings presented here may not generalize outside of Malawi.

5. Conclusion

ACEs are much more prevalent in the low-income country under study than in middle- and high-income countries surveyed in prior research (Giano et al., 2020; Le et al., 2022; Manyema & Richter, 2019; A. L. Soares et al., 2016). Furthermore, few predictors of childhood adversity highlighted in prior studies are significantly associated with cumulative ACEs exposure among adolescents in Malawi. Mothers’ mental health in childhood nonetheless emerges as a significant predictor of adolescents’ self-reported ACEs. This finding underscores the importance of interventions focusing on mothers’ mental health as a key resource for improving child-related outcomes in high-adversity contexts (Tol et al., 2020).

Acknowledgements

The authors thank the respondents for their time and willingness to share their experiences. We also thank the staff (field supervisors, interviewers, counselors, and many others) at Invest in Knowledge International for their time, effort, and support of this project. In particular, we thank James Mwera and Andrew Zulu for oversight of the extensive data collection. Research reported in this publication was supported by the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Award Numbers R01HD090988. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Funding:

Research reported in this publication was supported by the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Award Numbers R01HD090988. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Footnotes

Conflict of Interest

The authors declare no conflict of interest.

Financial Disclosure

The authors report no financial disclosure.

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