Associated factors at different treatment outcomes (cured, completed, defaulted, and death) among TB/HIV co-infected patients in East Coast Malaysia: 5-year record review (2016 - 2020)

Associated factors at different treatment outcomes (cured, completed, defaulted, and death) among TB/HIV co-infected patients in East Coast Malaysia: 5-year record review (2016 - 2020) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Associated factors at different treatment outcomes (cured, completed, defaulted, and death) among TB/HIV co-infected patients in East Coast Malaysia: 5-year record review (2016 - 2020) Siti Romaino Mohd Nor, Nyi Nyi Naing, Mat Zuki Mat Jaeb This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6910886/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Oct, 2025 Read the published version in BMC Infectious Diseases → Version 1 posted 22 You are reading this latest preprint version Abstract Background Tuberculosis (TB) and human immunodeficiency virus (HIV) co-infection pose a substantial public health problem, particularly in high-prevalence areas. This study uses an epidemiological model to analyze the sociodemographic and clinical characteristics related to various TB treatment outcomes (cured, completed, defaulted, and death) among TB/HIV co-infected patients on the East Coast of Malaysia over five years. Methods This cross-sectional study used secondary data from the e-Notifikasi for Tuberculosis Information System (TBIS) from January 2016 to December 2020 and was conducted at the State TB Organizer or TB/Leprosy Unit, Jabatan Kesihatan Negeri (JKN) Kelantan, Terengganu, and Pahang. Data was analyzed using multinomial logistic regression with IBM SPSS Statistics version 25.0 and STATA-14. Ethics permission was received from the Medical Research Ethics Committee (MREC), Ministry of Health (MOH). Results There were 14,289 TB cases, with 1,292 (9.04%) being TB/HIV co-infected patients. However, 69 TB/HIV cases were excluded due to transfer, change of diagnosis, and still ongoing treatment. As a result, 1,223 TB/HIV co-infected patients were assessed. The prevalence of cured was 33.5% (410), completed 29.2% (357), defaulted 6.4% (78), and died 30.9% (378). There were no failures identified. Nine factors were determined to be statistically significant during the univariable analysis. The important variables discovered in the univariate study were then used for the multivariate analysis. The study found that the duration of treatment, diabetes mellitus, occupation, and Chest X-ray (CXR) status were all substantially linked with treatment completion. Age, duration of treatment, residency, Directly Observed Treatment Short-course (DOTS) status, case type, and CXR status significantly impacted treatment default. In contrast, duration of treatment, diabetes mellitus, DOTS, occupation, and CXR all had a substantial effect on death rates. Conclusion Understanding the factors that influence TB treatment outcomes is crucial for developing effective intervention strategies and enhancing patient outcomes. The findings of this study provide a comprehensive understanding of the relevant factors influencing treatment outcomes at all levels, which may aid in the development of more effective treatment techniques. Associated factors treatment outcomes cured completed defaulted death TB/HIV co-infected patients Figures Figure 1 Background Tuberculosis (TB) and human immunodeficiency virus (HIV) co-infection provide a serious global public health concern, especially in areas where both diseases are endemic. TB is a prominent cause of morbidity and mortality among people living with HIV (PLHIV), as the virus weakens the immune system, rendering people more vulnerable to TB infection and complicating treatment ( 1 ). According to the World Health Organization (WHO), TB is the primary cause of Death among HIV-positive people, accounting for one-third of HIV-related deaths worldwide ( 2 ). In Malaysia, the dual burden of TB and HIV is particularly concerning. The Ministry of Health (MOH) has observed a consistent increase in the number of TB/HIV co-infected patients, notably on the East Coast. Effective TB management and treatment in this population are crucial for improving health outcomes and limiting the spread of both illnesses. Understanding the factors that determine different treatment outcomes (cure, complete, default, and death) is critical for establishing targeted interventions and enhancing patient care. The success of TB treatment is typically measured by these outcomes, which reflect different levels of adherence to treatment protocols, patient health status, and healthcare system efficiency ( 3 ). Identifying the factors associated with each treatment outcome is crucial for enhancing patient survival and success rates ( 4 ). Previous studies have identified various factors influencing TB treatment outcomes, including sociodemographic characteristics, clinical comorbidities, and healthcare-related factors ( 5 ). However, there is a lack of availability of research specifically examining these factors among TB/HIV co-infected patients in East Coast Malaysia, particularly at different treatment outcomes. This gap in the literature highlights the need for research that not only identifies these factors but also differentiates their impact across the spectrum of treatment outcomes. Despite the known challenges, limited research has specifically addressed the factors influencing TB treatment outcomes among TB/HIV co-infected patients in Malaysia. Previous studies have often focused on either TB or HIV in isolation without adequately addressing the complexities of co-infection ( 6 ). Moreover, regional variations in healthcare infrastructure, socioeconomic conditions, and demographic profiles may lead to different outcomes, underscoring the importance of localized research. This study aims to assess the sociodemographic characteristics (age, gender, race, residency, occupation), clinical characteristics (duration of treatment, diabetes mellitus, Bacillus Calmette–Guerin (BCG) scar, Chest X-ray (CXR) status, TB case category, smoking status, Directly Observed Therapy Shortcourse (DOTS), detection method), prevalence of TB treatment outcomes (cured, completed, defaulted, failure and death) and associated factors at different treatment outcomes among TB/HIV co-infected patients in East Coast Malaysia for 5 years. The findings of this study have significant public health implications. By understanding the determinants of treatment success and unsuccess, healthcare providers can develop more effective, tailored interventions to address the needs of TB/HIV co-infected patients. This study will also contribute to the broader knowledge base, offering valuable data for policymakers and researchers focused on combating TB and HIV. In summary, this research attempts to enhance our understanding of TB treatment outcomes in the context of HIV co-infection in East Coast Malaysia, with the ultimate goal of improving patient care and public health outcomes. Materials and Methods Study Design, Study Participant, and Population This cross-sectional study utilised secondary data from January 2016 to December 2020, sourced from the e-Notifikasi for Tuberculosis Information System (TBIS) at the State TB organiser or TB/Leprosy Unit, Jabatan Kesihatan Negeri (JKN) in Kelantan, Terengganu, and Pahang. The population comprised all TB/HIV co-infected patients in East Coast Malaysia (Kelantan, Terengganu, and Pahang) who met the study's inclusion criteria. Inclusion Criteria & Exclusion Criteria The study included all TB/HIV co-infected patients aged 18 and above who tested positive for TB and HIV, confirmed for TB and HIV, enrolled in TBIS between January 2016 and December 2020, and had a TB treatment outcome such as cured, complete, defaulted, failure, or death. Non-Malaysian citizens, those transferred to other treatment centres, those with changes in diagnosis, those lost to follow-up, and those receiving continuing treatment at the time of data extraction were excluded. Sample Size Determination and Sampling Method The sample size was calculated using PS software based on proportions. The parameter for this estimation included a level of significance at 5%, a power of 80%, and a ratio of 1:1. An uncorrected chi-square statistic was used to evaluate this null hypothesis. According to the sample size calculations, the largest sample size was 230 patients per group. Assuming a 10% dropout, the total number of patients should be at least 1,278. Convenience sampling was employed to achieve the required sample size. Data Collection The person in charge at the State TB organizer or TB/Leprosy Unit has retrieved and downloaded the data from the TBIS online system, including the patient's sociodemographic ( age, gender, race, residency, occupation ), clinical characteristics ( duration of treatment, diabetes mellitus , BCG scar, CXR status, TB case category, smoking status, DOTS, detection method ), and TB treatment outcome into Microsoft Excel for patients identified as TB/HIV co-infection. However, a few parameters, such as level of education, treatment regimen, source of notification, HAART treatment, marital status, and monthly income, were not fully recorded. Therefore, the researcher requests data from the Disease Control Division, MOH, to obtain the complete database. Unfortunately, we still haven't received that information. All the required information in this study was extracted in July 2022. Then, the researcher exports the data from Microsoft Excel to IBM SPSS Statistics version 25.0 and STATA 14 for further analysis. The data analysis divided the final TB treatment outcome into four treatment outcomes (cured, completed, defaulted, and dead) due to the failure outcomes being recorded as zero cases. Variables Independent Variables : Age, Gender, Race, Residency, Occupation , Duration of treatment, Diabetes mellitus , BCG scar, CXR status, TB case category, Smoking Status, DOTS, Detection method Dependent Variables : Cured, completed, defaulted, and dead. Data Analysis The descriptive data were summarized using IBM SPSS Statistics version 25.0. The data was examined, validated, and cleansed to detect inaccuracies or missing numbers. Categorical data were summarized as frequency (n) and percentage (%). Due to the normal distribution, numerical data were summarized as mean and standard deviation (SD). The prevalence of TB treatment outcomes was expressed as a percentage (%). The multinomial logistic regression was analyzed using the STATA program, Version 14, to assess the relationship between independent variables (e.g., demographic factors, clinical characteristics) and the categorical outcome of TB treatment (cured, completed, defaulted, or died) among TB/HIV co-infected patients. This strategy is appropriate given the nominal character of the dependent variable, which has more than two unsorted categories. A multinomial logistic regression model was used to calculate the log odds of each treatment outcome compared to the reference group (cured). Independent variables were selected based on their theoretical relevance and prior research. Effect modification was assessed using interaction terms between two variables. Regression analysis aims to create a model that is the best fit, parsimonious, physiologically plausible, and statistically significant. Operational definitions According to the Clinical Practice Guidelines for Management of Tuberculosis (7), the following operational terminology; Cured: A Pulmonary TB (PTB) patient who had bacteriologically confirmed TB at the start of treatment and tested negative for smears or cultures in the final month of treatment and on at least one earlier occasion. Completed: A TB patient who completed treatment without evidence of failure but has no record of negative sputum smear or culture results in the last month of treatment or on at least one previous occasion, either due to lack of testing or unavailable results. Failed: A TB patient whose sputum smear or culture is positive in month 5 or after treatment. Default: A patient who has stopped treatment for two months or more. Death: A TB patient who passed away before or during treatment. The severity of the lesion on the X-ray film was used to characterize the CXR presentation following diagnosis. It was classified into; No lesion if the CXR showed no lesions, Minimal if the CXR showed a few lesions, Moderate advance if the CXR showed many lesions, Far advanced if the CXR revealed extensive lesions or a military appearance, and Not done if the CXR was not performed during the diagnosis. Results Sociodemographic Characteristics of Study Participants There were 14,289 TB cases, with 1,292 (9.0%) TB/HIV co-infections in East Coast Malaysia from 2016 to 2020. However, 69 TB/HIV cases were excluded due to transferring to another treatment centre, change of diagnosis, and still ongoing treatment. Therefore, 1,223 TB/HIV co-infected patients were evaluated. Of these 1,223 TB/HIV cases, their age ranged from 18 to 77 years, with a mean (SD) age of 42.1 (12.1) years. The majority of cases involved males (82.4%), Malays (92.1%), and individuals residing in rural areas (77.8%), with other occupations also represented (45.6%). Clinical Characteristics of Study Participants The duration of TB treatment ranged from 0 to 21 months with a mean (SD) of 5.6 (3.9) months. There were 89.3% non-diabetes mellitus cases, 94.8% had a BCG scar, 47.3% had minimal lesions on CXR during diagnosis, 85.3% were new TB cases, 56.7% were smokers, 86.6% were under DOTS, and 87.8% had a passive detection method. Table 1 illustrates the descriptive statistics at different TB treatment outcomes among TB/HIV co-infected patients in East Coast Malaysia (n = 1,223). Table 1 Descriptive Statistics at Different TB Treatment Outcomes among TB/HIV Co-infected Patients in East Coast Malaysia (n = 1,223) Variable Cured (Reference) Completed Defaulted Death Total n = 410 n = 357 n = 78 n = 378 n = 1,223 Age (years) 44.7 ± 13.0* 39.4 ± 11.8)* 37.8 ± 8.6* 42.7 ± 11.1* 42.1 ± 12.1* Duration of treatment (months) 7.3 ± 2.41* 8.1 ± 2.9* 4.3 ± 3.6* 1.6 ± 2.5* 5.6 ± 3.9* Sex Male 336 (82.0) 283 (79.3) 68 (87.2) 321 (84.9) 1008 (82.4) Female 74 (18.1) 74 (20.7) 10 (12.8) 57 (15.1) 215 (17.6) Race Malays 384 (93.7) 337 (94.4) 72 (92.3) 333 (88.1) 1126 (92.1) Non-Malays 26 (6.3) 20 (5.6) 6 (7.7) 45 (11.9) 97 (7.9) Residency Urban 68 (16.6) 83 (23.3) 28 (35.9) 92 (24.3) 271 (22.2) Rural 342 (83.4) 274 (76.8) 50 (64.1) 286 (75.7) 952 (77.8) Occupation Government staff 15 (3.7) 33 (9.2%) 1 (1.3) 17 (4.5) 66 (5.4) Unemployed 153 (37.3) 138 (38.7) 32 (41.0) 185 (48.9) 508 (41.5) Prisoners 32 (7.8) 23 (6.4) 11 (14.1) 25 (6.6) 91 (7.4) Others 210 (51.2) 163 (45.7) 34 (43.6) 151 (40.0) 558 (45.6) Diabetes mellitus No 324 (79.0) 341 (95.5) 76 (97.4) 351 (92.9) 1092 (89.3) Yes 86 (21.0) 16 (4.5) 2 (2.6) 27 (7.1) 131 (10.7) BCG Scar Yes 392 (95.6) 341 (95.5) 77 (98.7) 349 (92.3) 1159 (94.8) No 18 (4.4) 16 (4.5) 1 (1.3) 29 (7.7) 64 (5.2) CXR status during the diagnosis No lesion 15 (3.7) 87 (24.4) 15 (19.2) 43 (11.4) 160 (13.1) Minimal 207 (50.5) 171 (47.9) 36 (46.2) 165 (43.7) 579 (47.3) Moderately advanced 169 (41.2) 86 (24.1) 24 (30.8) 141 (37.3) 420 (34.3) Far advanced 18 (4.4) 4 (1.1) 1 (1.3) 26 (6.9) 49 (4.0) Not done 1 (0.2) 9 (2.5) 2 (2.6) 3 (0.8) 15 (1.2) Category case New 356 (86.8) 310 (86.8) 57 (73.1) 320 (84.7) 1043 (85.3) Recurrent 42 (10.2) 39 (10.9) 12 (15.4) 45 (11.9) 138 (11.3) After fail/default 12 (2.9) 8 (2.2) 9 (11.5) 13 (3.4) 42 (3.4) Smoking status No 178 (43.4) 187 (52.4) 24 (30.8) 141 (37.3) 530 (43.3) Yes 232 (56.6) 170 (47.6) 54 (69.2) 237 (62.7) 693 (56.7) DOT Yes 409 (99.8) 352 (98.6) 62 (79.5) 236 (62.4) 1059 (86.6) No 1 (0.2) 5 (1.4) 16 (20.5) 142 (37.6) 164 (13.4) Detection method Active 21 (5.1) 13 (3.6) 9 (11.5) 12 (3.2) 55 (4.5) Passive 350 (85.4) 319 (89.4) 60 (76.9) 345 (91.3) 1074 (87.8) Screening 39 (9.5) 25 (7.0) 9 (11.5) 21 (5.6) 94 (7.7) mean ± SD* The Prevalence of TB Treatment Outcomes In this study, the prevalence of TB treatment outcomes among 1,223 TB/HIV co-infected patients was 33.5% (410) cured, 29.2% (357) completed, 6.4% (78) defaulted, and 30.9% (378) death. There were no failure cases identified, as shown in Fig. 1 . For this reason, the multinomial logistic regression analysis excluded the failure outcomes from the subsequent analysis step. Thus, the analysis and reporting of TB treatment outcomes were limited to four categories: cured (as the reference), completed, defaulted, and died. Associated Factors In the completed treatment outcome analysis, several variables were found to be significantly associated with the likelihood of completing treatment, specifically duration of treatment, diabetes mellitus, occupation, and CXR status during diagnosis. With the increase in the 1-month duration of treatment, the chance of achieving a complete treatment outcome was estimated to increase by 8% (aRRR = 1.08, 95% CI = 1.02, 1.15, P = 0.005). The presence of diabetes mellitus is negatively associated with TB treatment completion. Patients with underlying diabetes mellitus have an 80% lower chance of completing treatment compared to those without diabetes mellitus (aRRR = 0.20, 95% CI = 10.12, 0.39, P < 0.001). Patients working as government servants were estimated to have a 2.44 times higher chance of achieving a complete treatment outcome compared to unemployed patients (aRRR = 2.44, 95% CI = 1.15, 5.11, P = 0.019). Patients with minimal lesions of CXR were estimated to have an 84% lower chance of achieving a complete treatment outcome compared to patients with no lesion CXR (aRRR = 0.16, 95% CI = 0.09, 0.29, P < 0.001). Similarly, patients with moderately advanced disease were estimated to have a 90% lower chance of achieving a complete treatment outcome compared to patients with no lesion on CXR (aRRR = 0.10, 95% CI = 0.05, 0.19, P < 0.001). Patients with far advanced were estimated to have a 95% lower chance of achieving a complete treatment outcome compared to patients with no lesion CXR (aRRR = 0.05, 95% CI = 0.15, 0.18, P < 0.001). In the default treatment outcome, the variables associated with an increased likelihood of defaulting on TB treatment compared to being cured were age, duration of treatment, residency, DOT, case category, and CXR status at diagnosis. With the increase of 1-year in age, the chance of having a defaulted treatment outcome is estimated to decrease by 5% (aRRR = 0.95, 95% CI = 0.93, 0.98, P = 0.001). With the increase of 1-month in the duration of treatment, the chance of having a defaulted treatment outcome is estimated to decrease by 36% (aRRR = 0.64, 95% CI = 0.56, 0.72, P < 0.001). Patients in rural areas were estimated to have a 57% lower chance of defaulting on treatment outcomes than those in urban areas (aRRR = 0.43, 95% CI = 0.23, 0.79, P = 0.007). Patients without a DOT program were estimated to have a 45.96 times higher chance of having defaulted treatment outcomes compared to patients with a DOT program (aRRR = 45.96, 95% CI = 4.82, 438.53, P = 0.001). Patients with recurrent case TB were estimated to have a 2.41 times higher chance of having defaulted treatment outcomes compared to those with new case TB (aRRR = 2.41, 95% CI = 1.07, 5.41, P = 0.033). Patients after failure/default case TB were estimated to have a 5.38 times higher chance of defaulting on treatment outcomes compared to those with new case TB (aRRR = 5.38, 95% CI = 1.77, 16.33, P = 0.003). Patients with minimal, moderately advanced, and far advanced had lower odds of defaulting compared to those with no lesions on CXR. Specifically, patients with minimal lesions had a 79% lower chance of defaulting (aRRR = 0.21, 95% CI = 0.09, 0.52, P = 0.001); patients with moderately advanced lesions had an 84% lower chance of defaulting (aRRR = 0.16, 95% CI = 0.66, 0.40, P < 0.001); and patients with far advanced lesions had a 93% lower chance of defaulting (aRRR = 0.07, 95% CI = 0.01, 0.66, P = 0.020). Meanwhile, in the death outcome, several variables were found to be significantly associated with death as an outcome of treatment when compared to being cured, such as duration of treatment, diabetes mellitus, DOT, occupation, and CXR status during the diagnosis. With a 1-month increase in treatment duration, the chance of a death outcome is estimated to decrease by 59% (aRRR = 0.41, 95% CI = 0.36, 0.46, P < 0.001). Patients with underlying diabetes mellitus were estimated to have a 66% lower chance of death compared to patients without diabetes mellitus (aRRR = 0.34, 95% CI = 0.13, 0.79, P = 0.013). Patients without a DOT program were estimated to have a 29.41 times higher chance of having death outcomes compared to patients with a DOT program (aRRR = 29.41, 95% CI = 3.14, 275.18, P = 0.003). Patients working in other types of employment were estimated to have a 47% lower chance of having death outcomes compared to unemployed patients (aRRR = 0.53, 95% CI = 10.32, 0.88, P = 0.014). In terms of CXR presentation, patients with minimal lesions were estimated to have a 75% lower chance of having death outcomes compared to patients with no CXR lesion (aRRR = 0.25, 95% CI = 0.11, 0.59, P = 0.001). Patients with moderately advanced were estimated to have an 81% lower chance of having death outcomes compared to patients with no lesion CXR (aRRR = 0.19, 95% CI = 0.08, 0.46, P < 0.001). The multinomial logistic regression analysis highlighted that different variables influence the likelihood of various TB treatment outcomes. Our study revealed that both the duration of treatment and CXR status during diagnosis were consistently significant predictors across all treatment outcomes (completed, defaulted, and death), indicating that these variables are important predictors for multiple treatment outcomes, not just one specific outcome. The final regression model is presented in Table 2 . Table 2 Associated Factors at Different TB Treatment Outcomes (Completed, Defaulted, and Death) (n = 1,223) Completed Defaulted Death Variable b aRRR (95% CI) P- Value b aRRR (95% CI) P - Value b aRRR (95% CI) P - Value Age -0.01 0.99 (0.97, 1.00) 0.064 -0.05 0.95 (0.93, 0.98) 0.001 -0.01 0.99 (0.97, 1.10) 0.296 Duration of treatment 0.08 1.08 (1.02, 1.15) 0.005 -0.45 0.64 (0.56, 0.72) < 0.001 -0.9 0.41 (0.36, 0.46) < 0.001 Residency Urban 0 1 0 1 0 1 Rural -0.29 0.75 (0.50, 1.11) 0.15 -0.86 0.43 (0.23, 0.79) 0.007 -0.1 0.95 (0.53, 1.69) 0.849 Diabetes mellitus No 0 1 0 1 0 1 Yes -0.63 0.120 (0.11, 0.39) < 0.001 -1.38 0.25 (0.06, 1.16) 0.077 -1.1 0.34 (0.13, 0.79) 0.013 Smoking status No 0 1 0 1 0 1 Yes -0.31 0.73 (0.52, 1.02) 0.065 0.59 1.97 (0.98, 3.28) 0.057 0.3 1.393 (0.85, 2.28) 0.185 DOTS Yes 0 1 0 1 0 1 No 1.92 6.84 (0.77, 61.07) 0.085 3.83 45.96 (4.82, 438.53) 0.001 3.4 29.41 (3.14, 275.18) 0.003 Category case New 0 1 0 1 0 1 Recurrent 0.01 1.11 (0.66, 1.85) 0.703 0.88 2.41 (1.07, 5.41) 0.033 0.6 1.87 (0.93, 3.78) 0.08 After failure/ default -0.34 0.71 (0.26, 1.93) 0.504 1.68 5.38 (1.77, 16.33) 0.003 0.9 2.36(0.72, 7.75) 0.158 Occupation Not working 0 1 0 1 0 1 Gov staff 0.89 2.44 (1.15, 5.11) 0.019 -0.75 0.48 (0.06, 4.08) 0.497 0.2 1.26 (0.39, 4.07) 0.705 Prisoners -0.40 0.67 (0.36, 1.28) 0.227 0.47 1.61 (0.65, 3.95) 0.302 0.1 1.13 (0.48, 2.64) 0.783 Others -0.02 0.98 (0.69, 1.38) 0.904 -0.31 0.73 (0.40, 1.33) 0.306 -0.6 0.53 (0.32, 0.88) 0.014 CXR status during the diagnosis No lesion 0 1 0 1 0 1 Minimal -1.86 0.16 (0.09, 0.29) < 0.001 -1.55 0.21 (0.09, 0.52) 0.001 -1.4 0.25 (0.11, 0.59) 0.001 Moderate advance -2.28 0.10 (0.05, 0.19) < 0.001 -1.86 0.16 (0.66, 0.40) < 0.001 -1.7 0.19 (0.08, 0.46) < 0.001 Far advance -2.94 0.05 (0.15, 0.18) < 0.001 -2.68 0.07 (0.01, 0.66) 0.02 -1.3 0.28 (0.07, 1.10) 0.069 Not done 0.07 1.08 (0.12, 9.48) 0.948 0.38 1.47 (0.08, 28.73) 0.802 -0.8 0.47 (0.02, 10.04) 0.629 Regression coefficient (b), Adjusted Relative Risk Ratio (aRRR) Footnote: Multinomial logistic regression was applied. The linearity of the continuous variable was checked and reported to be linear. Multicollinearity and interaction were unlikely. The overall fit of the model was checked and reported to be the Hosmer-Lemeshow test (completed: P = 0.467; defaulted: P = 0.053; Death: P = 0.000), Pearson chi-square test (completed: P = 0.337; defaulted: P = 0.000; death: P = 0.000), overall correctly classified percentage (completed: 70.1%; defaulted: 91.6%; Death: 93.3%), Area under the ROC curve (completed: 0.771 (95% CI: 0.74, 0.804); defaulted: 0.898 (95% CI: 0.85, 0.94); Death: 0.843 (95% CI: 0.78, 0.91). Regression diagnostic was performed by estimated logistic probability (p), Leverage (h), covariate pattern (n), Hosmer and Lemeshow Delta chi-squared influence statistic (dx2), Hosmer and Lemeshow Delta-D influence statistic (dd), and Pregibon Delta-Beta influence statistic (db). Influential outliers were identified by checking per cent changes in the regression coefficient (b) set at 20%. Discussion This study aims to assess the sociodemographic characteristics, clinical characteristics, prevalence of TB treatment outcomes, and associated factors at different TB treatment outcomes (cured, completed, defaulted, and death) among TB/HIV co-infected patients in East Coast Malaysia for five ( 5 ) years. It included 1,223 cases, with 410 (33.5%) cured, 357 (29.2%) completed, 78 (6.4%) defaulted, and 378 (30.9%) death outcomes. There were no failure cases identified. Sociodemographic Characteristics of Study Participants The sociodemographic findings of this study were similar to the study of 703 cases of TB/HIV co-infection in Kelantan between 2014 and 2018 in terms of age, gender, race, and residency. However, the results differed in terms of occupation in this study. Other occupation types accounted for 45.6%, whereas in the previous study, these types comprised 27.9% ( 5 ). The difference in occupational status could be due to variations in the study population, socioeconomic status, temporal factors, or other contextual variables that were not controlled for. Clinical Characteristics of Study Participants In this study, the overall mean (SD) of TB treatment duration among TB/HIV co-infected patients was 5.6 (3.9) months. This duration falls within the range reported in a study conducted in Ethiopia, where treatment durations of two to seven months were associated with successful outcomes ( 8 ). The similarity between the mean duration of treatment in our study and the effective duration reported in Ethiopia suggests that adherence to a treatment period of around six to seven months may contribute to better TB treatment outcomes among TB/HIV co-infected patients. This aligns with the standard World Health Organisation (WHO) guidelines for TB treatment, which recommend a minimum of six months of therapy for drug-susceptible TB, provided the patient adheres to the treatment regimen without interruptions. In this study, the proportion of non-diabetic patients, minimal lesions of CXR during diagnosis, and smokers were lower than the figures reported in Kelantan between 2014 and 2018 ( 5 ). These differences may suggest changes in the demographic or clinical profile of TB/HIV co-infected patients over time. Despite these differences, our study found similar proportions of patients with BCG scars, those under DOT supervision, and those diagnosed through passive detection methods, compared to previous findings from Kelantan. This study also found that the proportion of new TB cases was higher (85.3%) than the previously reported figure (82.8%) in Kelantan. The higher proportion of new cases might indicate improved case detection efforts, perhaps due to increased public awareness, better diagnostic facilities, or enhanced screening programs. The Prevalence of TB Treatment Outcomes In terms of the prevalence of TB treatment outcomes, this study shows that 33.5% had been cured. For cured outcomes, this prevalence was lower than among TB/HIV co-infected patients (91.3%) in Jakarta, Indonesia ( 9 ). However, it was higher than the study among TB patients attending the Public Hospitals in Harar Town, Eastern Ethiopia (30.4%) ( 10 ), TB/HIV co-infection in Kelantan itself (19.8%) ( 5 ), in Kwabre East Municipality of Ghana (14.3%) ( 11 ), and among 2,150 TB patients in Southwest Ethiopia (4.0%) ( 12 ). These differences in prevalence can be attributed to several factors, including variations in healthcare systems, TB control programs, patient populations, and socioeconomic conditions. This study also found notable differences in the prevalence of completed treatment outcomes among TB/HIV co-infected patients compared to reports from other countries. This study reported that 29.2% of participants had completed outcomes, which is lower than the rates observed in a study conducted at Public Hospitals of Harar Town, Eastern Ethiopia (56.7%) ( 13 ) and a study at Gondar University Referral Hospital, Northwest Ethiopia (66.9%) ( 13 ). However, it was higher than among smear-positive TB patients in the Kepong district, Kuala Lumpur, Malaysia (1.71%) ( 14 ). Differences can influence these variations in treatment completion rates in healthcare systems, patient characteristics, and programmatic approaches to TB care. The observed differences in the prevalence of completed treatment outcomes highlight the need to strengthen efforts to support patient adherence and access to care in our setting. Addressing barriers to treatment completion, such as improving healthcare access, enhancing patient support systems, integrating TB and HIV care, and managing comorbid conditions, will be critical for improving outcomes. Additionally, aligning our monitoring and reporting practices with global standards will help ensure accurate comparisons with other countries and provide a clearer understanding of where improvements are needed. The default treatment among TB/HIV co-infected patients in this study was 6.4%. This rate is lower than what has been reported among TB/HIV co-infected patients (7.0%) in South Africa ( 15 ) and among TB patients (9.9%) in a Nigerian state ( 16 ). However, it is higher than the default rate reported among TB/HIV co-infected patients (2.3%) at the Greater Accra Regional Hospital in Ghana ( 17 ). The finding suggests opportunities for further improvement in patient retention and treatment adherence. Identifying and adopting best practices from settings with lower default rates could help further reduce defaults. Additionally, this study found a death rate of 30.9% among TB/HIV co-infected patients. This rate is lower than the reported among 667 TB/HIV co-infected patients (32.8%) in Kelantan ( 5 ) but higher than the reported among HIV co-infected patients (5.5%) in Southwest Ethiopia ( 12 ) and the reported among TB patients (10.2%) in Malaysia ( 18 ). Therefore, the observed death rate in this study highlights a need for continued efforts to reduce mortality among TB/HIV co-infected patients. Understanding the factors influencing TB treatment outcomes at different treatment outcomes is crucial for devising effective intervention strategies and improving health outcomes specifically. This study employed a multinomial logistic regression model with a cured outcome as the reference group. Completed Treatment This study identified several variables significantly associated with the likelihood of completing TB treatment among TB/HIV co-infected patients, including the duration of treatment, the presence of diabetes mellitus, occupation, and CXR status during the diagnosis. Each variable contributes differently to the probability of treatment completion, offering valuable insights into patient management and informing targeted interventions to enhance outcomes. Patients with longer treatment durations had higher odds of completing the treatment. With the increase in the 1-month duration of treatment, the chance for complete treatment outcome was estimated to be 84% (aRRR = 1.084, 95% CI = 1.02, 1.19, P = 0.005). This result suggests that adhering to treatment regimens for extended periods improves treatment outcomes. Patients who remain engaged in their care and continue treatment over time may be more likely to complete the treatment. Additionally, it could imply that early and sustained engagement in treatment, supported by healthcare providers, enhances the likelihood of completion. It was supported by the WHO, which emphasizes the importance of completing the entire course of treatment ( 19 ). The presence of diabetes mellitus is negatively associated with TB treatment completion, with patients having underlying diabetes mellitus having an 80.4% lower chance of completing treatment compared to those without diabetes mellitus (aRRR = 0.196, 95% CI = 0.11, 0.39, P < 0.001). This finding is consistent with previous reports that diabetes mellitus is a significant risk factor for TB ( 20 , 21 , 22 ). Diabetes mellitus may exacerbate TB symptoms, affect drug metabolism, or cause additional complications, making it more challenging for patients to adhere to and complete TB treatment. Moreover, diabetes mellitus may affect immune function, leading to slower recovery or increased side effects from TB medications, further reducing the likelihood of completing treatment. This highlights the need for integrated care approaches that address both TB and diabetes mellitus concurrently, including careful monitoring, tailored treatment plans, and patient education on managing both conditions. Certain occupations were found to be significantly linked to the completion of TB treatment. Patients working as government servants were estimated to have 2.44 times the chance of having a complete treatment outcome compared to unemployed patients (aRRR = 2.435, 95% CI = 1.15, 5.11, P = 0.019). This finding suggests that employment, particularly stable jobs such as government service, may provide patients with better access to healthcare resources, social support, and financial stability, all of which can facilitate treatment adherence and completion. Government employees may have better health benefits, easier access to medical leave, or more flexible schedules that allow them to attend follow-up appointments and adhere to treatment regimens. In contrast, unemployed patients may face financial constraints, a lack of social support, or limited access to healthcare, which can lead to lower treatment completion rates. This underscores the importance of addressing social determinants of health, such as employment status, to improve TB treatment outcomes. Abnormal CXR findings were also significantly associated with completed treatment outcomes. Patients with minimal lesions of CXR were estimated to have 84.4% less chance of having a complete treatment outcome than patients with no lesions (aRRR = 0.156, 95% CI = 0.09, 0.29, P < 0.001). Similarly, patients with moderately advanced disease were estimated to have an 89.8% lower chance of achieving a complete treatment outcome than patients with no lesions (aRRR = 0.102, 95% CI = 0.05–0.19, P < 0.001). Patients with far advanced were estimated to have 94.7% less chance of a complete treatment than patients with no lesions (aRRR = 0.053, 95% CI = 0.15, 0.18, P < 0.001). These findings suggest that more severe CXR abnormalities (i.e., moderately advanced or far advanced) are associated with lower odds of treatment completion. The study shows that CXR findings were associated with treatment success, with results of advanced observation having a low success rate compared to no lesion or minimal ( 23 ). This could be because patients with more severe TB disease are likely to experience more significant symptoms, adverse effects, or complications, which may lead to treatment discontinuation or default. On the other hand, patients with lesion CXR findings may have a less severe form of the disease or better overall health, making them more likely to complete treatment. The lower likelihood of treatment completion among patients with minimal or more severe CXR findings indicates a need for targeted interventions to support these patients. For those with minimal findings, it is crucial to recognize the importance of completing treatment, even if their symptoms are mild or improve rapidly. For those with severe findings, providing additional medical support, managing side effects, and offering social and psychological support may improve treatment adherence and completion. Defaulted Treatment This study identified several variables significantly associated with defaulted TB treatment, including age, treatment duration, residency, DOTS, case category, and CXR status at the time of diagnosis. The study found that older age was significantly associated with higher odds of defaulting on treatment. With the increase of 1-year in age, the chance was estimated to be 4.7% less chance to have a defaulted treatment outcome (aRRR = 0.953, 95% CI = 0.93, 0.98, P = 0.001). This finding may suggest that older patients are potentially more experienced in managing chronic conditions and adhering to treatment regimens. Earlier African studies have identified different effects of demographic factors on treatment default. Other studies have found that patients over 25 are more likely to default than their younger ( 24 , 25 ). However, other researchers ( 26 , 27 ) have found no connection between treatment default and the age variable. Alternatively, it might indicate that older patients are more likely to have a supportive network or better access to healthcare resources. Further investigation into the reasons behind this association could provide insights into how age-related factors influence treatment adherence and default. Prolonged treatment duration was a significant predictor of defaulting. With the increase of 1-month in the duration of treatment, the chance was estimated to be 36.4% less chance to have a defaulted treatment outcome (aRRR = 0.636, 95% CI = 0.56, 0.72, P < 0.001). This result suggests that longer treatment durations may increase the complexity of adhering to the regimen, leading to higher default rates. This study is supported by the study among TB patients in Northeast Ethiopia ( 28 ), which reported that 90.4% defaulted on the treatment in the continuous phase due to the long course of TB treatment being complex. This is also supported by a study in Uganda ( 28 ) that indicates a correlation between the length of TB treatment and defaulting from treatment. Extended treatment periods may contribute to patient fatigue, side effects, or loss of motivation, ultimately leading to treatment default. To address this issue, it is crucial to provide ongoing support and counselling to help patients stay engaged and motivated throughout their treatment. The area of residence (urban vs. rural) was found to be a significant factor. Patients in rural areas were estimated to have a 57.5% lower chance of defaulting treatment outcomes than those in urban areas (aRRR = 0.425, 95% CI = 0.23, 0.79, P = 0.007). This finding suggests that rural patients are less likely to default on treatment, which may be attributed to stronger community support systems or effective local health initiatives in rural areas. It was supported by a study among PTB patients in Yemen ( 29 ) that indicated that patients in rural areas had a higher risk of non-compliance compared to metropolitan areas, and the study among TB patients in western Ethiopia ( 30 ) which reported that patients were more likely to have a successful treatment if they were urban residents compared to rural residents. Alternatively, it could reflect differences in access to healthcare services, with urban patients potentially facing more significant barriers to treatment adherence. Participation in DOTS was significantly linked to defaulting outcomes. Patients without a DOTS program were estimated to have a 45.96 times higher chance of having defaulted treatment outcomes compared to patients with a DOTS program (aRRR = 45.958, 95% CI = 4.82, 438.53, P = 0.001). DOTS ensures patients receive supervised and consistent treatment, which is crucial for adherence and successful outcomes. A study among PTB patients in Kepong district, Kuala Lumpur, Malaysia ( 14 ) did not show a significant association between DOTS and unsuccessful treatment outcomes. A study among TB patients in West Bengal, India ( 31 ) reported that the factor independently associated with defaulting was the DOT program. The significant association underscores the need to expand DOTS coverage and ensure that all patients have access to this crucial component of TB care. Different case categories were significantly associated with treatment default. Patients with recurrent case TB category were estimated to have a 2.41 times higher chance of having defaulted treatment outcomes compared to the new case TB category (aRRR = 2.409, 95% CI = 1.07, 5.41, P = 0.033). Patients with after failure/default case TB category were estimated to have a 5.38 times chance of defaulting treatment outcome compared to the new case TB category (aRRR = 5.379, 95% CI = 1.77, 16.33, P = 0.003). A study done among TB patients in western Ethiopia ( 30 ) indicated that patients were less likely to have a successful treatment if they were retreatment cases compared to patients with new TB cases. These findings suggest that patients with recurrent or previously failed TB cases are more likely to default on treatment. This could be due to factors such as treatment fatigue, lack of confidence in the treatment regimen, or challenges in managing recurrent disease. Enhanced support and tailored interventions for these patients, including additional counselling and monitoring, could help reduce default rates among these high-risk groups. Abnormal CXR findings are also significantly associated with treatment default. Patients with minimal, moderately advanced, and far advanced had lower odds of defaulting compared to those with no lesions of CXR findings. Specifically, patients with minimal had 78.7% less chance of defaulting (aRRR = 0.213, 95% CI = 0.09, 0.52, P = 0.001); moderately advanced had 84.5% less chance of defaulting (aRRR = 0.155, 95% CI = 0.66, 0.40, P < 0.001); and far advanced had 93.1% less chance of defaulting (aRRR = 0.069, 95% CI = 0.01, 0.66, P = 0.020). This suggests that patients with more severe chest X-ray (CXR) abnormalities are less likely to default on treatment. This could be because severe TB cases often require more intensive management and close monitoring, which may help ensure adherence. Conversely, patients with less severe disease might not perceive the need for strict adherence as urgent, potentially leading to higher default rates. Targeted interventions to reinforce the importance of completing treatment, regardless of disease severity, could help improve adherence rates among all patients. Death This study highlights several variables significantly associated with death outcomes among TB/HIV co-infected patients in East Coast Malaysia, including treatment duration, diabetes mellitus, DOTS, occupation, and CXR status during the diagnosis. Longer duration of treatment was significantly associated with a higher likelihood of death. With the increase of 1-month in the duration of treatment, the chance was estimated to be 59.4% less chance of death (aRRR = 0.406, 95% CI = 0.36, 0.46, P < 0.001). This finding indicates that patients who remain on treatment for an extended period have a lower risk of death. It could reflect that prolonged treatment allows for better disease control and reduces mortality risk, possibly due to improved management of both TB and HIV. Alternatively, it may suggest that patients who are more engaged in treatment are generally healthier or have better access to healthcare resources, leading to improved survival rates. It is also possible that more severe cases with a higher risk of death might be excluded from the study if they do not complete the extended treatment period. Enhanced patient support and monitoring throughout treatment are crucial for maintaining adherence and reducing mortality. Diabetes mellitus was also a significant predictor of death. Contrary to some expectations, patients with underlying diabetes mellitus were estimated to have a 65.8% lower chance of death compared to patients without diabetes mellitus (aRRR = 0.342, 95% CI = 0.13, 0.79, P = 0.013). This result is unexpected, as diabetes mellitus is typically associated with worse outcomes in TB patients due to its impact on immune function and treatment response. One possible explanation could be that patients with diabetes mellitus are receiving more comprehensive care or are under more rigorous monitoring due to their comorbidity, which might contribute to better management and lower mortality. Alternatively, there may be residual confounding or differences in patient characteristics between those with diabetes mellitus and those without. Further research is needed to explore this counterintuitive finding and understand the underlying factors that contribute to it. Participation in DOTS is also associated with death outcomes. Patients without a DOTS program were estimated to have 29.41 times the chance of having death outcomes compared to patients with a DOTS program (aRRR = 29.412, 95% CI = 3.14, 275.18, P = 0.003). This strong association highlights the crucial role of DOTS in enhancing patient outcomes. In 2015, it was reported that 89.6% of patients practiced DOTS for TB case management and treatment. A study using data from the National Registry in Malaysia ( 6 ) showed that patients who did not receive DOTS were nine ( 9 ) times more likely to have unsuccessful treatment outcomes and death than those who had received DOTS. Therefore, they recommend better adherence using DOTS approaches to improve the treatment outcomes. Specific occupations were found to be significantly linked to the likelihood of death. Patients who are working in other types were estimated to have 46.9% less chance of having death outcomes compared to unemployed patients (aRRR = 0.531, 95% CI = 10.32, 0.88, P = 0.014). This finding suggests that employment status might influence treatment outcomes and mortality. Employed patients may have better access to healthcare resources, financial stability, and social support, which can contribute to improved health management and lower mortality rates. In contrast, unemployed patients may face barriers such as financial constraints or limited access to healthcare, which increases their risk of poor outcomes. The study done in Southwest Ethiopia indicated that mortality among TB/HIV co-infection was high among participants with economic hardships and poor incomes ( 32 ). Addressing these socioeconomic factors through targeted interventions could help improve the survival rates among unemployed patients. The abnormal CXR results were found to be strongly associated with death. Patients with minimal lesions were estimated to have 74.9% less chance of having death outcomes compared to patients with no lesions (aRRR = 0.251, 95% CI = 0.11, 0.59, P = 0.001). Patients with moderately advanced were estimated to have 80.8% less chance of having death outcomes compared to patients with no lesion (aRRR = 0.192, 95% CI = 0.07, 0.46, P < 0.001). This indicates that patients with less severe CXR abnormalities (minimal or moderate advanced) have a lower risk of death compared to those with more severe abnormalities. The severity of CXR findings often reflects the extent of lung damage and disease progression, which is closely linked to treatment outcomes. Patients with severe CXR findings might have more advanced disease or complications, contributing to higher mortality. This emphasizes the importance of early diagnosis and treatment of TB to prevent severe disease progression and reduce mortality. These findings regarding the factors significantly associated with death outcomes among TB/HIV co-infected patients, such as treatment duration, diabetes mellitus, DOTS, occupation, and CXR status during the diagnosis, contradicted the study in South Africa ( 33 ), which reported that lower CD4 count, lower BMI, presence of TB-related symptoms, detectable LAM, and more severe anaemia were significant risk factors for death. Recommendation The results of this study demonstrate that various factors significantly influence TB treatment outcomes among TB/HIV co-infected patients. Duration of treatment and CXR status during the diagnosis emerged as consistent predictors across all treatment outcomes, indicating their critical role in influencing the likelihood of completing treatment, defaulting, or death. These findings suggest that the length of treatment and radiological findings play an essential procedure in determining TB treatment success or unsuccess. Additionally, factors such as diabetes mellitus, occupation, and DOTS were associated with higher likelihoods of treatment default or death, underscoring the need for targeted interventions aimed at improving treatment adherence and survival in these vulnerable populations. By identifying the associated factors, this study offers valuable insights for healthcare providers and policymakers to develop targeted strategies that improve TB treatment outcomes. Special attention should be given to patients with comorbidities like diabetes, those from specific occupational backgrounds, and those requiring intensive DOTS supervision. Strengthening early detection and management of these factors can lead to better TB treatment outcomes, potentially reducing default and mortality rates among TB/HIV co-infected patients in East Coast Malaysia. Implications By strengthening TB/HIV collaborative efforts and tailoring interventions based on patient characteristics, healthcare providers can improve treatment success and reduce the risk of default and Death among TB/HIV co-infected patients in East Coast Malaysia. Limitations In this study, the secondary data were utilized from the MyTB database, which introduced several limitations affecting data quality and interpretability. Firstly, it was observed that certain variables were not consistently recorded. Different terms were used, such as occupation and education level. These inconsistencies required researchers to undertake considerable preprocessing, such as recoding and standardize the dataset. Secondly, the original data in the MyTB registry may also be subject to response biases, especially for fields based on patient self-report, such as occupation, education level, or comorbidities. Additionally, healthcare providers recording this information may have inconsistently interpreted or entered responses, which further contributes to data variability. Secondary databases, such as MyTB, are designed for administrative or clinical surveillance rather than research, which means that certain relevant variables may be missing or inadequately captured. Because the researchers did not collect the original data themselves, they had limited control over the accuracy, completeness, or consistency of the records. Being aware of these potential issues and taking steps to mitigate them can help improve the quality and reliability of studies using secondary data from a registry. Future Research This study suggested several areas for further research, such as longitudinal studies, to better understand the causal relationships between the identified factors and TB treatment outcomes. Prospective studies and intervention trials are needed to explore further and address the factors influencing TB treatment outcomes in TB/HIV co-infected populations. Conclusion This study provides insights into the challenges of managing TB treatment among TB/HIV co-infected patients. Variables such as treatment duration and CXR findings appear to be associated with completed treatment, default, and death. Additionally, diabetes mellitus status, occupation, and the DOT program may influence adherence and survival. These findings suggest that context-specific interventions, particularly for patients with comorbidities, certain occupational groups, and those requiring closer treatment supervision, could support improved TB care. Further investigation and early intervention strategies may contribute to better treatment outcomes among TB/HIV co-infected patients in East Coast Malaysia. Abbreviations aRRR: Adjusted Relative Risk Ratio; BCG: Bacillus Calmette–Guerin;CI: Confidence Interval; CXR: Chest X-ray; DOTS: Directly Observed Therapy Shortcourse; HIV: human immunodeficiency virus; JKN: Jabatan Kesihatan Negeri; MOH: Ministry of Health; MREC: Medical Research Ethics Committee; PLHIV: people living with HIV; PTB: Pulmonary TB; SD: standard deviation; TB: Tuberculosis; TBIS: Tuberculosis Information System; WHO: World Health Organization Declarations Ethics Approval and consent to participate The study was conducted in compliance with ethical principles outlined in the Declaration of Helsinki and the Malaysian Good Clinical Practice Guideline. This study obtained ethical approval from the UniSZA Human Research Ethics Committee (UHREC) and the Medical Research Ethics Committee (MREC), MOH (NMRR-19-2628-50776 (IIR)). The requirement for informed consent to participate was waived by both UHREC and MREC as the study involved analysis of anonymized secondary data retrieved solely from the national TB Information System (TBIS) registry, which is is a digital platform or database designed to collect, manage, and analyse data related to TB cases and their management with no direct patient contact. This waiver is in accordance with Malaysian national regulations on medical research ethics. Clinical Trial Number Not applicable Consent for Publication Not Applicable Availability of data and materials The dataset is available, but to protect subject privacy, it cannot be shared openly. Anyone requesting this dataset should consult the TB Sector in the Ministry of Health, Malaysia. Competing interests The authors declare no conflict of interest. Funding This study has not received any funding sources. Author Contribution Declaration Siti Romaino Mohd Nor contributed to the conceptualization, methodology, statistical analysis, interpretation of results, drafting, and revising of the manuscript. Nyi Nyi Naing contributed to the interpretation of the results, reviewed the manuscript, and approved the final draft of the manuscript. Mat Zuki Mat Jaeb contributed to the review of the manuscript and approved the final draft. Acknowledgments The authors thank the Director-General of Health, Malaysia, for his kind permission to publish these research findings, as well as the organizations that contributed to the study. References Darraj MA, Abdulhaq AA, Yassin A, Mubarki S, Shalaby HM, Keynan Y et al. Tuberculosis among people living with HIV/AIDS in Jazan Region, Southwestern Saudi Arabia. J Infect Public Health [Internet]. 2021;14(11):1571–7. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1876034121002707 World Health Organization. Global Tuberculosis Report 2019. 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Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6910886","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":477975552,"identity":"d29b7afa-ba63-4006-8670-de03eb582838","order_by":0,"name":"Siti Romaino Mohd Nor","email":"","orcid":"","institution":"Sultan Zainal Abidin University","correspondingAuthor":false,"prefix":"","firstName":"Siti","middleName":"Romaino Mohd","lastName":"Nor","suffix":""},{"id":477975553,"identity":"a69518fc-1d4d-47b2-989d-6d0fa6517d30","order_by":1,"name":"Nyi Nyi Naing","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYJACaSDmYWBvYGBIgAgYEKPFgIeB5wCJWhgYJBLgAvi1yM9IPni7oOaPjO7MN2YPHvypk2dgb94mwZhzGKcWgxtpydYzjhnwmN3OMTdIbGMzbOA5VibBuA2PFokcM2keNrAWM4nEBh7GBqAIXi3yM/K/SfP8A2q5ecZMIuGPhH2D/Bv8Whhu5LBJ87YBtdzgAWphM0hskODBr8XgzDNja94+Yx6zM2llEoltCcltPGnFFonb0nE7rD354W2eb3L2ZscPb5P88afOtp/98MYbH7dZ43aYQAKaABuISGBoxq2F/wB28TrcWkbBKBgFo2CkAQC+TU2kcCeKrgAAAABJRU5ErkJggg==","orcid":"","institution":"Sultan Zainal Abidin University","correspondingAuthor":true,"prefix":"","firstName":"Nyi","middleName":"Nyi","lastName":"Naing","suffix":""},{"id":477975554,"identity":"a7f576f2-f481-40fa-9c7c-49bf46737cd6","order_by":2,"name":"Mat Zuki Mat Jaeb","email":"","orcid":"","institution":"Hospital Raja Perempuan Zainab II","correspondingAuthor":false,"prefix":"","firstName":"Mat","middleName":"Zuki Mat","lastName":"Jaeb","suffix":""}],"badges":[],"createdAt":"2025-06-17 06:24:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6910886/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6910886/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12879-025-11649-0","type":"published","date":"2025-10-08T15:56:51+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":85827916,"identity":"f5bdae2c-9fbc-49d4-bef8-7def3850a7f4","added_by":"auto","created_at":"2025-07-02 07:24:24","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":46620,"visible":true,"origin":"","legend":"\u003cp\u003eThe Prevalence of TB Treatment Outcomesamong TB/HIV Co-infected Patients in East Coast Malaysia between 2016 and 2020 (n = 1,223).\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6910886/v1/dca9e44378407647662f1802.jpg"},{"id":93419457,"identity":"efb947ea-8acc-4b2f-a5cf-f306bd063fed","added_by":"auto","created_at":"2025-10-13 16:01:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1436165,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6910886/v1/a4b934ca-8b53-4fd4-8335-37adcf4974c9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Associated factors at different treatment outcomes (cured, completed, defaulted, and death) among TB/HIV co-infected patients in East Coast Malaysia: 5-year record review (2016 - 2020)","fulltext":[{"header":"Background","content":"\u003cp\u003eTuberculosis (TB) and human immunodeficiency virus (HIV) co-infection provide a serious global public health concern, especially in areas where both diseases are endemic. TB is a prominent cause of morbidity and mortality among people living with HIV (PLHIV), as the virus weakens the immune system, rendering people more vulnerable to TB infection and complicating treatment (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). According to the World Health Organization (WHO), TB is the primary cause of Death among HIV-positive people, accounting for one-third of HIV-related deaths worldwide (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Malaysia, the dual burden of TB and HIV is particularly concerning. The Ministry of Health (MOH) has observed a consistent increase in the number of TB/HIV co-infected patients, notably on the East Coast. Effective TB management and treatment in this population are crucial for improving health outcomes and limiting the spread of both illnesses. Understanding the factors that determine different treatment outcomes (cure, complete, default, and death) is critical for establishing targeted interventions and enhancing patient care.\u003c/p\u003e \u003cp\u003eThe success of TB treatment is typically measured by these outcomes, which reflect different levels of adherence to treatment protocols, patient health status, and healthcare system efficiency (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Identifying the factors associated with each treatment outcome is crucial for enhancing patient survival and success rates (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Previous studies have identified various factors influencing TB treatment outcomes, including sociodemographic characteristics, clinical comorbidities, and healthcare-related factors (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). However, there is a lack of availability of research specifically examining these factors among TB/HIV co-infected patients in East Coast Malaysia, particularly at different treatment outcomes. This gap in the literature highlights the need for research that not only identifies these factors but also differentiates their impact across the spectrum of treatment outcomes.\u003c/p\u003e \u003cp\u003eDespite the known challenges, limited research has specifically addressed the factors influencing TB treatment outcomes among TB/HIV co-infected patients in Malaysia. Previous studies have often focused on either TB or HIV in isolation without adequately addressing the complexities of co-infection (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Moreover, regional variations in healthcare infrastructure, socioeconomic conditions, and demographic profiles may lead to different outcomes, underscoring the importance of localized research. This study aims to assess the sociodemographic characteristics (age, gender, race, residency, occupation), clinical characteristics (duration of treatment, diabetes mellitus, Bacillus Calmette\u0026ndash;Guerin (BCG) scar, Chest X-ray (CXR) status, TB case category, smoking status, Directly Observed Therapy Shortcourse (DOTS), detection method), prevalence of TB treatment outcomes (cured, completed, defaulted, failure and death) and associated factors at different treatment outcomes among TB/HIV co-infected patients in East Coast Malaysia for 5 years.\u003c/p\u003e \u003cp\u003eThe findings of this study have significant public health implications. By understanding the determinants of treatment success and unsuccess, healthcare providers can develop more effective, tailored interventions to address the needs of TB/HIV co-infected patients. This study will also contribute to the broader knowledge base, offering valuable data for policymakers and researchers focused on combating TB and HIV. In summary, this research attempts to enhance our understanding of TB treatment outcomes in the context of HIV co-infection in East Coast Malaysia, with the ultimate goal of improving patient care and public health outcomes.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003ch4\u003e\u003cstrong\u003eStudy Design, Study Participant, and Population\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eThis cross-sectional study utilised secondary data from January 2016 to December 2020, sourced from the e-Notifikasi for Tuberculosis Information System (TBIS) at the State TB organiser or TB/Leprosy Unit, Jabatan Kesihatan Negeri (JKN) in Kelantan, Terengganu, and Pahang. The population comprised all TB/HIV co-infected patients in East Coast Malaysia (Kelantan, Terengganu, and Pahang) who met the study\u0026apos;s inclusion criteria.\u0026nbsp;\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003eInclusion Criteria \u0026amp; Exclusion Criteria\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eThe study included all TB/HIV co-infected patients aged 18 and above who tested positive for TB and HIV, confirmed for TB and HIV, enrolled in TBIS between January 2016 and December 2020, and had a TB treatment outcome such as cured, complete, defaulted, failure, or death. Non-Malaysian citizens, those transferred to other treatment centres, those with changes in diagnosis, those lost to follow-up, and those receiving continuing treatment at the time of data extraction were excluded.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003eSample Size Determination and Sampling Method\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eThe sample size was calculated using PS software based on proportions. The parameter for this estimation included a level of significance at 5%, a power of 80%, and a ratio of 1:1. An uncorrected chi-square statistic was used to evaluate this null hypothesis. According to the sample size calculations, the largest sample size was 230 patients per group. Assuming a 10% dropout, the total number of patients should be at least 1,278. Convenience sampling was employed to achieve the required sample size.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003eData Collection\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eThe person in charge at the State TB organizer or TB/Leprosy Unit has retrieved and downloaded the data from the TBIS online system, including the patient\u0026apos;s sociodemographic (\u003cstrong\u003eage, gender, race, residency, occupation\u003c/strong\u003e), clinical characteristics (\u003cstrong\u003eduration of treatment,\u0026nbsp;\u003c/strong\u003ediabetes mellitus\u003cstrong\u003e, BCG scar, CXR status, TB case category, smoking status, DOTS, detection method\u003c/strong\u003e), and TB treatment outcome into Microsoft Excel for patients identified as TB/HIV co-infection. However, a few parameters, such as level of education, treatment regimen, source of notification, HAART treatment, marital status, and monthly income, were not fully recorded. Therefore, the researcher requests data from the Disease Control Division, MOH, to obtain the complete database. Unfortunately, we still haven\u0026apos;t received that information. All the required information in this study was extracted in July 2022.\u0026nbsp;Then, the researcher exports the data from\u0026nbsp;Microsoft\u0026nbsp;Excel to\u0026nbsp;IBM SPSS Statistics version 25.0 and STATA 14\u0026nbsp;for further analysis.\u0026nbsp;The data analysis divided the final TB treatment outcome into four treatment outcomes (cured, completed, defaulted, and dead) due to the failure outcomes being recorded as zero cases.\u0026nbsp;\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003e\u003cstrong\u003eIndependent Variables\u003c/strong\u003e: \u003cstrong\u003eAge, Gender, Race, Residency, Occupation\u003c/strong\u003e, \u003cstrong\u003eDuration of treatment,\u0026nbsp;\u003c/strong\u003eDiabetes mellitus\u003cstrong\u003e, BCG scar, CXR status, TB case category, Smoking Status, DOTS, Detection method\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDependent Variables\u003c/strong\u003e: Cured, completed, defaulted, and dead.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003eData Analysis\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eThe descriptive data were summarized using IBM SPSS Statistics version 25.0. The data was examined, validated, and cleansed to detect inaccuracies or missing numbers. Categorical data were summarized as frequency (n) and percentage (%). Due to the normal distribution, numerical data were summarized as mean and standard deviation (SD). The prevalence of TB treatment outcomes was expressed as a percentage (%). The multinomial logistic regression was analyzed using the STATA program, Version 14, to assess the relationship between independent variables (e.g., demographic factors, clinical characteristics) and the categorical outcome of TB treatment (cured, completed, defaulted, or died) among TB/HIV co-infected patients. This strategy is appropriate given the nominal character of the dependent variable, which has more than two unsorted categories. A multinomial logistic regression model was used to calculate the log odds of each treatment outcome compared to the reference group (cured). Independent variables were selected based on their theoretical relevance and prior research. Effect modification was assessed using interaction terms between two variables. Regression analysis aims to create a model that is the best fit, parsimonious, physiologically plausible, and statistically significant.\u003c/p\u003e\n\u003ch3\u003eOperational definitions\u003c/h3\u003e\n\u003cp\u003eAccording to the Clinical Practice Guidelines for Management of Tuberculosis (7), the following\u0026nbsp;operational terminology;\u0026nbsp;\u003c/p\u003e\n\u003col style=\"list-style-type: lower-roman;\"\u003e\n \u003cli\u003eCured: A Pulmonary TB (PTB) patient who had bacteriologically confirmed TB at the start of treatment and tested negative for smears or cultures in the final month of treatment and on at least one earlier occasion.\u003c/li\u003e\n \u003cli\u003eCompleted: A TB patient who completed treatment without evidence of failure but has no record of negative sputum smear or culture results in the last month of treatment or on at least one previous occasion, either due to lack of testing or unavailable results.\u003c/li\u003e\n \u003cli\u003eFailed: A TB patient whose sputum smear or culture is positive in month 5 or after treatment.\u003c/li\u003e\n \u003cli\u003eDefault: A patient who has stopped treatment for two months or more.\u003c/li\u003e\n \u003cli\u003eDeath: A TB patient who passed away before or during treatment.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe severity of the lesion on the X-ray film was used to characterize the CXR presentation following diagnosis. It was classified into;\u003c/p\u003e\n\u003col style=\"list-style-type: lower-roman;\"\u003e\n \u003cli\u003eNo lesion if the CXR showed no lesions,\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMinimal if the CXR showed a few lesions,\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eModerate advance if the CXR showed many lesions,\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eFar advanced if the CXR revealed extensive lesions or a military appearance, and\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eNot done if the CXR was not performed during the diagnosis.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSociodemographic Characteristics of Study Participants\u003c/h2\u003e \u003cp\u003eThere were 14,289 TB cases, with 1,292 (9.0%) TB/HIV co-infections in East Coast Malaysia from 2016 to 2020. However, 69 TB/HIV cases were excluded due to transferring to another treatment centre, change of diagnosis, and still ongoing treatment. Therefore, 1,223 TB/HIV co-infected patients were evaluated. Of these 1,223 TB/HIV cases, their age ranged from 18 to 77 years, with a mean (SD) age of 42.1 (12.1) years. The majority of cases involved males (82.4%), Malays (92.1%), and individuals residing in rural areas (77.8%), with other occupations also represented (45.6%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eClinical Characteristics of Study Participants\u003c/h2\u003e \u003cp\u003eThe duration of TB treatment ranged from 0 to 21 months with a mean (SD) of 5.6 (3.9) months. There were 89.3% non-diabetes mellitus cases, 94.8% had a BCG scar, 47.3% had minimal lesions on CXR during diagnosis, 85.3% were new TB cases, 56.7% were smokers, 86.6% were under DOTS, and 87.8% had a passive detection method. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the descriptive statistics at different TB treatment outcomes among TB/HIV co-infected patients in East Coast Malaysia (n\u0026thinsp;=\u0026thinsp;1,223).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics at Different TB Treatment Outcomes among TB/HIV Co-infected Patients in East Coast Malaysia (n\u0026thinsp;=\u0026thinsp;1,223)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCured (Reference)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCompleted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDefaulted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDeath\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;1,223\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.7\u0026thinsp;\u0026plusmn;\u0026thinsp;13.0*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.4\u0026thinsp;\u0026plusmn;\u0026thinsp;11.8)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.6*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42.7\u0026thinsp;\u0026plusmn;\u0026thinsp;11.1*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42.1\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of treatment (months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.41*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e336 (82.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e283 (79.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68 (87.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e321 (84.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1008 (82.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74 (18.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74 (20.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57 (15.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e215 (17.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMalays\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e384 (93.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e337 (94.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72 (92.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e333 (88.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1126 (92.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Malays\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e97 (7.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68 (16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83 (23.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28 (35.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e92 (24.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e271 (22.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e342 (83.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e274 (76.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50 (64.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e286 (75.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e952 (77.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGovernment staff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (9.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e66 (5.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e153 (37.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138 (38.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32 (41.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e185 (48.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e508 (41.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrisoners\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 (14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25 (6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e91 (7.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e210 (51.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e163 (45.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34 (43.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e151 (40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e558 (45.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e324 (79.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e341 (95.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76 (97.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e351 (92.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1092 (89.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86 (21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e131 (10.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCG Scar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e392 (95.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e341 (95.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77 (98.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e349 (92.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1159 (94.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e64 (5.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXR status during the diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo lesion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87 (24.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15 (19.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43 (11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e160 (13.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinimal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e207 (50.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e171 (47.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36 (46.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e165 (43.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e579 (47.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerately advanced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e169 (41.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86 (24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 (30.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e141 (37.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e420 (34.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFar advanced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e49 (4.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot done\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15 (1.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory case\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNew\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e356 (86.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e310 (86.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57 (73.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e320 (84.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1043 (85.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRecurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e138 (11.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAfter fail/default\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42 (3.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e178 (43.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e187 (52.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 (30.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e141 (37.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e530 (43.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e232 (56.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e170 (47.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54 (69.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e237 (62.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e693 (56.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDOT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e409 (99.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e352 (98.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62 (79.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e236 (62.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1059 (86.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16 (20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e142 (37.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e164 (13.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDetection method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e55 (4.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePassive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e350 (85.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e319 (89.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60 (76.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e345 (91.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1074 (87.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScreening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e94 (7.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003emean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD*\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eThe Prevalence of TB Treatment Outcomes\u003c/h2\u003e \u003cp\u003eIn this study, the prevalence of TB treatment outcomes among 1,223 TB/HIV co-infected patients was 33.5% (410) cured, 29.2% (357) completed, 6.4% (78) defaulted, and 30.9% (378) death. There were no failure cases identified, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. For this reason, the multinomial logistic regression analysis excluded the failure outcomes from the subsequent analysis step. Thus, the analysis and reporting of TB treatment outcomes were limited to four categories: cured (as the reference), completed, defaulted, and died.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAssociated Factors\u003c/h2\u003e \u003cp\u003eIn the completed treatment outcome analysis, several variables were found to be significantly associated with the likelihood of completing treatment, specifically duration of treatment, diabetes mellitus, occupation, and CXR status during diagnosis. With the increase in the 1-month duration of treatment, the chance of achieving a complete treatment outcome was estimated to increase by 8% (aRRR\u0026thinsp;=\u0026thinsp;1.08, 95% CI\u0026thinsp;=\u0026thinsp;1.02, 1.15, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005). The presence of diabetes mellitus is negatively associated with TB treatment completion. Patients with underlying diabetes mellitus have an 80% lower chance of completing treatment compared to those without diabetes mellitus (aRRR\u0026thinsp;=\u0026thinsp;0.20, 95% CI\u0026thinsp;=\u0026thinsp;10.12, 0.39, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients working as government servants were estimated to have a 2.44 times higher chance of achieving a complete treatment outcome compared to unemployed patients (aRRR\u0026thinsp;=\u0026thinsp;2.44, 95% CI\u0026thinsp;=\u0026thinsp;1.15, 5.11, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019). Patients with minimal lesions of CXR were estimated to have an 84% lower chance of achieving a complete treatment outcome compared to patients with no lesion CXR (aRRR\u0026thinsp;=\u0026thinsp;0.16, 95% CI\u0026thinsp;=\u0026thinsp;0.09, 0.29, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, patients with moderately advanced disease were estimated to have a 90% lower chance of achieving a complete treatment outcome compared to patients with no lesion on CXR (aRRR\u0026thinsp;=\u0026thinsp;0.10, 95% CI\u0026thinsp;=\u0026thinsp;0.05, 0.19, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients with far advanced were estimated to have a 95% lower chance of achieving a complete treatment outcome compared to patients with no lesion CXR (aRRR\u0026thinsp;=\u0026thinsp;0.05, 95% CI\u0026thinsp;=\u0026thinsp;0.15, 0.18, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eIn the default treatment outcome, the variables associated with an increased likelihood of defaulting on TB treatment compared to being cured were age, duration of treatment, residency, DOT, case category, and CXR status at diagnosis. With the increase of 1-year in age, the chance of having a defaulted treatment outcome is estimated to decrease by 5% (aRRR\u0026thinsp;=\u0026thinsp;0.95, 95% CI\u0026thinsp;=\u0026thinsp;0.93, 0.98, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). With the increase of 1-month in the duration of treatment, the chance of having a defaulted treatment outcome is estimated to decrease by 36% (aRRR\u0026thinsp;=\u0026thinsp;0.64, 95% CI\u0026thinsp;=\u0026thinsp;0.56, 0.72, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients in rural areas were estimated to have a 57% lower chance of defaulting on treatment outcomes than those in urban areas (aRRR\u0026thinsp;=\u0026thinsp;0.43, 95% CI\u0026thinsp;=\u0026thinsp;0.23, 0.79, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007). Patients without a DOT program were estimated to have a 45.96 times higher chance of having defaulted treatment outcomes compared to patients with a DOT program (aRRR\u0026thinsp;=\u0026thinsp;45.96, 95% CI\u0026thinsp;=\u0026thinsp;4.82, 438.53, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). Patients with recurrent case TB were estimated to have a 2.41 times higher chance of having defaulted treatment outcomes compared to those with new case TB (aRRR\u0026thinsp;=\u0026thinsp;2.41, 95% CI\u0026thinsp;=\u0026thinsp;1.07, 5.41, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033). Patients after failure/default case TB were estimated to have a 5.38 times higher chance of defaulting on treatment outcomes compared to those with new case TB (aRRR\u0026thinsp;=\u0026thinsp;5.38, 95% CI\u0026thinsp;=\u0026thinsp;1.77, 16.33, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003). Patients with minimal, moderately advanced, and far advanced had lower odds of defaulting compared to those with no lesions on CXR. Specifically, patients with minimal lesions had a 79% lower chance of defaulting (aRRR\u0026thinsp;=\u0026thinsp;0.21, 95% CI\u0026thinsp;=\u0026thinsp;0.09, 0.52, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001); patients with moderately advanced lesions had an 84% lower chance of defaulting (aRRR\u0026thinsp;=\u0026thinsp;0.16, 95% CI\u0026thinsp;=\u0026thinsp;0.66, 0.40, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001); and patients with far advanced lesions had a 93% lower chance of defaulting (aRRR\u0026thinsp;=\u0026thinsp;0.07, 95% CI\u0026thinsp;=\u0026thinsp;0.01, 0.66, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020).\u003c/p\u003e \u003cp\u003eMeanwhile, in the death outcome, several variables were found to be significantly associated with death as an outcome of treatment when compared to being cured, such as duration of treatment, diabetes mellitus, DOT, occupation, and CXR status during the diagnosis. With a 1-month increase in treatment duration, the chance of a death outcome is estimated to decrease by 59% (aRRR\u0026thinsp;=\u0026thinsp;0.41, 95% CI\u0026thinsp;=\u0026thinsp;0.36, 0.46, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients with underlying diabetes mellitus were estimated to have a 66% lower chance of death compared to patients without diabetes mellitus (aRRR\u0026thinsp;=\u0026thinsp;0.34, 95% CI\u0026thinsp;=\u0026thinsp;0.13, 0.79, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013). Patients without a DOT program were estimated to have a 29.41 times higher chance of having death outcomes compared to patients with a DOT program (aRRR\u0026thinsp;=\u0026thinsp;29.41, 95% CI\u0026thinsp;=\u0026thinsp;3.14, 275.18, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003). Patients working in other types of employment were estimated to have a 47% lower chance of having death outcomes compared to unemployed patients (aRRR\u0026thinsp;=\u0026thinsp;0.53, 95% CI\u0026thinsp;=\u0026thinsp;10.32, 0.88, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014). In terms of CXR presentation, patients with minimal lesions were estimated to have a 75% lower chance of having death outcomes compared to patients with no CXR lesion (aRRR\u0026thinsp;=\u0026thinsp;0.25, 95% CI\u0026thinsp;=\u0026thinsp;0.11, 0.59, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). Patients with moderately advanced were estimated to have an 81% lower chance of having death outcomes compared to patients with no lesion CXR (aRRR\u0026thinsp;=\u0026thinsp;0.19, 95% CI\u0026thinsp;=\u0026thinsp;0.08, 0.46, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eThe multinomial logistic regression analysis highlighted that different variables influence the likelihood of various TB treatment outcomes. Our study revealed that both the duration of treatment and CXR status during diagnosis were consistently significant predictors across all treatment outcomes (completed, defaulted, and death), indicating that these variables are important predictors for multiple treatment outcomes, not just one specific outcome. The final regression model is presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociated Factors at Different TB Treatment Outcomes (Completed, Defaulted, and Death) (n\u0026thinsp;=\u0026thinsp;1,223)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCompleted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDefaulted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDeath\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eaRRR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003e Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eaRRR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e- Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eaRRR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e- Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99 (0.97, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.95 (0.93, 0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.99 (0.97, 1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.296\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08 (1.02, 1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.64 (0.56, 0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.41 (0.36, 0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75 (0.50, 1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.43 (0.23, 0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.95 (0.53, 1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.120 (0.11, 0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.25 (0.06, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.34 (0.13, 0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.73 (0.52, 1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.97 (0.98, 3.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.393 (0.85, 2.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDOTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.84 (0.77, 61.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e45.96 (4.82, 438.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e29.41 (3.14, 275.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory case\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNew\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRecurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.11 (0.66, 1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.41 (1.07, 5.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.87 (0.93, 3.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAfter failure/ default\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71 (0.26, 1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.38 (1.77, 16.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.36(0.72, 7.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot working\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGov staff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.44 (1.15, 5.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.48 (0.06, 4.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.26 (0.39, 4.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrisoners\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.67 (0.36, 1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.61 (0.65, 3.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.13 (0.48, 2.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98 (0.69, 1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.73 (0.40, 1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.53 (0.32, 0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXR status during the diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo lesion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinimal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.16 (0.09, 0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.21 (0.09, 0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.25 (0.11, 0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate advance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.10 (0.05, 0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.16 (0.66, 0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.19 (0.08, 0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFar advance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05 (0.15, 0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.07 (0.01, 0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.28 (0.07, 1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot done\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08 (0.12, 9.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.47 (0.08, 28.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.47 (0.02, 10.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.629\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRegression coefficient (b), Adjusted Relative Risk Ratio (aRRR)\u003c/p\u003e \u003cp\u003eFootnote:\u003c/p\u003e \u003cp\u003eMultinomial logistic regression was applied.\u003c/p\u003e \u003cp\u003eThe linearity of the continuous variable was checked and reported to be linear.\u003c/p\u003e \u003cp\u003eMulticollinearity and interaction were unlikely.\u003c/p\u003e \u003cp\u003eThe overall fit of the model was checked and reported to be the Hosmer-Lemeshow test (completed: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.467; defaulted: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.053; Death: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000), Pearson chi-square test (completed: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.337; defaulted: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000; death: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000), overall correctly classified percentage (completed: 70.1%; defaulted: 91.6%; Death: 93.3%), Area under the ROC curve (completed: 0.771 (95% CI: 0.74, 0.804); defaulted: 0.898 (95% CI: 0.85, 0.94); Death: 0.843 (95% CI: 0.78, 0.91).\u003c/p\u003e \u003cp\u003eRegression diagnostic was performed by estimated logistic probability (p), Leverage (h), covariate pattern (n), Hosmer and Lemeshow Delta chi-squared influence statistic (dx2), Hosmer and Lemeshow Delta-D influence statistic (dd), and Pregibon Delta-Beta influence statistic (db).\u003c/p\u003e \u003cp\u003eInfluential outliers were identified by checking per cent changes in the regression coefficient (b) set at 20%.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aims to assess the sociodemographic characteristics, clinical characteristics, prevalence of TB treatment outcomes, and associated factors at different TB treatment outcomes (cured, completed, defaulted, and death) among TB/HIV co-infected patients in East Coast Malaysia for five (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) years. It included 1,223 cases, with 410 (33.5%) cured, 357 (29.2%) completed, 78 (6.4%) defaulted, and 378 (30.9%) death outcomes. There were no failure cases identified.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eSociodemographic Characteristics of Study Participants\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe sociodemographic findings of this study were similar to the study of 703 cases of TB/HIV co-infection in Kelantan between 2014 and 2018 in terms of age, gender, race, and residency. However, the results differed in terms of occupation in this study. Other occupation types accounted for 45.6%, whereas in the previous study, these types comprised 27.9% (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). The difference in occupational status could be due to variations in the study population, socioeconomic status, temporal factors, or other contextual variables that were not controlled for.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eClinical Characteristics of Study Participants\u003c/h2\u003e \u003cp\u003eIn this study, the overall mean (SD) of TB treatment duration among TB/HIV co-infected patients was 5.6 (3.9) months. This duration falls within the range reported in a study conducted in Ethiopia, where treatment durations of two to seven months were associated with successful outcomes (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The similarity between the mean duration of treatment in our study and the effective duration reported in Ethiopia suggests that adherence to a treatment period of around six to seven months may contribute to better TB treatment outcomes among TB/HIV co-infected patients. This aligns with the standard World Health Organisation (WHO) guidelines for TB treatment, which recommend a minimum of six months of therapy for drug-susceptible TB, provided the patient adheres to the treatment regimen without interruptions.\u003c/p\u003e \u003cp\u003eIn this study, the proportion of non-diabetic patients, minimal lesions of CXR during diagnosis, and smokers were lower than the figures reported in Kelantan between 2014 and 2018 (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). These differences may suggest changes in the demographic or clinical profile of TB/HIV co-infected patients over time. Despite these differences, our study found similar proportions of patients with BCG scars, those under DOT supervision, and those diagnosed through passive detection methods, compared to previous findings from Kelantan. This study also found that the proportion of new TB cases was higher (85.3%) than the previously reported figure (82.8%) in Kelantan. The higher proportion of new cases might indicate improved case detection efforts, perhaps due to increased public awareness, better diagnostic facilities, or enhanced screening programs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eThe Prevalence of TB Treatment Outcomes\u003c/h2\u003e \u003cp\u003eIn terms of the prevalence of TB treatment outcomes, this study shows that 33.5% had been cured. For cured outcomes, this prevalence was lower than among TB/HIV co-infected patients (91.3%) in Jakarta, Indonesia (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). However, it was higher than the study among TB patients attending the Public Hospitals in Harar Town, Eastern Ethiopia (30.4%) (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), TB/HIV co-infection in Kelantan itself (19.8%) (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), in Kwabre East Municipality of Ghana (14.3%) (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), and among 2,150 TB patients in Southwest Ethiopia (4.0%) (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). These differences in prevalence can be attributed to several factors, including variations in healthcare systems, TB control programs, patient populations, and socioeconomic conditions.\u003c/p\u003e \u003cp\u003eThis study also found notable differences in the prevalence of completed treatment outcomes among TB/HIV co-infected patients compared to reports from other countries. This study reported that 29.2% of participants had completed outcomes, which is lower than the rates observed in a study conducted at Public Hospitals of Harar Town, Eastern Ethiopia (56.7%) (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) and a study at Gondar University Referral Hospital, Northwest Ethiopia (66.9%) (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). However, it was higher than among smear-positive TB patients in the Kepong district, Kuala Lumpur, Malaysia (1.71%) (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Differences can influence these variations in treatment completion rates in healthcare systems, patient characteristics, and programmatic approaches to TB care. The observed differences in the prevalence of completed treatment outcomes highlight the need to strengthen efforts to support patient adherence and access to care in our setting. Addressing barriers to treatment completion, such as improving healthcare access, enhancing patient support systems, integrating TB and HIV care, and managing comorbid conditions, will be critical for improving outcomes. Additionally, aligning our monitoring and reporting practices with global standards will help ensure accurate comparisons with other countries and provide a clearer understanding of where improvements are needed.\u003c/p\u003e \u003cp\u003eThe default treatment among TB/HIV co-infected patients in this study was 6.4%. This rate is lower than what has been reported among TB/HIV co-infected patients (7.0%) in South Africa (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) and among TB patients (9.9%) in a Nigerian state (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). However, it is higher than the default rate reported among TB/HIV co-infected patients (2.3%) at the Greater Accra Regional Hospital in Ghana (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The finding suggests opportunities for further improvement in patient retention and treatment adherence. Identifying and adopting best practices from settings with lower default rates could help further reduce defaults.\u003c/p\u003e \u003cp\u003eAdditionally, this study found a death rate of 30.9% among TB/HIV co-infected patients. This rate is lower than the reported among 667 TB/HIV co-infected patients (32.8%) in Kelantan (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) but higher than the reported among HIV co-infected patients (5.5%) in Southwest Ethiopia (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) and the reported among TB patients (10.2%) in Malaysia (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Therefore, the observed death rate in this study highlights a need for continued efforts to reduce mortality among TB/HIV co-infected patients.\u003c/p\u003e \u003cp\u003eUnderstanding the factors influencing TB treatment outcomes at different treatment outcomes is crucial for devising effective intervention strategies and improving health outcomes specifically. This study employed a multinomial logistic regression model with a cured outcome as the reference group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eCompleted Treatment\u003c/h2\u003e \u003cp\u003eThis study identified several variables significantly associated with the likelihood of completing TB treatment among TB/HIV co-infected patients, including the duration of treatment, the presence of diabetes mellitus, occupation, and CXR status during the diagnosis. Each variable contributes differently to the probability of treatment completion, offering valuable insights into patient management and informing targeted interventions to enhance outcomes. Patients with longer treatment durations had higher odds of completing the treatment. With the increase in the 1-month duration of treatment, the chance for complete treatment outcome was estimated to be 84% (aRRR\u0026thinsp;=\u0026thinsp;1.084, 95% CI\u0026thinsp;=\u0026thinsp;1.02, 1.19, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005). This result suggests that adhering to treatment regimens for extended periods improves treatment outcomes. Patients who remain engaged in their care and continue treatment over time may be more likely to complete the treatment. Additionally, it could imply that early and sustained engagement in treatment, supported by healthcare providers, enhances the likelihood of completion. It was supported by the WHO, which emphasizes the importance of completing the entire course of treatment (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe presence of diabetes mellitus is negatively associated with TB treatment completion, with patients having underlying diabetes mellitus having an 80.4% lower chance of completing treatment compared to those without diabetes mellitus (aRRR\u0026thinsp;=\u0026thinsp;0.196, 95% CI\u0026thinsp;=\u0026thinsp;0.11, 0.39, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This finding is consistent with previous reports that diabetes mellitus is a significant risk factor for TB (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Diabetes mellitus may exacerbate TB symptoms, affect drug metabolism, or cause additional complications, making it more challenging for patients to adhere to and complete TB treatment. Moreover, diabetes mellitus may affect immune function, leading to slower recovery or increased side effects from TB medications, further reducing the likelihood of completing treatment. This highlights the need for integrated care approaches that address both TB and diabetes mellitus concurrently, including careful monitoring, tailored treatment plans, and patient education on managing both conditions.\u003c/p\u003e \u003cp\u003eCertain occupations were found to be significantly linked to the completion of TB treatment. Patients working as government servants were estimated to have 2.44 times the chance of having a complete treatment outcome compared to unemployed patients (aRRR\u0026thinsp;=\u0026thinsp;2.435, 95% CI\u0026thinsp;=\u0026thinsp;1.15, 5.11, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019). This finding suggests that employment, particularly stable jobs such as government service, may provide patients with better access to healthcare resources, social support, and financial stability, all of which can facilitate treatment adherence and completion. Government employees may have better health benefits, easier access to medical leave, or more flexible schedules that allow them to attend follow-up appointments and adhere to treatment regimens. In contrast, unemployed patients may face financial constraints, a lack of social support, or limited access to healthcare, which can lead to lower treatment completion rates. This underscores the importance of addressing social determinants of health, such as employment status, to improve TB treatment outcomes.\u003c/p\u003e \u003cp\u003eAbnormal CXR findings were also significantly associated with completed treatment outcomes. Patients with minimal lesions of CXR were estimated to have 84.4% less chance of having a complete treatment outcome than patients with no lesions (aRRR\u0026thinsp;=\u0026thinsp;0.156, 95% CI\u0026thinsp;=\u0026thinsp;0.09, 0.29, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, patients with moderately advanced disease were estimated to have an 89.8% lower chance of achieving a complete treatment outcome than patients with no lesions (aRRR\u0026thinsp;=\u0026thinsp;0.102, 95% CI\u0026thinsp;=\u0026thinsp;0.05\u0026ndash;0.19, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients with far advanced were estimated to have 94.7% less chance of a complete treatment than patients with no lesions (aRRR\u0026thinsp;=\u0026thinsp;0.053, 95% CI\u0026thinsp;=\u0026thinsp;0.15, 0.18, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings suggest that more severe CXR abnormalities (i.e., moderately advanced or far advanced) are associated with lower odds of treatment completion.\u003c/p\u003e \u003cp\u003eThe study shows that CXR findings were associated with treatment success, with results of advanced observation having a low success rate compared to no lesion or minimal (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). This could be because patients with more severe TB disease are likely to experience more significant symptoms, adverse effects, or complications, which may lead to treatment discontinuation or default. On the other hand, patients with lesion CXR findings may have a less severe form of the disease or better overall health, making them more likely to complete treatment. The lower likelihood of treatment completion among patients with minimal or more severe CXR findings indicates a need for targeted interventions to support these patients. For those with minimal findings, it is crucial to recognize the importance of completing treatment, even if their symptoms are mild or improve rapidly. For those with severe findings, providing additional medical support, managing side effects, and offering social and psychological support may improve treatment adherence and completion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eDefaulted Treatment\u003c/h2\u003e \u003cp\u003eThis study identified several variables significantly associated with defaulted TB treatment, including age, treatment duration, residency, DOTS, case category, and CXR status at the time of diagnosis. The study found that older age was significantly associated with higher odds of defaulting on treatment. With the increase of 1-year in age, the chance was estimated to be 4.7% less chance to have a defaulted treatment outcome (aRRR\u0026thinsp;=\u0026thinsp;0.953, 95% CI\u0026thinsp;=\u0026thinsp;0.93, 0.98, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). This finding may suggest that older patients are potentially more experienced in managing chronic conditions and adhering to treatment regimens. Earlier African studies have identified different effects of demographic factors on treatment default. Other studies have found that patients over 25 are more likely to default than their younger (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). However, other researchers (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) have found no connection between treatment default and the age variable. Alternatively, it might indicate that older patients are more likely to have a supportive network or better access to healthcare resources. Further investigation into the reasons behind this association could provide insights into how age-related factors influence treatment adherence and default.\u003c/p\u003e \u003cp\u003eProlonged treatment duration was a significant predictor of defaulting. With the increase of 1-month in the duration of treatment, the chance was estimated to be 36.4% less chance to have a defaulted treatment outcome (aRRR\u0026thinsp;=\u0026thinsp;0.636, 95% CI\u0026thinsp;=\u0026thinsp;0.56, 0.72, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This result suggests that longer treatment durations may increase the complexity of adhering to the regimen, leading to higher default rates. This study is supported by the study among TB patients in Northeast Ethiopia (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), which reported that 90.4% defaulted on the treatment in the continuous phase due to the long course of TB treatment being complex. This is also supported by a study in Uganda (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) that indicates a correlation between the length of TB treatment and defaulting from treatment. Extended treatment periods may contribute to patient fatigue, side effects, or loss of motivation, ultimately leading to treatment default. To address this issue, it is crucial to provide ongoing support and counselling to help patients stay engaged and motivated throughout their treatment.\u003c/p\u003e \u003cp\u003eThe area of residence (urban vs. rural) was found to be a significant factor. Patients in rural areas were estimated to have a 57.5% lower chance of defaulting treatment outcomes than those in urban areas (aRRR\u0026thinsp;=\u0026thinsp;0.425, 95% CI\u0026thinsp;=\u0026thinsp;0.23, 0.79, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007). This finding suggests that rural patients are less likely to default on treatment, which may be attributed to stronger community support systems or effective local health initiatives in rural areas. It was supported by a study among PTB patients in Yemen (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) that indicated that patients in rural areas had a higher risk of non-compliance compared to metropolitan areas, and the study among TB patients in western Ethiopia (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) which reported that patients were more likely to have a successful treatment if they were urban residents compared to rural residents. Alternatively, it could reflect differences in access to healthcare services, with urban patients potentially facing more significant barriers to treatment adherence.\u003c/p\u003e \u003cp\u003eParticipation in DOTS was significantly linked to defaulting outcomes. Patients without a DOTS program were estimated to have a 45.96 times higher chance of having defaulted treatment outcomes compared to patients with a DOTS program (aRRR\u0026thinsp;=\u0026thinsp;45.958, 95% CI\u0026thinsp;=\u0026thinsp;4.82, 438.53, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). DOTS ensures patients receive supervised and consistent treatment, which is crucial for adherence and successful outcomes. A study among PTB patients in Kepong district, Kuala Lumpur, Malaysia (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) did not show a significant association between DOTS and unsuccessful treatment outcomes. A study among TB patients in West Bengal, India (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) reported that the factor independently associated with defaulting was the DOT program. The significant association underscores the need to expand DOTS coverage and ensure that all patients have access to this crucial component of TB care.\u003c/p\u003e \u003cp\u003eDifferent case categories were significantly associated with treatment default. Patients with recurrent case TB category were estimated to have a 2.41 times higher chance of having defaulted treatment outcomes compared to the new case TB category (aRRR\u0026thinsp;=\u0026thinsp;2.409, 95% CI\u0026thinsp;=\u0026thinsp;1.07, 5.41, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033). Patients with after failure/default case TB category were estimated to have a 5.38 times chance of defaulting treatment outcome compared to the new case TB category (aRRR\u0026thinsp;=\u0026thinsp;5.379, 95% CI\u0026thinsp;=\u0026thinsp;1.77, 16.33, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003). A study done among TB patients in western Ethiopia (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) indicated that patients were less likely to have a successful treatment if they were retreatment cases compared to patients with new TB cases. These findings suggest that patients with recurrent or previously failed TB cases are more likely to default on treatment. This could be due to factors such as treatment fatigue, lack of confidence in the treatment regimen, or challenges in managing recurrent disease. Enhanced support and tailored interventions for these patients, including additional counselling and monitoring, could help reduce default rates among these high-risk groups.\u003c/p\u003e \u003cp\u003eAbnormal CXR findings are also significantly associated with treatment default. Patients with minimal, moderately advanced, and far advanced had lower odds of defaulting compared to those with no lesions of CXR findings. Specifically, patients with minimal had 78.7% less chance of defaulting (aRRR\u0026thinsp;=\u0026thinsp;0.213, 95% CI\u0026thinsp;=\u0026thinsp;0.09, 0.52, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001); moderately advanced had 84.5% less chance of defaulting (aRRR\u0026thinsp;=\u0026thinsp;0.155, 95% CI\u0026thinsp;=\u0026thinsp;0.66, 0.40, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001); and far advanced had 93.1% less chance of defaulting (aRRR\u0026thinsp;=\u0026thinsp;0.069, 95% CI\u0026thinsp;=\u0026thinsp;0.01, 0.66, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020). This suggests that patients with more severe chest X-ray (CXR) abnormalities are less likely to default on treatment. This could be because severe TB cases often require more intensive management and close monitoring, which may help ensure adherence. Conversely, patients with less severe disease might not perceive the need for strict adherence as urgent, potentially leading to higher default rates. Targeted interventions to reinforce the importance of completing treatment, regardless of disease severity, could help improve adherence rates among all patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eDeath\u003c/h2\u003e \u003cp\u003eThis study highlights several variables significantly associated with death outcomes among TB/HIV co-infected patients in East Coast Malaysia, including treatment duration, diabetes mellitus, DOTS, occupation, and CXR status during the diagnosis.\u003c/p\u003e \u003cp\u003eLonger duration of treatment was significantly associated with a higher likelihood of death. With the increase of 1-month in the duration of treatment, the chance was estimated to be 59.4% less chance of death (aRRR\u0026thinsp;=\u0026thinsp;0.406, 95% CI\u0026thinsp;=\u0026thinsp;0.36, 0.46, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This finding indicates that patients who remain on treatment for an extended period have a lower risk of death. It could reflect that prolonged treatment allows for better disease control and reduces mortality risk, possibly due to improved management of both TB and HIV. Alternatively, it may suggest that patients who are more engaged in treatment are generally healthier or have better access to healthcare resources, leading to improved survival rates. It is also possible that more severe cases with a higher risk of death might be excluded from the study if they do not complete the extended treatment period. Enhanced patient support and monitoring throughout treatment are crucial for maintaining adherence and reducing mortality.\u003c/p\u003e \u003cp\u003eDiabetes mellitus was also a significant predictor of death. Contrary to some expectations, patients with underlying diabetes mellitus were estimated to have a 65.8% lower chance of death compared to patients without diabetes mellitus (aRRR\u0026thinsp;=\u0026thinsp;0.342, 95% CI\u0026thinsp;=\u0026thinsp;0.13, 0.79, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013). This result is unexpected, as diabetes mellitus is typically associated with worse outcomes in TB patients due to its impact on immune function and treatment response. One possible explanation could be that patients with diabetes mellitus are receiving more comprehensive care or are under more rigorous monitoring due to their comorbidity, which might contribute to better management and lower mortality. Alternatively, there may be residual confounding or differences in patient characteristics between those with diabetes mellitus and those without. Further research is needed to explore this counterintuitive finding and understand the underlying factors that contribute to it.\u003c/p\u003e \u003cp\u003eParticipation in DOTS is also associated with death outcomes. Patients without a DOTS program were estimated to have 29.41 times the chance of having death outcomes compared to patients with a DOTS program (aRRR\u0026thinsp;=\u0026thinsp;29.412, 95% CI\u0026thinsp;=\u0026thinsp;3.14, 275.18, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003). This strong association highlights the crucial role of DOTS in enhancing patient outcomes. In 2015, it was reported that 89.6% of patients practiced DOTS for TB case management and treatment. A study using data from the National Registry in Malaysia (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) showed that patients who did not receive DOTS were nine (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) times more likely to have unsuccessful treatment outcomes and death than those who had received DOTS. Therefore, they recommend better adherence using DOTS approaches to improve the treatment outcomes.\u003c/p\u003e \u003cp\u003eSpecific occupations were found to be significantly linked to the likelihood of death. Patients who are working in other types were estimated to have 46.9% less chance of having death outcomes compared to unemployed patients (aRRR\u0026thinsp;=\u0026thinsp;0.531, 95% CI\u0026thinsp;=\u0026thinsp;10.32, 0.88, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014). This finding suggests that employment status might influence treatment outcomes and mortality. Employed patients may have better access to healthcare resources, financial stability, and social support, which can contribute to improved health management and lower mortality rates. In contrast, unemployed patients may face barriers such as financial constraints or limited access to healthcare, which increases their risk of poor outcomes. The study done in Southwest Ethiopia indicated that mortality among TB/HIV co-infection was high among participants with economic hardships and poor incomes (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Addressing these socioeconomic factors through targeted interventions could help improve the survival rates among unemployed patients.\u003c/p\u003e \u003cp\u003eThe abnormal CXR results were found to be strongly associated with death. Patients with minimal lesions were estimated to have 74.9% less chance of having death outcomes compared to patients with no lesions (aRRR\u0026thinsp;=\u0026thinsp;0.251, 95% CI\u0026thinsp;=\u0026thinsp;0.11, 0.59, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). Patients with moderately advanced were estimated to have 80.8% less chance of having death outcomes compared to patients with no lesion (aRRR\u0026thinsp;=\u0026thinsp;0.192, 95% CI\u0026thinsp;=\u0026thinsp;0.07, 0.46, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This indicates that patients with less severe CXR abnormalities (minimal or moderate advanced) have a lower risk of death compared to those with more severe abnormalities. The severity of CXR findings often reflects the extent of lung damage and disease progression, which is closely linked to treatment outcomes. Patients with severe CXR findings might have more advanced disease or complications, contributing to higher mortality. This emphasizes the importance of early diagnosis and treatment of TB to prevent severe disease progression and reduce mortality.\u003c/p\u003e \u003cp\u003eThese findings regarding the factors significantly associated with death outcomes among TB/HIV co-infected patients, such as treatment duration, diabetes mellitus, DOTS, occupation, and CXR status during the diagnosis, contradicted the study in South Africa (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), which reported that lower CD4 count, lower BMI, presence of TB-related symptoms, detectable LAM, and more severe anaemia were significant risk factors for death.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eRecommendation\u003c/h2\u003e \u003cp\u003eThe results of this study demonstrate that various factors significantly influence TB treatment outcomes among TB/HIV co-infected patients. Duration of treatment and CXR status during the diagnosis emerged as consistent predictors across all treatment outcomes, indicating their critical role in influencing the likelihood of completing treatment, defaulting, or death. These findings suggest that the length of treatment and radiological findings play an essential procedure in determining TB treatment success or unsuccess. Additionally, factors such as diabetes mellitus, occupation, and DOTS were associated with higher likelihoods of treatment default or death, underscoring the need for targeted interventions aimed at improving treatment adherence and survival in these vulnerable populations. By identifying the associated factors, this study offers valuable insights for healthcare providers and policymakers to develop targeted strategies that improve TB treatment outcomes. Special attention should be given to patients with comorbidities like diabetes, those from specific occupational backgrounds, and those requiring intensive DOTS supervision. Strengthening early detection and management of these factors can lead to better TB treatment outcomes, potentially reducing default and mortality rates among TB/HIV co-infected patients in East Coast Malaysia.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eImplications\u003c/h2\u003e \u003cp\u003eBy strengthening TB/HIV collaborative efforts and tailoring interventions based on patient characteristics, healthcare providers can improve treatment success and reduce the risk of default and Death among TB/HIV co-infected patients in East Coast Malaysia.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eIn this study, the secondary data were utilized from the MyTB database, which introduced several limitations affecting data quality and interpretability. Firstly, it was observed that certain variables were not consistently recorded. Different terms were used, such as occupation and education level. These inconsistencies required researchers to undertake considerable preprocessing, such as recoding and standardize the dataset. Secondly, the original data in the MyTB registry may also be subject to response biases, especially for fields based on patient self-report, such as occupation, education level, or comorbidities.\u003c/p\u003e \u003cp\u003eAdditionally, healthcare providers recording this information may have inconsistently interpreted or entered responses, which further contributes to data variability. Secondary databases, such as MyTB, are designed for administrative or clinical surveillance rather than research, which means that certain relevant variables may be missing or inadequately captured. Because the researchers did not collect the original data themselves, they had limited control over the accuracy, completeness, or consistency of the records. Being aware of these potential issues and taking steps to mitigate them can help improve the quality and reliability of studies using secondary data from a registry.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eFuture Research\u003c/h2\u003e \u003cp\u003eThis study suggested several areas for further research, such as longitudinal studies, to better understand the causal relationships between the identified factors and TB treatment outcomes. Prospective studies and intervention trials are needed to explore further and address the factors influencing TB treatment outcomes in TB/HIV co-infected populations.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides insights into the challenges of managing TB treatment among TB/HIV co-infected patients. Variables such as treatment duration and CXR findings appear to be associated with completed treatment, default, and death. Additionally, diabetes mellitus status, occupation, and the DOT program may influence adherence and survival. These findings suggest that context-specific interventions, particularly for patients with comorbidities, certain occupational groups, and those requiring closer treatment supervision, could support improved TB care. Further investigation and early intervention strategies may contribute to better treatment outcomes among TB/HIV co-infected patients in East Coast Malaysia.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eaRRR: Adjusted Relative Risk Ratio; \u003cstrong\u003eBCG:\u003c/strong\u003e Bacillus Calmette\u0026ndash;Guerin;CI: Confidence Interval; \u003cstrong\u003eCXR:\u0026nbsp;\u003c/strong\u003eChest X-ray; \u003cstrong\u003eDOTS:\u003c/strong\u003e Directly Observed Therapy Shortcourse; HIV: human immunodeficiency virus; JKN: Jabatan Kesihatan Negeri; MOH: Ministry of Health; MREC: Medical Research Ethics Committee; PLHIV: people living with HIV; PTB: Pulmonary TB; SD: standard deviation; TB: Tuberculosis; TBIS: Tuberculosis Information System; WHO: World Health Organization\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003eEthics Approval\u0026nbsp;and consent to participate\u003c/h3\u003e\n\u003cp\u003eThe study was conducted in compliance with ethical principles outlined in the Declaration of Helsinki and the Malaysian Good Clinical Practice Guideline. This study obtained ethical approval from\u0026nbsp;the UniSZA Human Research Ethics Committee (UHREC) and the Medical Research Ethics Committee (MREC), MOH (NMRR-19-2628-50776 (IIR)).\u0026nbsp;The requirement for informed consent to participate was waived by both UHREC and MREC as the study involved analysis of anonymized secondary data retrieved solely from the national TB Information System (TBIS) registry, which is is a digital platform or database designed to collect, manage, and analyse data related to TB cases and their management with no direct patient contact. This waiver is in accordance with Malaysian national regulations on medical research ethics.\u003c/p\u003e\n\u003ch3\u003eClinical Trial Number\u003c/h3\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch3\u003eConsent for Publication\u003c/h3\u003e\n\u003cp\u003eNot Applicable\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eAvailability of data and materials\u003c/h3\u003e\n\u003cp\u003eThe dataset is available, but\u0026nbsp;to protect subject privacy, it cannot be shared openly. Anyone requesting this dataset should consult the TB Sector in the Ministry of Health, Malaysia.\u003c/p\u003e\n\u003ch3\u003eCompeting interests\u003c/h3\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003ch3\u003eFunding\u003c/h3\u003e\n\u003cp\u003eThis study has not received any funding sources.\u003c/p\u003e\n\u003ch3\u003eAuthor Contribution Declaration\u003c/h3\u003e\n\u003cp\u003eSiti Romaino Mohd Nor contributed to the conceptualization, methodology, statistical analysis, interpretation of results, drafting, and revising of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNyi Nyi Naing contributed to the interpretation of the results, reviewed the manuscript, and approved the final draft of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMat Zuki Mat Jaeb contributed to the review of the manuscript and approved the final draft.\u003c/p\u003e\n\u003ch3\u003eAcknowledgments\u003c/h3\u003e\n\u003cp\u003eThe authors thank the Director-General of Health, Malaysia, for his kind permission to publish these research findings, as well as the organizations that contributed to the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDarraj MA, Abdulhaq AA, Yassin A, Mubarki S, Shalaby HM, Keynan Y et al. 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Risk Factors Associated with Default among Tuberculosis Patients in Darjeeling District of West Bengal, India. J Fam Med Prim Care. 2015;4(3):388.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGesesew H, Tsehaineh B, Massa D, Tesfay A, Kahsay H, Mwanri L. The Role of Social Determinants on Tuberculosis/HIV Co-Infection Mortality in Southwest Ethiopia: A Retrospective Cohort Study. BMC Res Notes. 2016;9(1):4\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCalderwood CJ, Tlali M, Karat AS, Hoffmann CJ, Charalambous S, Johnson S et al. Risk Factors for Hospitalization or Death among Adults with Advanced HIV at Enrollment for Care in South Africa: A Secondary Analysis of the TB Fast Track Trial. Open Forum Infect Dis. 2022;9(7).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Associated factors, treatment outcomes, cured, completed, defaulted, death, TB/HIV co-infected patients","lastPublishedDoi":"10.21203/rs.3.rs-6910886/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6910886/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTuberculosis (TB) and human immunodeficiency virus (HIV) co-infection pose a substantial public health problem, particularly in high-prevalence areas. This study uses an epidemiological model to analyze the sociodemographic and clinical characteristics related to various TB treatment outcomes (cured, completed, defaulted, and death) among TB/HIV co-infected patients on the East Coast of Malaysia over five years.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis cross-sectional study used secondary data from the e-Notifikasi for Tuberculosis Information System (TBIS) from January 2016 to December 2020 and was conducted at the State TB Organizer or TB/Leprosy Unit, Jabatan Kesihatan Negeri (JKN) Kelantan, Terengganu, and Pahang. Data was analyzed using multinomial logistic regression with IBM SPSS Statistics version 25.0 and STATA-14. Ethics permission was received from the Medical Research Ethics Committee (MREC), Ministry of Health (MOH).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThere were 14,289 TB cases, with 1,292 (9.04%) being TB/HIV co-infected patients. However, 69 TB/HIV cases were excluded due to transfer, change of diagnosis, and still ongoing treatment. As a result, 1,223 TB/HIV co-infected patients were assessed. The prevalence of cured was 33.5% (410), completed 29.2% (357), defaulted 6.4% (78), and died 30.9% (378). There were no failures identified. Nine factors were determined to be statistically significant during the univariable analysis. The important variables discovered in the univariate study were then used for the multivariate analysis. The study found that the duration of treatment, diabetes mellitus, occupation, and Chest X-ray (CXR) status were all substantially linked with treatment completion. Age, duration of treatment, residency, Directly Observed Treatment Short-course (DOTS) status, case type, and CXR status significantly impacted treatment default. In contrast, duration of treatment, diabetes mellitus, DOTS, occupation, and CXR all had a substantial effect on death rates.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eUnderstanding the factors that influence TB treatment outcomes is crucial for developing effective intervention strategies and enhancing patient outcomes. The findings of this study provide a comprehensive understanding of the relevant factors influencing treatment outcomes at all levels, which may aid in the development of more effective treatment techniques.\u003c/p\u003e","manuscriptTitle":"Associated factors at different treatment outcomes (cured, completed, defaulted, and death) among TB/HIV co-infected patients in East Coast Malaysia: 5-year record review (2016 - 2020)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-02 07:23:41","doi":"10.21203/rs.3.rs-6910886/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision 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