Evidence map›Paper›PMID 31533661›Full record

ArticleBMC infectious diseases2019

Incidence and predictors of lost to follow-up among drug-resistant tuberculosis patients at University of Gondar Comprehensive Specialized Hospital, Northwest Ethiopia: a retrospective follow-up study.

Getahun Molla Kassa, Alemayehu Shimeka Teferra, Haileab Fekadu Wolde, Atalay Goshu Muluneh, Mehari Woldemariam Merid

Open access · goldAbstract read
In one paragraph

Article in BMC infectious diseases, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 2 pooled it
2.5field-weighted citation impact, top 11% of its field
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 22 citations in OpenAlex.

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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

5 authors at 1 institution in 1 country.

Getahun Molla KassaDepartment of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences and Specialized Comprehensive Hospital, University of Gondar, Gondar, Ethiopia. getahunm8@gmail.com.ORCID http://orcid.org/0000-0002-9926-7797
Alemayehu Shimeka TeferraDepartment of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences and Specialized Comprehensive Hospital, University of Gondar, Gondar, Ethiopia.
Haileab Fekadu WoldeDepartment of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences and Specialized Comprehensive Hospital, University of Gondar, Gondar, Ethiopia.
Atalay Goshu MulunehDepartment of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences and Specialized Comprehensive Hospital, University of Gondar, Gondar, Ethiopia.
Mehari Woldemariam MeridDepartment of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences and Specialized Comprehensive Hospital, University of Gondar, Gondar, Ethiopia.
University of Gondar · ET

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe emergence of Drug-Resistance Tuberculosis (DR-TB) is an increasing global public health problem. Lost to Follow-up (LTFU) from DR-TB treatment remains a major barrier to tuberculosis epidemic control and better treatment outcome. In Ethiopia, evidences on the incidence and predictors of LTFU are scarce. Thus, this study aimed to determine the incidence and identify the predictors of LTFU among DR-TB patients.

methodsA retrospective follow-up study was conducted among a total of 332 DR-TB patients at the University of Gondar comprehensive specialized hospital. Data were retrieved from patient records from September 2010 to December 2017 and entered in to Epi-data 4.2.0.0 and analysed using Stata14.1 software. The risk was estimated using the Nelson-Aalen cumulative hazard curve. A log-rank test was used for survival comparisons between categories of independent variables. The Gompertz regression model was fitted, and hazard ratio with a 95% confidence interval (CI) was used to measure the strength of associations. Variables with less than 0.05 p-values in the multivariable model were considered as significantly associated with LTFU.

resultsAmong a total of 332 patient records reviewed, 206 (62.05%) were male. The median age was 30 years (Inter Quartile Range (IQR): 23-40). Forty-one (12.35%) of the participants had no history of TB treatment, while a quarter of were TB-HIV co-infected. Closely all (92.17%) of the patients had pulmonary tuberculosis. The median follow up time was 20.37 months (IQR: 11.02, 21.80). Thirty-six (10.84%) patients were lost from follow-up with an incidence rate of 6.47 (95% CI: 4.67, 8.97)/1000 Person Months (PM). Homelessness (Adjusted Hazard Ratio (AHR) =2.51, 95%CI: 1.15, 5.45) and treatment enrolment year from 2013 to 2014 (AHR = 3.25, 95% CI: 1.30, 8.13) were significant predictors of LTFU.

conclusionThis study indicated that LTFU among DR-TB registered patients was high in the first six months compared to subsequent months. Homelessness and year of treatment enrolment were independent predictors of LTFU, requiring more economic support to patients in order to ensure treatment completion. This result can be generalized to patients who are using DR-TB treatment in similar settings.

Indexed as

AdultEthiopiaFemaleHospitals, SpecialHumansIncidenceLost to Follow-UpMaleMiddle AgedProportional Hazards ModelsRegression AnalysisRetrospective StudiesTuberculosis, Multidrug-ResistantYoung AdultDrug-resistant tuberculosisIncidenceLost to follow-upPredictorsRetrospective study

Identifiers

PMID31533661
PMCPMC6751642
OpenAlexW2974082739

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.