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Wang J, Zhang Y, Rao Q, Liu C, Du H, Cao X, Xi M. Factors affecting the readiness for hospital discharge of initially treated pulmonary tuberculosis patients in China: a phenomenological study. BMC Public Health 2024; 24:2312. [PMID: 39187780 PMCID: PMC11346029 DOI: 10.1186/s12889-024-19793-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Accepted: 08/14/2024] [Indexed: 08/28/2024] Open
Abstract
BACKGROUND Despite readiness for hospital discharge widespread popularity since readiness for hospital discharge introduction in 1979 and extensive study, readiness for hospital discharge among pulmonary tuberculosis (PTB) patients has not yet been investigated. Moreover, the factors influencing this process remain unclear. OBJECTIVE The objective of this study was to investigate the factors influencing readiness for hospital discharge in initially treated PTB patients using the capability, opportunity, motivation-behavior (COM-B) model. METHODS This phenomenological study was conducted from December 2023 to March 2024. Face-to-face individual interviews were conducted with 18 initially treated patients with PTB according to a semistructured interview guide developed on the basis of the COM-B model. The interview data were subjected to analysis using NVivo 14 software and Colaizzi's method. RESULTS As a result, 6 themes and 14 subthemes were identified. Physical capability for readiness for hospital discharge (subthemes included poor health status, early acquisition of adequate knowledge about PTB, inadequate knowledge about readiness for hospital discharge), psychological capability for readiness for hospital discharge(subthemes included false perceptions about readiness for hospital discharge, high treatment adherence), physical opportunity for readiness for hospital discharge (subthemes included high continuity of transition healthcare, insufficient financial support, insufficient informational support), social opportunity for readiness for hospital discharge (subthemes included stigmatization, inadequate emotional support), reflective motivation for readiness for hospital discharge (subthemes included lack of reflection on coping with difficulties, intention to develop a readiness for hospital discharge plan), and automatic motivation for readiness for hospital discharge (subthemes included strong desire to be cured, negative emotions). CONCLUSION We established factors related to readiness for hospital discharge in initially treated PTB patients in terms of capability, opportunity and motivation, which can inform the future development of readiness for hospital discharge plans. To improve patients' readiness for hospital discharge, patients need to be motivated to plan and desire readiness for hospital discharge, patients' knowledge and treatment adherence should be improved, and patients' transition healthcare continuity and emotional support should be focused on. Moreover, the quality of readiness for hospital discharge and discharge education should be assessed in a timely manner to identify impeding factors and provide interventions.
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Affiliation(s)
- Jiani Wang
- School of Nursing, University of South China, Hengyang, China
- University of South China - Hunan Province Tideng Medical Technology Limited Culture Company Wisdom Nursing Postgraduate Joint Cultivation Base, Hengyang, China
| | - Yuan Zhang
- Department of Pulmonary Tuberculosis, Changsha Central Hospital Affiliated to University of South China, Changsha, China
| | - Qin Rao
- Department of Pulmonary Tuberculosis, Changsha Central Hospital Affiliated to University of South China, Changsha, China
| | - Chenhuan Liu
- School of Nursing, Zunyi Medical University, Zunyi, China
| | - Hengxu Du
- School of Nursing, University of South China, Hengyang, China
- University of South China - Hunan Province Tideng Medical Technology Limited Culture Company Wisdom Nursing Postgraduate Joint Cultivation Base, Hengyang, China
| | - Xiaohua Cao
- Department of Pulmonary Tuberculosis, Changsha Central Hospital Affiliated to University of South China, Changsha, China
| | - Mingxia Xi
- Department of Pulmonary Tuberculosis, Changsha Central Hospital Affiliated to University of South China, Changsha, China.
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Ridolfi F, Amorim G, Peetluk LS, Haas DW, Staats C, Araújo-Pereira M, Cordeiro-Santos M, Kritski AL, Figueiredo MC, Andrade BB, Rolla VC, Sterling TR. Prediction Models for Adverse Drug Reactions During Tuberculosis Treatment in Brazil. J Infect Dis 2024; 229:813-823. [PMID: 38262629 PMCID: PMC10938211 DOI: 10.1093/infdis/jiae025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 01/04/2024] [Accepted: 01/22/2024] [Indexed: 01/25/2024] Open
Abstract
BACKGROUND Tuberculosis (TB) treatment-related adverse drug reactions (TB-ADRs) can negatively affect adherence and treatment success rates. METHODS We developed prediction models for TB-ADRs, considering participants with drug-susceptible pulmonary TB who initiated standard TB therapy. TB-ADRs were determined by the physician attending the participant, assessing causality to TB drugs, the affected organ system, and grade. Potential baseline predictors of TB-ADR included concomitant medication (CM) use, human immunodeficiency virus (HIV) status, glycated hemoglobin (HbA1c), age, body mass index (BMI), sex, substance use, and TB drug metabolism variables (NAT2 acetylator profiles). The models were developed through bootstrapped backward selection. Cox regression was used to evaluate TB-ADR risk. RESULTS There were 156 TB-ADRs among 102 of the 945 (11%) participants included. Most TB-ADRs were hepatic (n = 82 [53%]), of moderate severity (grade 2; n = 121 [78%]), and occurred in NAT2 slow acetylators (n = 62 [61%]). The main prediction model included CM use, HbA1c, alcohol use, HIV seropositivity, BMI, and age, with robust performance (c-statistic = 0.79 [95% confidence interval {CI}, .74-.83) and fit (optimism-corrected slope and intercept of -0.09 and 0.94, respectively). An alternative model replacing BMI with NAT2 had similar performance. HIV seropositivity (hazard ratio [HR], 2.68 [95% CI, 1.75-4.09]) and CM use (HR, 5.26 [95% CI, 2.63-10.52]) increased TB-ADR risk. CONCLUSIONS The models, with clinical variables and with NAT2, were highly predictive of TB-ADRs.
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Affiliation(s)
- Felipe Ridolfi
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Instituto Nacional de Infectologia Evandro Chagas, Fundação Oswaldo Cruz, Rio de Janeiro, Rio de Janeiro, Brazil
| | - Gustavo Amorim
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Lauren S Peetluk
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Optum Epidemiology, Boston, Massachusetts, USA
| | - David W Haas
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Cody Staats
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Mariana Araújo-Pereira
- Multinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative, Salvador, Bahia, Brazil
- Faculdade de Tecnologia e Ciências, Curso de Medicina, Salvador, Bahia, Brazil
| | - Marcelo Cordeiro-Santos
- Fundação Medicina Tropical Dr Heitor Vieira Dourado, Manaus, Amazonas, Brazil
- Universidade do Estado do Amazonas, Manaus, Amazonas, Brazil
| | - Afrânio L Kritski
- Faculdade de Medicina, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Rio de Janeiro, Brazil
| | - Marina C Figueiredo
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Bruno B Andrade
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Multinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative, Salvador, Bahia, Brazil
- Faculdade de Tecnologia e Ciências, Curso de Medicina, Salvador, Bahia, Brazil
- Laboratório de Pesquisa Clínica e Translacional, Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Bahia, Brazil
- Curso de Medicina, Universidade Salvador, Salvador, Bahia, Brazil
- Curso de Medicina, Escola Bahiana de Medicina e Saúde Pública, Salvador, Bahia, Brazil
| | - Valeria C Rolla
- Instituto Nacional de Infectologia Evandro Chagas, Fundação Oswaldo Cruz, Rio de Janeiro, Rio de Janeiro, Brazil
| | - Timothy R Sterling
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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Osawa T, Watanabe M, Morimoto K, Yoshiyama T, Matsuda S, Fujiwara K, Furuuchi K, Shimoda M, Ito M, Kodama T, Uesugi F, Okumura M, Tanaka Y, Sasaki Y, Ogata H, Goto H, Kudoh S, Ohta K. Activities of Daily Living, Hypoxemia, and Lymphocytes Score for Predicting Mortality Risk in Patients With Pulmonary TB. Chest 2024; 165:267-277. [PMID: 37726072 DOI: 10.1016/j.chest.2023.09.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 09/08/2023] [Accepted: 09/12/2023] [Indexed: 09/21/2023] Open
Abstract
BACKGROUND A clinically applicable mortality risk prediction system for pulmonary TB may improve treatment outcomes, but no easy-to-calculate and accurate score has yet been reported. The aim of this study was to construct a simple and objective disease severity score for patients with pulmonary TB. RESEARCH QUESTION Does a clinical score consisting of simple objective factors predict the mortality risk of patients with pulmonary TB? STUDY DESIGN AND METHODS The data set from our previous prospective study that recruited patients newly diagnosed with pulmonary TB was used for the development cohort. Patients for the validation cohort were prospectively recruited between March 2021 and September 2022. The primary end point was all-cause in-hospital mortality. Using Cox proportional hazards regression, a mortality risk prediction model was optimized in the development cohort. The disease severity score was developed by assigning integral points to each variate. RESULTS The data from 252 patients in the development cohort and 165 patients in the validation cohort were analyzed, of whom 39 (15.5%) and 17 (10.3%), respectively, died in the hospital. The disease severity score (named the AHL score) included three clinical parameters: activities of daily living (semi-dependent, 1 point; totally dependent, 2 points); hypoxemia (1 point), and lymphocytes (< 720/μL, 1 point). This score showed good discrimination with a C statistic of 0.902 in the development cohort and 0.842 in the validation cohort. We stratified the score into three groups (scores of 0, 1-2, and 3-4), which clearly corresponded to low (0% and 1.3%), intermediate (13.5% and 8.9%), and high (55.8% and 39.3%) mortality risk in the development and validation cohorts. INTERPRETATION The easy-to-calculate AHL disease severity score for patients with pulmonary TB was able to categorize patients into three mortality risk groups with great accuracy. CLINICAL TRIAL REGISTRATION University Hospital Medical Information Network Center; No. UMIN000012727 and No. UMIN000043849; URL: www.umin.ac.jp.
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Affiliation(s)
- Takeshi Osawa
- Department of Respiratory Medicine, Fukujuji Hospital, Japan Anti-Tuberculosis Association (JATA), Tokyo, Japan
| | - Masato Watanabe
- Department of Respiratory Medicine, Kyorin University School of Medicine, Tokyo, Japan.
| | - Kozo Morimoto
- Department of Respiratory Medicine, Fukujuji Hospital, Japan Anti-Tuberculosis Association (JATA), Tokyo, Japan; Division of Clinical Research, Fukujuji Hospital, Japan Anti-Tuberculosis Association (JATA), Tokyo, Japan
| | - Takashi Yoshiyama
- Department of Respiratory Medicine, Fukujuji Hospital, Japan Anti-Tuberculosis Association (JATA), Tokyo, Japan; Research Institute of Tuberculosis, Japan Anti-Tuberculosis Association (JATA), Tokyo, Japan
| | - Shuichi Matsuda
- Department of Respiratory Medicine, Fukujuji Hospital, Japan Anti-Tuberculosis Association (JATA), Tokyo, Japan
| | - Keiji Fujiwara
- Department of Respiratory Medicine, Fukujuji Hospital, Japan Anti-Tuberculosis Association (JATA), Tokyo, Japan
| | - Koji Furuuchi
- Department of Respiratory Medicine, Fukujuji Hospital, Japan Anti-Tuberculosis Association (JATA), Tokyo, Japan
| | - Masafumi Shimoda
- Department of Respiratory Medicine, Fukujuji Hospital, Japan Anti-Tuberculosis Association (JATA), Tokyo, Japan
| | - Masashi Ito
- Department of Respiratory Medicine, Fukujuji Hospital, Japan Anti-Tuberculosis Association (JATA), Tokyo, Japan
| | - Tatsuya Kodama
- Department of Respiratory Medicine, Fukujuji Hospital, Japan Anti-Tuberculosis Association (JATA), Tokyo, Japan
| | - Fumiko Uesugi
- Department of Respiratory Medicine, Fukujuji Hospital, Japan Anti-Tuberculosis Association (JATA), Tokyo, Japan
| | - Masao Okumura
- Department of Respiratory Medicine, Fukujuji Hospital, Japan Anti-Tuberculosis Association (JATA), Tokyo, Japan
| | - Yoshiaki Tanaka
- Department of Respiratory Medicine, Fukujuji Hospital, Japan Anti-Tuberculosis Association (JATA), Tokyo, Japan
| | - Yuka Sasaki
- Department of Respiratory Medicine, Fukujuji Hospital, Japan Anti-Tuberculosis Association (JATA), Tokyo, Japan
| | - Hideo Ogata
- Department of Respiratory Medicine, Fukujuji Hospital, Japan Anti-Tuberculosis Association (JATA), Tokyo, Japan
| | - Hajime Goto
- Department of Respiratory Medicine, Fukujuji Hospital, Japan Anti-Tuberculosis Association (JATA), Tokyo, Japan
| | - Shoji Kudoh
- Department of Respiratory Medicine, Fukujuji Hospital, Japan Anti-Tuberculosis Association (JATA), Tokyo, Japan
| | - Ken Ohta
- Department of Respiratory Medicine, Fukujuji Hospital, Japan Anti-Tuberculosis Association (JATA), Tokyo, Japan
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Li D, Tang SY, Lei S, Xie HB, Li LQ. A nomogram for predicting mortality of patients initially diagnosed with primary pulmonary tuberculosis in Hunan province, China: a retrospective study. Front Cell Infect Microbiol 2023; 13:1179369. [PMID: 37333854 PMCID: PMC10272565 DOI: 10.3389/fcimb.2023.1179369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 05/05/2023] [Indexed: 06/20/2023] Open
Abstract
Objective According to the Global Tuberculosis Report for three consecutive years, tuberculosis (TB) is the second leading infectious killer. Primary pulmonary tuberculosis (PTB) leads to the highest mortality among TB diseases. Regretfully, no previous studies targeted the PTB of a specific type or in a specific course, so models established in previous studies cannot be accurately feasible for clinical treatments. This study aimed to construct a nomogram prognostic model to quickly recognize death-related risk factors in patients initially diagnosed with PTB to intervene and treat high-risk patients as early as possible in the clinic to reduce mortality. Methods We retrospectively analyzed the clinical data of 1,809 in-hospital patients initially diagnosed with primary PTB at Hunan Chest Hospital from January 1, 2019, to December 31, 2019. Binary logistic regression analysis was used to identify the risk factors. A nomogram prognostic model for mortality prediction was constructed using R software and was validated using a validation set. Results Univariate and multivariate logistic regression analyses revealed that drinking, hepatitis B virus (HBV), body mass index (BMI), age, albumin (ALB), and hemoglobin (Hb) were six independent predictors of death in in-hospital patients initially diagnosed with primary PTB. Based on these predictors, a nomogram prognostic model was established with high prediction accuracy, of which the area under the curve (AUC) was 0.881 (95% confidence interval [Cl]: 0.777-0.847), the sensitivity was 84.7%, and the specificity was 77.7%.Internal and external validations confirmed that the constructed model fit the real situation well. Conclusion The constructed nomogram prognostic model can recognize risk factors and accurately predict the mortality of patients initially diagnosed with primary PTB. This is expected to guide early clinical intervention and treatment for high-risk patients.
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Affiliation(s)
- Dan Li
- Xiangya School of Nursing, Central South University, Changsha, Hunan, China
- College of Applied Technology, Hunan Open University, Changsha, Hunan, China
| | - Si-Yuan Tang
- Xiangya School of Nursing, Central South University, Changsha, Hunan, China
| | - Sheng Lei
- Interventional Radiology Center, Hunan Chest Hospital, Changsha, Hunan, China
| | - He-Bin Xie
- Department of Drug Clinical Trial Institutions, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, Hunan, China
| | - Lin-Qi Li
- School of Public Health, University of South China, Hengyang, China
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Zhan M, Xue H, Wang Y, Wu Z, Wen Q, Shi X, Wang J. A clinical indicator-based prognostic model predicting treatment outcomes of pulmonary tuberculosis: a prospective cohort study. BMC Infect Dis 2023; 23:101. [PMID: 36803117 PMCID: PMC9940065 DOI: 10.1186/s12879-023-08053-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 02/03/2023] [Indexed: 02/22/2023] Open
Abstract
OBJECTIVES Identifying prognostic factors helps optimize the treatment regimen and promote favorable outcomes. We conducted a prospective cohort study on patients with pulmonary tuberculosis to construct a clinical indicator-based model and estimate its performance. METHODS We performed a two-stage study by recruiting 346 pulmonary tuberculosis patients diagnosed between 2016 and 2018 in Dafeng city as the training cohort and 132 patients diagnosed between 2018 and 2019 in Nanjing city as the external validation population. We generated a risk score based on blood and biochemistry examination indicators by the least absolute shrinkage and selection operator (LASSO) Cox regression. Univariate and multivariate Cox regression models were used to assess the risk score, and the strength of association was expressed as the hazard ratio (HR) and 95% confidence interval (CI). We plotted the receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC). Internal validation was conducted by 10-fold cross-validation. RESULTS Ten significant indicators (PLT, PCV, LYMPH, MONO%, NEUT, NEUT%, TBTL, ALT, UA, and Cys-C) were selected to generate the risk score. Clinical indicator-based score (HR: 10.018, 95% CI: 4.904-20.468, P < 0.001), symptom-based score (HR: 1.356, 95% CI: 1.079-1.704, P = 0.009), pulmonary cavity (HR: 0.242, 95% CI: 0.087-0.674, P = 0.007), treatment history (HR: 2.810, 95% CI: 1.137-6.948, P = 0.025), and tobacco smoking (HR: 2.499, 95% CI: 1.097-5.691, P = 0.029) were significantly related to the treatment outcomes. The AUC was 0.766 (95% CI: 0.649-0.863) in the training cohort and 0.796 (95% CI: 0.630-0.928) in the validation dataset. CONCLUSION In addition to the traditional predictive factors, the clinical indicator-based risk score determined in this study has a good prediction effect on the prognosis of tuberculosis.
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Affiliation(s)
- Mengyao Zhan
- grid.89957.3a0000 0000 9255 8984Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Ave. Nanjing, 211166 Nanjing, China
| | - Hao Xue
- Department of Chronic Communicable Diseases, Yancheng Center for Disease Control and Prevention, 224002 Yancheng, China
| | - Yuting Wang
- grid.89957.3a0000 0000 9255 8984Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Ave. Nanjing, 211166 Nanjing, China
| | - Zhuchao Wu
- grid.89957.3a0000 0000 9255 8984Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Ave. Nanjing, 211166 Nanjing, China
| | - Qin Wen
- grid.89957.3a0000 0000 9255 8984Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Ave. Nanjing, 211166 Nanjing, China
| | - Xinling Shi
- grid.89957.3a0000 0000 9255 8984Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Ave. Nanjing, 211166 Nanjing, China
| | - Jianming Wang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Ave. Nanjing, 211166, Nanjing, China. .,Department of Epidemiology, Gusu School, Nanjing Medical University, 211166, Nanjing, China.
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Ridolfi F, Peetluk L, Amorim G, Turner M, Figueiredo M, Cordeiro-Santos M, Cavalcante S, Kritski A, Durovni B, Andrade B, Sterling TR, Rolla V. Tuberculosis Treatment Outcomes in Brazil: Different Predictors for Each Type of Unsuccessful Outcome. Clin Infect Dis 2023; 76:e930-e937. [PMID: 35788646 PMCID: PMC10169436 DOI: 10.1093/cid/ciac541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 06/20/2022] [Accepted: 06/30/2022] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Successful tuberculosis (TB) treatment is necessary for disease control. The World Health Organization (WHO) has a target TB treatment success rate of ≥90%. We assessed whether the different types of unfavorable TB treatment outcome had different predictors. METHODS Using data from Regional Prospective Observational Research for Tuberculosis-Brazil, we evaluated biological and behavioral factors associated with each component of unsuccessful TB outcomes, recently updated by WHO (death, loss to follow-up [LTFU], and treatment failure). We included culture-confirmed, drug-susceptible, pulmonary TB participants receiving standard treatment in 2015-2019. Multinomial logistic regression models with inverse probability weighting were used to evaluate the distinct determinants of each unsuccessful outcome. RESULTS Of 915 participants included, 727 (79%) were successfully treated, 118 (13%) were LTFU, 44 (5%) had treatment failure, and 26 (3%) died. LTFU was associated with current drug-use (adjusted odds ratio [aOR] = 5.3; 95% confidence interval [CI], 3.0-9.4), current tobacco use (aOR = 2.9; 95% CI, 1.7-4.9), and being a person with HIV (PWH) (aOR = 2.0; 95% CI, 1.1-3.5). Treatment failure was associated with PWH (aOR = 2.7; 95% CI, 1.2-6.2) and having diabetes (aOR = 2.2; 95% CI, 1.1-4.4). Death was associated with anemia (aOR = 5.3; 95% CI, 1.4-19.7), diabetes (aOR = 3.1; 95% CI, 1.4-6.7), and PWH (aOR = 3.9; 95% CI, 1.3-11.4). Direct observed therapy was protective for treatment failure (aOR = 0.5; 95% CI, .3-.9) and death (aOR = 0.5; 95% CI, .2-1.0). CONCLUSIONS The treatment success rate was below the WHO target. Behavioral factors were most associated with LTFU, whereas clinical comorbidities were correlated with treatment failure and death. Because determinants of unsuccessful outcomes are distinct, different intervention strategies may be needed to improve TB outcomes.
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Affiliation(s)
- Felipe Ridolfi
- Instituto Nacional de Infectologia Evandro Chagas (INI), Fiocruz, Rio de Janeiro, Brazil
| | - Lauren Peetluk
- Division of Epidemiology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Gustavo Amorim
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, USA
| | - Megan Turner
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Marina Figueiredo
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Marcelo Cordeiro-Santos
- Fundação de Medicina Tropical Dr. Heitor Vieira Dourado (FMT), Manaus, Brazil
- Universidade do Estado do Amazonas (UEA), Manaus, Brazil
| | - Solange Cavalcante
- Clínica de Família Rinaldo Delamare, Rocinha, Rio de Janeiro, Brazil
- Universidade Federal do Rio de Janeiro (UFRJ), Faculdade de Medicina, Rio de Janeiro, Brazil
| | - Afrânio Kritski
- Universidade Federal do Rio de Janeiro (UFRJ), Faculdade de Medicina, Rio de Janeiro, Brazil
| | - Betina Durovni
- Centro de Estudos Estratégicos, Fiocruz, Rio de Janeiro, Brazil
| | - Bruno Andrade
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
- Laboratório de Inflamação e Biomarcadores, Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Brazil
- Multinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative, Salvador, Brazil
- Faculdade de Medicina, Universidade Federal da Bahia, Salvador, Brazil
- Curso de Medicina, Universidade Salvador (UNIFACS), Salvador, Brazil
- Curso de Medicina, Escola Bahiana de Medicina e Saúde Pública (EBMSP), Salvador, Brazil
- Instituto Brasileiro para Investigação da Tuberculose, Fundação José Silveira, Salvador, Brazil
| | - Timothy R Sterling
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Valeria Rolla
- Instituto Nacional de Infectologia Evandro Chagas (INI), Fiocruz, Rio de Janeiro, Brazil
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