# Geospatial Codistribution of Tuberculosis and Diabetes Mellitus in Indonesia

Indra Dwinata, Tsheten Tsheten, Ansariadi Ansariadi, Fasil Wagnew, Kefyalew Addis Alene, I Nyoman Sutarsa, Paula Moraga, I Wayan Gede Artawan Eka Putra, Matthew Kelly

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# Geospatial Codistribution of Tuberculosis and Diabetes Mellitus in Indonesia

Indra Dwinata

indra@unhas.ac.id

Hasanuddin University: Universitas Hasanuddin https://orcid.org/0000-0001-9279-7755

Tsheten Tsheten

NCEPH: National Centre for Epidemiology and Population Health

Ansariadi Ansariadi

Hasanuddin University: Universitas Hasanuddin

Fasil Wagnew

NCEPH: National Centre for Epidemiology and Population Health

Kefyalew Addis Alene

Curtin University School of Population Health

I Nyoman Sutarsa

ANU College of Medicine and Health Sciences: Australian National University College of Health and Medicine

Paula Moraga

KAUST: King Abdullah University of Science and Technology

I Wayan Gede Artawan Eka Putra

Udayana University: Universitas Udayana

Matthew Kelly

NCEPH: National Centre for Epidemiology and Population Health

## Research Article

**Keywords:** Tuberculosis, Diabetes Mellitus, Geospatial analysis, Indonesia

**Posted Date:** August 21st, 2025

**DOI:** https://doi.org/10.21203/rs.3.rs-7296473/v1

**License:** This work is licensed under a Creative Commons Attribution 4.0 International License.

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# Geospatial Codistribution of Tuberculosis and Diabetes Mellitus in Indonesia

Indra Dwinata<sup>a</sup>, Tsheten Tsheten<sup>b</sup>, Ansariadi Ansariadi<sup>a</sup>, Fasil Wagnew<sup>b,c,d</sup>, Kefyalew Addis Alene<sup>d,e</sup>, I Nyoman Sutarsa<sup>f,g</sup>, Paula Moraga<sup>h</sup>, I Wayan Gede Artawan Eka Putra<sup>g</sup>, Matthew Kelly<sup>b</sup>

<sup>a</sup> Department of Epidemiology, Faculty of Public Health, Hasanuddin University, Indonesia

<sup>b</sup> National Centre for Epidemiology and Population Health (NCEPH), College of Law, Governance and Policy, Australian National University, Canberra, Australia

<sup>c</sup> College of Health Sciences, Debre Markos University, Debre Markos P.O. Box 269, Ethiopia

<sup>d</sup> Geospatial and Tuberculosis Research Team, Telethon Kids Institute, Nedlands, Western Australia, Australia

<sup>e</sup> School of Population Health, Faculty of Health Sciences, Curtin University, Bentley, Western Australia, Australia

<sup>f</sup> School of Medicine and Psychology, College of Science and Medicine, Australian National University

<sup>g</sup> School of Public Health, Faculty of Medicine, Udayana University

<sup>h</sup> Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia

**Funding** : None

**Corresponding Author**

Indra Dwinata
Department of Epidemiology, Faculty of Public Health, Hasanuddin University
Jln Perintis Kemerdekaan km.10 Makassar, South Sulawesi, 90242 Indonesia
indra@unhas.ac.id
**Abstract**

**Background** Tuberculosis (TB) and diabetes mellitus (DM) co-morbidity is a growing public health challenge, particularly in Indonesia, where TB incidence remains high and DM prevalence is increasing. DM co-morbidity is known to increase the risk of TB incidence and have negative effects on TB treatment outcomes. This study aims to analyze the geographical co-distribution of TB and DM and their sociodemographic determinants in Indonesia, to help inform public health response and targeting of screening programs.

**Methods** Using data from the 2023 Indonesian Health Survey (SKI), we applied a Bayesian geostatistical model to estimate disease prevalence and assess associations with key sociodemographic factors.

**Results** The proportion of the poor population is significantly associated with higher TB prevalence (0.106; 95% CrI: 0.039, 0.174), while population density has a strong positive correlation with DM prevalence (0.198; 95% CrI: 0.156, 0.241). Proportion of the poor population (-0.053; 95% CrI: -0.096, -0.009) and hospital services (-0.071; 95% CrI: -0.116, -0.027) show a negative association with DM prevalence.

**Conclusion** Spatial analysis revealed significant regional variations, with high TB-DM co-distribution observed in rapidly urbanizing and high-poverty districts, including parts of West Java, East Java, Sumatra, and Kalimantan. These findings emphasize the need for strengthened TB-DM integration in healthcare services, especially in areas that have a high prevalence of both diseases. Strengthening integrated disease management strategies in local areas can help mitigate the burden of TB and DM in Indonesia.

**Keywords** : Tuberculosis, Diabetes Mellitus, Geospatial analysis, Indonesia
**Background**

The co-occurrence of tuberculosis (TB) and Diabetes mellitus (DM) has become a major public health concern worldwide, impacting numerous countries. The incidence of TB is 2-5 times higher for those with diabetes compared with non-diabetics.¹ DM negatively impacts TB treatment outcomes, contributing to the progression of TB disease. Treatment outcomes are significantly worse for those with DM, and mortality risk is up to 6 times higher. ²⁻⁴ The global prevalence of TB patients experiencing DM comorbidity has been estimated at 13.73%.⁴

Two-thirds of the total TB cases globally come from 8 countries, and Indonesia has the world's second-largest TB burden.⁵ The incidence rate of TB in Indonesia was estimated at 354 per 100,000 population and the mortality rate at 52 per 100,000 population in 2022.⁵ Indonesia has also experienced significant demographic and epidemiological transitions, resulting in a rising prevalence of DM.⁶ The age-adjusted prevalence of DM has increased from 5% to nearly 10% among adults during the past decade in Indonesia.⁷ Significantly, almost fifty per cent of diabetics in Indonesia remain undiagnosed or are not having their condition managed.⁸ In low- and middle-income countries, such as Indonesia, where TB incidence is elevated and DM prevalence is rising,⁹ the comorbidity of TB and DM may hinder the attainment of global End TB goals to reduce TB incidence by 90% and TB mortality by 95% from 2015 to 2035.¹⁰ In Indonesia, DM is the third largest cause of TB incidence, following smoking and undernutrition.¹¹

Following the WHO Collaborative Framework for Care and Control of TB and DM, the Indonesian Ministry of Health is implementing bi-directional TB-DM screening programs to improve case detection and management.¹² At present this program is being introduced in selected districts across Indonesia and is concentrating first on screening diabetic patients at primary healthcare centres for tuberculosis. ¹³ However, the completeness of this screening program has not yet been assessed. To optimise the targeting and implementation of these programs, understanding spatial disease distribution can play a critical role, particularly in a geographically diverse country like Indonesia.¹⁴,¹⁵ The geographical distribution of TB and DM can vary significantly across regions due to shared factors, including socio-economic conditions, environmental influences, and access to healthcare.¹⁶,¹⁷ Traditional epidemiological approaches often overlook these geographic patterns, limiting the ability to identify high-risk areas and allocate resources efficiently.¹⁷,¹⁸ By employing spatial analysis, we can map disease prevalence, detect clusters of higher disease burden, and identify spatial disparities that could inform targeted interventions.¹⁹⁻²¹ However, the use of these methods for TB disease in Indonesia is so far restricted to the island of Java and small area, and spatial assessments of TB-DM co-morbidity have not yet been conducted.²²⁻²⁴ While individual-level co-morbidity studies exist, limited research examines their spatial co-distribution across lower administrative levels in Indonesia using nationally representative data. Identifying high-risk areas for TB-DM co-morbidity is crucial for cost-effective public health interventions. This study aims to examine the spatial co-distribution of TB-DM and socio-demographic factors in Indonesia and to identify potential co-morbidity hot spots using spatial analysis and epidemiological modelling.

**Materials and Methods**

**Study Setting**

Indonesia is the world's largest archipelagic nation, consisting of more than 17,000 islands, with five main islands: Java, Sumatra, Kalimantan, Sulawesi, and Papua. The country spans a vast tropical region with varied climatic and topographical conditions, influencing population distribution and disease patterns.²⁵ Indonesia has a population of approximately 277 million people, making it the fourth most populous country globally.²⁶ The population is unevenly distributed, with Java hosting around 56% of the total population despite constituting only 7% of the country's land area.²⁷ Indonesia's government structure is decentralized, with a hierarchical administrative system that consists of four main levels: province, district, sub-district, and village. Indonesia's healthcare system comprises a mix of public and private providers, with the government running a national health insurance scheme known as Jaminan Kesehatan Nasional (JKN). Indonesia's healthcare system is categorized into three service levels, which are Primary Healthcare centers (Puskesmas, clinic or family doctors), Secondary referral healthcare facilities (general hospitals), and Tertiary healthcare facilities (advanced hospitals with highly specialised services).²⁷ Services for the diagnosis, treatment and bidirectional Screening of TB -DM are available at all levels.²⁸

**Study design**

An ecological study was conducted to map and predict the spatial distribution of TB and DM prevalence, estimate the relationship between socio-demographic factors and TB and DM prevalence, and identify districts with high prevalence of both TB and DM in Indonesia. The unit of analysis was the 514 districts in Indonesia.

**Data Sources**

Data on TB and DM cases was obtained from the aggregate data of the Indonesian Health Survey (SKI) 2023. SKI is a national survey with a cross-sectional design which was used to generate prevalence estimates of TB and
DM at the district and National levels. Using probability proportional to size (PPS) sampling, the sample size was estimated at 345,000 households in 34,500 census blocks.²⁹ Data for the prevalence of TB and DM were weighted using sampling weight to ensure an equal probability of selection in the SKI 2023 data. A detailed explanation of the sampling techniques for the SKI can be found in another source.³⁰

Data on covariates related to TB and DM at the district level, including population density, poverty, smoking ratio, hospital and health center service ratio, were obtained from the Badan Pusat Statistik Indonesia (BPS)²⁶, Ministry of Home Affairs.³¹ and Ministry of Health E-database.³²

## Variables and operational definitions

### Outcome

Our primary outcome measures were the prevalence of TB and DM. We also generated estimates of the geographical overlap of TB and DM based on the predicted prevalence of TB and DM.

### Covariates

The covariates used in this study have been identified to be associated with either TB, DM or both in other studies. These included population density, poverty prevalence, smoking ratio, and hospital and health center service availability ratio.

### Operational Definition

<table><caption>Table 1. Variables and their operational definition as used in the study</caption><thead><tr><td>Variable</td><td>Definition</td><td>Data source</td></tr></thead><tbody><tr><td>TB Cases</td><td>Respondents who had been diagnosed with TB, in the year preceding the survey, by health workers through sputum examination, chest X-ray or both at all ages ≥ 1 year.</td><td>Indonesian Health Survey (SKI) 2023</td></tr><tr><td>DM Cases</td><td>Respondents diagnosed with DM by health workers in the population age group ≥18 years.</td><td>Indonesian Health Survey (SKI) 2023</td></tr><tr><td>Population density</td><td>Total population divided by the size of the area.</td><td>E-database Ministry of Home Affairs Indonesia</td></tr><tr><td>Poor Population</td><td>The number of people below the poverty line divided by the total population at the same period expressed in per cent.</td><td>Statistics Indonesia (BPS)</td></tr><tr><td>Percentage of smoker</td><td>Percentage of Population Aged 5 Years and Over Who Smoked Tobacco within the Last Month</td><td>Statistics Indonesia (BPS)</td></tr><tr><td>Ratio of Hospital</td><td>The ratio of the population to the number of hospitals in each district</td><td>E-database Ministry of Health</td></tr><tr><td>Ratio of Primary Health Care (PHC)</td><td>The ratio of the population to the number of PHCs in each district</td><td>E-database Ministry of Health</td></tr></tbody></table>

## Data Analysis

### Descriptive analysis

This study utilizes data from the 2023 Indonesian Health Survey (Survey Kesehatan Indonesia, SKI), The detailed methodology of the SKI is published elsewhere.³⁰ Prevalence of TB was calculated for each district among 877,531 survey participants in all age groups. For DM prevalence, the population analyzed was those aged above 18 years or 651,159. Prevalence was generated by considering weighting based on region according to SKI 2023 guidelines.

### Geospatial model

We employed Bayesian model-based geostatistics (MBG) to investigate the spatial risk and identify socio-demographic and health system risk factors of TB and DM in Indonesia. Predicted prevalence models were constructed using binomial logistic regression models, by incorporating fixed and random effects. Correlation between variables was assessed using Pearson's method (supplementary file S1).
We developed six different models for each disease (DM and TB) (Table 2), with details presented as a supplementary file (S2). Below, we demonstrate the model development process for DM. Similar approaches were followed for TB.

Let $Y_j$ denote the number of DM cases and $N_j$ The number of people screened at location j for $j = 1, \dots, 514$.

Let $p_i$ denote the modelled prevalence at location j. Then $Y_{ij}$ is modelled using a binomial distribution as:

$$Y_j \sim \text{Binomial}(N_i, p_j)$$

Using logit link function (which transforms the probabilities in [0, 1] interval to real values in $(-\infty, +\infty)$), the association between predicted prevalence and linear combination of predictors is expressed as:

$$\text{logit}(p_j) = \log\left(\frac{p_j}{1-p_j}\right) = \alpha + \sum_{k=1}^{3} \beta_k X_{jk} + \emptyset_j + u_j$$

Where:

* $\alpha$ is an intercept.

* $\sum_{k=1}^{3} \beta_k X_{jk}$ is a linear combination of predictors, where:

* $\beta_1$ (effect of population density).

* $\beta_2$ (effect of poverty).

* $\beta_3$ (effect of Hospital service availability).

* $\beta_4$ (effect of PHC service availability).

* $\emptyset_j$ is a spatial structured random effect, representing the spatial dependency.

* $u_j$ the spatially unstructured random effects capturing non-spatial variability.

As shown above, to account for the spatial autocorrelation at the district level, we used conditional autoregressive (CAR) prior distributions $\emptyset_j^{33,34}$. We implemented Besag-York-Mollié (BYM) model, which accounts for both structured and unstructured random effects.³⁵ Spatial relationships between districts were structured using an adjacency matrix $W$, with elements $w_{ij}$ composed of zeros and ones, where $w_{ij} = 1$ if and only if districts $i$ and $j$ are neighbors ($i \sim j$), 0 otherwise.

$$w_{ij} = \begin{cases} 1, & \text{if districts } i \text{ and } j \text{ are neighbors} \\ 0, & \text{otherwise} \end{cases}$$

Then, the CAR model is represented by the following conditional distribution:

$$\emptyset_j | \emptyset_{-j} \sim N \left( \sum_{i \sim j} \frac{\emptyset_i}{w_{j+}}, \frac{\sigma^2}{w_{j+}} \right) \\ u_j \sim N(0, \tau^2)$$

Where, $w_{j+}$ represents the “number of neighbors” for district $j$, $i \sim j$ represents the neighbouring districts adjacent to $j$, and $\sigma^2$ is the spatial variation (variance of the spatial random effect), $\tau^2$ is the variance of the unstructured random effect that captures area-specific variability that is independent of the spatial structure (unexplained heterogeneity that doesn't follow any geographic pattern).

We used the integrated nested Laplace approximation (INLA) method to approximate Bayesian inference.³⁶ For prior specification, we selected default vague priors for both parameters and hyperparameters implemented in R-INLA package. The posterior mean and 95% credible intervals (CRIs) were estimated for all the parameters. Along with spatial predicted prevalence maps, we also calculated exceedance probabilities for both TB and DM using thresholds that would be useful for policymaking. In both cases, the national prevalence was used as a threshold for each disease. Furthermore, we created overlapping maps for DM and TB to explore spatial patterns. Districts that have prevalence values in quintiles 4 and 5 were categorised as high prevalence, while those in quintiles 1 and 2 were categorised as low prevalence. This approach allows for the detection of regions where the prevalence of two or more diseases reaches its highest point simultaneously. Deviance information criteria (DIC) was used for investigating the goodness-of-fit and selecting the most parsimonious model supplementary file (S3).³⁷

Finally, we calculated quintiles of both DM and TB prevalence across all districts, and classified districts as 1) high prevalence for both conditions (quintile 1 for both) 2) low prevalence TB (below 1st quintile), high DM (quintile 1) prevalence 3) high prevalence TB (quintile 1), low prevalence DM (below 1st quintile) and 4) low prevalence for both conditions (below 1st quintile).

## Results

### Prevalence of TB and DM by sociodemographic characteristics

TB prevalence stands at 0.30%, while DM was considerably higher at 2.37%. Prevalence of both diseases increased with age, with the highest rates observed among those aged 40 years and above - TB at 0.42% and DM at 4.37%. Gender differences were evident, with males having a higher TB prevalence (0.38%) compared to women (0.22%), while women showed a greater burden of DM (2.85%) than men (1.90%).

Urban residence, lower education levels, and informal employment were consistently associated with higher prevalence for both TB and DM. TB prevalence was slightly higher in urban areas (0.32%) compared to rural
areas (0.27%), with individuals having lower education (0.33%) and informal employment (0.40%) being affected more. A similar pattern was seen for DM, with urban residents (2.90%), less educated individuals (2.62%), and non-working individuals (2.90%) showing the highest prevalence. All these differences were statistically significant (Table 2).

<table><caption>Table 2. Weighted prevalence of TB and DM by sociodemographic characteristics</caption><thead><tr><th>Variable</th><th>Total population (N)</th><th>Weighted prevalence of TB (%)</th><th>p-value</th><th>Total population (N)</th><th>Weighted prevalence of DM (%)</th><th>p-value</th></tr></thead><tbody><tr><td>Total</td><td>877,531</td><td>0.30</td><td></td><td>651,159</td><td>2.37</td><td></td></tr><tr><td>Age groups (year)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>&lt; 18</td><td>274,549</td><td>0.22</td><td>&lt;0.001</td><td>-</td><td></td><td>&lt;0.001</td></tr><tr><td>18-29</td><td>112,273</td><td>0.22</td><td></td><td>112,273</td><td>0.09</td><td></td></tr><tr><td>30-39</td><td>136,717</td><td>0.27</td><td></td><td>136,717</td><td>0.46</td><td></td></tr><tr><td>40+</td><td>353,992</td><td>0.42</td><td></td><td>353,992</td><td>4.37</td><td></td></tr><tr><td>Gender</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Male</td><td>414,262</td><td>0.38</td><td>&lt;0.001</td><td>273,590</td><td>1.90</td><td>&lt;0.001</td></tr><tr><td>Female</td><td>463,269</td><td>0.22</td><td></td><td>3S29,392</td><td>2.85</td><td></td></tr><tr><td>Place of residence</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Urban</td><td>469,549</td><td>0.32</td><td>0.024</td><td>323,091</td><td>2.90</td><td>&lt;0.001</td></tr><tr><td>Rural</td><td>407,982</td><td>0.27</td><td></td><td>279,891</td><td>1.63</td><td></td></tr><tr><td>Education</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low (≤junior high school)</td><td>521,375</td><td>0.33</td><td>&lt;0.001</td><td>334,564</td><td>2.62</td><td>&lt;0.001</td></tr><tr><td>High (≥senior high school)</td><td>269,810</td><td>0.23</td><td></td><td>268,418</td><td>2.07</td><td></td></tr><tr><td>Occupation</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Not work/school</td><td>311,243</td><td>0.30</td><td>&lt;0.001</td><td>204,554</td><td>2.90</td><td>&lt;0.001</td></tr><tr><td>Formal Employment</td><td>171,576</td><td>0.28</td><td></td><td>170,960</td><td>2.26</td><td></td></tr><tr><td>Informal Employment</td><td>175,723</td><td>0.40</td><td></td><td>175,113</td><td>1.62</td><td></td></tr></tbody></table>

## Spatial association between TB, DM and covariates

From the comparison of logistic regression models for explaining spatial heterogeneity, we found the full BYM2 model with covariates (model 6) had the lowest DIC values for both DM and TB and was used for the interpretation of the findings (supplementary file (S3)). The results in Table 3 illustrate that the proportion of the poor population (0.106; 95% CrI: 0.039, 0.174) is significantly and positively associated with TB. Population density (0.069; 95% CrI: -0.021, 0.159) shows a positive association with TB, although the effect is not statistically significant, the same is true for PHC and hospital health services. For DM population density (0.198; 95% CrI: 0.156, 0.241) has a significant positive association with DM. The proportion of the poor population (-0.053; 95% CrI: -0.096, -0.009) shows a significant negative association with DM and also hospital services -0.071 (-0.116, -0.027).

Spatial Random Effect for TB (0.150; 95% CrI: 0.042, 0.329) exhibits significant spatial variation, highlighting geographical heterogeneity in TB risk, while DM (0.029; 95% CrI: 0.001, 0.128) also shows spatial variability, albeit weaker. Both TB (2.505; 95% CrI: 2.030, 3.060) and DM (5.008; 95% CrI: 4.214, 5.905) exhibit substantial non-spatial variability, indicating influences from unobserved factors not related to spatial location.
<table><caption>Table 3. Regression coefficients for random and fixed effects of TB and DM.</caption><thead><tr><th rowspan="2">Variable</th><th>TB</th><th>DM</th></tr><tr><th>Coeff, Posterior mean (95% CrI)</th><th>Coeff, Posterior mean (95% CrI)</th></tr></thead><tbody><tr><td>Fixed Effect</td><td></td><td></td></tr><tr><td>Intercept</td><td>-6.032 (-6.102,-5.963)</td><td>-3.817 (-3.861,-3.774)</td></tr><tr><td>Population Density</td><td>0.069 (-0.021,0.159)</td><td>0.198 (0.156,0.241)</td></tr><tr><td>Poor Population</td><td>0.106 (0.039,0.174)</td><td>-0.053 (-0.096,-0.009)</td></tr><tr><td>Hospital Services</td><td>0.040 (-0.029,0.110)</td><td>-0.071 (-0.116,-0.027)</td></tr><tr><td>PHC Services</td><td>-0.088 (-0.178,0.001)</td><td>-</td></tr><tr><td>Random Effect</td><td></td><td></td></tr><tr><td>Spatial Random Effect</td><td>0.150 (0.042, 0.329)</td><td>0.029 (0.001,0.128)</td></tr><tr><td>Non-Spatial Random Effect</td><td>2.505 (2.030,3.060)</td><td>5.008 (4.214,5.905)</td></tr></tbody></table>

## Geospatial distribution of TB and DM prevalence

Figure 1 (a) shows the spatial distribution of predicted TB prevalence across districts in Indonesia. The TB prevalence varies between 0.1% and 3.0%. Eastern Indonesia, particularly Papua, exhibits the highest TB prevalence. Several districts in West Java and Banten Provinces also had a high prevalence of more than 0.39%. In contrast, many parts of western Indonesia, such as Sumatra, East Java and Bali, showed lower TB prevalence. Figure 1(b) illustrates the probability that TB prevalence exceeds the national average of 0.3% in Indonesia. This map aligns closely with the predicted prevalence map, showing higher probabilities in regions where higher predicted prevalence was observed.
<figure><img src="image_2.png" /><figcaption>Figure 1. Predicted prevalence map of TB (a) and the probability that the prevalence is higher than 0.025 (b)</figcaption></figure>

The map of the structured random effect for TB showed significant spatial clustering with higher values in eastern Indonesia (Papua Island) and parts of Java, Sumatra and Kalimantan (Figure 1 (a)). The map of the unstructured random effect (b) showed wide heterogeneity across the districts with higher values observed in central and eastern Indonesia. These spatial variations are independent of location and are more reflective of region-specific random noise or unexplained factors that do not follow a spatial pattern (Figure 2 (b)).
<figure><img src="image_3.png" /><figcaption>**Figure 2.** Spatial distribution of posterior means of structured (a) and unstructured random effects (b) of TB</figcaption></figure>

Figure 3 (a) shows the spatial distribution of predicted prevalence of DM across districts in Indonesia. The prevalence ranged from 0.7% to 6.3% with higher values predicted in West Indonesia in parts of Java and Sumatra (Central Java, East Java and Riau). The spatial distribution of DM prevalence is more heterogeneous than that of TB, reflecting a different pattern of disease risk. Likewise, the exceedance probability map showed higher probabilities in regions with a higher prevalence of DM (Figure 3 (b)).
<figure><img src="image_4.png" /><figcaption>Figure 3. Predicted prevalence map of DM (a) and probability that the prevalence is higher than 0.025</figcaption></figure>

The map of the structured random effects for DM showed notable spatial clustering across Indonesia. Darker regions indicating higher prevalence were observed in Java Island (East Java, DI Yogyakarta, DKI Jakarta), central Indonesia (Kalimantan and Sulawesi) and certain areas in Sumatra. (Figure 4 (a)). The map of the unstructured random effects showed considerable spatial heterogeneity across districts. Unlike the structured map, this map revealed random distribution of DM with higher prevalence noted mostly in parts of central and eastern Indonesia (Figure 4 (b)).
<figure><img src="image_5.png" /><figcaption>**Figure 4.** Spatial distribution of posterior means of structured (a) and unstructured random effects (b) of DM</figcaption></figure>

Spatial overlap of TB and DM

The map in **Figure 5** illustrates the spatial overlap of TB and DM based on prevalence quintiles. Regions marked in red represent areas where both TB and DM prevalence are in the top quintile, indicating significant public health burdens. A few districts exhibited a geographical overlap in the expected TB and DM prevalence, with the most significant clustering in West Java and Banten province, some areas in Aceh in the north of Sumatra Island, districts in Kalimantan Island (east Kalimantan and Center of Kalimantan), and some parts in the north of Sulawesi also had overlapping high TB and high DM. Regions with high TB and low DM are also of importance, the low prevalence of DM may be due to limited case-finding rates (DM screening), especially in Eastern Indonesia and rural areas.
<figure><img src="image_6.png" /><figcaption>Figure 5. Spatial overlapping map of predicted TB and DM</figcaption></figure>

Discussion

This study highlights the significant spatial variation in both TB, DM and both diseases across Indonesia’s 514 districts. Noteworthy is the high levels of TB-DM co-distribution identified in urban, but high poverty, districts particularly in the Western part of Java. These results indicate the need for strengthening integrated health services for TB-DM co-morbidity in these areas and the prioritisation of bi-directional screening programs.

Spatial Distribution of TB in Indonesia

This study showed spatial variation in TB prevalence among 514 districts in Indonesia. The geographical distribution of cases presented trends and proportions congruent with prior research and studies.15,38 This suggests that there are disparities related to health facilities, socioeconomic and environmental aspects in different regions. We found TB prevalence was concentrated in the Java region, including districts located in West Java, Banten and DKI Jakarta, as well as districts on Papua Island.

In addition to this, our results also show that the prevalence of TB in Indonesia in 2023 is higher in urban areas. One possible explanation is the higher housing density in cities than in villages and the contribution of urban slums, increasing transmission risk. Another interesting result of our study is that the Eastern Indonesian region of Papua has a high risk of TB despite the low population density in the region, which may be due to the high co-infection of TB with HIV in the region.39 In addition, high rates of malnutrition and stunting in Papua, contribute to a weakened immune system, making individuals more susceptible to TB.40,41 Papua is one of the most impoverished regions in Indonesia and access to healthcare is limited as many areas of Papua are geographically isolated.42

Spatial Distribution of DM in Indonesia

The study also identified significant spatial variations in the relationship between socio-demographic variables and DM. Extreme diversity was observed in the prevalence of DM based on self-report. These results show that there is a significant clustering and random distribution in the prevalence of DM in various regions, especially in Java Island, including East Java, DI Yogyakarta, and DKI Jakarta. This distribution pattern presents proportions that are in accordance with previous research.43 The spatial pattern of DM prevalence is concentrated in high-population and urban areas. This may be due to higher economic development in urban areas which is often correlated with an increase in the burden of DM due to lifestyle changes. This is also in line with spatial research on DM globally, including in India, which found that there is a grouping of DM cases in the Southern part of India, which is a rapidly growing urban area.44 While in Brazil the spatial pattern of DM individual socioeconomic characteristics and environment is influenced by individual socioeconomic characteristics and environment.45 Importantly, urban areas have easier access to health services so they may report a higher prevalence of DM due to a higher likelihood of being screened and diagnosed.

Many provinces in the eastern part of the country, such as Papua, West Papua and Maluku report lower prevalence rates of DM in this study. However, this lower prevalence may not necessarily reflect a truly lower burden of disease, but Healthcare facilities in Eastern Indonesia are often more limited in terms of resources,
workforce, and diagnostic equipment.⁴⁶ Primary healthcare centers (Puskesmas) in these regions may lack routine
screening programs for DM, leading to underdiagnosis and underreporting.

Spatial Overlap TB and DM in Indonesia

The findings of this study highlight significant spatial heterogeneity in the prevalence of TB and DM across districts in Indonesia. Hotspots of TB and DM were identified in regions characterized by high population density and elevated urban poverty rates for instance districts in west Java, East Java, Aceh and several areas on the islands of Kalimantan, and Sulawesi, in congruence with similar studies identifying social determinants and local health system capacity as critical factors in the spatial distribution of these diseases.⁴⁷⁻⁴⁹ These spatial disparities suggest that socio-demographic, socio-economic status, and healthcare-related factors, play a critical role in shaping the geographical distribution of TB and DM. This observation aligns with global trends where socioeconomically disadvantaged populations often experience a disproportionate burden of TB and DM, exacerbating health inequities.

In the Indonesian context, these findings are particularly relevant given the country's dual burden of communicable and non-communicable diseases. Indonesia is the second-highest TB-burdened country globally. Meanwhile, the prevalence of DM among adults has nearly doubled over the past decade, and projections indicate a continued rise in DM prevalence in Indonesia, increasing the challenge for TB management.⁴⁸ The co-morbidity of TB and DM presents unique challenges to public health efforts, as DM increases the risk of developing active TB by two- to three-fold and worsens TB treatment outcomes. Additionally, the spatial clustering observed in this study mirrors findings from previous studies in Java, Indonesia, which linked high TB prevalence to factors such as poverty, poor and limited healthcare access.²² This suggests that existing health inequities are driving the overlapping burden of TB and DM in specific districts, underscoring the need for localized interventions tailored to these vulnerable populations, for example, implementing Community-Based Integrated TB-DM Screening in High-Burden Urban Slums areas and Strengthening TB-DM Services in Remote Eastern Indonesia. These findings contribute to the growing body of evidence advocating for integrated and targeted approaches to TB and DM management in Indonesia. Policies addressing the shared risk factors for both diseases—such as improving access to healthcare services, reducing poverty, and promoting healthy lifestyles—are critical. For example, the implementation of bi-directional screening programs for TB and DM, as recommended by the World Health Organization (WHO), could enhance early detection and improve treatment outcomes. Moreover, addressing the spatial disparities through geographically targeted interventions could optimize resource allocation and reduce the health burden in high-risk districts. These findings highlight the need for a comprehensive and context-specific strategy to mitigate the impact of TB and DM co-morbidity on Indonesia's public health system while advancing global efforts toward achieving WHO End TB targets.⁵⁰

Limitation of study

This study has several limitations that should be considered when interpreting the findings. First, the
analysis relied on aggregated data from the Indonesian Health Survey (SKI) 2023, which may obscure individual-
level variations in TB and DM co-morbidity. This ecological approach does not account for the individual risk
factors and their direct interactions, potentially leading to ecological fallacy. Second, the study used secondary
data sources, which may introduce inconsistencies due to variations in data collection methods and reporting
accuracy across districts. In particular, the self-reported nature of the data relies on both good participant recall
and the availability of screening services which would allow participants to be aware of their disease status. For
instance, underreporting or screening programs for the detection of diseases in rural or resource-limited settings
could have led to an underestimation of TB and DM prevalence in certain areas. Finally, potential confounders,
such as nutritional status and co-infections like HIV, were not included in the analysis despite their known impact
on TB and DM prevalence. Addressing these limitations in future research, including the integration of
longitudinal and individual-level data, could provide a more comprehensive understanding of TB and DM spatial
in Indonesia.

This study provides critical insights into the dual burden of TB and DM in Indonesia, highlighting the need for a geographically targeted approach to disease prevention and management that accounts for the diverse geographic and socio-economic conditions across regions. Within Indonesia's decentralized healthcare systems, TB and DM control interventions must be implemented adaptively by local governments, considering the unique characteristics of each region. The integration of TB-DM services should be strengthened through community-based strategies, such as enhancing primary healthcare capacity, implementing bi-directional screening in local health centres (puskesmas), and optimizing community health workers to reach vulnerable populations. Additionally, resource allocation should prioritize high-burden areas, particularly impoverished urban settings and remote regions with limited healthcare access. Strengthening coordination between national and local governments in health program planning and financing will be key to ensuring effective interventions. Further studies incorporating spatiotemporal trends could support the development of evidence-based policies that enhance the sustainability and effectiveness of TB and DM control efforts in Indonesia.
**Conclusion**

This study provides critical insights into the dual burden of TB and DM in Indonesia, highlighting the need for a geographically targeted approach to disease prevention and management that accounts for the diverse geographic and socio-economic conditions across regions. Within Indonesia's decentralized healthcare systems, TB and DM control interventions must be implemented adaptively by local governments, considering the unique characteristics of each region. The integration of TB-DM services should be strengthened through community-based strategies, such as enhancing primary healthcare capacity, implementing bi-directional screening in local health centers (puskesmas), and optimizing community health workers to reach vulnerable populations. Additionally, resource allocation should prioritize high-burden areas, particularly impoverished urban settings and remote regions with limited healthcare access. Strengthening coordination between national and local governments in health program planning and financing will be key to ensuring effective interventions. Further studies incorporating spatiotemporal trends could support the development of evidence-based policies that enhance the sustainability and effectiveness of TB and DM control efforts in Indonesia.

**Declarations**

**Ethical Approval and consent to participate**

Ethical approval was provided by the Ethics Committee of Health Polytechnics Jakarta II Ministry of Health Republik Indonesia with the registration number LB.02.01/I/KE/L/287/2023

**Consent for publication**

Not applicable

**Availability of data and materials**

Data used in this study were obtained from the Indonesian government Ministry of Health Republik Indonesia under permission and we are unable to share these data further. Data access to the Indonesian National Health Survey 2023 can be applied for at this website: https://www.badankebijakan.kemkes.go.id/data-mikro-ski

**Competing interests**

All other authors no competing interests

**Funding**

None

**Authors Contributors**

ID : contributed towards literature search, data analysis and interpretation, and writing the manuscript; TT : contributed towards study design, data analysis and interpretation. AA : interpretation of the results and critical review of the manuscript; FW, KAA, INS, PM, IWG : critical review of the manuscript; MK : Conceptualisation, data interpretation, reviewing and editing manuscript.

**Acknowledgements**

The authors would like to express their sincere gratitude to the Future Research Talent Program at the Australian National University (ANU) and Universitas Hasanuddin for providing the opportunity to learn and collaborate with researchers at the National Centre for Epidemiology and Population Health Studies (NCEPHS). This collaboration has been invaluable in enriching the research experience and fostering meaningful academic exchanges.

**AI use statement**

During the preparation of this work the author(s) used [Chat GPT, Grammarly, and Quillbot] in order to [improve language and readability of the paper]. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.
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