Evidence map›Paper›PMID 28335816›Full record

ArticleInfectious diseases of poverty2017

Diagnostic delay and associated factors among patients with pulmonary tuberculosis in Dar es Salaam, Tanzania.

Khadija Said, Jerry Hella, Grace Mhalu, Mary Chiryankubi, Edward Masika, Thomas Maroa, Francis Mhimbira, Neema Kapalata, Lukas Fenner

Open access · goldAbstract read
In one paragraph

Article in Infectious diseases of poverty, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
47citing papers in PubMed, 2 pooled it
3.6field-weighted citation impact, top 7% 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

47 citing papers in PubMed, 2 syntheses or guidelines pooled it, 65 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

9 authors at 3 institutions in 2 countries.

Khadija SaidIfakara Health Institute, Bagamoyo Research and Training Centre (BRTC), P O Box 74, Bagamoyo, Tanzania. ksaid@ihi.or.tz.
Jerry HellaIfakara Health Institute, Bagamoyo Research and Training Centre (BRTC), P O Box 74, Bagamoyo, Tanzania.
Grace MhaluIfakara Health Institute, Bagamoyo Research and Training Centre (BRTC), P O Box 74, Bagamoyo, Tanzania.
Mary ChiryankubiNational Tuberculosis and Leprosy Programme, Dar es Salaam, Tanzania.
Edward MasikaNational Tuberculosis and Leprosy Programme, Dar es Salaam, Tanzania.
Thomas MaroaIfakara Health Institute, Bagamoyo Research and Training Centre (BRTC), P O Box 74, Bagamoyo, Tanzania.
Francis MhimbiraIfakara Health Institute, Bagamoyo Research and Training Centre (BRTC), P O Box 74, Bagamoyo, Tanzania.
Neema KapalataNational Tuberculosis and Leprosy Programme, Dar es Salaam, Tanzania.
Lukas FennerIfakara Health Institute, Bagamoyo Research and Training Centre (BRTC), P O Box 74, Bagamoyo, Tanzania. lukas.fenner@unibas.ch.
Ifakara Health Institute · TZSwiss Tropical and Public Health Institute · CHUniversity of Basel · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTanzania is among the 30 countries with the highest tuberculosis (TB) burdens. Because TB has a long infectious period, early diagnosis is not only important for reducing transmission, but also for improving treatment outcomes. We assessed diagnostic delay and associated factors among infectious TB patients.

methodsWe interviewed new smear-positive adult pulmonary TB patients enrolled in an ongoing TB cohort study in Dar es Salaam, Tanzania, between November 2013 and June 2015. TB patients were interviewed to collect information on socio-demographics, socio-economic status, health-seeking behaviour, and residential geocodes. We categorized diagnostic delay into ≤ 3 or > 3 weeks. We used logistic regression models to identify risk factors for diagnostic delay, presented as crude (OR) and adjusted Odds Ratios (aOR). We also assessed association between geographical distance (incremental increase of 500 meters between household and the nearest pharmacy) with binary outcomes.

resultsWe analysed 513 patients with a median age of 34 years (interquartile range 27-41); 353 (69%) were men. Overall, 444 (87%) reported seeking care from health care providers prior to TB diagnosis, of whom 211 (48%) sought care > 2 times. Only six (1%) visited traditional healers before TB diagnosis. Diagnostic delay was positively associated with absence of chest pain (aOR = 7.97, 95% confidence intervals [CI]: 3.15-20.19; P < 0.001), and presence of hemoptysis (aOR = 25.37, 95% CI: 11.15-57.74; P < 0.001) and negatively associated with use of medication prior to TB diagnosis (aOR = 0.31, 95% CI: 0.14-0.71; P = 0.01). Age, sex, HIV status, education level, household income, and visiting health care facilities (HCFs) were not associated with diagnostic delay. Patients living far from pharmacies were less likely to visit a HCF (incremental increase of distance versus visit to any facility: OR = 0.51, 95% CI: 0.28-0.96; P = 0.037).

conclusionsTB diagnostic delay was common in Dar es Salaam, and was more likely among patients without prior use of medication and presenting with hemoptysis. Geographical distance to HCFs may have an impact on health-seeking behaviour. Increasing community awareness of TB signs and symptoms could further reduce diagnostic delays and interrupt TB transmission.

Indexed as

AdolescentAdultAgedAmbulatory Care FacilitiesCohort StudiesDelayed DiagnosisEarly DiagnosisFemaleGeographic MappingHumansMaleMiddle AgedPatient Acceptance of Health CareSocial ClassTanzaniaTreatment OutcomeDiagnostic delayGeographic information systemHealth-seekingPharmacyTanzaniaTransmissionTuberculosis

Identifiers

PMID28335816
PMCPMC5364704
OpenAlexW2597853028

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.