Hoddinott J, Alderman H, Behrman JR, Haddad L, Horton S. The economic rationale for investing in stunting reduction. Matern Child Nutr. 2013;9(S2):69–82.
Lutter CK, Daelmans BMEG, de Onis M, Kothari MT, Ruel MT, Arimond M, et al. Undernutrition, poor feeding practices, and low coverage of key nutrition interventions. Pediatrics. 2011;128(6):e1418–27. https://doi.org/10.1542/peds.2011-1392.
Deborah A, Kavita S, Elisabeth S, Lesley O, Tara K, Joyceline K, et al. Reducing malnutrition in Tanzania: estimates to support nutrition advocacy: Tanzania PROFILES 2014. Reducing malnutrition in Tanzania: estimates to support nutrition advocacy Tanzania PROFILES 2014. 2014. Available from: https://www.fantaproject.org/sites/default/files/resources/Tanzania-PROFILES-Report-2014-June2017.pdf.
De Onis M, Blössner M. WHO Global Database on Child Growth and Malnutrition. Programme of Nutrition World Health Organization Geneva. 1997.
Olofin I, McDonald CM, Ezzati M, Flaxman S, Black RE, Fawzi WW, et al. Associations of suboptimal growth with all-cause and cause-specific mortality in children under five years: a pooled analysis of ten prospective studies. PLoS One. 2013;8(5).
Huicho L, Segura ER, Huayanay-Espinoza CA, de Guzman JN, Restrepo-Méndez MC, Tam Y, et al. Child health and nutrition in Peru within an antipoverty political agenda: a Countdown to 2015 country case study. Lancet Glob Heal. 2016;4(6):e414-26.
UNICEF, WHO, WBG. Levels and trends in child malnutrition UNICEF / WHO / World Bank Group Joint Child Malnutrition Estimates Key findings of the 2021 edition. World Heal Organ [Internet]. 2021;1–32. Available from: https://www.who.int/publications/i/item/9789240025257.
Crochemore I, Silva M, França G V, Barros AJD, Amouzou A, Krasevec J, et al. Declining Stunting Prevalence in Low- and Middle-Income Countries. Community Int Nutr. 2018.
MoHCDGEC. Tanzania National Nutrition Survey 2018. Dodoma; 2019. Available from: https://www.unicef.org/tanzania/media/2141/file/TanzaniaNationalNutritionSurvey2018.pdf.
Howe LD, Galobardes B, Matijasevich A, Gordon D, Johnston D, Onwujekwe O, et al. Measuring socio-economic position for epidemiological studies in low-and middle-income countries: a methods of measurement in epidemiology paper. Int J Epidemiol. 2012;41(3):871–86.
Darin-Mattsson A, Fors S, Kåreholt I. Different indicators of socioeconomic status and their relative importance as determinants of health in old age. Int J Equity Health. 2017;16(1):1–11.
Omer W, Al-Hadithi T. Developing a socioeconomic index for health research in Iraq. East Mediterr Heal J. 2017;23(10):670–7.
Psaki SR, Seidman JC, Miller M, Gottlieb M, Bhutta ZA, Ahmed T, et al. Measuring socioeconomic status in multicountry studies: results from the eight-country MAL-ED study. Popul Health Metr. 2014;12(1):1–11.
Frost MB, Forste R, Haas DW. Maternal education and child nutritional status in Bolivia: finding the links. Soc Sci Med. 2005;60(2):395–407.
D’Errico A, Ricceri F, Stringhini S, Carmeli C, Kivimaki M, Bartley M, et al. Socioeconomic indicators in epidemiologic research: A practical example from the LIFEPATH study. Vol. 12, PLoS ONE. 2017. 1–32 p.
Wagstaff A, Watanabe N. Socioeconomic Inequalities in Child Malnutrition in the Developing World. The Development Dilemma. 2000.
Baye K, Laillou A, Chitweke S. Socio-economic inequalities in child stunting reduction in sub-Saharan Africa. Nutrients. 2020;12(1):1–11.
Rabbani A, Khan A, Yusuf S, Adams A. Trends and determinants of inequities in childhood stunting in Bangladesh from 1996/7 to 2014. Int J Equity Health. 2016. https://doi.org/10.1186/s12939-016-0477-7.
Fenta HM, Zewotir T, Muluneh EK. Spatial data analysis of malnutrition among children under-five years in Ethiopia. BMC Med Res Methodol. 2021;21(1):1–13.
Nandy S, Irving M, Gordon D, Subramanian SV, Davey SG. Poverty, child undernutrition and morbidity: new evidence from india. Bull World Health Organ. 2017;011650(04):309–15.
Kazembe LN. An additive regression model for investigating the relationship between childhood health and socio-economic status. Spat Spatiotemporal Epidemiol. 2013;6:71–84. https://doi.org/10.1016/j.sste.2013.07.002.
Emina JB, Kandala N, Inungu J, Ye Y. Maternal education and child nutritional status in the democratic republic of Congo. J Public Health Epidemiol. 2011;3(12):576–92.
Seboka BT, Hailegebreal S, Mamo TT, Yehualashet DE, Gilano G, Kabthymer RH, et al. Spatial trends and projections of chronic malnutrition among children under 5 years of age in Ethiopia from 2011 to 2019: a geographically weighted regression analysis. J Heal Popul Nutr. 2022;41(1):1–17. https://doi.org/10.1186/s41043-022-00309-7.
Adeyemi RA, Zewotir T, Ramroop S. Joint spatial mapping of childhood anemia and malnutrition in sub-Saharan Africa: a cross-sectional study of small-scale geographical disparities. African Health Sci. 2019;19(3):2692–712.
Kazembe LN. Mapping socio-economic inequalities in health status among Malawian children: a mixed model approach. In: Manda SOM, Kandala N-B, Ghilagaber G, editors. Advanced techniques for modelling maternal and child health in Africa. 2014. https://doi.org/10.1007/978-94-007-6778-2_5
Habyarimana F, Zewotir T, Ramroop S, Ayele DG. Spatial distribution of determinants of malnutrition of children under five years in Rwanda: simultaneous measurement of three anthropometric indices. J Hum Ecol. 2016;54(3):138–49.
Musheiguza E, Mahande MJ, Malamala E, Msuya SE, Charles F, Philemon R, et al. Inequalities in stunting among under-five children in Tanzania: decomposing the concentration indexes using demographic health surveys from 2004/5 to 2015/6. Int J Equity Health. 2021;20(1):1–10.
Mgongo M, Chotta NAS, Hashim TH, Uriyo JG, Damian DJ, Stray-pedersen B, et al. Underweight , Stunting and Wasting among Children in Kilimanjaro Region, Tanzania ; a population-based cross-sectional study. 2017;1–12.
Mohammed S, Asfaw ZG. Bayesian Gaussian regression analysis of malnutrition for children under five years of age in Ethiopia, EMDHS 2014. Arch Public Heal. 2018;76(1):1–11.
Takele K, Zewotir T, Ndanguza D. Spatial joint modelling of children under-five malnutrition in Ethiopia. J Hum Ecol. 2020;70(1–3):132–42.
Lindtjørn B. Spatial variation of child stunting and maternal malnutrition after controlling for known risk factors in a drought-prone rural community in. Glob Heal. 2021;87(1):1–14.
Jahanpour OF, Okango EL, Todd J, Mwambi H, Mahande MJ. Role of clusters in exclusive breastfeeding practices in Tanzania: a secondary analysis study using demographic and health survey data (2015/2016). Front Pediatr. 2022. https://doi.org/10.3389/fped.2022.939706/full.
Sunguya BF, Zhu S, Mpembeni R, Huang J. Trends in prevalence and determinants of stunting in Tanzania: an analysis of Tanzania demographic health surveys (1991–2016). Nutr J. 2019;18(1):1–13.
Novignon J, Nonvignon J. Socioeconomic status and the prevalence of fever in children under age five: Evidence from four sub-Saharan African countries. BMC Res Notes. 2012;5.
Moshi CC, Sebastian PJ, Mushumbusi DG, Azizi KA, Meghji WP, Kitunda ME, et al. Determinants of underweight among children aged 0–23 months in Tanzania. Food Sci Nutr. 2022;10(4):1167–74.
Chirande L, Charwe D, Mbwana H, Victor R, Kimboka S, Issaka AI, et al. Determinants of stunting and severe stunting among under-fives in Tanzania: evidence from the 2010 cross-sectional household survey. BMC Pediatr. 2015;15(1):1–13. https://doi.org/10.1186/s12887-015-0482-9.
Modern G, Sauli E, Mpolya E. Correlates of diarrhea and stunting among under-five children in Ruvuma, Tanzania; a hospital-based cross-sectional study. Sci African. 2020;8:e00430. https://doi.org/10.1016/j.sciaf.2020.e00430.
Nattey C, Masanja H, Klipstein-Grobusch K. Relationship between household socio-economic status and under-five mortality in Rufiji DSS. Tanzania Glob Health Action. 2017;2013(6):19278.
Khamis AG, Mwanri AW, Ntwenya JE, Kreppel K. The influence of dietary diversity on the nutritional status of children between 6 and 23 months of age in Tanzania. BMC Pediatr. 2019;19(1):1–9.
Altare C, Delbiso TD, Mutwiri GM, Kopplow R, Guha-sapir D. Factors associated with stunting among pre-school children in southern highlands of Tanzania. 2016; 390–408.
Khamis AG, Mwanri AW, Kreppel K, Kwesigabo G. The burden and correlates of childhood undernutrition in Tanzania according to composite index of anthropometric failure. BMC Nutr. 2020;6(39):1–13.
Makori N, Kassim N, Kinabo J, Matemu A. Factors associated with stunting in Dodoma Region, Tanzania. African J Food Agric Nutr Dev. 2018;18(3):13842–61.
Chirande L, Charwe D, Mbwana H, Victor R, Kimboka S, Issaka AI, et al. Determinants of stunting and severe stunting among under-fives in Tanzania: evidence from the 2010 cross-sectional household survey. BMC Pediatr. 2015;15(1):1–13. https://doi.org/10.1186/s12887-015-0482-9.
Vittinghoff E, Shiboski SC, McCulloch CE, Glidden DV. Regression methods in biostatistics: linear, logistics, survival, and reapeated measures models. Second Edi. Gail M, Krickeberg K, Samet JM, Tsiatis A, Wong W, editors. vol. 23, Int J Neural Syst. 2013.
Austin PC, Merlo J. Intermediate and advanced topics in multilevel logistic regression analysis. Stat Med. 2017;36(20):3257–77.
Zitzmann S, Lüdtke O, Robitzsch A, Hecht M. On the performance of bayesian approaches in small samples: a comment on Smid, McNeish, Miocevic, van de Schoot (2020). Struct Equ Model A Multidiscip J. 2021;28(1):40–50. https://doi.org/10.1080/10705511.2020.1752216.
Baldwin SA, Fellingham GW. Bayesian methods for the analysis of small sample multilevel data with a complex variance structure. Psychol Methods. 2013;18(2):151–64.
Maas CJM, Hox JJ. Sufficient sample sizes for multilevel modeling. Methodology. 2005;1(3):86–92.
Rutstein SO, Rojas G. Guide to DHS statistics [Internet]. Calverton, MD: ORC Macro. 2006. Available from: http://www.measuredhs.com/pubs/pdf/DHSG1/Guide_to_DHS_Statistics_29Oct2012_DHSG1.pdf%5Cn, http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.431.8235&rep=rep1&type=pdf
MoHCDGEC. Tanzania Demographic and Health Survey and Malaria Indicator Survey 2015–2016 [Internet]. Government Statistician ICF. 2016. Available from: https://dhsprogram.com/pubs/pdf/FR321/FR321.pdf.
Onis M De, Onyango A, M DO. WHO child growth standards. Paediatr Croat Suppl. 2008;52(SUPP.1):13–7. Available from: http://hpps.kbsplit.hr/hpps-2008/pdf/dok03.pdf%5Cn, http://cdrwww.who.int/entity/childgrowth/publications/ca_symposium_comparison/en/.
Asmare AA, Agmas YA. Determinants of coexistence of stunting, wasting, and underweight among children under five years in the Gambia; evidence from 2019/20 Gambian demographic health survey: application of multivariate binary logistic regression model. BMC Public Health. 2022;22(1):1–13. https://doi.org/10.1186/s12889-022-14000-3.
Haile D, Azage M, Mola T, Rainey R. Exploring spatial variations and factors associated with childhood stunting in Ethiopia: spatial and multilevel analysis. BMC Pediatr. 2016;16(1):1–14. https://doi.org/10.1186/s12887-016-0587-9.
Taleke BA, Gezle LD, Alamneh TS. Pooled prevalence of stunting and associated factors among children aged 6–59 months in Sub-Saharan Africa countries: a Bayesian multilevel approach. PLoS One. 2022;17(10):1–19. https://doi.org/10.1371/journal.pone.0275889.
Sarma H, Khan JR, Asaduzzaman M, Uddin F, Tarannum S, Hasan MM, et al. Factors influencing the prevalence of stunting among children aged below five years in Bangladesh. Food Nutr Bull. 2017;38(3):291–301.
UNESCO. International standard classification of education (ISCED) 2011. Vol. 5, Prospects. Succursale Centre-Ville, Canada: UNESCO; 1975.
ILO. International Standard Classification of Occupations: ISCO-08 [Internet]. Vol. I, Department of Statistics International Labour Office. Geneva; 2008. http://www.ilo.org/public/english/bureau/stat/isco/index.htm.
Glenn J, Laput J, Go2 GL. Disentangling the Effects of Maternal Employment on Child Stunting in the Philippines1 Disentangling the Effects of Maternal Employment on Child Stunting in the Philippines. Philipp J Dev. 2022;46(1).
Montgomery MR, Gragnolati M, Burke KA, Paredes E. Measuring living standards with proxy variables. Demography. 2000;37(2):155–74.
WHO/UNICEF. Progress on Sanitation and DrWorld Health Organization Unicef Joint Monitoring Programme for Water Supply and Sanitationinking Water: Update 2010. WHO Library. Geneva; 2010.
WHO\UNICEF. Progress on household drinking water, sanitation and hygiene 2000–2020: five years into the SDGs. Joint Water Supply, & Sanitation Monitoring Programme. Geneva; 2021. Available from: http://apps.who.int/bookorders.
Vyas S, Kumaranayake L. Constructing socio-economic status indices: How to use principal components analysis. Health Policy Plan. 2006;21(6):459–68.
Filmer D, Pritchett LH. Estimating wealth effects without expenditure data-or tears: an application to educational enrollments in states of India. Demography. 2001;38(1):115–32.
Krefis AC, Schwarz NG, Nkrumah B, Acquah S, Loag W, Sarpong N, et al. Principal component analysis of socioeconomic factors and their association with malaria in children from the Ashanti region. Ghana Malar J. 2010;9(1):1–7.
Wolfinger R, O’Connell M. Generalized linear mixed models: a pseudo-likelihood approach. J Stat Comput Simul. 1993;48(3–4):233–43.
Bolker BM, Brooks ME, Clark CJ, Geange SW, Poulsen JR, Stevens MHH, et al. Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol Evol. 2009;24(3):127–35.
Myung IJ. Tutorial on maximum likelihood estimation. J Math Psychol. 2003;47(1):90–100.
Boedeker P. Hierarchical linear modeling with maximum likelihood, restricted maximum likelihood, and fully Bayesian estimation. Pract Assessment Res Eval. 2017;22(2):1–10.
Corbeil RR, Searle SR. Restricted maximum likelihood (REML) estimation of variance components in the mixed model. Taylor Fr. 2016;18(1):31–8.
Peugh JL. A practical guide to multilevel modeling. J Sch Psychol. 2010;48(1):85–112. https://doi.org/10.1016/j.jsp.2009.09.002.
Held L, Bové DS. Applied statistical inference: likelihood and bayes. Berlin Heidelberg: Springer-Verlag; 2014.
Berger JO. J.O. Berger-1985-Statistical Decision Theory and Bayesian.pdf. Second. New York: Springer-Verlag; 1985.
Box GEP, Tiao GC. Bayesian Inference in Statistical Analysis. Bayesian Inference in Statistical Analysis. 1992.
Zitzmann S, Helm C, Hecht M. Prior specification for more stable bayesian estimation of multilevel latent variable models in small samples: a comparative investigation of two different approaches. Front Psychol. 2021;11(January):1–11.
Holtmann J, Koch T, Lochner K, Eid M. A comparison of ML, WLSMV, and Bayesian methods for multilevel structural equation models in small samples: a simulation study. Multivariate Behav Res. 2016;51(5):661–80.
Zondervan-Zwijnenburg M, Peeters M, Depaoli S, Van de Schoot R. Where do priors come from? applying guidelines to construct informative priors in small sample research. Res Hum Dev. 2017;14(4):305–20. https://doi.org/10.1080/15427609.2017.1370966.
Zellner A, Siow A. Posterior odds ratios for selected regression hypotheses. Trab Estad Y Investig Oper. 1980;31(1):585–603.
Hicks T, Rodríguez-Campos L, Choi JH. Bayesian posterior odds ratios: statistical tools for collaborative evaluations. Am J Eval. 2018;39(2):278–89.
Gelman A. Struggles with survey weighting and regression modeling. Stat Sci. 2007;22(2):153–64.
Gelman A, Carlin JB, Stern HS, Rubin DB. Bayesian data analysis: texts in statistical science. Second. Evolution. CRC Press; 1998. p 346.
Akombi BJ. Stunting, wasting and underweight in Sub-Saharan Africa: a systematic review. Int J Environ Res. 2017;14(8):1–18.
Demilew YM. Factors associated with mothers’ knowledge on infant and young child feeding recommendation in slum areas of Bahir Dar City, Ethiopia: cross sectional study. BMC Res Notes. 2017;10(1):1–7.
Joseph G, Haque SS, Moqueet N, Hoo YR. Children Need Clean Water to Grow: E. Coli Contamination of Drinking Water and Childhood Nutrition in Bangladesh. Children Need Clean Water to Grow: E. Coli Contamination of Drinking Water and Childhood Nutrition in Bangladesh. World Bank Group; 2019.
Ikeda N, Irie Y, Shibuya K. Determinants of reduced child stunting in Cambodia: analysis of pooled data from three demographic and health surveys. Bull World Health Organ. 2013;91(5):341–9.
Wamani H, Tylleskär T, Åstrøm AN, Tumwine JK, Peterson S. Mothers’ education but not fathers’ education, household assets or land ownership is the best predictor of child health inequalities in rural Uganda. Int J Equity Health. 2004;3(1):1–8.
Kavosi E, Rostami ZH, Kavosi Z, Nasihatkon A, Moghadami M, Heidari M. Prevalence and determinants of under-nutrition among children under six: a cross-sectional survey in Fars province. Iran Int J Heal Policy Manag. 2014;3(2):71–6.
Abeway S, Gebremichael B, Murugan R, Assefa M, Adinew YM. Stunting and its determinants among children aged 6—59 Months in Northern Ethiopia: a cross-sectional study. J Nutr Metab. 2018;2018.
Budhathoki SS, Bhandari A, Gurung R, Gurung A, Kc A. Stunting among under 5-year—olds in Nepal: trends and risk factors. Matern Child Health J. 2019. https://doi.org/10.1007/s10995-019-02817-1.
Mzumara B, Bwembya P, Halwiindi H, Mugode R, Banda J. Factors associated with stunting among children below five years of age in Zambia: evidence from the 2014 Zambia demographic and health survey. BMC Nutr. 2018;4(1):1–8.
Win H, Shafique S, Mizan S, Wallenborn J, Probst-Hensch N, Fink G. Association between mother’s work status and child stunting in urban slums: a cross-sectional assessment of 346 child-mother dyads in Dhaka, Bangladesh. Arch Public Heal. 2022;80(1):1–16. https://doi.org/10.1186/s13690-022-00948-6.
Amaha ND, Woldeamanuel BT. Maternal factors associated with moderate and severe stunting in Ethiopian children: analysis of some environmental factors based on 2016 demographic health survey. Nutr J. 2021;20(1):1–9.
Braveman PA, Cubbin C, Egerter S, Chideya S, Marchi KS, Metzler M, et al. Socioeconomic status in health research: one size does not fit all. JAMA. 2005;294(22):2879.
Krieger N, Williams DR, Moss NE. Measuring social class in us public health research: concepts, methodologies, and guidelines. Annu Rev Public Health. 1997;18(16):341–78.
Wang Y, Wang B. Multidimensional poverty measure and analysis: a case study from Hechi City, China. Springerplus. 2016;5(1).
Næss Ø, Claussen B, Thelle DS, Smith GD. Four indicators of socioeconomic position: relative ranking across causes of death. Scand J Public Health. 2005;33(3):215–21.
Martikainen P, Blomgren J, Valkonen T. Change in the total and independent effects of education and occupational social class on mortality: Analyses of all Finnish men and women in the period 1971–2000. J Epidemiol Commun Health. 2007;61(6):499–505.
Savitz DA, Wellenius GA. Can cross-sectional studies contribute to causal inference? It depends. Am J Epidemiol. 2022;00(00):1–3.
Rosenman R, Tennekoon V. Measuring bias in self-reported data. Int J Behav Healthc Res. 2011;2(4):320.
Althubaiti A. Information bias in health research: definition, pitfalls, and adjustment methods. J Multidiscip Healthc [Internet]. 2016 May;9:211. Available from: https://www.dovepress.com/information-bias-in-health-research-definition-pitfalls-and-adjustment-peer-reviewed-article-JMDH.
Add Comment