Evidence map›Paper›PMID 33833283›Full record

ArticleScientific reports2021

Spatial distribution and risk factors of adverse treatment outcomes of tuberculosis in Guizhou, China, 2013-2018.

Jian Zhou, Xiaoxue Ma, Juan Tian, Feng Hong, Jinlan Li

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
0.7field-weighted citation impact, top 27% 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

8 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Observational
  5. Article
  6. Article
  7. Factors Associated with Medication Adherence in Elderly Individuals with Tuberculosis: A Qualitative Study.The Canadian journal of infectious diseases & medical microbiology = Journal canadien des maladies infectieuses et de la microbiologie medicale · 2023
    Article
  8. Article
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 4 institutions in 1 country.

Jian Zhou *School of Public Health, The Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, 550025, Guizhou, China.
Xiaoxue Ma *School of Public Health, The Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, 550025, Guizhou, China.
Juan TianThe First People's Hospital of Guiyang, Guiyang, 550001, Guizhou, China.
Feng HongSchool of Public Health, The Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, 550025, Guizhou, China. 519490967@qq.com.
Jinlan LiGuizhou Provincial Center for Disease Control and Prevention, The Institute of Tuberculosis Control and Prevention, Guiyang, 550001, Guizhou, China. lijinlanpublic@126.com.
Guiyang Medical University · CNAffiliated Hospital of Guizhou Medical University · CNGuizhou Center for Disease Control and Prevention · CNThe First People's Hospital of Guiyang · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The incidence of Tuberculosis (TB) in Guizhou province has ranked to be the top four among the 31 China provinces. The spatial distribution and influencing factors of adverse outcomes of TB in Guizhou are unclear. In our study, the cases information of TB in Guizhou province from 2013 to 2018 was collected, we analyzed the spatial distribution and clusters of five adverse outcomes of TB with ArcMap10.2 software, used logistics regression analysis to assessed risk factors and used Chi-square analysis to analyze variation trend of the five adverse outcomes. A total of 237, 806 cases information of TB were collected. The proportion of adverse outcomes in TB patients was 6.18%, among which adverse reactions accounted for 1.05%, lost to follow-up accounted for 1.44%, treatment failed accounted for 1.15%, died accounted for 2.31%, switch to MDR accounted for 0.24%. The component ratio of adverse outcomes showed an upward trend (P < 0.05).Regional clustering existed in each of adverse outcomes (P < 0.05). There were high-risk minorities, gender, age, occupation, type of diagnosis, Therapeutic category existed in adverse outcomes of TB. Miao and Dong had a higher risk in adverse reaction of TB compared with Han. Women had a higher risk in adverse reactions than men, and a lower risk of lost to follow-up, failed, and died. Retreated patients had a higher risk of adverse outcomes. Timely monitoring and active intervention should be carried out for some high-risk areas and groups, including middle-aged and elderly patients, rural patients, floating patients, severe patients and retreated patients during the process of patient diagnosis and treatment.

Indexed as

Antitubercular AgentsChinaCluster AnalysisFemaleHumansIncidenceMaleRisk FactorsTreatment OutcomeTuberculosis, PulmonaryAntitubercular Agents

Identifiers

PMID33833283
PMCPMC8032654
OpenAlexW3141141110

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.