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Impact of adverse childhood experiences on analgesia-related outcomes: a systematic review. BACKGROUND: There is well-established evidence linking adverse childhood experiences (ACEs) and chronic pain in adulthood. It is less clear how ACE exposure might influence the response to chronic pain treatment. In this systematic review, we synthesise the literature assessing the impact of ACE exposure on outcomes relating to the use, benefits, and harms of analgesic medications (analgesia-related outcomes). METHODS: We searched seven databases from inception to September 26, 2023, for studies investigating adverse events in childhood (<18 yr) and any analgesia-related outcome during adulthood (≥18 yr). Title/abstract screening, full-text review, data extraction, and risk of bias assessment were performed independently by two authors. Given the high degree of study heterogeneity, a narrative synthesis was performed. RESULTS: From 7531 records, 66 studies met inclusion criteria, involving 137 395 participants. Analgesia-related outcomes were classed into six categories: use of analgesics (n=12), analgesic side-effects (n=4), substance misuse (n=45), lifetime drug overdose (n=2), endogenous pain signalling (n=4), and other outcomes (n=2). No studies assessed the effect of ACE exposure on the potential benefits of analgesics. ACE exposure was associated with greater use of analgesic medication, higher incidence of analgesic medication side-effects, greater risk and severity of substance misuse, greater risk of drug overdose, and greater risk of attempted suicide in opioid dependency. CONCLUSIONS: Adverse childhood experience exposure is associated with poor analgesia-related outcomes, so individual assessment adverse childhood experiences is important when considering the treatment of chronic pain. However, significant gaps in the literature remain, especially relating to the use and harms of non opioid analgesics. SYSTEMATIC REVIEW PROTOCOL: CRD42023389870 (PROSPERO).

We conducted a systematic review that was registered in the International Prospective Register of Systematic Reviews (PROSPERO) on September 15, 2023 (CRD42023389870). We reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We developed a search strategy based on previously published literature reviews and refined it following input from subject experts, an academic librarian, and our patient and public partners (Supplementary Table S1). The strategy included strings for ACEs, generic analgesia terms, specific analgesic groups, and 59 individual analgesics taken from relevant sections of the British National Formulary.54 The strategy did not include strings for any specific outcome to allow identification of all possible outcomes. We searched the following seven databases from inception to September 26, 2023: APA PsycNET, CINAHL Plus, Cochrane CENTRAL, Embase, MEDLINE, Scopus, and Web of Science. The search results were imported into Covidence (Veritas Health Innovation, Melbourne, VIC, Australia), which automatically identified and removed duplicate entries. Two reviewers (DS and MK) independently performed title/abstract screening (inter-rater reliability using Cohen's kappa = 0.45) and full-text review (Cohen's kappa = 0.90). Discrepancies were resolved by consensus discussion.

ion, Melbourne, VIC, Australia), which automatically identified and removed duplicate entries. Two reviewers (DS and MK) independently performed title/abstract screening (inter-rater reliability using Cohen's kappa = 0.45) and full-text review (Cohen's kappa = 0.90). Discrepancies were resolved by consensus discussion. Reports were eligible for review if they included adults (≥18 yr), adverse events that had occurred during childhood (<18 yr), and any analgesia-related outcome. Reports that only assessed adverse events in adulthood or analgesia-related outcomes in children were excluded. The following study designs were eligible: randomised controlled trials, cohort studies, case–control studies, cross-sectional studies, and review articles with meta-analysis. Editorials, case reports, and conference abstracts were excluded. Systematic reviews without a meta-analysis and narrative synthesis review articles were also excluded; however, their reference lists were screened for relevant citations.

–control studies, cross-sectional studies, and review articles with meta-analysis. Editorials, case reports, and conference abstracts were excluded. Systematic reviews without a meta-analysis and narrative synthesis review articles were also excluded; however, their reference lists were screened for relevant citations. Two reviewers (DS and either MK or KB) independently performed data extraction into Microsoft Excel (Microsoft Corporation, Redmond, WA, USA) using a pre-agreed template. Discrepancies were resolved by consensus discussion. Data extracted from each report included study details (author, year, study design, sample cohort, sample size, and sample country of origin), patient characteristics (age and sex), ACE information (definition, childhood cut-off age, number of ACEs, list of ACEs, and ACE prevalence), analgesia-related outcome information (outcome measured and outcome definition), and analysis parameters (effect size and confidence intervals).

e, and sample country of origin), patient characteristics (age and sex), ACE information (definition, childhood cut-off age, number of ACEs, list of ACEs, and ACE prevalence), analgesia-related outcome information (outcome measured and outcome definition), and analysis parameters (effect size and confidence intervals). Two reviewers (DS and either MK or KB) independently performed risk of bias assessments of each included study using either the Risk Of Bias In Non-randomized Studies of Exposures (ROBINS-E) tool for observational studies or the Risk of Bias 2 (RoB 2) tool for randomised trials.55,56 The ROBINS-E tool assesses the risk of bias across seven domains: confounding; measurement of the exposure, participant selection, postexposure interventions, missing data, measurement of the outcome, and selection of the reported result. The RoB 2 tool assesses the risk of bias across five domains: randomisation, deviation from the intended intervention, missing outcome data, measurement of the outcome, and selection of the reported result. Discrepancies were resolved by consensus discussion.

rement of the outcome, and selection of the reported result. The RoB 2 tool assesses the risk of bias across five domains: randomisation, deviation from the intended intervention, missing outcome data, measurement of the outcome, and selection of the reported result. Discrepancies were resolved by consensus discussion. We made no assumptions about the types of analgesia-related outcomes that could have been identified; however, we ultimately classed the included papers into the following six outcome categories: use of analgesics, analgesic side-effects, substance misuse, lifetime drug overdose, endogenous pain signalling, and other outcomes. When considering the harmful use of substances, we opted to use the term substance misuse rather than substance use disorder (or equivalent) as the latter implies that diagnostic criteria have been met, whereas the former encompasses a broader range of harmful scenarios.57 All statistical analyses were performed in R version 4.2.2 using the RStudio integrated development environment (RStudio Team, Boston, MA, USA). To avoid repetition of individual participant data, where multiple studies analysed the same patient cohort, we selected the study with the largest sample size. Meta-analysis of prevalence was performed with the meta package, using logit transformations within a generalised linear mixed model and reporting the random-effects model.58,59

n of individual participant data, where multiple studies analysed the same patient cohort, we selected the study with the largest sample size. Meta-analysis of prevalence was performed with the meta package, using logit transformations within a generalised linear mixed model and reporting the random-effects model.58,59 This review had input from members of the Chronic Pain Advisory Group (CPAG), who are part of the Consortium Against Pain Inequality (CAPE).60 CPAG consists of eight individuals with lived experiences of ACEs and chronic pain. The group has experience in systematic review co-production and provided feedback on the choice of topic and framing of the research question.

Two reviewers (DS and either MK or KB) independently performed data extraction into Microsoft Excel (Microsoft Corporation, Redmond, WA, USA) using a pre-agreed template. Discrepancies were resolved by consensus discussion. Data extracted from each report included study details (author, year, study design, sample cohort, sample size, and sample country of origin), patient characteristics (age and sex), ACE information (definition, childhood cut-off age, number of ACEs, list of ACEs, and ACE prevalence), analgesia-related outcome information (outcome measured and outcome definition), and analysis parameters (effect size and confidence intervals). Two reviewers (DS and either MK or KB) independently performed risk of bias assessments of each included study using either the Risk Of Bias In Non-randomized Studies of Exposures (ROBINS-E) tool for observational studies or the Risk of Bias 2 (RoB 2) tool for randomised trials.55,56 The ROBINS-E tool assesses the risk of bias across seven domains: confounding; measurement of the exposure, participant selection, postexposure interventions, missing data, measurement of the outcome, and selection of the reported result. The RoB 2 tool assesses the risk of bias across five domains: randomisation, deviation from the intended intervention, missing outcome data, measurement of the outcome, and selection of the reported result. Discrepancies were resolved by consensus discussion.

This review had input from members of the Chronic Pain Advisory Group (CPAG), who are part of the Consortium Against Pain Inequality (CAPE).60 CPAG consists of eight individuals with lived experiences of ACEs and chronic pain. The group has experience in systematic review co-production and provided feedback on the choice of topic and framing of the research question.

The search identified 7531 records, of which 66 met the inclusion criteria (Fig. 1). Sixty-five studies were observational, and one was a randomised controlled trial. The total participant count (discounting duplicated cohorts) was 137 395. The majority of studies were from North American (n=47, 71.2%), European (n=11, 16.7%), or Australian (n=6, 9.1%) populations; African and Middle Eastern populations were represented by one study each (1.5% each). The summary characteristics can be found in Supplementary Table S2.Fig 1Flow chart of selection of studies into the systematic review. ACE, adverse childhood experience.Fig 1 Flow chart of selection of studies into the systematic review. ACE, adverse childhood experience. Risk of bias assessment was performed on 65 observational studies using the ROBINS-E tool (Supplementary Table S3).55 Overall most studies were at ‘high risk’ (n=21, 31.8%) or ‘very high risk’ (n=44, 66.7%) of bias.

The search identified 7531 records, of which 66 met the inclusion criteria (Fig. 1). Sixty-five studies were observational, and one was a randomised controlled trial. The total participant count (discounting duplicated cohorts) was 137 395. The majority of studies were from North American (n=47, 71.2%), European (n=11, 16.7%), or Australian (n=6, 9.1%) populations; African and Middle Eastern populations were represented by one study each (1.5% each). The summary characteristics can be found in Supplementary Table S2.Fig 1Flow chart of selection of studies into the systematic review. ACE, adverse childhood experience.Fig 1 Flow chart of selection of studies into the systematic review. ACE, adverse childhood experience. Risk of bias assessment was performed on 65 observational studies using the ROBINS-E tool (Supplementary Table S3).55 Overall most studies were at ‘high risk’ (n=21, 31.8%) or ‘very high risk’ (n=44, 66.7%) of bias. There were some consistent risks observed across the studies, especially in domain 1 (risk of bias attributed to confounding) and domain 3 (risk of bias attributed to participant selection). In domain 1, most studies were ‘high risk’ or ‘very high risk’ (n=59, 89.4%) as either they performed unadjusted analysis, introducing a risk of confounding bias, or they controlled for variables that could have been affected by ACE exposure (e.g. coexisting mental health disorders), increasing the risk of overadjustment bias. In domain 3, many studies were ‘high risk’ or ‘very high risk’ (n=43, 65.2%) as participant selection was based on characteristics that could have been influenced by ACE exposure (e.g. recruitment of participants attending a healthcare service), introducing a risk of selection bias. The remaining studies were deemed as having ‘some concerns’ (n=23, 34.8%) as participant selection occurred at a time after ACE exposure, introducing a risk of survivorship bias.

have been influenced by ACE exposure (e.g. recruitment of participants attending a healthcare service), introducing a risk of selection bias. The remaining studies were deemed as having ‘some concerns’ (n=23, 34.8%) as participant selection occurred at a time after ACE exposure, introducing a risk of survivorship bias. Differences in risk of bias were seen in domain 2 (risk of bias attributed to exposure measurement) and domain 5 (risk of bias attributed to missing data). In domain 2, some studies were at ‘high risk’ as they used a narrow or atypical measure of ACEs (n=15, 22.7%); others were graded as having ‘some concerns’ as they used a broader but still incomplete measure of ACEs (n=30, 45.5%); and the remainder were at ‘low risk’ as they used an established or comprehensive list of ACEs (n=21, 31.8%). In domain 5, many studies were at ‘high risk’ or ‘very high risk’ as they failed to acknowledge or appropriately address missing data (n=43, 65.2%); a few were graded as having ‘some concerns’ (n=3, 4.5%) as they had a significant amount of missing data (>10% of exposure, outcome, or confounders) but mitigated this with appropriate strategies; and the remainder were at ‘low risk’ as they reported low levels (<10%) of missing data (n=20, 30.3%). Risk of bias assessment was performed on one interventional study using the RoB 2 tool (Supplementary Table S4).56 This study was graded as having ‘some concerns’ overall because of concerns in domain 1 (risk of bias attributed to randomisation), and domain 5 (risk of bias attributed to selection of the reported result).

Risk of bias assessment was performed on one interventional study using the RoB 2 tool (Supplementary Table S4).56 This study was graded as having ‘some concerns’ overall because of concerns in domain 1 (risk of bias attributed to randomisation), and domain 5 (risk of bias attributed to selection of the reported result). There were differences in the way that the concept of ACEs was defined and measured across the 66 studies (Supplementary Table S5). Twenty-two different terms were used (although most were variations on similar themes), with the most common being ‘adverse childhood experiences’ (n=22, 33.3%), ‘childhood trauma’ (n=11, 16.7%), and ‘childhood maltreatment’ (n=7, 10.6%). Nearly two-thirds of studies (n=42, 63.6%) did not provide a formal definition of their term of choice. The upper age limit for childhood ranged from <12 to <19 yr, with the most common being <18 (n=26, 39.4%), although over a third of studies (n=27, 40.9%) did not report the age cut-off used.

ltreatment’ (n=7, 10.6%). Nearly two-thirds of studies (n=42, 63.6%) did not provide a formal definition of their term of choice. The upper age limit for childhood ranged from <12 to <19 yr, with the most common being <18 (n=26, 39.4%), although over a third of studies (n=27, 40.9%) did not report the age cut-off used. In total, 45 different ACEs were assessed, with a median per study of 5 (range 1–27). The most frequently assessed ACEs were sexual abuse (n=55, 83.3%), physical abuse (n=52, 78.8%), and emotional abuse (n=44, 66.7%) (Supplementary Table S6). Twenty studies (30.3%) provided sufficient data to allow for a meta-analysis of the prevalence of exposure to any ACE; the pooled prevalence was 64.1% (95% confidence interval [CI] 51.3–75.2%). However, the inter-study heterogeneity was high (I2=99.4%, Cochran Q=3417, P<0.001) (Fig. 2).Fig 2Meta-analysis of prevalence of exposure to any ACE. ACE, adverse childhood experience; CI, confidence interval.Fig 2 Meta-analysis of prevalence of exposure to any ACE. ACE, adverse childhood experience; CI, confidence interval. Twenty-three studies (34.8%) compared their outcome of interest with individual ACE types (e.g. sexual or physical abuse). Twenty-eight studies (42.4%) used ACE counts (the sum of the different types of ACEs measured), 10 studies (15.2%) used ACE scores (incorporating ACE frequency or severity into the measurement), and five studies (7.6%) used other methods of ACE grouping (e.g. latent class analysis, ‘low’ vs ‘high’ trauma).

ical abuse). Twenty-eight studies (42.4%) used ACE counts (the sum of the different types of ACEs measured), 10 studies (15.2%) used ACE scores (incorporating ACE frequency or severity into the measurement), and five studies (7.6%) used other methods of ACE grouping (e.g. latent class analysis, ‘low’ vs ‘high’ trauma). Twelve studies (involving 27 281 participants) investigated the use of analgesic medication (Table 1).61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72 Compared with adults with no/low ACE exposure, high ACE exposure was associated with a higher number of analgesic medications in 2/2 studies, use of over-the-counter analgesic medications in 1/1 study, use of any prescription analgesic medications in 1/2 studies, use of prescription opioids in 3/7 studies, and use of NSAIDs in 1/1 study, but not with use of benzodiazepines (1/1 study). Although four studies failed to show an association between ACEs and use of prescription opioids, these studies tended to have a smaller sample size (n=80, 113, 235, and 865) compared with the three studies that did show an association (n=230, 2999, and 14 800) and so may have been underpowered. The largest study, by Austin and colleagues,62 found that a history of any childhood abuse was associated with a 51% higher odds of recent prescription opioid use. We found no studies that examined whether ACE exposure influenced the potential benefits arising from use of analgesics (e.g. improvements in pain or functional status).Table 1Impact of adverse childhood experiences on the use of analgesics.

abuse was associated with a 51% higher odds of recent prescription opioid use. We found no studies that examined whether ACE exposure influenced the potential benefits arising from use of analgesics (e.g. improvements in pain or functional status).Table 1Impact of adverse childhood experiences on the use of analgesics. ACE, adverse childhood experience; CI, confidence interval; NR, not reported; OR, odds ratio.Table 1Author and yrParticipant populationSample sizeExposureOutcomeOutcome toolRelationshipAlexander 1998Women with fibromyalgia attending an outpatient rheumatology clinic75Sexual abuse and physical abuseMedication usageNumber of pain medicationsA history of abuse was associated with a higher number of pain medications (t-test, P=0.025).Austin 2018aNational Longitudinal Study of Adolescent to Adult Health (Add Health), waves 1 and 4: a nationally representative cohort of adolescents aged 12–19 yr recruited from schools and followed up for more than 20 yr14 800Childhood abuseRecent prescription opioid useStructured interview asking about use of prescription opioids in the last 4 weeksA history of any childhood abuse (logistic regression, OR 1.51, 95% CI 1.24–1.82), emotional abuse (logistic regression, OR 1.57, 95% CI 1.29–1.90), and physical abuse (logistic regression, OR 1.46, 95% CI 1.14–1.87) were associated with greater odds of recent prescription opioid use.A history of sexual abuse was not associated with greater odds of recent prescription opioid use (logistic regression, OR 1.14, 95% CI 0.72–1.80).Baumann-Larsen 2023Trøndelag Health Study (HUNT), Young-HUNT3 and Young-HUNT4: adolescents aged 13–19 yr living in the Nord-Trøndelag region (Young-HUNT3) followed up as adults (Young-HUNT4)2947Potentially traumatic eventsUse of over-the-counter analgesics to treat musculoskeletal painUse of over-the-counter analgesics to treat headacheSelf-completed questionnaire asking about use of over-the-counter analgesics in the last monthBullying (ordinal logistic regression, OR 1.84, 95% CI 1.28–2.66), physical violence (ordinal logistic regression, OR 1.53, 95% CI 1.07–2.19), sexual abuse (ordinal logistic regression, OR 1.70, 95% CI 1.12–2.57), witness to violence (ordinal logistic regression, OR 1.40, 95% CI 1.07–1.84), severe illness or death of someone close (ordinal logistic regression, OR 1.38, 95% CI 1.06–1.80), and severe accident, disaster, or other traumatic event (ordinal logistic regression, OR 1.40, 95% CI 1.07–1.84) were associated with use of over the counter a

nce (ordinal logistic regression, OR 1.40, 95% CI 1.07–1.84), severe illness or death of someone close (ordinal logistic regression, OR 1.38, 95% CI 1.06–1.80), and severe accident, disaster, or other traumatic event (ordinal logistic regression, OR 1.40, 95% CI 1.07–1.84) were associated with use of over the counter a nalgesics to treat musculoskeletal pain.Bullying (ordinal logistic regression, OR 1.40, 95% CI 1.02–1.92), witness to violence (ordinal logistic regression, OR 1.28, 95% CI 1.03–1.59), and disease or death of someone close (ordinal logistic regression, OR 1.44, 95% CI 1.17–1.78) were associated with use of over-the-counter analgesics to treat headache.Physical violence (ordinal logistic regression, OR 1.23, 95% CI 0.90–1.68), sexual abuse (ordinal logistic regression, OR 1.23, 95% CI 0.85–1.77), and severe accident, disaster, or other traumatic event (ordinal logistic regression, OR 1.12, 95% CI 0.93–1.35) were not associated with the use of over-the-counter analgesics to treat headache.Griego 2022Women with chronic pelvic pain attending outpatient OB/GYN or pain clinics113ACEsUsed opioids in the last 3 monthsNRACE count was not associated with use of opioids in the last 3 months (logistic regression, OR 1.05, 95% CI 0.92–1.19).Lee 2023Midlife in the United States study (MIDUS), wave 2: a nationally representative sample of adults865Childhood abuseOpioid prescriptionMedication review by study staffEmotional abuse (logistic regression, OR 0.69, 95% CI 0.34–1.44), physical abuse (logistic regression, OR 0.45, 95% CI 0.19–1.02), and sexual abuse (logistic regression, OR 1.12, 95% CI 0.60–2.11) were not associated with opioid prescription.Pierce 2019Adults with current opioid use attending an outpatient pain clinic1785History of abuseCurrent benzodiazepine useSelf-completed questionnaire asking about current benzodiazepine useChildhood physical or sexual abuse was not associated with current benzodiazepine use (logistic regression, OR 1.08, 95% CI 0.63–1.84).Sansone 2010Adults attending an outpatient internal medicine clinic.80Childhood traumaTotal number of pain medicationsNarcotic pain medication useNSAID medication useOther pain medication use (gabapentin, duloxetine, and amitriptyline)Review of the preceding 4 weeks of the medical recordA higher number of childhood trauma types was associated with a higher number of pain prescriptions (Pearson's correlation, r=0.34, P<0.0.1), higher NSAID medication use (Pearson's correlation, r=0.24, P<0.05), and higher other p

pentin, duloxetine, and amitriptyline)Review of the preceding 4 weeks of the medical recordA higher number of childhood trauma types was associated with a higher number of pain prescriptions (Pearson's correlation, r=0.34, P<0.0.1), higher NSAID medication use (Pearson's correlation, r=0.24, P<0.05), and higher other p ain medication use (Pearson's correlation, r=0.28, P<0.05).The number of childhood trauma types was not associated with use of narcotic medications (Pearson's correlation, r=0.18, P>0.05).Testa 2023Pregnancy Risk Assessment Monitoring System (PRAMS), North Dakota and South Dakota populations: routine surveillance system for mothers who have recently given birth2999ACEsAny prescription opioid useSelf-completed paper questionnaire or structured interview asking about prescription pain reliever use during the most recent pregnancy≥3 ACEs was associated with higher odds of using prescription opioids during pregnancy (logistic regression; 0 ACEs: referent; 1 ACE: OR 1.88, 95% CI 0.98–3.64; 2 ACEs: OR 1.99, 95% CI 0.90–4.37; ≥3 ACEs: OR 2.44, 95% CI 1.32–4.50).Williams 2020Adults with a history of at least one type of interpersonal trauma (intimate partner violence, sexual assault, and/or ACEs) recruited through public advertising230ACEsPrescription opioid useSelf-completed online questionnaire asking if they had received a prescription for pain medication in the last yearHigher ACE count was associated with greater odds of opioid prescription (logistic regression, OR 1.10, 95% CI 1.01–1.20).Williams 2021Adults with a history of at least one type of interpersonal trauma (intimate partner violence, sexual assault, and/or ACEs) recruited through public advertising235ACEsPrescription opioid useSelf-completed online questionnaire asking if they had received a prescription for pain medication in the last yearACE count was not associated with prescription opioid use (logistic regression, OR 1.07, 95% CI 0.94–1.23).Wuest 2007Women's Health Effects Study (WHES): English speaking women who had left an abusive partner in the preceding 3–36 months and who had a positive Abuse Assessment Screen309Abused as a childPrescription pain medication useStructured interview asking about use of prescription pain medications in the last monthA history of child abuse was associated with higher prescription pain medication use (χ2, P=0.02).You 2019Adults attending a university3073Childhood adversityPrescription pain medication useSelf-completed online questionnaire asking to report current me

asking about use of prescription pain medications in the last monthA history of child abuse was associated with higher prescription pain medication use (χ2, P=0.02).You 2019Adults attending a university3073Childhood adversityPrescription pain medication useSelf-completed online questionnaire asking to report current me dicationsEarly traumatic inventory self-report score was not associated with prescription pain medication use (logistic regression, full result not provided, P>0.075). Impact of adverse childhood experiences on the use of analgesics. ACE, adverse childhood experience; CI, confidence interval; NR, not reported; OR, odds ratio.

dicationsEarly traumatic inventory self-report score was not associated with prescription pain medication use (logistic regression, full result not provided, P>0.075). Impact of adverse childhood experiences on the use of analgesics. ACE, adverse childhood experience; CI, confidence interval; NR, not reported; OR, odds ratio. Four studies (involving 3491 participants) investigated analgesic medication side-effects (Table 2).73, 74, 75, 76 The largest study (n=3118) examined the most side-effects and found that people with a history of abuse were more likely to report ‘any side-effect’ and more likely to report 10 out of the 15 individual side-effects than those without a history of abuse.76 The side-effects considered included some that are often associated with opioid and gabapentinoid use (such as constipation, drowsiness, and nausea).76 However, Bottiroli and colleagues73 reported no influence of childhood trauma history (i.e. abuse and neglect) on the persistence of medication overuse-associated headache. In a double-blind placebo-controlled randomised trial by Carlyle and colleagues,74 after an intramuscular injection of morphine, participants with a history of childhood trauma reported higher ratings for the pleasurable side-effects (such as ‘feeling high’ and ‘liking drug effects’) and lower ratings for the disagreeable side-effects (such as ‘disliking drug effects’) than those with no such history. These findings were not replicated in a hospital-based study by the same research group, where day-case surgery patients were given an opioid infusion before general anaesthesia and asked similar questions.75Table 2Impact of adverse childhood experiences on analgesic side-effects.

cts’) than those with no such history. These findings were not replicated in a hospital-based study by the same research group, where day-case surgery patients were given an opioid infusion before general anaesthesia and asked similar questions.75Table 2Impact of adverse childhood experiences on analgesic side-effects. CI, confidence interval; NR, not reported; OR, odds ratio.Table 2Author and yrParticipant populationSample sizeExposureOutcomeOutcome toolRelationshipBottiroli 2019Adults with chronic migraine and medication overuse headache attending an inpatient detoxification clinic166Childhood traumaPersistence of analgesia overuse 2 months after an analgesia detoxification programmePersistence of chronic headache 2 months after an analgesia detoxification programmeClinical assessment by a neurologistNumber of emotional trauma events was associated with persistence of analgesia overuse at 2 months (logistic regression, OR 11.10, 95% CI 1.15–106.84).Total number of trauma events, number of physical trauma events, and sexual abuse history were not associated with the persistence of analgesia overuse at 2 months (logistic regression; data not reported).Total number of trauma events, number of emotional trauma events, number of physical trauma events, and sexual abuse history were not associated with the persistence of chronic headache at 2 months (logistic regression; data not reported).Carlyle 2021Adults recruited through ‘ … convenience and snowball sampling via participant databases, poster advertisements, and word of mouth … ’52Childhood traumaFeeling the drug effectsFeeling highLiking drug effectsWanting more of the drugDisliking drug effectsSelf-completed questionnaire with questions from the Drug Effect Questionnaire, completed at baseline and then 15, 30, 45, 60, 90, 120, and 150 min after the administration of intramuscular morphine (0.15 mg/kg)No significant difference between high trauma and no trauma groups for ‘feeling the drug effects’ (mixed-effects random intercept models, P>0.284).Compared with no trauma group, the high trauma group had higher ratings for ‘feeling high’ at 30 min after morphine administration (mixed-effects random intercept models, P=0.047), ‘liking drug effects’ at all time points (mixed-effects random intercept models, P<0.010), and ‘wanting more of the drug’ at all time points (mixed-effects random intercept models, P<0.001).Compared with high trauma group, the low trauma group had higher ratings for ‘disliking drug effects’ at 90 and 150 mi

, P=0.047), ‘liking drug effects’ at all time points (mixed-effects random intercept models, P<0.010), and ‘wanting more of the drug’ at all time points (mixed-effects random intercept models, P<0.001).Compared with high trauma group, the low trauma group had higher ratings for ‘disliking drug effects’ at 90 and 150 mi n after morphine administration (mixed-effects random intercept models, P=0.004 and P<0.001, respectively).Carlyle 2023Healthy adults (American Society of Anaesthesiologists grade 1–2) attending a day-case surgery unit155Childhood adversityPost-opioid likingPost-opioid feeling goodPost-opioid feeling highPost-opioid dislikingPost-opioid feeling anxiousStructured interview using questions from the Drug Effect Questionnaire after the start of a remifentanil or oxycodone intravenous infusion, 5 min before a general anaestheticHigher total childhood trauma questionnaire score was associated with lower post-opioid liking (linear regression, β=–0.06, 95% CI –0.11 to –0.01).Total childhood trauma questionnaire score was not associated with post-opioid feeling good (linear regression, β=0.01, 95% CI –0.03 to 0.04), post-opioid feeling high (linear regression, β=–0.01, 95% CI –0.05 to 0.04), post-opioid disliking (linear regression, β=0.01, 95% CI –0.04 to 0.06), or post-opioid feeling anxious (linear regression, β=0.03, 95% CI –0.02 to 0.07).Pierce 2020Adults attending an outpatient pain clinic3118Child abuseAny side-effectDrowsinessConstipationFatigueDizzinessNausea or vomitingConfusionItchinessOther stomach/bowel upsetMental impairmentDiarrhoeaSwelling of hands and/or feetDifficulty urinatingHeart palpitationsShortness of breathHallucinationsNRCompared with people with no abuse history, people with an abuse history reported higher proportions of any side-effect (χ2, P<0.001), drowsiness (χ2, P<0.001), constipation (χ2, P<0.001), fatigue (χ2, P<0.001), nausea or vomiting (χ2, P<0.001), confusion (χ2, P<0.001), itchiness (χ2, P=0.002), other stomach/bowel upset (χ2, P<0.001), mental impairment (χ2, P<0.001), diarrhoea (χ2, P<0.001), and heart palpitations (χ2, P<0.001).There was no difference in reporting of dizziness (χ2, P=0.007 [Bonferonni-corrected threshold of 0.003]), swelling of hands and/or feet (χ2, P=0.028 [Bonferonni-corrected threshold of 0.003]), difficulty urinating (χ2, P=0.049 [Bonferonni-corrected threshold of 0.003]), shortness of breath (χ2, P=0.103), or hallucinations (χ2, P=0.076).

ence in reporting of dizziness (χ2, P=0.007 [Bonferonni-corrected threshold of 0.003]), swelling of hands and/or feet (χ2, P=0.028 [Bonferonni-corrected threshold of 0.003]), difficulty urinating (χ2, P=0.049 [Bonferonni-corrected threshold of 0.003]), shortness of breath (χ2, P=0.103), or hallucinations (χ2, P=0.076). Impact of adverse childhood experiences on analgesic side-effects. CI, confidence interval; NR, not reported; OR, odds ratio.

ence in reporting of dizziness (χ2, P=0.007 [Bonferonni-corrected threshold of 0.003]), swelling of hands and/or feet (χ2, P=0.028 [Bonferonni-corrected threshold of 0.003]), difficulty urinating (χ2, P=0.049 [Bonferonni-corrected threshold of 0.003]), shortness of breath (χ2, P=0.103), or hallucinations (χ2, P=0.076). Impact of adverse childhood experiences on analgesic side-effects. CI, confidence interval; NR, not reported; OR, odds ratio. Forty-five studies (involving 104 550 participants) investigated outcomes relating to substance misuse (Table 3).69,70,77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119 Of these, 44 studies examined opioids, and six examined sedatives.Table 3Impact of adverse childhood experiences on substance misuse. ACE, adverse childhood experience; CI, confidence interval; HR, hazard ratio; OR, odds ratio.Table 3Author and yrParticipant populationSample sizeExposureOutcomeOutcome toolRelationshipAfifi 2012National Epidemiological Study of Alcohol and Related Conditions (NESARC), wave 2: a nationwide nationally representative household survey of adults34 653Childhood maltreatmentSedative substance use disorderOpioid (unspecified) substance use disorderHeroin substance use disorderStructured interview using questions from the Alcohol Use Disorder and Associated Disabilities Interview Schedule–DSM IV (AUDADIS-IV)In males and females, physical abuse, sexual abuse, emotional abuse, physical neglect, and emotional neglect were associated with sedative substance use disorder (logistic regression; full results not reported here).In males and females, physical abuse, sexual abuse, emotional abuse, physical neglect, and emotional neglect were associated with opioid substance use disorder (logistic regression; full results not reported here).In males and females, physical abuse, sexual abuse, emotional abuse, and emotional neglect were associated with heroin substance use disorder (logistic regression; full results not reported here).

emotional neglect were associated with opioid substance use disorder (logistic regression; full results not reported here).In males and females, physical abuse, sexual abuse, emotional abuse, and emotional neglect were associated with heroin substance use disorder (logistic regression; full results not reported here). In females, but not in males, physical neglect was associated with heroin substance use disorder (logistic regression; full results not reported here).Ararso 2021National Longitudinal Study of Adolescent to Adult Health (Add Health), waves 1, 3, and 4: a nationally representative cohort of adolescents aged 12–19 yr recruited from schools and followed up for more than 20 yr12 288Child abuse and homelessnessPrescription opioid misuseStructured interview asking about prescription opioid useAt wave 3, compared with those with no history of abuse or homelessness, participants with abuse only (logistic regression, OR 1.41, 95% CI 1.17–1.69), homelessness only (logistic regression, OR 1.50, 95% CI 1.00–2.27), and abuse + homelessness (logistic regression, OR 3.23, 95% CI 2.11–4.93) had a higher odds of prescription opioid misuse.At wave 4, compared with those with no history of abuse or homelessness, participants with abuse only had a higher odds of prescription opioid misuse (logistic regression, OR 1.67, 95% CI 1.23–2.27).At wave 4, compared with those with no history of abuse or homelessness, participants with homelessness only (logistic regression, OR 1.58, 95% CI 0.84–2.94) and abuse + homelessness (logistic regression, OR 1.91, 95% CI 0.84–4.32) had no difference in the odds of prescription opioid misuse.Austin 2018bNational Longitudinal Study of Adolescent to Adult Health (Add Health), waves 1 and 3: a nationally-representative cohort of adolescents aged 12–19 yr recruited from schools and followed up for more than 20 yr14 322Childhood abuse and neglectPrescription opioid misuseStructured interview asking about prescription opioid use since wave 1Childhood abuse and neglect was associated with prescription opioid misuse (structural equation modelling, β=0.232, SE 0.022, P<0.001).Browne 1998Adults attending the outpatient National Drug Treatment Centre52Sexual abuse and physical abuseAge of first opiate useDuration of opiate useSemistructured interviewSexual abuse was associated with a younger age of first opiate use (t-test, P=0.01).Sexual abuse was not associated with duration of opiate use (t-test; data not reported).Physical abuse was not associated with age

tre52Sexual abuse and physical abuseAge of first opiate useDuration of opiate useSemistructured interviewSexual abuse was associated with a younger age of first opiate use (t-test, P=0.01).Sexual abuse was not associated with duration of opiate use (t-test; data not reported).Physical abuse was not associated with age of first opiate abuse or duration of opiate use (t-test; data not reported).Carr 2023Adults with opioid use disorder attending an outpatient addiction treatment centre171Childhood adversityOpioid addiction severitySelf-completed questionnaire using the Recognizing Addictive Disorder (RAD) scoreHigher ACE count was associated with higher opioid addiction severity score (linear regression, β=1.70, 95% CI 0.26–3.13).Conroy 2009Cases: adults attending opioid pharmacotherapy clinicsControls: adults recruited through public advertisingCases: 967Controls: 346Childhood maltreatmentOpioid-dependence (unspecified)Cases determined through enrolment in an opioid pharmacotherapy programmeIn males, physical abuse was associated with being a case (logistic regression, OR 1.6, 95% CI 1.0–2.4).In males, sexual abuse (logistic regression, OR 0.7, 95% CI 0.5–1.1), emotional abuse (logistic regression, OR 1.2, 95% CI 0.8–1.9), and neglect (logistic regression, OR 1.2, 95% CI 0.8–1.8) were not associated with being a case.In females, physical abuse (logistic regression, OR 1.1, 95% CI 0.7–1.7), sexual abuse (logistic regression, OR 1.2, 95% CI 0.8–2.0), emotional abuse (logistic regression, OR 0.9, 95% CI 0.6–1.4), and neglect (logistic regression, OR 0.7, 95% CI 0.4–1.3) were not associated with being a case.Davis 2022Adolescents recruited from 16 middle schools and followed up annually for 12 yr, waves 8–122880VictimisationLatency to opioid misuse (prescription and illicit)Self-completed online questionnaire with questions on past-year heroin use and past-year prescription narcotic medication useCompared with the ‘low all’ ACE class, all classes were associated with a shorter latency to opioid misuse: ‘sexual abuse and indirect violence + high trauma characteristics’ (discrete time survival mixture analysis, HR 1.98, 95% CI 1.09–3.63), ‘high all + high trauma characteristics’ (discrete time survival mixture analysis, HR 2.00, 95% CI 1.32–3.03), and ‘chronic emotional abuse + trusted perpetrator’ (discrete time survival mixture analysis, HR 2.02, 95% CI 1.42–2.87).Derefinko 2019Adults attending an outpatient opioid use disorder clinic87ACEsOpioid relapsePositive result in patient s

eristics’ (discrete time survival mixture analysis, HR 2.00, 95% CI 1.32–3.03), and ‘chronic emotional abuse + trusted perpetrator’ (discrete time survival mixture analysis, HR 2.02, 95% CI 1.42–2.87).Derefinko 2019Adults attending an outpatient opioid use disorder clinic87ACEsOpioid relapsePositive result in patient s elf-report, urine drug screen, or prescription drug database resultHigher ACE count was associated with greater odds of opioid relapse (logistic regression, OR 1.17, 95% CI 1.05–1.30).Dunn 2022Adults with a history of heroin or prescription opioid use recruited online through Amazon Mechanical Turk310Early life traumaOpioid use disorder (prescription and illicit)Opioid use disorder severity (prescription and illicit)Opioid withdrawal severitySelf-completed online questionnaire using questions from the DSM-V checklist for opioid use disorderTotal trauma score (t-test, P<0.001), general trauma score (t-test, P<0.001), physical trauma score (t-test, P=0.013), emotional trauma score (t-test, P=0.015), and sexual trauma score (t-test, P<0.001) were associated with opioid use disorder.Total trauma score (χ2, P<0.001), general trauma score (χ2, P<0.001), physical trauma score (χ2, P<0.001), emotional trauma score (χ2, P=0.002), and sexual trauma score (χ2, P<0.001) were associated with opioid use disorder severity.Total trauma score (Pearson's correlation, r=0.240, P<0.001), general trauma score (Pearson's correlation, r=0.238, P<0.001), physical trauma score (Pearson's correlation, r=0.151, P=0.008), emotional trauma score (Pearson's correlation, r=0.172, P=0.002), and sexual trauma score (Pearson's correlation, r=0.240, P=0.003) were associated with opioid withdrawal severity.Eaves 2021Adults incarcerated at a county detention facility96ACEsRecent heroin useRecent other opiate useSelf-completed questionnaire asking about heroin/opiate use in the last 30 days before admission to jailACE count was associated with recent other opiate use (logistic regression, OR 1.26, 95% CI 1.00–1.61).ACE count was not associated with recent heroin use (logistic regression, OR 1.17, 95% CI 0.95–1.44).Elhammady 2014Cases: adults with opioid dependence syndrome, heroin dependence, or methadone prescription attending an outpatient addiction clinicControls: friends and family members of casesCases: 120Controls: 100Child sexual abuse, child physical abuse, and parental history of drug/alcohol misuseSeverity of opioid dependenceSemistructured interview using the Severity of Dependence Scale

ndence, or methadone prescription attending an outpatient addiction clinicControls: friends and family members of casesCases: 120Controls: 100Child sexual abuse, child physical abuse, and parental history of drug/alcohol misuseSeverity of opioid dependenceSemistructured interview using the Severity of Dependence Scale and the Leeds Dependence QuestionnaireSexual abuse (Pearson's correlation, r=0.185, P=0.043), physical abuse (Pearson's correlation, r=0.306, P=0.001), and parent drug/alcohol misuse (Pearson's correlation, r=0.245, P=0.007) were associated with Severity of Dependence Scale score.Sexual abuse (Pearson's correlation, r=0.180, p=0.049), physical abuse (Pearson's correlation, r=0.231, p=0.011), and parent drug/alcohol misuse (Pearson's correlation, r=0.285, P=0.002) were associated with Leeds Dependence Questionnaire score.Fortson 2021Adults attending a university1402ACEsRisk for opioid misuseSelf-completed online questionnaire using questions from the Screener and Opioid Assessment for Patients with Pain (SOAPP)ACE count was associated with greater odds of being high risk for opioid misuse (logistic regression; 0 ACEs: referent; 1–3 ACEs: OR 1.96, 95% CI 1.46–2.65; >4 ACEs: OR 2.93, 95% CI 1.95–4.39).Fuss 2023Vape shop Advertising, Place characteristics, and Effects Surveillance study (VAPES): adults aged 18–34 yr recruited online through Facebook and Reddit and followed up for 1 yr2975ACEsLifetime opioid useSelf-completed online questionnaire asking about lifetime opioid use, with participants categorised into (1) no opioid use, (2) prescription opioid use, (3) nonmedical prescription opioid use, and (4) heroin use groupsThose in the no-opioid group had a lower ACE count than those in the prescription opioid group (logistic regression, OR 0.92, 95% CI 0.87–0.97).

about lifetime opioid use, with participants categorised into (1) no opioid use, (2) prescription opioid use, (3) nonmedical prescription opioid use, and (4) heroin use groupsThose in the no-opioid group had a lower ACE count than those in the prescription opioid group (logistic regression, OR 0.92, 95% CI 0.87–0.97). Those in the heroin group had a higher ACE count than those in the prescription opioid group (logistic regression, OR 1.24, 95% CI 1.11–1.39).There was no difference in ACE counts between the nonmedical prescription opioid group and the prescription opioid group (logistic regression, OR 1.03, 95% CI 0.94–1.14).Garami 2019Cases: adults with a history of opiate addiction attending an opioid treatment programme.Controls: adults recruited via word of mouth from the communityCases: 36Controls: 33Childhood traumaOpiate addiction (unspecified)Likelihood of being a case (i.e.

id group (logistic regression, OR 1.03, 95% CI 0.94–1.14).Garami 2019Cases: adults with a history of opiate addiction attending an opioid treatment programme.Controls: adults recruited via word of mouth from the communityCases: 36Controls: 33Childhood traumaOpiate addiction (unspecified)Likelihood of being a case (i.e. having a history of opiate addiction and being treated at the clinic)Higher childhood trauma questionnaire total score was associated with being a case (logistic regression, OR 1.11, 95% CI 1.04–1.18).Garland 2019Women with chronic pain and regular opioid analgesic use in the last 90 days attending primary care and specialty pain clinics36ACEsOpioid use disorder severity (prescription)Negative emotional cue-elicited opioid cravingStructured interview using the Mini-International Neuropsychiatric Interview (MINI), followed by an experimental procedure in which participants were asked to rate opioid craving before and after exposure to negative affective imagesACE count was associated with opioid use disorder severity (linear regression, β=0.30, no 95% CI given, P=0.04).ACE count was associated with cue-elicited craving scores (linear regression, β=0.53, no 95% CI given, P=0.002).Guarino 2021Adults aged 18–29 yr with prescription opioid or heroin use in the preceding 30 days539ACEsAge at initiation of nonmedical prescription opioid useAge at initiation of snorting of nonmedical prescription opioidsAge at initiation of injection of nonmedical prescription opioidsAge at initiation of heroin useAge at initiation of injection of heroinStructured interview asking about age of initiation of various opioid use behaviours, with younger age defined as lowest quartile and older age was defined as highest quartileHigher ACE count was associated with younger age at initiation of nonmedical prescription opioid use (logistic regression, OR 1.23, 95% CI 1.12–1.43), younger age at initiation of snorting of non-medication prescription opioids (logistic regression, OR 1.16, 95% CI 1.05–1.28), younger age at initiation of heroin use (logistic regression, OR 1.17, 95% CI 1.03–1.32), and younger age at initiation of injection of heroin (logistic regression, OR 1.13, 95% CI 1.02–1.25).ACE count was not associated with younger age at initiation of injection of nonmedication prescription opioids (logistic regression, OR 1.12, 95% CI 0.97–1.30).Heffernan 2000Adults attending an inpatient psychiatric hospital763Childhood abuseOpiate useStructured interview asking about opiate useChildhood

CI 1.02–1.25).ACE count was not associated with younger age at initiation of injection of nonmedication prescription opioids (logistic regression, OR 1.12, 95% CI 0.97–1.30).Heffernan 2000Adults attending an inpatient psychiatric hospital763Childhood abuseOpiate useStructured interview asking about opiate useChildhood physical and/or sexual abuse was associated with opiate use (logistic regression, OR 2.68, 95% CI 2.27–3.10).Khoury 2010Adults attending outpatient general medical and OB/GYN clinics587Childhood traumatic experiencesLifetime heroin/opiate usePast 30 days heroin/opiate useStructured interview using the Kreek–McHugh–Schluger–Kellogg scalePhysical abuse score was associated with lifetime heroin/opiate use (Pearson's correlation, r=0.123, P<0.01) and past 30 days heroin/opiate use (Pearson's correlation, r=0.251, P<0.001).Sexual abuse score was not associated with lifetime heroin/opiate use (Pearson's correlation, r=0.038, P>0.01) or past 30 days heroin/opiate use (Pearson's correlation, r=–0.011, P>0.01).Emotional abuse score was not associated with lifetime heroin/opiate use (Pearson's correlation, r=0.045, P>0.01) or past 30 days heroin/opiate use (Pearson's correlation, r=0.144, P>0.01).Kors 2022Pregnant women (second trimester or later) attending a high-risk pregnancy clinic93Childhood maltreatmentOpioid use during pregnancy (prescription and illicit)Urine analysis for prescribed or nonprescribed opioidsSexual abuse was associated with opioid use during pregnancy (logistic regression, OR 3.17, 95% CI not provided, P=0.03).Physical abuse (logistic regression, OR 1.21, 95% CI not provided, P=0.73), neglect (logistic regression, OR 1.24, 95% CI not provided, P=0.44), and emotional abuse (logistic regression, OR 1.10, 95% CI not provided, P=0.72) were not associated with opioid use during pregnancy.Kumar 2016Adults attending an outpatient buprenorphine treatment programme113Childhood traumaDropping out of a buprenorphine treatment programmePhase advancement in a buprenorphine treatment programmeAbsence >8 days during the first 90 days of the programmeAdvancement from weekly to biweekly visits during the first 90 days of the programmeModerate/severe physical neglect score (logistic regression, OR 4.84, 95% CI 1.33–17.65) and moderate/severe emotional neglect score (logistic regression, OR 8.27, 95% CI 1.51–45.39) were associated with greater odds of dropping out of the buprenorphine treatment programme.Moderate/severe physical abuse, emotional abuse, and sexual abus

ysical neglect score (logistic regression, OR 4.84, 95% CI 1.33–17.65) and moderate/severe emotional neglect score (logistic regression, OR 8.27, 95% CI 1.51–45.39) were associated with greater odds of dropping out of the buprenorphine treatment programme.Moderate/severe physical abuse, emotional abuse, and sexual abus e were not associated with dropping out of the buprenorphine treatment program (χ2; data not provided).Moderate/severe physical abuse, emotional abuse, sexual abuse, physical neglect, and emotional neglect were not associated with phase advancement in the buprenorphine treatment (χ2; data not provided).Larance 2018Adults with a history of heroin dependence receiving opioid agonist treatment1149Childhood maltreatmentTransition from heroin use to dependenceTransition from heroin dependence to treatment seekingStructured interview using questions from the Semi-Structured Assessment of the Genetics of Alcoholism–Australia (SSAGA-OZ)Childhood maltreatment count was associated with higher odds of transitioning from heroin use to dependence (multivariate discrete-time survival analysis, OR 1.11, 95% CI 1.04–1.18).Childhood maltreatment count was not associated with higher odds of transitioning from heroin dependence to treatment seeking (multivariate discrete-time survival analysis, OR 1.01, 95% CI 0.95–1.07).Lynskey 2006The Australian Twin Study: twins recruited from schools in Australia and followed up through adulthood6265Childhood sexual abuse; childhood physical abuseOpioid + sedative abuse/dependenceStructured telephone interview using the modified Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA) tool, with subsequent latent class analysis identifying five substance use classesCompared with the ‘low use’ class, the ‘opioid + sedative’ class had higher odds of physical abuse (logistic regression, OR 3.8, 95% CI 2.8–5.3) and sexual abuse (logistic regression, OR 4.0, 95% CI 2.6–6.0).Martin 2023Adults with substance use disorder recruited via media advertising565ACEsOpioid useStructured interview with questions on history of opioid use.Compared with people with tobacco use only, people with opioid use had higher odds of reporting high ACE counts (ordinal logistic regression, OR 2.21, 95% CI 1.27–3.86).Compared with people with tobacco use only, people with opioid use had higher odds of reporting household dysfunction (logistic regression, OR 2.67, 95% CI 1.30–5.49).There was no association between people with tobacco use only and people with opioid

ACE counts (ordinal logistic regression, OR 2.21, 95% CI 1.27–3.86).Compared with people with tobacco use only, people with opioid use had higher odds of reporting household dysfunction (logistic regression, OR 2.67, 95% CI 1.30–5.49).There was no association between people with tobacco use only and people with opioid use when reporting emotional/physical abuse (logistic regression, OR 1.46, 95% CI 0.79–2.69), sexual abuse (logistic regression, OR 1.54, 95% CI 0.74–3.20), or neglect (logistic regression, OR 1.89, 95% CI 0.95–3.75).McDonagh 2023Adults attending substance use services104ACEsAge of first opiate useStructured interview using the Opiate Treatment Index (OTI).Higher ACE count was associated with a younger age of first opiate use (Spearman correlation, r=–0.278, P<0.01).Meadows 2023Adults recruited through public advertising107ACEsAge of initiation of nonmedical prescription opioid useAge of initiation of non-medical prescription benzodiazepine useSelf-completed questionnaire with a drug history screening tool followed by an ‘ … an in-depth questionnaire to evaluate use patterns, including age of initiation … ‘.Higher ACE count was associated with a younger age of initiation of nonmedication prescription opioid use (Cox proportional hazard regression, OR 1.22, 95% CI 1.05–1.43).Higher ACE count was not associated with a younger age of initiation of nonmedical prescription benzodiazepine Cox proportional hazard regression, OR 1.03, 95% CI 0.90–1.19).Merrick 2020Behavioural Risk Factor Surveillance System (BRFSS), Montana and Florida populations: adults responding to a telephone surveyMontana: 8726Florida: 27 545ACEsPrescription opioid misuseMontana: self-completed questionnaire asking about use of prescription opioids at a higher frequency/dose than prescribed or without a prescription.Florida: self-completed questionnaire asking about use of prescription opioids without a prescription or for the experience/feelingMontana: Higher ACE count was associated with higher odds of using prescribed opioids at a higher frequency/dose than prescribed (logistic regression; 0 ACEs: referent; 1–2 ACEs: OR 2.82, 95% CI 1.06–7.46; ≥3 ACEs: OR 4.73, 95% CI 1.79–12.51) and using opioids without a prescription (logistic regression; 0 ACEs: referent; 1–2 ACEs: OR 3.53, 95% CI 2.11–5.92; ≥3 ACEs: OR 7.13, 95% CI 4.32–11.77).Florida: Higher ACE count was associated with higher odds of using prescription opioids without a prescription or for the experience/feeling (logistic regression; 0

and using opioids without a prescription (logistic regression; 0 ACEs: referent; 1–2 ACEs: OR 3.53, 95% CI 2.11–5.92; ≥3 ACEs: OR 7.13, 95% CI 4.32–11.77).Florida: Higher ACE count was associated with higher odds of using prescription opioids without a prescription or for the experience/feeling (logistic regression; 0 ACEs: referent; 1–2 ACEs: OR 1.55, 95% CI 0.98–2.45; ≥3 ACEs: OR 3.08, 95% CI 1.65–5.75).Mirhashem 2017Adults with a history of opioid use >1 yr84Childhood maltreatmentSubstance related problemsSelf-completed questionnaire using the Short Inventory of Problems-Revised (SIPS-R) tool, which measures the negative effects of drug usePhysical neglect score (Pearson's correlation, r=0.06, P>0.05), emotional neglect score (Pearson's correlation, r=–0.02, P>0.05), sexual abuse score (Pearson's correlation, r=0.03, P>0.05), physical abuse score (Pearson's correlation, r=0.05, P>0.05), and emotional abuse score (Pearson's correlation, r=0.17, P>0.05) were not associated with higher substance related problems.Myers 2014National Epidemiological Study of Alcohol and Related Conditions (NESARC), wave 2: a nationwide nationally representative household survey of adults34 653Childhood adversityDisordered opiate use in the last 12 months (unspecified)Structured interview using questions from the Alcohol Use Disorder and Associated Disabilities Interview Schedule–DSM IV (AUDADIS-IV)Childhood adversity count was associated with greater odds of disordered opiate use in the last 12 months (logistic regression; 0 ACE: referent; 1–2 ACE: OR 15.8, 95% CI 1.72–145.5; ≥3 ACE: OR 19.5, 95% 1.82–208.1).Naqavi 2011Cases: opiate dependent adults attending drug treatment centres.Controls: adults who visited neighbourhood clinics for reasons other than addiction treatmentCases: 212Controls: 216Childhood maltreatmentOpiate dependencyLikelihood of being a case (i.e.

15.8, 95% CI 1.72–145.5; ≥3 ACE: OR 19.5, 95% 1.82–208.1).Naqavi 2011Cases: opiate dependent adults attending drug treatment centres.Controls: adults who visited neighbourhood clinics for reasons other than addiction treatmentCases: 212Controls: 216Childhood maltreatmentOpiate dependencyLikelihood of being a case (i.e. having a history of opiate dependency and being treated at the clinic).Emotional abuse (logistic regression, OR 5.06, 95% CI 2.30–11.18), sexual abuse (logistic regression, OR 1.89, 95% CI 1.04–3.43), and physical neglect (logistic regression, OR 1.96, 95% CI 1.21–3.18) were associated with being a case.Physical abuse and emotional neglect were not associated with being a case (logistic regression; data not provided).Nelson 2006The Australian Twin Study: twins recruited from schools in Australia and followed up through adulthood6050Childhood sexual abuseOpioid abuse/dependenceSedative abuse/dependenceStructured telephone interview using the modified Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA) toolSexual abuse was associated with a high risk for opioid abuse/dependence (Cox proportional hazard regression, HR 2.60, 95% CI 1.60–4.22) and sedative abuse/dependence (Cox proportional hazard regression, HR 2.61, 95% CI 1.43–4.78).When looking at same-sex twins with different sexual abuse histories, sexual abuse was associated with a high risk of opioid abuse/dependence (conditional logistic regression, OR 6.50, 95% CI 1.47–28.80).When looking at same-sex twins with different sexual abuse histories, sexual abuse was not associated with sedative abuse/dependence.Onu 2021Adults attending a university, recruited from local student accommodation and hostels301ACEsTramadol abuseSelf-completed questionnaire using questions from the Tramadol Abuse ScaleHigher ACE count was associated with a higher tramadol abuse score (Pearson's correlation, r=0.33, P<0.001).Pakdaman 2021Adults attending a university3899ACEsPrescription opioid misusePrescription sedative misuseSelf-completed online questionnaire asking about use of prescription drugs without a prescription (response classes were antidepressants, painkillers, sedatives, and stimulants)Higher ACE count was associated with higher odds of prescription opiate misuse (logistic regression, OR 1.21, 95% CI 1.13–1.30) and prescription sedative misuse (logistic regression, OR 1.33, 95% CI 1.22–1.44).Quinn 2016National Longitudinal Study of Adolescent to Adult Health (Add Health), waves 3, and 4: a nationally repres

ACE count was associated with higher odds of prescription opiate misuse (logistic regression, OR 1.21, 95% CI 1.13–1.30) and prescription sedative misuse (logistic regression, OR 1.33, 95% CI 1.22–1.44).Quinn 2016National Longitudinal Study of Adolescent to Adult Health (Add Health), waves 3, and 4: a nationally repres entative cohort of adolescents aged 12–19 yr recruited from schools and followed up for more than 20 yr12 288Childhood traumaPrescription pain reliever misuseStructured interview asking about prescription pain reliever useAt wave 3, ACE count was associated with prescription pain reliever misuse (logistic regression; 0 ACEs: referent; 1 ACE: OR 1.34, 95% CI 1.14–1.58; 2 ACEs: OR 1.58, 95% CI 1.25–1.99; 3 ACEs: OR 1.70, 95% CI 1.29–2.25; 4 ACEs: OR 2.17, 95% CI 1.49–3.15; ≥5 ACEs: OR 1.79, 95% CI 1.05–3.07).At wave 4, ACE count was associated with prescription pain reliever misuse (logistic regression; 0 ACEs: referent; 1 ACE: OR 1.46, 95% CI 1.12–1.91; 2 ACEs: OR 1.71, 95% CI 1.23–2.36; 3 ACEs: OR 2.16, 95% CI 1.43–3.26; 4 ACEs: OR 2.70, 95% CI 1.62–4.52; ≥5 ACEs: OR 3.09, 95% CI 1.52–6.30).Santo Jr 2022Pain and Opioids in Treatment (POINT) study: adults prescribed regulated opioids for chronic noncancer pain recruited from community pharmacies1514Childhood traumaOpioid use disorder (prescription and illicit)Structured interview following the Composite International Diagnostic Interview (CIDI) v3 asking about opioid use in the past 12 monthsCompared with the ‘low exposure’ ACE class, the ‘emotional and sexual abuse’ class (logistic regression, OR 1.75, 95% CI 1.25–2.34) and the ‘high all’ class (logistic regression, OR 2.75, 95% CI 2.04–3.70) were associated with opioid use disorder.Sartor 2014Adults with opioid dependence attending for an alternative research study3513Childhood risk factorsTransition time from first opioid use to opioid dependenceStructured interview based on questions covering ‘ … age at first use … ’ and ‘ … age at dependence onset, defined as the age at which full dependence criteria were met … ’Severe physical abuse was associated with quicker transition time (ordinal logistic regression, OR 1.50, 95% CI 1.17–1.91).Other ACEs were not associated with quicker transition (data not reported).Stein 2017Adults attending an inpatient opioid detoxification programme457ACEsAge of initiating opioid useRecent intravenous drug useStructured interview asking about lifetime opioid use and intravenous use within the last monthHigher ACE count was associa

were not associated with quicker transition (data not reported).Stein 2017Adults attending an inpatient opioid detoxification programme457ACEsAge of initiating opioid useRecent intravenous drug useStructured interview asking about lifetime opioid use and intravenous use within the last monthHigher ACE count was associa ted with younger age of opioid initiation (linear regression, β=–0.50, 95% CI –0.70 to –0.29).Higher ACE count was associated with greater odds of recent intravenous drug use (logistic regression, OR 1.11, 95% CI 1.02–1.20).Tang 2020National Epidemiological Study of Alcohol and Related Conditions (NESARC), wave 3: a nationwide nationally representative household survey of adults36 309ACEsPast-year prescription opioid misuseLifetime prescription opioid misuseEarly-onset status of prescription opioid misusePast-year opioid use disorderLifetime opioid use disorderStructured interview with questions on painkiller use and from the Alcohol Use Disorder and Associated Disabilities Interview Schedule-5 (AUDADIS-5), with ‘early onset’ defined as ≤17Higher ACE count was associated with past-year prescription opioid misuse (logistic regression, OR 1.03, 95% CI 1.00–1.06), lifetime prescription opioid misuse (logistic regression, OR 1.04, 95% CI 1.02–1.06), early-onset status of prescription opioid misuse (logistic regression, OR 1.07, 95% CI 1.03–1.10), and lifetime opioid use disorder (logistic regression, OR 1.06, 95% CI 1.02–1.10).ACE count was not associated with past-year opioid use disorder (logistic regression, OR 1.05, 95% CI 0.99–1.10).Taplin 2014Adults with a history of opioid injection who had previously participated in the North American Opiate Medication Initiative (NAOMI)87Childhood traumaAge of first injection of opioidsStructured interview using ‘ … the Family History section of the European Addiction Severity Index (EuropASI) … ’All forms of childhood trauma were associated with a younger age of first injection of opioids (regression models, unclear adjustments): emotional abuse, 1.19% decrease in age (95% CI 0.06–2.32); physical abuse, 1.44% decrease in age (95% CI 0.20–2.67); sexual abuse, 1.31% decrease in age (95% CI 0.33–2.29); emotional neglect, 1.69% decrease in age (95% CI 0.48–2.88); and physical neglect, 2.07% decrease in age (95% CI 0.64–3.48).Thiesset 2023Adults with a history of opioid use disorder identified through the University of Utah's health system's electronic data warehouse124ACEsAcknowledging a history of opioid use disorderSelf-comp

neglect, 1.69% decrease in age (95% CI 0.48–2.88); and physical neglect, 2.07% decrease in age (95% CI 0.64–3.48).Thiesset 2023Adults with a history of opioid use disorder identified through the University of Utah's health system's electronic data warehouse124ACEsAcknowledging a history of opioid use disorderSelf-comp leted online questionnaire asking about opioid use disorder, including ‘ … a history of addiction … ’ and ‘ … a history of using opioids prescribed to another person … ’Exposure to ≥4 ACEs was associated with acknowledging a history of opioid use disorder on the survey (χ2, P=0.04).Tomassi 2017Adults aged 18–54 yr with first presentation of psychosis attending community mental health centres345Childhood traumaLifetime use of heroinStructured interview using the Cannabis Experiences Questionnaire (assesses cannabis, cocaine, and heroin)Severe sexual abuse (logistic regression, OR 12.6, 95% CI 2.7–58.1) and severe physical abuse (logistic regression, OR 3.7, 95% CI 1.2–11.3) were associated with lifetime heroin use.There was no difference between the trauma and no-trauma groups and lifetime heroin use (χ2, P=0.74).Vogel 2011Adults with opioid dependence attending outpatient clinics193Traumatic childhood experiencesProlonged benzodiazepine useLifetime benzodiazepine useSelf-completed questionnaire asking questions on prolonged benzodiazepine use (>2 months) and any lifetime benzodiazepine useHigher childhood trauma questionnaire score was associated with prolonged benzodiazepine use (logistic regression, OR 1.53, 95% CI 1.11–2.11).There was no association between childhood trauma questionnaire score and lifetime benzodiazepine use (Mann-Whitney-U test, P=0.21).Wang 2021National Epidemiological Study of Alcohol and Related Conditions (NESARC), wave 3: a nationwide nationally representative household survey of adults33 613ACEsPrescription opioid misuseStructured interview asking about nonmedical use of prescription opioids in the last 12 monthsHigher ACE count was associated with greater odds of prescription opioid misuse in the last year (generalised structural equation modelling, OR 1.09, 95% CI 1.05–1.13).Widom 2006Children <12 yr with court-substantiated abuse/neglect between 1967 and 1971 and matched nonabused controls, followed up for more than 20 yr892Child abuse and/or neglectLifetime heroin usePast-year heroin useStructured interview asking about drug use patterns, including lifetime and past-year useAbuse/neglect status was not associated with lifetime hero

iated abuse/neglect between 1967 and 1971 and matched nonabused controls, followed up for more than 20 yr892Child abuse and/or neglectLifetime heroin usePast-year heroin useStructured interview asking about drug use patterns, including lifetime and past-year useAbuse/neglect status was not associated with lifetime hero in use (logistic regression, OR 1.29, 95% CI 0.67–2.50) or with past-year heroin use (logistic regression, OR 1.60, 95% CI 0.14–17.69).Williams 2020Adults with a history of at least one type of interpersonal trauma (intimate partner violence, sexual assault, or ACEs) recruited through public advertising230ACEsOpioid misuse (prescription and illicit)Self-completed online questionnaire with questions on past-year heroin use, past-year prescription pain medication use without a prescription, and the PROMIS1 Prescription Pain Medication Misuse 7a ScaleACE count was not associated with opioid misuse (logistic regression, OR 1.10, 95% CI 0.99–1.22).Williams 2021Adults with a history of at least one type of interpersonal trauma (intimate partner violence, sexual assault, or ACEs) recruited through public advertising235ACEsOpioid misuse (prescription and illicit)Self-completed online questionnaire with questions on past-year heroin use, past-year prescription pain medication use without a prescription, and the PROMIS1 Prescription Pain Medication Misuse 7a ScaleHigher ACE count was associated with greater odds of opioid misuse (logistic regression, OR 1.32, 95% CI 1.16–1.49).

)Self-completed online questionnaire with questions on past-year heroin use, past-year prescription pain medication use without a prescription, and the PROMIS1 Prescription Pain Medication Misuse 7a ScaleHigher ACE count was associated with greater odds of opioid misuse (logistic regression, OR 1.32, 95% CI 1.16–1.49). Impact of adverse childhood experiences on substance misuse. ACE, adverse childhood experience; CI, confidence interval; HR, hazard ratio; OR, odds ratio.

)Self-completed online questionnaire with questions on past-year heroin use, past-year prescription pain medication use without a prescription, and the PROMIS1 Prescription Pain Medication Misuse 7a ScaleHigher ACE count was associated with greater odds of opioid misuse (logistic regression, OR 1.32, 95% CI 1.16–1.49). Impact of adverse childhood experiences on substance misuse. ACE, adverse childhood experience; CI, confidence interval; HR, hazard ratio; OR, odds ratio. ACE exposure was associated with prescription opioid misuse in all seven of the included studies that examined this relationship (7/7 studies), illicit opioid misuse in 2/5 studies, and unspecified opioid misuse in 12/15 studies. A number of the 45 included studies reported outcomes relating to different aspects of opioid misuse development and progression. Compared with those who had experienced no/low ACE exposure, high ACE exposure was associated with a higher risk for opioid misuse in 1/1 study, an earlier age of first opioid misuse in 5/6 studies, an earlier age of first opioid injection in 2/2 studies, a shorter latency from opioid use to misuse in 2/3 studies, a higher severity of opioid use disorder in 4/4 studies, a higher severity of opioid withdrawal in 1/1 study, and a greater likelihood of opioid relapse in 1/1 study. Compared with no/low ACE exposure, high ACE exposure was not associated with the duration of opioid misuse in 1/1 study, and had an equivocal association with engagement within a rehabilitation programme in 1/1 study. Compared with lower ACE exposure, high ACE exposure was associated with sedative misuse in 4/5 studies but was not associated with an earlier age of first sedative use in 1/1 study.

e duration of opioid misuse in 1/1 study, and had an equivocal association with engagement within a rehabilitation programme in 1/1 study. Compared with lower ACE exposure, high ACE exposure was associated with sedative misuse in 4/5 studies but was not associated with an earlier age of first sedative use in 1/1 study. Two studies (involving 658 participants) investigated lifetime incidence of drug (including opioid) overdose (Table 4).112,120 In both studies, a higher ACE count was associated with a greater likelihood of lifetime drug overdose.Table 4Impact of ACEs on lifetime drug overdose. ACE, adverse childhood experience; CI, confidence interval; OR, odds ratio; RR, relative risk.Table 4Author and yrParticipant populationSample sizeExposureOutcomeOutcome toolRelationshipEl-Bassel 2019Women participating in Project PACT (a couple-focused randomised clinical trial of an HIV prevention intervention for men undergoing community corrections and their female intimate partners) who reported lifetime use of illicit drugs201Childhood adversityLifetime overdoseStructured interview asking about lifetime overdose (defined as loss of consciousness) of heroin, opioid pain relievers, or tranquiliserHigher childhood adversity count was associated with greater risk of lifetime overdose (generalised linear model, RR 1.3, 95% CI 1.1–1.6).Stein 2017Adults attending an inpatient opioid detoxification programme457ACEsLifetime overdoseStructured interview asking about lifetime overdoseHigher ACE count was associated with greater odds of lifetime overdose (logistic regression, OR 1.10, 95% CI 1.02–1.20).

se (generalised linear model, RR 1.3, 95% CI 1.1–1.6).Stein 2017Adults attending an inpatient opioid detoxification programme457ACEsLifetime overdoseStructured interview asking about lifetime overdoseHigher ACE count was associated with greater odds of lifetime overdose (logistic regression, OR 1.10, 95% CI 1.02–1.20). Impact of ACEs on lifetime drug overdose. ACE, adverse childhood experience; CI, confidence interval; OR, odds ratio; RR, relative risk.

se (generalised linear model, RR 1.3, 95% CI 1.1–1.6).Stein 2017Adults attending an inpatient opioid detoxification programme457ACEsLifetime overdoseStructured interview asking about lifetime overdoseHigher ACE count was associated with greater odds of lifetime overdose (logistic regression, OR 1.10, 95% CI 1.02–1.20). Impact of ACEs on lifetime drug overdose. ACE, adverse childhood experience; CI, confidence interval; OR, odds ratio; RR, relative risk. Four studies (involving 353 participants) investigated endogenous pain signalling pathways (Table 5).121, 122, 123, 124 Zehetmeier and colleagues124 performed quantitative sensory testing and found no association between ACE exposure and pain thresholds in various sensory modalities. Hill and colleagues122 found no association between ACE exposure and μ-opioid receptor binding in two regions of the brain (nucleus accumbens and amygdala). Two studies looked at the hypothalamic–pituitary–adrenal (HPA) axis: Groh and colleagues121 showed that ACE exposure was associated with a lower baseline cortisol level and a slower decrease in cortisol following an intravenous opioid agonist (diamorphine), and Lovallo and colleagues123 found that ACE exposure was associated with a smaller cortisol increase after an oral opioid antagonist (naltrexone).Table 5Impact of adverse childhood experiences on endogenous pain signalling. ACTH, adrenocorticotropic hormone; anova, analysis of variance.Table 5Author and yrParticipant populationSample sizeExposureOutcomeOutcome toolRelationshipGroh 2020Adults with opioid dependency attending a diamorphine maintenance treatment clinic15Childhood traumaCortisol response to diamorphineACTH response to diamorphinePlasma cortisol/ACTH measured at –15, 15, 60, 180, and 300 min relative to intravenous diamorphine injectionCompared with the mild trauma group, the severe trauma group had a lower baseline cortisol (anova, F[1.140]=39.93, P<0.001).

ent clinic15Childhood traumaCortisol response to diamorphineACTH response to diamorphinePlasma cortisol/ACTH measured at –15, 15, 60, 180, and 300 min relative to intravenous diamorphine injectionCompared with the mild trauma group, the severe trauma group had a lower baseline cortisol (anova, F[1.140]=39.93, P<0.001). Compared with the mild trauma group, the severe trauma group had a slower decrease in cortisol following diamorphine injection (anova, F[1.6]=9.38, P=0.022).Trauma group status was not associated with baseline ACTH (results not provided) or the decrease in ACTH following diamorphine injection trauma (anova, F[1.6]=1.69, P=0.242).Hill 2022Adults recruited through public advertising75Childhood maltreatmentMu opioid receptor density in the nucleus accumbens and amygdalaBinding of radiolabelled opioid (11C-carfentanil) administered by intravenous infusion to the nucleus accumbens and amygdala, assessed by MRI and PET scanChildhood trauma questionnaire score was not associated with Mu opioid receptor binding (linear mixed-effects model, F=3.28.

sity in the nucleus accumbens and amygdalaBinding of radiolabelled opioid (11C-carfentanil) administered by intravenous infusion to the nucleus accumbens and amygdala, assessed by MRI and PET scanChildhood trauma questionnaire score was not associated with Mu opioid receptor binding (linear mixed-effects model, F=3.28. P=0.074).Lovallo 2018Oklahoma Family Health Patterns Project: healthy women aged 18–30 yr72Early-life adversityCortisol response to naltrexoneSubjective distress and activation scores in response to naltrexoneSalivary cortisol measured every 30 mins for 180 mins after oral naltrexone or placebo ingestionVisual analogue scales on distress (five items) and activation (five items) completed every 60 min for 180 min after oral naltrexone or placebo ingestionHigher early-life adversity score was associated with a smaller cortisol increase in response to oral naltrexone (anova, F=3.51, P=0.035).Higher early-life adversity score was associated with a smaller/negligible increase in distress score (anova, F=4.13, P=0.02).Early-life adversity score was not associated with activation score (anova, full results not provided, P>0.278).Zehetmeier 2023Pregnant women (third trimester) attending an inpatient obstetric clinic191Adverse childhood experiencesQuantitative sensory assessment (mechanical detection threshold, mechanical pain threshold, pressure pain threshold, and conditioned pain modulation)Full description of sensory testing protocol not reported hereThere was no association between trauma exposure and quantitative sensory assessment parameters: mechanical detection threshold (anova, F=0.20, P=0.654), mechanical pain threshold (anova, F=0.01, P=0.911), pressure pain threshold (anova, F=0.18, P=0.671), and conditioned pain modulation (anova, F=0.03, P=0.861).

ed hereThere was no association between trauma exposure and quantitative sensory assessment parameters: mechanical detection threshold (anova, F=0.20, P=0.654), mechanical pain threshold (anova, F=0.01, P=0.911), pressure pain threshold (anova, F=0.18, P=0.671), and conditioned pain modulation (anova, F=0.03, P=0.861). Impact of adverse childhood experiences on endogenous pain signalling. ACTH, adrenocorticotropic hormone; anova, analysis of variance.

ed hereThere was no association between trauma exposure and quantitative sensory assessment parameters: mechanical detection threshold (anova, F=0.20, P=0.654), mechanical pain threshold (anova, F=0.01, P=0.911), pressure pain threshold (anova, F=0.18, P=0.671), and conditioned pain modulation (anova, F=0.03, P=0.861). Impact of adverse childhood experiences on endogenous pain signalling. ACTH, adrenocorticotropic hormone; anova, analysis of variance. Two studies involved outcomes that did not fit into the above five categories (Table 6).125,126 Roy and colleagues125 reported that some types of ACE (emotional abuse, sexual abuse, and emotional neglect) were associated with attempted suicide in adults with opioid dependency. Smith and colleagues126 reported that ACE exposure was not associated with use of the recreational herbal agent, kratom, which contains constituents known to activate the μ-opioid receptor.127Table 6Impact of ACEs on other analgesia-related outcomes. ACE, adverse childhood experience; CI, confidence interval; OR, odds ratio.Table 6Author and yrParticipant populationSample sizeExposureOutcomeOutcome toolRelationshipRoy 2002Adults with opiate dependency attending substance abuse clinics246Childhood traumaAttempted suicide‘ … a self-destructive act with some intent to end one's life that was not self-mutilatory in nature … ’Emotional abuse: OR 8.90 (95% CI 1.28–68.50)Sexual abuse: OR 12.90 (95% CI 1.47–165.20)Emotional neglect: OR 5.70 (95% CI 1.17–28.70)No association for physical abuse or physical neglect (data not provided)Smith 2022Adults with a history of alcohol, prescription opioid, illicit opioid, kratom, or illicit stimulant use in the last 6 months, recruited through Amazon Mechanical Turk1510ACEsLifetime kratom useSelf-completed online questionnaire with a question about lifetime kratom useIn unadjusted analysis, ACE count was higher in the lifetime kratom use group than in the no kratom use group (t-test, Cohen's d=–0.34, P=0.001).In adjusted analysis, there was no significant association between total ACE count and lifetime kratom use (logistic regression, OR 0.94, 95% CI 0.89–1.03).

lifetime kratom useIn unadjusted analysis, ACE count was higher in the lifetime kratom use group than in the no kratom use group (t-test, Cohen's d=–0.34, P=0.001).In adjusted analysis, there was no significant association between total ACE count and lifetime kratom use (logistic regression, OR 0.94, 95% CI 0.89–1.03). Impact of ACEs on other analgesia-related outcomes. ACE, adverse childhood experience; CI, confidence interval; OR, odds ratio.

Risk of bias assessment was performed on 65 observational studies using the ROBINS-E tool (Supplementary Table S3).55 Overall most studies were at ‘high risk’ (n=21, 31.8%) or ‘very high risk’ (n=44, 66.7%) of bias. There were some consistent risks observed across the studies, especially in domain 1 (risk of bias attributed to confounding) and domain 3 (risk of bias attributed to participant selection). In domain 1, most studies were ‘high risk’ or ‘very high risk’ (n=59, 89.4%) as either they performed unadjusted analysis, introducing a risk of confounding bias, or they controlled for variables that could have been affected by ACE exposure (e.g. coexisting mental health disorders), increasing the risk of overadjustment bias. In domain 3, many studies were ‘high risk’ or ‘very high risk’ (n=43, 65.2%) as participant selection was based on characteristics that could have been influenced by ACE exposure (e.g. recruitment of participants attending a healthcare service), introducing a risk of selection bias. The remaining studies were deemed as having ‘some concerns’ (n=23, 34.8%) as participant selection occurred at a time after ACE exposure, introducing a risk of survivorship bias.

There were differences in the way that the concept of ACEs was defined and measured across the 66 studies (Supplementary Table S5). Twenty-two different terms were used (although most were variations on similar themes), with the most common being ‘adverse childhood experiences’ (n=22, 33.3%), ‘childhood trauma’ (n=11, 16.7%), and ‘childhood maltreatment’ (n=7, 10.6%). Nearly two-thirds of studies (n=42, 63.6%) did not provide a formal definition of their term of choice. The upper age limit for childhood ranged from <12 to <19 yr, with the most common being <18 (n=26, 39.4%), although over a third of studies (n=27, 40.9%) did not report the age cut-off used. In total, 45 different ACEs were assessed, with a median per study of 5 (range 1–27). The most frequently assessed ACEs were sexual abuse (n=55, 83.3%), physical abuse (n=52, 78.8%), and emotional abuse (n=44, 66.7%) (Supplementary Table S6). Twenty studies (30.3%) provided sufficient data to allow for a meta-analysis of the prevalence of exposure to any ACE; the pooled prevalence was 64.1% (95% confidence interval [CI] 51.3–75.2%). However, the inter-study heterogeneity was high (I2=99.4%, Cochran Q=3417, P<0.001) (Fig. 2).Fig 2Meta-analysis of prevalence of exposure to any ACE. ACE, adverse childhood experience; CI, confidence interval.Fig 2 Meta-analysis of prevalence of exposure to any ACE. ACE, adverse childhood experience; CI, confidence interval.

In total, 45 different ACEs were assessed, with a median per study of 5 (range 1–27). The most frequently assessed ACEs were sexual abuse (n=55, 83.3%), physical abuse (n=52, 78.8%), and emotional abuse (n=44, 66.7%) (Supplementary Table S6). Twenty studies (30.3%) provided sufficient data to allow for a meta-analysis of the prevalence of exposure to any ACE; the pooled prevalence was 64.1% (95% confidence interval [CI] 51.3–75.2%). However, the inter-study heterogeneity was high (I2=99.4%, Cochran Q=3417, P<0.001) (Fig. 2).Fig 2Meta-analysis of prevalence of exposure to any ACE. ACE, adverse childhood experience; CI, confidence interval.Fig 2 Meta-analysis of prevalence of exposure to any ACE. ACE, adverse childhood experience; CI, confidence interval. Twenty-three studies (34.8%) compared their outcome of interest with individual ACE types (e.g. sexual or physical abuse). Twenty-eight studies (42.4%) used ACE counts (the sum of the different types of ACEs measured), 10 studies (15.2%) used ACE scores (incorporating ACE frequency or severity into the measurement), and five studies (7.6%) used other methods of ACE grouping (e.g. latent class analysis, ‘low’ vs ‘high’ trauma).

Twelve studies (involving 27 281 participants) investigated the use of analgesic medication (Table 1).61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72 Compared with adults with no/low ACE exposure, high ACE exposure was associated with a higher number of analgesic medications in 2/2 studies, use of over-the-counter analgesic medications in 1/1 study, use of any prescription analgesic medications in 1/2 studies, use of prescription opioids in 3/7 studies, and use of NSAIDs in 1/1 study, but not with use of benzodiazepines (1/1 study). Although four studies failed to show an association between ACEs and use of prescription opioids, these studies tended to have a smaller sample size (n=80, 113, 235, and 865) compared with the three studies that did show an association (n=230, 2999, and 14 800) and so may have been underpowered. The largest study, by Austin and colleagues,62 found that a history of any childhood abuse was associated with a 51% higher odds of recent prescription opioid use. We found no studies that examined whether ACE exposure influenced the potential benefits arising from use of analgesics (e.g. improvements in pain or functional status).Table 1Impact of adverse childhood experiences on the use of analgesics.

Four studies (involving 3491 participants) investigated analgesic medication side-effects (Table 2).73, 74, 75, 76 The largest study (n=3118) examined the most side-effects and found that people with a history of abuse were more likely to report ‘any side-effect’ and more likely to report 10 out of the 15 individual side-effects than those without a history of abuse.76 The side-effects considered included some that are often associated with opioid and gabapentinoid use (such as constipation, drowsiness, and nausea).76 However, Bottiroli and colleagues73 reported no influence of childhood trauma history (i.e. abuse and neglect) on the persistence of medication overuse-associated headache. In a double-blind placebo-controlled randomised trial by Carlyle and colleagues,74 after an intramuscular injection of morphine, participants with a history of childhood trauma reported higher ratings for the pleasurable side-effects (such as ‘feeling high’ and ‘liking drug effects’) and lower ratings for the disagreeable side-effects (such as ‘disliking drug effects’) than those with no such history. These findings were not replicated in a hospital-based study by the same research group, where day-case surgery patients were given an opioid infusion before general anaesthesia and asked similar questions.75Table 2Impact of adverse childhood experiences on analgesic side-effects.

Forty-five studies (involving 104 550 participants) investigated outcomes relating to substance misuse (Table 3).69,70,77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119 Of these, 44 studies examined opioids, and six examined sedatives.Table 3Impact of adverse childhood experiences on substance misuse. ACE, adverse childhood experience; CI, confidence interval; HR, hazard ratio; OR, odds ratio.Table 3Author and yrParticipant populationSample sizeExposureOutcomeOutcome toolRelationshipAfifi 2012National Epidemiological Study of Alcohol and Related Conditions (NESARC), wave 2: a nationwide nationally representative household survey of adults34 653Childhood maltreatmentSedative substance use disorderOpioid (unspecified) substance use disorderHeroin substance use disorderStructured interview using questions from the Alcohol Use Disorder and Associated Disabilities Interview Schedule–DSM IV (AUDADIS-IV)In males and females, physical abuse, sexual abuse, emotional abuse, physical neglect, and emotional neglect were associated with sedative substance use disorder (logistic regression; full results not reported here).In males and females, physical abuse, sexual abuse, emotional abuse, physical neglect, and emotional neglect were associated with opioid substance use disorder (logistic regression; full results not reported here).In males and females, physical abuse, sexual abuse, emotional abuse, and emotional neglect were associated with heroin substance use disorder (logistic regression; full results not reported here).

Two studies (involving 658 participants) investigated lifetime incidence of drug (including opioid) overdose (Table 4).112,120 In both studies, a higher ACE count was associated with a greater likelihood of lifetime drug overdose.Table 4Impact of ACEs on lifetime drug overdose. ACE, adverse childhood experience; CI, confidence interval; OR, odds ratio; RR, relative risk.Table 4Author and yrParticipant populationSample sizeExposureOutcomeOutcome toolRelationshipEl-Bassel 2019Women participating in Project PACT (a couple-focused randomised clinical trial of an HIV prevention intervention for men undergoing community corrections and their female intimate partners) who reported lifetime use of illicit drugs201Childhood adversityLifetime overdoseStructured interview asking about lifetime overdose (defined as loss of consciousness) of heroin, opioid pain relievers, or tranquiliserHigher childhood adversity count was associated with greater risk of lifetime overdose (generalised linear model, RR 1.3, 95% CI 1.1–1.6).Stein 2017Adults attending an inpatient opioid detoxification programme457ACEsLifetime overdoseStructured interview asking about lifetime overdoseHigher ACE count was associated with greater odds of lifetime overdose (logistic regression, OR 1.10, 95% CI 1.02–1.20). Impact of ACEs on lifetime drug overdose. ACE, adverse childhood experience; CI, confidence interval; OR, odds ratio; RR, relative risk.

Four studies (involving 353 participants) investigated endogenous pain signalling pathways (Table 5).121, 122, 123, 124 Zehetmeier and colleagues124 performed quantitative sensory testing and found no association between ACE exposure and pain thresholds in various sensory modalities. Hill and colleagues122 found no association between ACE exposure and μ-opioid receptor binding in two regions of the brain (nucleus accumbens and amygdala). Two studies looked at the hypothalamic–pituitary–adrenal (HPA) axis: Groh and colleagues121 showed that ACE exposure was associated with a lower baseline cortisol level and a slower decrease in cortisol following an intravenous opioid agonist (diamorphine), and Lovallo and colleagues123 found that ACE exposure was associated with a smaller cortisol increase after an oral opioid antagonist (naltrexone).Table 5Impact of adverse childhood experiences on endogenous pain signalling. ACTH, adrenocorticotropic hormone; anova, analysis of variance.Table 5Author and yrParticipant populationSample sizeExposureOutcomeOutcome toolRelationshipGroh 2020Adults with opioid dependency attending a diamorphine maintenance treatment clinic15Childhood traumaCortisol response to diamorphineACTH response to diamorphinePlasma cortisol/ACTH measured at –15, 15, 60, 180, and 300 min relative to intravenous diamorphine injectionCompared with the mild trauma group, the severe trauma group had a lower baseline cortisol (anova, F[1.140]=39.93, P<0.001).

Two studies involved outcomes that did not fit into the above five categories (Table 6).125,126 Roy and colleagues125 reported that some types of ACE (emotional abuse, sexual abuse, and emotional neglect) were associated with attempted suicide in adults with opioid dependency. Smith and colleagues126 reported that ACE exposure was not associated with use of the recreational herbal agent, kratom, which contains constituents known to activate the μ-opioid receptor.127Table 6Impact of ACEs on other analgesia-related outcomes. ACE, adverse childhood experience; CI, confidence interval; OR, odds ratio.Table 6Author and yrParticipant populationSample sizeExposureOutcomeOutcome toolRelationshipRoy 2002Adults with opiate dependency attending substance abuse clinics246Childhood traumaAttempted suicide‘ … a self-destructive act with some intent to end one's life that was not self-mutilatory in nature … ’Emotional abuse: OR 8.90 (95% CI 1.28–68.50)Sexual abuse: OR 12.90 (95% CI 1.47–165.20)Emotional neglect: OR 5.70 (95% CI 1.17–28.70)No association for physical abuse or physical neglect (data not provided)Smith 2022Adults with a history of alcohol, prescription opioid, illicit opioid, kratom, or illicit stimulant use in the last 6 months, recruited through Amazon Mechanical Turk1510ACEsLifetime kratom useSelf-completed online questionnaire with a question about lifetime kratom useIn unadjusted analysis, ACE count was higher in the lifetime kratom use group than in the no kratom use group (t-test, Cohen's d=–0.34, P=0.001).In adjusted analysis, there was no significant association between total ACE count and lifetime kratom use (logistic regression, OR 0.94, 95% CI 0.89–1.03).

In this systematic review, we synthesised the literature on adverse childhood experiences and a range of analgesia-related outcomes, using data from 137 395 participants (66 studies). In general, high ACE exposure was associated with poorer outcomes than no/low ACE exposure. However, there were differences in the way that the included studies defined, measured, and analysed their key variables, so this synthesis should be interpreted with these limitations in mind. When considering analgesic medication use, our results showed that compared with no/low ACE exposure, high ACE exposure was generally associated with greater use. No data were presented in the included studies to allow us to draw firm conclusions as to why this may be the case; it could reflect the higher prevalence of chronic pain in ACE-exposed individuals, a greater severity of chronic pain experienced, a greater perceived need for treatment, a greater risk of analgesic misuse, or some other factors altogether. We found it notable that no studies reported on whether ACEs influenced the benefits of analgesic medications (e.g. by affecting functional status or pain scores).

greater severity of chronic pain experienced, a greater perceived need for treatment, a greater risk of analgesic misuse, or some other factors altogether. We found it notable that no studies reported on whether ACEs influenced the benefits of analgesic medications (e.g. by affecting functional status or pain scores). When considering analgesic medication harms, our results showed that compared with no/low ACE exposure, high ACE exposure was generally associated with greater likelihood of harm. The majority of the included studies, especially in the context of substance misuse, looked specifically at opioids, which is perhaps unsurprising given the high profile given to the opioid crisis in both the scientific and popular press.33, 34, 35,128 Opioid-related harms included a higher incidence of analgesic medication side-effects, greater risk and severity of substance misuse, greater risk of drug overdose, and greater risk of attempted suicide in those misusing opioids. The only other drug class to be represented was sedatives (e.g. benzodiazepines), which are not formal analgesics and whose use in the management of chronic pain is generally not recommended.129 We included them in our search strategy as they are sometimes prescribed for pain, although the association between ACE exposure and sedative misuse summarised here may provide another reason to caution against their regular use. We found it notable that other analgesics with an established risk of harm were not investigated; for example, we found no studies that specifically investigated ACE exposure and gabapentinoid outcomes, which in recent years have been shown to have similar public health risks as opioids.34,43,49

inst their regular use. We found it notable that other analgesics with an established risk of harm were not investigated; for example, we found no studies that specifically investigated ACE exposure and gabapentinoid outcomes, which in recent years have been shown to have similar public health risks as opioids.34,43,49 The links between ACEs and opioid-related harms found in our review were concordant with similar reviews focussing specifically on ACEs and opioid use.130,131 Two of the included studies hinted at a possible mechanism for this relationship: high ACE exposure was associated with changes to the HPA axis, altering the stress hormone responses to opioid agonists and antagonists.121,123 This would be in line with existing evidence showing that people with ACE exposure have blunted cortisol reactivity to stressors132 and the general mechanisms proposed to contribute to the syndrome of ‘toxic stress’.19 However, the relationship between ACEs and the HPA axis is complicated, with a recent meta-analysis suggesting that ACE factors (e.g. type of adversity) and participant factors (e.g. racial background) may influence the strength and direction of the correlation.133 It is of note that one included study used an objective measure of pain sensation (quantitative sensory testing) but did not find an association between ACEs and pain thresholds in various sensory modalities.124 However, the population in this study was pregnant women in the third trimester, so it is unclear how generalisable this would be to the broader adult population. It is likely that the long-term consequences of ACEs occur through multiple pathways, although specific evidence relating ACEs to analgesia-related outcomes is currently lacking.

on in this study was pregnant women in the third trimester, so it is unclear how generalisable this would be to the broader adult population. It is likely that the long-term consequences of ACEs occur through multiple pathways, although specific evidence relating ACEs to analgesia-related outcomes is currently lacking. There are a number of strengths to our review. Firstly, we used a robust methodology, including a broad search string that made no assumptions about the type of analgesia-related outcome that might be discovered. As a result, we are confident that we have identified the relevant literature. Secondly, our findings are based on data from a large number of participants (n=137 395) from a range of contexts, including healthcare settings and dedicated research cohorts, which we believe will reflect a variety of real-world contexts.

As a result, we are confident that we have identified the relevant literature. Secondly, our findings are based on data from a large number of participants (n=137 395) from a range of contexts, including healthcare settings and dedicated research cohorts, which we believe will reflect a variety of real-world contexts. However, there are also some limitations to the generalisability of our findings, which are predominantly attributed to the nature of the studies included in the review. Firstly, there was substantial heterogeneity in the definition and operationalisation of ACEs across the included studies, with the upper age threshold for childhood ranging from <12 to <19 yr and the number of included ACE types ranging from 1 to 27. This is an issue for any synthesis of ACE research, as there is no consistency or consensus opinion on what constitutes an ACE.134 Furthermore, our patient and public involvement group strongly highlighted the limitation of an assumption central to most ACEs research: that different ACEs in the same person or the same ACE in different people carry the same burden of stress. Ten included studies partially addressed this by incorporating measures of frequency or severity into an ACE score, but these were objective assessments that did not directly assess the subjective impact of an ACE. It also added an additional layer of heterogeneity that made comparisons between studies more difficult.

Ten included studies partially addressed this by incorporating measures of frequency or severity into an ACE score, but these were objective assessments that did not directly assess the subjective impact of an ACE. It also added an additional layer of heterogeneity that made comparisons between studies more difficult. Secondly, the included studies used retrospective participant reports of ACE exposure and so were at risk of recall and reporting bias. This is the case for much of the research into ACEs, especially given the potential ethical issues arising from prospective ACE measurement without intervention. Two studies that compared prospective measurements of ACEs during childhood with retrospective recall of ACEs during adulthood found inconsistent results.135,136 Recall and reporting bias may also be influenced by later life experiences: our patient and public involvement group reported their perception that people exposed to ACEs who subsequently had supportive relationships seemed less likely to have poor long-term outcomes than those who experienced abusive relationships in adulthood. Such adult circumstances are rarely captured in ACE research and were not considered moderators in any of the studies included in this review.

e exposed to ACEs who subsequently had supportive relationships seemed less likely to have poor long-term outcomes than those who experienced abusive relationships in adulthood. Such adult circumstances are rarely captured in ACE research and were not considered moderators in any of the studies included in this review. Thirdly, there is undoubtedly a close relationship between ACEs and childhood socioeconomic status (SES),137 but for our review, it was not possible to separate the effects of the two variables on our outcomes owing to the limited reporting of childhood SES in the included studies. The studies that did acknowledge SES used measures from adulthood (e.g. income level and education level) that could have been influenced by ACEs in earlier life and therefore introduced another potential source of bias attributed to confounding (ROBINS-E domain 1; Supplementary Table S3). Furthermore, different measures of SES may produce different apparent effects; thus, it remains difficult to adequately isolate the impact of each concept.138

fluenced by ACEs in earlier life and therefore introduced another potential source of bias attributed to confounding (ROBINS-E domain 1; Supplementary Table S3). Furthermore, different measures of SES may produce different apparent effects; thus, it remains difficult to adequately isolate the impact of each concept.138 Fourthly, although a number of the included studies incorporated analysis of factors that may moderate the relationship between ACEs and analgesia-related outcomes, the heterogeneity of these variables across studies precluded any meaningful synthesis. Potential factors that were identified (all by just one study each) included the following: the presence of adolescent pain,79 perceived stress,85 any past or existing health condition,88 sociosexuality,107 a centralised pain phenotype,76 pain catastrophising,76 internalising symptoms (e.g. anxiety and depression),113 and externalising symptoms (e.g. aggression and delinquency).113 There are likely to be additional factors not described here that may also influence this relationship. For example, demographic factors (e.g. gender), co-morbid health conditions (especially mental health conditions such as posttraumatic stress disorder), and co-administration of multiple pharmacologic agents were not evaluated.

ikely to be additional factors not described here that may also influence this relationship. For example, demographic factors (e.g. gender), co-morbid health conditions (especially mental health conditions such as posttraumatic stress disorder), and co-administration of multiple pharmacologic agents were not evaluated. Our review aligns with previously published work reporting the impact of ACEs on long-term health outcomes1, 2, 3, 4, 5, 6, 7, 8, 9 and advances our knowledge on the specific field of ACEs and chronic pain. It lends additional weight to the benefit of adopting a trauma-informed model of care, in which the potential long-term impacts of negative experiences in childhood are acknowledged in the assessment and management of disease.139,140 Although widespread screening for ACEs is contro-versial,141, 142, 143, 144 assessment of ACEs in chronic pain settings may have benefits by identifying those at greater risk of analgesic harms—either in those who are newly starting medication or in those who are established on analgesics and who may benefit from a prescribing review. At the very least, it provides some context for management discussions between individuals living with chronic pain and healthcare professionals. However, we should keep in mind that the evidence about the impact of ACEs on the effectiveness of analgesics and the harms of non-opioid analgesics is still patchy at best.

At the very least, it provides some context for management discussions between individuals living with chronic pain and healthcare professionals. However, we should keep in mind that the evidence about the impact of ACEs on the effectiveness of analgesics and the harms of non-opioid analgesics is still patchy at best. Our review focused on the pharmacological management of chronic pain, but this is by no means the only management option available. Indeed, recent guidelines advocate for a move away from traditional analgesics (e.g. opioids) with deprescribing encouraged where appropriate.145 The impact of ACEs on the nonpharmacological management of chronic pain is also an area where more research is required, although a recent study found that ACE exposure did not influence the improvements in pain and functional status after an interdisciplinary pain rehabilitation programme.146 More broadly, the evidence for interventions to support people exposed to ACEs is mixed, with no clear consensus on the best approach.147,148 The reality is that each individual's experience of ACEs is different; therefore, they will likely require different levels and modalities of support.

ehabilitation programme.146 More broadly, the evidence for interventions to support people exposed to ACEs is mixed, with no clear consensus on the best approach.147,148 The reality is that each individual's experience of ACEs is different; therefore, they will likely require different levels and modalities of support. In summary, this systematic review evaluated the literature on adverse childhood experiences and a range of analgesia-related outcomes. In general, higher adverse childhood experience exposure was associated with poorer outcomes than lower/no exposure. This included greater use of analgesic medication, greater incidence of analgesic medication side-effects, greater risk and severity of substance misuse, greater risk of drug overdose, and greater risk of attempted suicide in opioid dependency. Higher adverse childhood experience exposure was also associated with changes to the HPA axis, altering stress hormone responses to opioid agonists and antagonists. However, the evidence base is heterogenous, and there are still gaps in our understanding. Nevertheless, on the basis of current evidence, we believe that adopting trauma-informed practices in settings where adverse childhood experience exposures are common (e.g. the chronic pain clinic) is justified and may help to improve the management of chronic pain.

Study conception and design: DS, LC Acquisition of data: DS, MK, KB Analysis and interpretation of data: DS, MK, KB, BS, TH, LM, LC Drafting of final manuscript: DS Revision and critical appraisal of the manuscript: MK, KB, BS, TH, LM, LC Final approval of the submitted manuscript: DS, MK, KB, BS, TH, LM, LC

The authors are members of the Advanced Pain Discovery Platform (APDP) supported by 10.13039/100014013UK Research and Innovation (UKRI), Versus Arthritis, and Eli Lilly. DS is a fellow on the Multimorbidity Doctoral Training Programme for Health Professionals, which is supported by the Wellcome Trust (223499/Z/21/Z). MK is funded by The National Institute of Academic Anaesthesia (NIAA) (WKR0-2022-0028). BS and LC are supported by an APDP grant as part of the Partnership for Assessment and Investigation of Neuropathic Pain: Studies Tracking Outcomes, Risks and Mechanisms (PAINSTORM) consortium (MR/W002388/1). TH and LC are supported by an APDP grant as part of the Consortium Against Pain Inequality (MR/W002566/1). The funding bodies had no role in study design, data collection/analysis/interpretation, report writing, or the decision to submit the manuscript for publication.