Accumulation and sensitive period effects for childhood abuse and financial hardship on depressive symptoms in late adolescence.

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Learn more: PMC Disclaimer | PMC Copyright Notice J Mood Anxiety Disord . 2026 Mar 8;14:100171. doi: 10.1016/j.xjmad.2026.100171 Search in PMC Search in PubMed View in NLM Catalog Add to search Accumulation and sensitive period effects for childhood abuse and financial hardship on depressive symptoms in late adolescence ☆ Erin C Dunn Erin C Dunn a Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States b Department of Psychiatry, Harvard Medical School, Boston, MA, United States c Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, United States d Department of Sociology, College of Liberal Arts, Purdue University, West Lafayette, IN, United States Find articles by Erin C Dunn a, b, c, d, ⁎, 1 , Theresa W Cheng Theresa W Cheng a Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States b Department of Psychiatry, Harvard Medical School, Boston, MA, United States c Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, United States Find articles by Theresa W Cheng a, b, c , Yiwen Zhu Yiwen Zhu a Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States Find articles by Yiwen Zhu a , Alexandre A Lussier Alexandre A Lussier a Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States b Department of Psychiatry, Harvard Medical School, Boston, MA, United States c Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, United States Find articles by Alexandre A Lussier a, b, c , Katherine N Thompson Katherine N Thompson d Department of Sociology, College of Liberal Arts, Purdue University, West Lafayette, IN, United States Find articles by Katherine N Thompson d , Andrew DAC Smith Andrew DAC Smith e Mathematics and Statistics Research Group, University of the West of England, Bristol, United Kingdom Find articles by Andrew DAC Smith e , Henning Tiemeier Henning Tiemeier f Department of Child Psychiatry, Erasmus Medical Center, Rotterdam, the Netherlands g Department of Social and Behavioral Science, Harvard T.H. Chan School of Public Health, Boston, MA, United States Find articles by Henning Tiemeier f, g , Ezra S Susser Ezra S Susser h Department of Epidemiology, Mailman School of Public Health, Columbia University, United States i New York State Psychiatric Institute, New York, NY, United States Find articles by Ezra S Susser h, i Author information Article notes Copyright and License information a Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States b Department of Psychiatry, Harvard Medical School, Boston, MA, United States c Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, United States d Department of Sociology, College of Liberal Arts, Purdue University, West Lafayette, IN, United States e Mathematics and Statistics Research Group, University of the West of England, Bristol, United Kingdom f Department of Child Psychiatry, Erasmus Medical Center, Rotterdam, the Netherlands g Department of Social and Behavioral Science, Harvard T.H. Chan School of Public Health, Boston, MA, United States h Department of Epidemiology, Mailman School of Public Health, Columbia University, United States i New York State Psychiatric Institute, New York, NY, United States ⁎ Correspondence to: Purdue University, College of Liberal Arts, Department of Sociology, Innovation Hall, IO 277, 625 W. Michigan Street, Indianapolis, IN 46202, United States. [email protected] 1 Website: www.thedunnlab.com Received 2024 Oct 11; Revised 2026 Feb 26; Accepted 2026 Mar 4; Collection date 2026 Jun. © 2026 The Authors This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). PMC Copyright notice PMCID: PMC13092055  PMID: 42011421 Abstract Background Childhood adversity is a potent and modifiable risk factor for depression. Few studies have investigated how the developmental timing of adversity exposure shapes depression risk. We investigated whether there were sensitive periods, or age stages when two types of adversity (caregiver abuse, financial hardship) had stronger associations with depressive symptoms in late adolescence. Methods Data came from a prospective, longitudinal birth cohort of children in Avon, England (Avon Longitudinal Study of Parents and Children). Caregivers reported their children’s adversity exposure at least seven times between 0 and 18 years of age. Child participants self-reported depressive symptoms (Short Mood and Feelings Questionnaire) at 18.5 years old. We used a structured life course modeling approach (SLCMA) to characterize how sensitive period and accumulation hypotheses explained variation in depressive symptoms. Results For females, accumulation best explained variation in depressive symptoms for both adversity types, with each additional developmental period of exposure associated with a 0.33-unit increase in depressive symptoms for caregiver abuse and 0.31-unit increase for financial hardship. For males, sensitive period hypotheses were selected for exposure to caregiver abuse (exposure at age 9 =2.13-unit and age 3.9 =1.46-unit increase), while an accumulation hypothesis was selected for exposure to financial hardship (0.40-unit increase in depression). Conclusions Accumulation rather than sensitive period hypotheses generally best explained the relationship between adversity and depressive symptoms in late adolescence, but findings varied by sex and adversity type. These findings highlight the importance of considering multiple life course hypotheses, sex, and adversity type when investigating the downstream impacts of adversity. Keywords: Adversity, Adolescence, Depression, Child development, ALSPAC 1. Introduction Childhood adversity is among the strongest determinants of depression, estimated to at least double the risk of a first onset of depression throughout the lifespan [1] , [2] . Despite major developmental changes over the first two decades of life, little is known about how the timing of childhood adversity shapes depression risk. Pin-pointing how the developmental timing of childhood adversity exposure underlies risk for depression may help clinicians and public health experts tailor therapies and preventative interventions to specific life stages when those efforts may yield even more potent impacts [3] . Many scientists hypothesize that there are sensitive periods in childhood when the brain is especially susceptible to life experience, including childhood adversity exposure [4] , [5] . However, evidence supporting sensitive periods for the effect of adversity on depression is mixed, with studies finding evidence of sensitive periods in early childhood [6] , [7] , [8] , later childhood [9] , [10] , [11] , and adolescence [12] – and also no evidence for sensitive periods at all [13] , [14] , [15] , [16] , [17] . As noted by a recent systematic review [18] , there are several possible explanations for these inconsistent results. First, studies examining potential sensitive periods for childhood adversity rarely consider other theories described in life course epidemiology [19] or developmental psychopathology [20] . For example, accumulation of risk is another hypothesis describing the relationship between adversity and later risk for depression; this model posits that multiple occurences of adversity over time confer cumulative or additive risk, regardless of the developmental timing of exposure [21] , [22] . Research investigating both accumulation and sensitive periods hypotheses simultaneously may determine that such hypothesized effects operate together, or that unique effects of one hypothesis are more readily identifiable when controlling for the other. Second, many studies assess sensitive periods by lumping diverse adversity types. This grouping approach was popularized following seminal studies of Adverse Childhood Experiences (ACE scores) [23] . Aggregation approaches have been criticized for being unable to clarify the mechanisms of adversity due to their implicit assumption that different types of adversity operate identically [24] . Studies using disaggregated adversities have found that adversity-related sensitive period effects indeed vary by type [25] , [26] . Therefore, separation of adversity types may support the identification of specific mechanisms for the effects of childhood adversity on psychopathology risk. [Notably, approaches focused on specific types of adversity have also faced criticisms related to difficulties in isolating the impacts of correlated adversity exposures [24] ]. Finally, divergent sensitive period findings might be explained by when depression outcomes are measured. Prospective studies investigating time-dependent effects of adversity on behavioral and developmental outcomes during childhood have generally not found strong support for sensitive periods [13] , [14] . By contrast, studies examining outcomes in adolescence and adulthood generally find more support for sensitive periods, with exposure between birth and age 5 conferring greater risk [7] , [8] , [26] . Studies examining depression outcomes in childhood only may fail to identify time-dependent adversity effects manifesting further in the life course and/or sensitive periods occurring later in development [11] . Indeed, unless quite large in sample size, studies of depressive symptoms in childhood may not have sufficient signal, as only about 10% of depression cases occur before adolescence [27] . 1.1. The current study In this study, we investigated in a large population-based cohort whether there were sensitive periods for the effects of two types of childhood adversity on depressive symptoms in late adolescence. We brought three innovations to address challenges in prior literature. First, using a novel analytic technique, called the structured life course modeling approach, [28] , [29] , we tested for possible sensitive period effects, while also considering potential adversity accumulation. Second, in contrast to prior research on aggregated adversity scores, we separately examined two types of adversity – physical or emotional abuse by a caregiver and financial hardship – that are commonly known to influence risk for depressive disorders [30] , [31] , [32] . Finally, to account for potential longer-term sequela of childhood adversity, we examined depressive symptoms during the developmental period of emerging adulthood (age 18.5 years), a period of transition from late adolescence into early adulthood that is a critical time for the emergence of depression following childhood adversity [33] , [34] . Indeed, this developmental time period is an age of heightened social instability and stressful life transitions [35] , [36] when depression rates have been shown to increase [37] , [38] . In doing so, we expand upon prior work examining associations between the timing of childhood adversity on psychopathology symptoms during late childhood [25] . 2. Methods 2.1. Sample and procedures Data came from the Avon Longitudinal Study of Parents and Children (ALSPAC), a prospective, longitudinal birth cohort of children born to mothers living in the county of Avon, England, with estimated delivery dates between April 1991 and December 1992 [39] , [40] . Eligible pregnant women agreed to participate (n = 14,541 pregnancies), resulting in 14,062 eligible live births, of which 13,988 children were alive at 1 year of age. An additional 913 children who met inclusion criteria, but whose mothers did not participate before birth, were enrolled after age 7 [41] . The total sample size for analyses using any data collected after the age of seven is therefore 15,454 pregnancies, resulting in 15,589 foetuses. Of these, 14,901 were alive at 1 year of age. ALSPAC children have been followed from birth through early adulthood, and data collection continues. Further details, including a fully searchable data dictionary, are available at http://www.bristol.ac.uk/alspac/researchers/our-data/ . Of note, we report N = 14,884 alive at 1 year of age in the manuscript; this discrepancy is because the 14,901 participants described in the ALSPAC cohort profile additionally includes triplets and quadruplets as well as 7 participants who withdrew in the intervening years between the cohort profile and the dataset made available to us by ALSPAC. Out of 14,884 enrolled children alive at 1 year of age, 3376 children had complete data on depressive symptoms at age 18.5, the outcome time-point of interest. We further restricted this sample to singleton births (analytic sample N = 3263; see Figure S1 for how we arrived at the final analytic sample). Compared to the full sample, the analytic sample had a greater proportion of females, White people, and was less disadvantaged, including by socioeconomic status (e.g., higher maternal education and home ownership rates) and other markers; participants also experienced lower rates of financial hardship across measurement occasions, though levels of exposure to caregiver abuse and depressive symptoms on the Short Mood and Feelings Questionnaire (SMFQ) were similar ( Table S1 ). 2.2. Measures 2.2.1. Exposure to adversity Exposure to adversity was measured on at least seven occasions between birth and age 18 via parental report to mailed questionnaires (see Table S2 for more details). The time period specified for the occurrence of each exposure was specific to the experiences of the child at the time of that assessment, or in the period since the last assessment. 2.2.1.1. Caregiver physical or emotional abuse Participants were coded as exposed to physical or emotional abuse if the mother, partner, or both responded affirmatively to any of the following items: (1) Your partner was physically cruel to your children; (2) You were physically cruel to your children; (3) Your partner was emotionally cruel to your children; (4) You were emotionally cruel to your children. These items were asked to mothers at nine time-points and to partners at eight time-points (see Table S2 ). Participants were informed prior to responding that their responses were confidential. 2.2.1.2. Financial hardship Mothers reported at seven time-points the family’s difficulty (1 = not difficult; 2 = slightly difficult; 3 = fairly difficult; 4 = very difficult) affording the following: (a) items for the child; (b) rent or mortgage; (c) heating; (d) clothing; and (e) food (see Table S2 ). Participants were coded as exposed to financial hardship if their mothers reported that paying for three or more of these necessities was at least fairly difficult or that these items were paid for by the government. This cut-point is as stringent [25] or more stringent [42] than those used in other ALSPAC-based studies and thus captures fairly severe instances of financial hardship. 2.2.1.3. Timing of adversity To make full use of the available data, we separately analyzed each measurement occasion for both types of adversity. However, to help interpret our findings, we conceptually grouped the results (from these separate measurement occasions) into five developmental periods, generally consistent with prior research [25] , [43] , [44] . The developmental periods were: very early childhood (i.e., before 3 years of age, including the 8 month, age 1.75, and age 2.75 assessment), early childhood (i.e., 3–5 years of age, including the age 3.9 and age 5.1 assessment), middle childhood (i.e., 6–7 years of age, including age 6 and age 7 assessment), late childhood (i.e., including age 9 and 11 assessment), and adolescence (i.e., including the age 18 assessment). 2.2.2. Depressive symptoms Depressive symptoms were self-reported by mail at age 18.5 years using the 13-item SMFQ [45] , [46] . The SMFQ is commonly used in population-based studies [47] , [48] to measure affective and cognitive components of depression (e.g., “I felt miserable or unhappy”; “I thought I could never be as good as others.”). Items on the SMFQ are rated on a three-point scale (0 = not at all, 1 = sometimes, or 2 = true) to capture the severity of symptoms within the past two weeks. The SMFQ captures self-reported depressive symptoms and not clinical depression. That said, the SMFQ correlates highly with questionnaire and interview measures of psychopathology and clinician-rated diagnoses of depression in community child and adolescent samples [49] , [50] , [51] , [52] . Studies in ALSPAC have found the SMFQ’s internal consistency to be good ( α >0.80) [53] . 2.2.3. Covariates We controlled for the following covariates, measured at the time of the child’s birth (see Supporting Information ): child race/ethnicity, maternal age, maternal marital status, number of mother’s previous pregnancies, highest level of maternal education, and homeownership status. Maternal education and homeownership are routinely used in ALSPAC-based studies to capture social class and were included for reasons described in detail in Supporting Information [54] , [55] . All covariates were selected as potential confounders based on previous literature linking them to both childhood adversity exposure [56] , [57] and risk for psychopathology in adolescence [58] , [59] . Maternal psychopathology (measured 8 months postnatally using the Edinburgh Postnatal Depression Scale; [60] was also included due to previous work suggesting that mother’s mental health may impact reporting of their children’s adversity exposures [61] , [62] and children’s risk for mental health problems [63] , [64] , [65] . 2.3. Multiple imputation for missing data Compared to people who were missing data on any exposure or covariate (n = 1782), participants with complete data (n = 1481) differed by sex, race, socioeconomic status, financial hardship exposure, maternal psychopathology, and depressive symptom severity at age 18.5, but did not differ in reports of caregiver physical or emotional abuse ( Table S3 ). To reduce potential bias and minimize loss of power due to attrition, we performed multiple imputation on the covariates and adversity exposure measures; subsequent analyses used 20 imputed datasets (see Supporting Information ). Separate imputed datasets were generated for each adversity and each sex (four total datasets). 2.4. Analyses To examine the time-dependent associations between adversity and depressive symptoms, we used the two-stage structured life course modeling approach [SLCMA; pronounced slick-mah; [28] , [29] , [66] ]. The SLCMA uses Least Angle Regression [LARS; 67 ] to compare multiple competing hypotheses simultaneously. The major benefit of LARS, compared to other structured life course modeling methods, is its greater statistical power [29] and that it can be used in conjunction with post-selection inference methods to generate unbiased effect estimates [68] . We use the SLCMA to identify which developmental hypothesis has the greatest explanatory power for how child adversity influences later depressive symptoms. A demonstration of how to use the SLCMA has been described in detail elsewhere [28] , and the SLCMA has been used in multiple life course epidemiology studies by our research group [44] and others [69] , [70] . 2.4.1. Testing for sensitive periods and accumulation of risk We examined whether adversity exposure during specific time periods and/or accumulated exposure to adversity was associated with increased risk of depressive symptoms in late adolescence. To test sensitive period hypotheses , we generated a set of encoded variables (see Supporting Information ) indicating the presence versus absence of exposure to adversity at each measurement occasion. To test an accumulation of risk hypothesis, we generated a single variable denoting the number of developmental periods of exposure. Procedures were repeated for both adversity types, and analyses were performed separately by sex, given sex-specific patterns of adversity exposure [71] , [72] and depressive symptoms [73] , [74] , [75] . We performed sex-stratified analyses because our aim was to identify unique life course hypotheses for exposure-outcome associations by sex (in contrast to testing for statistical interactions between sex and adversity, which would capture differences in magnitude of the same hypotheses by sex). In the first stage of analysis, we entered all encoded variables into the LARS procedure to select the best-fitting hypothesis or combination of hypotheses. We then visually inspected the resulting elbow plots (see Supporting Information ) to select hypotheses for further investigation. In the second stage of the analysis, we estimated the effect sizes and corresponding confidence intervals of each selected hypothesis using a post-selection inference method known as selective inference [76] . Unlike ordinary least squares estimation, this approach adjusts estimates to account for the fact that hypotheses were selected from among a priori life course hypotheses. We adjusted for covariates using partitioned regression (i.e., the Frisch-Waugh-Lovell theorem), which can improve the power to detect differences between groups in penalized regression analyses [77] , [78] . 3. Results 3.1. Exposure to adversity About one-third of the sample experienced one of the two adversities before age 18 (34.2%; n = 1117). Exposure to financial hardship was more common (22.5% in females; 22.3% in males) than exposure to caregiver abuse (18.2% in females; 18.9% in males), and levels of financial hardship generally declined as children aged ( Table 1 ). Rates of physical and emotional abuse were more balanced across the sexes compared to some previous UK literature, which has reported slightly higher rates of emotional, sexual, and domestic abuse in girls [79] . Further, exposures were moderately correlated across time ( r values between 0.42 and 0.82), with neighboring time periods being more highly correlated than distal ones ( Table S4 ). Table 1. Exposure to childhood caregiver physical or emotional abuse and financial hardship in the total ALSPAC analytic sample (n = 3263) and by developmental time period of exposure. Caregiver physical or emotional abuse Financial hardship Females Males Females Males N (%) N (%) N (%) N (%) Unexposed 1718 81.81 943 81.08 1627 77.48 904 77.73 Exposed 382 18.19 220 18.92 473 22.52 259 22.27 Age at Exposure Very Early Childhood 179 -- 103 -- 367 -- 214 -- Age 8 mo. 73 3.85 41 3.78 184 9.75 123 11.39 Age 1.75 78 4.18 44 4.11 209 11.46 111 10.57 Age 2.75 112 6.07 63 5.89 196 10.87 112 10.74 Early Childhood 175 -- 107 -- 134 -- 63 -- Age 3.9 101 5.57 53 4.97 --- --- --- --- Age 5.1 115 6.45 75 7.24 134 7.68 63 5.42 Middle Childhood 112 -- 48 -- 91 -- 44 -- Age 6 112 6.33 48 4.63 --- --- --- --- Age 7 --- --- --- --- 91 5.25 44 4.34 Late Childhood Age 9 49 2.69 29 2.77 --- --- --- --- Age 11 41 2.24 25 2.35 69 3.93 35 3.43 Adolescence Age 18 67 5.12 39 4.65 64 5.25 39 4.91 Open in a new tab Percentages for each age represent proportions of those exposed out of the total sample with reported data for that variable. For caregiver physical or emotional abuse, questions about the child’s exposure to abuse referred to time periods ranging from the prior 8 months to 3 years (since approximately the date of previous questionnaire), while questions on financial hardship asked about current hardship (see Supplemental Information for details). --- indicates that the variable was not assessed at the corresponding time point Total possible accumulation scores ranged from 0 (no exposure) to 7 (exposed to financial hardship at all measurement occasions) or 9 (exposed to caregiver abuse at all measurement occasions) and followed an exponential decay distribution, with low mean exposure levels across sex and adversity type (instances of caregiver abuse, out of 9: M=0.36, SD=0.97 in females; M=0.36, SD=0.96 in males; instances of financial hardship, out of 7: M=0.45, SD=1.06 in females; M=0.45, SD=1.05 in males). Among participants who had been exposed during at least one time period, mean exposure levels were also comparable (caregiver abuse: M=1.96, SD=1.44 in females; M=1.90, SD=1.39 in males; financial hardship: M=2.00, SD=1.37 in females; M=2.03, SD=1.31 in males), confirming the comparability in exposure levels across sex and adversity types. Exposure to caregiver abuse and financial hardship were moderately to highly correlated over time ( r values between 0.34 and 0.85; Table S4 ), but not high enough to cause problems with identifying hypotheses (LARS can estimate models in the presence of even high collinearity). Adversity exposure was patterned by sociodemographic characteristics. Participants who were exposed to adversity were more likely to be born to mothers who were unmarried, did not own their homes, had more previous pregnancies, and had higher levels of psychopathology (all p < .01); they were also more likely to have higher depressive symptoms at age 18.5 years (p < .001; Table S5 ). 3.2. Hypothesis selection LARS produces a sequence of best-fitting models with a different number of encoded variables. We visually examined plots of R 2 values against the number of variables in the LARS models to select hypotheses for interpretation ( Figure S2 ). We selected hypotheses that preceded an ‘elbow’ in the plot – that is, a point at which adding another variable would cause relatively little improvement in R 2 but removing a variable would cause a relatively large decrease in R 2 . In the event of more than one potential elbow, we focused on the more parsimonious model. Across three of four models, accumulation was selected as the sole variable. Thus, among the hypotheses considered, accumulation explained the most variance under this selection procedure. For caregiver abuse in males, however, we identified sensitive periods at 3.9 and 9 years of age. 3.3. Model findings In each case, the identified hypotheses were carried over to the effect estimation stage, or stage two of the SLCMA. Effect estimates, p-values, and 95% confidence intervals are presented in Table 2 . For females, being exposed to caregiver abuse in each additional developmental period was associated with a 0.33-unit (5.3% of a standard deviation) increase in depressive symptoms on the SMFQ at age 18 (p = .037). Similarly, each additional developmental period of exposure to financial hardship was associated with a 0.31-unit (5.0% of a standard deviation) increase in depressive symptoms (p = .039). Table 2. Results of SLCMA showing the life course model type that best explained the relationship between each type of adversity and depressive symptoms at age 18.5, stratified by sex and controlling for covariates (n = 3263). Model Selection Effect Estimation Females (n = 2100) Adversity Partial R 2 Selected hypothesis Coefficient SE p- value Lower CI Upper CI Caregiver abuse .37% Accumulation .33 .16 .037 -.06 2.83 Financial hardship .14% Accumulation .31 .15 .039 -.05 1.4 Males (n = 1163) Caregiver abuse .22% Exposure at age 9 y 2.13 .86 .025 -.009 3.83 Exposure at age 3.9 y 1.46 .66 .0502 -.34 2.77 Financial hardship .14% Accumulation .40 .13 .41 -1.36 .62 Open in a new tab Reported p-values test the null hypothesis that the beta estimate for the selected hypothesis did not differ from 0, controlling for covariates and, in cases where multiple variables are selected by LARS, controlling for any other selected hypotheses. Partial R 2 is the amount of remaining variation, after adjusting for covariates, explained by the model. Partial R 2 is adjusted for model selection. 95% confidence intervals (CIs) and p-values were calculated using post-selective inference [76] to adjust for model selection. Note that the classic duality between p-values and CIs does not hold after model selection. Due to differences in the way that CIs and p-values are conceptualized and calculated in post-selective inference, the CIs are more conservative than the p-values, resulting in occasions of CIs crossing the null while p < 0.05 [76] . For males, being exposed to caregiver abuse at age 9 was associated with a 2.13-unit (43.0% of a standard deviation) increase in depressive symptoms (p = .025), and being exposed at age 3.9 was associated with a 1.46 (29.5% of a standard deviation) increase in depressive symptoms (p = .0502). When considering exposure to financial hardship in males, exposure in each additional developmental period was associated with a 0.40-unit (8.0% of a standard deviation) increase in depressive symptoms (p = .41). Note that while the variance explained by these models is small, the effects estimates reported are for specific temporal patterns of exposure to specific adversity types; smaller levels of variance explained may therefore be expected, given the specificity of the models [25] . Further, a small proportion of explained variance should not be conflated with population impact. Even modest shifts in risk can have substantial public health benefits. For instance, while the timing of childhood exposure to adversity may account for a small proportion of individual-level variance in depression risk, among many other contributing factors, it can still translate into substantial population-level impact when such experiences are common [80] . 4. Discussion The main finding of this study was that, among accumulation of risk and sensitive period hypotheses, accumulation generally explained the most variation in late adolescents’ depressive symptoms related to caregiver physical and emotional abuse and financial hardship. These results are consistent with prior work in this sample examining psychopathology symptoms at 8 years of age [25] , and suggest that people chronically exposed to adversity in childhood are among those most vulnerable to depressive symptoms not just in childhood, but also in emerging adulthood. We identified one instance where a sensitive period explained the most variation in depressive symptoms. Specifically, caregiver abuse in males at age 9 was associated with over 40% of a standard deviation increase in depressive symptoms, on average. This finding is consistent with prior work showing maltreatment in late childhood most strongly predicts depressive symptoms in adolescence [10] . The present analyses provide fine-grained and specific predictions for future replication, as this sensitive period effect is specific to age, sex, and adversity type, which is underscored by the small explanatory power of these models. However, heterogeneity in population selection, study design, age groupings, adversity measurement, and outcome measurement may contribute to inconsistencies in this literature, underscoring the need for both in-depth individual studies and meta-analyses. To that end, our results can be interpreted in the context of a recent systematic review, which did not find stronger evidence for time-dependent effects on depression outcomes measured later in life [18] . Notably, however, that review only focused on one type of childhood adversity – child maltreatment (abuse, neglect) – and thus may not apply to financial instability. We encourage future research to further probe these relationships so data can be triangulated across studies and research design contexts. 4.1. Strengths and limitations The current study had several strengths. Data came from a large, population-based sample with prospective adversity measures. We used a novel analytic approach (SLCMA) that allowed us to simultaneously compare competing life course theoretical models (both accumulation and sensitive period hypotheses). Our results highlight the importance of testing both life course models simultaneously. We also examined caregiver abuse and financial hardship separately, rather than combining these exposures, allowing us to identify patterns specific to adversity types that were not highly correlated. Moreover, compared to previous ALSPAC-based studies of sensitive periods [25] , we examined outcomes farther out into development, exploring the possibility that some sensitive-period differences may not emerge immediately after exposure, but could lag in time from the occurrence of exposure to outcome presentation. We previously observed evidence for these latency effects from childhood adversity [81] . Our study also had limitations. First, we studied depressive symptoms within a general population cohort. Therefore, we cannot directly extend our findings to major depression diagnoses. We hope future studies expand these findings to focus on diagnoses and other symptom measures. Second, one consequence of examining depressive symptoms further out into development was a high attrition rate. Our analytic sample was substantially smaller than the recruited sample, and attrition was patterned by sociodemographic characteristics and severity of psychopathology, indicating our estimates may not be representative of the original, full study sample. Although we retained a substantial proportion of the full sample through the use of multiple imputation to address missing information on exposures and covariates, such efforts did not recover the representativeness of the original sample. Third, we were unable to control for time-varying covariates (such as family support or relationships) using SLCMA, as SLCMA is not yet able to accommodate such time-varying measures. However, we are working now on methods to address this limitation through adaptations to the SLCMA. Fourth, our adversity exposure measures relied on parental reports, which may create inaccuracies due to parental nondisclosure, particularly regarding abuse [82] . Alternative approaches to mitigate parental bias include court-documented records or self-reports of adversity obtained from children/adolescents as they are developing or retrospectively when they are adults. Retrospective self-reports of childhood maltreatment (ascertained from participants, not their parents) have been shown to predict mental health problems even in the absence of objective measures (such as court-documented reports) [83] , [84] . The subjective experience of adversity could therefore play a key role in the development of mental health problems, and the parent-reports used in our study could show different patterns of results compared to participant self-reports or more objective records. For example, if parents underreported the prevalence of childhood adversity, our modelling approach may have been compromised, as exposed children with a higher risk of mental health problems would instead be classified as non-exposed to adversity, impacting our ability to detect differences in later depression levels based on adversity exposure. We therefore urge research to triangulate and assess multiple forms of adverse experiences and disentangle the effects of parent reports and children’s interpretations or perceptions of their experiences in relation to accumulation and sensitive period hypotheses. 4.2. Conclusion Understanding the relative magnitude and relevance of accumulation and sensitive period life history hypotheses in the relationship between childhood adversity and psychopathology may help inform prevention and intervention efforts. Results from this study suggest the effects of childhood adversity (caregiver abuse and financial hardship) on depressive symptoms in late adolescence are predominantly characterized by the accumulation of risk hypothesis, although sensitive periods were identified for caregiver abuse in males at ages 3.9 and 9. Ethical Standards and Informed Consent Ethical approval for the study was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees. Informed consent for the use of data collected via questionnaires and clinics was obtained from participants following the recommendations of the ALSPAC Ethics and Law Committee at the time. For a full overview of ethics approvals please refer to ALSPAC’s online statement at: https://www.bristol.ac.uk/media-library/sites/alspac/documents/governance/Research_Ethics_Committee_approval_references.pdf Funding The UK Medical Research Council and Wellcome (Grant ref: 217065/Z/19/Z) and the University of Bristol provide core support for ALSPAC. This publication is the work of the authors, who will serve as guarantors for the contents of this paper. A comprehensive list of grants funding is available on the ALSPAC website ( http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf ); this research was specifically funded by Wellcome Trust and MRC (Grant ref: 092731) and the National Institute of Mental Health of the National Institutes of Health (E.C.D., Award Number K01MH102403 and R01MH113930). The work of HT on this project was supported by a Dutch Research Council grant (NWO-ZonMW: 016.VICI.170.200). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Erin Dunn, ScD, MPH reports financial support was provided by National Institute of Mental Health. Henning Tiemeier, MD, PhD reports financial support was provided by Dutch Research Council. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgement We are extremely grateful to all the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. The authors thank Katie Davis and Janine Cerutti for their contributions to earlier drafts of the manuscript, as well as Brooke Smith for her assistance with data analysis. Footnotes ☆ A member of the Editorial Board is an author of this article. Editorial Board members are not involved in decisions about papers which they have written themselves or have been written by family members or colleagues or which relate to products or services in which the editor has an interest. Any such submission is subject to all of the journal's usual procedures, with peer review handled independently of the relevant editor and their research groups. 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