@article{10.11648/j.wjph.20251003.30,
  author = {Ombaka Ogolla and Richard Simwa and Cheruiyot Kipkoech},
  title = {Frailty Modeling of Child Stunting in Coast Province, Kenya: Analysis Using KDHS 2022 Data
},
  journal = {World Journal of Public Health},
  volume = {10},
  number = {3},
  pages = {389-397},
  doi = {10.11648/j.wjph.20251003.30},
  url = {https://doi.org/10.11648/j.wjph.20251003.30},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.wjph.20251003.30},
  abstract = {Child stunting reduction is the first of 6 goals in the Global Nutrition Targets for 2025 and a key indicator in the second Sustainable Development Goal of Zero Hunger. The prevalence of undernutrition is decreasing in many parts of the developing world, but challenges remain in many countries. For instance,the prevalence of stunting is 30.7% in Africa - higher than the global average of 22.0%. In Kenya, more than a quarter of children under the age of five, or two million children, have stunted growth. Stunting is the most frequent form of under-nutrition among young children. If not addressed, it has devastating long-term effects, including diminished mental and physical development.Child under-nutrition in Kenya has decreased in recent years. Levels of child stunting fell from 35.2% in 2009 to 26% in 2014 and wasting from 7% in 2009 to 4% in 2015. In Kenya, Coast Province has the highest stunting rate with (30.8%) and the lowest in Nairobi Province (17.2%). Despite this advancement, the world is still unlikely to achieve that goal in the global nutrition targets. Our study intends to investigate on crucial prognostic factors influencing child stunting in Coast, Kenya. The principal objective of this paper is to determine the effect of socioeconomic and demographic variables on child stunting in presence of dependencies in clusters and households. The study then uses variable selection technique which is an artificial intelligence techniques to select covariates with the highest predictive power from the robust KDHS 2022 data. Additionally, a proportional hazards assumption test was carried out for the chosen covariates. Those covariates that satisfied the proportionality assumption were finally included in the frailty model to takes care of the presence of dependencies within the households. Data used was based on the Kenya Demographic and Health Survey (KDHS 2022), which was collected by use of questionnaires. Child stunting from the, KDHS 2022 data, was analyzed in an age period: stunting from the age of 12 months to the age of 60 months, referred to as “child stunting”. from the age of 12 months to the age of 60 months, referred to as “child stunting”.
},
 year = {2025}
}

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TY  - JOUR
T1  - Frailty Modeling of Child Stunting in Coast Province, Kenya: Analysis Using KDHS 2022 Data

AU  - Ombaka Ogolla
AU  - Richard Simwa
AU  - Cheruiyot Kipkoech
Y1  - 2025/09/05
PY  - 2025
N1  - https://doi.org/10.11648/j.wjph.20251003.30
DO  - 10.11648/j.wjph.20251003.30
T2  - World Journal of Public Health
JF  - World Journal of Public Health
JO  - World Journal of Public Health
SP  - 389
EP  - 397
PB  - Science Publishing Group
SN  - 2637-6059
UR  - https://doi.org/10.11648/j.wjph.20251003.30
AB  - Child stunting reduction is the first of 6 goals in the Global Nutrition Targets for 2025 and a key indicator in the second Sustainable Development Goal of Zero Hunger. The prevalence of undernutrition is decreasing in many parts of the developing world, but challenges remain in many countries. For instance,the prevalence of stunting is 30.7% in Africa - higher than the global average of 22.0%. In Kenya, more than a quarter of children under the age of five, or two million children, have stunted growth. Stunting is the most frequent form of under-nutrition among young children. If not addressed, it has devastating long-term effects, including diminished mental and physical development.Child under-nutrition in Kenya has decreased in recent years. Levels of child stunting fell from 35.2% in 2009 to 26% in 2014 and wasting from 7% in 2009 to 4% in 2015. In Kenya, Coast Province has the highest stunting rate with (30.8%) and the lowest in Nairobi Province (17.2%). Despite this advancement, the world is still unlikely to achieve that goal in the global nutrition targets. Our study intends to investigate on crucial prognostic factors influencing child stunting in Coast, Kenya. The principal objective of this paper is to determine the effect of socioeconomic and demographic variables on child stunting in presence of dependencies in clusters and households. The study then uses variable selection technique which is an artificial intelligence techniques to select covariates with the highest predictive power from the robust KDHS 2022 data. Additionally, a proportional hazards assumption test was carried out for the chosen covariates. Those covariates that satisfied the proportionality assumption were finally included in the frailty model to takes care of the presence of dependencies within the households. Data used was based on the Kenya Demographic and Health Survey (KDHS 2022), which was collected by use of questionnaires. Child stunting from the, KDHS 2022 data, was analyzed in an age period: stunting from the age of 12 months to the age of 60 months, referred to as “child stunting”. from the age of 12 months to the age of 60 months, referred to as “child stunting”.

VL  - 10
IS  - 3
ER  - 

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