Evidence map›Paper›PMID 36828495›Full record

ArticleTropical medicine and infectious disease2023

Diagnosis and Treatment Pathway of MDR/RR-TB in Taizhou, Zhejiang Province, China.

Jingting Lu, Yuanyuan Xu, Zhipeng Li, Xiaoxiao Chen, Haijiang Lin, Qi Zhao

Abstract read
In one paragraph

Article in Tropical medicine and infectious disease, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

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

7 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

6 authors.

Jingting LuSchool of Public Health, Fudan University, Shanghai 200032, China.
Yuanyuan XuTaizhou Center for Disease Control and Prevention, Zhejiang 318000, China.
Zhipeng LiSchool of Public Health, Fudan University, Shanghai 200032, China.
Xiaoxiao ChenTaizhou Center for Disease Control and Prevention, Zhejiang 318000, China.
Haijiang LinTaizhou Center for Disease Control and Prevention, Zhejiang 318000, China.
Qi ZhaoSchool of Public Health, Fudan University, Shanghai 200032, China.ORCID 0000-0003-0140-3920

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aims to understand MDR/RR-TB patients' experience from seeking TB-related health care to diagnosis and treatment completion, as well as the social determinants with the waiting time for DST and treatment, pre-treatment attrition, and treatment outcome based on a retrospective cohort study. Univariate and multi-variate logistic regressions were used to analyze the associated factors. The median time of waiting time for DST and treatment was 24.00 and 30.00 days, respectively. Non-residential patients (aOR: 2.89, 95% CI: 1.14-7.70), registered before 2018 (aOR: 19.93, 95% CI: 8.99-48.51), first visited a county-level hospital (aOR: 4.65, 95% CI: 1.08-21.67), sputum smear-negative (aOR: 3.54, 95% CI: 1.28-10.16), and comorbid with pneumoconiosis (aOR: 7.10, 95% CI: 1.23-47.98) had a longer DST delay. The pre-treatment attrition was 26.9% (82/305). Elderly, non-residential patients and patients registered before 2018 were more likely to refuse MDR/RR treatment. However, in housekeeping/unemployment and farmer/fisherman, recurrent patients tended to take therapeutic measures actively. The successful treatment rate was 62.1% (105/169). Elderly, comorbidity with diabetes and sputum smear conversion time >1 month may lead to poorer outcomes. Immediate interventions should be taken to smooth diagnosis and treatment pathways and improve the social protections further so as to encourage patients to cooperate with the treatment actively.

Indexed as

attritionfactorsMDR/RR-TBtreatment outcomewaiting time

Identifiers

PMID36828495
PMCPMC9964905

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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.