The association of multidimensional household poverty with child and mother psychopathology wellbeing trajectories using a prospective longitudinal cohort in Ireland.

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David J O Driscoll, Ali S Khashan, Linda M O Keeffe, Elizabeth Kiely This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4565907/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: The association between multidimensional household poverty (MHP) and child and mother psychopathology trajectories is not well understood. The aim of this paper is to explore this association. Methods: We analysed 9241 infants and mothers recruited at 9-months (m) and 32-years (y) respectively from the Growing-up-in-Ireland study and followed up when the child was aged 3, 5, 7, 9 and 13y. MHP was derived from monetary, subjective and material poverty questionnaires completed by mothers before infant age 3y using latent-class-analysis. Confounder-adjusted linear spline multilevel models were used to examine the association between MHP before 3y and trajectories of child (3-to-13y) and mother (34-to-46y) psychopathology wellbeing measures (Strengths-and-difficulties-questionnaire and Centre-for-epidemiological-studies-depression-scale, respectively). Results: In adjusted models, MHP before 3y was associated with a higher mean difference(MD) (MD:0.67, 95%CI 0.41,0.92) in child psychopathology at 3y and this was broadly similar at age 13y (MD:0.87, 95% CI 0.57,1.17). MHP prior to 3y was associated with a higher mother psychopathology when her child was age 3y (MD:1.07, 95% CI 0.90,1.23) and this persisted albeit with a slight reduction in magnitude at age 13y of the child (MD:0.72, 95% CI 0.53,0.90). Conclusions: . Though replication in other cohorts is required, findings suggest that MHP exposure in child infancy may have early negative life course impacts on mother and child wellbeing that persist for up to a decade. If causal, these findings underscore the importance of early life course policy interventions to prevent and ameliorate poverty to reduce long term psychopathology of mothers and their children. Figures Figure 1 Figure 2 Take home message What is already known on this subject? Single dimensions of poverty (e.g., monetary) are associated with psychopathology. The association between multidimensional household poverty (MHP) and child and mother psychopathology trajectories in early life is not well understood. What this study adds? This study shows that early childhood and early motherhood MHP exposure is associated with higher psychopathology scores in the mother and in the child by age 3 years and this difference largely persists for the following decade of life in both. Findings suggest early anti-poverty policy interventions during the first few years of motherhood and a child’s life may be important in improving a child’s and mother’s psychological wellbeing over time. Introduction There is strong evidence of a dose dependent relationship between adverse childhood experiences (ACEs) and poor outcomes (e.g., obesity, mental and physical illness etc.) [ 1 – 3 ]. Household poverty experienced during childhood is an ACE [ 3 , 4 ]. Most studies to date have focussed on unidimensional measures of poverty such as monetary poverty (e.g., lowest decile income), and demonstrated associations with poor psychopathology [ 5 – 7 ]. However, there are calls for the use of multidimensional measures of poverty (The Lancet Child Adolescent Health, 2019). In 2018, using the World Bank multidimensional poverty headcount ratio, approximately 15 percentage of people in the world lived in multidimensional poverty compared to nine percentage if only measured by monetary poverty alone [ 8 ]. Defining multidimensional poverty is challenging as there is no agreement about what dimensions of poverty (beyond income) should be included [ 9 , 10 ]. The Sustainable Developmental Goals defines multidimensional poverty as poverty in all its dynamic forms everywhere [ 11 ]. However, it is not practical nor is data available to take this all-encompassing approach to studying poverty. Instead, it is often necessary to define MHP using multiple indicators such as low income or an inability to afford standard household items etc. This multi-dimensional approach as utilised in this study allows greater flexibility to take into account more subgroups within a population that may be experiencing different types of poverty. Analysis of the Early Childhood Longitudinal Study in the United States, (1998–1999) highlighted using growth curve modelling that multidimensional poverty from 5 to 10 years was associated with internalising and externalizing behaviours in young children from 5 to 10 years [ 12 ]. However, there are few studies concerned with the child’s and mother’s experience of MHP in early life and their psychopathology trajectories. Understanding and addressing poverty in a child’s early life when a mother is caring for a young child, is an optimal developmentally sensitive period to target effective social and health policy interventions (e.g., education) to improve psychopathology wellbeing. Using a nationally representative prospective cohort (Growing Up in Ireland (GUI), we examined the association between MHP prior to 3 years of children and (i) trajectories of psychopathology in children from 3 years to 13 years (ii) trajectories of psychopathology in mothers during the same time period (from 3 to 13 years of their child) when mothers were aged (mean) 34 years to 46 years. Methods Data Using the Irish national child benefit register as a sampling frame, children and their primary caregivers (99.7% mothers and hereafter referred to as mothers) were recruited to the Infant Cohort in 2008 at child age 9 months (n = 11,134) (mean (SD) mother age 32 years (5.3)). Children were aged 3, (n = 9,793), 5 (n = 9,001), 7 (n = 5344), 9 (n = 8,032) and 13 (n = 6,655) years at follow-up and mothers were mean age (SD) 34 (5.5), 36 (5.4), 41 (5.3), and 46 (5.1) years at these follow ups. The Infant Cohort is estimated to represent 1-in-9 children born in 2008 in Ireland (n = 11, 194, included in the first wave). A detailed description of the study design, interview method, and follow-up procedure is provided by Quail and colleagues [ 13 , 14 ]. All waves had equivalent interview protocols. The GUI study received ethical approval from the Irish Department of Health and Children Research Ethics Committee. All participants gave informed consent to enrol in the GUI study. This study is in accordance with the ethical standards as per the Declaration of Helsinki (1964) and subsequent amendments. Exposure Multidimensional Household Poverty A mother completed a face-to-face questionnaire when their child was at age 9 months and 3 years. Questions on lowest quintile of equivalised household income, ease or difficulty in making ends meet and total score of material deprivation index were used to derive measures of “monetary”, “subjective" and “material“ poverty respectively and used in our latent class analysis of MHP [ 15 ]. Table S1 includes further information on the specific questions asked and possible responses given, including the categorisation used here for our analysis. Outcomes Child general psychopathology The mother completed a Strengths and Difficulties Questionnaire (SDQ) for each wave when their child was 3, 5, 7, 9, and 13 years of age. The SDQ was developed to detect active signs of emotional difficulties, hyperactivity, conduct behaviour, peer problems and prosocial issues in children [ 16 – 24 ]. (See supplementary material for additional information). Mother depressive psychopathology Mothers completed the Centre for epidemiological studies depression scale (8-item) (CESD-8), an eight- item questionnaire, when the mother was mean (SD) age in years 34 (5.5), 37 (5.4), 41 (5.3) and 46 (5.1). Table S2 provides a breakdown of CESD-8 questions) [ 25 – 27 ]. Potential confounders We used a directed acyclic graph (DAG) to identify potential confounders ( Figure S1 , Figure S2 ). Mother and child DAG have similar confounders. These included household factors (home-ownership, social class, household composition (i.e., single parent or two parent)), mother factors (age, and level of educational attainment (obtained a degree)), and mother health-related characteristics measured prior to exposure (has a chronic health condition, and depression score). Statistical analysis Latent class analysis – to derive a multidimensional household poverty exposure. Similar to previous methods used in GUI, latent class analysis was used to create a MHP exposure variable using the three poverty exposure variables (monetary, subjective, material poverty, see Table S1 ) measured when the child was 9 months or 3 years of age. The optimal number of latent classes was determined by fitting four models (one-class to four-classes) using two fit indices: the Bayesian Information Criteria (BIC), and the Akaike Information Criteria (AIC). Smaller values of BIC and AIC indicated a better model fit. We looked at model entropy values, in which values closer to one indicated a good classification and that each class had sufficient participants. Finally, we qualitatively assessed each output. Analyses of MHP as described here were completed separately at 9 months and 3 years. [ 15 ]. We then created a MHP exposure prior to 3 years as either experiencing same at 9 months or 3 years of age. All data analysis was carried out using the statistical software package Stata (v.17). Primary analyses – repeated measures analysis We used linear spline multilevel modelling (2 levels: measurement occasion and individual) to examine the association between MHP and trajectories of total, internalising and externalising SDQ scores)and mother CES-D depression scores. Linear spline multilevel models estimate mean trajectories of the outcome while accounting for the non-independence (i.e., clustering) of repeated measurements within individuals and differences in the number and timing of measurements between individuals (using all available data from all eligible participants under a missing at random assumption) (32–34). Linear splines allow knot points to be fit at different ages or stages to derive periods in which change is approximately linear [ 28 ]. Linear spline periods were chosen to reflect ages in whole years that were closest to mean age at interview and hence where the density of measures was greatest. Age (in years) was centred at the first available measure of outcome data, i.e., when child was 3 years and their mother was 34 years. For child outcomes, we had four knots at 5, 7, 9, 13 years, this produced four different linear slopes of the repeated outcome measure (i.e., SDQ): 3 to ≤ 5, 5 to ≤ 7, 7 to ≤9, 9 to ≤13 years. For mother outcomes, we had three knots at mean ages 37, 41 and 46 years, corresponding to the child ages above of 5, 9 and 13 years, producing three different linear slopes of repeated outcome measure (i.e., CESD-8): 34 to ≤37, 37 to ≤41, 41 to ≤46 years. All models included individual level random effects for the intercept and each linear spline period. For inclusion in the child psychopathology analysis, participants required data on MHP and at least one measure of child psychopathology from 3 years to 13 years and complete confounder data. For inclusion in the mother psychopathology analyses, participants required data on MHP and at least one measure of parent psychopathology when their child was 3, 5, 9 or 13 years of age and complete confounder data. Model fit statistics compared observed values of each outcome at each age or stage with those predicted by the models. All data analysis was carried out using the statistical software package Stata (v.17). Sensitivity and supplementary analyses We examined whether the associations of MHP at 9 months and 3 years separately were similar to the results of our main analysis (MHP at 9 months or 3 years). We explored the difference between included and excluded participants in our analysis sample due to missing exposure, confounder or outcome data. We describe the number of outcome measurements (e.g., SDQ, CESD-8) that were included and not included. We repeated our main analysis restricting analysis to participants with complete outcome data at each follow up period. We investigated if some of our confounders could also be mediators and repeated our main analysis adjusted only for mother age, mother education and mother depression status [ 31 ]. (See supplementary material for additional information on sensitivity methods). Some countries have distinct educational policies to provide resources to schools that are located in areas of deprivation. One such policy in Ireland introduced in 2005 is called the Delivering-Equality-of-Opportunity-in-Schools (DEIS) programme [ 32 , 33 ]. We examined whether our results for child and mother psychopathology differed by DEIS school attendance by performing a stratification analysis. Results Multidimensional Household Poverty – Latent class analysis Using all available data (n = 11,134), two latent classes were identified using the first wave (when child was 9 months of age) as the best fit by entropy ( Table S3 ), sample size per class and qualitatively ( Table S4 ). It was repeated for the second wave (when child was 3 years of age). The two classes identified were at risk and not at risk of multidimensional poverty. A composite multidimensional poverty exposure variable for further analysis was determined as being at risk in the first wave (when child was 9 months of age) or the second wave (when child was 3 years of age) Summary of demographics of Infant Cohort We had 11,134 children and mothers in our MHP latent class analysis to derive the classes, of these 9,241 (83%) had outcome and confounder data for inclusion in our analysis and 22.4% (n = 2,069) was exposed to poverty (Table 1 ). Households that experience poverty had lower home ownership, and lower parent educational attainment. Households that experienced poverty also had higher levels of a parent having chronic health problems, being of younger age and having higher depression scores. Table 1 Summary of demographic covariates and outcomes of participants used for analysis in the Irish Growing Up in Ireland - Infant Cohort (n = 9 241). No Poverty Poverty (n = 7 172) (n = 2 069) n (%) n (%) p-value * Covariates Gender (female) 3 533 (49.3) 9 88 (47.8) < 0.001 4 category household type < 0.001 1 parent, 1 child 262 (3.7) 187 (9) 1 parent, 2 + child 230 (3.2) 336 (16.2) 2 parents, 1 child 2 649 (36.9) 417 (20.2) 2 parents, 2 + child 4 031 (56.2) 1 129 (54.6) Equivalised household income (quintile) < 0.001 1st 788 (11) 1 054 (50.9) 2nd 1 257 (17.5) 452 (21.8) 3rd 1 523 (21.2) 288 (13.9) 4th 1 867 (26) 179 (8.7) 5th 1 737 (24.2) 96 (4.6) Home owner 5 532 (77.1) 899 (43.5) < 0.001 Home owner missing 2 (< 1) 3 (< 1) Relationship of PCG to child (parent) 7 172 (100) 2 068 (100) 0.063 PCG gender (female) 7 154 (99.7) 2 060 (99.6) 0.17 PCG age (yrs.) < 0.001 < 26 921 (12.8) 575 (27.8) 27–30 1 411 (19.7) 466 (22.5) 31–35 2 839 (39.6) 549 (26.5) 36–39 1 545 (21.5) 341 (16.5) 40+ 456 (6.4) 138 (6.7) PCG degree 3 064 (42.7) 388 (18.8) < 0.001 PCG degree missing 1 (< 1) 1 (< 1) PCG Chronic health problem 725 (10.1) 323 (15.6) < 0.001 Resourced Disadvantage School 1 466 (23.8) 654 (38.9) < 0.001 Outcome (mean (SD)) Total SDQ score at 3 yrs. 7.4 (4.4) 9.1 (5.1) < 0.001 5 yrs. 6.9 (4.5) 8.4 (5.4) < 0.001 7 yrs. 6.8 (5.1) 8.7 (6.2) < 0.001 9 yrs. 6.8 (5.1) 8.7 (6.1) < 0.001 13 yrs. 6.7 (5.4) 8.6 (6.5) < 0.001 Parental Depression score at child age 9 m. 2.1 (3.1) 3.6 (4.6) < 0.001 3 yrs. 2.0 (3.0) 3.7 (4.7) < 0.001 5 yrs. 1.9 (2.9) 3.2 (4.4) < 0.001 9 yrs. 2.0 (2.9) 3.2 (4.3) < 0.001 13 yrs. 2.8 (3.2) 3.7 (4.1) < 0.001 Abbreviations : SDQ strengths and difficulties questionnaire,, PCG Primary Care Giver, yrs. years, m months, SD (standard deviation). * Pearson's chi-square test, statistical significance p < 0.05. Primary analyses Child psychopathology Mean trajectories of child psychopathology wellbeing scores (SDQ total difficulties, internalising and externalising scores) from 3 years to 13 years by MHP prior to age 3 are demonstrated in Table 2 and Fig. 1 . In adjusted models, MHP prior to 3 years was associated with higher total difficulties scores at 3 (Mean difference (MD):0.67, 95% CI 0.41, 0.92), 5 years (MD:0.71, 95% CI 0.45, 0.97), 7 years (MD:1.14, 95% CI 0.75, 1.53), 9 years (MD:1.00, 95% CI 0.68, 1.31), and 13 years (MD:0.87, 95% CI 0.57, 1.17). MHP prior to 3 years of age was associated with higher internalising scores at 3 years (MD:0.36, 95% CI 0.18, 0.55), 5 years (MD:0.38, 95% CI 0.20, 0.56), 7 years (MD:0.50, 95% CI 0.27, 0.73), 9 years (MD:0.53, 95% CI 0.33, 0.73), and 13 years (MD:0.45, 95% CI 0.27, 0.63). Similarly, MHP prior to 3 years of age was associated with higher externalising scores at 3 years (MD:0.31 95% CI 0.18, 0.43), 5 years (MD:0.33, 95% CI 0.19, 0.47), 7 years (MD:0.64, 95% CI 0.41, 0.87), 9 years (MD:0.47, 95% CI 0.29, 0.65), and 13 years (MD:0.42, 95% CI 0.25, 0.60). Overall, there was no strong evidence of associations between MHP and change in each linear spline from 3 to 13 years. Table 2 Mean trajectories of child psychopathology scores (SDQ total difficulties, externalising and internalising scores) at 3, 5, 7, 9 and 13 years of age by multidimensional household poverty prior to 3 years of age using the Irish Growing Up in Ireland – Infant Cohort. Unadjusted Adjusted No Poverty Poverty No Poverty vs Poverty No Poverty Poverty No Poverty vs Poverty SDQ Mean trajectory (95% CI) Mean trajectory (95% CI) Mean difference in trajectory (95% CI) Mean trajectory (95% CI) Mean trajectory (95% CI) Mean difference in trajectory comparing Poverty to no Poverty (95% CI) Total SDQ Difficulties Score MD Age 3 yrs. 7.45 (7.37,7.56) 9.03 (8.81, 9.25) 1.56 (1.32, 1.80) 6.90 (6.49, 7.32) 7.57 (7.14, 8.01) 0.67 (0.41,0.92) \(\varDelta\) 3 to 5 yrs. -0.29 (-0.33,-0.24) -0.29(-0.40,-0.17) 0.00 (-0.12, 0.13) -0.60(-0.81,-0.38) -0.58(-0.80,-0.35) 0.02 (-0.11, 0.16) MD Age 5 yrs. 6.90 (6.79, 7.00) 8.46 (8.23, 8.69) 1.56 (1.31, 1.82) 5.71 (5.27, 6.15) 6.42 (5.96, 6.88) 0.71 (0.45, 0.97) \(\varDelta\) 5 to 7 yrs. 0.10 (0.03, 0.16) 0.41 (0.24, 0.58) 0.32 (0.13, 0.50) 0.50 (0.22, 0.77) 0.71 (0.41, 1.02) 0.21 (0.03, 0.40) MD Age 7 yrs. 7.09 (6.95, 7.22) 9.28 (8.92, 9.64) 2.19 (1.81, 2.58) 6.70 (6.12, 7.29) 7.84 (7.20, 8.48) 1.14 (0.75, 1.53) \(\varDelta\) 7 to 9 yrs. -0.12 (-0.18,-0.07) -0.25(-0.42,-0.09) -0.13 (-0.30, 0.04) -0.43(-0.68,-0.18) -0.50(-0.79,-0.22) -0.07 (-0.24,0.10) MD Age 9 yrs. 6.84 (6.72, 6.96) 8.77 (8.49, 9.05) 1.93 (1.63, 2.24) 5.84 (5.33, 6.36) 6.84 (6.31, 7.37) 1.00 (0.68, 1.31) \(\varDelta\) 9 to 13 yrs. 0.01 (-0.02, 0.04) -0.05 (-0.12,0.03) -0.05 (-0.14, 0.03) 0.39 (0.26, 0.53) 0.33 (0.19, 0.48) -0.06 (-0.15,0.02) MD Age 13 yrs. 6.86 (6.74, 6.97) 8.68 (8.41, 8.95) 1.82 (1.53, 2.12) 6.63 (6.14, 7.13) 7.50 (6.98, 8.03) 0.87 (0.57, 1.17) SDQ Externalising Score MD Age 3 yrs. 5.09 (5.02, 5.16) 6.06 (5.90, 6.22) 0.97 (0.79, 1.14) 4.57 (4.26, 4.87) 4.93 (4.61, 5.24) 0.36 (0.18, 0.55) \(\varDelta\) 3 to 5 yrs. -0.27 (-0.31,-0.24) -0.29(-0.37,-0.21) -0.02 (-0.11, 0.07) -0.47(-0.62,-0.32) -0.46(-0.62,-0.30) 0.01 (-0.09, 0.10) MD Age 5 yrs. 4.54 (4.47, 4.61) 5.47 (5.31, 5.64) 0.93 (0.75, 1.11) 3.63 (3.32, 3.94) 4.01 (3.69, 4.33) 0.38 (0.20, 0.56) \(\varDelta\) 5 to 7 yrs. -0.15 (-0.19,-0.11) -0.07 (-0.17, 0.03) 0.08 (-0.03, 0.19) 0.12 (-0.06, 0.29) 0.17 (-0.01, 0.36) 0.06 (-0.06, 0.17) MD Age 7 yrs. 4.24 (4.15, 4.32) 5.34 (5.13, 5.54) 1.10 (0.88, 1.33) 3.86 (3.50, 4.22) 4.36 (3.97, 4.74) 0.50 (0.27, 0.73) \(\varDelta\) 7 to 9 yrs. -0.11 (-0.15,-0.08) -0.13(-0.22,-0.03) -0.02 (-0.12, 0.08) -0.27(-0.42,-0.11) -0.25(-0.42,-0.08) 0.02 (-0.09, 0.12) MD Age 9 yrs. 4.01 (3.94, 4.09) 5.08 (4.90, 5.26) 1.07 (0.87, 1.26) 3.32 (3.00, 3.65) 3.86 (3.52, 4.19) 0.53 (0.33, 0.73) \(\varDelta\) 9 to 13 yrs. -0.13 (-0.15,-0.11) -0.18(-0.22,-0.13) -0.05 (-0.10, 0.00) 0.00 (-0.09, 0.08) -0.04 (-0.13, 0.05) -0.04 (-0.09, 0.01) MD Age 13 yrs. 3.75 (3.68, 3.82) 4.72 (4.56, 4.88) 0.97 (0.80, 1.15) 3.32 (3.02, 3.61) 3.77 (3.46, 4.08) 0.45 (0.27, 0.63) SDQ Internalising Score MD Age 3 yrs. 2.38 (2.33, 2.43) 2.98 (2.87, 3.08) 0.60 (0.49, 0.72) 2.34 (2.13, 2.54) 2.64 (2.43, 2.86) 0.31 (0.18, 0.43) \(\varDelta\) 3 to 5 yrs. -0.01 (-0.04, 0.01) 0.00 (-0.06, 0.06) 0.01 (-0.05, 0.08) -0.13(-0.25,-0.01) -0.12 (-0.24,0.01) 0.01 (-0.06, 0.09) MD Age 5 yrs. 2.35 (2.30, 2.40) 2.98 (2.86, 3.10) 0.63 (0.50, 0.76) 2.08 (1.86, 2.30) 2.41 (2.18, 2.64) 0.33 (0.19, 0.47) \(\varDelta\) 5 to 7 yrs. 0.24 (0.21, 0.28) 0.48 (0.37, 0.58) 0.23 (0.12, 0.34) 0.36 (0.20, 0.53) 0.52 (0.34, 0.71) 0.16 (0.04, 0.27) MD Age 7 yrs. 2.84 (2.76, 2.91) 3.93 (3.72, 4.14) 1.09 (0.87, 1.32) 2.81 (2.48, 3.14) 3.45 (3.08, 3.82) 0.64 (0.41, 0.87) \(\varDelta\) 7 to 9 yrs. -0.01 (-0.04, 0.03) -0.12(-0.22,-0.02) -0.11(-0.22,-0.01) -0.14(-0.30, 0.01) -0.23(-0.41,-0.06) -0.09 (-0.20, 0.02) MD Age 9 yrs. 2.82 (2.75, 2.89) 3.69 (3.54, 3.84) 0.87 (0.70, 1.04) 2.52 (2.24, 2.81) 2.99 (2.70, 3.28) 0.47 (0.29, 0.65) \(\varDelta\) 9 to 13 yrs. 0.14 (0.12, 0.16) 0.13 (0.08, 0.18) -0.01 (-0.06, 0.05) 0.40 (0.31, 0.49) 0.38 (0.28, 0.47) -0.02 (-0.08, 0.04) MD Age 13 yrs. 3.10 (3.03, 3.16) 3.95 (3.80, 4.11) 0.86 (0.69, 1.02) 3.32 (3.03, 3.60) 3.74 (3.45, 4.04) 0.42 (0.25, 0.60) Adjusted for : household composition, household home owner, mother age, mother education, mother chronic illness and mother depression. MD mean difference in SDQ score \(\varDelta\) mean difference in change per year of SDQ score. Abbreviations : yrs. years of age, SDQ Strengths and Difficulties Questionnaire, CI 95% Confidence Interval. Mother psychopathology Mean trajectories of mother psychopathology wellbeing scores (depression scores) over a similar time period from children age 3 to 13 years when mothers were mean age 34 to 46 years by MHP prior to age 3 of their child (i.e., first 3 years of motherhood to that child), are demonstrated in Table 3 and Fig. 2 . In adjusted models, MHP prior to 3 years was associated with higher psychopathology scores when the mother’s child was 3 years (MD:1.07, 95% CI 0.90, 1.23), 5 years (MD:0.96, 95% CI 0.78, 1.13), 9 years (MD:0.85, 95% CI 0.70, 0.99) and 13 years (MD:0.72, 95% CI 0.53, 0.90). Overall, there was no strong evidence of associations between MHP and change in each linear spline over this time period. Table 3 Mean trajectories of maternal psychopathology (depression) scores at 3, 5, 9 and 13 years by multidimensional household poverty prior to 3 years in the child (mean maternal age 34 years) using the Irish Growing Up in Ireland – Infant Cohort. Unadjusted Adjusted No Poverty Poverty No Poverty vs Poverty No Poverty Poverty No Poverty vs Poverty Mean trajectory (95% CI) Mean trajectory (95% CI) Mean difference in trajectory (95% CI) Mean trajectory (95% CI) Mean trajectory (95% CI) Mean difference in trajectory comparing Poverty to no Poverty (95% CI) Maternal Depression Scores MD 3 yrs. 1.99 (1.92, 2.07) 3.62 (3.47, 3.76) 1.62 (1.46, 1.79) 1.33 (1.04, 1.63) 2.40 (2.10, 2.70) 1.07 (0.90, 1.23) \(\varDelta\) 3 to 5 yrs. -0.06 (-0.09,-0.02) -0.15 (-0.23,-0.07) -0.10 (-0.18,-0.01) 0.13 (-0.04, 0.29) 0.07 (-0.10, 0.24) -0.06 (-0.15, 0.04) MD 5 yrs. 1.88 (1.81, 1.96) 3.31 (3.17, 3.46) 1.43 (1.27, 1.60) 1.59 (1.29, 1.90) 2.55 (2.23, 2.86) 0.96 (0.78, 1.13) \(\varDelta\) 5 to 9 yrs. 0.03 (0.01, 0.05) 0.00 (-0.04, 0.04) -0.04 (-0.08, 0.01) 0.08 (-0.01, 0.17) 0.03 (-0.06, 0.12) -0.05 (-0.10, 0.00) MD 9 yrs. 1.95 (1.88, 2.01) 3.31 (3.18, 3.44) 1.36 (1.22, 1.51) 1.75 (1.49, 2.02) 2.60 (2.33, 2.87) 0.85 (0.70, 0.99) \(\varDelta\) 9 to 13 yrs. 0.21 (0.19, 0.24) 0.13 (0.06, 0.19) -0.08 (-0.15,-0.01) 0.20 (0.08, 0.32) 0.13 (0.01, 0.26) -0.06 (-0.14, 0.01) MD 13 yrs. 2.37 (2.30, 2.45) 3.57 (3.41, 3.73) 1.20 (1.02, 1.37) 2.15 (1.84, 2.46) 2.87 (2.54, 3.19) 0.72 (0.53, 0.90) Adjusted for : household composition, household home owner, mother age, mother education, mother chronic illness and mother depression. \(\varDelta \text{m}\text{e}\text{a}\text{n} \text{d}\text{i}\text{f}\text{f}\text{e}\text{r}\text{e}\text{n}\text{c}\text{e} \text{i}\text{n} \text{c}\text{h}\text{a}\text{n}\text{g}\text{e} \text{p}\text{e}\text{r} \text{y}\text{e}\text{a}\text{r} \text{o}\text{f} \text{d}\text{e}\text{p}\text{r}\text{e}\text{s}\text{s}\text{i}\text{o}\text{n} \text{s}\text{c}\text{o}\text{r}\text{e}.\) Abbreviations : yrs. years of age, CI 95% Confidence Interval. Sensitivity and supplementary analyses Findings for associations of MHP at 9 months and 3 years were similar to our main results (MHP at 9 months or 3 years) ( Table S5 and Table S6 ). A description of included and excluded participants from analyses are available in Table S9 . The mean (SD) number of SDQ scores per child participants was 3.5 (1.6) and we had similar results when we restricted our sample to those participants with complete outcome (SDQ score) data ( Table S7, Table S11 ). The mean (SD) number of depression scores per mother participants was 2.7 (1.25) and we had similar results when we restricted our sample to those participants with complete outcome (depression score) data (Table S8, Table S10) . Sensitivity analyses with adjustment only for age and maternal education and without adjustment for remaining confounders, which may potentially be a mediator were similar to main analysis ( Table S12, Table S13) . (See supplementary material for additional information). Mean trajectories of child psychopathology scores from 3 years to 13 years by MHP prior to age 3 years, stratified by subsequent attendance at a school in a geographical area of deprivation (DEIS school) are demonstrated in Table S14 and Figure S3 . In adjusted analysis, MHP prior to 3 years was more strongly associated with total difficulty scores among DEIS school attendees compared with children who did not attend a DEIS school. For example, MHP prior to 3 years was associated with higher total difficulties at age 3 years (MD: 0.33, 95% CI 0.12, 0.55) in DEIS school attendees compared with non-DEIS school attendees and this persisted at age 13 years (MD: 0.49, 95% CI 0.01, 0.98). Mean trajectories of mother psychopathology scores over a similar time period as main analyses from child age 3 to 13 years when mothers were mean age 34 to 46 years by MHP prior to age 3 of their child (i.e., first 3 years of motherhood to that child) were stratified by attendance at a school in a geographical area of deprivation (DEIS school) and are demonstrated in Table S15 and Figure S4 . In adjusted analysis, MHP prior to 3 years was associated with higher psychopathology scores at 5 years (MD: 0.29, 95% CI 0.01, 0.57) and 13 years (MD: 0.35 CI 0.02, 0.68) and the scores spanned the null at 3 years (MD: 0.14, 95% CI -0.01, 0.30) and 9 years (MD: 0.15, 95% CI -0.09, 0.39) if the mother’s child attended a DEIS school compared to a mother’s child not attending a DEIS school. Discussion In this large contemporary Irish prospective cohort study of over 9,000 children and mothers, we found some evidence of associations of MHP before the child was age 3 years with measures of psychopathology in children and their mothers at child age 3 years. Although we found no strong evidence of associations with rates of change in psychopathology scores over time, the differences at age 3 years persisted up to a decade later, to age 13 years in the child and to age 47 years in mothers. Though replication in other cohorts is required, our findings may underscore the importance of early life course anti-poverty policy interventions to prevent and ameliorate poverty with the aim to improve long term psychological wellbeing of mothers and their children. González and colleagues investigated multidimensional poverty at 7–11 years of age and similar to our study demonstrated an association with higher internalising and externalising difficulties in young people (7–11 years of age) in two regions in Spain (n = 394, and n = 382). However, our work adds to these findings by showing that some of the differences seen in later childhood perhaps are driven by associations that arise in infancy and persist throughout childhood. [ 34 ]. Using the Raine study (n = 2 900 pregnancies) in Western Australia, Tearne and colleagues used linear mixed regression models with random intercept and slope to investigate the changes in mental health trajectories from 2 to 14 years of age following exposure of a prenatal adversity (e.g., household income below the poverty line (monetary)) [ 35 ]. Our findings are comparable and provide further evidence of the importance of the perinatal and postnatal period and up to 3 years. While we used MHP, Evans et al demonstrated the association of unidimensional poverty (0–9 years of age) and trajectory of internalizing and externalizing difficulties from 9 to 24 years of age [ 36 ]. Our findings highlight the continued importance of not allowing children to be born into poverty and that poverty remains a significant potential contributor to child developmental psychopathology. Although the association between poverty and depression features in parents is well documented [ 37 , 38 ], we could not identify a similar study that investigated the association of MHP during this important developmentally sensitive period of life (i.e., first 3 years) and both mother and child psychopathology outcomes. Thus our findings provide emerging evidence of same. This study contains limitations. First, the MHP exposure variable was classed as not at risk of poverty and at risk of poverty, and there may be other latent classes of poverty not identified. Second, our MHP did not include education level or attainment, social class status or other at-risk-of poverty factors. Third, the outcome measures are not clinical diagnosis. Fourth, loss to follow up occurred in this longitudinal cohort, and our included sample for analysis did have a lower proportion of single parent households, higher parent age profiles, and higher educational attainment levels indicating the possibility of attrition of families of lower socioeconomic status. While acknowledging that this is a limitation, we did have a large sample size for analysis and when we limited our analysis to participants with complete outcome data, we had similar findings. Fifth, we endeavoured to control for all known and available confounders. However, residual confounding may persist due to the inclusion of poorly measured confounders and also due to being unable to adjust for known confounders we had not measured such as area level deprivation. Sixth, It is important to acknowledge that DEIS schools are located in areas of deprivation and that DEIS attendees and their families (not all) may have a higher exposure of combined MHP and area of deprivation resulting in higher psychopathology scores anyway. As we were not able to adjust for area level of deprivation and DEIS school, our DEIS analysis needs to be interpreted with caution since residual confounding by area level of deprivation is likely, but also because DEIS school attendance is a likely mediator of the association. For a less biased examination of the role of DEIS school attendance as a mediator with interaction in our analyses, causal mediation methods would be required, which are beyond the scope of this paper. Finally, the available dataset was during a time period just post the 2008 international and national recessions. As such, the first few years (up to 3 years of age) of poverty in our sample, may have had unmeasured lagged poverty duration for a household. The strengths of this study include a broad conceptual definition and measure of MHP in contrast to unidimensional approaches. We used a large contemporary and recent nationally representative cohort making our results generalisable to the Irish population. This study was measured prospectively, with limited loss to follow up. Moreover, our analytical approach maximised the available sample size (i.e., a participant was included if they had the exposure, confounders and at least one outcome measurement) thereby minimising selection bias compared with complete case approaches. Conclusion In this large prospective nationally representative cohort, we demonstrated that MHP is associated with psychopathology from early childhood to early adolescence in children and in mothers during the same time period. Further work is required to better understand these associations, specifically whether these associations are causal and whether they are transient during childhood and dissipate in late adolescence. Declarations Declaration of conflicting interests The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding Linda M O’Keeffe is funded by a Health Research Board of Ireland Emerging Investigator Award (grant ref: EIA-FA-2019-007 SCaRLeT). Author Contribution DJODRoles: Conceptualization, formal analysis, investigation, methodology, project administration, validation, writing – original draft preparation. EKRoles: Conceptualization, methodology, in Acknowledgements Growing Up in Ireland (GUI) is funded by the Department of Children, Equality, Disability, Integration and Youth (DCEDIY). It is managed by DCEDIY in association with the Central Statistics Office (CSO). Results in this report are based on analyses of data from Research Microdata Files provided by the Central Statistics Office (CSO). Neither the CSO nor DCEDIY take any responsibility for the views expressed or the outputs generated from these analyses. References Bellis MA, Hughes K, Ford K et al (2019) Life course health consequences and associated annual costs of adverse childhood experiences across Europe and North America: a systematic review and meta-analysis. Lancet Public Health 4:e517–e528. https://doi.org/10.1016/S2468-2667(19)30145-8 Hughes M, Tucker W Poverty as an Adverse Childhood Experience. N C Med J 79:124–126 Lee K, Zhang L (2022) Cumulative Effects of Poverty on Children’s Social-Emotional Development: Absolute Poverty and Relative Poverty. Community Ment Health J 58:930–943. https://doi.org/10.1007/s10597-021-00901-x Flouri E, Midouhas E, Joshi H, Sullivan A (2015) Neighbourhood social fragmentation and the mental health of children in poverty. 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J Child Psychol Psychiatry 49:1304–1312. https://doi.org/10.1111/j.1469-7610.2008.01942.x Bracke P, Levecque K, Van de Velde S (2008) The psychometric properties of the CES-D depression inventory and the estimation of cross-national differences in the true prevalence of depression. In: Dag van de sociologie, Abstracts Karim J, Weisz R, Bibi Z, ur Rehman S (2015) Validation of the Eight-Item Center for Epidemiologic Studies Depression Scale (CES-D) Among Older Adults. Curr Psychol 34:681–692. https://doi.org/10.1007/s12144-014-9281-y Missinne S, Vandeviver C, Van de Velde S, Bracke P (2014) Measurement equivalence of the CES-D 8 depression-scale among the ageing population in eleven European countries. Soc Sci Res 46:38–47. https://doi.org/10.1016/j.ssresearch.2014.02.006 O’Keeffe LM, Tilling K, Bell JA et al (2023) Sex-specific trajectories of molecular cardiometabolic traits from childhood to young adulthood. Heart 109:674–685. https://doi.org/10.1136/heartjnl-2022-321347 O’Keeffe LM, Yelverton CA, Bartels HC et al (2023) Application of multilevel linear spline models for analysis of growth trajectories in a cohort with repeat antenatal and postnatal measures of growth: a prospective cohort study. BMJ Open 13:e065701. https://doi.org/10.1136/bmjopen-2022-065701 O’Keeffe LM, Simpkin AJ, Tilling K et al (2018) Sex-specific trajectories of measures of cardiovascular health during childhood and adolescence: A prospective cohort study. Atherosclerosis 278:190–196. https://doi.org/10.1016/j.atherosclerosis.2018.09.030 van Zwieten A, Tennant PWG, Kelly-Irving M et al (2022) Avoiding overadjustment bias in social epidemiology through appropriate covariate selection: a primer. J Clin Epidemiol 149:127–136. https://doi.org/10.1016/j.jclinepi.2022.05.021 (2020) DEIS Delivering Equality of Opportunity In Schools. https://www.gov.ie/en/policy-information/4018ea-deis-delivering-equality-of-opportunity-in-schools/ . Accessed 23 Aug 2023 Fenwick A, Kinsella B, Harford J (2022) Promoting academic resilience in DEIS schools. Ir Educational Stud 41:513–530. https://doi.org/10.1080/03323315.2022.2094107 González L, Estarlich M, Murcia M et al (2021) Poverty, social exclusion, and mental health: the role of the family context in children aged 7–11 years INMA mother-and-child cohort study. Eur Child Adolesc Psychiatry. https://doi.org/10.1007/s00787-021-01848-w Tearne JE, Allen KL, Herbison CE et al (2015) The association between prenatal environment and children’s mental health trajectories from 2 to 14 years. Eur Child Adolesc Psychiatry 24:1015–1024. https://doi.org/10.1007/s00787-014-0651-7 Evans GW, France KD (2022) Childhood Poverty and Psychological Wellbeing: The Mediating Role of Cumulative Risk Exposure. Dev Psychopathol 34:911–921. https://doi.org/10.1017/S0954579420001947 McGovern ME, Rokicki S, Reichman NE (2022) Maternal depression and economic well-being: A quasi-experimental approach. Soc Sci Med 305:115017. https://doi.org/10.1016/j.socscimed.2022.115017 Zhang X, Zhang Y, Vasilenko SA (2022) The longitudinal relationships among poverty, material hardship, and maternal depression in the USA: a latent growth mediation model. Arch Womens Ment Health 25:763–770. https://doi.org/10.1007/s00737-022-01238-4 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4565907","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":317232596,"identity":"11471dea-1e1f-4a09-98b8-d008dd00fef5","order_by":0,"name":"David J O Driscoll","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYDCCA0DEw8DMwAbiPCiwgQkSqyXBII04LQwgLQwQLYcJu4vv+BnDA28qrPP5+A8/fJBgcD6xv/0A44EPeLRInskxODjnTLplm0SasUGCwe3EGWcSGA7OwKPF4EBawmHetsMGbBI8bBIgLRskGBgO8+DTcv4ZVAv/GZCWcxAtf/BpuZF8AKKFIQek5QBECz7vS954fADkF6DDwH5JNp5xJrHhYA8eLXznE5s/AEPMQL4fGGIfKuxk+9sPH/7wA581WABjA4kaRsEoGAWjYBSgAwAyVlObIKhEhAAAAABJRU5ErkJggg==","orcid":"","institution":"School of Public Health, University College Cork","correspondingAuthor":true,"prefix":"","firstName":"David","middleName":"J O","lastName":"Driscoll","suffix":""},{"id":317232598,"identity":"710e5125-1132-4a5e-a5ea-b943c0bb1958","order_by":1,"name":"Ali S Khashan","email":"","orcid":"","institution":"School of Public Health, University College Cork","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"S","lastName":"Khashan","suffix":""},{"id":317232601,"identity":"1f19bdea-cdeb-46b6-88c6-f0ef48efc39c","order_by":2,"name":"Linda M O Keeffe","email":"","orcid":"","institution":"School of Public Health, University College Cork","correspondingAuthor":false,"prefix":"","firstName":"Linda","middleName":"M O","lastName":"Keeffe","suffix":""},{"id":317232602,"identity":"58d7beb0-1b7b-4ddc-adf9-6da098a66511","order_by":3,"name":"Elizabeth Kiely","email":"","orcid":"","institution":"School of Applied Social Studies","correspondingAuthor":false,"prefix":"","firstName":"Elizabeth","middleName":"","lastName":"Kiely","suffix":""}],"badges":[],"createdAt":"2024-06-11 18:21:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4565907/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4565907/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59966166,"identity":"d90a1897-672d-4045-9a79-e0eb9c06684d","added_by":"auto","created_at":"2024-07-10 02:05:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":105025,"visible":true,"origin":"","legend":"\u003cp\u003eTrajectories of child psychopathology scores (\u003cstrong\u003eA\u003c/strong\u003e) total difficulties score, (\u003cstrong\u003eB\u003c/strong\u003e) internalising difficulties score, and (\u003cstrong\u003eC\u003c/strong\u003e) externalising difficulties score from 3 years to 13 years by multidimensional household poverty (MHP) exposure prior to 3 years, from adjusted analysis (mother age, mother education, household composition, home ownership, mother chronic health status and depression status) using the Irish Growing Up in Ireland – Infant Cohort. (95% confidence intervals)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4565907/v1/9f5c05293543e75bced56942.png"},{"id":59966817,"identity":"aedeed21-0f36-46e1-b0b8-70a88ffd781d","added_by":"auto","created_at":"2024-07-10 02:13:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":71078,"visible":true,"origin":"","legend":"\u003cp\u003eTrajectories of maternal psychopathology score (depression score) from 3 to 13 years following multidimensional household poverty (MHP) exposure prior to 3 years in the child (mean maternal age 34), from adjusted analysis (mother age, mother education, household composition, home ownership, mother chronic health status and depression status) using the Irish Growing Up in Ireland – Infant Cohort. (95% confidence intervals)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4565907/v1/db5f15cc8f83a575790dc586.png"},{"id":73002306,"identity":"effa605a-9d3d-4e5a-84ab-40a658c96d80","added_by":"auto","created_at":"2025-01-05 17:01:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1394493,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4565907/v1/5242c6b9-779c-4419-8df0-4ea19530b693.pdf"},{"id":59966168,"identity":"af078875-fc5f-4e3b-b091-64f44c5cda6a","added_by":"auto","created_at":"2024-07-10 02:05:12","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":984549,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4565907/v1/70bb52f78fcaba4294361e7e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The association of multidimensional household poverty with child and mother psychopathology wellbeing trajectories using a prospective longitudinal cohort in Ireland.","fulltext":[{"header":"Take home message","content":"\u003cp\u003e\u003cem\u003eWhat is already known on this subject?\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSingle dimensions of poverty (e.g., monetary) are associated with psychopathology. The association between multidimensional household poverty (MHP) and child and mother psychopathology trajectories in early life is not well understood.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eWhat this study adds?\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study shows that early childhood and early motherhood MHP exposure is associated with higher psychopathology scores in the mother and in the child by age 3 years and this difference largely persists for the following decade of life in both. Findings suggest early anti-poverty policy interventions during the first few years of motherhood and a child\u0026rsquo;s life may be important in improving a child\u0026rsquo;s and mother\u0026rsquo;s psychological wellbeing over time.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eThere is strong evidence of a dose dependent relationship between adverse childhood experiences (ACEs) and poor outcomes (e.g., obesity, mental and physical illness etc.) [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Household poverty experienced during childhood is an ACE [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMost studies to date have focussed on unidimensional measures of poverty such as monetary poverty (e.g., lowest decile income), and demonstrated associations with poor psychopathology [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, there are calls for the use of multidimensional measures of poverty (The Lancet Child Adolescent Health, 2019). In 2018, using the World Bank multidimensional poverty headcount ratio, approximately 15 percentage of people in the world lived in multidimensional poverty compared to nine percentage if only measured by monetary poverty alone [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Defining multidimensional poverty is challenging as there is no agreement about what dimensions of poverty (beyond income) should be included [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The Sustainable Developmental Goals defines multidimensional poverty as \u003cem\u003epoverty in all its dynamic forms everywhere\u003c/em\u003e [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, it is not practical nor is data available to take this all-encompassing approach to studying poverty. Instead, it is often necessary to define MHP using multiple indicators such as low income or an inability to afford standard household items etc. This multi-dimensional approach as utilised in this study allows greater flexibility to take into account more subgroups within a population that may be experiencing different types of poverty.\u003c/p\u003e \u003cp\u003eAnalysis of the Early Childhood Longitudinal Study in the United States, (1998\u0026ndash;1999) highlighted using growth curve modelling that multidimensional poverty from 5 to 10 years was associated with internalising and externalizing behaviours in young children from 5 to 10 years [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, there are few studies concerned with the child\u0026rsquo;s and mother\u0026rsquo;s experience of MHP in early life and their psychopathology trajectories. Understanding and addressing poverty in a child\u0026rsquo;s early life when a mother is caring for a young child, is an optimal developmentally sensitive period to target effective social and health policy interventions (e.g., education) to improve psychopathology wellbeing.\u003c/p\u003e \u003cp\u003eUsing a nationally representative prospective cohort (Growing Up in Ireland (GUI), we examined the association between MHP prior to 3 years of children and (i) trajectories of psychopathology in children from 3 years to 13 years (ii) trajectories of psychopathology in mothers during the same time period (from 3 to 13 years of their child) when mothers were aged (mean) 34 years to 46 years.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData\u003c/h2\u003e \u003cp\u003e Using the Irish national child benefit register as a sampling frame, children and their primary caregivers (99.7% mothers and hereafter referred to as mothers) were recruited to the Infant Cohort in 2008 at child age 9 months (n\u0026thinsp;=\u0026thinsp;11,134) (mean (SD) mother age 32 years (5.3)). Children were aged 3, (n\u0026thinsp;=\u0026thinsp;9,793), 5 (n\u0026thinsp;=\u0026thinsp;9,001), 7 (n\u0026thinsp;=\u0026thinsp;5344), 9 (n\u0026thinsp;=\u0026thinsp;8,032) and 13 (n\u0026thinsp;=\u0026thinsp;6,655) years at follow-up and mothers were mean age (SD) 34 (5.5), 36 (5.4), 41 (5.3), and 46 (5.1) years at these follow ups. The Infant Cohort is estimated to represent 1-in-9 children born in 2008 in Ireland (n\u0026thinsp;=\u0026thinsp;11, 194, included in the first wave). A detailed description of the study design, interview method, and follow-up procedure is provided by Quail and colleagues [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. All waves had equivalent interview protocols. The GUI study received ethical approval from the Irish Department of Health and Children Research Ethics Committee. All participants gave informed consent to enrol in the GUI study. This study is in accordance with the ethical standards as per the Declaration of Helsinki (1964) and subsequent amendments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eExposure\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eMultidimensional Household Poverty\u003c/h2\u003e \u003cp\u003eA mother completed a face-to-face questionnaire when their child was at age 9 months and 3 years. Questions on lowest quintile of equivalised household income, ease or difficulty in making ends meet and total score of material deprivation index were used to derive measures of \u0026ldquo;monetary\u0026rdquo;, \u0026ldquo;subjective\" and \u0026ldquo;material\u0026ldquo; poverty respectively and used in our latent class analysis of MHP [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e includes further information on the specific questions asked and possible responses given, including the categorisation used here for our analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eOutcomes\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section4\"\u003e \u003ch2\u003eChild general psychopathology\u003c/h2\u003e \u003cp\u003eThe mother completed a Strengths and Difficulties Questionnaire (SDQ) for each wave when their child was 3, 5, 7, 9, and 13 years of age. The SDQ was developed to detect active signs of emotional difficulties, hyperactivity, conduct behaviour, peer problems and prosocial issues in children [\u003cspan additionalcitationids=\"CR17 CR18 CR19 CR20 CR21 CR22 CR23\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. (See supplementary material for additional information).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003eMother depressive psychopathology\u003c/h2\u003e \u003cp\u003eMothers completed the Centre for epidemiological studies depression scale (8-item) (CESD-8), an eight- item questionnaire, when the mother was mean (SD) age in years 34 (5.5), 37 (5.4), 41 (5.3) and 46 (5.1). \u003cb\u003eTable S2\u003c/b\u003e provides a breakdown of CESD-8 questions) [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003ePotential confounders\u003c/h2\u003e \u003cp\u003eWe used a directed acyclic graph (DAG) to identify potential confounders (\u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e, \u003cb\u003eFigure S2\u003c/b\u003e). Mother and child DAG have similar confounders. These included household factors (home-ownership, social class, household composition (i.e., single parent or two parent)), mother factors (age, and level of educational attainment (obtained a degree)), and mother health-related characteristics measured prior to exposure (has a chronic health condition, and depression score).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003e \u003cb\u003eLatent class analysis \u0026ndash; to derive a multidimensional household poverty exposure.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSimilar to previous methods used in GUI, latent class analysis was used to create a MHP exposure variable using the three poverty exposure variables (monetary, subjective, material poverty, see \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e) measured when the child was 9 months or 3 years of age. The optimal number of latent classes was determined by fitting four models (one-class to four-classes) using two fit indices: the Bayesian Information Criteria (BIC), and the Akaike Information Criteria (AIC). Smaller values of BIC and AIC indicated a better model fit. We looked at model entropy values, in which values closer to one indicated a good classification and that each class had sufficient participants. Finally, we qualitatively assessed each output. Analyses of MHP as described here were completed separately at 9 months and 3 years. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. We then created a MHP exposure prior to 3 years as either experiencing same at 9 months or 3 years of age. All data analysis was carried out using the statistical software package Stata (v.17).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePrimary analyses \u0026ndash; repeated measures analysis\u003c/h2\u003e \u003cp\u003eWe used linear spline multilevel modelling (2 levels: measurement occasion and individual) to examine the association between MHP and trajectories of total, internalising and externalising SDQ scores)and mother CES-D depression scores. Linear spline multilevel models estimate mean trajectories of the outcome while accounting for the non-independence (i.e., clustering) of repeated measurements within individuals and differences in the number and timing of measurements between individuals (using all available data from all eligible participants under a missing at random assumption) (32\u0026ndash;34). Linear splines allow knot points to be fit at different ages or stages to derive periods in which change is approximately linear [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Linear spline periods were chosen to reflect ages in whole years that were closest to mean age at interview and hence where the density of measures was greatest.\u003c/p\u003e \u003cp\u003eAge (in years) was centred at the first available measure of outcome data, i.e., when child was 3 years and their mother was 34 years. For child outcomes, we had four knots at 5, 7, 9, 13 years, this produced four different linear slopes of the repeated outcome measure (i.e., SDQ): 3 to \u0026le; 5, 5 to \u0026le; 7, 7 to \u0026le;9, 9 to \u0026le;13 years. For mother outcomes, we had three knots at mean ages 37, 41 and 46 years, corresponding to the child ages above of 5, 9 and 13 years, producing three different linear slopes of repeated outcome measure (i.e., CESD-8): 34 to \u0026le;37, 37 to \u0026le;41, 41 to \u0026le;46 years. All models included individual level random effects for the intercept and each linear spline period. For inclusion in the child psychopathology analysis, participants required data on MHP and at least one measure of child psychopathology from 3 years to 13 years and complete confounder data. For inclusion in the mother psychopathology analyses, participants required data on MHP and at least one measure of parent psychopathology when their child was 3, 5, 9 or 13 years of age and complete confounder data. Model fit statistics compared observed values of each outcome at each age or stage with those predicted by the models.\u003c/p\u003e \u003cp\u003eAll data analysis was carried out using the statistical software package Stata (v.17).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity and supplementary analyses\u003c/h2\u003e \u003cp\u003eWe examined whether the associations of MHP at 9 months and 3 years separately were similar to the results of our main analysis (MHP at 9 months or 3 years). We explored the difference between included and excluded participants in our analysis sample due to missing exposure, confounder or outcome data. We describe the number of outcome measurements (e.g., SDQ, CESD-8) that were included and not included. We repeated our main analysis restricting analysis to participants with complete outcome data at each follow up period. We investigated if some of our confounders could also be mediators and repeated our main analysis adjusted only for mother age, mother education and mother depression status [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. (See supplementary material for additional information on sensitivity methods). Some countries have distinct educational policies to provide resources to schools that are located in areas of deprivation. One such policy in Ireland introduced in 2005 is called the Delivering-Equality-of-Opportunity-in-Schools (DEIS) programme [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. We examined whether our results for child and mother psychopathology differed by DEIS school attendance by performing a stratification analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMultidimensional Household Poverty \u0026ndash; Latent class analysis\u003c/h2\u003e \u003cp\u003eUsing all available data (n\u0026thinsp;=\u0026thinsp;11,134), two latent classes were identified using the first wave (when child was 9 months of age) as the best fit by entropy (\u003cb\u003eTable S3\u003c/b\u003e), sample size per class and qualitatively (\u003cb\u003eTable S4\u003c/b\u003e). It was repeated for the second wave (when child was 3 years of age). The two classes identified were at risk and not at risk of multidimensional poverty. A composite multidimensional poverty exposure variable for further analysis was determined as being at risk in the first wave (when child was 9 months of age) or the second wave (when child was 3 years of age)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSummary of demographics of Infant Cohort\u003c/h2\u003e \u003cp\u003eWe had 11,134 children and mothers in our MHP latent class analysis to derive the classes, of these 9,241 (83%) had outcome and confounder data for inclusion in our analysis and 22.4% (n\u0026thinsp;=\u0026thinsp;2,069) was exposed to poverty (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Households that experience poverty had lower home ownership, and lower parent educational attainment. Households that experienced poverty also had higher levels of a parent having chronic health problems, being of younger age and having higher depression scores.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of demographic covariates and outcomes of participants used for analysis in the Irish Growing Up in Ireland - Infant Cohort (n\u0026thinsp;=\u0026thinsp;9 241).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNo Poverty\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ePoverty\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;7 172)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2 069)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCovariates\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(49.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(47.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4 category household type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 parent, 1 child\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 parent, 2\u0026thinsp;+\u0026thinsp;child\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(16.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 parents, 1 child\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(36.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 parents, 2\u0026thinsp;+\u0026thinsp;child\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(56.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(54.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEquivalised household income (quintile)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1st\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(50.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2nd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(21.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3rd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(21.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHome owner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(77.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(43.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHome owner missing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u0026lt;\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(\u0026lt;\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelationship of PCG to child (parent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCG gender (female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(99.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(99.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCG age (yrs.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(19.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(39.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(16.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCG degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(42.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCG degree missing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u0026lt;\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(\u0026lt;\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCG Chronic health problem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResourced Disadvantage School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(23.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(38.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOutcome (mean (SD))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal SDQ score at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParental Depression score at child age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9 m.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAbbreviations\u003c/b\u003e: SDQ strengths and difficulties questionnaire,, PCG Primary Care Giver, yrs. years, m months, SD (standard deviation). * Pearson's chi-square test, statistical significance p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePrimary analyses\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003eChild psychopathology\u003c/h2\u003e \u003cp\u003eMean trajectories of child psychopathology wellbeing scores (SDQ total difficulties, internalising and externalising scores) from 3 years to 13 years by MHP prior to age 3 are demonstrated in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In adjusted models, MHP prior to 3 years was associated with higher total difficulties scores at 3 (Mean difference (MD):0.67, 95% CI 0.41, 0.92), 5 years (MD:0.71, 95% CI 0.45, 0.97), 7 years (MD:1.14, 95% CI 0.75, 1.53), 9 years (MD:1.00, 95% CI 0.68, 1.31), and 13 years (MD:0.87, 95% CI 0.57, 1.17). MHP prior to 3 years of age was associated with higher internalising scores at 3 years (MD:0.36, 95% CI 0.18, 0.55), 5 years (MD:0.38, 95% CI 0.20, 0.56), 7 years (MD:0.50, 95% CI 0.27, 0.73), 9 years (MD:0.53, 95% CI 0.33, 0.73), and 13 years (MD:0.45, 95% CI 0.27, 0.63). Similarly, MHP prior to 3 years of age was associated with higher externalising scores at 3 years (MD:0.31 95% CI 0.18, 0.43), 5 years (MD:0.33, 95% CI 0.19, 0.47), 7 years (MD:0.64, 95% CI 0.41, 0.87), 9 years (MD:0.47, 95% CI 0.29, 0.65), and 13 years (MD:0.42, 95% CI 0.25, 0.60). Overall, there was no strong evidence of associations between MHP and change in each linear spline from 3 to 13 years.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean trajectories of child psychopathology scores (SDQ total difficulties, externalising and internalising scores) at 3, 5, 7, 9 and 13 years of age by multidimensional household poverty prior to 3 years of age using the Irish Growing Up in Ireland \u0026ndash; Infant Cohort.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Poverty\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePoverty\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo Poverty vs Poverty\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo Poverty\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePoverty\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo Poverty vs Poverty\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean trajectory (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean trajectory (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean difference in trajectory (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean trajectory (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean trajectory (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean difference in trajectory comparing Poverty to no Poverty (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal SDQ Difficulties Score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD Age 3 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.45 (7.37,7.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.03 (8.81, 9.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.56 (1.32, 1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.90 (6.49, 7.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.57 (7.14, 8.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.67 (0.41,0.92)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta\\)\u003c/span\u003e\u003c/span\u003e 3 to 5 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.29 (-0.33,-0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.29(-0.40,-0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00 (-0.12, 0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.60(-0.81,-0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.58(-0.80,-0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.02 (-0.11, 0.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD Age 5 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.90 (6.79, 7.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.46 (8.23, 8.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.56 (1.31, 1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.71 (5.27, 6.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.42 (5.96, 6.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.71 (0.45, 0.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta\\)\u003c/span\u003e\u003c/span\u003e 5 to 7 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.10 (0.03, 0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.41 (0.24, 0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32 (0.13, 0.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.50 (0.22, 0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.71 (0.41, 1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.21 (0.03, 0.40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD Age 7 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.09 (6.95, 7.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.28 (8.92, 9.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.19 (1.81, 2.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.70 (6.12, 7.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.84 (7.20, 8.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.14 (0.75, 1.53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta\\)\u003c/span\u003e\u003c/span\u003e 7 to 9 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.12 (-0.18,-0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.25(-0.42,-0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.13 (-0.30, 0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.43(-0.68,-0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.50(-0.79,-0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.07 (-0.24,0.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD Age 9 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.84 (6.72, 6.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.77 (8.49, 9.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.93 (1.63, 2.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.84 (5.33, 6.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.84 (6.31, 7.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00 (0.68, 1.31)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta\\)\u003c/span\u003e\u003c/span\u003e 9 to 13 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01 (-0.02, 0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.05 (-0.12,0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.05 (-0.14, 0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.39 (0.26, 0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.33 (0.19, 0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.06 (-0.15,0.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD Age 13 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.86 (6.74, 6.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.68 (8.41, 8.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.82 (1.53, 2.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.63 (6.14, 7.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.50 (6.98, 8.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.87 (0.57, 1.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSDQ Externalising Score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD Age 3 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.09 (5.02, 5.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.06 (5.90, 6.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97 (0.79, 1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.57 (4.26, 4.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.93 (4.61, 5.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.36 (0.18, 0.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta\\)\u003c/span\u003e\u003c/span\u003e 3 to 5 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.27 (-0.31,-0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.29(-0.37,-0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.02 (-0.11, 0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.47(-0.62,-0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.46(-0.62,-0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01 (-0.09, 0.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD Age 5 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.54 (4.47, 4.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.47 (5.31, 5.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93 (0.75, 1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.63 (3.32, 3.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.01 (3.69, 4.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.38 (0.20, 0.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta\\)\u003c/span\u003e\u003c/span\u003e 5 to 7 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.15 (-0.19,-0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.07 (-0.17, 0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08 (-0.03, 0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.12 (-0.06, 0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.17 (-0.01, 0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.06 (-0.06, 0.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD Age 7 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.24 (4.15, 4.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.34 (5.13, 5.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.10 (0.88, 1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.86 (3.50, 4.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.36 (3.97, 4.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.50 (0.27, 0.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta\\)\u003c/span\u003e\u003c/span\u003e 7 to 9 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.11 (-0.15,-0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.13(-0.22,-0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.02 (-0.12, 0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.27(-0.42,-0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.25(-0.42,-0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.02 (-0.09, 0.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD Age 9 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.01 (3.94, 4.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.08 (4.90, 5.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07 (0.87, 1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.32 (3.00, 3.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.86 (3.52, 4.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.53 (0.33, 0.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta\\)\u003c/span\u003e\u003c/span\u003e 9 to 13 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.13 (-0.15,-0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.18(-0.22,-0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.05 (-0.10, 0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00 (-0.09, 0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.04 (-0.13, 0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.04 (-0.09, 0.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD Age 13 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.75 (3.68, 3.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.72 (4.56, 4.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97 (0.80, 1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.32 (3.02, 3.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.77 (3.46, 4.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.45 (0.27, 0.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSDQ Internalising Score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD Age 3 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.38 (2.33, 2.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.98 (2.87, 3.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.60 (0.49, 0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.34 (2.13, 2.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.64 (2.43, 2.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.31 (0.18, 0.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta\\)\u003c/span\u003e\u003c/span\u003e 3 to 5 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.01 (-0.04, 0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00 (-0.06, 0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01 (-0.05, 0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.13(-0.25,-0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.12 (-0.24,0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01 (-0.06, 0.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD Age 5 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.35 (2.30, 2.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.98 (2.86, 3.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.63 (0.50, 0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.08 (1.86, 2.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.41 (2.18, 2.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.33 (0.19, 0.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta\\)\u003c/span\u003e\u003c/span\u003e 5 to 7 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.24 (0.21, 0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.48 (0.37, 0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23 (0.12, 0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.36 (0.20, 0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.52 (0.34, 0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.16 (0.04, 0.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD Age 7 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.84 (2.76, 2.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.93 (3.72, 4.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.09 (0.87, 1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.81 (2.48, 3.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.45 (3.08, 3.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.64 (0.41, 0.87)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta\\)\u003c/span\u003e\u003c/span\u003e 7 to 9 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.01 (-0.04, 0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.12(-0.22,-0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.11(-0.22,-0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.14(-0.30, 0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.23(-0.41,-0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.09 (-0.20, 0.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD Age 9 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.82 (2.75, 2.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.69 (3.54, 3.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.87 (0.70, 1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.52 (2.24, 2.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.99 (2.70, 3.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.47 (0.29, 0.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta\\)\u003c/span\u003e\u003c/span\u003e 9 to 13 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.14 (0.12, 0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.13 (0.08, 0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.01 (-0.06, 0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.40 (0.31, 0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.38 (0.28, 0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.02 (-0.08, 0.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD Age 13 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.10 (3.03, 3.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.95 (3.80, 4.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.86 (0.69, 1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.32 (3.03, 3.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.74 (3.45, 4.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.42 (0.25, 0.60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAdjusted for\u003c/b\u003e: household composition, household home owner, mother age, mother education, mother chronic illness and mother depression. MD mean difference in SDQ score \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta\\)\u003c/span\u003e\u003c/span\u003e mean difference in change per year of SDQ score.\u003c/p\u003e \u003cp\u003e\u003cb\u003eAbbreviations\u003c/b\u003e: yrs. years of age, SDQ Strengths and Difficulties Questionnaire, CI 95% Confidence Interval.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eMother psychopathology\u003c/h2\u003e \u003cp\u003eMean trajectories of mother psychopathology wellbeing scores (depression scores) over a similar time period from children age 3 to 13 years when mothers were mean age 34 to 46 years by MHP prior to age 3 of their child (i.e., first 3 years of motherhood to that child), are demonstrated in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In adjusted models, MHP prior to 3 years was associated with higher psychopathology scores when the mother\u0026rsquo;s child was 3 years (MD:1.07, 95% CI 0.90, 1.23), 5 years (MD:0.96, 95% CI 0.78, 1.13), 9 years (MD:0.85, 95% CI 0.70, 0.99) and 13 years (MD:0.72, 95% CI 0.53, 0.90). Overall, there was no strong evidence of associations between MHP and change in each linear spline over this time period.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean trajectories of maternal psychopathology (depression) scores at 3, 5, 9 and 13 years by multidimensional household poverty prior to 3 years in the child (mean maternal age 34 years) using the Irish Growing Up in Ireland \u0026ndash; Infant Cohort.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Poverty\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePoverty\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo Poverty vs Poverty\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo Poverty\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePoverty\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo Poverty vs Poverty\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean trajectory (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean trajectory (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean difference in trajectory (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean trajectory (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean trajectory (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean difference in trajectory comparing Poverty to no Poverty (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMaternal Depression Scores\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD 3 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.99 (1.92, 2.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.62 (3.47, 3.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.62 (1.46, 1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.33 (1.04, 1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.40 (2.10, 2.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.07 (0.90, 1.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta\\)\u003c/span\u003e\u003c/span\u003e 3 to 5 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.06 (-0.09,-0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.15 (-0.23,-0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.10 (-0.18,-0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.13 (-0.04, 0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07 (-0.10, 0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.06 (-0.15, 0.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD 5 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.88 (1.81, 1.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.31 (3.17, 3.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.43 (1.27, 1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.59 (1.29, 1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.55 (2.23, 2.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.96 (0.78, 1.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta\\)\u003c/span\u003e\u003c/span\u003e 5 to 9 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03 (0.01, 0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00 (-0.04, 0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.04 (-0.08, 0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08 (-0.01, 0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03 (-0.06, 0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.05 (-0.10, 0.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD 9 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.95 (1.88, 2.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.31 (3.18, 3.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.36 (1.22, 1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.75 (1.49, 2.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.60 (2.33, 2.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.85 (0.70, 0.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta\\)\u003c/span\u003e\u003c/span\u003e 9 to 13 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.21 (0.19, 0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.13 (0.06, 0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.08 (-0.15,-0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.20 (0.08, 0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.13 (0.01, 0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.06 (-0.14, 0.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMD 13 yrs.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.37 (2.30, 2.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.57 (3.41, 3.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.20 (1.02, 1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.15 (1.84, 2.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.87 (2.54, 3.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.72 (0.53, 0.90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAdjusted for\u003c/b\u003e: household composition, household home owner, mother age, mother education, mother chronic illness and mother depression. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta \\text{m}\\text{e}\\text{a}\\text{n} \\text{d}\\text{i}\\text{f}\\text{f}\\text{e}\\text{r}\\text{e}\\text{n}\\text{c}\\text{e} \\text{i}\\text{n} \\text{c}\\text{h}\\text{a}\\text{n}\\text{g}\\text{e} \\text{p}\\text{e}\\text{r} \\text{y}\\text{e}\\text{a}\\text{r} \\text{o}\\text{f} \\text{d}\\text{e}\\text{p}\\text{r}\\text{e}\\text{s}\\text{s}\\text{i}\\text{o}\\text{n} \\text{s}\\text{c}\\text{o}\\text{r}\\text{e}.\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAbbreviations\u003c/b\u003e: yrs. years of age, CI 95% Confidence Interval.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity and supplementary analyses\u003c/h2\u003e \u003cp\u003eFindings for associations of MHP at 9 months and 3 years were similar to our main results (MHP at 9 months or 3 years) (\u003cb\u003eTable S5 and Table S6\u003c/b\u003e). A description of included and excluded participants from analyses are available in \u003cb\u003eTable S9\u003c/b\u003e. The mean (SD) number of SDQ scores per child participants was 3.5 (1.6) and we had similar results when we restricted our sample to those participants with complete outcome (SDQ score) data (\u003cb\u003eTable S7, Table S11\u003c/b\u003e). The mean (SD) number of depression scores per mother participants was 2.7 (1.25) and we had similar results when we restricted our sample to those participants with complete outcome (depression score) data \u003cb\u003e(Table S8, Table S10)\u003c/b\u003e. Sensitivity analyses with adjustment only for age and maternal education and without adjustment for remaining confounders, which may potentially be a mediator were similar to main analysis (\u003cb\u003eTable S12, Table S13)\u003c/b\u003e. (See supplementary material for additional information).\u003c/p\u003e \u003cp\u003eMean trajectories of child psychopathology scores from 3 years to 13 years by MHP prior to age 3 years, stratified by subsequent attendance at a school in a geographical area of deprivation (DEIS school) are demonstrated in \u003cb\u003eTable S14\u003c/b\u003e and \u003cb\u003eFigure S3\u003c/b\u003e. In adjusted analysis, MHP prior to 3 years was more strongly associated with total difficulty scores among DEIS school attendees compared with children who did not attend a DEIS school. For example, MHP prior to 3 years was associated with higher total difficulties at age 3 years (MD: 0.33, 95% CI 0.12, 0.55) in DEIS school attendees compared with non-DEIS school attendees and this persisted at age 13 years (MD: 0.49, 95% CI 0.01, 0.98). Mean trajectories of mother psychopathology scores over a similar time period as main analyses from child age 3 to 13 years when mothers were mean age 34 to 46 years by MHP prior to age 3 of their child (i.e., first 3 years of motherhood to that child) were stratified by attendance at a school in a geographical area of deprivation (DEIS school) and are demonstrated in \u003cb\u003eTable S15\u003c/b\u003e and \u003cb\u003eFigure S4\u003c/b\u003e. In adjusted analysis, MHP prior to 3 years was associated with higher psychopathology scores at 5 years (MD: 0.29, 95% CI 0.01, 0.57) and 13 years (MD: 0.35 CI 0.02, 0.68) and the scores spanned the null at 3 years (MD: 0.14, 95% CI -0.01, 0.30) and 9 years (MD: 0.15, 95% CI -0.09, 0.39) if the mother\u0026rsquo;s child attended a DEIS school compared to a mother\u0026rsquo;s child not attending a DEIS school.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large contemporary Irish prospective cohort study of over 9,000 children and mothers, we found some evidence of associations of MHP before the child was age 3 years with measures of psychopathology in children and their mothers at child age 3 years. Although we found no strong evidence of associations with rates of change in psychopathology scores over time, the differences at age 3 years persisted up to a decade later, to age 13 years in the child and to age 47 years in mothers. Though replication in other cohorts is required, our findings may underscore the importance of early life course anti-poverty policy interventions to prevent and ameliorate poverty with the aim to improve long term psychological wellbeing of mothers and their children.\u003c/p\u003e \u003cp\u003eGonz\u0026aacute;lez and colleagues investigated multidimensional poverty at 7\u0026ndash;11 years of age and similar to our study demonstrated an association with higher internalising and externalising difficulties in young people (7\u0026ndash;11 years of age) in two regions in Spain (n\u0026thinsp;=\u0026thinsp;394, and n\u0026thinsp;=\u0026thinsp;382). However, our work adds to these findings by showing that some of the differences seen in later childhood perhaps are driven by associations that arise in infancy and persist throughout childhood. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Using the Raine study (n\u0026thinsp;=\u0026thinsp;2 900 pregnancies) in Western Australia, Tearne and colleagues used linear mixed regression models with random intercept and slope to investigate the changes in mental health trajectories from 2 to 14 years of age following exposure of a prenatal adversity (e.g., household income below the poverty line (monetary)) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Our findings are comparable and provide further evidence of the importance of the perinatal and postnatal period and up to 3 years. While we used MHP, Evans et al demonstrated the association of unidimensional poverty (0\u0026ndash;9 years of age) and trajectory of internalizing and externalizing difficulties from 9 to 24 years of age [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Our findings highlight the continued importance of not allowing children to be born into poverty and that poverty remains a significant potential contributor to child developmental psychopathology. Although the association between poverty and depression features in parents is well documented [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], we could not identify a similar study that investigated the association of MHP during this important developmentally sensitive period of life (i.e., first 3 years) and both mother and child psychopathology outcomes. Thus our findings provide emerging evidence of same.\u003c/p\u003e \u003cp\u003eThis study contains limitations. First, the MHP exposure variable was classed as not at risk of poverty and at risk of poverty, and there may be other latent classes of poverty not identified. Second, our MHP did not include education level or attainment, social class status or other at-risk-of poverty factors. Third, the outcome measures are not clinical diagnosis. Fourth, loss to follow up occurred in this longitudinal cohort, and our included sample for analysis did have a lower proportion of single parent households, higher parent age profiles, and higher educational attainment levels indicating the possibility of attrition of families of lower socioeconomic status. While acknowledging that this is a limitation, we did have a large sample size for analysis and when we limited our analysis to participants with complete outcome data, we had similar findings. Fifth, we endeavoured to control for all known and available confounders. However, residual confounding may persist due to the inclusion of poorly measured confounders and also due to being unable to adjust for known confounders we had not measured such as area level deprivation. Sixth, It is important to acknowledge that DEIS schools are located in areas of deprivation and that DEIS attendees and their families (not all) may have a higher exposure of combined MHP and area of deprivation resulting in higher psychopathology scores anyway. As we were not able to adjust for area level of deprivation and DEIS school, our DEIS analysis needs to be interpreted with caution since residual confounding by area level of deprivation is likely, but also because DEIS school attendance is a likely mediator of the association. For a less biased examination of the role of DEIS school attendance as a mediator with interaction in our analyses, causal mediation methods would be required, which are beyond the scope of this paper. Finally, the available dataset was during a time period just post the 2008 international and national recessions. As such, the first few years (up to 3 years of age) of poverty in our sample, may have had unmeasured lagged poverty duration for a household.\u003c/p\u003e \u003cp\u003eThe strengths of this study include a broad conceptual definition and measure of MHP in contrast to unidimensional approaches. We used a large contemporary and recent nationally representative cohort making our results generalisable to the Irish population. This study was measured prospectively, with limited loss to follow up. Moreover, our analytical approach maximised the available sample size (i.e., a participant was included if they had the exposure, confounders and at least one outcome measurement) thereby minimising selection bias compared with complete case approaches.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this large prospective nationally representative cohort, we demonstrated that MHP is associated with psychopathology from early childhood to early adolescence in children and in mothers during the same time period. Further work is required to better understand these associations, specifically whether these associations are causal and whether they are transient during childhood and dissipate in late adolescence.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of conflicting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eLinda M O\u0026rsquo;Keeffe is funded by a Health Research Board of Ireland Emerging Investigator Award (grant ref: EIA-FA-2019-007 SCaRLeT).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eDJODRoles: Conceptualization, formal analysis, investigation, methodology, project administration, validation, writing \u0026ndash; original draft preparation. EKRoles: Conceptualization, methodology, in\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGrowing Up in Ireland (GUI) is funded by the Department of Children, Equality, Disability, Integration and Youth (DCEDIY). It is managed by DCEDIY in association with the Central Statistics Office (CSO). Results in this report are based on analyses of data from Research Microdata Files provided by the Central Statistics Office (CSO). Neither the CSO nor DCEDIY take any responsibility for the views expressed or the outputs generated from these analyses.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBellis MA, Hughes K, Ford K et al (2019) Life course health consequences and associated annual costs of adverse childhood experiences across Europe and North America: a systematic review and meta-analysis. 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Arch Womens Ment Health 25:763\u0026ndash;770. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00737-022-01238-4\u003c/span\u003e\u003cspan address=\"10.1007/s00737-022-01238-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4565907/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4565907/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThe association between multidimensional household poverty (MHP) and child and mother psychopathology trajectories is not well understood. The aim of this paper is to explore this association.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe analysed 9241 infants and mothers recruited at 9-months (m) and 32-years (y) \u0026nbsp;respectively from the Growing-up-in-Ireland study and followed up when the child was aged 3, 5, 7, 9 and 13y. MHP was derived from monetary, subjective and material poverty questionnaires completed by mothers before infant age 3y using latent-class-analysis. Confounder-adjusted linear spline multilevel models were used to examine the association between MHP before 3y and trajectories of child (3-to-13y) and mother (34-to-46y) psychopathology wellbeing measures (Strengths-and-difficulties-questionnaire and Centre-for-epidemiological-studies-depression-scale, respectively).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e In adjusted models, MHP before 3y was associated with a higher mean difference(MD) (MD:0.67, 95%CI 0.41,0.92) in child psychopathology at 3y and this was broadly similar at age 13y (MD:0.87, 95% CI 0.57,1.17). \u0026nbsp;MHP prior to 3y was associated with a higher mother psychopathology when her child was age 3y (MD:1.07, 95% CI 0.90,1.23) \u0026nbsp;and this persisted albeit with a slight reduction in magnitude at age 13y of the child (MD:0.72, 95% CI 0.53,0.90).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e. Though replication in other cohorts is required, findings suggest that MHP exposure in child infancy may have early negative life course impacts on mother and child wellbeing that persist for up to a decade. \u0026nbsp;If causal, these findings underscore the importance of early life course policy interventions to prevent and ameliorate poverty to reduce long term psychopathology of mothers and their children.\u003c/p\u003e","manuscriptTitle":"The association of multidimensional household poverty with child and mother psychopathology wellbeing trajectories using a prospective longitudinal cohort in Ireland.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-10 02:05:07","doi":"10.21203/rs.3.rs-4565907/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c251e8f2-c816-4a89-928c-8c957e082b28","owner":[],"postedDate":"July 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-05T16:53:14+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-10 02:05:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4565907","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4565907","identity":"rs-4565907","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}