Who
Population: 514 districts in Indonesia
Data Source Population: ~345,000 households from 34,500 census blocks
Subgroups Identified:
Age ≥40 years (higher TB and DM prevalence)
Gender differences:
TB higher in males (0.38%) vs females (0.22%)
DM higher in females (2.85%) vs males (1.90%)
Higher-risk groups:
Urban residents
Lower education levels
Informal or non-working populations
Data Sources:
2023 Indonesian Health Survey (SKI)
Badan Pusat Statistik (BPS)
Ministry of Health databases
Ministry of Home Affairs data
What
Study Focus
Mapping spatial distribution of:
Tuberculosis (TB)
Diabetes Mellitus (DM)
Identifying:
High-risk districts
Socio-demographic risk factors
Co-occurring TB-DM burden
Key Findings
National prevalence
TB: 0.30%
DM: 2.37%
Age ≥40 years
TB: 0.42%
DM: 4.37%
Socio-demographic Associations
TB
Poverty positively associated
β = 0.015 (95% CrI: 0.005–0.024)
Population density: positive but not significant
DM
Population density positively associated
β = 0.059 (95% CrI: 0.039–0.080)
Poverty negatively associated
β = −0.007 (95% CrI: −0.013 to −0.001)
Geographic Patterns
High TB prevalence
Papua (highest)
West Java
Banten
High DM prevalence
Central Java
East Java
Riau
Sumatra regions
High TB-DM overlap (62 districts >50% probability)
West Java
Banten
Aceh
East Kalimantan
Central Kalimantan
North Sulawesi
Authors' Conclusions
TB and DM co-burden shows distinct but overlapping spatial patterns
High-risk areas often:
Urban
High population density
Low-income settings
Policy Implications
Targeted geographic interventions recommended:
Integrated TB-DM screening
Strengthening primary care
Resource prioritization for high-burden districts
Improved referral pathways
Community health worker engagement
When
Data year: 2023 Indonesian Health Survey (SKI)
Study type: Cross-sectional spatial analysis
Follow-up: Not applicable (ecological cross-sectional)
Where
Country: Indonesia
Geographic unit: 514 districts
Settings:
National survey data
District-level socio-demographic indicators
Spatial modelling using district shapefiles
Why
TB and diabetes increasingly co-occur
Limited understanding of:
Spatial overlap
Shared risk factors
Geographic clustering
Objective:
Identify districts with dual burden
Inform targeted policy and healthcare planning
How
Study Design
Ecological spatial study
Cross-sectional national survey data
Level of Evidence:
Observational ecological modelling study
Methods
Bayesian Model-Based Geostatistics (MBG)
Binomial logistic regression
Fixed + random effects (BYM2 model)
Model Evaluation
Deviance Information Criterion (DIC)
WAIC
RMSE
Probability Integral Transform (PIT)
Fivefold cross-validation
Spatial Analysis
Quintile classification
Joint exceedance probability mapping
Residual spatial random effect mapping
Covariates Included
Population density
Poverty proportion
Hospital service ratio
Primary healthcare availability
Limitations (Implied / Reported)
Ecological design (district-level aggregation)
Non-public dataset access
Potential under-diagnosis in remote areas
Cross-sectional data limits causal inference
Strength of Evidence
Moderate (Ecological spatial modelling study)
Strong national dataset
No causal inference possible
Narrative Summary
This ecological spatial study analyzed data from 514 districts in Indonesia using the 2023 Indonesian Health Survey to examine the geographic distribution and co-occurrence of tuberculosis (0.30%) and diabetes mellitus (2.37%). Using Bayesian geostatistical modelling, the study identified significant associations between TB and poverty, and between DM and population density, with overlapping high-burden districts concentrated in West Java, Banten, Aceh, Kalimantan, and North Sulawesi. Approximately 62 districts showed high joint probability of TB-DM co-occurrence. Urban residence, lower education, and informal employment were associated with higher prevalence of both diseases. The findings highlight geographic clustering and emphasize the need for integrated TB-DM screening, targeted resource allocation, and strengthened primary healthcare in high-burden districts within Indonesia's decentralized health system.
Source: Dwinata I, Tsheten T, Ansariadi A, Wagnew F, Alene KA, Sutarsa IN, Moraga P, Putra IW, Kelly M. Geospatial codistribution of tuberculosis and diabetes mellitus in Indonesia. Infectious Diseases of Poverty. 2026 Mar 30;15(1):37.
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