This paper enumerates and characterizes latent classes of adverse childhood experiences and investigates how they relate to prenatal substance use (i.e., smoking, alcohol, and other drugs) and poor infant outcomes (i.e., infant prematurity and low birthweight) across eight low- and middle-income countries (LMICs).
A total of 1189 mother-infant dyads from the Evidence for Better Lives Study cohort were recruited. Latent class analysis using the Bolck, Croon, and Hagenaars (BCH) 3-step method with auxiliary multilevel logistic regressions was performed.
Three high-risk classes and one low-risk class emerged: (1)
Our results highlight the multifaceted nature of ACEs and underline the potential importance of exposure to childhood adversities on behaviors and outcomes in the perinatal period. This can inform the design of antenatal support to better address these challenges.
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Felitti and colleagues provided seminal evidence for a strong gradient relationship between adverse childhood experiences (ACEs) and poor health [
One important pathway may be via the impact of maternal behaviors during pregnancy. Embryonic and fetal exposure to teratogens during this critical period can disrupt growth and development which may lead to long term deficits [
While most investigations of links between ACEs and substance use adopt a cumulative risk approach that assumes an additive and linear step-wise relationship between risk factors and outcomes examined [
The value of LCA to meet this need has been increasingly demonstrated in ACEs studies that use the technique to parse the heterogeneity in ACE exposure into potentially meaningful risk profiles [
This study examined the number and characterization of latent classes of childhood adversity in a prospective birth cohort study involving mother-infant dyads residing across eight diverse low- and middle-income countries (LMICs). Additionally, we explored the relationships between latent ACEs, prenatal substance use, and poor infant outcomes (i.e., infant prematurity and low birthweight). We hypothesized that high-risk latent ACE classes, characteristic of high levels of child maltreatment and household dysfunction, are more likely to be associated with prenatal substance use and adverse infant outcomes. Additionally, we hypothesized that salient risk factors (e.g., sexual abuse, physical abuse) have synergistic effects and are more predictive of adverse outcomes than other ACE domains (e.g., parental divorce).
To the best of our knowledge, this is the first study to explore how the latent typologies of maternal ACEs link to prenatal substance use and poor infant outcomes in LMICs. Of the.
few studies to apply LCA to determine typologies of risk of early childhood adversity using the nine domains of the pioneering ACE study [
All participants were taking part in the Evidence for Better Lives Study (EBLS) [
The 29-item ACE-IQ [
The
Between three to six months postpartum, participants reported on offspring birth weight (‘how big was your child when he/she was born?’) and maternal week of birth (‘how many weeks pregnant were you when your baby was born?’). Following the definition of the World Health Organization (WHO) on infant prematurity [
The following covariates were adjusted for in the analyses: age, perceived socioeconomic status (SES), and educational level. Perceived SES was assessed using the
Missing data were handled using multiple imputations (MI) using the mice package in
Latent Class Analysis using the Bolck, Croon, and Hagenaars (BCH) 3-step method with auxiliary multilevel logistic regressions on the distal outcomes were performed using
There is not a singular method used when determining the ‘optimal’ model; thus, a host of fit indices were utilized: Bayesian Information Criterion (BIC), Sample-size Adjusted Bayesian Information Criterion (SABIC), Consistent Akaike Information Criterion (CAIC) and Approximate Weight of Evidence Criterion (AWE) [
Characterization of the resultant classes of the selected model was based on posterior membership probabilities and strong item-class relationships. Strong item-class relationships must fulfil two features: within-class homogeneity and between-class separation [
To examine the associations of the latent classes and the covariates on the distal outcomes, the second run of the BCH method was employed by estimating the multilevel logistic regression models conditional on the latent class variable saved as BCH weights [
The study was composed of 1189 women in their third trimester of pregnancy (mean age 28.7; standard deviatio
The class enumeration process was terminated at the Fit statistics and classification coefficients K par BIC SABIC CAIC AWE BFk,k+1 LMR-LRT BLRT 1 19 -10,401.019 20,936.687 20,876.336 20,879.51 20,889.01 < 3 - 2 39 -9296.006 18,868.394 18,744.515 18,751.04 18,770.54 < 3 < .001 3 59 -9049.660 18,517.437 18,330.031 18,339.90 18,369.40 < 0 < .05 < .001 4 79 -8919.640 18,148.198 18,161.42 18,200.92 > 4 < .001 < .001 5 99 -8863.024 18,427.635 18,129.74 18,179.24 0.60 < .001 6 119 -8812.199 18,467.719 18,089.729 - 0.74 < .001 Note: Model-estimated, Class-specific Item Response Probabilities Based on the Unconditional 4-class LCA Adverse Childhood Experiences Class 1 Class 2 Class 3 Class 4 1. was a problem drinker or alcoholic, or misused street or prescription drugs? 0.446 0.556 2. was depressed, mentally ill or suicidal? 3. was ever sent to jail or prison? 0.434 4. Were your parents ever separated or divorced? 0.511 0.666 5. Did your mother, father or guardian die? 0.341 0.389 0.354 6. being yelled at, screamed at, sworn at, insulted or humiliated? 7. being slapped, kicked, punched or beaten up? 0.573 8. being hit or cut with an object, such as a stick (or cane), bottle, club, knife, whip etc.? 0.567 0.671 9. yell, scream or swear at you, insult or humiliate you? 10. threaten to, or actually, abandon you or throw you out of the house? 0.480 0.649 11. spank, slap, kick, punch or beat you up? 0.664 12. hit or cut you with an object, such as a stick (or cane), bottle, club, knife, whip etc.? 0.428 0.539 13. touch or fondle you in a sexual way when you did not want them to? 14. make you touch their body in a sexual way when you did not want them to? 15. attempt oral, anal, or vaginal intercourse with you when you did not want them to? 16. actually have oral, anal, or vaginal intercourse with you when you did not want them to? 17. How often did your parents/guardians not give you enough food even when they could easily have done so? 0.376 18. How often were your parents/guardians too drunk or intoxicated by drugs to take care of you? 19. How often did our parents/guardians not send you to school even when it was available? 0.339 Note: Model-Estimated item response odds ratios for all latent class comparisons bason on the 4-class unconditional LCA ACE Class 1 vs 2 Class 1 vs 3 Class 1 vs 4 Class 2 vs 3 Class 2 vs 4 Class 3 vs 4 1. was a problem drinker or alcoholic, or misused street or prescription drugs? 0.64 3.20 3.98 2. was depressed, mentally ill or suicidal? 0.88 4.68 3.16 3. was ever sent to jail or prison? 0.56 2.35 4. Were your parents ever separated or divorced? 0.52 3.25 1.83 5. Did your mother, father or guardian die? 0.81 1.45 0.95 1.77 1.16 0.65 6. being yelled at, screamed at, sworn at, insulted or humiliated? 1.73 *** 7. being slapped, kicked, punched or beaten up? 0.50 2.31 4.63 8. being hit or cut with an object, such as a stick (or cane), bottle, club, knife, whip etc.? 0.64 3.56 9. yell, scream or swear at you, insult or humiliate you? 0.48 1.71 3.57 10. threaten to, or actually, abandon you or throw you out of the house? 0.50 11. spank, slap, kick, punch or beat you up? 1.38 2.58 1.86 12. hit or cut you with an object, such as a stick (or cane), bottle, club, knife, whip etc.? 0.64 2.17 3.38 13. touch or fondle you in a sexual way when you did not want them to? 2.61 2.86 14. make you touch their body in a sexual way when you did not want them to? 2.87 2.29 15. attempt oral, anal, or vaginal intercourse with you when you did not want them to? 0.57 2.09 3.65 16. actually have oral, anal, or vaginal intercourse with you when you did not want them to? 3.07 2.49 0.81 17. How often did your parents/guardians not give you enough food even when they could easily have done so? 0.69 0.88 18. How often were your parents/guardians too drunk or intoxicated by drugs to take care of you? 1.20 19. How often did our parents/guardians not send you to school even when it was available.? 0.69 3.70 3.53 0.96 Note: Maternal adverse childhood experiences profile plot. Class 1:
Class 1, with an estimated proportion of 7%
Class 2 with an estimated proportion of 13%
Class 3, with an estimated proportion of 40%
Class 4, with an estimated proportion of 40%
The class-specific threshold values and significance tests for each distal outcome are presented in Table Significant differences between class-specific thresholds of distal outcomes using χ2tests Class (threshold) Class 1 ( Class 2 ( Class 3 ( Prenatal alcohol use c1 (1.086) c2 (0.864) 0.018 c3 (1.645) 0.035 0.017 c4 (2.651) 0.008 -0.010 -0.027 Prenatal heavy alcohol use c1 (2.372) c2 (2.581) -0.401 c3 (3.216) -0.061 -0.019 c4 (4.279) -0.094 -0.053 -0.033 Prenatal tobacco use c1 (1.119) c2 (1.137) 0.015 c3 (2.144) 0.036 0.020 c4 (2.791) 0.003 -0.012 -0.033 Prenatal heavy tobacco use c1(1.463) c2(1.472) -0.007 c3(2.919) -0.053 -1.552 c4(3.347) -0.006 0.002 0.553 Prenatal other drug use c1(2.010) c2(2.501) -0.058 c3(2.744) 0.064 0.122 c4(3.701) -0.071 Low birth weight c1(2.221) c2(2.031) c3(2.833) 0.014 c4(2.094) -0.087 0.044 Infant prematurity c1(2.150) c2(2.446) 0.069 c3(2.414) 0.035 -0.034 c4(2.437) 0.027 -0.042 -0.008 The values under column one are class specific threshold values for each distal outcome. The values in columns two to four are the results from the pairwise significance tests * =
To the best of our knowledge, this is the first adverse childhood experiences (ACEs) study to focus on a cohort of mothers residing in eight diverse LMICs to provide a more global perspective on the impact of ACEs. Using the Evidence for Better Lives Study (EBLS) dataset, the number and characterizations of latent ACEs and their associations with prenatal substance use and poor infant outcomes were explored. The findings suggested high prevalence and co-occurrence of maternal ACEs in this cohort, with 39% having experienced ≥ 4 ACEs which is higher than in ACE studies involving pregnant women in HICs [
A model with four distinct latent classes was judged optimal for these data, with classes labelled:
Previous studies have used the cumulative risk approach to demonstrate a dose–response relationship between maternal ACEs and prenatal smoking and alcohol use [
Prenatal other drug use was present in 6% of the study sample; this is comparable to other ACE studies involving a cohort of pregnant women residing in a HIC (3.1%) [
A review of developmental resilience science literature shows that some individuals are able to positively adapt in the face of cumulative and severe exposure to childhood adversities [
The study has some potentially important implications for policy and practice. Primarily, our results highlight the importance of considering the multifaceted nature of ACEs. Concurrent with the continuous growth of the ACE field of study is the increasing number of proponents for universal ACEs screening as part of standard medical assessment [
Despite the concern raised about their use as screens, ACE questionnaires or similar tools can be used to introduce the sensitive topic to expectant mothers, followed by a systematic clinical assessment of the nature of their childhood adversity with detailed discussion of, among other things, developmental chronicity, frequency, severity, and how this exposure is currently impacting their behavior and wellbeing [
This study has several limitations. First, convenience sampling constrained generalizability to the wider population. Second, principal investigators from each site selected health care providers that provided antenatal check-ups. Only mothers who were able to visit the local health centers had the chance to participate in the study; mothers who were not able to attend routine check-ups and in turn might be more vulnerable and at higher risk of poor pregnancy outcomes were not recruited. Third, the psychometric measures were translated from English into nine local languages: Afrikaans, IsiXhosa, Romanian, Tagalog, Tamil, Twi, Romanian, Sinhala, Urdu, and Vietnamese. Thus, it is possible that the translated items may not have been homogenous in their meaning across the nine languages. To give an example, the ACE item on parental death was coded as missing for Pakistan because the translation of the item in Urdu was not representative of the original question. This may partially explain why class homogeneity has been low for this item across all classes. Fourth, information on exposure to ACEs were collected from retrospective and self-administered reports, which have been shown to exhibit high false-negative scores [
The results further our understanding of the dynamic and multifaceted nature of ACEs. Contrary to previous research, there were insufficient evidence linking exposure to multiple ACEs to prenatal substance use and poor infant outcomes. The findings highlight the importance of bring more attention to various parameters of risk exposure (i.e., severity, duration, chronicity). Additionally, more ACE research grounded on LMICs with focus on exploring the impact of socioecological factors can help optimize interventions for both mother and child.
Not applicable.
All authors were engaged in the overall conceptualization, study design, investigation, and findings interpretation. CLH, FM, ALM, LGS, and DF were involved in the analysis and draft writing of the manuscript. All authors contributed to provide critical review and revision of the manuscript. All authors have reviewed and approved this manuscript.
The work of the Evidence for Better Lives Study was supported by the Jacobs Foundation, UBS Optimus Foundation, Fondation Botnar, the Consuelo Zobel Alger Foundation, the British Academy, the Cambridge Humanities Research Grants Scheme, the ESRC Impact Acceleration Account Programme, a Queensland University of Technology Postgraduate Research Award, Higher Degree Research Student Supplementary Research Funding from Queensland University of Technology, the University of Edinburgh College Office for the College of Arts, the Humanities and Social Sciences SFC ODA Global Challenges Internal Fund, the University of Cambridge GCRF Quality Research Fund and the Wolfson Professor of Criminology Discretionary Fund. CLH is supported by the Edinburgh Centre for Data, Culture and Society. FM was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme [Grant Agreement Number 852787] and the UK Research and Innovation Global Challenges Research Fund [ES/S008101/1]. The views expressed are those of the authors and not necessarily those of the funding bodies. For the purpose of open access, the author has applied a ‘Creative Commons Attribution (CC BY) license to any Author Accepted Manuscript version arising from this submission.
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
The Evidence for Better Lives Study protocol for recruitment and collection of data has been approved by the following ethics boards: University of Cambridge, School of Social Sciences (18/180) and the Human Biology Research Ethics Committee, UK HBREC.2018.27; University of the Philippines Manila—Research Ethics Board, Philippines, UPMREB 2018–558-01; The Institutional Ethics Committee of Hue University of Medicine and Pharmacy, Vietnam, H2018/430; University of Kelaniya, Faculty of Medicine, Ethics Review Committee, Sri Lanka, P/208/11/2018; National Bioethics Committee (NBC), Pakistan, 4–87/ NBC-364/19/1487; Consiliul Stiintific—Universitatea Babes-Bolyai, Romania, 18.362/11.10.2018; University of Cape Town, Department of Health, Western Cape Government, South Africa, WC_201911_009; Health Impact Assessment—Western Cape Government; University of Cape Town, Faculty of Health Sciences, Human Research Ethics Committee, South Africa, 057/2019. Health Research Ethics Committee (HREC) at Stellenbosch University, South Africa, N18/09/099; Ghana Health Service Ethics Review Committee, GHS-ERC008/11/18; University of the West Indies Ethics Committee, Jamaica, ECP 212, 17/18. All participants provided written informed consent prior to participation. The study was carried out in accordance with the Declaration of Helsinki regarding the ethical conduct of medical research involving human subjects. For a more comprehensive discussion of the data collection process, please see Evidence for Better Lives Consortium [
Not applicable.
The authors declare that they have no competing interests.
Adverse Childhood Experiences
Adverse Childhood Experiences – International Questionnaire
Akaike Information Criterion
Alcohol, Smoking and Substance Involvement Screening Test
Approximate Weight of Evidence Criterion
Bolck, Croon, and Hagenaars
Bayes Factor
Bayesian Information Criterion
Bootstrapped likelihood ratio test
Consistent Akaike Information Criterion
Demographic and Health Survey
Evidence for Better Lives Study
High income countries
Latent Class Analysis
Log-likelihood
Low- and middle-income countries
Lo-Mendel-Rubin likelihood ratio test
Lanza, Tan, and Bray approach
Multiple Imputations
Maximum Likelihood three-step approach
Sample-size Adjusted Bayesian Information Criterion
Socioeconomic Status
World Health Organization
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