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E-publishing of this PDF file has been approved by the authors. Healthc Low-resour S 2025 [Online ahead of print] To cite this Article: Rahmayanti D, Isa M. Risk factors for tuberculosis patients with diabetes mellitus in Indonesia: a case control study. Healthc Low-resour S doi: 10.4081/hls.2025.13887 ©The Author(s), 2025 Licensee PAGEPress, Italy Note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries should be directed to the corresponding author for the article. All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher. Submitted: 11 April 2025 Accepted: 12 November 2025 Early access: 22 December 2025 https://www.pagepressjournals.org/index.php/hls/index https://www.pagepress.org/site Risk factors for tuberculosis patients with diabetes mellitus in Indonesia: a case control study Devi Rahmayanti,1 Mohamad Isa2 1Department of Fundamental Nursing, School of Nursing, Faculty of Medicine and Health Science, Universitas Lambung Mangkurat, Banjarmasin; 2Department of Pulmonology and Respiratory Medicine, Faculty of Medicine and Health Science, Universitas Lambung Mangkurat, Banjarmasin, Indonesia Correspondence: Devi Rahmayanti, School of Nursing, Faculty of Medicine and Health Science, Universitas Lambung Mangkurat, Indonesia. Tel.: +62.812.51241405 E-mail: devi.rahmayanti@ulm.ac.id Key words: case-control study; demographic; risk factors; tuberculosis. Contributions: DR, conceptualization, data curation, formal analysis, methodology, validation, visualization, writing – original draft, review & editing; MI, conceptualization, investigation, methodology, validation, and writing – original draft, review & editing; DR and MI, conceptualization, methodology, formal analysis, validation, and writing – original draft, review & editing. Conflict of interest: the authors declare no conflict of interest. Funding: this research did not receive external funding Ethics approval and consent to participate: all of procedure of this study was granted by ethical clerance from Faculty of Medicine and Health Science, Universitas Lambung Mangkurat, number 407/KEPK-FK ULM/EC/XI/2023. Patient consent for publication: we obtained informed consent from respondents before collecting the data. mailto:devi.rahmayanti@ulm.ac.id Availability of data and materials: all data generated or analyzed during this study are included in this published article. Acknowledgement: the authors would like to express their gratitude to the Banjar Regency Community Health Center and all respondents for their willingness to participate in this study, as well as to everyone who has contributed to this research. Abstract Tuberculosis (TB) is a chronic infectious disease caused by Mycobacterium Tuberculosis (Mtb), and its burden is exacerbated by the increasing prevalence of Non-Insulin-Dependent Diabetes Mellitus (NIDDM). This case-control study aimed to identify risk factors associated with TB among patients with NIDDM in Banjar Regency, Indonesia. A total of 93 respondents with TB or TB with NIDDM were included using a purposive sampling technique. Data were collected via questionnaires and medical records, analyzing demographic characteristics, history of TB exposure, and health parameters. The results showed that gender, education level, smoking history, hospitalization history, and a family history of TB were significantly associated with TB among NIDDM patients. These findings highlight the need for targeted interventions to reduce TB risk among individuals with NIDDM, including enhanced screening, public health education, and lifestyle modifications. Introduction Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis.1 Globally, TB is one of the 10 causes of death and ranks first among single infectious agents.2 Indonesia is ranked second out of eight countries with 8.5% TB.3 According to the World Health Organization (WHO), Indonesia ranks second after India in terms of the global Tuberculosis (TB) burden.4 Globally, there were an estimated 10.6 million new TB cases in 2022, with more than half of them concentrated in eight high-burden countries, including Indonesia.5 The burden is unequally distributed across provinces, with the highest numbers recorded in West Jawa.6 These figures highlight Indonesia’s critical role in the global TB epidemic and the urgent need for regionally targeted interventions. It's important to note that underreporting and underdiagnosis of TB have been significant challenges in Indonesia. The WHO estimated that 15.6% of detected TB cases were underreported, with variations among different types of health facilities.7 In summary, while the WHO provides comprehensive national data on TB in Indonesia for 2018, detailed statistics for specific provinces and cities, including South Kalimantan.8 Although the number of TB cases has decreased, the burden of TB disease is getting heavier due to the increasing cases of Non-Insulin-Dependent Diabetes Mellitus (NIDDM). NIDDM cases can increase the risk of TB infection by 1.5 fold.9 TB patients with diabetes are more likely to experience treatment failure and relapse compared to those without diabetes, increasing their risk of developing multidrug-resistant.10 Cellular immune response is impaired in patients with NIDDM, despite its crucial role in controlling TB infection. There is a decrease in lymphocyte count in TB patients with NIDDM compared to those without diabetes. Additionally, levels of TNF-α and IFN-γ are elevated in patients with both TB and NIDDM. This indicates a weakened cellular immune response, which requires stronger stimulation for optimal activation.11, 12 Data from Indonesia health basic research indicated that men are more vulnerable to TB than women, partly due to smoking habits, which are nearly twice as common among men.13 Smoking exacerbates tuberculosis by impairing alveolar macrophage function, suppressing T- cell mediated immunity, damaging pulmonary mucosal defenses, and contributing to poor treatment outcomes.14 The highest incidence of TB occurs in 45–54 age group (17.3%), followed by the 25–34 age group (16.8%) and the 15–24 age group (16.7%).15 TB is more prevalent among individuals aged below 60 years with a Body mass index > 25.16 TB screening for NIDDM clients, especially sociodemographic was limit.17,18 Therefore, this study is needed to explore the factors contributing to TB in NIDDM patients. Materials and Methods All of procedure of this study was granted by ethical clerance from Faculty of Medicine and Health Science, Universitas Lambung Mangkurat, number 407/KEPK-FK ULM/EC/XI/2023. This study was a the case-control study approach and conducted from February to November 2023 in the Health Care Center of Banjar Regency, South Kalimantan, Indonesia. The sample size was determined using the formula for testing the difference between two proportions.19 The calculation was based on a significance level of 5% (α = 0.05; Zα = 1.96), study power of 80% (β = 0.20; Zβ = 0.84), an expected odds ratio (OR) of 3.3, and an assumed proportion of exposure among controls (P2) of 0.50. Using these parameters, the estimated proportion of exposure among cases (P1) was calculated as 0.50, with the average proportion (P) of 0.40. Substituting into the formula yielded a minimum required sample size of 48 subjects. Total sample in this study were 93 respondents with pulmonary TB or pulmonary TB with NIDDM. We used consecutive sampling technique. Inclusion criteria were pulmonary TB patients diagnosed at the Banjar Regency Health Center who agreed to participate in the study. Cases were defined as pulmonary TB patients with NIDDM, while controls were pulmonary TB patients without NIDDM. Exclusion criteria were patients with extrapulmonary TB, TB-HIV co-infection, or incomplete medical records. Data collection was carried out using primary and secondary data. This study used questionnaire and data from medical records. The data will be analyzed with a SPSS. Descriptive statistics are used to measure the frequency and percentage random blood glucose, acid-fast bacilli sputum smear and sociodemographic of respondents. We used chi-square test determine if there is a significant association betweencharacteristic/sociodemographic and NIDDM Statistical significance (p < 0.05) was used as a criterion to include or exclude the hypothesis. Variables with a p-value < 0.05 in the bivariate analysis were then included in a multivariate logistic regression model to control for potential confounders and to determine independent risk factors. Results Table 1 showed the characteristic of respondents of TB. All of respondents had positive acid bacilli sputum smear. In term of age, respondents in risk age was 46.3% and not risk age was 53.7%. Most of respondents were female (86.1%) and had high school degree (59.1%). In term of job, more than a half of respondents had risk job (51.7%) and normal body mass index (52.6%), however there were 45.1% of respondents had underweight body mass index. 100% repondents had history of smoking, and more than half had history of family with TB (51.7%). 81.7% of respondents never hospitalized and 71 % of respondents was TB patients with NIDDM. Patients TB with NIDDM was become case study. Table 2 showed risk factors of TB among TB patients NIDDM. It listes that gender (p value: 0.000), education (p value: 0.005), history of smooking (p value: 0.000), history of hospitalized (p value: 0.000), and family with TB history (p value: 0.000) were factors that related to TB with NIDDM. The multivariate logistic regression analysis showed that the model had a Cox & Snell R Square value of 0.150 and a Nagelkerke R Square value of 0.231. This indicates that the independent variables included in the model were able to explain approximately 21.3% of the variability in the dependent variable. The remaining 78.7% of the variability was influenced by other factors outside the model. Discussion The objective of this study was to identify the risk factors for t TB with NIDDM. The results showed that gender, education, history of smooking, history of hospitalized, and family with TB history were factors that related to TB with NIDDM. This study found no relationship between age and TB with NIDDM. This finding contrasts with previous research in Indonesia that mentioned a correlation between young age and the incidence of TB with NIDDM.20 The majority of TB cases occur in individuals aged 15–55 years, which is considered the productive age group. During this period, people often prioritize work, leading to reduced rest and a weakened immune system.15 However, there was study mentioned that most TB with NIDDM were men aged 47 years or older, attributing the higher prevalence of NIDDM in older individuals to decreased immunity and age-related frailty, which increase vulnerability to TB. Based on these studies, it showed that the relationship between age and the prevalence of TB in individuals with NIDDM may vary across different populations and settings.21 The statistical test analysis revealed there was significant relationship between gender and TB in individuals with NIDDM. This finding aligns with previous studies indicating a relationship between gender and TB.21 TB is generally more prevalent in men than in women. Study among individuals with TB and NIDDM showed that from 1,243 smear-positive TB cases found that 63.2% were male. Interestingly, the prevalence of NIDDM among these TB patients was higher in women (22.9%) compared to men (9.2%), suggesting that while TB is generally more prevalent in men, women with TB are more likely to have concurrent NIDDM.22,23 This may be attributed to men often having unhealthy habits such as smoking24, 25 and alcohol consumption.26 These study conclude that there is a relationship between education and TB with NIDDM. This is in line with previous study that there is a relationship between the level of knowledge and the TB cases with NIDDM.27 These results show that the higher or better a person's knowledge was related with adherence in medication.28 Level of education greatly influences health literacy. If TB patients do not understand the benefits of taking medication regularly and regular check-ups, the patient will discontinue treatment, resulting in resistance.29 The analysis results showed no significant relationship between job and the incidence of TB with NIDDM. However, previous study mentioned that work environment factors can influence a person’s risk of exposure to disease.30 Poor working conditions contribute to a higher risk of TB infection, particularly among individuals in occupations such as drivers, laborers, and pedicab drivers, compared to those working in office settings. Factors such as exposure to respirable silica dust, lack of personal protective equipment, and poor living conditions, including overcrowding and inadequate ventilation, contributed to this increased risk.31 Additionally, occupational factors are also closely linked to income levels.32 Families with little or no income have lower purchasing power, making it difficult to meet adequate nutritional needs. This often leads to malnutrition, which weakens the immune system and increases vulnerability to various diseases, especially TB.32 This study revealed no significant association between body mass index (BMI) and tuberculosis with diabetes mellitus. Theoretically, BMI is related to TB;33 however, weight gain may occur until BMI returns to normal in TB patients who have completed the initial intensive phase of treatment (2–8 weeks). In addition to completing the standard 6 month treatment regimen, maintaining adequate nutrition is also essential. We assumed that the risk of tuberculosis increases regardless of BMI, as diabetes mellitus serves as a dominant factor that may overshadow the effect of BMI. Therefore, it is recommended that respondents maintain normal nutritional status by consuming nutritious food, routinely measuring weight and height so that their bmi is known periodically, in addition to increasing the coverage of bcg immunization in infants in order to prevent TB because it can increase the body's immune system. The history of hospital admissions showed a statistically significant association with TB patients who also have NIDDM The coexistence of these conditions can result in more severe disease manifestations and complications, often requiring hospitalization for comprehensive management. Previous study mentioned that people with NIDDM have a 1.5-fold increased risk of developing TB. The risk is even higher among individuals using insulin for NIDDM management. This increased susceptibility underscores the importance of comprehensive management strategies, including potential hospital admissions, to address the complications arising from the coexistence of TB and NIDDM.34 The analysis showed that all TB patients in the study were smokers, either active or passive, and there was a link between smoking, TB and NIDDM. Smoking may worsen TB by affecting the body's defense system.35 It can impair the lungs' ability to clear mucus, weaken immune cells like macrophages and Natural Killer (NK) cells, and disrupt other immune responses. Research also suggests that smoking is related to the severity of lung damage in TB and the speed of recovery. The longer a person smokes, the greater the risk, as harmful toxins from cigarettes build up in the body. Smoking and TB together create a dangerous combination, as smoking increases the spread of infection, turns latent TB into an active disease, and makes the illness more severe.36 Family history of TB was related to TB patient with NIDDM. A family history of TB suggests prolonged exposure to Mtb, increasing the likelihood of latent TB infection and some genetic factors may make individuals in the same family more susceptible to TB.37 And the respondents close contact with a TB -infected family member increases the risk of acquiring the infection, especially in crowded living conditions.38 Further, the logistic regression model explained 21.3% of the variability in TB among patients with NIDDM, indicating that gender, education level, smoking history, hospitalization history, and family history of TB are important but not exclusive predictors. The remaining unexplained variability suggests that other biological, behavioral, and environmental factors also contribute significantly to the risk of TB in this population. This study has several limitations that should be considered when interpreting the findings. first, the relatively small sample size may limit the generalizability of the results to broader populations. a larger, more diverse sample would provide a more comprehensive understanding of the relationship between TB with NIDDM. second, as a case-control study, this research identifies associations but does not establish causation between the examined risk factors and TB among NIDDM patients. additionally, some data, such as smoking history and past TB exposure, were self-reported, which may introduce recall bias. Conclusions This study identified key risk factors for TB among individuals with NIDDM, including gender, education level, smoking history, hospitalization history, and a family history of TB. The findings of this study have significant implications for TB prevention and management among individuals. Public health interventions should focus on targeted strategies to reduce TB risk in NIDDM patients, including early screening. Health education programs are essential to raise awareness about risk factors such as smoking and non-adherence to treatment, encouraging patients to adopt healthier lifestyles. Additionally, regular TB screening for individuals with NIDDM, particularly those with a family history of TB or other identified risk factors, should be prioritized to facilitate early detection and treatment. References 1. Miggiano R, Rizzi M, Ferraris DM. Mycobacterium tuberculosis pathogenesis, infection prevention and treatment. Pathogens 2020;9:385. 2. Daley CL. The global fight against tuberculosis. Thoracic Surgery Clinics 2019;29:19-25. 3. WHO G. Global tuberculosis report 2020. Glob Tuberc Rep 2020;2020. 4. WHO. Tuberculosis 2025. Available from: https://www.who.int/news-room/fact- sheets/detail/tuberculosis 5. WHO. World Health Organization Data 2024. 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Multi-ancestry meta-analysis of host genetic susceptibility to tuberculosis identifies shared genetic architecture. eLife 2024;13:e84394. 38. Kokiwar PR, Soodi Reddy AK. Diabetes mellitus as a risk factor for tuberculosis: a community based case control study. Indian J Community Med 2024;49:617-21. Table 1. Sociodemographic and clinical characteristic of respondents. Characteristic Frequency Percentage N = 93 % Characteristic Frequency Percentage N = 93 % Age Not risk (>55 years old) 50 53.7 Risk (15-55 years old) 43 46.3 Gender Female 13 13.9 Male 80 86.1 Education Elementary School 33 35.4 High School 55 59.1 University 5 0.5 Job Not Risk 45 48.3 Risk 48 51.7 Body mass index Underweight 42 45.1 Normal 49 52.6 Obesity 2 2.3 History of smoking Passive or active 93 100 Family history of TB Yes 45 48.3 No 48 51.7 History of hospitalized Yes 17 18.2 No 76 81.8 Random Blood Glucose Yes 27 29 No 66 71 Acid-fast bacilli sputum smear Yes 93 100 Characteristic Frequency Percentage N = 93 % No 0 0 Table 2. Bivariate analysis of factors that related with TB among TB patients with NIDDM. Variable Tuberculosis p-value Case (%) Control (%) Age 0.778 Not risk (>55 years old) 11 (40.7) 39 (59.1) Risk (15-55 years old) 16 (59.3) 27 (40.9) Gender 0.000 Female 13 (48.1) 0 (0) Male 14 (51.9) 66 (100 ) Education 0.005 Elementary school 16 (59.2) 17 (25.8) High school 8 (29.6) 47 (71.2) University 3 (11.2) 2 (3.0) Job 0.171 Risk 16 (59.3) 29 (43.9) Not risk 11 (40.7) 37 (56.1) BMI 0.873 Underweight 9 (33.3) 34 (50) Normal 18 (66.7) 32 (47) Obesity 0 (0) 2 (3) History of Smooking 0.000 Yes 14 (51.9) 66 (100) No History of hospitalized 13 (48.1) 0 (0) Yes 7 (25.9) 10 (15.2) 0.000 No 20 (74.1) 56 (84.8) Family history of TB Yes 16 (59.3) 29 (43.9) 0.000 No 11 (40.7) 37 (56.1) Table 3. Logistic regression. Model Fit Measure Value Cox & Snell R Square 0.150 Nagelkerke R Square 0.231 R² (Interpretation) 0.213