Trial reportBMC infectious diseases2017
Screening in Maternity to Ascertain Tuberculosis Status (SMATS) study.
Trial report in BMC infectious diseases, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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.
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.
Who cites it
5 citing papers in PubMed.
- Prevalence and burden of HIV-TB co-infection among pregnant women: A systematic review and meta-analysis.Journal of Taibah University Medical Sciences · 2026Review
- Cross sectional survey of maternal and newborn quality of care in Niger: Selected findings, lessons learned and recommendations.PLOS global public health · 2024Article
- HIV/Tuberculosis Coinfection in Pregnancy and the Postpartum Period.Journal of clinical medicine · 2023Review
- Detecting tuberculosis in pregnant and postpartum women in Eswatini.African journal of laboratory medicine · 2020Article
- Lateral flow urine lipoarabinomannan assay for detecting active tuberculosis in people living with HIV.The Cochrane database of systematic reviews · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
backgroundDiagnosis of tuberculosis is difficult among pregnant women because the signs and symptoms of the disease, such as fatigue, shortness of breath, sweating, cough, and mild fever are similar to some manifestations of pregnancy. It is particularly challenging among HIV-infected women as symptoms are often masked or atypical. Currently, WHO recommends a standard four-symptom screening tool for pregnant and lactating women. There is evidence from South Africa that this screening tool (which, despite complex symptomology in this population, recommends identification of patients with weight loss, fever, current cough and night sweats), may be missing true active TB cases. However there exist several laboratory and clinical procedures that have the potential to improve the sensitivity and specificity of this screening tool.
methodsThis study will evaluate the sensitivity and specificity of the current TB screening tool for pregnant and lactating women, both HIV positive and negative. We will also assess several different enhanced screening algorithm using LAM, IGRA, TST and chest radiography and clinical/laboratory procedures and tests. The study will use a cross-sectional analytical study design involving pregnant and lactating women up to six months post-delivery attending antenatal or postnatal care, respectively in one of three selected public health units in Swaziland. Participants will be consecutively enrolled and will be in one of four groups of interest: HIV infected pregnant women, non-HIV infected pregnant women, HIV infected lactating women and non-HIV infected lactating women. DISCUSSION: We expect in conducting all procedures on all participants regardless of result of the symptom screening we may experience a high refusal rate. However, this risk will be mitigated by the long data collection period of five or more months.
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Registered trials
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.