Non-communicable disease multi-comorbidities and risks of unfavourable tuberculosis treatment outcomes among routine tuberculosis patients, Southern Ethiopia

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This study aims to estimate comorbidity and risk factors of unfavourable tuberculosis treatment outcomes among tuberculosis patient Methods and material A prospective follow-up study was conducted and the study population were screened by standard and validated procedure for exposure categorization; and followed until treatment completion, respective to six months. A bivariate and multiple logistic regression model was developed for risk estimates of odds ratio after descriptive statics proportion and mean explored and it was presented in tables and figures. Result The majority (92.5%) of tuberculosis patients had successful treatment outcomes, of which 46.5% and 42.28% were completed and cured, respectively. TB patients comorbid with any one NCD (DM, HPN, cancer, COPD, CVD), comorbid with any two or more NCDs (DM, HPN, cancer, COPD, CVD), TB patients who had waist circumference 72–79 cm/ 78–89 cm, and care facility as health centre were found to be an independent predictor for the unfavourable TB treatment outcomes Conclusion Even though the majority of TB patients had a treatment success rate, multi-comorbidity negatively predicted the treatment success. For better TB treatment outcomes policymakers should develop an early detection and management platform for identified risk factors in available TB programs. Non-communicable disease risk factors tuberculosis treatment outcomes comorbidity Figures Figure 1 Figure 2 1. Introduction Tuberculosis (TB) is a major public health problem globally, and one of the top 22 countries affected by TB is Ethiopia ( 1 ) with a predictable incidence and prevalence rate of 2.1/1000 and 2.0 /1000, respectively ( 2 ). Ethiopia is the 3rd highest country in Sub-Saharan Africa, with a prevalence rate of 4.36% in diabetes mellitus (DM) ( 3 ). And more than 1.3 million cases of DM in Ethiopia ( 4 ) NCDs( non-communicable diseases) are risk factors for TB, especially for progression from infection to disease due to the negative impact on host defence mechanisms against mycobacterium TB; NCDs and their attributes complicate treatment and management of TB, due to clinical challenges ( 5 , 6 ). Chronic disease challenges are growing unequally among developing countries, and non-communicable diseases were raised from 6.7 to 8.5 million from 2000 to 2012 respectively in South East Asia ( 7 ). Global population health data suggest that TB control targets achieving and addressing TB intervention programs delayed by TB and NCD comorbidities ( 8 , 9 ). This contributes to the increment of the TB burden and its attributes of smoking and alcohol use at the community level ( 8 , 10 ). The common comorbid non-infectious diseases comorbid with TB are DM, hypertension (HPN), heart diseases, chronic obstructed pulmonary diseases (COPD), and cancer ( 11 ); and shared risk factors such as smoking, poor diet, and harmful use of alcohol, which need to be addressed for effective prevention ( 5 , 12 – 14 ) In South Africa, the prevalence of comorbidity (with one NCD) was 26.9% and multi-morbidity (with two or more NCDs) was 20.9%, alcohol use (24.3%), tobacco use (15.0%), hypertension (8.9%), ischaemic heart disease or angina (7.5%), arthritis (4.5%), type 2 DM (4.1%), asthma (3.5%), cancer or malignant neoplasms (2.1%), chronic lung disease (1.9%) and dyslipidemia (1.6%) ( 15 ). Nationally, the DM prevalence among TB patients (13.5%) is higher than the population prevalence (2.0% -6.5%) and it has a residence difference between Urban (5.1%) and Rural (2.1%) ( 16 – 18 ). Eight preventable attributable factors for NCDs are tobacco use, alcohol use, inadequate physical exercise, less consumption of fruits and vegetables, DM, hypertension, and obesity ( 19 ). These variables negatively affect the TB treatment success rate ( 20 – 23 ). Those TB patients comorbid with NCDs delay the treatment and detection and are attributed to TB transmissions ( 24 ). The WHO defined TB standard international controlling indicators cured, completed, failed, died and defaulted; its proportion was mutually reported as died, treatment failed and defaulted as unfavourable treatment outcomes, whereas cured and completed were reported as favourable treatment outcomes ( 25 , 26 ). The national strategic plan for ending TB reported that the relapse of TB somewhat increased and the TB treatment success rate (88.5%), is below the TB end strategic plan (90%) ( 27 ). Also globally in 2019, most international regions and the majority of TB prevalent areas are not in line with the End TB strategy’s 2020 milestones, the decrease in TB incidence rate (6.3%) and the number of deaths (11%) were reported between 2015 and 2018 ( 28 – 30 ) In Nigeria, (81.4%) of TB patients had favourable treatment outcomes, of which 46.3% were cured and 35.1% completed the treatment, however among 18.6% had unfavourable treatment outcomes 9.8% lost to follow-up, 6.5% died and 1.5% have failed the treatment ( 31 ). Similarly, in Ethiopia, the majority of notifiable TB patients completed the treatment and 77.9% had a cure rate, however in the Wolaga Oromia region 17.2% were cured, 2.9% lost to follow-up, 4.8% died and 0.4% failed the treatment ( 29 , 32 ). The 2030 and 2035 Sustainable Development Goals(SGD) targets of decreasing TB incidence rate, death; and NCDs premature death rate were not achieved as planned ( 24 ) In general, globally there is little evidence on factors affecting tuberculosis treatment outcomes, which is more focused on DM, single risk estimates and differ in outcome measurement ( 33 – 39 ); and in Ethiopia, a few descriptive studies were conducted using secondary data, and further follow-up researches were highly recommended ( 32 , 40 , 41 ), and no evidence of risk estimates of unfavourable tuberculosis treatment outcomes related to NCDs comorbidities and shared risk factors using a prospective follow-up study design. This prospective study provided strong evidence on both NCDs and risk estimates of unfavourable tuberculosis treatment outcomes among routine tuberculosis patients for policy development and to provide quality and integrated service for both diseases 2. Methods and materials 2.1 Study site and setting The study was conducted in the Hadiya Zone Hospital TB treatment centre from January 2022 to, June 2023 for six months. The administration of the zone is divided into 7 urban districts and 10 rural districts with 303 rural kebele, and 29 urban kebele. The total population was 1,705,902 (male 848,695 and female 857,225) with 4 district hospitals, 1 referral hospital, 61 health centres, and around 305 health stations. In the study site, TB were reported based on the standard national and international guidelines. In 2020, among 1630 TB patients, 781 pulmonary positive TB, 579 pulmonary negative TB, and 270 extra pulmonary TB were reported. Presently, the study site applied chronic care as a new program, however, it functioned only in selected TB care facilities. In the selected site in 2021 from 73333 screened participants, about 397 hypertension patients liked to care, even though the health professionals were not motivated and most healthcare facilities did not have chronic disease screening equipment 2.2 Study design and population This study is the continuity of the previous project on NCDs and risk factors comorbidity identification and categorization published at https://www.dovepress.com/getfile.php?fileID=94717 . A single-arm follow-up study was carried out on routine tuberculosis patients, through screening common NCDs and risk factors for two months and following these patients for four months until completion of treatment to measure outcomes. The eligibility criteria were those study participants who were enrolled in previous projects and willing to continue until the completion of treatment. TB patients who have a previous history of unfavourable treatment outcomes (e.g. relapse) and early death that occurred before the survey were excluded from the study participants. These eligible study participants were followed until treatment completion 2.3 Data collection and procedure Initially, baseline data were assessed from three general hospitals, one referral hospital and three health centres following ethical consensus. These study sites were selected based on the eligibility criteria of both Non-communicable and tuberculosis disease continuous detection, diagnosis and treatment services availability. Before enrolling the study participants (TB patients) for screening and follow-up, the study participants were selected again by eligibility criteria. First, all newly enrolled tuberculosis patients in the direct observational treatment (DOT) program were screened for NCD and its risk factors through the two stages WHO Step-wise screening procedure. After assessing the baseline survey data, the portfolio and per forma were developed for each TB patient. Next, a single-arm follow-up study was undertaken on screened TB patients and followed until completion of treatment to observe TB treatment outcomes. The two stages of the WHO step-wise screening procedure were used to measure the exposure status for tuberculosis treatment outcomes. This tool was formerly validated and tested in different settings ( 42 , 43 ). Data were collected through the standard WHO measures, structured interview questionnaires and reviewing medical records at the start, and end of treatment to determine outcomes (supplmentary1). The end-line data were collected after six months of follow-up time concerning each TB patient treatment period. Patient's clinical data were observed for outcomes every 2 months after the baseline survey because the participant's registration was cascaded by using an open cohort follow-up nature. 2.4 Variables The exposure variables were common comorbid NCD and risk factors include elevated blood pressure, elevated blood glucose, cigarette smoking/ tobacco use, alcohol use, malnutrition (body mass index (BMI), waist circumference insufficient physical activity, and inadequate fruit and vegetable consumption). These variables were measured using the WHO score scales formerly validated and tested in different settings( 42 , 43 ). Those tuberculosis patients on the direct observation treatment (DOT) program and newly initiated treatment were screened for exposure within two months. Sociodemographic factors ( age, sex, residence, contact person, address, phone number, and others), whereas clinical and TB-related variables (treatment centre, TB types, treatment and health-seeking delay) were explanatory variables for tuberculosis treatment outcomes. The outcome variables were measured using the standard global definition of the WHO( 44 ). These outcomes were categorized into cured, treatment completed, treatment failed, and loss of follow-up, death, and not evaluation. Deaths, treatment failure, and loss of follow-up are defined as unfavourable treatment outcomes, whereas favourable treatment outcomes include treatment completed and cured. Follow-up and adherence: Once the TB patients on the DOT program were screened consecutively for two months for NCDs and risk factors, which were used for baseline data, participants were consulted and frequently contacted based on their voluntary participation for adherence. 2.5 Sample size and sampling procedure Once the cluster and the participant’s size in the care centre were identified, NCDs and risk factors were screened by the WHO Step-wise screening procedure. The sample size, sampling procedure; and variables measurement and categorization that were collected and analyzed were published as baseline data in a peer-reviewed journal: https://doi.org/10.2147/IBPC.S432251 2.6 Data analysis and interpretation We used STATA v.14 software for analysis, after importing the EXCELL datasheet. Formerly, baseline data were described by the percentages, mean and other statistics of socio-demographic, exposure variables and clinical data of tuberculosis patients, which were presented by tables, and published at https://doi.org/10.2147/IBPC.S432251 . These survey data were used to estimate the risk of unfavourable TB treatment outcomes, and which were explained using logistic regression analysis. Before model development, the TB treatment outcomes were presented by using the figure Both bivariate and multivariate logistic regression models were developed to assess the significantly associated variables with the tuberculosis treatment unfavourable. While we identified the associated variables of unfavourable, the dependent variable was coded as 1 if the tuberculosis treatment outcome was unfavourable and coded as 0 if outcome is favorable. First, a bivariate logistic regression model was constructed by identifying the significant association at p-value ≤ 0.05 with 95% CI between the response and explanatory variables. Next, those significantly associated variables at p-value ≤ 0.05 with 95% CI in the binary logistic regression model were entered into multiple regression analyses to identify the predictors of unfavourable tuberculosis treatment outcomes. Before multiple logistic regression model development multicollinearity was checked among the explanatory variables. Similarly, the model appropriateness was checked by Hosmer-Lemeshow test statistics to see the goodness-of-fit test. Finally, the estimates were reported by considering the 95% CI statistical significance of COR, and AOR using tables 3. Result 3.1 Baseline characteristics of participants and exposure variables Before exposure categorization, we enrolled all TB patients in the DOT program; and due to the small sample size, we recruited consecutively newly registered TB patients for treatment until sample size adequacy. Those who were not ready to die and who had the previous treatment outcome were excluded from baseline characterization. Based on these criteria the participant's sociodemographic variable descriptions; and NCDs and risk factors prevalence were explored in a previous project and can be accessed from the baseline data published article on reputable journal https://doi.org/10.2147/IBPC.S432251 3.2 Tuberculosis treatment outcomes Among the total tuberculosis patients (443), the majority of 410 (92.60%) had favourable treatment outcomes (Fig. 1 ), of which 206 (46.5%) and 187(42.28%) were completed and cured, respectively, however from unfavourable treatment outcomes 33 (7.40%), 15(3.39%), 10(2.26%) and 7(1.58%) were loss to follow up, failure and died, respectively (Fig. 2 ). 3.3 Determinants of Tuberculosis Treatment Success Rate Bivariate analysis showed that TB patients' comorbid with NCDs, waist circumferences, tobacco smoke, alcohol use, rural residences, and health centres as health care facilities were candidate variables for multivariable analysis at p-value ≤ 0.05 and have a crude association with the occurrence of a TB treatment unfavourable and also collectively entered to multiple logistic regression model to identify predictors. After adjusting for the covariate, the multiple logistic regression analysis showed that tuberculosis patients comorbid with NCDs, waist circumferences, and health facilities as health centres were found to be independent predictors for unfavourable TB treatment at p-value ≤ 0.05. Accordingly, TB patients comorbid with any one NCD (DM, HPN, cancer, COPD, CVD) had 0.25 times less odd of TB treatment favourable outcomes as compared to those TB patients without comorbid (AOR = 0.25; 95%CI = 0.07–0.83 at P value = 0.024). TB patients who were comorbid with any two or more NCDs (DM, HPN, cancer, COPD, CVD) had 0.16 times less odd of treatment favourable as compared to those TB patients without comorbid (AOR = 0.16; 95%CI = 0.04–0.66 at P value = 0.012). The odds of having tuberculosis treatment favourable outcomes were 5.23 times more likely to have a waist circumference of 72–79 cm for females or 78–89 cm for males (obese) as compared to those TB patients who had a waist circumference less than 72cm for female or 78 cm for male (AOR = 5.23; 95%CI = 1.52–18.10 at P value = 0.009). Those TB patients who followed treatment at the health centre were about 0.37 times less likely to have a favourable treatment outcome compared to TB patients who followed the treatment at hospitals (AOR = 0.37; 95%CI = 0.15–0.95 at P value = 0.038) (Table 4 ) Table 4 Predictors of unfavourable tuberculosis treatment outcomes in Southern Ethiopia Variable Category Treatment outcomes COR 95% CI AOR 95% CI favourable unfavourable TB-NCD Comorbidity status Not comorbid 364 24 Reference Comorbid 28 5 0.37 0.13, 1.04 0.25* 0.07, 0.83 Multi-comorbid 18 4 0.29 0.09 ,0.95 0.16* 0.04, 0.66 Waist circumference < 72/78 cm 269 29 Reference 72–79/78–89 cm 123 3 4.42* 1.32, 14.79 5.23* 1.51, 18.10 ≥ 80/90 cm 18 1 1.94 0.25, 15.07 5.88 0.60, 57.50 Alcohol use Not drinker 372 24 Reference Drinker 38 9 0.27 0.12, 0.63 0.41 0.09, 1.87 Cigarette smoking Not smoker 378 26 Reference Smoker 32 7 0.31 0.13, 0.78 1.01 0.19, 5.09 Health facility Hospital 203 9 Reference Health center 207 24 0.38* 0.17, 0.84 0.37* 0.15, 0.95 Residence Urban 367 24 Reference Rural 43 9 0.31* 0.14, 0.72 0.43 0.17, 1.09 4. Discussion This study revealed that 92.5% of tuberculosis patients had favourable treatment outcomes, This findings were in line with the study conducted in Jimma, Ethiopia 88.3% ( 45 ) and the WHO 2030 international target of ≥ 90% ( 46 ). This might be time interconnection and similarity in outcome measurement but it contradicted the national pooled estimate of 86% and Nigeria's 81.4% ( 31 ). The variation might be reported time and study settings. Accordingly, the current study showed from the total participants, 46.28%, 46.50%, 1.58%, 2.25%, and 3.39% were cured, completed the treatment, died, failed and lost to follow-up, respectively. This result is contradicted by the study done in different countries, in Ethiopia 20.2% were cured, 68.0% completed treatment 4.8% died, 0.4% failed the therapy, and 3.2% cases were lost to follow-up ( 45 ). And in Nigeria, treatment outcome showed a total success rate of 81.4%, (cured 46.3% and completed treatment 35.1%, default 9.8%, died 6.5% and failed treatment 1.5% ( 31 ). This difference might be due to the study time, and sample size variation. Those TB patients who followed treatment at the health centre were less likely to have favourable treatment outcomes compared to TB patients who followed the treatment at hospitals. This result is in line with different studies done in Nigeria, and Vietnam the presence of physicians and nurses, and access to health services are predisposing and enabling health system factors for the successful treatment of tuberculosis ( 47 – 50 ). This might be in higher institutions like hospitals with adequate resources, skilled manpower and quality services. TB patients who are comorbid with any one NCD (DM, HPN, cancer, COPD, CVD) had 0.25 times fewer odds of treatment favourable outcomes as compared to those TB patients without comorbid. This is similar to the previously reported systematic review study that diabetes is associated with an increased risk of failure and death during tuberculosis treatment ( 33 – 35 ); patients with diabetes have a risk ratio (RR) for the combined outcome of failure and death ( 51 ); and the presence of some NCDs is associated with slower sputum and culture conversion both higher relapse and TB mortality rates, and poor TB treatment outcomes ( 52 – 54 ). This could be due to the presence of comorbidity, comorbid TB patients with NCDs might have delayed initiation of treatment, loss to follow up and face drug interaction side effects. The odds of having favourable tuberculosis treatment outcomes were more likely among TB patients who had waist circumference 72–79 cm/78–89 cm as compared to those TB patients who had waist circumference less than 72cm/ 78 cm. This finding is supported by different studies conducted in Addis Ababa, Ethiopia ( 55 ), where BMI ≥ 18.5kg/m2 was an independent predictor for successful treatment outcomes and in South India ( 56 ). This might be because TB patients are often malnourished, and malnourished people are at higher risk of death as their immune systems are decreasing. 5. Conclusion The TB treatment success rate was higher compared with a national pooled estimate and it indicated an early achievement of the WHO 2030 international target, SDG of reducing TB deaths by 90 percent by 2030. Even though the majority of TB patients had a treatment success rate, the TB patients followed treatment at the health centre, comorbid with any one or more than two NCDs (DM, HPN, cancer, COPD, CVD), had waist circumference 72–79 cm/78–89 cm were negatively predicted the treatment success. For better TB treatment outcomes policymakers should develop an early detection and management platform for identified risk factors in available TB programs and the government should avail NCD screening services at health centers. The health professional should counsel and educate TB patients on NCDs and risk factors Abbreviations NCDs non-communicable diseases TB tuberculosis DM diabetes mellitus CVD Cardiovascular disease COPD Chronic obstructed pulmonary disease SDG Sustainable Development Goals DOT Direct observation treatment WHO World Health Organization BMI body mass index Declarations Ethics approval and consent to participate The ethical principles related to protecting human subjects in this research were followed as outlined in the Declaration of Helsinki. Before the study was conducted, the ethics review committee of Wolaita Sodo University approved the study protocol with approval number: WSU/41/33/1356). Using this approved letter, the research team communicated with the study site administration of the Hadiya Zone Office to secure permission before starting data collection. Informed written voluntary consent, which was accepted and approved by the Ethics Committee, was obtained from the participants after securing their confidentiality and rights. Data and material availability Datasets supporting this finding can be accessed based on a reasonable justification from the corresponding author. The baseline data and methodological description can be retrieved from https://www.dovepress.com/getfile.php?fileID=94717. The data were not publicly available due to privacy or ethical restrictions. Competing interest The authors declare that they have no competing interests Author Contributions Investigation, data collection, writing-original draft and management: MHN; Conceptualization, study design and data analysis: MHN, YKL; Manuscript review and editing: KDG, EWW, YKL. All authors read and approved the final manuscript Acknowledgement The authors acknowledge Wolaita Sodo University for overall coordination, from preparation to finalizing the research project. Gratitude extends to Wachemo University and Hadiya Zone health office managers and coordinators for their information, and to the implementer and patients to provide pertinent information for this project achievement factors Funding statement Not granted by the known organization for financial support Author Details 1* Department of Public Health, Wachemo University, Southern Ethiopia. 2 Department of Public Health, Wolaita Sodo University, South Region, Ethiopia 3 Department of Health, Behavior and Society, Jimma University, Oromia region, Ethiopia. Consent for publication Not applicable References ABABA A. Federal Democratic Republic of Ethiopia Ministry of Health CLTSH Verification and Certification Protocol. 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Treatment outcomes in tuberculosis patients with diabetes: a polytomous analysis using a Brazilian surveillance system. PLoS One. 2014;9(7):e100082. Jørgensen ME, Faurholt-Jepsen D. Is there an effect of glucose-lowering treatment on incidence and prognosis of tuberculosis? A systematic review. Current diabetes reports. 2014;14(7):505. George JT. Outcomes of Sputum Positive Pulmonary Tuberculosis in Patients with Diabetes Mellitus: A Prospective Observational Cohort Study: Christian Medical College, Vellore; 2019. Sahile Z, Tezera R, Haile Mariam D, Collins J, Ali JH. Nutritional status and TB treatment outcomes in Addis Ababa, Ethiopia: an ambi-directional cohort study. Plos one. 2021;16(3):e0247945. Ramakrishnan C, Rajendran K, Jacob PG, Fox W, Radhakrishna S. The role of diet in the treatment of pulmonary tuberculosis: an evaluation in a controlled chemotherapy study in home and sanatorium patients in south India. Bulletin of the World Health Organization. 1961;25(3):339. Additional Declarations No competing interests reported. Supplementary Files screeningprocedureandDatacollection.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 20 Apr, 2024 Editor invited by journal 29 Mar, 2024 Submission checks completed at journal 29 Mar, 2024 First submitted to journal 29 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4094027","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":500003592,"identity":"2c92d5d5-784b-11f0-907b-06cc9d20a69f","order_by":0,"name":"Mengistu Nunemo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYHACNiBmlmNjZmx8AGTx8BGrxZifnbnZAKSFjVgtiTP72dskYFy8QLe999iDnzusGTccZmyr/JpjJ8PGwPzw0Q08WszOnEs37D2TzmwA1HJbdlsy0GFsxsY5+LTcyDGT4G07zAbWIrmNGaiFh00ar5b7b8wk/7Yd5gFpKZbcVk+Elhs8ZtJAWyQkmxnbGD9uO0yEljM5ZtKybekG/MyMzdKM247zsDET8svxM2aSb9us69v4jz/8+HNbtT0/e/PDx/i0oABmHjBJrHIQYPxBiupRMApGwSgYMQAAs15Dn5Zr8bUAAAAASUVORK5CYII=","orcid":"","institution":"Wachemo University","correspondingAuthor":true,"prefix":"","firstName":"Mengistu","middleName":"","lastName":"Nunemo","suffix":""},{"id":500003624,"identity":"32a0e835-784b-11f0-907b-06cc9d20a69f","order_by":1,"name":"Kassa Gidebo","email":"","orcid":"","institution":"Wolaita Sodo University","correspondingAuthor":false,"prefix":"","firstName":"Kassa","middleName":"","lastName":"Gidebo","suffix":""},{"id":500003628,"identity":"38b823a8-784b-11f0-907b-06cc9d20a69f","order_by":2,"name":"Eskinder Woticha","email":"","orcid":"","institution":"Wolaita Sodo University","correspondingAuthor":false,"prefix":"","firstName":"Eskinder","middleName":"","lastName":"Woticha","suffix":""},{"id":500003655,"identity":"40479e26-784b-11f0-907b-06cc9d20a69f","order_by":3,"name":"Yohannes Lemu","email":"","orcid":"","institution":"Jimma University","correspondingAuthor":false,"prefix":"","firstName":"Yohannes","middleName":"","lastName":"Lemu","suffix":""}],"badges":[],"createdAt":"2024-03-13 15:16:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4094027/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4094027/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89386389,"identity":"746e684a-c24e-436d-ac32-e7bc0f4c3058","added_by":"auto","created_at":"2025-08-19 12:35:50","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":53968,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of tuberculosis treatment success rate, central Ethiopia\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4094027/v1/23c47aa954e987c58e540cdb.jpg"},{"id":89386390,"identity":"28f6215a-f194-4ff2-8aa8-be27ba53e1d8","added_by":"auto","created_at":"2025-08-19 12:35:50","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":88500,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of tuberculosis treatment outcomes, in Southern Ethiopia\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4094027/v1/9d4e611673ecb6e4a48b9932.jpg"},{"id":89389873,"identity":"f0e8a5cc-8f71-4ae8-8b3f-c36443e69287","added_by":"auto","created_at":"2025-08-19 12:51:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":874756,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4094027/v1/38ae26ef-3081-4122-bc78-d18520bf4c68.pdf"},{"id":89386393,"identity":"96bfdade-2b1a-4acb-b524-ed0901ee5144","added_by":"auto","created_at":"2025-08-19 12:35:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":575164,"visible":true,"origin":"","legend":"","description":"","filename":"screeningprocedureandDatacollection.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4094027/v1/c590427032a5c61b2744b602.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Non-communicable disease multi-comorbidities and risks of unfavourable tuberculosis treatment outcomes among routine tuberculosis patients, Southern Ethiopia","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eTuberculosis (TB) is a major public health problem globally, and one of the top 22 countries affected by TB is Ethiopia (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) with a predictable incidence and prevalence rate of 2.1/1000 and 2.0 /1000, respectively (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Ethiopia is the 3rd highest country in Sub-Saharan Africa, with a prevalence rate of 4.36% in diabetes mellitus (DM) (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). And more than 1.3\u0026nbsp;million cases of DM in Ethiopia (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eNCDs( non-communicable diseases) are risk factors for TB, especially for progression from infection to disease due to the negative impact on host defence mechanisms against mycobacterium TB; NCDs and their attributes complicate treatment and management of TB, due to clinical challenges (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Chronic disease challenges are growing unequally among developing countries, and non-communicable diseases were raised from 6.7 to 8.5\u0026nbsp;million from 2000 to 2012 respectively in South East Asia (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGlobal population health data suggest that TB control targets achieving and addressing TB intervention programs delayed by TB and NCD comorbidities (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). This contributes to the increment of the TB burden and its attributes of smoking and alcohol use at the community level (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The common comorbid non-infectious diseases comorbid with TB are DM, hypertension (HPN), heart diseases, chronic obstructed pulmonary diseases (COPD), and cancer (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e); and shared risk factors such as smoking, poor diet, and harmful use of alcohol, which need to be addressed for effective prevention (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eIn South Africa, the prevalence of comorbidity (with one NCD) was 26.9% and multi-morbidity (with two or more NCDs) was 20.9%, alcohol use (24.3%), tobacco use (15.0%), hypertension (8.9%), ischaemic heart disease or angina (7.5%), arthritis (4.5%), type 2 DM (4.1%), asthma (3.5%), cancer or malignant neoplasms (2.1%), chronic lung disease (1.9%) and dyslipidemia (1.6%) (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Nationally, the DM prevalence among TB patients (13.5%) is higher than the population prevalence (2.0% -6.5%) and it has a residence difference between Urban (5.1%) and Rural (2.1%) (\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEight preventable attributable factors for NCDs are tobacco use, alcohol use, inadequate physical exercise, less consumption of fruits and vegetables, DM, hypertension, and obesity (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). These variables negatively affect the TB treatment success rate (\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Those TB patients comorbid with NCDs delay the treatment and detection and are attributed to TB transmissions (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe WHO defined TB standard international controlling indicators cured, completed, failed, died and defaulted; its proportion was mutually reported as died, treatment failed and defaulted as unfavourable treatment outcomes, whereas cured and completed were reported as favourable treatment outcomes (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe national strategic plan for ending TB reported that the relapse of TB somewhat increased and the TB treatment success rate (88.5%), is below the TB end strategic plan (90%) (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Also globally in 2019, most international regions and the majority of TB prevalent areas are not in line with the End TB strategy\u0026rsquo;s 2020 milestones, the decrease in TB incidence rate (6.3%) and the number of deaths (11%) were reported between 2015 and 2018 (\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eIn Nigeria, (81.4%) of TB patients had favourable treatment outcomes, of which 46.3% were cured and 35.1% completed the treatment, however among 18.6% had unfavourable treatment outcomes 9.8% lost to follow-up, 6.5% died and 1.5% have failed the treatment (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Similarly, in Ethiopia, the majority of notifiable TB patients completed the treatment and 77.9% had a cure rate, however in the Wolaga Oromia region 17.2% were cured, 2.9% lost to follow-up, 4.8% died and 0.4% failed the treatment (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). The 2030 and 2035 Sustainable Development Goals(SGD) targets of decreasing TB incidence rate, death; and NCDs premature death rate were not achieved as planned (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eIn general, globally there is little evidence on factors affecting tuberculosis treatment outcomes, which is more focused on DM, single risk estimates and differ in outcome measurement (\u003cspan additionalcitationids=\"CR34 CR35 CR36 CR37 CR38\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e); and in Ethiopia, a few descriptive studies were conducted using secondary data, and further follow-up researches were highly recommended (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e), and no evidence of risk estimates of unfavourable tuberculosis treatment outcomes related to NCDs comorbidities and shared risk factors using a prospective follow-up study design. This prospective study provided strong evidence on both NCDs and risk estimates of unfavourable tuberculosis treatment outcomes among routine tuberculosis patients for policy development and to provide quality and integrated service for both diseases\u003c/p\u003e"},{"header":"2. Methods and materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study site and setting\u003c/h2\u003e\u003cp\u003eThe study was conducted in the Hadiya Zone Hospital TB treatment centre from January 2022 to, June 2023 for six months. The administration of the zone is divided into 7 urban districts and 10 rural districts with 303 rural kebele, and 29 urban kebele. The total population was 1,705,902 (male 848,695 and female 857,225) with 4 district hospitals, 1 referral hospital, 61 health centres, and around 305 health stations.\u003c/p\u003e\u003cp\u003e In the study site, TB were reported based on the standard national and international guidelines. In 2020, among 1630 TB patients, 781 pulmonary positive TB, 579 pulmonary negative TB, and 270 extra pulmonary TB were reported. Presently, the study site applied chronic care as a new program, however, it functioned only in selected TB care facilities. In the selected site in 2021 from 73333 screened participants, about 397 hypertension patients liked to care, even though the health professionals were not motivated and most healthcare facilities did not have chronic disease screening equipment\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Study design and population\u003c/h2\u003e\u003cp\u003eThis study is the continuity of the previous project on NCDs and risk factors comorbidity identification and categorization published at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.dovepress.com/getfile.php?fileID=94717\u003c/span\u003e\u003cspan address=\"https://www.dovepress.com/getfile.php?fileID=94717\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. A single-arm follow-up study was carried out on routine tuberculosis patients, through screening common NCDs and risk factors for two months and following these patients for four months until completion of treatment to measure outcomes.\u003c/p\u003e\u003cp\u003eThe eligibility criteria were those study participants who were enrolled in previous projects and willing to continue until the completion of treatment. TB patients who have a previous history of unfavourable treatment outcomes (e.g. relapse) and early death that occurred before the survey were excluded from the study participants. These eligible study participants were followed until treatment completion\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Data collection and procedure\u003c/h2\u003e\u003cp\u003eInitially, baseline data were assessed from three general hospitals, one referral hospital and three health centres following ethical consensus. These study sites were selected based on the eligibility criteria of both Non-communicable and tuberculosis disease continuous detection, diagnosis and treatment services availability. Before enrolling the study participants (TB patients) for screening and follow-up, the study participants were selected again by eligibility criteria. First, all newly enrolled tuberculosis patients in the direct observational treatment (DOT) program were screened for NCD and its risk factors through the two stages WHO Step-wise screening procedure. After assessing the baseline survey data, the portfolio and per forma were developed for each TB patient. Next, a single-arm follow-up study was undertaken on screened TB patients and followed until completion of treatment to observe TB treatment outcomes.\u003c/p\u003e\u003cp\u003eThe two stages of the WHO step-wise screening procedure were used to measure the exposure status for tuberculosis treatment outcomes. This tool was formerly validated and tested in different settings (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Data were collected through the standard WHO measures, structured interview questionnaires and reviewing medical records at the start, and end of treatment to determine outcomes (supplmentary1). The end-line data were collected after six months of follow-up time concerning each TB patient treatment period. Patient's clinical data were observed for outcomes every 2 months after the baseline survey because the participant's registration was cascaded by using an open cohort follow-up nature.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Variables\u003c/h2\u003e\u003cp\u003eThe exposure variables were common comorbid NCD and risk factors include elevated blood pressure, elevated blood glucose, cigarette smoking/ tobacco use, alcohol use, malnutrition (body mass index (BMI), waist circumference insufficient physical activity, and inadequate fruit and vegetable consumption). These variables were measured using the WHO score scales formerly validated and tested in different settings(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Those tuberculosis patients on the direct observation treatment (DOT) program and newly initiated treatment were screened for exposure within two months. Sociodemographic factors ( age, sex, residence, contact person, address, phone number, and others), whereas clinical and TB-related variables (treatment centre, TB types, treatment and health-seeking delay) were explanatory variables for tuberculosis treatment outcomes.\u003c/p\u003e\u003cp\u003eThe outcome variables were measured using the standard global definition of the WHO(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). These outcomes were categorized into cured, treatment completed, treatment failed, and loss of follow-up, death, and not evaluation. Deaths, treatment failure, and loss of follow-up are defined as unfavourable treatment outcomes, whereas favourable treatment outcomes include treatment completed and cured. Follow-up and adherence: Once the TB patients on the DOT program were screened consecutively for two months for NCDs and risk factors, which were used for baseline data, participants were consulted and frequently contacted based on their voluntary participation for adherence.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Sample size and sampling procedure\u003c/h2\u003e\u003cp\u003eOnce the cluster and the participant\u0026rsquo;s size in the care centre were identified, NCDs and risk factors were screened by the WHO Step-wise screening procedure. The sample size, sampling procedure; and variables measurement and categorization that were collected and analyzed were published as baseline data in a peer-reviewed journal: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2147/IBPC.S432251\u003c/span\u003e\u003cspan address=\"10.2147/IBPC.S432251\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Data analysis and interpretation\u003c/h2\u003e\u003cp\u003eWe used STATA v.14 software for analysis, after importing the EXCELL datasheet. Formerly, baseline data were described by the percentages, mean and other statistics of socio-demographic, exposure variables and clinical data of tuberculosis patients, which were presented by tables, and published at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2147/IBPC.S432251\u003c/span\u003e\u003cspan address=\"10.2147/IBPC.S432251\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. These survey data were used to estimate the risk of unfavourable TB treatment outcomes, and which were explained using logistic regression analysis. Before model development, the TB treatment outcomes were presented by using the figure\u003c/p\u003e\u003cp\u003eBoth bivariate and multivariate logistic regression models were developed to assess the significantly associated variables with the tuberculosis treatment unfavourable. While we identified the associated variables of unfavourable, the dependent variable was coded as 1 if the tuberculosis treatment outcome was unfavourable and coded as 0 if outcome is favorable. First, a bivariate logistic regression model was constructed by identifying the significant association at p-value\u0026thinsp;\u0026le;\u0026thinsp;0.05 with 95% CI between the response and explanatory variables. Next, those significantly associated variables at p-value\u0026thinsp;\u0026le;\u0026thinsp;0.05 with 95% CI in the binary logistic regression model were entered into multiple regression analyses to identify the predictors of unfavourable tuberculosis treatment outcomes. Before multiple logistic regression model development multicollinearity was checked among the explanatory variables. Similarly, the model appropriateness was checked by Hosmer-Lemeshow test statistics to see the goodness-of-fit test. Finally, the estimates were reported by considering the 95% CI statistical significance of COR, and AOR using tables\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Baseline characteristics of participants and exposure variables\u003c/h2\u003e\u003cp\u003eBefore exposure categorization, we enrolled all TB patients in the DOT program; and due to the small sample size, we recruited consecutively newly registered TB patients for treatment until sample size adequacy. Those who were not ready to die and who had the previous treatment outcome were excluded from baseline characterization. Based on these criteria the participant's sociodemographic variable descriptions; and NCDs and risk factors prevalence were explored in a previous project and can be accessed from the baseline data published article on reputable journal \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2147/IBPC.S432251\u003c/span\u003e\u003cspan address=\"10.2147/IBPC.S432251\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Tuberculosis treatment outcomes\u003c/h2\u003e\u003cp\u003eAmong the total tuberculosis patients (443), the majority of 410 (92.60%) had favourable treatment outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), of which 206 (46.5%) and 187(42.28%) were completed and cured, respectively, however from unfavourable treatment outcomes 33 (7.40%), 15(3.39%), 10(2.26%) and 7(1.58%) were loss to follow up, failure and died, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Determinants of Tuberculosis Treatment Success Rate\u003c/h2\u003e\u003cp\u003e Bivariate analysis showed that TB patients' comorbid with NCDs, waist circumferences, tobacco smoke, alcohol use, rural residences, and health centres as health care facilities were candidate variables for multivariable analysis at p-value\u0026thinsp;\u0026le;\u0026thinsp;0.05 and have a crude association with the occurrence of a TB treatment unfavourable and also collectively entered to multiple logistic regression model to identify predictors.\u003c/p\u003e\u003cp\u003eAfter adjusting for the covariate, the multiple logistic regression analysis showed that tuberculosis patients comorbid with NCDs, waist circumferences, and health facilities as health centres were found to be independent predictors for unfavourable TB treatment at p-value\u0026thinsp;\u0026le;\u0026thinsp;0.05.\u003c/p\u003e\u003cp\u003eAccordingly, TB patients comorbid with any one NCD (DM, HPN, cancer, COPD, CVD) had 0.25 times less odd of TB treatment favourable outcomes as compared to those TB patients without comorbid (AOR\u0026thinsp;=\u0026thinsp;0.25; 95%CI\u0026thinsp;=\u0026thinsp;0.07\u0026ndash;0.83 at P value\u0026thinsp;=\u0026thinsp;0.024). TB patients who were comorbid with any two or more NCDs (DM, HPN, cancer, COPD, CVD) had 0.16 times less odd of treatment favourable as compared to those TB patients without comorbid (AOR\u0026thinsp;=\u0026thinsp;0.16; 95%CI\u0026thinsp;=\u0026thinsp;0.04\u0026ndash;0.66 at P value\u0026thinsp;=\u0026thinsp;0.012). The odds of having tuberculosis treatment favourable outcomes were 5.23 times more likely to have a waist circumference of 72\u0026ndash;79 cm for females or 78\u0026ndash;89 cm for males (obese) as compared to those TB patients who had a waist circumference less than 72cm for female or 78 cm for male (AOR\u0026thinsp;=\u0026thinsp;5.23; 95%CI\u0026thinsp;=\u0026thinsp;1.52\u0026ndash;18.10 at P value\u0026thinsp;=\u0026thinsp;0.009). Those TB patients who followed treatment at the health centre were about 0.37 times less likely to have a favourable treatment outcome compared to TB patients who followed the treatment at hospitals (AOR\u0026thinsp;=\u0026thinsp;0.37; 95%CI\u0026thinsp;=\u0026thinsp;0.15\u0026ndash;0.95 at P value\u0026thinsp;=\u0026thinsp;0.038) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\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 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePredictors of unfavourable tuberculosis treatment outcomes in Southern Ethiopia\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eTreatment outcomes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCOR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAOR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003efavourable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eunfavourable\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eTB-NCD Comorbidity status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot comorbid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e364\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eComorbid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.13, 1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.25*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.07, 0.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMulti-comorbid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.09 ,0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.16*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.04, 0.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eWaist circumference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;72/78 cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e269\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72\u0026ndash;79/78\u0026ndash;89 cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.42*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.32, 14.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.23*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.51, 18.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;80/90 cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18\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\u003cp\u003e1.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.25, 15.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.60, 57.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAlcohol use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot drinker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e372\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDrinker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.12, 0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.09, 1.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCigarette smoking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e378\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSmoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.13, 0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.19, 5.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHealth facility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHospital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHealth center\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.38*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.17, 0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.37*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.15, 0.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eResidence\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\u003e367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.31*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.14, 0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.17, 1.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study revealed that 92.5% of tuberculosis patients had favourable treatment outcomes, This findings were in line with the study conducted in Jimma, Ethiopia 88.3% (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e) and the WHO 2030 international target of \u0026ge;\u0026thinsp;90% (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). This might be time interconnection and similarity in outcome measurement but it contradicted the national pooled estimate of 86% and Nigeria's 81.4% (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). The variation might be reported time and study settings.\u003c/p\u003e\u003cp\u003eAccordingly, the current study showed from the total participants, 46.28%, 46.50%, 1.58%, 2.25%, and 3.39% were cured, completed the treatment, died, failed and lost to follow-up, respectively. This result is contradicted by the study done in different countries, in Ethiopia 20.2% were cured, 68.0% completed treatment 4.8% died, 0.4% failed the therapy, and 3.2% cases were lost to follow-up (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). And in Nigeria, treatment outcome showed a total success rate of 81.4%, (cured 46.3% and completed treatment 35.1%, default 9.8%, died 6.5% and failed treatment 1.5% (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). This difference might be due to the study time, and sample size variation.\u003c/p\u003e\u003cp\u003eThose TB patients who followed treatment at the health centre were less likely to have favourable treatment outcomes compared to TB patients who followed the treatment at hospitals. This result is in line with different studies done in Nigeria, and Vietnam the presence of physicians and nurses, and access to health services are predisposing and enabling health system factors for the successful treatment of tuberculosis (\u003cspan additionalcitationids=\"CR48 CR49\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). This might be in higher institutions like hospitals with adequate resources, skilled manpower and quality services.\u003c/p\u003e\u003cp\u003eTB patients who are comorbid with any one NCD (DM, HPN, cancer, COPD, CVD) had 0.25 times fewer odds of treatment favourable outcomes as compared to those TB patients without comorbid. This is similar to the previously reported systematic review study that diabetes is associated with an increased risk of failure and death during tuberculosis treatment (\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e); patients with diabetes have a risk ratio (RR) for the combined outcome of failure and death (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e); and the presence of some NCDs is associated with slower sputum and culture conversion both higher relapse and TB mortality rates, and poor TB treatment outcomes (\u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). This could be due to the presence of comorbidity, comorbid TB patients with NCDs might have delayed initiation of treatment, loss to follow up and face drug interaction side effects.\u003c/p\u003e\u003cp\u003eThe odds of having favourable tuberculosis treatment outcomes were more likely among TB patients who had waist circumference 72\u0026ndash;79 cm/78\u0026ndash;89 cm as compared to those TB patients who had waist circumference less than 72cm/ 78 cm. This finding is supported by different studies conducted in Addis Ababa, Ethiopia (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e), where BMI\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;18.5kg/m2 was an independent predictor for successful treatment outcomes and in South India (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). This might be because TB patients are often malnourished, and malnourished people are at higher risk of death as their immune systems are decreasing.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe TB treatment success rate was higher compared with a national pooled estimate and it indicated an early achievement of the WHO 2030 international target, SDG of reducing TB deaths by 90 percent by 2030. Even though the majority of TB patients had a treatment success rate, the TB patients followed treatment at the health centre, comorbid with any one or more than two NCDs (DM, HPN, cancer, COPD, CVD), had waist circumference 72\u0026ndash;79 cm/78\u0026ndash;89 cm were negatively predicted the treatment success. For better TB treatment outcomes policymakers should develop an early detection and management platform for identified risk factors in available TB programs and the government should avail NCD screening services at health centers. The health professional should counsel and educate TB patients on NCDs and risk factors\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNCDs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003enon-communicable diseases\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTB\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003etuberculosis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ediabetes mellitus\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCVD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCardiovascular disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCOPD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eChronic obstructed pulmonary disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSDG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSustainable Development Goals\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDOT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDirect observation treatment\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWorld Health Organization\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ebody mass index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003e\u003cstrong\u003eEthics approval and consent to participate \u0026nbsp;\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe ethical principles related to protecting human subjects in this research were followed as outlined in the Declaration of Helsinki. Before the study was conducted, the ethics review committee of Wolaita Sodo University approved the study protocol with approval number: WSU/41/33/1356). Using this approved letter, the research team communicated with the study site administration of the Hadiya Zone Office to secure permission before starting data collection. Informed written voluntary consent, which was accepted and approved by the Ethics Committee, was obtained from the participants after securing their confidentiality and rights.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eData and material availability\u0026nbsp;\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003e\u0026nbsp;Datasets supporting this finding can be accessed based on a reasonable justification from the corresponding author. The baseline data and methodological description can be retrieved from https://www.dovepress.com/getfile.php?fileID=94717. The data were not publicly available due to privacy or ethical restrictions.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eCompeting interest\u0026nbsp;\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e\u0026nbsp;Author Contributions\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eInvestigation, data collection, writing-original draft and management: MHN; Conceptualization, study design and data analysis: MHN, YKL; Manuscript review and editing: KDG, EWW, YKL. All authors read and approved the final manuscript\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe authors acknowledge Wolaita Sodo University for overall coordination, from preparation to finalizing the research project. Gratitude extends to Wachemo University and Hadiya Zone health office managers and coordinators for their information, and to the implementer and patients to provide pertinent information for this project achievement\u0026nbsp;factors\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e\u0026nbsp;Funding statement\u0026nbsp;\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eNot granted by the known organization for financial support\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Details\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1*\u0026nbsp;\u003c/sup\u003eDepartment of Public Health, Wachemo University, Southern Ethiopia.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u0026nbsp;2\u003c/sup\u003eDepartment of Public Health, Wolaita Sodo University, South Region, Ethiopia\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003csup\u003e3\u003c/sup\u003eDepartment of Health, Behavior and Society, Jimma University, Oromia region, Ethiopia.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eABABA A. 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Factors associated with localization of tuberculosis disease among patients in a high burden country: A health facility-based comparative study in Ethiopia. Journal of Clinical Tuberculosis and Other Mycobacterial Diseases. 2021;23:100231.\u003c/li\u003e\n\u003cli\u003eTola A, Minshore KM, Ayele Y, Mekuria AN. Tuberculosis treatment outcomes and associated factors among TB patients attending public hospitals in Harar town, Eastern Ethiopia: a five-year retrospective study. Tuberculosis research and treatment. 2019;2019.\u003c/li\u003e\n\u003cli\u003eAmarchand R, Krishnan A, Saraf DS, Mathur P, Shukla DK, Nath LM. Lessons for addressing noncommunicable diseases within a primary health-care system from the Ballabgarh project, India. WHO South-East Asia journal of public health. 2015;4(2):130-7.\u003c/li\u003e\n\u003cli\u003eMisra A, Chowbey P, Makkar B, Vikram N, Wasir J, Chadha D, et al. 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Factors associated with interruption of treatment among Pulmonary Tuberculosis patients in Plateau State, Nigeria. 2011. Pan African Medical Journal. 2014;17(1).\u003c/li\u003e\n\u003cli\u003eVIETNAM HIVI, RELAPSE SWE. BELITUNGNURSING. 2019.\u003c/li\u003e\n\u003cli\u003ePutra KWR, Toonsiri C. Factors related to the successful treatment of tuberculosis: A literature review. Belitung Nursing Journal. 2019;5(4):136-46.\u003c/li\u003e\n\u003cli\u003eOWUOR MO. SOCIAL OUTCOMES OF HEALTHCARE-SEEKING BEHAVIOUR AMONG PERSONS LIVING WITH DIABETES IN RONGO SUB-COUNTY, MIGORI COUNTY, KENYA 2022.\u003c/li\u003e\n\u003cli\u003eBaker MA, Harries AD, Jeon CY, Hart JE, Kapur A, L\u0026ouml;nnroth K, et al. The impact of diabetes on tuberculosis treatment outcomes: a systematic review. BMC medicine. 2011;9(1):1-15.\u003c/li\u003e\n\u003cli\u003eReis-Santos B, Gomes T, Locatelli R, de Oliveira ER, Sanchez MN, Horta BL, et al. Treatment outcomes in tuberculosis patients with diabetes: a polytomous analysis using a Brazilian surveillance system. PLoS One. 2014;9(7):e100082.\u003c/li\u003e\n\u003cli\u003eJ\u0026oslash;rgensen ME, Faurholt-Jepsen D. Is there an effect of glucose-lowering treatment on incidence and prognosis of tuberculosis? A systematic review. Current diabetes reports. 2014;14(7):505.\u003c/li\u003e\n\u003cli\u003eGeorge JT. Outcomes of Sputum Positive Pulmonary Tuberculosis in Patients with Diabetes Mellitus: A Prospective Observational Cohort Study: Christian Medical College, Vellore; 2019.\u003c/li\u003e\n\u003cli\u003eSahile Z, Tezera R, Haile Mariam D, Collins J, Ali JH. Nutritional status and TB treatment outcomes in Addis Ababa, Ethiopia: an ambi-directional cohort study. Plos one. 2021;16(3):e0247945.\u003c/li\u003e\n\u003cli\u003eRamakrishnan C, Rajendran K, Jacob PG, Fox W, Radhakrishna S. The role of diet in the treatment of pulmonary tuberculosis: an evaluation in a controlled chemotherapy study in home and sanatorium patients in south India. Bulletin of the World Health Organization. 1961;25(3):339.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"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":"Non-communicable disease, risk factors, tuberculosis treatment outcomes, comorbidity","lastPublishedDoi":"10.21203/rs.3.rs-4094027/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4094027/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEven though non-communicable diseases are associated with adverse or poor TB treatment outcomes, there are a few recorded base studies conducted on determinants of tuberculosis treatment outcomes, which are more concentrated on diabetes mellitus. This study aims to estimate comorbidity and risk factors of unfavourable tuberculosis treatment outcomes among tuberculosis patient\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA prospective follow-up study was conducted and the study population were screened by standard and validated procedure for exposure categorization; and followed until treatment completion, respective to six months. A bivariate and multiple logistic regression model was developed for risk estimates of odds ratio after descriptive statics proportion and mean explored and it was presented in tables and figures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe majority (92.5%) of tuberculosis patients had successful treatment outcomes, of which 46.5% and 42.28% were completed and cured, respectively. TB patients comorbid with any one NCD (DM, HPN, cancer, COPD, CVD), comorbid with any two or more NCDs (DM, HPN, cancer, COPD, CVD), TB patients who had waist circumference 72–79 cm/ 78–89 cm, and care facility as health centre were found to be an independent predictor for the unfavourable TB treatment outcomes\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEven though the majority of TB patients had a treatment success rate, multi-comorbidity negatively predicted the treatment success. For better TB treatment outcomes policymakers should develop an early detection and management platform for identified risk factors in available TB programs.\u003c/p\u003e","manuscriptTitle":"Non-communicable disease multi-comorbidities and risks of unfavourable tuberculosis treatment outcomes among routine tuberculosis patients, Southern Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-19 12:27:45","doi":"10.21203/rs.3.rs-4094027/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2024-04-20T10:49:29+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-03-29T10:04:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-29T08:36:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2024-03-29T08:35:24+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"5f34c25f-1ff6-4173-b4c2-8b7499f35ae7","owner":[],"postedDate":"August 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-08-19T12:27:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-19 12:27:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4094027","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4094027","identity":"rs-4094027","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}