THE SCORING PREDICTORS OF STUNTING BASED ON THE EPIDEMIOLOGICAL TRIAD
Keywords:Stunting, Scoring Predictor, Epidemiological Triad
The scoring predictor of stunting is intended to predict or assess the risk of a child experiencing stunting. The epidemiological triad approach includes three important elements, namely the host, agent, and environment related to stunting. The steps in developing a scoring predictor of stunting include identifying variables and data, analysing data to understand the relationships between variables, developing predictive models, and model validation to ensure model performance and accuracy. After the model is developed, implementation and evaluation of the model become important in applying the model on a wider scale as well as monitoring the performance and effectiveness of the model in preventing and overcoming stunting. The ultimate goal of the scoring predictor of stunting is to reduce the prevalence of stunting and improve the quality of life for children by preventing the adverse effects that this condition of malnutrition may cause in the long term.
Abdulla, F., Rahman, A., Hossain, Md.M., 2023. Prevalence and risk predictors of childhood stunting in Bangladesh. PLOSONE18, e0279901. https://doi.org/10.1371/journal.pone.0279901
Arifuddin, A., 2023. Multivariate analysis, logistic regression analysis of presentation and interpretation analysis, in: Data Management and Analysis. Global Executive Technology, Padang, West Sumatra.
Arifuddin, A., Prihatni, Y., Setiawan, A., Wahyuni, RD, Nur, AF, Dyastuti, E., Arifuddin, H., 2023. Epidemiological Model of Stunting Determinants in Indonesia. Healthy Tadulako Journal 9(2), 224–234. https://doi.org/10.22487/htj.v9i2.928
Arnett, DK, Claas, SA, 2017. Introduction to Epidemiology, in: Clinical and Translational Science. Elsevier, pp. 53–69. https://doi.org/10.1016/B978-0-12-802101-9.00004-1
Kemenkes RI, 2020. Health Data and Information Window: Stunting Situation in Indonesia. Center for Health Data and Information Ministry of Health of the Republic of Indonesia, Jakarta.
Lukman, TNE, Anwar, F., Riyadi, H., Harjomidjojo, H., Martianto, D., 2022. Responsive Prediction Model of Stunting in Toddlers in Indonesia. Curr. Res. Nutr. Food Sci. J. 10, 302–310. https://doi.org/10.12944/CRNFSJ.10.1.25
Mukuku, O., Mutombo, AM, Kamona, LK, Lubala, TK, Mawaw, PM, Aloni, MN, Wembonyama, SO, Luboya, ON, 2019. Predictive Model for the Risk of Severe Acute Malnutrition in Children. J.Nutr. Metab. 2019, 1–7. https://doi.org/0.1155/2019/4740825
Ndagijimana, S., Kabano, IH, Masabo, E., Ntaganda, JM, 2023. Prediction of Stunting Among Under-5 Children in Rwanda Using Machine Learning Techniques. J. Prev. med. Pubs. Health 56, 41–49. https://doi.org/10.3961/jpmph.22.388
Nur, AF, Munir, A., Setiawati, T., Dyastuti, NE, Arifuddin, H., Arifuddin, A., 2023. Analysis Determinants of Incomplete Immunization in Children: Systematic Literature Review. Healthy Tadulako Journal 9(1). https://jurnal.fk.untad.ac.id/index.php/htj/article/view/772
Simbolon, D., Suryani, D., Yorita, E., 2019. Prediction Model and Scoring System in Prevention and Control of Stunting Problems in Under Five-Year-Olds in Indonesia. J. Health. Mass. 15, 160–170. https://doi.org/10.15294/kemas.v15i2.13415
Soni, A., Fahey, N., Ash, A., Bhutta, Z., Li, W., Simas, TM, Nimbalkar, S., Allison, J., 2022. Predictive algorithm to stratify newborns at-risk for child undernutrition in India: Secondary analysis of the National Family Health Survey-4. J. Globe. Health 12, 04040. https://doi.org/10.7189/jogh.12.04040
Usman, E., Yanis, A., Nindrea, RD, 2020. Scoring System in Prediction of Stunting Risk Among Children in West Sumatra Province, Indonesia. syst. Rev. Pharm. 11.
WHO, 2019. Child Stunting. World Health Stats. Data Vis. Dashboards.
WHO, UNICEF, World Bank Group, 2021. Levels and Trends in Child Malnutrition.
How to Cite
Copyright (c) 2023 Healthy Tadulako Journal (Jurnal Kesehatan Tadulako)
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.