Evidence map›Paper›PMID 37061308›Full record

Trial reportThe Lancet. Global health2023

Digital adherence technologies to improve tuberculosis treatment outcomes in China: a cluster-randomised superiority trial.

Xiaoqiu Liu, Jennifer Thompson, Haiyan Dong, Sedona Sweeney, Xue Li, Yanli Yuan, Xiaomeng Wang, Wangrui He, Bruce Thomas, Caihong Xu and 7 more

Open access · goldAbstract readEquivalence TrialRandomized Controlled Trial
In one paragraph

Trial report in The Lancet. Global health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed, 5 pooled it
10.9field-weighted citation impact, top 1% 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

37 citing papers in PubMed, 5 syntheses or guidelines pooled it, 56 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Trial
  7. Trial
  8. Trial
  9. Trial
  10. Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. Review
  16. Article
  17. Observational
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

17 authors at 5 institutions in 3 countries.

Xiaoqiu LiuNational Center for TB Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Jennifer ThompsonDepartment of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
Haiyan DongPATH China Office, Shanghai, China.
Sedona SweeneyDepartment of Global Health and Development, London School of Hygiene & Tropical Medicine, London, UK.
Xue LiNational Center for TB Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Yanli YuanJilin Provincial Center for Disease Control and Prevention, Changchun, China.
Xiaomeng WangZhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China.
Wangrui HeJiangxi Provincial Center for Disease Control and Prevention, Nanchang, China.
Bruce ThomasArcady Group, Richmond, VA, USA.
Caihong XuNational Center for TB Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Dongmei HuNational Center for TB Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Anna VassallDepartment of Global Health and Development, London School of Hygiene & Tropical Medicine, London, UK.
Shitong HuanBill & Melinda Gates Foundation China Office, Beijing, China.
Hui ZhangNational Center for TB Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Shiwen JiangNational Center for TB Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Katherine FieldingDepartment of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
Yanlin ZhaoNational Center for TB Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China. Electronic address: zhaoyl@chinacdc.cn.
Chinese Center For Disease Control and Prevention · CNLondon School of Hygiene & Tropical Medicine · GBBill & Melinda Gates Foundation · USNanchang Center for Disease Control and Prevention · CNZhejiang Center for Disease Control and Prevention · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDrug-sensitive tuberculosis treatment requires 6 months of therapy, so adherence problems are common. Digital adherence technologies might improve tuberculosis treatment outcomes. We aimed to evaluate the effect of a daily reminder medication monitor, monthly review of adherence data by the health-care provider, and differentiated care for patients with adherence issues, on tuberculosis treatment adherence and outcomes.

methodsWe did a cluster-randomised superiority trial across four prefectures in China. 24 counties or districts (clusters) were randomly assigned (1:1) to intervention or control groups. We enrolled patients aged 18 years or older with GeneXpert-positive, rifampicin-sensitive pulmonary tuberculosis, who were receiving daily fixed-dose combination treatment. Patients in the intervention group received a medication monitor for daily drug-dosing reminders, monthly review of adherence data by health-care provider, and management of poor adherence; and patients in the control group received routine care (silent-mode monitor-measured adherence). Only the independent endpoints review committee who assessed endpoint data for some participants were masked to study group assignment. Patients were followed up (with sputum solid culture) at 12 and 18 months. The primary outcome was a composite of death, loss to follow-up, treatment failure, switch to multidrug-resistant tuberculosis treatment, or tuberculosis recurrence by 18 months from treatment start, analysed in the intention-to-treat population. Analysis accounted for study design with multiple imputation for the primary outcome. This trial is now complete and is registered with ISRCTN, 35812455.

findingsBetween Jan 26, 2017, and April 3, 2019, 15 257 patients were assessed for eligibility and 3074 were enrolled, 2686 (87%) of whom were included in the intention-to-treat population. 1909 (71%) of 2686 patients were male, 777 (29%) were female, and the median age was 44 years (IQR 29-58). By 18 months from treatment start, using multiple imputation for missing outcomes, 239 (16% [geometric mean of cluster-level proportion]) of 1388 patients in the control group and 224 (16%) of 1298 in the intervention group had a primary composite outcome event (289 [62%] of 463 events were loss to follow-up during treatment and 42 [9%] were tuberculosis recurrence). The intervention had no effect on risk of the primary composite outcome (adjusted risk ratio 1·01, 95% CI 0·73-1·40).

interpretationOur digital medication monitor intervention had no effect on unfavourable outcomes, which included loss to follow-up during treatment, tuberculosis recurrence, death, and treatment failure. There was a failure to change patient management following identification of treatment non-adherence at monthly reviews. A better understanding of adherence patterns and how they relate to poor outcomes, coupled with a more timely review of adherence data and improved implementation of differentiated care, may be required.

fundingBill & Melinda Gates Foundation.

Indexed as

TuberculosisTuberculosis, Multidrug-ResistantTuberculosis, PulmonaryAdultChinaFemaleHumansMaleMedication AdherenceTreatment Outcome

Identifiers

PMID37061308
PMCPMC10126227
OpenAlexW4365519904

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.