The impact of multidimensional disadvantage over childhood on developmental outcomes in Australia

The impact of multidimensional disadvantage over childhood on developmental outcomes in Australia Abstract Background Understanding the relationship between different aspects of disadvantage over time and domains of child development will facilitate the formulation of more precise policy responses. We examined the association between exposure to aspects of disadvantage over the childhood period (from 0–9 years) and child development at 10–11 years. Methods We used data from the nationally representative birth cohort of the Longitudinal Study of Australian Children (n = 4979). Generalized linear models with log-Poisson link were used to estimate the association between previously derived disadvantage trajectories (in each of four lenses of sociodemographic, geographic environments, health conditions and risk factors, and a composite of these) and risk of poor child developmental outcomes. Population-attributable fractions were calculated to quantify the potential benefit of providing all children with optimal conditions for each developmental outcome. Results Trajectories of disadvantage were associated with developmental outcomes: children in the most disadvantaged composite trajectory had seven times higher risk of poor outcomes on two or more developmental domains, compared with those most advantaged. Trajectories of disadvantage in different lenses were varyingly associated with the child development domains of socio-emotional adjustment, physical functioning and learning competencies. Exposure to the most advantaged trajectory across all lenses could reduce poor developmental outcomes by as much as 70%. Conclusions Exposure to disadvantage over time is associated with adverse child development outcomes. Developmental outcomes varied with the aspects of disadvantage experienced, highlighting potential targets for more precise policy responses. The findings provide evidence to stimulate advocacy and action to reduce child inequities. Health inequity, disadvantage, child development, longitudinal, adversity Key Messages The more disadvantaged a child’s trajectory, the higher the risk of poor developmental outcomes by late childhood. Exposure to different aspects of disadvantage over childhood showed varying associations with children’s socio-emotional adjustment, physical functioning and learning competencies. At a population level, addressing all aspects of disadvantage and providing optimal conditions across childhood could reduce poor developmental outcomes by as much as 70%. Understanding associations between specific aspects of disadvantage and child development domains is necessary for greater precision in policy responses. Introduction Early childhood development lays the foundation for health, well-being, and productivity over the life course.1,2 Brain development during early childhood provides the architecture for ongoing skills development and is highly sensitive to external influences, such as exposure to disadvantage.3,4 As a consequence, early disadvantage adversely affects children’s development,5,6 with more adverse outcomes for each increment of increasing disadvantage.7,8 This pattern exists in all countries, across children’s developmental domains.1,9,10 It reflects inequitable developmental differences that are unjust and preventable.1,11 The World Health Organization’s (WHO’s) Commission on Social Determinants of Health has called for the elimination of inequitable health outcomes within a generation.1,12 To achieve this, public health and public policy interventions, targeted during early childhood, are needed to reduce inequities in three major domains of child development: socio-emotional adjustment, physical functioning and learning competencies.1,2 Each of these domains contributes to children’s current and future functioning and achievement, including school success, economic and social participation and health.2 Public policy responses should be informed by robust data to determine the current extent of child inequities across these aspects of child development, guide the development of intervention targets and monitor progress.13,14 Building the evidence base to address child inequities is complicated by the necessarily complex nature of measuring and conceptualizing disadvantage, or relative position in a social hierarchy.15,16 Recently, we developed and tested a multidimensional framework of child disadvantage17 (Figure 1) based on the bio-ecological perspective that child development occurs within a complex system of interacting environments18 and is shaped by the circumstances in which children live, learn and grow.19 The resulting four-factor model is structured around the inter-related social determinant ‘lenses’ of sociodemographic factors, geographic environments, health conditions and risk factors.17 Longitudinal analysis in the Australian population has revealed different patterns of stability and change for each disadvantage lens over childhood, offering diverse opportunities for intervention (Goldfeld et al., submitted for publication). Figure 1 View largeDownload slide Framework of child disadvantage aligning to a social determinants and bioecological perspective, reproduced from Goldfeld et al.17 Examples of relevant indicators within each lens and level are shown. It is expected that disadvantage experienced through each of these lenses will overlap and influence inequities in complex ways.19. Figure 1 View largeDownload slide Framework of child disadvantage aligning to a social determinants and bioecological perspective, reproduced from Goldfeld et al.17 Examples of relevant indicators within each lens and level are shown. It is expected that disadvantage experienced through each of these lenses will overlap and influence inequities in complex ways.19. It is likely that there is a more complex interplay than has been previously reported between the lenses of disadvantage, time and child development domains.20,16 Sanson et al.16 found that children’s socio-emotional adjustment, physical functioning and learning competencies at 8–9 years had both overlapping and unique risk factors at the child-, family- and community-levels (measured at 4–5 and 6–7 years). Better determination of the relationship of differential (and potentially modifiable) childhood exposures to disadvantage with variable aspects of child development will help to avoid underestimating the extent of inequities, and to develop more precisely tailored interventions to redress them. To address this gap in the literature, we examine the relationship between trajectories of disadvantage over the childhood period (0–9 years) and children’s development by late childhood (10–11 years). Three complementary research questions are addressed. First, what is the association of children’s overall disadvantage trajectories with their socio-emotional adjustment, physical functioning and learning competencies? Second, are there varying associations between trajectories of disadvantage within different lenses (sociodemographic, geographic environments, health conditions and risk factors) with these developmental outcomes? Third, what would be the potential reduction in the proportion of children with poor developmental outcomes, if policy and intervention efforts could successfully redress disadvantage and achieve optimal conditions for all children? Method Data source The Longitudinal Study of Australian Children (LSAC) is a nationally representative sample of two cohorts of Australian children—the birth cohort (B-cohort) of 5107 infants, and the kindergarten cohort (K-cohort) of 4983 4-year-olds—each of which commenced in May 2004.21 The LSAC design and sampling methodology is documented elsewhere.21,22 In short, a complex survey design was used to select a sample that was broadly representative of all Australian children except those living in remote areas.22 Data were collected on children’s development as well as family and community characteristics. Multiple information sources were used, including parent interview, direct child assessments and observational measures, parent and teacher self-report questionnaires and linkage to administrative datasets. This paper uses data from the B-cohort (51.2% male) of 5107 infants, followed up over six waves (Figure 2). Sample attrition of the children recruited into the B-cohort has been gradual. The retained sample by Wave 6 was 73.7% of the original sample, comparing well with similar cohort studies (e.g. Duncan and Gibson23). Non-response has been higher for some subpopulations, such as families where mothers had lower educational attainment or less stable housing tenure.24 Survey methods weighting is used to account for the probability of selecting each child into the study and non-response,24 and was applied to all analyses described herein. LSAC is conducted under approval of the Australian Institute of Family Studies Human Research Ethics Review Board. Figure 2 View largeDownload slide Flowchart of cohort attrition from original sample in the B-cohort of the Longitudinal Study of Australian children. Figure 2 View largeDownload slide Flowchart of cohort attrition from original sample in the B-cohort of the Longitudinal Study of Australian children. Measures Exposure: disadvantage trajectories from 0–1 to 8–9 years Childhood disadvantage was represented by four social determinants lenses (Figure 1): sociodemographic factors (characteristics that define subpopulation groups); geographic environments (characteristics of the places in which children live); health conditions (diagnosable medical problems); and risk factors (attributes, characteristics and exposures that increase the likelihood of poor child outcomes).17 In a previous study (Goldfeld et al., submitted for publication) we used confirmatory factor analysis to generate factor scores reflecting each of these lenses at every time point from 0–1 to 8–9 years. Overall, 25 indicators were consistently available in each of these waves (see Supplementary Table 1, available as Supplementary data at IJE online). Group-based trajectory modelling25 was then used to identify groups of children with similar patterns of disadvantage exposure over time, according to both composite disadvantage (average across the four lenses) and for each lens separately (Goldfeld et al., submitted for publication). Four trajectories were identified for each individual lens and for composite disadvantage from 0–1 to 8–9 years (Figure 3). Children in the trajectory group with consistently higher scores were labelled as ‘most disadvantaged’, and children in the trajectory with consistently lower scores over time were labelled as ‘most advantaged’. Figure 3 View largeDownload slide Trajectories of disadvantage for composite disadvantage and within each lens from 0–1 to 8–9 years (n = 5107). Higher scores indicate higher levels of disadvantage. The bold line in each plot represents the mean score, noting the different y-axis scales. Figure 3 View largeDownload slide Trajectories of disadvantage for composite disadvantage and within each lens from 0–1 to 8–9 years (n = 5107). Higher scores indicate higher levels of disadvantage. The bold line in each plot represents the mean score, noting the different y-axis scales. The resulting trajectories were used in the current study as a measure of disadvantage exposure over the childhood period, for children’s overall experiences (composite disadvantage) and within the specific lenses of sociodemographic, geographic environments, health conditions and risk factors. In the current study, some trajectories were combined due to small numbers. For example, children in the most extreme trajectory group (‘most advantaged’, n = 62) were combined with the ‘advantaged’ (n = 1035) trajectory for the sociodemographic lens. Child development outcomes: socio-emotional adjustment, physical functioning and learning competencies at 10–11 years Child development was measured across the domains of socio-emotional adjustment (e.g. social competence and mental health), physical functioning (e.g. motor skills) and learning competencies (e.g. literacy and numeracy). An outcome index has previously been developed and validated within LSAC for Waves 1–3,26,27 as a means of summarizing progress within the three developmental domains of health and physical development, social and emotional functioning and learning competencies. We replicated the outcome index at Wave 6 using the same indicators previously used at Wave 3, with some exceptions (Table 1). We did not include specific language measures such as the Peabody Picture Vocabulary Test because it was not measured at Wave 6. In addition, indicators in the physical functioning domain measuring child health conditions and illness (e.g. ongoing health problems) at Wave 3 were excluded, given our focus on physical functioning. Table 1 Indicators used to measure each child developmental domain at 10–11 years Domain Sub-domain Measure Details Socio-emotional adjustment Social competence Strengths and Difficulties Questionnaire (SDQ) prosocial (5 items; e.g. is kind to younger children (reversed scored)) and peer problems (5 items; e.g. rather solitary, tends to play alone) subscales Parent rated truth of statements about child’s behaviour in the past 6 months from 0 = not true to 2 = certainly true Internalizing SDQ emotional symptoms (5 items; e.g. many worries, often seems worried) subscale Externalizing SDQ hyperactivity (5 items, e.g. easily distracted, concentration wanders) and conduct problems (5 items, e.g. steals from home, school or elsewhere) subscales Physical functioning Motor skills Paediatric Quality of Life Inventory physical functioning subscale (8 items, e.g. the child has had a problem participating in sports activity or exercise) Parent rated the frequency of problems in the past month from 1 = never to 5 = almost always Learning competencies Language and literacy Academic Rating Scale (ARS) of Language and Literacy subscale (from the Early Childhood Longitudinal Study – Kindergarten) assessing performance on language tasks including reading, writing and oral communication (e.g. reads fluently) (9 items) Teacher rated performance from 1 = not yet proficient to 5 = proficient Numeracy ARS Mathematical Thinking subscale (from the Early Childhood Longitudinal Study – Kindergarten) assessing the child’s ability to perform various mathematical tasks (e.g. models, reads, writes and compares fractions) (10 items) Non-verbal cognition Wechsler Intelligence Scale for Children IV (WISC-IV) matrix reasoning test assessing visual information processing and abstract reasoning Direct assessment during home interview Domain Sub-domain Measure Details Socio-emotional adjustment Social competence Strengths and Difficulties Questionnaire (SDQ) prosocial (5 items; e.g. is kind to younger children (reversed scored)) and peer problems (5 items; e.g. rather solitary, tends to play alone) subscales Parent rated truth of statements about child’s behaviour in the past 6 months from 0 = not true to 2 = certainly true Internalizing SDQ emotional symptoms (5 items; e.g. many worries, often seems worried) subscale Externalizing SDQ hyperactivity (5 items, e.g. easily distracted, concentration wanders) and conduct problems (5 items, e.g. steals from home, school or elsewhere) subscales Physical functioning Motor skills Paediatric Quality of Life Inventory physical functioning subscale (8 items, e.g. the child has had a problem participating in sports activity or exercise) Parent rated the frequency of problems in the past month from 1 = never to 5 = almost always Learning competencies Language and literacy Academic Rating Scale (ARS) of Language and Literacy subscale (from the Early Childhood Longitudinal Study – Kindergarten) assessing performance on language tasks including reading, writing and oral communication (e.g. reads fluently) (9 items) Teacher rated performance from 1 = not yet proficient to 5 = proficient Numeracy ARS Mathematical Thinking subscale (from the Early Childhood Longitudinal Study – Kindergarten) assessing the child’s ability to perform various mathematical tasks (e.g. models, reads, writes and compares fractions) (10 items) Non-verbal cognition Wechsler Intelligence Scale for Children IV (WISC-IV) matrix reasoning test assessing visual information processing and abstract reasoning Direct assessment during home interview The following measures from the physical health domain of the Wave 3 outcome index were not included in the Wave 6 outcome index due to lack of alignment with our conceptualization of the domain as normative physical functioning: overall rating of health; special health care needs; weight status; gross motor coordination. The Peabody Picture Vocabulary Test was also not included in the learning competencies domain as this measure was not available at Wave 6. Table 1 Indicators used to measure each child developmental domain at 10–11 years Domain Sub-domain Measure Details Socio-emotional adjustment Social competence Strengths and Difficulties Questionnaire (SDQ) prosocial (5 items; e.g. is kind to younger children (reversed scored)) and peer problems (5 items; e.g. rather solitary, tends to play alone) subscales Parent rated truth of statements about child’s behaviour in the past 6 months from 0 = not true to 2 = certainly true Internalizing SDQ emotional symptoms (5 items; e.g. many worries, often seems worried) subscale Externalizing SDQ hyperactivity (5 items, e.g. easily distracted, concentration wanders) and conduct problems (5 items, e.g. steals from home, school or elsewhere) subscales Physical functioning Motor skills Paediatric Quality of Life Inventory physical functioning subscale (8 items, e.g. the child has had a problem participating in sports activity or exercise) Parent rated the frequency of problems in the past month from 1 = never to 5 = almost always Learning competencies Language and literacy Academic Rating Scale (ARS) of Language and Literacy subscale (from the Early Childhood Longitudinal Study – Kindergarten) assessing performance on language tasks including reading, writing and oral communication (e.g. reads fluently) (9 items) Teacher rated performance from 1 = not yet proficient to 5 = proficient Numeracy ARS Mathematical Thinking subscale (from the Early Childhood Longitudinal Study – Kindergarten) assessing the child’s ability to perform various mathematical tasks (e.g. models, reads, writes and compares fractions) (10 items) Non-verbal cognition Wechsler Intelligence Scale for Children IV (WISC-IV) matrix reasoning test assessing visual information processing and abstract reasoning Direct assessment during home interview Domain Sub-domain Measure Details Socio-emotional adjustment Social competence Strengths and Difficulties Questionnaire (SDQ) prosocial (5 items; e.g. is kind to younger children (reversed scored)) and peer problems (5 items; e.g. rather solitary, tends to play alone) subscales Parent rated truth of statements about child’s behaviour in the past 6 months from 0 = not true to 2 = certainly true Internalizing SDQ emotional symptoms (5 items; e.g. many worries, often seems worried) subscale Externalizing SDQ hyperactivity (5 items, e.g. easily distracted, concentration wanders) and conduct problems (5 items, e.g. steals from home, school or elsewhere) subscales Physical functioning Motor skills Paediatric Quality of Life Inventory physical functioning subscale (8 items, e.g. the child has had a problem participating in sports activity or exercise) Parent rated the frequency of problems in the past month from 1 = never to 5 = almost always Learning competencies Language and literacy Academic Rating Scale (ARS) of Language and Literacy subscale (from the Early Childhood Longitudinal Study – Kindergarten) assessing performance on language tasks including reading, writing and oral communication (e.g. reads fluently) (9 items) Teacher rated performance from 1 = not yet proficient to 5 = proficient Numeracy ARS Mathematical Thinking subscale (from the Early Childhood Longitudinal Study – Kindergarten) assessing the child’s ability to perform various mathematical tasks (e.g. models, reads, writes and compares fractions) (10 items) Non-verbal cognition Wechsler Intelligence Scale for Children IV (WISC-IV) matrix reasoning test assessing visual information processing and abstract reasoning Direct assessment during home interview The following measures from the physical health domain of the Wave 3 outcome index were not included in the Wave 6 outcome index due to lack of alignment with our conceptualization of the domain as normative physical functioning: overall rating of health; special health care needs; weight status; gross motor coordination. The Peabody Picture Vocabulary Test was also not included in the learning competencies domain as this measure was not available at Wave 6. Following the procedures used to calculate the outcome index in previous LSAC waves,27,28 scores for the three developmental domains were calculated by: (i) standardizing all outcome variables (i.e. mean = 0, standard deviation = 1) and averaging them into sub-domain scores; (ii) standardizing sub-domain scores and combining them into continuous domain scores; and (iii) standardizing the domain scores and obtaining cut-offs to identify the bottom 15% of the sample for each domain, reflecting those with the poorest outcomes in that domain. We also categorized children according to whether they fell into the lowest 15% on two or more domains of development (n = 496, 9.7%). Statistical analysis The associations between trajectories of disadvantage from 0–1 to 8–9 years and child development outcomes at 10–11 years were examined using generalized linear models with log-Poisson link. For each of the three developmental domains, we estimated the relative risk (RR) of being in the lowest 15% in each developmental domain associated with disadvantage trajectories in the sociodemographic, geographic environments, health conditions and risk factors lenses, as well as for composite disadvantage. Given that the most vulnerable children are those with difficulties across multiple developmental domains, we also estimated the RR of poor outcomes on two or more developmental domains associated with disadvantage trajectories. Both the unadjusted and the adjusted effects of disadvantage trajectories in each lens were estimated. The adjusted models estimate the association of each lens with the outcomes after accounting for the influence of children’s trajectory membership on all other lenses. The most advantaged trajectory was the reference category in all analyses, based on the assumption that all children have the right to sufficient resources and living standards that allow them to achieve their optimal potential.29 This aligns with the approach adopted by WHO, to fully represent the impact of inequity on child outcomes.30 Population-attributable fractions (PAFs) were also estimated from the models (adjusted for all other lenses) using the maximum likelihood estimates method proposed by Greenland and Drescher.31 As an approach for representing the extent of health inequities, PAFs reflect the percentage reduction in an adverse outcome that would occur if exposure to disadvantage were reduced to an optimal scenario, assuming that disadvantage causes the adverse outcome.30,32 We estimated the percentage reduction in poor outcomes in each developmental domain which could be achieved if all other children had the same exposure as those in the optimal trajectory (most advantaged). Higher PAFs indicate greater potential reduction in poor developmental outcomes. PAFs were calculated using the punaf command in Stata.33 Separation between exposure and outcome for these analyses was ensured as recommended by Goldfeld et al.17 To account for potential overlap with the outcomes, children reported by parents as having a mental health, physical or learning condition or disability were excluded from all analyses (n = 152, 2.5%). Findings were similar when all (n = 5107) children were included (data not shown). Multiple imputation by chained equations was used to deal with missing data in all analyses under the missing at random assumption.34 Twenty imputed datasets were created using regression switching.35 The imputation model included all exposures (sociodemographic, geographic environments, health conditions, risk factors and composite disadvantage) and outcomes (socio-emotional adjustment, physical functioning and learning competencies) and an auxiliary variable (maternal age) to help predict missing data. Results were combined using Rubin’s rules.36 All analyses were conducted using Stata SE version 14.2.37 Results from imputed data are reported. Results Associations between composite disadvantage trajectories and developmental outcomes The more disadvantaged a child’s trajectory, the higher the risk of poor developmental outcomes by 10–11 years of age (Figure 4). Compared with children in the most advantaged composite trajectory, there was a 7-fold increased risk of having poor outcomes in two or more domains in the most disadvantaged composite trajectory. Figure 4 View largeDownload slide Relative risk of poor outcomes (bottom 15%) on each developmental domain and on two or more developmental domains associated with composite disadvantage. Children reported by parents as having a condition impacting on their development in these areas (n = 128, 2.5%) were excluded. Figure 4 View largeDownload slide Relative risk of poor outcomes (bottom 15%) on each developmental domain and on two or more developmental domains associated with composite disadvantage. Children reported by parents as having a condition impacting on their development in these areas (n = 128, 2.5%) were excluded. Associations between lens trajectories and developmental outcomes For each disadvantage lens, the unadjusted estimates show that children following more disadvantaged trajectories had a higher risk of poor developmental outcomes (Table 2). In the adjusted models, different lenses of disadvantage were associated with different developmental outcomes. Only disadvantage trajectories in the sociodemographic lens were associated with learning competencies after adjusting for other lenses, whereas disadvantage trajectories in the sociodemographic and health conditions lenses were associated with physical functioning. Trajectories in all disadvantage lenses were associated with socio-emotional adjustment. Table 2 Relative risk (RR) of poor developmental outcomes (lowest 15%) according to trajectory membership for each disadvantage lens (n = 4979a) Disadvantage lenses % Socio-emotional adjustment Physical functioning Learning competencies RR (95% CI) Adjusted RR (95% CI) RR (95% CI) Adjusted RR (95% CI) RR (95% CI) Adjusted RR (95% CI) Sociodemographic  Most advantagedb/advantaged 25.8 Ref Ref Ref Ref Ref Ref  Disadvantaged 49.5 1.35 1.08 1.27 1.16 1.75 1.70 (1.05, 1.73) (0.83, 1.41) (1.03, 1.57) (0.93, 1.45) (1.37, 2.22) (1.33, 2.20)  Most disadvantaged 24.7 2.36 1.41 1.91 1.40 2.72 2.57 (1.88, 2.95) (1.08, 1.85) (1.52, 2.38) (1.08, 1.81) (2.16, 3.42) (1.94, 3.41) Geographic environments  Most advantaged 17.1 Ref Ref Ref Ref Ref Ref  Advantaged 36.3 1.28 1.12 1.14 1.00 1.36 1.03 (0.94, 1.72) (0.83, 1.50) (0.87, 1.48) (0.76, 1.31) (1.02, 1.82) (0.77, 1.38)  Disadvantaged 31.6 1.88 1.45 1.22 0.95 1.51 1.11 (1.45, 2.43) (1.10, 1.92) (0.95, 1.55) (0.73, 1.24) (1.16, 1.98) (0.83, 1.50)  Most disadvantaged 15.0 2.22 1.50 1.58 1.09 1.92 1.15 (1.69, 2.94) (1.10, 2.04) (1.19, 2.11) (0.79, 1.52) (1.43, 2.56) (0.83, 1.60) Health conditions  Advantaged 67.6 Ref Ref Ref Ref Ref Ref  Disadvantaged 22.7 2.03 1.31 2.02 1.53 1.25 1.20 (1.71, 2.42) (0.97, 1.76) (1.67, 2.44) (1.12, 2.08) (1.06, 1.49) (0.89, 1.61)  Increasing disadvantage 4.6 2.46 1.52 2.09 1.56 1.44 1.29 (1.81, 3.35) (1.02, 2.26) (1.49, 2.93) (1.01, 2.39) (0.99, 2.09) (0.81, 2.05)  Most disadvantaged 5.1 2.95 1.47 2.73 1.95 1.49 1.39 (2.33, 3.74) (0.94, 2.28) (2.10, 3.55) (1.17, 3.26) (1.08, 2.06) (0.78, 2.49) Risk factors  Advantaged 65.1 Ref Ref Ref Ref Ref Ref  Disadvantaged 24.9 2.01 1.45 1.94 1.33 1.18 0.88 (1.69, 2.40) (1.10, 1.94) (1.58, 2.39) (0.97, 1.82) (0.98, 1.42) (0.65, 1.18)  Intermediate disadvantageb/ most disadvantaged 10.0 2.81 1.73 2.35 1.26 1.29 0.77 (2.33, 3.39) (1.18, 2.53) (1.93, 2.88) (0.83, 1.90) (1.02, 1.62) (0.48, 1.23) Disadvantage lenses % Socio-emotional adjustment Physical functioning Learning competencies RR (95% CI) Adjusted RR (95% CI) RR (95% CI) Adjusted RR (95% CI) RR (95% CI) Adjusted RR (95% CI) Sociodemographic  Most advantagedb/advantaged 25.8 Ref Ref Ref Ref Ref Ref  Disadvantaged 49.5 1.35 1.08 1.27 1.16 1.75 1.70 (1.05, 1.73) (0.83, 1.41) (1.03, 1.57) (0.93, 1.45) (1.37, 2.22) (1.33, 2.20)  Most disadvantaged 24.7 2.36 1.41 1.91 1.40 2.72 2.57 (1.88, 2.95) (1.08, 1.85) (1.52, 2.38) (1.08, 1.81) (2.16, 3.42) (1.94, 3.41) Geographic environments  Most advantaged 17.1 Ref Ref Ref Ref Ref Ref  Advantaged 36.3 1.28 1.12 1.14 1.00 1.36 1.03 (0.94, 1.72) (0.83, 1.50) (0.87, 1.48) (0.76, 1.31) (1.02, 1.82) (0.77, 1.38)  Disadvantaged 31.6 1.88 1.45 1.22 0.95 1.51 1.11 (1.45, 2.43) (1.10, 1.92) (0.95, 1.55) (0.73, 1.24) (1.16, 1.98) (0.83, 1.50)  Most disadvantaged 15.0 2.22 1.50 1.58 1.09 1.92 1.15 (1.69, 2.94) (1.10, 2.04) (1.19, 2.11) (0.79, 1.52) (1.43, 2.56) (0.83, 1.60) Health conditions  Advantaged 67.6 Ref Ref Ref Ref Ref Ref  Disadvantaged 22.7 2.03 1.31 2.02 1.53 1.25 1.20 (1.71, 2.42) (0.97, 1.76) (1.67, 2.44) (1.12, 2.08) (1.06, 1.49) (0.89, 1.61)  Increasing disadvantage 4.6 2.46 1.52 2.09 1.56 1.44 1.29 (1.81, 3.35) (1.02, 2.26) (1.49, 2.93) (1.01, 2.39) (0.99, 2.09) (0.81, 2.05)  Most disadvantaged 5.1 2.95 1.47 2.73 1.95 1.49 1.39 (2.33, 3.74) (0.94, 2.28) (2.10, 3.55) (1.17, 3.26) (1.08, 2.06) (0.78, 2.49) Risk factors  Advantaged 65.1 Ref Ref Ref Ref Ref Ref  Disadvantaged 24.9 2.01 1.45 1.94 1.33 1.18 0.88 (1.69, 2.40) (1.10, 1.94) (1.58, 2.39) (0.97, 1.82) (0.98, 1.42) (0.65, 1.18)  Intermediate disadvantageb/ most disadvantaged 10.0 2.81 1.73 2.35 1.26 1.29 0.77 (2.33, 3.39) (1.18, 2.53) (1.93, 2.88) (0.83, 1.90) (1.02, 1.62) (0.48, 1.23) RRs are adjusted for trajectory membership in all other lenses. Ref, reference; CI, confidence interval. a Analysis uses the imputed sample (data were imputed for participants with missing outcomes and exposures). Children reported by parents as having a condition related to the outcomes (n = 128, 2.5%) were excluded from the analyses. b Combined due to small numbers in trajectory group. Table 2 Relative risk (RR) of poor developmental outcomes (lowest 15%) according to trajectory membership for each disadvantage lens (n = 4979a) Disadvantage lenses % Socio-emotional adjustment Physical functioning Learning competencies RR (95% CI) Adjusted RR (95% CI) RR (95% CI) Adjusted RR (95% CI) RR (95% CI) Adjusted RR (95% CI) Sociodemographic  Most advantagedb/advantaged 25.8 Ref Ref Ref Ref Ref Ref  Disadvantaged 49.5 1.35 1.08 1.27 1.16 1.75 1.70 (1.05, 1.73) (0.83, 1.41) (1.03, 1.57) (0.93, 1.45) (1.37, 2.22) (1.33, 2.20)  Most disadvantaged 24.7 2.36 1.41 1.91 1.40 2.72 2.57 (1.88, 2.95) (1.08, 1.85) (1.52, 2.38) (1.08, 1.81) (2.16, 3.42) (1.94, 3.41) Geographic environments  Most advantaged 17.1 Ref Ref Ref Ref Ref Ref  Advantaged 36.3 1.28 1.12 1.14 1.00 1.36 1.03 (0.94, 1.72) (0.83, 1.50) (0.87, 1.48) (0.76, 1.31) (1.02, 1.82) (0.77, 1.38)  Disadvantaged 31.6 1.88 1.45 1.22 0.95 1.51 1.11 (1.45, 2.43) (1.10, 1.92) (0.95, 1.55) (0.73, 1.24) (1.16, 1.98) (0.83, 1.50)  Most disadvantaged 15.0 2.22 1.50 1.58 1.09 1.92 1.15 (1.69, 2.94) (1.10, 2.04) (1.19, 2.11) (0.79, 1.52) (1.43, 2.56) (0.83, 1.60) Health conditions  Advantaged 67.6 Ref Ref Ref Ref Ref Ref  Disadvantaged 22.7 2.03 1.31 2.02 1.53 1.25 1.20 (1.71, 2.42) (0.97, 1.76) (1.67, 2.44) (1.12, 2.08) (1.06, 1.49) (0.89, 1.61)  Increasing disadvantage 4.6 2.46 1.52 2.09 1.56 1.44 1.29 (1.81, 3.35) (1.02, 2.26) (1.49, 2.93) (1.01, 2.39) (0.99, 2.09) (0.81, 2.05)  Most disadvantaged 5.1 2.95 1.47 2.73 1.95 1.49 1.39 (2.33, 3.74) (0.94, 2.28) (2.10, 3.55) (1.17, 3.26) (1.08, 2.06) (0.78, 2.49) Risk factors  Advantaged 65.1 Ref Ref Ref Ref Ref Ref  Disadvantaged 24.9 2.01 1.45 1.94 1.33 1.18 0.88 (1.69, 2.40) (1.10, 1.94) (1.58, 2.39) (0.97, 1.82) (0.98, 1.42) (0.65, 1.18)  Intermediate disadvantageb/ most disadvantaged 10.0 2.81 1.73 2.35 1.26 1.29 0.77 (2.33, 3.39) (1.18, 2.53) (1.93, 2.88) (0.83, 1.90) (1.02, 1.62) (0.48, 1.23) Disadvantage lenses % Socio-emotional adjustment Physical functioning Learning competencies RR (95% CI) Adjusted RR (95% CI) RR (95% CI) Adjusted RR (95% CI) RR (95% CI) Adjusted RR (95% CI) Sociodemographic  Most advantagedb/advantaged 25.8 Ref Ref Ref Ref Ref Ref  Disadvantaged 49.5 1.35 1.08 1.27 1.16 1.75 1.70 (1.05, 1.73) (0.83, 1.41) (1.03, 1.57) (0.93, 1.45) (1.37, 2.22) (1.33, 2.20)  Most disadvantaged 24.7 2.36 1.41 1.91 1.40 2.72 2.57 (1.88, 2.95) (1.08, 1.85) (1.52, 2.38) (1.08, 1.81) (2.16, 3.42) (1.94, 3.41) Geographic environments  Most advantaged 17.1 Ref Ref Ref Ref Ref Ref  Advantaged 36.3 1.28 1.12 1.14 1.00 1.36 1.03 (0.94, 1.72) (0.83, 1.50) (0.87, 1.48) (0.76, 1.31) (1.02, 1.82) (0.77, 1.38)  Disadvantaged 31.6 1.88 1.45 1.22 0.95 1.51 1.11 (1.45, 2.43) (1.10, 1.92) (0.95, 1.55) (0.73, 1.24) (1.16, 1.98) (0.83, 1.50)  Most disadvantaged 15.0 2.22 1.50 1.58 1.09 1.92 1.15 (1.69, 2.94) (1.10, 2.04) (1.19, 2.11) (0.79, 1.52) (1.43, 2.56) (0.83, 1.60) Health conditions  Advantaged 67.6 Ref Ref Ref Ref Ref Ref  Disadvantaged 22.7 2.03 1.31 2.02 1.53 1.25 1.20 (1.71, 2.42) (0.97, 1.76) (1.67, 2.44) (1.12, 2.08) (1.06, 1.49) (0.89, 1.61)  Increasing disadvantage 4.6 2.46 1.52 2.09 1.56 1.44 1.29 (1.81, 3.35) (1.02, 2.26) (1.49, 2.93) (1.01, 2.39) (0.99, 2.09) (0.81, 2.05)  Most disadvantaged 5.1 2.95 1.47 2.73 1.95 1.49 1.39 (2.33, 3.74) (0.94, 2.28) (2.10, 3.55) (1.17, 3.26) (1.08, 2.06) (0.78, 2.49) Risk factors  Advantaged 65.1 Ref Ref Ref Ref Ref Ref  Disadvantaged 24.9 2.01 1.45 1.94 1.33 1.18 0.88 (1.69, 2.40) (1.10, 1.94) (1.58, 2.39) (0.97, 1.82) (0.98, 1.42) (0.65, 1.18)  Intermediate disadvantageb/ most disadvantaged 10.0 2.81 1.73 2.35 1.26 1.29 0.77 (2.33, 3.39) (1.18, 2.53) (1.93, 2.88) (0.83, 1.90) (1.02, 1.62) (0.48, 1.23) RRs are adjusted for trajectory membership in all other lenses. Ref, reference; CI, confidence interval. a Analysis uses the imputed sample (data were imputed for participants with missing outcomes and exposures). Children reported by parents as having a condition related to the outcomes (n = 128, 2.5%) were excluded from the analyses. b Combined due to small numbers in trajectory group. Population attributable fractions Table 3 shows the PAFs for each of the adjusted models. These findings assume that the estimated relationships between disadvantage trajectories and developmental outcomes approximate the true causal effect. If all other children had the same exposure as those in the optimal composite trajectory (i.e. the most advantaged), the proportion of children with poor developmental outcomes in two or more domains could be reduced by as much as 70%. Poor developmental outcomes in the socio-emotional, physical functioning and learning competencies domains could be reduced by 59%, 48% and 55%, respectively. When examining the impact of providing optimal conditions on each lens (i.e. all children have the same exposure as that of those in the optimal trajectory on that specific lens), the potential reduction in poor outcomes varied across developmental domains. For example, the proportion of children with poor learning competencies could be reduced by as much as 43% by providing optimal conditions on the sociodemographic lens. In Supplementary Table 2 (available as Supplementary data at IJE online), we estimated PAFs corresponding to each individual trajectory (within all lenses and composite disadvantage), rather than for all non-optimal trajectories combined. Table 3 Population-attributable fractions showing the proportional reduction in poor developmental outcomes that could be achieved if all children had the same exposure as the optimal trajectory, if the estimated relationships between disadvantage and developmental outcomes approximates the true causal effect (n = 4979a) Socio-emotional adjustment Physical functioning Learning competencies Poor outcome on 2+ domains PAF % (95% CI) PAF % (95% CI) PAF % (95% CI) PAF % (95% CI) Disadvantage lenses  Sociodemographic 14.58 (11.41, 17.64) 16.40 (12.48, 19.23) 43.02 (40.65, 45.29) 33.07 (29.44, 36.52)  Geographic environments 22.92 (19.27, 26.41) −0.29 (−4.36, 3.62) 7.63 (3.35, 11.72) 17.92 (12.71, 22.83)  Health conditions 13.94 (11.71, 16.12) 18.98 (17.10, 20.82) 8.47 (6.72, 10.18) 22.60 (20.18, 24.96)  Risk factors 19.33 (17.22, 21.37) 12.33 (10.10, 14.56) −7.48 (−9.96, −5.07) 10.15 (6.98, 13.20)  Composite disadvantage 59.04 (56.06, 61.82) 48.38 (45.12, 51.46) 54.81 (51.70, 57.72) 70.00 (66.70, 72.93) Socio-emotional adjustment Physical functioning Learning competencies Poor outcome on 2+ domains PAF % (95% CI) PAF % (95% CI) PAF % (95% CI) PAF % (95% CI) Disadvantage lenses  Sociodemographic 14.58 (11.41, 17.64) 16.40 (12.48, 19.23) 43.02 (40.65, 45.29) 33.07 (29.44, 36.52)  Geographic environments 22.92 (19.27, 26.41) −0.29 (−4.36, 3.62) 7.63 (3.35, 11.72) 17.92 (12.71, 22.83)  Health conditions 13.94 (11.71, 16.12) 18.98 (17.10, 20.82) 8.47 (6.72, 10.18) 22.60 (20.18, 24.96)  Risk factors 19.33 (17.22, 21.37) 12.33 (10.10, 14.56) −7.48 (−9.96, −5.07) 10.15 (6.98, 13.20)  Composite disadvantage 59.04 (56.06, 61.82) 48.38 (45.12, 51.46) 54.81 (51.70, 57.72) 70.00 (66.70, 72.93) The reference group is the optimal trajectory. The PAF is calculated by comparing scenario 1 (a hypothetical scenario in which all children were in the optimal trajectory, e.g. most advantaged trajectory) with scenario 0 (the real world in which there are children in the optimal trajectory and children in other more disadvantaged trajectories). PAF,  population-attributable fractions, reported as percentages. a Analysis uses the imputed sample (data were imputed for participants with missing outcomes and exposures). Children reported by parents as having a condition related to the outcomes (n = 128, 2.5%) were excluded from the analyses. Table 3 Population-attributable fractions showing the proportional reduction in poor developmental outcomes that could be achieved if all children had the same exposure as the optimal trajectory, if the estimated relationships between disadvantage and developmental outcomes approximates the true causal effect (n = 4979a) Socio-emotional adjustment Physical functioning Learning competencies Poor outcome on 2+ domains PAF % (95% CI) PAF % (95% CI) PAF % (95% CI) PAF % (95% CI) Disadvantage lenses  Sociodemographic 14.58 (11.41, 17.64) 16.40 (12.48, 19.23) 43.02 (40.65, 45.29) 33.07 (29.44, 36.52)  Geographic environments 22.92 (19.27, 26.41) −0.29 (−4.36, 3.62) 7.63 (3.35, 11.72) 17.92 (12.71, 22.83)  Health conditions 13.94 (11.71, 16.12) 18.98 (17.10, 20.82) 8.47 (6.72, 10.18) 22.60 (20.18, 24.96)  Risk factors 19.33 (17.22, 21.37) 12.33 (10.10, 14.56) −7.48 (−9.96, −5.07) 10.15 (6.98, 13.20)  Composite disadvantage 59.04 (56.06, 61.82) 48.38 (45.12, 51.46) 54.81 (51.70, 57.72) 70.00 (66.70, 72.93) Socio-emotional adjustment Physical functioning Learning competencies Poor outcome on 2+ domains PAF % (95% CI) PAF % (95% CI) PAF % (95% CI) PAF % (95% CI) Disadvantage lenses  Sociodemographic 14.58 (11.41, 17.64) 16.40 (12.48, 19.23) 43.02 (40.65, 45.29) 33.07 (29.44, 36.52)  Geographic environments 22.92 (19.27, 26.41) −0.29 (−4.36, 3.62) 7.63 (3.35, 11.72) 17.92 (12.71, 22.83)  Health conditions 13.94 (11.71, 16.12) 18.98 (17.10, 20.82) 8.47 (6.72, 10.18) 22.60 (20.18, 24.96)  Risk factors 19.33 (17.22, 21.37) 12.33 (10.10, 14.56) −7.48 (−9.96, −5.07) 10.15 (6.98, 13.20)  Composite disadvantage 59.04 (56.06, 61.82) 48.38 (45.12, 51.46) 54.81 (51.70, 57.72) 70.00 (66.70, 72.93) The reference group is the optimal trajectory. The PAF is calculated by comparing scenario 1 (a hypothetical scenario in which all children were in the optimal trajectory, e.g. most advantaged trajectory) with scenario 0 (the real world in which there are children in the optimal trajectory and children in other more disadvantaged trajectories). PAF,  population-attributable fractions, reported as percentages. a Analysis uses the imputed sample (data were imputed for participants with missing outcomes and exposures). Children reported by parents as having a condition related to the outcomes (n = 128, 2.5%) were excluded from the analyses. Discussion This study found strong evidence of an association between the composite disadvantage trajectories from 0–9 years and all major domains of development at 10–11 years (socio-emotional adjustment, physical functioning and learning competencies). Rather than a threshold, we found that the more disadvantaged a child’s composite trajectory, the higher the risk of poor developmental outcomes by 10–11 years of age. Thus, inequities in child development affect a larger proportion of the population than just those who are most severely deprived.38,39 These findings are consistent with previous research reporting on trajectories of other indicators of disadvantage (e.g. income).40 It also aligns with our growing understanding of brain development; evidence continues to elucidate how stress arising from continued and compounded experiences of disadvantage can alter brain architecture and physiological systems to adversely affect many aspects of children’s health and development.41 At a more granular level, the different lenses of disadvantage were varyingly associated with poor outcomes in each developmental domain, aligning with the findings of Sanson et al.16 who found both overlapping and distinct risk factors across domains. Children can have different combinations of exposure to disadvantage across the four lenses and over time (Goldfeld et al., submitted for publication). The impact of these varied experiences could manifest in different ways across developmental domains, depending on how directly or pervasively they undermine the child’s opportunities for development in that area. For example, when considering learning competencies, exposure to a more disadvantaged pathway on the sociodemographic lens seemed particularly relevant. Parents’ own educational experiences, captured within this lens, are shown to be closely associated with their capacity to provide a quality home learning environment, which is a critical resource for early academic development.42 We further quantified the substantial reduction in these poor developmental outcomes (as much as 70%) which could be achieved if policy and intervention efforts successfully redressed disadvantage. This reinforces findings from previous research, demonstrating the potential for profound reduction in child morbidity given optimal social conditions.30 To allow all children the same conditions as those most advantaged, we would need to capitalize on those potentially modifiable factors that will generate a strong return on investment; this would likely require cooperation across a range of government portfolios. Achieving this reduction in child development inequities would have far-reaching implications for policy in terms of generating savings in health, education and welfare budgets.32 It would also be expected to generate significant improvements for productivity, given that healthy child development translates to improved human capital in the longer term.43 Most importantly it would ensure that all children achieve their potential.29 Strengths and limitations The breadth and richness of data available within LSAC over multiple waves enabled exploration of children’s exposure to disadvantage from 0–9 years, allowing us to better capture its relationship to child development. As with any study of this duration there has been attrition, and this was greatest for the most disadvantaged children. We have used survey weighting and multiple imputation to reduce (but we cannot eliminate) potential selection bias.21,34 We measured socio-emotional adjustment, physical functioning and learning competencies at a single time point (10–11 years), but like disadvantage, development also unfolds over time. How developmental trajectories might change in relation to disadvantage exposure over time should be explored in future work (e.g. whether gaps in developmental trajectories amplify over time in response to disadvantage exposure).44 The findings should also be replicated in other cohorts and populations outside Australia, as it is possible that the intersections of specific lenses and outcomes might vary between country contexts. The PAFs are intended to provide a useful metric for quantifying the extent of the relationship between disadvantage and children’s development, taking into account both the prevalence and the relative risk of the exposure.30 The PAFs rest on the assumption that the relationships between disadvantage trajectories and developmental outcomes are similar to the true causal effect. Testing these causal assumptions is an important step for future work, but should not preclude more immediate policy effort in this area. Implications These findings unambiguously demonstrate that child inequities are a significant concern in Australia, as undoubtedly they are for other high-income countries.10 These differential outcomes have lifelong consequences that track forward over time; gaps in academic performance by the end of childhood are only likely to widen throughout the rest of the school years.45 Clearly, there is both a need and an opportunity for improved precision in policy development and intervention in this area. It cannot be assumed that inequities across all aspects of child development share the same drivers or follow the same paths. Precision public health means providing the right intervention, to the right population and at the right time, to optimize the outcome—thereby maximising the public dollar.20 Failure to achieve this precision may mean that gaps in some developmental domains do not close, or do not converge fast enough. This study helps to signpost where further research (including policy research) could be focused, although causal effects and specific intervention targets cannot be inferred from these findings which likely reflect a complex interaction between the child, their biology and the multiple and diverse factors influencing their experiences across ecological settings and over time.46 Research now needs to untangle the causal pathways and temporal relationships of the many inter-related indicators within each of the lenses, some of which may be more strongly related to developmental outcomes than others, or more relevant at different periods in children’s development. For example, existing evidence suggests that intervening in the early years of life is one of the most critical and cost-effective ways of reducing inequities and should remain a priority for all governments.1 Understanding what contributes to variability in outcomes for children exposed to the same disadvantage trajectory will also help to more precisely define target population groups for intervention.20,47 It will be important to quantify the potential return on investment for intervening on the most powerful causal drivers, or combinations of drivers, to help prioritize policy interventions. Conclusions These findings provide a comprehensive account of the relationship between exposure to disadvantage over early childhood and later developmental outcomes; this evidence can be used to stimulate advocacy and action to reduce child inequities in Australia. Whereas the overall relationship between disadvantage and development was strong, disadvantage lenses were variably associated with different developmental domains, signalling the value of precision policy approaches. Researchers, service providers and policy makers should delve further into these relationships to determine, develop and test specific policies and programmes that can best reduce inequities in children’s developmental outcomes. Acknowledgements The Changing Children’s Chances investigator team oversees this programme of work, and includes Prof. Sharon Goldfeld, Prof. Katrina Williams, A/Prof. Gerry Redmond, Prof. Frank Oberklaid, A/Prof. Hannah Badland, Prof. Gary Freed, Dr Fiona Mensah, A/Prof. Sue Woolfenden, Dr Jenny Proimos, Dr Amanda Kvalsvig and Dr Jianfei Gong, with thanks to Emeritus Professor Nick Spencer for his feedback on this manuscript. This paper uses unit record data from Growing Up in Australia, the Longitudinal Study of Australian Children. The study is conducted in partnership with the Department of Social Services (DSS), the Australian Institute of Family Studies (AIFS) and the Australian Bureau of Statistics (ABS). The findings and views reported in this paper are those of the authors and should not be attributed to the DSS, the AIFS or the ABS. Funding This research is funded by Australian Research Council Discovery Grant DP160101735, and was supported by the Victorian Government's Operational Infrastructure Support Program. S.G. is supported by Australian National Health and Medical Research Council (NHMRC) Career Development Fellowship 1082922, and F.M. is supported by NHMRC Career Development Fellowship 1111160. H.B. is supported by an RMIT University VC Senior Research Fellowship. Conflict of interest: None declared. References 1 Commission on Social Determinants of Health . Closing the Gap in a Generation: Health Equity Through Action on the Social Determinants of Health. 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Closing the gap in a generation: health equity through action on the social determinants of health . Lancet 2008 ; 372 : 1661 – 69 . Google Scholar Crossref Search ADS PubMed 13 Hertzman C , Williams R. Making early childhood count . CMAJ 2009 ; 180 : 68 – 71 . Google Scholar Crossref Search ADS PubMed 14 Goldfeld S , Oberklaid F. Maintaining an agenda for children: the role of data in linking policy, politics and outcomes . Med J Aust 2005 ; 183 : 209 – 11 . Google Scholar PubMed 15 Braveman P. Health disparities and health equity: concepts and measurement . Annu Rev Public Health 2006 ; 27 : 167 – 94 . Google Scholar Crossref Search ADS PubMed 16 Sanson A , Smart D , Misson S. Children's socio-emotional, physical, and cognitive outcomes: do they share the same drivers? Aust J Psychol 2011 ; 63 : 56 – 74 . Google Scholar Crossref Search ADS 17 Goldfeld S , O'Connor M , Cloney D et al. Understanding child disadvantage from a social determinants perspective . J Epidemiol Community Health 2018 ; 72 : 223 – 29 . Google Scholar Crossref Search ADS PubMed 18 Bronfenbrenner U. Making Human Beings Human: Bioecological Perspectives on Human Development . Thousand Oaks, FL : Sage Publications , 2004 . 19 Koh H , Oppenheimer S , Massin-Short S , Emmons K , Geller A , Viswanath K. Translating research evidence into practice to reduce health disparities: a social determinants approach . Am J Public Health 2010 ; 100 : S72 – 80 . Google Scholar Crossref Search ADS PubMed 20 Khoury M , Iademarco M , Riley W. Precision public health for the era of precision medicine . Am J Prev Med 2016 ; 50 : 398 – 401 . Google Scholar Crossref Search ADS PubMed 21 Soloff C , Lawrence D , Misson S , Johnstone R. LSAC Technical Paper No. 3. Wave 1 Weighting and Non-response . Melbourne, VIC : Australian Institute of Family Studies , 2006 . 22 Soloff C , Lawrence D , Johnstone R. LSAC Technical Paper No. 1. Sample Design . Melbourne, VIC : Australian Institute of Family Studies , 2005 . 23 Duncan G , Gibson C. Selection and Attrition in the NICHD Childcare Study’s Analyses of the Impacts of Childcare Quality on Child Outcomes . Evanston, IL : Northwestern University , 2000 . 24 Norton A , Monahan K. LSAC Technical Paper No. 15. Wave 6 Weighting and Non-response . Melbourne, VIC : Australian Bureau of Statistics , 2015 . 25 Jones B , Nagin D. A note on a STATA plugin for estimating group-based trajectory models . Sociol Methods Res 2013 ; 42 : 608 – 13 . Google Scholar Crossref Search ADS 26 Sanson A , Misson S , Hawkins M , Berthelsen D ; The LSAC Research Consortium . The development and validation of Australian indices of child development—part I: conceptualisation and development . Child Indicators Res 2010 ; 3 : 275 – 92 . Google Scholar Crossref Search ADS 27 Misson S , Sanson A , Berthelsen D et al. LSAC Research Paper No. 50. Tracking Children's Development over Time: the Longitudinal Study of Australian Children Outcome Indices, Waves 2 and 3 . Melbourne, VIC : Australian Institute of Family Studies , 2011 . 28 Sanson A , Misson S ; Outcome Index Working Group . LSAC Technical Paper No. 2. Summarising Children's Wellbeing: the LSAC Outcome Index . Melbourne, VIC : Australian Institute of Family Studies , 2005 . 29 Redmond G. To their fullest potential? Conceptualising the adequacy of children's living standards for their development . Int J Child Rights 2014 ; 22 : 618 – 40 . Google Scholar Crossref Search ADS 30 Spencer N. Child health inequities . Paediatr Child Health 2010 ; 20 : 157 – 62 . Google Scholar Crossref Search ADS 31 Greenland S , Drescher K. Maximum likelihood estimation of the attributable fraction from logistic models . Biometrics 1993 ; 49 : 865 – 72 . Google Scholar Crossref Search ADS PubMed 32 Spencer N. European Society for Social Pediatrics and Child Health (ESSOP) Position Statement: social inequalities in child health—towards equity and social justice in child health outcomes . Child Care Health Dev 2008 ; 34 : 631 –3 4 . Google Scholar Crossref Search ADS PubMed 33 Newson R. Attributable and unattributable risks and fractions and other scenario comparisons . Stata J 2013 ; 13 : 672 – 98 . 34 White I , Royston P , Wood A. Multiple imputation using chained equations: issues and guidance for practice . Stat Med 2011 ; 30 : 377 – 99 . Google Scholar Crossref Search ADS PubMed 35 Royston P. Multiple imputation of missing values . Stata J 2004 ; 4 : 227 – 41 . 36 Rubin D. Multiple Imputation for Nonresponse in Surveys . New York, NY : Wiley , 1987 . 37 StataCorp . Stata Statistical Software: Release 14 . College Station, TX : StataCorp LP , 2015 . 38 Braveman P , Egerter S , Woolf S , Marks J. When do we know enough to recommend action on the social determinants of health? Am J Prev Med 2011 ; 40 : S58 – 66 . Google Scholar Crossref Search ADS PubMed 39 Latour-Perez J. Social inequalities in severity of illness . J Epidemiol Community Health 1999 ; 53 : 599 – 600 . Google Scholar Crossref Search ADS PubMed 40 Jackson M , Kiernan K , McLanahan S. Maternal education, changing family circumstances, and children’s skill development in the United States and UK . Ann Am Acad Pol Soc Sci 2017 ; 674 : 59 – 84 . Google Scholar Crossref Search ADS PubMed 41 Shonkoff J , Siegel B , Garner A et al. The lifelong effects of early childhood adversity and toxic stress . Pediatrics 2012 ; 129 : e232 – 46 . Google Scholar Crossref Search ADS PubMed 42 Magnuson K. Maternal education and children's academic achievement during middle childhood . Dev Psychol 2007 ; 43 : 1497 – 512 . Google Scholar Crossref Search ADS PubMed 43 Heckman J , Mosso S. The economics of human development and social mobility . Annu Rev Econom 2014 ; 6 : 689 – 733 . Google Scholar Crossref Search ADS PubMed 44 Feinstein L. Inequality in the early cognitive development of British children in the 1970 cohort . Economica 2003 ; 70 : 73 – 97 . Google Scholar Crossref Search ADS 45 Caro D. Socio-economic status and academic achievement trajectories from childhood to adolescence . Can J Educ 2009 ; 32 : 558 – 90 . 46 Woolfenden S , Williams K , Eapen V et al. Developmental vulnerability—don't investigate without a model in mind . Child Care Health Dev 2015 ; 41 : 337 – 45 . Google Scholar Crossref Search ADS PubMed 47 Willms J. Ten Hypotheses about Socioeconomic Gradients and Community Differences in Children’s Developmental Outcomes . Quebec, QC : Human Resources Development Canada , 2003 . © The Author(s) 2018; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Epidemiology Oxford University Press

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Oxford University Press
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© The Author(s) 2018; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association
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0300-5771
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1464-3685
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Abstract

Abstract Background Understanding the relationship between different aspects of disadvantage over time and domains of child development will facilitate the formulation of more precise policy responses. We examined the association between exposure to aspects of disadvantage over the childhood period (from 0–9 years) and child development at 10–11 years. Methods We used data from the nationally representative birth cohort of the Longitudinal Study of Australian Children (n = 4979). Generalized linear models with log-Poisson link were used to estimate the association between previously derived disadvantage trajectories (in each of four lenses of sociodemographic, geographic environments, health conditions and risk factors, and a composite of these) and risk of poor child developmental outcomes. Population-attributable fractions were calculated to quantify the potential benefit of providing all children with optimal conditions for each developmental outcome. Results Trajectories of disadvantage were associated with developmental outcomes: children in the most disadvantaged composite trajectory had seven times higher risk of poor outcomes on two or more developmental domains, compared with those most advantaged. Trajectories of disadvantage in different lenses were varyingly associated with the child development domains of socio-emotional adjustment, physical functioning and learning competencies. Exposure to the most advantaged trajectory across all lenses could reduce poor developmental outcomes by as much as 70%. Conclusions Exposure to disadvantage over time is associated with adverse child development outcomes. Developmental outcomes varied with the aspects of disadvantage experienced, highlighting potential targets for more precise policy responses. The findings provide evidence to stimulate advocacy and action to reduce child inequities. Health inequity, disadvantage, child development, longitudinal, adversity Key Messages The more disadvantaged a child’s trajectory, the higher the risk of poor developmental outcomes by late childhood. Exposure to different aspects of disadvantage over childhood showed varying associations with children’s socio-emotional adjustment, physical functioning and learning competencies. At a population level, addressing all aspects of disadvantage and providing optimal conditions across childhood could reduce poor developmental outcomes by as much as 70%. Understanding associations between specific aspects of disadvantage and child development domains is necessary for greater precision in policy responses. Introduction Early childhood development lays the foundation for health, well-being, and productivity over the life course.1,2 Brain development during early childhood provides the architecture for ongoing skills development and is highly sensitive to external influences, such as exposure to disadvantage.3,4 As a consequence, early disadvantage adversely affects children’s development,5,6 with more adverse outcomes for each increment of increasing disadvantage.7,8 This pattern exists in all countries, across children’s developmental domains.1,9,10 It reflects inequitable developmental differences that are unjust and preventable.1,11 The World Health Organization’s (WHO’s) Commission on Social Determinants of Health has called for the elimination of inequitable health outcomes within a generation.1,12 To achieve this, public health and public policy interventions, targeted during early childhood, are needed to reduce inequities in three major domains of child development: socio-emotional adjustment, physical functioning and learning competencies.1,2 Each of these domains contributes to children’s current and future functioning and achievement, including school success, economic and social participation and health.2 Public policy responses should be informed by robust data to determine the current extent of child inequities across these aspects of child development, guide the development of intervention targets and monitor progress.13,14 Building the evidence base to address child inequities is complicated by the necessarily complex nature of measuring and conceptualizing disadvantage, or relative position in a social hierarchy.15,16 Recently, we developed and tested a multidimensional framework of child disadvantage17 (Figure 1) based on the bio-ecological perspective that child development occurs within a complex system of interacting environments18 and is shaped by the circumstances in which children live, learn and grow.19 The resulting four-factor model is structured around the inter-related social determinant ‘lenses’ of sociodemographic factors, geographic environments, health conditions and risk factors.17 Longitudinal analysis in the Australian population has revealed different patterns of stability and change for each disadvantage lens over childhood, offering diverse opportunities for intervention (Goldfeld et al., submitted for publication). Figure 1 View largeDownload slide Framework of child disadvantage aligning to a social determinants and bioecological perspective, reproduced from Goldfeld et al.17 Examples of relevant indicators within each lens and level are shown. It is expected that disadvantage experienced through each of these lenses will overlap and influence inequities in complex ways.19. Figure 1 View largeDownload slide Framework of child disadvantage aligning to a social determinants and bioecological perspective, reproduced from Goldfeld et al.17 Examples of relevant indicators within each lens and level are shown. It is expected that disadvantage experienced through each of these lenses will overlap and influence inequities in complex ways.19. It is likely that there is a more complex interplay than has been previously reported between the lenses of disadvantage, time and child development domains.20,16 Sanson et al.16 found that children’s socio-emotional adjustment, physical functioning and learning competencies at 8–9 years had both overlapping and unique risk factors at the child-, family- and community-levels (measured at 4–5 and 6–7 years). Better determination of the relationship of differential (and potentially modifiable) childhood exposures to disadvantage with variable aspects of child development will help to avoid underestimating the extent of inequities, and to develop more precisely tailored interventions to redress them. To address this gap in the literature, we examine the relationship between trajectories of disadvantage over the childhood period (0–9 years) and children’s development by late childhood (10–11 years). Three complementary research questions are addressed. First, what is the association of children’s overall disadvantage trajectories with their socio-emotional adjustment, physical functioning and learning competencies? Second, are there varying associations between trajectories of disadvantage within different lenses (sociodemographic, geographic environments, health conditions and risk factors) with these developmental outcomes? Third, what would be the potential reduction in the proportion of children with poor developmental outcomes, if policy and intervention efforts could successfully redress disadvantage and achieve optimal conditions for all children? Method Data source The Longitudinal Study of Australian Children (LSAC) is a nationally representative sample of two cohorts of Australian children—the birth cohort (B-cohort) of 5107 infants, and the kindergarten cohort (K-cohort) of 4983 4-year-olds—each of which commenced in May 2004.21 The LSAC design and sampling methodology is documented elsewhere.21,22 In short, a complex survey design was used to select a sample that was broadly representative of all Australian children except those living in remote areas.22 Data were collected on children’s development as well as family and community characteristics. Multiple information sources were used, including parent interview, direct child assessments and observational measures, parent and teacher self-report questionnaires and linkage to administrative datasets. This paper uses data from the B-cohort (51.2% male) of 5107 infants, followed up over six waves (Figure 2). Sample attrition of the children recruited into the B-cohort has been gradual. The retained sample by Wave 6 was 73.7% of the original sample, comparing well with similar cohort studies (e.g. Duncan and Gibson23). Non-response has been higher for some subpopulations, such as families where mothers had lower educational attainment or less stable housing tenure.24 Survey methods weighting is used to account for the probability of selecting each child into the study and non-response,24 and was applied to all analyses described herein. LSAC is conducted under approval of the Australian Institute of Family Studies Human Research Ethics Review Board. Figure 2 View largeDownload slide Flowchart of cohort attrition from original sample in the B-cohort of the Longitudinal Study of Australian children. Figure 2 View largeDownload slide Flowchart of cohort attrition from original sample in the B-cohort of the Longitudinal Study of Australian children. Measures Exposure: disadvantage trajectories from 0–1 to 8–9 years Childhood disadvantage was represented by four social determinants lenses (Figure 1): sociodemographic factors (characteristics that define subpopulation groups); geographic environments (characteristics of the places in which children live); health conditions (diagnosable medical problems); and risk factors (attributes, characteristics and exposures that increase the likelihood of poor child outcomes).17 In a previous study (Goldfeld et al., submitted for publication) we used confirmatory factor analysis to generate factor scores reflecting each of these lenses at every time point from 0–1 to 8–9 years. Overall, 25 indicators were consistently available in each of these waves (see Supplementary Table 1, available as Supplementary data at IJE online). Group-based trajectory modelling25 was then used to identify groups of children with similar patterns of disadvantage exposure over time, according to both composite disadvantage (average across the four lenses) and for each lens separately (Goldfeld et al., submitted for publication). Four trajectories were identified for each individual lens and for composite disadvantage from 0–1 to 8–9 years (Figure 3). Children in the trajectory group with consistently higher scores were labelled as ‘most disadvantaged’, and children in the trajectory with consistently lower scores over time were labelled as ‘most advantaged’. Figure 3 View largeDownload slide Trajectories of disadvantage for composite disadvantage and within each lens from 0–1 to 8–9 years (n = 5107). Higher scores indicate higher levels of disadvantage. The bold line in each plot represents the mean score, noting the different y-axis scales. Figure 3 View largeDownload slide Trajectories of disadvantage for composite disadvantage and within each lens from 0–1 to 8–9 years (n = 5107). Higher scores indicate higher levels of disadvantage. The bold line in each plot represents the mean score, noting the different y-axis scales. The resulting trajectories were used in the current study as a measure of disadvantage exposure over the childhood period, for children’s overall experiences (composite disadvantage) and within the specific lenses of sociodemographic, geographic environments, health conditions and risk factors. In the current study, some trajectories were combined due to small numbers. For example, children in the most extreme trajectory group (‘most advantaged’, n = 62) were combined with the ‘advantaged’ (n = 1035) trajectory for the sociodemographic lens. Child development outcomes: socio-emotional adjustment, physical functioning and learning competencies at 10–11 years Child development was measured across the domains of socio-emotional adjustment (e.g. social competence and mental health), physical functioning (e.g. motor skills) and learning competencies (e.g. literacy and numeracy). An outcome index has previously been developed and validated within LSAC for Waves 1–3,26,27 as a means of summarizing progress within the three developmental domains of health and physical development, social and emotional functioning and learning competencies. We replicated the outcome index at Wave 6 using the same indicators previously used at Wave 3, with some exceptions (Table 1). We did not include specific language measures such as the Peabody Picture Vocabulary Test because it was not measured at Wave 6. In addition, indicators in the physical functioning domain measuring child health conditions and illness (e.g. ongoing health problems) at Wave 3 were excluded, given our focus on physical functioning. Table 1 Indicators used to measure each child developmental domain at 10–11 years Domain Sub-domain Measure Details Socio-emotional adjustment Social competence Strengths and Difficulties Questionnaire (SDQ) prosocial (5 items; e.g. is kind to younger children (reversed scored)) and peer problems (5 items; e.g. rather solitary, tends to play alone) subscales Parent rated truth of statements about child’s behaviour in the past 6 months from 0 = not true to 2 = certainly true Internalizing SDQ emotional symptoms (5 items; e.g. many worries, often seems worried) subscale Externalizing SDQ hyperactivity (5 items, e.g. easily distracted, concentration wanders) and conduct problems (5 items, e.g. steals from home, school or elsewhere) subscales Physical functioning Motor skills Paediatric Quality of Life Inventory physical functioning subscale (8 items, e.g. the child has had a problem participating in sports activity or exercise) Parent rated the frequency of problems in the past month from 1 = never to 5 = almost always Learning competencies Language and literacy Academic Rating Scale (ARS) of Language and Literacy subscale (from the Early Childhood Longitudinal Study – Kindergarten) assessing performance on language tasks including reading, writing and oral communication (e.g. reads fluently) (9 items) Teacher rated performance from 1 = not yet proficient to 5 = proficient Numeracy ARS Mathematical Thinking subscale (from the Early Childhood Longitudinal Study – Kindergarten) assessing the child’s ability to perform various mathematical tasks (e.g. models, reads, writes and compares fractions) (10 items) Non-verbal cognition Wechsler Intelligence Scale for Children IV (WISC-IV) matrix reasoning test assessing visual information processing and abstract reasoning Direct assessment during home interview Domain Sub-domain Measure Details Socio-emotional adjustment Social competence Strengths and Difficulties Questionnaire (SDQ) prosocial (5 items; e.g. is kind to younger children (reversed scored)) and peer problems (5 items; e.g. rather solitary, tends to play alone) subscales Parent rated truth of statements about child’s behaviour in the past 6 months from 0 = not true to 2 = certainly true Internalizing SDQ emotional symptoms (5 items; e.g. many worries, often seems worried) subscale Externalizing SDQ hyperactivity (5 items, e.g. easily distracted, concentration wanders) and conduct problems (5 items, e.g. steals from home, school or elsewhere) subscales Physical functioning Motor skills Paediatric Quality of Life Inventory physical functioning subscale (8 items, e.g. the child has had a problem participating in sports activity or exercise) Parent rated the frequency of problems in the past month from 1 = never to 5 = almost always Learning competencies Language and literacy Academic Rating Scale (ARS) of Language and Literacy subscale (from the Early Childhood Longitudinal Study – Kindergarten) assessing performance on language tasks including reading, writing and oral communication (e.g. reads fluently) (9 items) Teacher rated performance from 1 = not yet proficient to 5 = proficient Numeracy ARS Mathematical Thinking subscale (from the Early Childhood Longitudinal Study – Kindergarten) assessing the child’s ability to perform various mathematical tasks (e.g. models, reads, writes and compares fractions) (10 items) Non-verbal cognition Wechsler Intelligence Scale for Children IV (WISC-IV) matrix reasoning test assessing visual information processing and abstract reasoning Direct assessment during home interview The following measures from the physical health domain of the Wave 3 outcome index were not included in the Wave 6 outcome index due to lack of alignment with our conceptualization of the domain as normative physical functioning: overall rating of health; special health care needs; weight status; gross motor coordination. The Peabody Picture Vocabulary Test was also not included in the learning competencies domain as this measure was not available at Wave 6. Table 1 Indicators used to measure each child developmental domain at 10–11 years Domain Sub-domain Measure Details Socio-emotional adjustment Social competence Strengths and Difficulties Questionnaire (SDQ) prosocial (5 items; e.g. is kind to younger children (reversed scored)) and peer problems (5 items; e.g. rather solitary, tends to play alone) subscales Parent rated truth of statements about child’s behaviour in the past 6 months from 0 = not true to 2 = certainly true Internalizing SDQ emotional symptoms (5 items; e.g. many worries, often seems worried) subscale Externalizing SDQ hyperactivity (5 items, e.g. easily distracted, concentration wanders) and conduct problems (5 items, e.g. steals from home, school or elsewhere) subscales Physical functioning Motor skills Paediatric Quality of Life Inventory physical functioning subscale (8 items, e.g. the child has had a problem participating in sports activity or exercise) Parent rated the frequency of problems in the past month from 1 = never to 5 = almost always Learning competencies Language and literacy Academic Rating Scale (ARS) of Language and Literacy subscale (from the Early Childhood Longitudinal Study – Kindergarten) assessing performance on language tasks including reading, writing and oral communication (e.g. reads fluently) (9 items) Teacher rated performance from 1 = not yet proficient to 5 = proficient Numeracy ARS Mathematical Thinking subscale (from the Early Childhood Longitudinal Study – Kindergarten) assessing the child’s ability to perform various mathematical tasks (e.g. models, reads, writes and compares fractions) (10 items) Non-verbal cognition Wechsler Intelligence Scale for Children IV (WISC-IV) matrix reasoning test assessing visual information processing and abstract reasoning Direct assessment during home interview Domain Sub-domain Measure Details Socio-emotional adjustment Social competence Strengths and Difficulties Questionnaire (SDQ) prosocial (5 items; e.g. is kind to younger children (reversed scored)) and peer problems (5 items; e.g. rather solitary, tends to play alone) subscales Parent rated truth of statements about child’s behaviour in the past 6 months from 0 = not true to 2 = certainly true Internalizing SDQ emotional symptoms (5 items; e.g. many worries, often seems worried) subscale Externalizing SDQ hyperactivity (5 items, e.g. easily distracted, concentration wanders) and conduct problems (5 items, e.g. steals from home, school or elsewhere) subscales Physical functioning Motor skills Paediatric Quality of Life Inventory physical functioning subscale (8 items, e.g. the child has had a problem participating in sports activity or exercise) Parent rated the frequency of problems in the past month from 1 = never to 5 = almost always Learning competencies Language and literacy Academic Rating Scale (ARS) of Language and Literacy subscale (from the Early Childhood Longitudinal Study – Kindergarten) assessing performance on language tasks including reading, writing and oral communication (e.g. reads fluently) (9 items) Teacher rated performance from 1 = not yet proficient to 5 = proficient Numeracy ARS Mathematical Thinking subscale (from the Early Childhood Longitudinal Study – Kindergarten) assessing the child’s ability to perform various mathematical tasks (e.g. models, reads, writes and compares fractions) (10 items) Non-verbal cognition Wechsler Intelligence Scale for Children IV (WISC-IV) matrix reasoning test assessing visual information processing and abstract reasoning Direct assessment during home interview The following measures from the physical health domain of the Wave 3 outcome index were not included in the Wave 6 outcome index due to lack of alignment with our conceptualization of the domain as normative physical functioning: overall rating of health; special health care needs; weight status; gross motor coordination. The Peabody Picture Vocabulary Test was also not included in the learning competencies domain as this measure was not available at Wave 6. Following the procedures used to calculate the outcome index in previous LSAC waves,27,28 scores for the three developmental domains were calculated by: (i) standardizing all outcome variables (i.e. mean = 0, standard deviation = 1) and averaging them into sub-domain scores; (ii) standardizing sub-domain scores and combining them into continuous domain scores; and (iii) standardizing the domain scores and obtaining cut-offs to identify the bottom 15% of the sample for each domain, reflecting those with the poorest outcomes in that domain. We also categorized children according to whether they fell into the lowest 15% on two or more domains of development (n = 496, 9.7%). Statistical analysis The associations between trajectories of disadvantage from 0–1 to 8–9 years and child development outcomes at 10–11 years were examined using generalized linear models with log-Poisson link. For each of the three developmental domains, we estimated the relative risk (RR) of being in the lowest 15% in each developmental domain associated with disadvantage trajectories in the sociodemographic, geographic environments, health conditions and risk factors lenses, as well as for composite disadvantage. Given that the most vulnerable children are those with difficulties across multiple developmental domains, we also estimated the RR of poor outcomes on two or more developmental domains associated with disadvantage trajectories. Both the unadjusted and the adjusted effects of disadvantage trajectories in each lens were estimated. The adjusted models estimate the association of each lens with the outcomes after accounting for the influence of children’s trajectory membership on all other lenses. The most advantaged trajectory was the reference category in all analyses, based on the assumption that all children have the right to sufficient resources and living standards that allow them to achieve their optimal potential.29 This aligns with the approach adopted by WHO, to fully represent the impact of inequity on child outcomes.30 Population-attributable fractions (PAFs) were also estimated from the models (adjusted for all other lenses) using the maximum likelihood estimates method proposed by Greenland and Drescher.31 As an approach for representing the extent of health inequities, PAFs reflect the percentage reduction in an adverse outcome that would occur if exposure to disadvantage were reduced to an optimal scenario, assuming that disadvantage causes the adverse outcome.30,32 We estimated the percentage reduction in poor outcomes in each developmental domain which could be achieved if all other children had the same exposure as those in the optimal trajectory (most advantaged). Higher PAFs indicate greater potential reduction in poor developmental outcomes. PAFs were calculated using the punaf command in Stata.33 Separation between exposure and outcome for these analyses was ensured as recommended by Goldfeld et al.17 To account for potential overlap with the outcomes, children reported by parents as having a mental health, physical or learning condition or disability were excluded from all analyses (n = 152, 2.5%). Findings were similar when all (n = 5107) children were included (data not shown). Multiple imputation by chained equations was used to deal with missing data in all analyses under the missing at random assumption.34 Twenty imputed datasets were created using regression switching.35 The imputation model included all exposures (sociodemographic, geographic environments, health conditions, risk factors and composite disadvantage) and outcomes (socio-emotional adjustment, physical functioning and learning competencies) and an auxiliary variable (maternal age) to help predict missing data. Results were combined using Rubin’s rules.36 All analyses were conducted using Stata SE version 14.2.37 Results from imputed data are reported. Results Associations between composite disadvantage trajectories and developmental outcomes The more disadvantaged a child’s trajectory, the higher the risk of poor developmental outcomes by 10–11 years of age (Figure 4). Compared with children in the most advantaged composite trajectory, there was a 7-fold increased risk of having poor outcomes in two or more domains in the most disadvantaged composite trajectory. Figure 4 View largeDownload slide Relative risk of poor outcomes (bottom 15%) on each developmental domain and on two or more developmental domains associated with composite disadvantage. Children reported by parents as having a condition impacting on their development in these areas (n = 128, 2.5%) were excluded. Figure 4 View largeDownload slide Relative risk of poor outcomes (bottom 15%) on each developmental domain and on two or more developmental domains associated with composite disadvantage. Children reported by parents as having a condition impacting on their development in these areas (n = 128, 2.5%) were excluded. Associations between lens trajectories and developmental outcomes For each disadvantage lens, the unadjusted estimates show that children following more disadvantaged trajectories had a higher risk of poor developmental outcomes (Table 2). In the adjusted models, different lenses of disadvantage were associated with different developmental outcomes. Only disadvantage trajectories in the sociodemographic lens were associated with learning competencies after adjusting for other lenses, whereas disadvantage trajectories in the sociodemographic and health conditions lenses were associated with physical functioning. Trajectories in all disadvantage lenses were associated with socio-emotional adjustment. Table 2 Relative risk (RR) of poor developmental outcomes (lowest 15%) according to trajectory membership for each disadvantage lens (n = 4979a) Disadvantage lenses % Socio-emotional adjustment Physical functioning Learning competencies RR (95% CI) Adjusted RR (95% CI) RR (95% CI) Adjusted RR (95% CI) RR (95% CI) Adjusted RR (95% CI) Sociodemographic  Most advantagedb/advantaged 25.8 Ref Ref Ref Ref Ref Ref  Disadvantaged 49.5 1.35 1.08 1.27 1.16 1.75 1.70 (1.05, 1.73) (0.83, 1.41) (1.03, 1.57) (0.93, 1.45) (1.37, 2.22) (1.33, 2.20)  Most disadvantaged 24.7 2.36 1.41 1.91 1.40 2.72 2.57 (1.88, 2.95) (1.08, 1.85) (1.52, 2.38) (1.08, 1.81) (2.16, 3.42) (1.94, 3.41) Geographic environments  Most advantaged 17.1 Ref Ref Ref Ref Ref Ref  Advantaged 36.3 1.28 1.12 1.14 1.00 1.36 1.03 (0.94, 1.72) (0.83, 1.50) (0.87, 1.48) (0.76, 1.31) (1.02, 1.82) (0.77, 1.38)  Disadvantaged 31.6 1.88 1.45 1.22 0.95 1.51 1.11 (1.45, 2.43) (1.10, 1.92) (0.95, 1.55) (0.73, 1.24) (1.16, 1.98) (0.83, 1.50)  Most disadvantaged 15.0 2.22 1.50 1.58 1.09 1.92 1.15 (1.69, 2.94) (1.10, 2.04) (1.19, 2.11) (0.79, 1.52) (1.43, 2.56) (0.83, 1.60) Health conditions  Advantaged 67.6 Ref Ref Ref Ref Ref Ref  Disadvantaged 22.7 2.03 1.31 2.02 1.53 1.25 1.20 (1.71, 2.42) (0.97, 1.76) (1.67, 2.44) (1.12, 2.08) (1.06, 1.49) (0.89, 1.61)  Increasing disadvantage 4.6 2.46 1.52 2.09 1.56 1.44 1.29 (1.81, 3.35) (1.02, 2.26) (1.49, 2.93) (1.01, 2.39) (0.99, 2.09) (0.81, 2.05)  Most disadvantaged 5.1 2.95 1.47 2.73 1.95 1.49 1.39 (2.33, 3.74) (0.94, 2.28) (2.10, 3.55) (1.17, 3.26) (1.08, 2.06) (0.78, 2.49) Risk factors  Advantaged 65.1 Ref Ref Ref Ref Ref Ref  Disadvantaged 24.9 2.01 1.45 1.94 1.33 1.18 0.88 (1.69, 2.40) (1.10, 1.94) (1.58, 2.39) (0.97, 1.82) (0.98, 1.42) (0.65, 1.18)  Intermediate disadvantageb/ most disadvantaged 10.0 2.81 1.73 2.35 1.26 1.29 0.77 (2.33, 3.39) (1.18, 2.53) (1.93, 2.88) (0.83, 1.90) (1.02, 1.62) (0.48, 1.23) Disadvantage lenses % Socio-emotional adjustment Physical functioning Learning competencies RR (95% CI) Adjusted RR (95% CI) RR (95% CI) Adjusted RR (95% CI) RR (95% CI) Adjusted RR (95% CI) Sociodemographic  Most advantagedb/advantaged 25.8 Ref Ref Ref Ref Ref Ref  Disadvantaged 49.5 1.35 1.08 1.27 1.16 1.75 1.70 (1.05, 1.73) (0.83, 1.41) (1.03, 1.57) (0.93, 1.45) (1.37, 2.22) (1.33, 2.20)  Most disadvantaged 24.7 2.36 1.41 1.91 1.40 2.72 2.57 (1.88, 2.95) (1.08, 1.85) (1.52, 2.38) (1.08, 1.81) (2.16, 3.42) (1.94, 3.41) Geographic environments  Most advantaged 17.1 Ref Ref Ref Ref Ref Ref  Advantaged 36.3 1.28 1.12 1.14 1.00 1.36 1.03 (0.94, 1.72) (0.83, 1.50) (0.87, 1.48) (0.76, 1.31) (1.02, 1.82) (0.77, 1.38)  Disadvantaged 31.6 1.88 1.45 1.22 0.95 1.51 1.11 (1.45, 2.43) (1.10, 1.92) (0.95, 1.55) (0.73, 1.24) (1.16, 1.98) (0.83, 1.50)  Most disadvantaged 15.0 2.22 1.50 1.58 1.09 1.92 1.15 (1.69, 2.94) (1.10, 2.04) (1.19, 2.11) (0.79, 1.52) (1.43, 2.56) (0.83, 1.60) Health conditions  Advantaged 67.6 Ref Ref Ref Ref Ref Ref  Disadvantaged 22.7 2.03 1.31 2.02 1.53 1.25 1.20 (1.71, 2.42) (0.97, 1.76) (1.67, 2.44) (1.12, 2.08) (1.06, 1.49) (0.89, 1.61)  Increasing disadvantage 4.6 2.46 1.52 2.09 1.56 1.44 1.29 (1.81, 3.35) (1.02, 2.26) (1.49, 2.93) (1.01, 2.39) (0.99, 2.09) (0.81, 2.05)  Most disadvantaged 5.1 2.95 1.47 2.73 1.95 1.49 1.39 (2.33, 3.74) (0.94, 2.28) (2.10, 3.55) (1.17, 3.26) (1.08, 2.06) (0.78, 2.49) Risk factors  Advantaged 65.1 Ref Ref Ref Ref Ref Ref  Disadvantaged 24.9 2.01 1.45 1.94 1.33 1.18 0.88 (1.69, 2.40) (1.10, 1.94) (1.58, 2.39) (0.97, 1.82) (0.98, 1.42) (0.65, 1.18)  Intermediate disadvantageb/ most disadvantaged 10.0 2.81 1.73 2.35 1.26 1.29 0.77 (2.33, 3.39) (1.18, 2.53) (1.93, 2.88) (0.83, 1.90) (1.02, 1.62) (0.48, 1.23) RRs are adjusted for trajectory membership in all other lenses. Ref, reference; CI, confidence interval. a Analysis uses the imputed sample (data were imputed for participants with missing outcomes and exposures). Children reported by parents as having a condition related to the outcomes (n = 128, 2.5%) were excluded from the analyses. b Combined due to small numbers in trajectory group. Table 2 Relative risk (RR) of poor developmental outcomes (lowest 15%) according to trajectory membership for each disadvantage lens (n = 4979a) Disadvantage lenses % Socio-emotional adjustment Physical functioning Learning competencies RR (95% CI) Adjusted RR (95% CI) RR (95% CI) Adjusted RR (95% CI) RR (95% CI) Adjusted RR (95% CI) Sociodemographic  Most advantagedb/advantaged 25.8 Ref Ref Ref Ref Ref Ref  Disadvantaged 49.5 1.35 1.08 1.27 1.16 1.75 1.70 (1.05, 1.73) (0.83, 1.41) (1.03, 1.57) (0.93, 1.45) (1.37, 2.22) (1.33, 2.20)  Most disadvantaged 24.7 2.36 1.41 1.91 1.40 2.72 2.57 (1.88, 2.95) (1.08, 1.85) (1.52, 2.38) (1.08, 1.81) (2.16, 3.42) (1.94, 3.41) Geographic environments  Most advantaged 17.1 Ref Ref Ref Ref Ref Ref  Advantaged 36.3 1.28 1.12 1.14 1.00 1.36 1.03 (0.94, 1.72) (0.83, 1.50) (0.87, 1.48) (0.76, 1.31) (1.02, 1.82) (0.77, 1.38)  Disadvantaged 31.6 1.88 1.45 1.22 0.95 1.51 1.11 (1.45, 2.43) (1.10, 1.92) (0.95, 1.55) (0.73, 1.24) (1.16, 1.98) (0.83, 1.50)  Most disadvantaged 15.0 2.22 1.50 1.58 1.09 1.92 1.15 (1.69, 2.94) (1.10, 2.04) (1.19, 2.11) (0.79, 1.52) (1.43, 2.56) (0.83, 1.60) Health conditions  Advantaged 67.6 Ref Ref Ref Ref Ref Ref  Disadvantaged 22.7 2.03 1.31 2.02 1.53 1.25 1.20 (1.71, 2.42) (0.97, 1.76) (1.67, 2.44) (1.12, 2.08) (1.06, 1.49) (0.89, 1.61)  Increasing disadvantage 4.6 2.46 1.52 2.09 1.56 1.44 1.29 (1.81, 3.35) (1.02, 2.26) (1.49, 2.93) (1.01, 2.39) (0.99, 2.09) (0.81, 2.05)  Most disadvantaged 5.1 2.95 1.47 2.73 1.95 1.49 1.39 (2.33, 3.74) (0.94, 2.28) (2.10, 3.55) (1.17, 3.26) (1.08, 2.06) (0.78, 2.49) Risk factors  Advantaged 65.1 Ref Ref Ref Ref Ref Ref  Disadvantaged 24.9 2.01 1.45 1.94 1.33 1.18 0.88 (1.69, 2.40) (1.10, 1.94) (1.58, 2.39) (0.97, 1.82) (0.98, 1.42) (0.65, 1.18)  Intermediate disadvantageb/ most disadvantaged 10.0 2.81 1.73 2.35 1.26 1.29 0.77 (2.33, 3.39) (1.18, 2.53) (1.93, 2.88) (0.83, 1.90) (1.02, 1.62) (0.48, 1.23) Disadvantage lenses % Socio-emotional adjustment Physical functioning Learning competencies RR (95% CI) Adjusted RR (95% CI) RR (95% CI) Adjusted RR (95% CI) RR (95% CI) Adjusted RR (95% CI) Sociodemographic  Most advantagedb/advantaged 25.8 Ref Ref Ref Ref Ref Ref  Disadvantaged 49.5 1.35 1.08 1.27 1.16 1.75 1.70 (1.05, 1.73) (0.83, 1.41) (1.03, 1.57) (0.93, 1.45) (1.37, 2.22) (1.33, 2.20)  Most disadvantaged 24.7 2.36 1.41 1.91 1.40 2.72 2.57 (1.88, 2.95) (1.08, 1.85) (1.52, 2.38) (1.08, 1.81) (2.16, 3.42) (1.94, 3.41) Geographic environments  Most advantaged 17.1 Ref Ref Ref Ref Ref Ref  Advantaged 36.3 1.28 1.12 1.14 1.00 1.36 1.03 (0.94, 1.72) (0.83, 1.50) (0.87, 1.48) (0.76, 1.31) (1.02, 1.82) (0.77, 1.38)  Disadvantaged 31.6 1.88 1.45 1.22 0.95 1.51 1.11 (1.45, 2.43) (1.10, 1.92) (0.95, 1.55) (0.73, 1.24) (1.16, 1.98) (0.83, 1.50)  Most disadvantaged 15.0 2.22 1.50 1.58 1.09 1.92 1.15 (1.69, 2.94) (1.10, 2.04) (1.19, 2.11) (0.79, 1.52) (1.43, 2.56) (0.83, 1.60) Health conditions  Advantaged 67.6 Ref Ref Ref Ref Ref Ref  Disadvantaged 22.7 2.03 1.31 2.02 1.53 1.25 1.20 (1.71, 2.42) (0.97, 1.76) (1.67, 2.44) (1.12, 2.08) (1.06, 1.49) (0.89, 1.61)  Increasing disadvantage 4.6 2.46 1.52 2.09 1.56 1.44 1.29 (1.81, 3.35) (1.02, 2.26) (1.49, 2.93) (1.01, 2.39) (0.99, 2.09) (0.81, 2.05)  Most disadvantaged 5.1 2.95 1.47 2.73 1.95 1.49 1.39 (2.33, 3.74) (0.94, 2.28) (2.10, 3.55) (1.17, 3.26) (1.08, 2.06) (0.78, 2.49) Risk factors  Advantaged 65.1 Ref Ref Ref Ref Ref Ref  Disadvantaged 24.9 2.01 1.45 1.94 1.33 1.18 0.88 (1.69, 2.40) (1.10, 1.94) (1.58, 2.39) (0.97, 1.82) (0.98, 1.42) (0.65, 1.18)  Intermediate disadvantageb/ most disadvantaged 10.0 2.81 1.73 2.35 1.26 1.29 0.77 (2.33, 3.39) (1.18, 2.53) (1.93, 2.88) (0.83, 1.90) (1.02, 1.62) (0.48, 1.23) RRs are adjusted for trajectory membership in all other lenses. Ref, reference; CI, confidence interval. a Analysis uses the imputed sample (data were imputed for participants with missing outcomes and exposures). Children reported by parents as having a condition related to the outcomes (n = 128, 2.5%) were excluded from the analyses. b Combined due to small numbers in trajectory group. Population attributable fractions Table 3 shows the PAFs for each of the adjusted models. These findings assume that the estimated relationships between disadvantage trajectories and developmental outcomes approximate the true causal effect. If all other children had the same exposure as those in the optimal composite trajectory (i.e. the most advantaged), the proportion of children with poor developmental outcomes in two or more domains could be reduced by as much as 70%. Poor developmental outcomes in the socio-emotional, physical functioning and learning competencies domains could be reduced by 59%, 48% and 55%, respectively. When examining the impact of providing optimal conditions on each lens (i.e. all children have the same exposure as that of those in the optimal trajectory on that specific lens), the potential reduction in poor outcomes varied across developmental domains. For example, the proportion of children with poor learning competencies could be reduced by as much as 43% by providing optimal conditions on the sociodemographic lens. In Supplementary Table 2 (available as Supplementary data at IJE online), we estimated PAFs corresponding to each individual trajectory (within all lenses and composite disadvantage), rather than for all non-optimal trajectories combined. Table 3 Population-attributable fractions showing the proportional reduction in poor developmental outcomes that could be achieved if all children had the same exposure as the optimal trajectory, if the estimated relationships between disadvantage and developmental outcomes approximates the true causal effect (n = 4979a) Socio-emotional adjustment Physical functioning Learning competencies Poor outcome on 2+ domains PAF % (95% CI) PAF % (95% CI) PAF % (95% CI) PAF % (95% CI) Disadvantage lenses  Sociodemographic 14.58 (11.41, 17.64) 16.40 (12.48, 19.23) 43.02 (40.65, 45.29) 33.07 (29.44, 36.52)  Geographic environments 22.92 (19.27, 26.41) −0.29 (−4.36, 3.62) 7.63 (3.35, 11.72) 17.92 (12.71, 22.83)  Health conditions 13.94 (11.71, 16.12) 18.98 (17.10, 20.82) 8.47 (6.72, 10.18) 22.60 (20.18, 24.96)  Risk factors 19.33 (17.22, 21.37) 12.33 (10.10, 14.56) −7.48 (−9.96, −5.07) 10.15 (6.98, 13.20)  Composite disadvantage 59.04 (56.06, 61.82) 48.38 (45.12, 51.46) 54.81 (51.70, 57.72) 70.00 (66.70, 72.93) Socio-emotional adjustment Physical functioning Learning competencies Poor outcome on 2+ domains PAF % (95% CI) PAF % (95% CI) PAF % (95% CI) PAF % (95% CI) Disadvantage lenses  Sociodemographic 14.58 (11.41, 17.64) 16.40 (12.48, 19.23) 43.02 (40.65, 45.29) 33.07 (29.44, 36.52)  Geographic environments 22.92 (19.27, 26.41) −0.29 (−4.36, 3.62) 7.63 (3.35, 11.72) 17.92 (12.71, 22.83)  Health conditions 13.94 (11.71, 16.12) 18.98 (17.10, 20.82) 8.47 (6.72, 10.18) 22.60 (20.18, 24.96)  Risk factors 19.33 (17.22, 21.37) 12.33 (10.10, 14.56) −7.48 (−9.96, −5.07) 10.15 (6.98, 13.20)  Composite disadvantage 59.04 (56.06, 61.82) 48.38 (45.12, 51.46) 54.81 (51.70, 57.72) 70.00 (66.70, 72.93) The reference group is the optimal trajectory. The PAF is calculated by comparing scenario 1 (a hypothetical scenario in which all children were in the optimal trajectory, e.g. most advantaged trajectory) with scenario 0 (the real world in which there are children in the optimal trajectory and children in other more disadvantaged trajectories). PAF,  population-attributable fractions, reported as percentages. a Analysis uses the imputed sample (data were imputed for participants with missing outcomes and exposures). Children reported by parents as having a condition related to the outcomes (n = 128, 2.5%) were excluded from the analyses. Table 3 Population-attributable fractions showing the proportional reduction in poor developmental outcomes that could be achieved if all children had the same exposure as the optimal trajectory, if the estimated relationships between disadvantage and developmental outcomes approximates the true causal effect (n = 4979a) Socio-emotional adjustment Physical functioning Learning competencies Poor outcome on 2+ domains PAF % (95% CI) PAF % (95% CI) PAF % (95% CI) PAF % (95% CI) Disadvantage lenses  Sociodemographic 14.58 (11.41, 17.64) 16.40 (12.48, 19.23) 43.02 (40.65, 45.29) 33.07 (29.44, 36.52)  Geographic environments 22.92 (19.27, 26.41) −0.29 (−4.36, 3.62) 7.63 (3.35, 11.72) 17.92 (12.71, 22.83)  Health conditions 13.94 (11.71, 16.12) 18.98 (17.10, 20.82) 8.47 (6.72, 10.18) 22.60 (20.18, 24.96)  Risk factors 19.33 (17.22, 21.37) 12.33 (10.10, 14.56) −7.48 (−9.96, −5.07) 10.15 (6.98, 13.20)  Composite disadvantage 59.04 (56.06, 61.82) 48.38 (45.12, 51.46) 54.81 (51.70, 57.72) 70.00 (66.70, 72.93) Socio-emotional adjustment Physical functioning Learning competencies Poor outcome on 2+ domains PAF % (95% CI) PAF % (95% CI) PAF % (95% CI) PAF % (95% CI) Disadvantage lenses  Sociodemographic 14.58 (11.41, 17.64) 16.40 (12.48, 19.23) 43.02 (40.65, 45.29) 33.07 (29.44, 36.52)  Geographic environments 22.92 (19.27, 26.41) −0.29 (−4.36, 3.62) 7.63 (3.35, 11.72) 17.92 (12.71, 22.83)  Health conditions 13.94 (11.71, 16.12) 18.98 (17.10, 20.82) 8.47 (6.72, 10.18) 22.60 (20.18, 24.96)  Risk factors 19.33 (17.22, 21.37) 12.33 (10.10, 14.56) −7.48 (−9.96, −5.07) 10.15 (6.98, 13.20)  Composite disadvantage 59.04 (56.06, 61.82) 48.38 (45.12, 51.46) 54.81 (51.70, 57.72) 70.00 (66.70, 72.93) The reference group is the optimal trajectory. The PAF is calculated by comparing scenario 1 (a hypothetical scenario in which all children were in the optimal trajectory, e.g. most advantaged trajectory) with scenario 0 (the real world in which there are children in the optimal trajectory and children in other more disadvantaged trajectories). PAF,  population-attributable fractions, reported as percentages. a Analysis uses the imputed sample (data were imputed for participants with missing outcomes and exposures). Children reported by parents as having a condition related to the outcomes (n = 128, 2.5%) were excluded from the analyses. Discussion This study found strong evidence of an association between the composite disadvantage trajectories from 0–9 years and all major domains of development at 10–11 years (socio-emotional adjustment, physical functioning and learning competencies). Rather than a threshold, we found that the more disadvantaged a child’s composite trajectory, the higher the risk of poor developmental outcomes by 10–11 years of age. Thus, inequities in child development affect a larger proportion of the population than just those who are most severely deprived.38,39 These findings are consistent with previous research reporting on trajectories of other indicators of disadvantage (e.g. income).40 It also aligns with our growing understanding of brain development; evidence continues to elucidate how stress arising from continued and compounded experiences of disadvantage can alter brain architecture and physiological systems to adversely affect many aspects of children’s health and development.41 At a more granular level, the different lenses of disadvantage were varyingly associated with poor outcomes in each developmental domain, aligning with the findings of Sanson et al.16 who found both overlapping and distinct risk factors across domains. Children can have different combinations of exposure to disadvantage across the four lenses and over time (Goldfeld et al., submitted for publication). The impact of these varied experiences could manifest in different ways across developmental domains, depending on how directly or pervasively they undermine the child’s opportunities for development in that area. For example, when considering learning competencies, exposure to a more disadvantaged pathway on the sociodemographic lens seemed particularly relevant. Parents’ own educational experiences, captured within this lens, are shown to be closely associated with their capacity to provide a quality home learning environment, which is a critical resource for early academic development.42 We further quantified the substantial reduction in these poor developmental outcomes (as much as 70%) which could be achieved if policy and intervention efforts successfully redressed disadvantage. This reinforces findings from previous research, demonstrating the potential for profound reduction in child morbidity given optimal social conditions.30 To allow all children the same conditions as those most advantaged, we would need to capitalize on those potentially modifiable factors that will generate a strong return on investment; this would likely require cooperation across a range of government portfolios. Achieving this reduction in child development inequities would have far-reaching implications for policy in terms of generating savings in health, education and welfare budgets.32 It would also be expected to generate significant improvements for productivity, given that healthy child development translates to improved human capital in the longer term.43 Most importantly it would ensure that all children achieve their potential.29 Strengths and limitations The breadth and richness of data available within LSAC over multiple waves enabled exploration of children’s exposure to disadvantage from 0–9 years, allowing us to better capture its relationship to child development. As with any study of this duration there has been attrition, and this was greatest for the most disadvantaged children. We have used survey weighting and multiple imputation to reduce (but we cannot eliminate) potential selection bias.21,34 We measured socio-emotional adjustment, physical functioning and learning competencies at a single time point (10–11 years), but like disadvantage, development also unfolds over time. How developmental trajectories might change in relation to disadvantage exposure over time should be explored in future work (e.g. whether gaps in developmental trajectories amplify over time in response to disadvantage exposure).44 The findings should also be replicated in other cohorts and populations outside Australia, as it is possible that the intersections of specific lenses and outcomes might vary between country contexts. The PAFs are intended to provide a useful metric for quantifying the extent of the relationship between disadvantage and children’s development, taking into account both the prevalence and the relative risk of the exposure.30 The PAFs rest on the assumption that the relationships between disadvantage trajectories and developmental outcomes are similar to the true causal effect. Testing these causal assumptions is an important step for future work, but should not preclude more immediate policy effort in this area. Implications These findings unambiguously demonstrate that child inequities are a significant concern in Australia, as undoubtedly they are for other high-income countries.10 These differential outcomes have lifelong consequences that track forward over time; gaps in academic performance by the end of childhood are only likely to widen throughout the rest of the school years.45 Clearly, there is both a need and an opportunity for improved precision in policy development and intervention in this area. It cannot be assumed that inequities across all aspects of child development share the same drivers or follow the same paths. Precision public health means providing the right intervention, to the right population and at the right time, to optimize the outcome—thereby maximising the public dollar.20 Failure to achieve this precision may mean that gaps in some developmental domains do not close, or do not converge fast enough. This study helps to signpost where further research (including policy research) could be focused, although causal effects and specific intervention targets cannot be inferred from these findings which likely reflect a complex interaction between the child, their biology and the multiple and diverse factors influencing their experiences across ecological settings and over time.46 Research now needs to untangle the causal pathways and temporal relationships of the many inter-related indicators within each of the lenses, some of which may be more strongly related to developmental outcomes than others, or more relevant at different periods in children’s development. For example, existing evidence suggests that intervening in the early years of life is one of the most critical and cost-effective ways of reducing inequities and should remain a priority for all governments.1 Understanding what contributes to variability in outcomes for children exposed to the same disadvantage trajectory will also help to more precisely define target population groups for intervention.20,47 It will be important to quantify the potential return on investment for intervening on the most powerful causal drivers, or combinations of drivers, to help prioritize policy interventions. Conclusions These findings provide a comprehensive account of the relationship between exposure to disadvantage over early childhood and later developmental outcomes; this evidence can be used to stimulate advocacy and action to reduce child inequities in Australia. Whereas the overall relationship between disadvantage and development was strong, disadvantage lenses were variably associated with different developmental domains, signalling the value of precision policy approaches. Researchers, service providers and policy makers should delve further into these relationships to determine, develop and test specific policies and programmes that can best reduce inequities in children’s developmental outcomes. Acknowledgements The Changing Children’s Chances investigator team oversees this programme of work, and includes Prof. Sharon Goldfeld, Prof. Katrina Williams, A/Prof. Gerry Redmond, Prof. Frank Oberklaid, A/Prof. Hannah Badland, Prof. Gary Freed, Dr Fiona Mensah, A/Prof. Sue Woolfenden, Dr Jenny Proimos, Dr Amanda Kvalsvig and Dr Jianfei Gong, with thanks to Emeritus Professor Nick Spencer for his feedback on this manuscript. This paper uses unit record data from Growing Up in Australia, the Longitudinal Study of Australian Children. The study is conducted in partnership with the Department of Social Services (DSS), the Australian Institute of Family Studies (AIFS) and the Australian Bureau of Statistics (ABS). The findings and views reported in this paper are those of the authors and should not be attributed to the DSS, the AIFS or the ABS. Funding This research is funded by Australian Research Council Discovery Grant DP160101735, and was supported by the Victorian Government's Operational Infrastructure Support Program. S.G. is supported by Australian National Health and Medical Research Council (NHMRC) Career Development Fellowship 1082922, and F.M. is supported by NHMRC Career Development Fellowship 1111160. H.B. is supported by an RMIT University VC Senior Research Fellowship. Conflict of interest: None declared. References 1 Commission on Social Determinants of Health . Closing the Gap in a Generation: Health Equity Through Action on the Social Determinants of Health. 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Published by Oxford University Press on behalf of the International Epidemiological Association This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Journal

International Journal of EpidemiologyOxford University Press

Published: Oct 1, 2018

References

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