Socioeconomic and Regional Inequalities in Early Childhood Development in Algeria: Evidence from the MICS-6 Survey

Socioeconomic and Regional Inequalities in Early Childhood Development in Algeria: Evidence from the MICS-6 Survey | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Socioeconomic and Regional Inequalities in Early Childhood Development in Algeria: Evidence from the MICS-6 Survey Boukhelif Meryem, Sidi-Yakhlef Adel, Moussouni Abdellatif, Chikhi Kamel This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9122949/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Early childhood development is increasingly recognized as a central dimension of child well-being and an essential component of child indicator frameworks used to monitor social progress. The objective of this study is to examine disparities in early childhood development in Algeria using nationally representative data from the 2019 Multiple Indicator Cluster Survey (MICS). The analysis covered all children aged between 36 and 59 months included in the database and used the Early Childhood Development Index (ECDI) as the main assessment criterion. Using complex sample logistic regression models, the total and direct associations between socio-economic factors and developmental status were assessed. A directed acyclic graph (DAG) guided covariate selection and informed the distinction between total association models and models adjusting for hypothesized mediating pathways (nutritional status and home stimulation). Interaction between household wealth and place of residence was also examined. Among the 6,146 children listed, 76.8% were developmentally on track. Data analysis showed strong wealth-related gradients: children in the poorest quintile had significantly higher odds of being not developmentally on track compared with those in the richest quintile. These disparities persisted after adjusting for maternal education, child characteristics, nutritional status, and home stimulation. Stunting was independently associated with developmental vulnerability, while book reading was associated with lower odds of developmental delay. Significant regional disparities were identified. Moreover, the association between wealth and developmental status varied by place of residence, indicating spatial heterogeneity in socioeconomic gradients. These findings demonstrate that early childhood developmental inequalities in Algeria are shaped by both structural socioeconomic disadvantage and geographic context. Investing in early childhood development as an equity-sensitive child indicator may support more targeted policies addressing household-level disadvantage and territorial disparities not only in Algeria but throughout North Africa. Early childhood development Socioeconomic inequalities Child well-being indicators Algeria Multiple Indicator Cluster Survey Figures Figure 1 Figure 2 Introduction Early childhood is a crucial stage in human development, during which fundamental cognitive, socio-emotional and physical abilities begin to take shape (United Nations Children’s Fund, 2023; Likhar et al., 2022 ). Several studies have demonstrated that disadvantages experienced during this period often extend beyond childhood, influencing educational attainment, health and social outcomes later in life. This is why increasing attention must be paid to the early identification and monitoring of developmental inequalities at the population level (Krafft and El-Kogali, 2015 ). The United Nations has included early childhood development in target 4.2 of the Sustainable Development Goals (SDGs), given its importance. By 2030, countries should ensure that every child has access to quality early childhood care and education, as well as early childhood development (ECD) in terms of health, learning and psychosocial well-being (Krafft and El-Kogali, 2015 ; Rahman et al., 2023 ). To address this need, UNICEF developed the Early Childhood Development Index (ECDI) , a standardized composite indicator designed to assess whether children up to 8 years of age are developmentally on track in key domains, including literacy-numeracy, physical development, learning, and socio-emotional skills (United Nations Children’s Fund, 2023). Although this index does not reflect the full complexity of child development, the ECDI is now routinely included in Multiple Indicator Cluster Surveys (MICS), enabling countries to assess the proportion of children who are developing normally in several areas (UNICEF, 2019 ). Beyond individual outcomes, the ECDI provide valuable tools for monitoring inequalities in early life conditions and identifying vulnerable populations. A growing body of literature has consistently reported associations between early childhood development and a range of socio-demographic characteristics, including household socio-economic status, parental education, and place of residence. These characteristics reflect broader living conditions that shape children’s exposure to learning environments, nutrition, health services, and psychosocial stimulation (Rahman et al., 2023 ; Akram et al., 2024 ). The family is the child's first environment for interaction from birth and plays a central role in stimulating, supporting and educating them (Roopnarine and Dede, 2018 ). These qualities are in turn influenced by the resources available to families for raising their children (income), their parenting style, and their ability to provide a rich and stimulating linguistic environment (parents' level of education). Furthermore, the sex of the child can influence early childhood development, particularly due to socio-cultural norms associated with gender (Akram et al., 2024 ; Wang et al., 2022 ). The environment in which children grow up plays a decisive role in early childhood development, as economic, cultural and social disparities can influence their well-being and social development (Rahman et al., 2023 ; Bizzego et al., 2020 ). Parental involvement in various learning activities with their children has been identified as the most important factor in children's cognitive and overall development (Roopnarine and Dede, 2018 ). Their participation through reading books had a significant association on children's reading and writing skills. In addition, playful activities between parents and children have also promoted their overall development (Rahman et al., 2023 ; Roopnarine and Dede, 2018 ). Another crucial factor with substantial developmental consequences is child nutrition. Proper nutrition during this crucial phase of life is essential, given that suboptimal nutrition can have detrimental effects on brain development and overall development (Akram et al., 2024 ). This nutritional deficiency is likely to have harmful effects on cognitive and academic performance, both in the short and long term (Bahru et al., 2019 ). Thus, factors related to the social environment can influence children's development in a positive or negative way, acting as risk or protective factors. However, although these SDH are clearly identified in the literature, the list of determinants often varies from one country to another, and the number of negative factors is significantly higher in poor countries (Krafft and El-Kogali, 2015 ). However, evidence remains uneven across regions, and empirical studies focusing on early childhood development indicators in North Africa are still scarce. In Algeria, early childhood development has received increasing policy attention in recent years; however, empirical evidence on population-level developmental inequalities remains limited. While Algeria has made substantial progress in child survival, education, and access to basic services, less is known about how socioeconomic status, geographic context, nutritional conditions, and home stimulation jointly shape early developmental outcomes. Existing national studies have focused primarily on health or education outcomes in isolation, and few analyses have integrated the multiple dimensions of early childhood development using standardised indicators. Other studies have relied on multivariable models including numerous correlated variables without explicit conceptual justification (Lassassi, 2021 ; Bedrouni and fratsa, 2025 ). This can lead to overfitting, particularly when intermediate variables are treated as confounding factors rather than as pathways through which socio-economic conditions influence development. In this context, the present study aims to examine socioeconomic and geographic inequalities in early childhood development as measured by the ECDI among Algerian children aged 36 to 59 months, based on nationally representative data from the MICS survey. By combining descriptive analyses with directed acyclic graphs (DAG) guided multivariable models, this study goes beyond documenting disparities to clarify how structural correlates, contextual factors, and modifiable pathways may jointly contribute to developmental inequalities. The use of DAGs as conceptual frameworks is increasingly recommended to clarify hypothetical relationships, distinguish confounding factors from mediators, and improve the transparency and interpretability of results. Furthermore, by explicitly testing whether wealth gradients differ between urban and rural settings, the analysis attempts to fill an important gap in the Algerian literature concerning context-specific vulnerabilities. By identifying socioeconomic gradients and territorial disparities in early childhood development, this study provides evidence that can inform equity-oriented early childhood policies in Algeria. Beyond the national context, the findings also contribute to the limited empirical literature on early childhood development inequalities in North Africa and other middle-income countries undergoing demographic and social transitions. This study contributes to the literature on early childhood development in three main ways. First, it is the first analysis to use the most recent national survey on socio-economic and geographical inequalities in early childhood development in Algeria using the Early Childhood Development Index (ECDI). Secondly, a directed acyclic graph (DAG)-guided modelling strategy was applied to distinguish total socio-economic associations from trajectory-adjusted associations, thereby improving transparency in model specification. Thirdly, the study explicitly examines spatial heterogeneity in socio-economic gradients by testing the interaction between household wealth and place of residence, which is an aspect rarely explored in research on early childhood development in North Africa in general and Algeria in particular. Methods This study is based on secondary analysis of a representative national data from the Multiple Indicator Cluster Survey (MICS 6), conducted in 2018–2019 by the Algerian Ministry of Health, Population and Hospital Reform, with technical and financial support from UNICEF and a financial contribution from UNFPA (Ministry of Health and Population of Algeria, 2021). MICS is a nationally representative household survey implemented using a stratified, multistage cluster sampling design to generate population-level estimates of key child and maternal indicators. The survey collects standardized information on early childhood development, health, nutrition, education, and household characteristics, thereby helping decision-makers to draw up policies and intervention programmes (UNICEF, 2019 ). For this analysis, we used the module on children under-5 to extract data assessing the early childhood development index. Collected data included a questionnaire administered to the child's mother or main carer, constructed by combining individual datasets on children, mothers and household characteristics. All children aged 36–59 months with complete information on the ECDI outcome and selected explanatory variables were included in the analysis i.e. 6146 children in the database (3173 boys and 2973 girls). Observations with missing outcome data were excluded. All estimates are representative of the national population of children aged 36–59 months in Algeria. Outcome variable: early childhood development The variable of interest, ECD, was estimated by the ECD index (ECDI), which has four domains: literacy- numeracy, learning, physical and social–emotional developed by UNICEF (Loizillon et al., 2017 ). The ECDI is widely used in international monitoring of early childhood development and forms part of the Sustainable Development Goal indicator framework (SDG indicator 4.2.1). For each domain assessed, a series of questions was administered, and reaching a predefined threshold of positive responses allowed for the conclusion that the child was developing adequately. The number of items in each domain of the ECDI varies; each child is categorized as on track or not in each of the domains. Literacy and numeracy were assessed based on meeting at least two of the following three requirements: identifying at least ten letters, reading at least four familiar words or names, and recognising numbers from 1 to 10. For the physical domain, children are required to be able to do at least one of the following: can pick up item with two fingers and does not often feel sick (on track when at least one in two items received a positive response). The learning domain is based on two observations aimed at assessing the child's progress: their ability to follow simple instructions correctly and their ability to carry out the instructions given to them independently. The socio-emotional assessment aimed to measure emotional and social functioning as well as sensory processing. It was based on three questions asked to the mother or guardian, with at least two affirmative answers allowing the child to be classified as normal. These questions focused on the child's ability to get along well with other children, the absence of aggressive behaviour (kicking, biting or hitting) and their ability to not be easily distracted. Children who met the criteria in at least three of the four domains assessed were considered to be on track for early childhood development. This classification is expressed as a binary variable, known as the “early childhood development index” (ECDI) (Loizillon et al., 2017 ; Rahman et al., 2023 ). Explanatory variables Selection of explanatory variables was guided by prior literature and a conceptual framework distinguishing structural correlates, potential confounders, and intermediate pathway variables. Structural socioeconomic and contextual correlates (household wealth quintile, place of residence, geographic region). Maternal and child characteristics (maternal education level, maternal economic activity, child age group, child sex). Nutritional status (stunting, underweight) and home stimulation indicators (book reading, storytelling, playing with the child). Conceptual framework and DAG-guided modelling strategy A directed acyclic graph (DAG) was developed to clarify hypothesized relationships between socioeconomic conditions and early childhood development and to guide covariate selection. The DAG distinguished between (1) upstream structural correlates (wealth, residence, region), (2) potential confounders (maternal education, maternal occupation, child characteristics), and (3) Intermediate pathway variables illustrating the mechanisms through which socio-economic conditions can influence development (stimulation at home and nutritional status). The use of a DAG was motivated by the need to avoid overfitting and to distinguish total associations from net associations of hypothetical mechanisms. In particular, nutritional status and home stimulation were conceptualised as pathways through which socioeconomic conditions may influence developmental outcomes rather than as confounders. The directed acyclic graph (Fig. 1 ) presents the hypothesized relationships between structural socioeconomic determinants, intermediate mechanisms, and early childhood development outcomes. Household wealth and place of residence were conceptualized as upstream structural exposures. Maternal education and region were treated as contextual confounders, while nutritional status and home stimulation were considered potential mediating pathways linking socioeconomic conditions to developmental outcomes. Child age and sex were included as individual-level covariates. The DAG guided covariate selection and informed the distinction between total and direct association models. Statistical analysis All analyses considered the complex design of the Multiple Indicator Cluster Survey (MICS 6) conducted in Algeria, including sample weighting, stratification, and clustering. A complex sampling design file was created using the weight variable (WEIGHT), strata (STRATA), and primary sampling unit (PSU). Weighted proportions and 95% confidence intervals (95% CI) were estimated for the overall ECDI and each domain, as well as for all explanatory variables. Descriptive analyses were conducted to present weighted distributions of ECDI and each developmental domain according to child, maternal, and household characteristics. These descriptive analyses were intended to provide an overview of population level patterns. To examine associations between socio-economic and contextual correlates and the likelihood of being not developmentally on track, complex samples logistic regression models were fitted with ECDI status as the dependent variable. Model specification was guided by a DAG developed a priori to formalize hypothesized causal relationships and to distinguish between confounders and intermediate pathway variables. Based on this framework, three complementary multivariable modelling strategies were implemented. First, a total association model was estimated to assess the adjusted association between structural socioeconomic correlates and ECDI. This model included household wealth quintile, residence, and geographic region as primary exposure variables, and was adjusted for potential confounders including maternal education, maternal occupation, child age, and child sex. Nutritional status and home stimulation variables were not included in this model, consistent with their conceptualization as intermediate pathways rather than confounders. Second, a direct association model was estimated by additionally including nutritional status indicators (stunting and underweight) and home stimulation indicators (book reading, storytelling, and playing with the child). Finally, to assess whether wealth-related inequalities varied according to place of residence, an interaction term between wealth quintile and residence was introduced. The change in effect was assessed using Wald F tests adjusted for the study design. The main effects in the interaction model were interpreted in relation to the interaction terms. Adjusted odds ratios (OR) with 95% confidence intervals (CI) are reported. Statistical significance was defined as p < 0.05. Multicollinearity among variables was evaluated using diagnostic measures, including the variance inflation factor (VIF) and tolerance statistics. Model diagnostics of the final multivariate binary logistic regression model indicated acceptable model fit. All analyses were conducted using IBM SPSS Statistics (Complex Samples module). Results The study involved 6146 children aged 36 to 59 months, including 3173 boys (51.6%) and 2973 girls (48.4%). Figure 2 presents the percentage of Children Developmentally on Track in ECDI and in each of its four domains. 39.1% of children showed development in literacy–numeracy, 97.1% in physical, 76.9% in social–emotional, 88.1% in learning, and 70.5% in at least 3 of the 4 domains. This prevalence is comparable to estimates reported in several middle-income countries participating in MICS surveys, although there are significant variations across regions and socioeconomic groups (Hlasny, 2017). Table 1 presents the weighted distribution of the four ECD domains and overall ECDI according to child, maternal, and household characteristics. Marked disparities were observed across socioeconomic and geographic characteristics. Literacy–numeracy showed the greatest variability across subgroups, whereas the physical domain displayed consistently high levels across most categories. Our results show a very clear socio-economic gradient across developmental domains. In literacy and numeracy, the proportion of children on track increased steadily, from 27.8% among the poorest to 51.5% among the richest. Similar gradients were observed for learning outcomes and overall ECDI. Children of mothers with a higher education perform better, particularly in Literacy-Numeracy (rising from 21.5% with no education to 54.3% in higher education). Differences were also evident in overall ECDI and learning. Urban children show significantly better outcomes in literacy–numeracy (44.1% vs. 32.1%) and learning domains when compared to rural ones. There are marked regional differences between the northern and highland regions in all four domains indicating geographical inequalities in early development. For example, children in the West Highlands had the lowest literacy–numeracy performance (25.4%), while those in the North-East exhibited relatively higher levels (50.3%). Expectedly, older children scored significantly higher than younger children in almost all domains of development, reflecting the progression of development with age. Girls also achieved slightly higher results than boys in most domains. Children who were stunted had consistently lower developmental performance than non-stunted children across domains. Home stimulation practices, particularly reading books and storytelling, have been associated with higher rates of development, particularly in literacy and numeracy. Table 1 Weighted distribution of the four domains of ECD and overall ECDI according to socio-demographic characteristics. Variables Literacy-numeracy Physical Social–emotional Learning Overall ECDI Number of children % % % % % Sex male 36.1 96.7 74.7 87.8 69.2 3173 Female 42.5 97.3 79.4 88.3 71.8 2973 Residence Urban 44.1 97.4 76.8 90.2 72.4 3611 Rural 32.1 96.5 77.2 85.2 67.2 2535 Geographic region North-Central 40.2 97.1 80.7 86.1 71.4 1981 North-East 50.3 97.7 78.4 92.2 76.2 778 North-West 37.3 96.3 79.4 88.3 69.4 924 Central Highlands 32.5 96.3 71.2 92.5 70.1 517 East Highlands 34.5 97.5 74.1 86.6 67.7 878 West Highlands 25.4 97.4 72.5 80.4 54.1 326 South 42.1 96.5 70.5 89.9 73.6 739 Age 3 years 27.3 96.5 77.5 85.3 64.7 3121 4 years 51.4 97.3 75.7 90.6 76.5 3018 Mother's education level Preschool or None 21.5 96.2 75.8 85.5 66.1 859 Primary 32.6 95.4 77.1 85.6 67.5 952 Middle 37.2 97.5 75.9 87.9 70.4 1985 Secondary 46.7 97.7 77.5 89.3 73.1 1388 Higher 54.3 97.3 77.8 91.2 75.8 965 Mother's occupation Occupied 56.5 93.2 61.9 90.9 76.2 608 Unoccupied 35.2 91.4 65.7 88.5 69.9 5556 Wealth index quintiles The poorest 27.8 95.7 75.2 84.8 65.6 1519 Second 35.9 96.1 77.1 86.1 69.6 1372 The middle 40.5 96.4 75.3 87.8 70.5 1251 The Fourth 45.4 98.9 78.2 90.6 74.6 1068 The richest 51.5 97.3 79.8 92.9 76.5 928 Stunting Normal 38.1 91.9 65.6 89.2 71.2 5586 Stunted 29.4 87.9 62.2 84.1 63.8 578 weight Normal 37.6 91.6 65.3 88.8 66.2 6025 Underweight 26.9 88.4 67.9 82.7 70.6 139 Reading books to child Yes 43.8 64.9 92.1 89.1 76.1 2615 No 29.5 65.7 91.3 88.4 67.2 3542 Telling stories to child Yes 52.7 64.2 91.6 89.2 73.1 2488 No 28.5 65.7 92.1 88.6 67.4 3675 Played with child Yes 39.6 64.8 93.6 89.0 71.6 3319 No 35.1 65.7 90.5 88.3 69.3 2845 Table 2 presents the total association (model 1) between socioeconomic and contextual factors and the likelihood of being not developmentally on track. Significant wealth gradients were observed. Children in the poorest quintile were 74% more likely to fall behind in their normal development than their peers in the richest quintile (OR = 1.74, 95% CI: 1.26–2.40). Significant elevated odds were also observed for the second and middle quintiles. This gradient indicates a clear socioeconomic stratification in developmental outcomes even before the start of formal schooling. Children living in rural areas had a slightly higher risk of developmental delay than children living in urban areas (OR = 1.14, 95% CI: 1.02–1.27). Substantial regional inequalities were identified. The highlands show the most unfavourable results compared with North Centre, particularly the western region, which shows a very significant disadvantage. Children living in the West Highlands had more than twice the odds of being not on track (OR = 2.40, 95% CI: 1.87–3.07), while those in the central and East Highlands also showed elevated odds respectively (OR = 1.51, 95% CI: 1.28–1.71 ; OR = 1.77, 95% CI: 1.39–2.31). Girls were found to be slightly more developmentally on track than boys in term of overall ECD status (OR = 0.72, 95% CI: 0.59–0.86). Younger children (36–47 months) had substantially higher odds of developmental delay compared with older children (OR = 1.72, 95% CI: 1.47–2.01). The results show that the association of maternal education on early childhood development is significant, lower maternal education levels were associated with higher odds of being not on track, particularly among children of mothers with preschool or no education (OR = 1.67, 95% CI: 1.19–2.36). Maternal occupation was not significantly associated with ECDI in the total model. Table 2 Total association between socioeconomic and contextual factors and early childhood development (ECDI) Variables Model 1 OR (95% CI) Signification Household wealth Poorest 1.74 (1.26–2.40) ** Second 1.40 (1.02–1.93) * Middle 1.32 (1.03–1.88) * Fourth 1.23 (0.91–1.66) — Richest 1 Place of residence Urban 1 Rural 1.14 (1.02–1.27) * Geographic region North Centre 1 North-East 1.23 (0.92–1.63) — North-West 1.05 (0.76–1.43) — Central Highlands 1.51 (1.28–1.71) ** East Highlands 1.77 (1.39–2.31) ** West Highlands 2.40 (1.87–3.07) *** South 0.84 (0.67–1.07) — Child sex Boys 1 Girls 0.72 (0.59–0.86) ** Maternal education (ref:) Preschool or None 1.67 (1.19–2.36) ** Primary 1.53 (1.09–2.16) * Middle 1.32 (0.98–1.78) — Secondary 1.32 (0.99–1.77) — Higher 1 Maternal occupation Not working 0.89 (0.66–1.22) — working 1 Child age (months) 36–47 1.72 (1.47–2.01) *** 48–59 1 Signification (Sig): *p < 0.05; **p < 0.01; ***p < 0.001; — NS Odds ratios (OR) and 95% confidence intervals (CI) were estimated using complex samples logistic regression accounting for sampling weights, stratification, and clustering. Model 1 estimates total associations and does not adjust for nutritional status or home stimulation variables, consistent with the DAG. After adjusting for nutritional status and home stimulation variables (Table 3 , Model 2), wealth-related disparities persisted, although effect sizes were slightly attenuated. The findings in the Table 3 show that children in the poorest quintile remained significantly more likely to be not on track (OR = 1.70, 95% CI: 1.23–2.35). Followed by children in the second and third quintiles, who also continued to show high probabilities. Stunting was significantly associated with higher odds of developmental vulnerability (OR = 1.40, 95% CI: 1.11–1.77), whereas the weight of children does not seem to have any significant association. Among stimulation indicators, book reading was protective (OR = 0.81, 95% CI: 0.67–0.98), while storytelling and playing were not significantly associated in the fully adjusted model. Regional disparities remained substantial, particularly for the West Highlands and East Highlands. Likewise, girls continued to have lower odds of being not on track compared with boys, and younger children remained at higher risk. Interaction model (Model 3) in Table 3 examined whether the association between household wealth and early childhood development differed between urban and rural settings. The statistically significant interaction term for the fourth wealth quintile in rural areas (OR = 3.63, 95% CI: 1.41–9.34) indicates that the association between household wealth and the odds of being not developmentally on track differs across urban and rural settings. The inclusion of the wealth × residence interaction term revealed evidence of effect modification, indicating that socioeconomic gradients in ECDI were not uniform across geographic contexts. In fact, the attenuation of the main wealth relationship in Model 3 reflects this reparameterisation rather than the disappearance of socio-economic inequalities, indicating that wealth-related disparities depend on context. Table 3 Direct association model for ECDI and effect modification by place of residence Variables Model 2 Model 3 OR (95% CI) Sig OR (95% CI) Sig Household wealth Poorest 1.70 (1.23–2.35) ** 1.26 (0.59–2.69) — Second 1.38 (1.01–1.90) * 0.89 (0.41–1.93) — Middle 1.38 (1.02–1.86) * 1.00 (0.45–2.21) — Fourth 1.22 (0.91–1.65) — 0.39 (0.16–0.97) * Richest 1 1 Place of residence Urban 1 1 Rural 0.98 (0.83–1.16) — 0.57 (0.27–1.22) — Region North Centre 1 North-East 0.85 (0.60–1.19) — — — North-West 0.99 (0.71–1.36) — — — Central Highlands 1.48 (1.22–1.64) * — — East Highlands 1.67 (1.30–2.15) ** — — West Highlands 1.88 (1.41–2.50) *** — — South 0.80 (0.60–1.07) — — — Child sex Boys 1 Girls 0.81 (0.68–0.94) * — — Maternal education Preschool or None 1.59 (1.13–2.25) * — — Primary 1.41 (1.00–2.00) — — — Middle 1.27 (0.94–1.72) — — — Secondary 1.30 (0.97–1.74) — — — Higher 1 Maternal occupation Not working 0.90 (0.66–1.23) — — — working 1 Child age (months) 36–47 1.70 (1.45–2.00) *** — — 48–59 Nutritional status Stunted 1.40 (1.11–1.77) ** — — Normal 1 Underweight 1.16 (0.72–1.86) — — — Normal 1 Home stimulation Book reading (yes vs no) 0.81 (0.67–0.98) * — — Storytelling (yes vs no) 0.97 (0.80–1.17) — — — Playing (yes vs no) 1.08 (0.92–1.28) — — — Wealth × Residence interaction Poorest × Rural — — 1.43 (0.62–3.28) — Second × Rural — — 1.79 (0.78–4.10) — Middle × Rural — — 1.50 (0.65–3.47) — Fourth × Rural — — 3.63 (1.41–9.34) * richest urban households (ref) — — 1 — Outcome: ECDI = NOT on track (reference = YES). Signification (Sig): *p < 0.05; **p < 0.01; ***p < 0.001; — NS. Model 2 (Direct association): Model 1 plus nutritional status (stunting, underweight) and home stimulation indicators (book reading, storytelling, playing). Model 3: Model 2 plus interaction term between household wealth and place of residence. Statistical significance was assessed using design-adjusted Wald F tests. Discussion Using nationally representative data from Algeria (MICS 2019), the present study highlights key socioeconomic and geographic inequalities in early childhood development. Three principal findings emerge. First, marked socioeconomic gradients were observed in early childhood development. Children from poorer households had consistently higher odds of being not developmentally on track, even after adjustment for maternal education, regional context, and child characteristics. Second, substantial regional disparities were identified, particularly in the Highlands regions. Third, the interaction analysis demonstrated that wealth-related inequalities differed between urban and rural contexts, indicating spatial heterogeneity in socioeconomic gradients. Importantly, wealth disparities persisted after adjusting for nutritional status and home stimulation indicators, suggesting that measured pathways explain only part of the observed inequalities. Socioeconomic gradients The wealth gradient observed in this study is consistent with extensive international research (Rahman et al., 2025; Alam et al., 2022 ; Hackman et al., 2010 ) demonstrating that early childhood development is strongly socially patterned. Large cross-national analyses have shown that children from lower-wealth households in low- and middle-income countries are significantly more likely to experience developmental vulnerability, particularly in literacy–numeracy and learning domains (Islam, 2024 ; Touhami et al., 2019 ). Family poverty can affect the ability to meet children's basic needs, including access to safe housing, nutritious food and quality childcare (Maggi et al., 2010 ). These findings are consistent with the Nurturing Care Framework, which highlights the importance of family resources, responsive caregiving, and learning opportunities in shaping early childhood development trajectories. Structural socioeconomic disadvantage may limit parents’ ability to provide stimulating learning environments and access quality early childhood services. As a result, inequalities in developmental outcomes may emerge well before formal schooling begins (Richter et al., 2017 ; Boutayeb and Helmert, 2021). Within the broader North African context, similar socioeconomic gradients have been documented in Morocco, Tunisia, and Egypt, where household wealth and maternal education consistently predict early developmental outcomes (Krafft and El-Kogali, 2015 ; Lu et al., 2016 ). Although Algeria has made considerable progress in maternal and child health and has relatively high maternal literacy and primary healthcare coverage compared with some neighboring countries, the persistence of wealth disparities underscores the structural nature of developmental inequality in the region. Urban–rural divides and regional disparities A key contribution of this study is the identification of effect modification by place of residence. Urban–rural disparities are a recurring theme in child development research. Studies in several North African countries have shown that rural children often have reduced access to early childhood education, learning materials, and structured preschool environments (Boutayeb and Helmert, 2021). These disparities are frequently associated with infrastructure gaps rather than solely household-level poverty (Prado-Galbarro et al., 2021 ). The interaction analysis suggests that socioeconomic gradients in early childhood development are not uniform across geographic contexts. In particular, the significant interaction observed for the fourth wealth quintile in rural areas indicates that moderate levels of household wealth may not translate into similar developmental advantages in rural settings as in urban areas. This pattern may reflect structural constraints in rural environments, such as limited access to early childhood education services, learning materials, and child-oriented infrastructure. In Algeria, while urban centers benefit from higher preschool enrollment rates and greater availability of private and public educational services (libraries, associations, cultural centres), rural areas may experience structural disadvantages, including geographic isolation and uneven service distribution. Thus, the interaction observed in this study reflects patterns consistent with regional structural inequalities rather than isolated household effects (Hlasny, 2019). Beyond the urban–rural divide, pronounced regional variation was observed in our results particularly in the Highland regions. Subnational disparities in early development have been documented across low- and middle-income countries and are often linked to uneven service distribution and economic development (Lu et al., 2020). Similar patterns have been reported in Tunisia and Egypt, where developmental outcomes vary substantially across provinces (Krafft and El-Kogali, 2015 ). These findings reinforce the importance of disaggregated monitoring. National averages may obscure meaningful territorial inequities. From a child well-being perspective, regional disparities highlight the need for place-based policy approaches targeting structurally disadvantaged areas. Nutritional status and home stimulation as pathways Consistent with prior literature, our results suggest that stunting was independently associated with higher odds of being not developmentally on track. This supports evidence linking chronic under nutrition with impaired cognitive and socio-emotional development (Black et al., 2013 ; Bornstein et al., 2021 ). Although stunting prevalence in Algeria is lower than in several sub-Saharan African countries and some neighboring nations, it remains a relevant risk factor for developmental vulnerability and may explain why nutritional adjustment attenuated but did not eliminate socioeconomic gradients. Across North Africa, the epidemiological transition has led to declining undernutrition but increasing double burdens of malnutrition. Even moderate levels of chronic undernutrition have been associated with developmental risk (Baye et al., 2020 ; Rahman et al., 2023 ). The protective association observed for book reading in this study aligns with research demonstrating that cognitive stimulation enhances language development and executive functioning (Britto et al., 2017 ), and several studies have affirmed that the family environment and parent-child interaction play an essential role in the development of a child's motor function (Alam et al., 2022 ; Saccani et al., 2013 ). Caring for young children is considered the primary responsibility of the mother, particularly in the Maghreb context. In fact, mothers with higher levels of education are more likely to create stimulating environments and use effective parenting techniques, which promotes more harmonious child development (Islam, 2024 ; Krafft and El-Kogali, 2015 ; Maggi et al., 2010 ). However, the persistence of wealth gradients after adjusting for nutritional and stimulation indicators suggests that socioeconomic inequalities extend beyond household-level practices. Structural correlates likely influence access to preschool education, neighborhood environments, and institutional resources not fully captured in survey data. Policy and monitoring implications The findings enrich to the existing literature by highlighting the position of early childhood development as a core child well-being indicator. They also contribute to the limited empirical evidence on early childhood development inequalities in North Africa, a region that remains underrepresented in the child indicators literature. In Algeria, strengthening early childhood development systems may therefore require integrated strategies that address household disadvantages (poverty reduction), the development of quality pre-school education, and targeted investments in rural areas. The results of the interaction suggest that improving the economic situation of households may prove insufficient in contexts where institutional and infrastructural constraints persist. These findings suggest that addressing developmental inequalities requires not only household-level interventions but also broader territorial policies targeting service availability and early childhood education infrastructure. Strengths and limitations Several strengths should be noted. First, the study uses nationally representative data with appropriate adjustment for complex survey design and data collection followed the well-defined criteria of the World Health Organization (UNICEF, 2019 ). Second, the use of a DAG framework strengthened model specification by distinguishing structural correlates from potential mediating mechanisms and reducing the risk of over adjustment. Third, the explicit assessment of interaction allowed identification of contextual heterogeneity in socioeconomic gradients. However, limitations must be acknowledged. The data were cross-sectional, which limits the possibility of studying the causal effect between socioeconomic conditions and developmental outcomes. The ECDI, while internationally standardized, provides a screening measure rather than a diagnostic assessment. ECDI refers to information reported by parents or caregivers and can therefore be subject to reporting bias. This study focuses exclusively on children aged 36–59 months, which prevents analysis of developmental changes beyond this age. The data from the MICS survey are limited to the variables available, which made it impossible to control for residual confounding from unmeasured contextual factors, such as preschool quality, neighborhood characteristics or the nutritional status of parents which can significantly influence ECD. Future research could address these limitations using longitudinal data and more detailed contextual measures. Conclusion Socioeconomic and geographic inequalities in early childhood development are evident in Algeria and reflect broader patterns observed across North Africa. Wealth gradients persist after adjustment for nutritional and stimulation pathways, and socioeconomic effects vary by place of residence. These findings suggest that developmental inequalities are shaped by both structural socioeconomic conditions and spatial context. Although the importance of protecting children remains one of the priorities of health and social protection policy in Algeria, there is an urgent need to address early childhood development disparities will likely require integrated policies targeting both household disadvantage and contextual infrastructure constraints. Declarations Author Contribution Meryem Boukhelif was in charge of conceptualization and formal analysis. Adel Sidi-Yakhlef was in charge of methodology. Abdellatif Moussouni and Kamel Chikhi contributed to writing of the first draft of the paper. All authors reviewed drafts of the manuscript, provided suggestions for refinement and provided approval for the version to be published. Acknowledgement The authors would like to thank the Algerian Ministry of Health, Population and Hospital Reform, UNICEF, UNFPA and all those involved in the development of this database. References Akram, S., Zahid, F., &Pervaiz, Z. (2024). Socioeconomic determinants of early childhood development: Evidence from Pakistan. Journal of Health, Population and Nutrition , 43 (1), 70. https://doi.org/10.1186/s41043-024-00569-5 . Alam, M. I., Mansur, M., & Barman, P. (2022). Early childhood development in Bangladesh and its socio-demographic determinants of importance. Early Child Development and Care , 192 (12), 1901–1920. https://doi.org/10.1080/03004430.2021.1951260 . Bedrouni, M., & fratsa, samir. (2025). Title: Beyond the Score: An In-Depth Analysis of Early Childhood (36 to 59 months) Development in Algeria using the ECDI (MICS6, 2019). Revue du développement et du management des resources humaine.12(2), 125-135. https://asjp.cerist.dz/en/article/283178. Bahru, B. A., Bosch, C., Birner, R., & Zeller, M. (2019). Drought and child undernutrition in Ethiopia: A longitudinal path analysis. PloS One , 14 (6), e0217821. https://doi.org/10.1371/journal.pone.0217821 . Baye, K., Laillou, A., &Chitweke, S. (2020). Socio-Economic Inequalities in Child Stunting Reduction in Sub-Saharan Africa. Nutrients , 12 (1), 253. https://doi.org/10.3390/nu12010253 . Bizzego, A., Lim, M., Schiavon, G., &Esposito, G. (2020). Children with Developmental Disabilities in Low- and Middle-Income Countries: More Neglected and Physically Punished. International Journal of Environmental Research and Public Health , 17 (19), 7009. https://doi.org/10.3390/ijerph17197009 . Black, R. E., Victora, C. G., Walker, S. P., Bhutta, Z. A., Christian, P., de Onis, M., Ezzati, M., Grantham-McGregor, S., Katz, J., Martorell, R., Uauy, R., & Maternal and Child Nutrition Study Group (2013). Maternal and child undernutrition and overweight in low-income and middle-income countries. Lancet (London, England), 382(9890), 427–451. https://doi.org/10.1016/S0140-6736(13)60937-X. Bornstein, M. H., Rothenberg, W. A., Lansford, J. E., Bradley, R. H., Deater-Deckard, K., Bizzego, A., & Esposito, G. (2021). Child Development in Low- and Middle-Income Countries. Pediatrics , 148 (5), e2021053180. https://doi.org/10.1542/peds.2021-053180 . Boutayeb, A., & Helmert, U. (2011). Social inequalities, regional disparities and health inequity in North African countries. International Journal for Equity in Health , 10 (1), 23. https://doi.org/10.1186/1475-9276-10-23 . Britto, P. R., Lye, S. J., Proulx, K., Yousafzai, A. K., Matthews, S. G., Vaivada, T., Perez-Escamilla, R., Rao, N., Ip, P., Fernald, L. C. H., MacMillan, H., Hanson, M., Wachs, T. D., Yao, H., Yoshikawa, H., Cerezo, A., Leckman, J. F., Bhutta, Z. A., & Early Childhood Development Interventions Review Group, for the Lancet Early Childhood Development Series Steering Committee (2017). Nurturing care: promoting early childhood development. Lancet (London, England), 389(10064), 91–102. https://doi.org/10.1016/S0140-6736(16)31390-3. Hackman, D. A., Farah, M. J., &Meaney, M. J. (2010). Socioeconomic status and the brain: Mechanistic insights from human and animal research. Nature Reviews. Neuroscience , 11 (9), 651–659. https://doi.org/10.1038/nrn2897 . Hlásny, V. (2019). Top expenditure distribution in Arab countries and the inequality puzzle. Journal of Economic and Social Measurement, 44(4), 177–201. https://doi.org/10.3233/JEM-200469 Islam, M. M. (2024). The gradient of social determinants of health and related inequalities and early childhood development: Analysis of two rounds of a cross-sectional survey. Journal of Paediatrics and Child Health , 60 (11), 716–723. https://doi.org/10.1111/jpc.16667 . Krafft, C., & El-Kogali, S. (2015). Expanding Opportunities for the Next Generation: Early Childhood Development in the Middle East and North Africa . Washington, DC: World Bank. https://doi.org/10.1596/978-1-4648-0323-9 . Lassassi, M. (2021). Does preschool improve child development and affect the quality of parent-child interaction? Evidence from Algeria. International Journal of Educational Development , 82 , 102354. https://doi.org/10.1016/j.ijedudev.2021.102354 . Likhar, A., Baghel, P., &Patil, M. (2022). Early Childhood Development and Social Determinants. Cureus . https://doi.org/10.7759/cureus.29500 . Loizillon, A.; Petrowski, N.; Britto, P.; Cappa, C. (2017) Development of the Early Childhood Development Index in MICS Surveys; UNICEF: New York, NY, USA. Lu, C., Black, M. M., & Richter, L. M. (2016). Risk of poor development in young children in low-income and middle-income countries: an estimation and analysis at the global, regional, and country level. The Lancet. Global health , 4 (12), e916–e922. https://doi.org/10.1016/S2214-109X(16)30266-2. Maggi, S., Irwin, L. J., Siddiqi, A., &Hertzman, C. (2010). The social determinants of early child development: An overview. Journal of Paediatrics and Child Health , 46 (11), 627–635. https://doi.org/10.1111/j.1440-1754.2010.01817.x Ministry of Health and Population (Algeria), United Nations Children's Fund (UNICEF) (2021) : Algeria Multiple Indicator Cluster Survey 2018-2019. New York, United States of America: United Nations Children's Fund (UNICEF). https://mics.unicef.org/surveys. Prado-Galbarro FJ, Pérez-Ferrer C, Ortigoza A, López-Olmedo NP, Braverman-Bronstein A, et al. (2021) Early childhood development and urban environment in Mexico. PLOS ONE 16(11): e0259946. https://doi.org/10.1371/journal.pone.0259946. Rahman, F., Tuli, S. N., Mondal, P., Sultana, S., Hossain, A., Kundu, S., Clara, A. A., &Hossain, A. (2023). Home environment factors associated with early childhood development in rural areas of Bangladesh: Evidence from a national survey. Frontiers in Public Health , 11 , 1209068. https://doi.org/10.3389/fpubh.2023.1209068 . Richter, L. M., Daelmans, B., Lombardi, J., Heymann, J., Boo, F. L., Behrman, J. R., Lu, C., Lucas, J. E., Perez-Escamilla, R., Dua, T., Bhutta, Z. A., Stenberg, K., Gertler, P., & Darmstadt, G. L. (2017). Investing in the foundation of sustainable development: Pathways to scale up for early childhood development. The Lancet , 389(10064), 103–118. https://doi.org/10.1016/S0140-6736(16)31698-1 Roopnarine JL, Dede YE. (2018). Paternal and maternal engagement in play, story telling, and reading in five Caribbean countries: associations with preschoolers’ literacy skills. Int J Play . 7:132–5. doi: 10.1080/21594937.2018.1496000. Saccani, R., Valentini, N. C., Pereira, K. R., Müller, A. B., &Gabbard, C. (2013). Associations of biological factors and affordances in the home with infant motor development. Pediatrics International: Official Journal of the Japan Pediatric Society, 55(2), 197–203. https://doi.org/10.1111/ped.12042. Touhami, A., Berenger, V., Lassassi, M. (2019). Le développement de la petite enfance et l’inégalité des chances dans les pays du Sud et de l’Est de la Méditerranée: Algérie, Maroc, Tunisie, Bosnie, Serbie et Ukraine with A. Touhami and M. Lassassi, FemiseResearchPapers: FEM43-18; available at https://www.euneighbours.eu/sites/default/files/publications/2019-01/FEM43-18.pdf UNICEF (2019) : Multiple Indicator Cluster Surveys (MICS). Available from: http://mics.unicef.org/surveys. United Nations Children's Fund . 2023. “The Early Childhood Development Index 2030: A New Measure of Early Childhood Development.” Wang, J., Wen, W., Sim, L., Li, X., Yan, J., & Kim, S. Y. (2022). Family Environment, Heritage Language Profiles, and Socioemotional Well-being of Mexican-origin Adolescents with First Generation Immigrant Parents. Journal of Youth and Adolescence , 51 (6), 1196–1209. https://doi.org/10.1007/s10964-022-01594-5 WHO Multicentre Growth Reference Study. WHO child growth standards based on length/height, weight and age. ActaPaediatr. (2006) 95:76–85. doi:10.1111/j.1651-2227.2006.tb02378.x. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9122949","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":619001760,"identity":"3f030401-adf2-4a01-b586-6bc3eca98aad","order_by":0,"name":"Boukhelif Meryem","email":"","orcid":"","institution":"University of Tlemcen","correspondingAuthor":false,"prefix":"","firstName":"Boukhelif","middleName":"","lastName":"Meryem","suffix":""},{"id":619001761,"identity":"c3c52353-139a-4893-af2d-3be9b5cd7145","order_by":1,"name":"Sidi-Yakhlef Adel","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABQ0lEQVRIie3RMUvDQBTA8RcCcXlwTvJKbP0KTw6KoNivknKQLKcWXDqVTunmnNLBryD4Ba5kcOkHCHSxFDp1CHS0qJdSoaRVV8H8x3f8eLkLQFXVnyxwDAA5MYDrQgeEsDM7gdPtuXuAwA5hqA37G8L4CwGn/0WezGb8PREDZQysL+oPpOayw1cks5txirBGcZLO5h24bJUITRaBcWKSMYVSJRxSM7sNLGGsjUIpE4j21mSajdOndkyBTJHTXtNOxsN3Rp5qz0dIvZI4y+5y+2EFiVaWfJBMLEnsltZUH71ZgiXCmbbX9wqipUI2xGT35pawrz3XEiqR88mCTbu4Cy7vJbIimiwLIpGmofSRIy6RxouavebrXv1xED372L0mMdAyD6DRECM1W2F378W2v8Z2HBw6gvKO3YT54bCqqqrqX/cJGQRqHroJI7UAAAAASUVORK5CYII=","orcid":"","institution":"University of Tlemcen","correspondingAuthor":true,"prefix":"","firstName":"Sidi-Yakhlef","middleName":"","lastName":"Adel","suffix":""},{"id":619001766,"identity":"7797af8b-0567-4c9c-9a9d-66529519622d","order_by":2,"name":"Moussouni Abdellatif","email":"","orcid":"","institution":"CNRPAH Tlemcen’s Station, National Centre for Prehistoric, Anthropological and Historical Research","correspondingAuthor":false,"prefix":"","firstName":"Moussouni","middleName":"","lastName":"Abdellatif","suffix":""},{"id":619001769,"identity":"8665c366-ed92-49f5-a4e0-fbbc2b61151b","order_by":3,"name":"Chikhi Kamel","email":"","orcid":"","institution":"Higher School of Management Tlemcen","correspondingAuthor":false,"prefix":"","firstName":"Chikhi","middleName":"","lastName":"Kamel","suffix":""}],"badges":[],"createdAt":"2026-03-14 13:38:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9122949/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9122949/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106545035,"identity":"d8e34db3-d8e1-4c4a-8b35-576f54eb3933","added_by":"auto","created_at":"2026-04-09 16:43:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":230360,"visible":true,"origin":"","legend":"\u003cp\u003eDirected acyclic graph (DAG) illustrating the conceptual framework linking socioeconomic determinants and early childhood development.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9122949/v1/2b85d948801c46f2bced1e1d.png"},{"id":106726868,"identity":"e53dc685-8652-4217-811d-defb3372ce07","added_by":"auto","created_at":"2026-04-12 18:37:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":42531,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of children aged 36–59 months who are developmentally on track in ECDI and its four domains.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9122949/v1/7cae0b0f4d227f1a6729590b.png"},{"id":106728208,"identity":"4503c0d0-8790-4851-bad9-395201adb102","added_by":"auto","created_at":"2026-04-12 18:42:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1284093,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9122949/v1/2b144ef7-9fae-4c9b-82fc-efe9c3827d90.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Socioeconomic and Regional Inequalities in Early Childhood Development in Algeria: Evidence from the MICS-6 Survey","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEarly childhood is a crucial stage in human development, during which fundamental cognitive, socio-emotional and physical abilities begin to take shape (United Nations Children\u0026rsquo;s Fund, 2023; Likhar et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Several studies have demonstrated that disadvantages experienced during this period often extend beyond childhood, influencing educational attainment, health and social outcomes later in life. This is why increasing attention must be paid to the early identification and monitoring of developmental inequalities at the population level (Krafft and El-Kogali, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe United Nations has included early childhood development in target 4.2 of the Sustainable Development Goals (SDGs), given its importance. By 2030, countries should ensure that every child has access to quality early childhood care and education, as well as early childhood development (ECD) in terms of health, learning and psychosocial well-being (Krafft and El-Kogali, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Rahman et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). To address this need, UNICEF developed the \u003cb\u003eEarly Childhood Development Index (ECDI)\u003c/b\u003e, a standardized composite indicator designed to assess whether children up to 8 years of age are developmentally on track in key domains, including literacy-numeracy, physical development, learning, and socio-emotional skills (United Nations Children\u0026rsquo;s Fund, 2023). Although this index does not reflect the full complexity of child development, the ECDI is now routinely included in Multiple Indicator Cluster Surveys (MICS), enabling countries to assess the proportion of children who are developing normally in several areas (UNICEF, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Beyond individual outcomes, the ECDI provide valuable tools for monitoring inequalities in early life conditions and identifying vulnerable populations.\u003c/p\u003e \u003cp\u003eA growing body of literature has consistently reported associations between early childhood development and a range of socio-demographic characteristics, including household socio-economic status, parental education, and place of residence. These characteristics reflect broader living conditions that shape children\u0026rsquo;s exposure to learning environments, nutrition, health services, and psychosocial stimulation (Rahman et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e ; Akram et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The family is the child's first environment for interaction from birth and plays a central role in stimulating, supporting and educating them (Roopnarine and Dede, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These qualities are in turn influenced by the resources available to families for raising their children (income), their parenting style, and their ability to provide a rich and stimulating linguistic environment (parents' level of education). Furthermore, the sex of the child can influence early childhood development, particularly due to socio-cultural norms associated with gender (Akram et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The environment in which children grow up plays a decisive role in early childhood development, as economic, cultural and social disparities can influence their well-being and social development (Rahman et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Bizzego et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Parental involvement in various learning activities with their children has been identified as the most important factor in children's cognitive and overall development (Roopnarine and Dede, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Their participation through reading books had a significant association on children's reading and writing skills. In addition, playful activities between parents and children have also promoted their overall development (Rahman et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Roopnarine and Dede, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Another crucial factor with substantial developmental consequences is child nutrition. Proper nutrition during this crucial phase of life is essential, given that suboptimal nutrition can have detrimental effects on brain development and overall development (Akram et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This nutritional deficiency is likely to have harmful effects on cognitive and academic performance, both in the short and long term (Bahru et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Thus, factors related to the social environment can influence children's development in a positive or negative way, acting as risk or protective factors. However, although these SDH are clearly identified in the literature, the list of determinants often varies from one country to another, and the number of negative factors is significantly higher in poor countries (Krafft and El-Kogali, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, evidence remains uneven across regions, and empirical studies focusing on early childhood development indicators in North Africa are still scarce. In Algeria, early childhood development has received increasing policy attention in recent years; however, empirical evidence on population-level developmental inequalities remains limited. While Algeria has made substantial progress in child survival, education, and access to basic services, less is known about how socioeconomic status, geographic context, nutritional conditions, and home stimulation jointly shape early developmental outcomes. Existing national studies have focused primarily on health or education outcomes in isolation, and few analyses have integrated the multiple dimensions of early childhood development using standardised indicators. Other studies have relied on multivariable models including numerous correlated variables without explicit conceptual justification (Lassassi, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Bedrouni and fratsa, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This can lead to overfitting, particularly when intermediate variables are treated as confounding factors rather than as pathways through which socio-economic conditions influence development.\u003c/p\u003e \u003cp\u003eIn this context, the present study aims to examine socioeconomic and geographic inequalities in early childhood development as measured by the ECDI among Algerian children aged 36 to 59 months, based on nationally representative data from the MICS survey. By combining descriptive analyses with directed acyclic graphs (DAG) guided multivariable models, this study goes beyond documenting disparities to clarify how structural correlates, contextual factors, and modifiable pathways may jointly contribute to developmental inequalities. The use of DAGs as conceptual frameworks is increasingly recommended to clarify hypothetical relationships, distinguish confounding factors from mediators, and improve the transparency and interpretability of results. Furthermore, by explicitly testing whether wealth gradients differ between urban and rural settings, the analysis attempts to fill an important gap in the Algerian literature concerning context-specific vulnerabilities.\u003c/p\u003e \u003cp\u003eBy identifying socioeconomic gradients and territorial disparities in early childhood development, this study provides evidence that can inform equity-oriented early childhood policies in Algeria. Beyond the national context, the findings also contribute to the limited empirical literature on early childhood development inequalities in North Africa and other middle-income countries undergoing demographic and social transitions. This study contributes to the literature on early childhood development in three main ways. First, it is the first analysis to use the most recent national survey on socio-economic and geographical inequalities in early childhood development in Algeria using the Early Childhood Development Index (ECDI). Secondly, a directed acyclic graph (DAG)-guided modelling strategy was applied to distinguish total socio-economic associations from trajectory-adjusted associations, thereby improving transparency in model specification. Thirdly, the study explicitly examines spatial heterogeneity in socio-economic gradients by testing the interaction between household wealth and place of residence, which is an aspect rarely explored in research on early childhood development in North Africa in general and Algeria in particular.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study is based on secondary analysis of a representative national data from the Multiple Indicator Cluster Survey (MICS 6), conducted in 2018\u0026ndash;2019 by the Algerian Ministry of Health, Population and Hospital Reform, with technical and financial support from UNICEF and a financial contribution from UNFPA (Ministry of Health and Population of Algeria, 2021). MICS is a nationally representative household survey implemented using a stratified, multistage cluster sampling design to generate population-level estimates of key child and maternal indicators. The survey collects standardized information on early childhood development, health, nutrition, education, and household characteristics, thereby helping decision-makers to draw up policies and intervention programmes (UNICEF, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For this analysis, we used the module on children under-5 to extract data assessing the early childhood development index. Collected data included a questionnaire administered to the child's mother or main carer, constructed by combining individual datasets on children, mothers and household characteristics. All children aged 36\u0026ndash;59 months with complete information on the ECDI outcome and selected explanatory variables were included in the analysis i.e. 6146 children in the database (3173 boys and 2973 girls). Observations with missing outcome data were excluded. All estimates are representative of the national population of children aged 36\u0026ndash;59 months in Algeria.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eOutcome variable: early childhood development\u003c/h2\u003e \u003cp\u003eThe variable of interest, ECD, was estimated by the ECD index (ECDI), which has four domains: literacy- numeracy, learning, physical and social\u0026ndash;emotional developed by UNICEF (Loizillon et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The ECDI is widely used in international monitoring of early childhood development and forms part of the Sustainable Development Goal indicator framework (SDG indicator 4.2.1). For each domain assessed, a series of questions was administered, and reaching a predefined threshold of positive responses allowed for the conclusion that the child was developing adequately. The number of items in each domain of the ECDI varies; each child is categorized as on track or not in each of the domains. Literacy and numeracy were assessed based on meeting at least two of the following three requirements: identifying at least ten letters, reading at least four familiar words or names, and recognising numbers from 1 to 10. For the physical domain, children are required to be able to do at least one of the following: can pick up item with two fingers and does not often feel sick (on track when at least one in two items received a positive response). The learning domain is based on two observations aimed at assessing the child's progress: their ability to follow simple instructions correctly and their ability to carry out the instructions given to them independently. The socio-emotional assessment aimed to measure emotional and social functioning as well as sensory processing. It was based on three questions asked to the mother or guardian, with at least two affirmative answers allowing the child to be classified as normal. These questions focused on the child's ability to get along well with other children, the absence of aggressive behaviour (kicking, biting or hitting) and their ability to not be easily distracted. Children who met the criteria in at least three of the four domains assessed were considered to be on track for early childhood development. This classification is expressed as a binary variable, known as the \u0026ldquo;early childhood development index\u0026rdquo; (ECDI) (Loizillon et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Rahman et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExplanatory variables\u003c/h3\u003e\n\u003cp\u003eSelection of explanatory variables was guided by prior literature and a conceptual framework distinguishing structural correlates, potential confounders, and intermediate pathway variables. Structural socioeconomic and contextual correlates (household wealth quintile, place of residence, geographic region). Maternal and child characteristics (maternal education level, maternal economic activity, child age group, child sex). Nutritional status (stunting, underweight) and home stimulation indicators (book reading, storytelling, playing with the child).\u003c/p\u003e\n\u003ch3\u003eConceptual framework and DAG-guided modelling strategy\u003c/h3\u003e\n\u003cp\u003eA directed acyclic graph (DAG) was developed to clarify hypothesized relationships between socioeconomic conditions and early childhood development and to guide covariate selection. The DAG distinguished between (1) upstream structural correlates (wealth, residence, region), (2) potential confounders (maternal education, maternal occupation, child characteristics), and (3) Intermediate pathway variables illustrating the mechanisms through which socio-economic conditions can influence development (stimulation at home and nutritional status). The use of a DAG was motivated by the need to avoid overfitting and to distinguish total associations from net associations of hypothetical mechanisms. In particular, nutritional status and home stimulation were conceptualised as pathways through which socioeconomic conditions may influence developmental outcomes rather than as confounders.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe directed acyclic graph (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) presents the hypothesized relationships between structural socioeconomic determinants, intermediate mechanisms, and early childhood development outcomes. Household wealth and place of residence were conceptualized as upstream structural exposures. Maternal education and region were treated as contextual confounders, while nutritional status and home stimulation were considered potential mediating pathways linking socioeconomic conditions to developmental outcomes. Child age and sex were included as individual-level covariates. The DAG guided covariate selection and informed the distinction between total and direct association models.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll analyses considered the complex design of the Multiple Indicator Cluster Survey (MICS 6) conducted in Algeria, including sample weighting, stratification, and clustering. A complex sampling design file was created using the weight variable (WEIGHT), strata (STRATA), and primary sampling unit (PSU). Weighted proportions and 95% confidence intervals (95% CI) were estimated for the overall ECDI and each domain, as well as for all explanatory variables. Descriptive analyses were conducted to present weighted distributions of ECDI and each developmental domain according to child, maternal, and household characteristics. These descriptive analyses were intended to provide an overview of population level patterns.\u003c/p\u003e \u003cp\u003eTo examine associations between socio-economic and contextual correlates and the likelihood of being not developmentally on track, complex samples logistic regression models were fitted with ECDI status as the dependent variable. Model specification was guided by a DAG developed a priori to formalize hypothesized causal relationships and to distinguish between confounders and intermediate pathway variables. Based on this framework, three complementary multivariable modelling strategies were implemented.\u003c/p\u003e \u003cp\u003eFirst, a total association model was estimated to assess the adjusted association between structural socioeconomic correlates and ECDI. This model included household wealth quintile, residence, and geographic region as primary exposure variables, and was adjusted for potential confounders including maternal education, maternal occupation, child age, and child sex. Nutritional status and home stimulation variables were not included in this model, consistent with their conceptualization as intermediate pathways rather than confounders.\u003c/p\u003e \u003cp\u003eSecond, a direct association model was estimated by additionally including nutritional status indicators (stunting and underweight) and home stimulation indicators (book reading, storytelling, and playing with the child).\u003c/p\u003e \u003cp\u003eFinally, to assess whether wealth-related inequalities varied according to place of residence, an interaction term between wealth quintile and residence was introduced. The change in effect was assessed using Wald F tests adjusted for the study design. The main effects in the interaction model were interpreted in relation to the interaction terms.\u003c/p\u003e \u003cp\u003eAdjusted odds ratios (OR) with 95% confidence intervals (CI) are reported. Statistical significance was defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Multicollinearity among variables was evaluated using diagnostic measures, including the variance inflation factor (VIF) and tolerance statistics. Model diagnostics of the final multivariate binary logistic regression model indicated acceptable model fit. All analyses were conducted using IBM SPSS Statistics (Complex Samples module).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe study involved 6146 children aged 36 to 59 months, including 3173 boys (51.6%) and 2973 girls (48.4%). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the percentage of Children Developmentally on Track in ECDI and in each of its four domains. 39.1% of children showed development in literacy\u0026ndash;numeracy, 97.1% in physical, 76.9% in social\u0026ndash;emotional, 88.1% in learning, and 70.5% in at least 3 of the 4 domains. This prevalence is comparable to estimates reported in several middle-income countries participating in MICS surveys, although there are significant variations across regions and socioeconomic groups (Hlasny, 2017).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the weighted distribution of the four ECD domains and overall ECDI according to child, maternal, and household characteristics. Marked disparities were observed across socioeconomic and geographic characteristics. Literacy\u0026ndash;numeracy showed the greatest variability across subgroups, whereas the physical domain displayed consistently high levels across most categories.\u003c/p\u003e \u003cp\u003eOur results show a very clear socio-economic gradient across developmental domains. In literacy and numeracy, the proportion of children on track increased steadily, from 27.8% among the poorest to 51.5% among the richest. Similar gradients were observed for learning outcomes and overall ECDI. Children of mothers with a higher education perform better, particularly in Literacy-Numeracy (rising from 21.5% with no education to 54.3% in higher education). Differences were also evident in overall ECDI and learning. Urban children show significantly better outcomes in literacy\u0026ndash;numeracy (44.1% vs. 32.1%) and learning domains when compared to rural ones. There are marked regional differences between the northern and highland regions in all four domains indicating geographical inequalities in early development. For example, children in the West Highlands had the lowest literacy\u0026ndash;numeracy performance (25.4%), while those in the North-East exhibited relatively higher levels (50.3%). Expectedly, older children scored significantly higher than younger children in almost all domains of development, reflecting the progression of development with age. Girls also achieved slightly higher results than boys in most domains. Children who were stunted had consistently lower developmental performance than non-stunted children across domains. Home stimulation practices, particularly reading books and storytelling, have been associated with higher rates of development, particularly in literacy and numeracy.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWeighted distribution of the four domains of ECD and overall ECDI according to socio-demographic characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLiteracy-numeracy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhysical\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSocial\u0026ndash;emotional\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLearning\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOverall ECDI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNumber of children\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3173\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2973\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e72.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3611\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e67.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2535\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eGeographic region\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth-Central\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1981\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth-East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e778\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth-West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e924\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Highlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e517\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast Highlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e67.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e878\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWest Highlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e326\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e739\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eMother's education level\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreschool or None\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e859\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e67.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e952\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1985\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1388\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e965\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eMother's occupation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e608\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnoccupied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5556\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eWealth index quintiles\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe poorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1519\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1372\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe middle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1251\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe Fourth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e74.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe richest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e928\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eStunting\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5586\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStunted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e578\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eweight\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eReading books to child\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2615\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e67.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3542\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eTelling stories to child\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2488\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e67.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3675\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003ePlayed with child\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3319\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2845\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the total association (model 1) between socioeconomic and contextual factors and the likelihood of being not developmentally on track. Significant wealth gradients were observed. Children in the poorest quintile were 74% more likely to fall behind in their normal development than their peers in the richest quintile (OR\u0026thinsp;=\u0026thinsp;1.74, 95% CI: 1.26\u0026ndash;2.40). Significant elevated odds were also observed for the second and middle quintiles. This gradient indicates a clear socioeconomic stratification in developmental outcomes even before the start of formal schooling. Children living in rural areas had a slightly higher risk of developmental delay than children living in urban areas (OR\u0026thinsp;=\u0026thinsp;1.14, 95% CI: 1.02\u0026ndash;1.27).\u003c/p\u003e \u003cp\u003eSubstantial regional inequalities were identified. The highlands show the most unfavourable results compared with North Centre, particularly the western region, which shows a very significant disadvantage. Children living in the West Highlands had more than twice the odds of being not on track (OR\u0026thinsp;=\u0026thinsp;2.40, 95% CI: 1.87\u0026ndash;3.07), while those in the central and East Highlands also showed elevated odds respectively (OR\u0026thinsp;=\u0026thinsp;1.51, 95% CI: 1.28\u0026ndash;1.71 ; OR\u0026thinsp;=\u0026thinsp;1.77, 95% CI: 1.39\u0026ndash;2.31).\u003c/p\u003e \u003cp\u003eGirls were found to be slightly more developmentally on track than boys in term of overall ECD status (OR\u0026thinsp;=\u0026thinsp;0.72, 95% CI: 0.59\u0026ndash;0.86). Younger children (36\u0026ndash;47 months) had substantially higher odds of developmental delay compared with older children (OR\u0026thinsp;=\u0026thinsp;1.72, 95% CI: 1.47\u0026ndash;2.01). The results show that the association of maternal education on early childhood development is significant, lower maternal education levels were associated with higher odds of being not on track, particularly among children of mothers with preschool or no education (OR\u0026thinsp;=\u0026thinsp;1.67, 95% CI: 1.19\u0026ndash;2.36). Maternal occupation was not significantly associated with ECDI in the total model.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTotal association between socioeconomic and contextual factors and early childhood development (ECDI)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSignification\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eHousehold wealth\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.74 (1.26\u0026ndash;2.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.40 (1.02\u0026ndash;1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.32 (1.03\u0026ndash;1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFourth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.23 (0.91\u0026ndash;1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003ePlace of residence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.14 (1.02\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eGeographic region\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth Centre\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth-East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.23 (0.92\u0026ndash;1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth-West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05 (0.76\u0026ndash;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Highlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.51 (1.28\u0026ndash;1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast Highlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.77 (1.39\u0026ndash;2.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWest Highlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.40 (1.87\u0026ndash;3.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.84 (0.67\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChild sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoys\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGirls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.72 (0.59\u0026ndash;0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eMaternal education (ref:)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreschool or None\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.67 (1.19\u0026ndash;2.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.53 (1.09\u0026ndash;2.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.32 (0.98\u0026ndash;1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.32 (0.99\u0026ndash;1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eMaternal occupation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot working\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.89 (0.66\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eworking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eChild age (months)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u0026ndash;47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.72 (1.47\u0026ndash;2.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e48\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eSignification (Sig): *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u0026mdash; NS\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOdds ratios (OR) and 95% confidence intervals (CI) were estimated using complex samples logistic regression accounting for sampling weights, stratification, and clustering.\u003c/p\u003e \u003cp\u003eModel 1 estimates total associations and does not adjust for nutritional status or home stimulation variables, consistent with the DAG.\u003c/p\u003e \u003cp\u003eAfter adjusting for nutritional status and home stimulation variables (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Model 2), wealth-related disparities persisted, although effect sizes were slightly attenuated. The findings in the Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e show that children in the poorest quintile remained significantly more likely to be not on track (OR\u0026thinsp;=\u0026thinsp;1.70, 95% CI: 1.23\u0026ndash;2.35). Followed by children in the second and third quintiles, who also continued to show high probabilities. Stunting was significantly associated with higher odds of developmental vulnerability (OR\u0026thinsp;=\u0026thinsp;1.40, 95% CI: 1.11\u0026ndash;1.77), whereas the weight of children does not seem to have any significant association. Among stimulation indicators, book reading was protective (OR\u0026thinsp;=\u0026thinsp;0.81, 95% CI: 0.67\u0026ndash;0.98), while storytelling and playing were not significantly associated in the fully adjusted model. Regional disparities remained substantial, particularly for the West Highlands and East Highlands. Likewise, girls continued to have lower odds of being not on track compared with boys, and younger children remained at higher risk.\u003c/p\u003e \u003cp\u003eInteraction model (Model 3) in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e examined whether the association between household wealth and early childhood development differed between urban and rural settings. The statistically significant interaction term for the fourth wealth quintile in rural areas (OR\u0026thinsp;=\u0026thinsp;3.63, 95% CI: 1.41\u0026ndash;9.34) indicates that the association between household wealth and the odds of being not developmentally on track differs across urban and rural settings. The inclusion of the wealth \u0026times; residence interaction term revealed evidence of effect modification, indicating that socioeconomic gradients in ECDI were not uniform across geographic contexts. In fact, the attenuation of the main wealth relationship in Model 3 reflects this reparameterisation rather than the disappearance of socio-economic inequalities, indicating that wealth-related disparities depend on context.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDirect association model for ECDI and effect modification by place of residence\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSig\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSig\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eHousehold wealth\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.70 (1.23\u0026ndash;2.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.26 (0.59\u0026ndash;2.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.38 (1.01\u0026ndash;1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89 (0.41\u0026ndash;1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.38 (1.02\u0026ndash;1.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (0.45\u0026ndash;2.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFourth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.22 (0.91\u0026ndash;1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.39 (0.16\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003ePlace of residence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.83\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.57 (0.27\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth Centre\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth-East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85 (0.60\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth-West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.71\u0026ndash;1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Highlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.48 (1.22\u0026ndash;1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast Highlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.67 (1.30\u0026ndash;2.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWest Highlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.88 (1.41\u0026ndash;2.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.80 (0.60\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eChild sex\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoys\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGirls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.81 (0.68\u0026ndash;0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eMaternal education\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreschool or None\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.59 (1.13\u0026ndash;2.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.41 (1.00\u0026ndash;2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.27 (0.94\u0026ndash;1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.30 (0.97\u0026ndash;1.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eMaternal occupation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot working\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.90 (0.66\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eworking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChild age (months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u0026ndash;47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.70 (1.45\u0026ndash;2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e48\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eNutritional status\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStunted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.40 (1.11\u0026ndash;1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.16 (0.72\u0026ndash;1.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eHome stimulation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBook reading (yes vs no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.81 (0.67\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStorytelling (yes vs no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.80\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlaying (yes vs no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08 (0.92\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eWealth \u0026times; Residence interaction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorest \u0026times; Rural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.43 (0.62\u0026ndash;3.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond \u0026times; Rural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.79 (0.78\u0026ndash;4.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle \u0026times; Rural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.50 (0.65\u0026ndash;3.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFourth \u0026times; Rural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.63 (1.41\u0026ndash;9.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erichest urban households (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOutcome: ECDI\u0026thinsp;=\u0026thinsp;NOT on track (reference\u0026thinsp;=\u0026thinsp;YES).\u003c/p\u003e \u003cp\u003eSignification (Sig): *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u0026mdash; NS.\u003c/p\u003e \u003cp\u003eModel 2 (Direct association): Model 1 plus nutritional status (stunting, underweight) and home stimulation indicators (book reading, storytelling, playing).\u003c/p\u003e \u003cp\u003eModel 3: Model 2 plus interaction term between household wealth and place of residence. Statistical significance was assessed using design-adjusted Wald F tests.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eUsing nationally representative data from Algeria (MICS 2019), the present study highlights key socioeconomic and geographic inequalities in early childhood development. Three principal findings emerge. First, marked socioeconomic gradients were observed in early childhood development. Children from poorer households had consistently higher odds of being not developmentally on track, even after adjustment for maternal education, regional context, and child characteristics. Second, substantial regional disparities were identified, particularly in the Highlands regions. Third, the interaction analysis demonstrated that wealth-related inequalities differed between urban and rural contexts, indicating spatial heterogeneity in socioeconomic gradients. Importantly, wealth disparities persisted after adjusting for nutritional status and home stimulation indicators, suggesting that measured pathways explain only part of the observed inequalities.\u003c/p\u003e\n\u003ch3\u003eSocioeconomic gradients\u003c/h3\u003e\n\u003cp\u003eThe wealth gradient observed in this study is consistent with extensive international research (Rahman et al., 2025; Alam et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hackman et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) demonstrating that early childhood development is strongly socially patterned. Large cross-national analyses have shown that children from lower-wealth households in low- and middle-income countries are significantly more likely to experience developmental vulnerability, particularly in literacy\u0026ndash;numeracy and learning domains (Islam, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e ; Touhami et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Family poverty can affect the ability to meet children's basic needs, including access to safe housing, nutritious food and quality childcare (Maggi et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese findings are consistent with the Nurturing Care Framework, which highlights the importance of family resources, responsive caregiving, and learning opportunities in shaping early childhood development trajectories. Structural socioeconomic disadvantage may limit parents\u0026rsquo; ability to provide stimulating learning environments and access quality early childhood services. As a result, inequalities in developmental outcomes may emerge well before formal schooling begins (Richter et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Boutayeb and Helmert, 2021).\u003c/p\u003e \u003cp\u003eWithin the broader North African context, similar socioeconomic gradients have been documented in Morocco, Tunisia, and Egypt, where household wealth and maternal education consistently predict early developmental outcomes (Krafft and El-Kogali, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e ; Lu et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Although Algeria has made considerable progress in maternal and child health and has relatively high maternal literacy and primary healthcare coverage compared with some neighboring countries, the persistence of wealth disparities underscores the structural nature of developmental inequality in the region.\u003c/p\u003e\n\u003ch3\u003eUrban–rural divides and regional disparities\u003c/h3\u003e\n\u003cp\u003eA key contribution of this study is the identification of effect modification by place of residence. Urban\u0026ndash;rural disparities are a recurring theme in child development research. Studies in several North African countries have shown that rural children often have reduced access to early childhood education, learning materials, and structured preschool environments (Boutayeb and Helmert, 2021). These disparities are frequently associated with infrastructure gaps rather than solely household-level poverty (Prado-Galbarro et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The interaction analysis suggests that socioeconomic gradients in early childhood development are not uniform across geographic contexts. In particular, the significant interaction observed for the fourth wealth quintile in rural areas indicates that moderate levels of household wealth may not translate into similar developmental advantages in rural settings as in urban areas. This pattern may reflect structural constraints in rural environments, such as limited access to early childhood education services, learning materials, and child-oriented infrastructure. In Algeria, while urban centers benefit from higher preschool enrollment rates and greater availability of private and public educational services (libraries, associations, cultural centres), rural areas may experience structural disadvantages, including geographic isolation and uneven service distribution. Thus, the interaction observed in this study reflects patterns consistent with regional structural inequalities rather than isolated household effects (Hlasny, 2019). Beyond the urban\u0026ndash;rural divide, pronounced regional variation was observed in our results particularly in the Highland regions. Subnational disparities in early development have been documented across low- and middle-income countries and are often linked to uneven service distribution and economic development (Lu et al., 2020). Similar patterns have been reported in Tunisia and Egypt, where developmental outcomes vary substantially across provinces (Krafft and El-Kogali, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese findings reinforce the importance of disaggregated monitoring. National averages may obscure meaningful territorial inequities. From a child well-being perspective, regional disparities highlight the need for place-based policy approaches targeting structurally disadvantaged areas.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eNutritional status and home stimulation as pathways\u003c/h2\u003e \u003cp\u003eConsistent with prior literature, our results suggest that stunting was independently associated with higher odds of being not developmentally on track. This supports evidence linking chronic under nutrition with impaired cognitive and socio-emotional development (Black et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e ; Bornstein et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Although stunting prevalence in Algeria is lower than in several sub-Saharan African countries and some neighboring nations, it remains a relevant risk factor for developmental vulnerability and may explain why nutritional adjustment attenuated but did not eliminate socioeconomic gradients. Across North Africa, the epidemiological transition has led to declining undernutrition but increasing double burdens of malnutrition. Even moderate levels of chronic undernutrition have been associated with developmental risk (Baye et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e ; Rahman et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe protective association observed for book reading in this study aligns with research demonstrating that cognitive stimulation enhances language development and executive functioning (Britto et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and several studies have affirmed that the family environment and parent-child interaction play an essential role in the development of a child's motor function (Alam et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e ; Saccani et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Caring for young children is considered the primary responsibility of the mother, particularly in the Maghreb context. In fact, mothers with higher levels of education are more likely to create stimulating environments and use effective parenting techniques, which promotes more harmonious child development (Islam, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Krafft and El-Kogali, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Maggi et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). However, the persistence of wealth gradients after adjusting for nutritional and stimulation indicators suggests that socioeconomic inequalities extend beyond household-level practices. Structural correlates likely influence access to preschool education, neighborhood environments, and institutional resources not fully captured in survey data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePolicy and monitoring implications\u003c/h2\u003e \u003cp\u003eThe findings enrich to the existing literature by highlighting the position of early childhood development as a core child well-being indicator. They also contribute to the limited empirical evidence on early childhood development inequalities in North Africa, a region that remains underrepresented in the child indicators literature.\u003c/p\u003e \u003cp\u003eIn Algeria, strengthening early childhood development systems may therefore require integrated strategies that address household disadvantages (poverty reduction), the development of quality pre-school education, and targeted investments in rural areas. The results of the interaction suggest that improving the economic situation of households may prove insufficient in contexts where institutional and infrastructural constraints persist. These findings suggest that addressing developmental inequalities requires not only household-level interventions but also broader territorial policies targeting service availability and early childhood education infrastructure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eSeveral strengths should be noted. First, the study uses nationally representative data with appropriate adjustment for complex survey design and data collection followed the well-defined criteria of the World Health Organization (UNICEF, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Second, the use of a DAG framework strengthened model specification by distinguishing structural correlates from potential mediating mechanisms and reducing the risk of over adjustment. Third, the explicit assessment of interaction allowed identification of contextual heterogeneity in socioeconomic gradients.\u003c/p\u003e \u003cp\u003eHowever, limitations must be acknowledged. The data were cross-sectional, which limits the possibility of studying the causal effect between socioeconomic conditions and developmental outcomes. The ECDI, while internationally standardized, provides a screening measure rather than a diagnostic assessment. ECDI refers to information reported by parents or caregivers and can therefore be subject to reporting bias. This study focuses exclusively on children aged 36\u0026ndash;59 months, which prevents analysis of developmental changes beyond this age. The data from the MICS survey are limited to the variables available, which made it impossible to control for residual confounding from unmeasured contextual factors, such as preschool quality, neighborhood characteristics or the nutritional status of parents which can significantly influence ECD. Future research could address these limitations using longitudinal data and more detailed contextual measures.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eSocioeconomic and geographic inequalities in early childhood development are evident in Algeria and reflect broader patterns observed across North Africa. Wealth gradients persist after adjustment for nutritional and stimulation pathways, and socioeconomic effects vary by place of residence. These findings suggest that developmental inequalities are shaped by both structural socioeconomic conditions and spatial context. Although the importance of protecting children remains one of the priorities of health and social protection policy in Algeria, there is an urgent need to address early childhood development disparities will likely require integrated policies targeting both household disadvantage and contextual infrastructure constraints.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMeryem Boukhelif was in charge of conceptualization and formal analysis. Adel Sidi-Yakhlef was in charge of methodology. Abdellatif Moussouni and Kamel Chikhi contributed to writing of the first draft of the paper. All authors reviewed drafts of the manuscript, provided suggestions for refinement and provided approval for the version to be published.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to thank the Algerian Ministry of Health, Population and Hospital Reform, UNICEF, UNFPA and all those involved in the development of this database.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkram, S., Zahid, F., \u0026amp;Pervaiz, Z. (2024). Socioeconomic determinants of early childhood development: Evidence from Pakistan. \u003cem\u003eJournal of Health, Population and Nutrition\u003c/em\u003e, \u003cem\u003e43\u003c/em\u003e(1), 70. https://doi.org/10.1186/s41043-024-00569-5\u003cu\u003e.\u003c/u\u003e\u003c/li\u003e\n\u003cli\u003eAlam, M. I., Mansur, M., \u0026amp; Barman, P. (2022). 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(2021) Early childhood development and urban environment in Mexico. \u003cem\u003ePLOS ONE\u003c/em\u003e 16(11): e0259946. https://doi.org/10.1371/journal.pone.0259946.\u003c/li\u003e\n\u003cli\u003eRahman, F., Tuli, S. N., Mondal, P., Sultana, S., Hossain, A., Kundu, S., Clara, A. A., \u0026amp;Hossain, A. (2023). Home environment factors associated with early childhood development in rural areas of Bangladesh: Evidence from a national survey. \u003cem\u003eFrontiers in Public Health\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e, 1209068. https://doi.org/10.3389/fpubh.2023.1209068\u003cu\u003e.\u003c/u\u003e\u003c/li\u003e\n\u003cli\u003eRichter, L. M., Daelmans, B., Lombardi, J., Heymann, J., Boo, F. L., Behrman, J. R., Lu, C., Lucas, J. E., Perez-Escamilla, R., Dua, T., Bhutta, Z. A., Stenberg, K., Gertler, P., \u0026amp; Darmstadt, G. L. (2017). 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Family Environment, Heritage Language Profiles, and Socioemotional Well-being of Mexican-origin Adolescents with First Generation Immigrant Parents. \u003cem\u003eJournal of Youth and Adolescence\u003c/em\u003e, \u003cem\u003e51\u003c/em\u003e(6), 1196\u0026ndash;1209. https://doi.org/10.1007/s10964-022-01594-5\u003c/li\u003e\n\u003cli\u003eWHO Multicentre Growth Reference Study. WHO child growth standards based on length/height, weight and age. ActaPaediatr. (2006) 95:76\u0026ndash;85. doi:10.1111/j.1651-2227.2006.tb02378.x.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Early childhood development, Socioeconomic inequalities, Child well-being indicators, Algeria, Multiple Indicator Cluster Survey","lastPublishedDoi":"10.21203/rs.3.rs-9122949/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9122949/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEarly childhood development is increasingly recognized as a central dimension of child well-being and an essential component of child indicator frameworks used to monitor social progress. The objective of this study is to examine disparities in early childhood development in Algeria using nationally representative data from the 2019 Multiple Indicator Cluster Survey (MICS).\u003c/p\u003e \u003cp\u003eThe analysis covered all children aged between 36 and 59 months included in the database and used the Early Childhood Development Index (ECDI) as the main assessment criterion. Using complex sample logistic regression models, the total and direct associations between socio-economic factors and developmental status were assessed. A directed acyclic graph (DAG) guided covariate selection and informed the distinction between total association models and models adjusting for hypothesized mediating pathways (nutritional status and home stimulation). Interaction between household wealth and place of residence was also examined.\u003c/p\u003e \u003cp\u003eAmong the 6,146 children listed, 76.8% were developmentally on track. Data analysis showed strong wealth-related gradients: children in the poorest quintile had significantly higher odds of being not developmentally on track compared with those in the richest quintile. These disparities persisted after adjusting for maternal education, child characteristics, nutritional status, and home stimulation. Stunting was independently associated with developmental vulnerability, while book reading was associated with lower odds of developmental delay. Significant regional disparities were identified. Moreover, the association between wealth and developmental status varied by place of residence, indicating spatial heterogeneity in socioeconomic gradients.\u003c/p\u003e \u003cp\u003eThese findings demonstrate that early childhood developmental inequalities in Algeria are shaped by both structural socioeconomic disadvantage and geographic context. Investing in early childhood development as an equity-sensitive child indicator may support more targeted policies addressing household-level disadvantage and territorial disparities not only in Algeria but throughout North Africa.\u003c/p\u003e","manuscriptTitle":"Socioeconomic and Regional Inequalities in Early Childhood Development in Algeria: Evidence from the MICS-6 Survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-09 16:43:29","doi":"10.21203/rs.3.rs-9122949/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"07af42ef-3493-4ab8-ab1a-aea57d8e93eb","owner":[],"postedDate":"April 9th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-17T06:55:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-11T16:23:16+00:00","index":25,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-02T18:35:39+00:00","index":24,"fulltext":""},{"type":"reviewerAgreed","content":"284023081373697110422173829648332595525","date":"2026-04-29T16:29:31+00:00","index":23,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-17T07:09:00+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-09 16:43:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9122949","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9122949","identity":"rs-9122949","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}