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Predictors of mortality and survival probability distribution among patients on tuberculosis treatment in Vihiga County, Kenya

BACKGROUND: Tuberculosis (TB) related mortality remains a serious impediment in ending TB epidemic. OBJECTIVE: To estimate survival probability and identify predictors, causes and conditions contributing to mortality among TB patients in Vihiga County. METHODS: A cohort of 291 patients from 20 purpo...

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Autores principales: Wekunda, Paul Waliaula, Aduda, Dickens S Omondi, Guyah, Bernard, Odongo, James
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Makerere Medical School 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10398452/
https://www.ncbi.nlm.nih.gov/pubmed/37545936
http://dx.doi.org/10.4314/ahs.v23i1.24
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author Wekunda, Paul Waliaula
Aduda, Dickens S Omondi
Guyah, Bernard
Odongo, James
author_facet Wekunda, Paul Waliaula
Aduda, Dickens S Omondi
Guyah, Bernard
Odongo, James
author_sort Wekunda, Paul Waliaula
collection PubMed
description BACKGROUND: Tuberculosis (TB) related mortality remains a serious impediment in ending TB epidemic. OBJECTIVE: To estimate survival probability and identify predictors, causes and conditions contributing to mortality among TB patients in Vihiga County. METHODS: A cohort of 291 patients from 20 purposively selected health facilities were prospectively considered. Data was obtained by validated questionnaires through face-to-face interviews. Survival probabilities were estimated using Kaplan-Meier method while Cox proportional hazard model identified predictors of TB mortality through calculation of hazard ratios at 95% confidence intervals. Mortality audit data was qualitatively categorized to elicit causes and conditions contributing to mortality. RESULTS: 209 (72%) were male, median age was 40 (IQR=32-53) years while TB/HIV coinfection rate was 35%. Overall, 45 (15%) patients died, majority (78% (log rank<0.001)) during intensive phase. The overall mortality rate was 32.2 (95% CI 23.5 - 43.1) deaths per 1000 person months and six months' survival probability was 0.838 (95% CI, 0.796-0.883). Mortality was higher (27%) among HIV positive than HIV negative (9%) TB patients. Independent predictors of mortality included; comorbidities (HR = 2.72, 95% CI,1.36–5.44, p< 0.005), severe illness (HR=5.06, 95% CI,1.59–16.1, p=0.006), HIV infection (HR=2.56, 95% CI,1.28–5.12, p=0.008) and smoking (HR=2.79, 95% CI,1.01–7.75, p=0.049). Independent predictors of mortality among HIV negative patients included; comorbidities (HR = 4.25, 95% CI; 1.15-15.7, p = 0.03) and being clinically diagnosed (HR = 4.8, 95% CI; 1.43-16, P = 0.01) while among HIV positive; they included smoking (HR = 4.05, 95% CI;1.03-16.0, P = 0.04), severe illness (HR = 5.84, 95% CI; 1.08-31.6, P = 0.04), severe malnutrition (HR = 4.56, 95% CI; 1.33-15.6, P = 0.01) and comorbidities (HR = 3.04, 95% CI; 1.03-8.97, p = 0.04). More than a half (52%) of mortality among HIV positive were ascribed to advanced HIV diseases while majority of (72%) of HIV negative patients died to TB related lung disease. Conditions contributing to mortality were largely patient and health system related. CONCLUSION: Risk of TB mortality is high and is attributable to comorbidities, severe illness, HIV and smoking. Causes and conditions contributing to TB mortality are multifaceted but modifiable. Improving TB/HIV care could reduce mortality in this setting.
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spelling pubmed-103984522023-08-04 Predictors of mortality and survival probability distribution among patients on tuberculosis treatment in Vihiga County, Kenya Wekunda, Paul Waliaula Aduda, Dickens S Omondi Guyah, Bernard Odongo, James Afr Health Sci Articles BACKGROUND: Tuberculosis (TB) related mortality remains a serious impediment in ending TB epidemic. OBJECTIVE: To estimate survival probability and identify predictors, causes and conditions contributing to mortality among TB patients in Vihiga County. METHODS: A cohort of 291 patients from 20 purposively selected health facilities were prospectively considered. Data was obtained by validated questionnaires through face-to-face interviews. Survival probabilities were estimated using Kaplan-Meier method while Cox proportional hazard model identified predictors of TB mortality through calculation of hazard ratios at 95% confidence intervals. Mortality audit data was qualitatively categorized to elicit causes and conditions contributing to mortality. RESULTS: 209 (72%) were male, median age was 40 (IQR=32-53) years while TB/HIV coinfection rate was 35%. Overall, 45 (15%) patients died, majority (78% (log rank<0.001)) during intensive phase. The overall mortality rate was 32.2 (95% CI 23.5 - 43.1) deaths per 1000 person months and six months' survival probability was 0.838 (95% CI, 0.796-0.883). Mortality was higher (27%) among HIV positive than HIV negative (9%) TB patients. Independent predictors of mortality included; comorbidities (HR = 2.72, 95% CI,1.36–5.44, p< 0.005), severe illness (HR=5.06, 95% CI,1.59–16.1, p=0.006), HIV infection (HR=2.56, 95% CI,1.28–5.12, p=0.008) and smoking (HR=2.79, 95% CI,1.01–7.75, p=0.049). Independent predictors of mortality among HIV negative patients included; comorbidities (HR = 4.25, 95% CI; 1.15-15.7, p = 0.03) and being clinically diagnosed (HR = 4.8, 95% CI; 1.43-16, P = 0.01) while among HIV positive; they included smoking (HR = 4.05, 95% CI;1.03-16.0, P = 0.04), severe illness (HR = 5.84, 95% CI; 1.08-31.6, P = 0.04), severe malnutrition (HR = 4.56, 95% CI; 1.33-15.6, P = 0.01) and comorbidities (HR = 3.04, 95% CI; 1.03-8.97, p = 0.04). More than a half (52%) of mortality among HIV positive were ascribed to advanced HIV diseases while majority of (72%) of HIV negative patients died to TB related lung disease. Conditions contributing to mortality were largely patient and health system related. CONCLUSION: Risk of TB mortality is high and is attributable to comorbidities, severe illness, HIV and smoking. Causes and conditions contributing to TB mortality are multifaceted but modifiable. Improving TB/HIV care could reduce mortality in this setting. Makerere Medical School 2023-03 /pmc/articles/PMC10398452/ /pubmed/37545936 http://dx.doi.org/10.4314/ahs.v23i1.24 Text en © 2023 Wekunda PW et al. https://creativecommons.org/licenses/by/4.0/Licensee African Health Sciences. This is an Open Access article distributed under the terms of the Creative commons Attribution License (https://creativecommons.org/licenses/BY/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Articles
Wekunda, Paul Waliaula
Aduda, Dickens S Omondi
Guyah, Bernard
Odongo, James
Predictors of mortality and survival probability distribution among patients on tuberculosis treatment in Vihiga County, Kenya
title Predictors of mortality and survival probability distribution among patients on tuberculosis treatment in Vihiga County, Kenya
title_full Predictors of mortality and survival probability distribution among patients on tuberculosis treatment in Vihiga County, Kenya
title_fullStr Predictors of mortality and survival probability distribution among patients on tuberculosis treatment in Vihiga County, Kenya
title_full_unstemmed Predictors of mortality and survival probability distribution among patients on tuberculosis treatment in Vihiga County, Kenya
title_short Predictors of mortality and survival probability distribution among patients on tuberculosis treatment in Vihiga County, Kenya
title_sort predictors of mortality and survival probability distribution among patients on tuberculosis treatment in vihiga county, kenya
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10398452/
https://www.ncbi.nlm.nih.gov/pubmed/37545936
http://dx.doi.org/10.4314/ahs.v23i1.24
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