Factors Affecting Stunting in Children under 5 Years of Age in Indonesia using Spatial Model

Kesuma, Zurnila Marli; Siregar, Latifah Rahayu; Fradinata, Edy; Fathinah, Aliya

doi:10.4103/ijph.ijph_1989_21

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…The risk factors that have been identified are short maternal stature, large household size, close pregnancy intervals, hypertension, poverty, open defecation and extreme temperatures. India In addition, research was conducted by Kesuma et al, (2022) on children under 5 years in Indonesia shows that there is a spatial autocorrelation in the annual data used, so that the condition of stunting in Indonesia from 2015 to 2019 is interrelated between provinces. The condition of stunting in Indonesia still needs attention, even though in general the percentage of stunted sufferers is decreasing.…”

Section: Discussionmentioning

confidence: 99%

Stunting Cases In Muaro Jambi District 2019–2021: Spatial Autocorrelation

Siregar

Syukri

Asparian

et al. 2023

IJHET

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Stunting is a major threat to the quality of Indonesian society. Stunting or short stature is a condition in which children have less height compared to their age. The 2020 Asian Development Bank report, Indonesia is the second highest country in the Southeast Asia region with a stunting prevalence of 31.8%. Children with stunting conditions will be at risk of having a level of intelligence that is not optimal and becomes more susceptible to disease. The impact of stunting is not only experienced by children but can affect in the future to adulthood, this can result in decreased levels of productivity. This study aims to determine the spatial pattern of the distribution of stunting cases in the Muaro Jambi Regency in 2019 – 2021. The research method uses an ecological study design with a spatial approach. The sample in this study used aggregate data of 22 working areas of the Community Health Centers in Muaro Jambi Regency. There is a trend of increasing stunting cases in Muaro Jambi Regency in 2019-2021 with a distribution pattern that is in the form of clusters or clusters and globally shows that there is a positive autocorrelation with the 2 working areas of the Puskesmas, which is calledhot spot area as well as being a priority in tackling stunting cases.

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Section: Discussionmentioning

confidence: 99%

Stunting Cases In Muaro Jambi District 2019–2021: Spatial Autocorrelation

Siregar

Syukri

Asparian

et al. 2023

IJHET

View full text Add to dashboard Cite

show abstract

“…As a result, the number of cases and population at risk of DHF varies. Therefore, it is important to plan which Disease Mapping has gained prominence within the field of Spatial Epidemiology due to its increasing relevance and applications [7]- [9], . The main objective of Disease Mapping is to estimate the spatial pattern of disease risk in a unit area and to identify areas that have a high level of disease risk [10] [11].…”

Section: Introductionmentioning

confidence: 99%

Spatial Estimation of Relative Risk for Dengue Fever in Aceh Province using Conditional Autoregressive Method

Rahayu

2023

J. Appl. Data Sci.

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The study aims to comprehensively analyze the spatial distribution and varying risk levels of Dengue Hemorrhagic Fever (DHF) within Aceh Province. The primary objective is to identify and delineate regions within Aceh Province that demonstrate diverse probabilities of DHF occurrences. By investigating the discrepancies in DHF cases and population susceptibility across districts and cities, the research intends to facilitate strategic planning and targeted interventions for disease mitigation and control. Utilizing secondary data sourced from the Aceh Provincial Health Profile spanning 2016 to 2022, this study employs the Bayesian Conditional Autoregressive (CAR) prior model Besag-York-Mollie (BYM). This statistiThe research significantly contributes to enhancing our understanding of DHF distribution patterns and associated risks within Aceh Province. By employing a robust statistical model and analyzing secondary health profile data, the study offers valuable insights into identifying areas with varying levels of DHF risk. The findings are pivotal in guiding evidence-based decision-making for targeted interventions, resource allocation, and strategic planning aimed at mitigating the impact of DHF in high-risk regions. The study's outcomes highlight notable fluctuations in mortality due to dengue cases within Aceh Province, particularly evident in the peaks observed during 2016 and 2022. Furthermore, through the Bayesian CAR (BYM) model, the research identifies districts and cities with varying relative risk values for DHF occurrences. Notably, Sabang city emerges with the highest relative risk value of 3.54, signifying elevated susceptibility, while Bener Meriah district demonstrates the lowest relative risk at 0.2, indicating lower vulnerability to DHF. These findings provide critical insights into the heterogeneous DHF risk landscape across Aceh Province, informing targeted interventions and planning strategies to effectively address the disease burden.

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Factors Affecting Stunting in Children under 5 Years of Age in Indonesia using Spatial Model

Cited by 2 publications

References 2 publications

Stunting Cases In Muaro Jambi District 2019–2021: Spatial Autocorrelation

Stunting Cases In Muaro Jambi District 2019–2021: Spatial Autocorrelation

Spatial Estimation of Relative Risk for Dengue Fever in Aceh Province using Conditional Autoregressive Method

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