Evidence map›Paper›PMID 37545936›Full record

ArticleAfrican health sciences2023

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

Paul Waliaula Wekunda, Dickens S Omondi Aduda, Bernard Guyah, James Odongo

Abstract read
In one paragraph

Article in African health sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Paul Waliaula WekundaDepartment of Health; Vihiga County Government, Kenya.
Dickens S Omondi AdudaSchool of Health Sciences: Directorate of Research, Innovation and Partnerships; Jaramogi Oginga Odinga University of Science and Technology.
Bernard GuyahDepartment of Biomedical Sciences; Maseno University.
James OdongoDepartment of Mathematics and applied sciences; Ramogi Institute of Advanced Technology.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

CoinfectionHIV InfectionsTuberculosisAdultAntitubercular AgentsFemaleHumansKenyaMaleProportional Hazards ModelsRetrospective StudiesRisk FactorsAntitubercular Agentssurvival distributionsTB mortalitytreatment outcomesVihiga

Identifiers

PMID37545936
PMCPMC10398452

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.