Evidence map›Paper›PMID 33166361›Full record

ArticlePloS one2020

Do electronic medication monitors improve tuberculosis treatment outcomes? Programmatic experience from China.

Ni Wang, Hemant Deepak Shewade, Pruthu Thekkur, Hui Zhang, Yanli Yuan, Xiaomeng Wang, Xiaolin Wang, Miaomiao Sun, Fei Huang

Open access · goldAbstract readEvaluation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in PloS one, 2020. 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
6.5field-weighted citation impact, top 3% 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, 31 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. A randomised controlled trial to evaluate a medication monitoring system for TB treatment.The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease · 2022
    Trial
  4. Article
  5. Article
  6. Article
  7. 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

9 authors at 4 institutions in 3 countries.

Ni WangNational Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.ORCID 0000-0002-1796-7803
Hemant Deepak ShewadeInternational Union against Tuberculosis and Lung Disease (The Union), Paris, France.ORCID 0000-0002-8242-1911
Pruthu ThekkurInternational Union against Tuberculosis and Lung Disease (The Union), Paris, France.
Hui ZhangNational Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Yanli YuanJilin Research Institute of Tuberculosis Control, Changchun, China.
Xiaomeng WangZhejiang province Center Disease Control and Prevention, Hangzhou, China.
Xiaolin WangThe Fourth People's Hospital of Ningxia Hui Autonomous Region, Yinchuan, China.
Miaomiao SunPATH China Program, Beijing, China.
Fei HuangNational Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.ORCID 0000-0001-8620-3869
Chinese Center For Disease Control and Prevention · CNWorld Health Organization Regional Office for South-East Asia · INZhejiang Center for Disease Control and Prevention · CNCenter for Tuberculosis Control of Guangdong Province · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn China, an indigenously developed electronic medication monitor (EMM) was used. EMM recorded each time the device was opened (no real time data), offering an indirect measure of tuberculosis treatment adherence. Previous study in China showed that the EMM uptake was satisfactory, missing adherence data were common in the information management system (25%) and shift to directly observed therapy (DOT) based on poor adherence documented by EMMs were seldom.

objectivesAmong people with tuberculosis notified in 30 counties (July-December 2018) where EMM supported self-administered therapy (SAT) was suggested to all eligible (no communication impairment, ambulatory), we assessed the relative differences in unfavourable outcomes and deaths among those started on EMM at baseline (within first month of diagnosis) when compared to SAT alone.

methodsThis was a cohort study using secondary data. We employed an intention to treat analysis, and used modified Poisson regression with robust variance estimates to assess the association.

resultsOf 1810 eligible people, 1047 used EMM at baseline and of them, 216 (20.1%) stopped using EMM midway. Of 763 people who did not use EMM at baseline, 267 (35.0%) started using EMM later during the treatment. Among those who started using EMM at baseline, 6.3% [95% CI: 4.9, 8.0] had unfavourable outcomes compared to 6.7% [95% CI: 5.1, 8.8] among those who did not (p = 0.746). Lesser deaths were observed in people who started EMM at baseline when compared to those who did not: 2.5% [95% CI: 1.7, 3.7] versus 3.5% [95% CI: 2.4, 5.2], p = 0.191. The lack of association remained after adjusting for potential confounders (occupation, TB classification and TB category).

conclusionUnder programmatic settings, we did not find significant differences in the outcomes. Optimization of EMMs by shifting to DOT when indicated, addressing the issue of missing data and ensuring continuous use is required.

Indexed as

Drug MonitoringAdolescentAdultAgedAntitubercular AgentsChinaFemaleHumansMaleMiddle AgedReminder SystemsTelemedicineTreatment OutcomeTuberculosisAntitubercular Agents

Identifiers

PMID33166361
PMCPMC7652336
OpenAlexW3104639550

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.