using Denver II subscales Cluster Analysis of Ethiopian children (6-60 months of age) living in extreme poverty in Jimma town of Ethiopia using Denver II subscales Wende Clarence Safari, Berhanu Nigussie Worku, Teklu Gemechu Abessa, Liesbeth Brückers, Marita Granitzer Hasselt University, Belgium. WHAT WE LEARNED The Denver II Jimma test, is an adapted version of the Denver II to the sociocultural context of children under-six years living in the Jimma zone of Ethiopia, and was intended to assess the developmental performance of children from extreme poverty families, in Jimma Ethiopia. The latent class cluster analysis (model based clustering classification) found three distinct and clinically relevant clusters in the data namely a Delayed Developmental Group, Questionable Developmental group and a Normal Developmental Group. A significant difference was found among the cluster of younger children and who have stunted growth. As younger and stunted children are more likely to be in the Delayed and Questionable Developmental Groups as compared to the Normal Group. Furthermore, children with poor performance in terms of personal social, gross motor and language are also more likely to be in the Delayed Developmental Group. ,mc INTRODUCTION RESULTS STUDY STRENGHTS, IMPLICATIONS, LIMITATIONS & RECOMMENDATION Children who live in extreme poverty are at high risk of developmental and health problems due to inadequate supportive environment, crowding and limited resources. The earlier poverty strikes in the developmental process, the more damaging and long-lasting its effects. Therefore it is important to identify these children as soon as possible to early intervene. To screen such children the Denver II test can be used. Identification and Interpretation of the clusters The results reveal that, within these data, it is possible to identify distinct subgroups within children, which suggests the possibility of alternative pathways to developmental growth. The 3-cluster solution (see Table 1) from the mixture models was chosen Normal Developmental group Boys = 158 (49.5%), Girls = 161 (50.5) Age: 24 to 61 months; Median age = 47.63 months Stunted = 83 (26%); Non stunted = 236 (74%) Children in this group had the averages of above one on all performance ratios. Older children were likely to be in the Normal Developmental Group. For stunting relative to non stunting children, the relative risks for being in Delayed group would be expected to increase by a factor of 8.48, given age is constant. Similar results was observed for children in the Questionable Group. Identification and Interpretation of the clusters The results reveal that, within these data, it is possible to identify distinct subgroups within children, which suggests the possibility of alternative pathways to developmental growth. The LCCA technique offers a mechanism to group data without losing the richness of information provided by both intra-child and inter children variability in developmental pathway. Clinically, examination of distinct developmental profiles may lead to improved accuracy in screening for developmental problems compared with the traditional use of single group assessment cut-off points. Results provide important implications for the design of future studies of developmental pathways for extreme poverty children at early age. Further research with longitudinal setting of the data need to be collected for children who are living at higher-risk of developmental delay, to validate the identified clusters as representative of developmental profiles patterns for children living in extreme poverty. To inform the government, policy makers and practitioners that, there should be strong emphases in the children’s health policies and interventions during early childhood development. Moreover, policies should focus more on the improved nutrition and psychosocial stimulation, and be implemented especially in extreme poverty areas. Table 1: Estimates of the obtained 3-clusters Clusters Developmental domain Mean* C.I* Delayed Developmental (Cluster 1) Personal social 0.96 (0.94, 0.98) Fine Motor 1.02 (1.01, 1.03) Language 0.92 (0.90, 0.93) Gross motor 0.98 (0.97, 1.00) Questionable Developmental (Cluster 2) 1.04 (0.97, 1.12) 1.06 (1.00, 1.12) 0.97 (0.92, 1.02) 1.00 (0.97, 1.06) Normal Developmental (Cluster 3) 1.01 (1.00, 1.03) 1.11 (1.10, 1.12) One of the children who live in an extreme poverty, performing a Denver II test. OBJECTIVES To determine the utility of cluster analysis as a statistical technique to organize the developmental outcomes (personal-social, language, fine and gross motor skills performance ratios) into clinically relevant groups for Ethiopian children living in extreme poverty. To compare demographic characteristics, nutritional indices, psychosocial, and social-economic factors across the obtained clusters. * Average performance ratios for each developmental domain under each cluster and their associated confidence interval. The average value of below one means children performed less than what was expected for their age. REFERENCES CITED Eldred, K., & Darrah, J. (2010). Using cluster analysis to interpret the variability of gross motor scores of children with typical development. Grantham-McGregor, S., Cheung, Y. B., Cueto, S., Glewwe, P., Richter, L., Strupp, B., & International Child Development Steering Group. (2007) The three clusters include; Delayed Developmental group Boys = 204 (49%); Girls = 211 (51%) Age: 5 to 51 months; Median = 22 months Stunted=218(53%); Non stunted=197(47%) Lower average values on personal-social, language and gross motor performance ratios Questionable Developmental group Boys=36 (42%); Girls= 49 (58%) Age: 5 to 27 months; Median = 10 months Stunted =24 (28%); Non stunted = 61 (72%) Lower average values on language and gross motor performance ratios METHODS & DATA A cross-sectional study of 819 children was conducted in Jimma town, from March 1 to September 2, 2013. We estimate a Latent Class Cluster Analysis (model based clustering or mixture model) with four domains of Denver-II Jimma, then a multinomial logistic regression was fitted to relate the obtained clusters and available covariates of interest. A total of eight mixture models were fitted using Mplus software with one to five clusters, and the models were compared using the Bayesian Information Criterion (BIC). Figure 3 : Graphical illustration of three clusters with age to predict cluster membership ACKNOWLEDGEMENTS Figure 3 shows much variability for younger children, and, these children are likely to be in the Questionable Developmental Group, middle age and older children are more likely to be under the Delayed and Normal Developmental Group, respectively. The data set is part of the the PhD project of Drs. Berhanu Nigussie Worku entitled “Developmental and growth status of children (6-60 months of age) in extreme poverty in Jimma town of Ethiopia: effects of developmental stimulation”.