Estimating Geographically Weighted Negative Binomial Regression (Gwnbr) Model and Mapping Spatial Patterns on Anemia Data in Indonesia

Q: What does this research mean for the field?

The factors associated with anemia among women of childbearing age in Indonesia exhibit significant spatial heterogeneity, driven primarily by pneumonia, malaria, and hepatitis in central and eastern regions, and by rural residence, acute respiratory infections, and tuberculosis in western regions. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Muhammad Nur Aidi; Yesan Tiara

doi:10.5281/zenodo.22093884

August 25, 2026Open Access

Estimating Geographically Weighted Negative Binomial Regression (Gwnbr) Model and Mapping Spatial Patterns on Anemia Data in Indonesia

View Full Paper

Ask AI

Bookmark

Share

Key Points

To model spatial heterogeneity and overdispersion in anemia cases among women of childbearing age across 33 Indonesian provinces using Geographically Weighted Negative Binomial Regression.
Applied a Geographically Weighted Negative Binomial Regression (GWNBR) model using geographic coordinates (latitude and longitude) as spatial weights to evaluate count data with overdispersion.
Estimated local regression parameters for anemia rates and co-occurring health and demographic variables across 33 provinces in Indonesia.
At least one explanatory variable significantly influenced anemia cases per 100 women of childbearing age in every analyzed province.
Anemia rates in central and eastern regions were positively associated with increased rates of pneumonia, malaria, and hepatitis.
Anemia rates in western regions were positively associated with rural residency (notably in Java and Kalimantan), acute respiratory infections (Sumatra and Kalimantan), and tuberculosis diagnoses.

No abstract yet

We only have this paper's title and publication details.

Read it at the source

Implication

Spatial modeling reveals regional infectious and geographic drivers of anemia among women of childbearing age, highlighting localized risk patterns.

Discussion

No takes yet. Share an insight, caveat, or question.

Cite This Study

Aidi et al. (2026) studied this question.

synapsesocial.com/papers/6a8fe99e10c91c1e926215d9 https://doi.org/10.5281/zenodo.22093884

View Full Paper

Ask AI

Bookmark

Share

Estimating Geographically Weighted Negative Binomial Regression (Gwnbr) Model and Mapping Spatial Patterns on Anemia Data in Indonesia

Key Points

No abstract yet

Implication

Discussion

Cite This Study

Estimating Geographically Weighted Negative Binomial Regression (Gwnbr) Model and Mapping Spatial Patterns on Anemia Data in Indonesia

Key Points

No abstract yet

Implication

Discussion

Cite This Study

Keep reading

Keep reading