Evidence map›Paper›PMID 38689228›Full record

ArticleBMC infectious diseases2024

Association between TB delay and TB treatment outcomes in HIV-TB co-infected patients: a study based on the multilevel propensity score method.

Rujun Liao, Lin Hu, Jie Yu, Ying Chen, Miaoshuang Chen, Jingmin Yan, Xin Li, Xinyue Han, Chunnong Jike, Gang Yu and 8 more

Abstract read
In one paragraph

Article in BMC infectious diseases, 2024. 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

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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

18 authors.

Rujun Liao *Center of Infectious Diseases, Research Center of Clinical Epidemiology and Evidence-Based Medicine, Innovation Insititute for Integration of Medicine and Engineering, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, People's Republic of China.
Lin Hu *Department of Epidemiology and Health Statistics, West China School of Public Health, West China Fourth Hospital, Sichuan University, Chengdu, 610041, Sichuan, People's Republic of China.
Jie YuDepartment of Epidemiology and Health Statistics, West China School of Public Health, West China Fourth Hospital, Sichuan University, Chengdu, 610041, Sichuan, People's Republic of China.
Ying ChenDepartment of Epidemiology and Health Statistics, West China School of Public Health, West China Fourth Hospital, Sichuan University, Chengdu, 610041, Sichuan, People's Republic of China.
Miaoshuang ChenDepartment of Epidemiology and Health Statistics, West China School of Public Health, West China Fourth Hospital, Sichuan University, Chengdu, 610041, Sichuan, People's Republic of China.
Jingmin YanDepartment of Epidemiology and Health Statistics, West China School of Public Health, West China Fourth Hospital, Sichuan University, Chengdu, 610041, Sichuan, People's Republic of China.
Xin LiDepartment of Epidemiology and Health Statistics, West China School of Public Health, West China Fourth Hospital, Sichuan University, Chengdu, 610041, Sichuan, People's Republic of China.
Xinyue HanDepartment of Epidemiology and Health Statistics, West China School of Public Health, West China Fourth Hospital, Sichuan University, Chengdu, 610041, Sichuan, People's Republic of China.
Chunnong JikeLiangshan Center for Disease Control and Prevention, Xichang, 615000, Sichuan, People's Republic of China.
Gang YuLiangshan Center for Disease Control and Prevention, Xichang, 615000, Sichuan, People's Republic of China.
Ju WangLiangshan Center for Disease Control and Prevention, Xichang, 615000, Sichuan, People's Republic of China.
Qiang LiaoLiangshan Center for Disease Control and Prevention, Xichang, 615000, Sichuan, People's Republic of China.
Lan XiaSichuan Center for Disease Control and Prevention, Chengdu, 610041, Sichuan, People's Republic of China.
Xuefei BaiSichuan Center for Disease Control and Prevention, Chengdu, 610041, Sichuan, People's Republic of China.
Jinhong ShiSichuan Center for Disease Control and Prevention, Chengdu, 610041, Sichuan, People's Republic of China.
Tian JiangEditorial department of Journal of Sichuan University (Medical Sciences), Sichuan University, Chengdu, CN, People's Republic of China.
Liang DuCenter of Infectious Diseases, Research Center of Clinical Epidemiology and Evidence-Based Medicine, Innovation Insititute for Integration of Medicine and Engineering, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, People's Republic of China. duliang0606@vip.sina.com.
Tao ZhangDepartment of Epidemiology and Health Statistics, West China School of Public Health, West China Fourth Hospital, Sichuan University, Chengdu, 610041, Sichuan, People's Republic of China. scdxzhangtao@163.com.

Funding

Central government funding items 2021zc02Chongqing Science and Technology Program cstc2020jscx-cylhX0003Health Commission of Sichuan province 20PJ092Key Research and development Project of Liangshan Prefecture Science and Technology Plan 22ZDYF0125Liangshan Yi autonomous prefecture Center for Disease Control and Prevention H210322National Natural Science Foundation of China 81602935Sichuan Science and Technology Program 2020YFS0015, 2020YFS0091, 2021YFS0001-LHSichuan Science and Technology Program 2022YFS0229Sichuan University Education Foundation 2018hhf-26
6 · The paper itself

Abstract

backgroundHIV-tuberculosis (HIV-TB) co-infection is a significant public health concern worldwide. TB delay, consisting of patient delay, diagnostic delay, treatment delay, increases the risk of adverse anti-TB treatment (ATT) outcomes. Except for individual level variables, differences in regional levels have been shown to impact the ATT outcomes. However, few studies appropriately considered possible individual and regional level confounding variables. In this study, we aimed to assess the association of TB delay on treatment outcomes in HIV-TB co-infected patients in Liangshan Yi Autonomous Prefecture (Liangshan Prefecture) of China, using a causal inference framework while taking into account individual and regional level factors.

methodsWe conducted a study to analyze data from 2068 patients with HIV-TB co-infection in Liangshan Prefecture from 2019 to 2022. To address potential confounding bias, we used a causal directed acyclic graph (DAG) to select appropriate confounding variables. Further, we controlled for these confounders through multilevel propensity score and inverse probability weighting (IPW).

resultsThe successful rate of ATT for patients with HIV-TB co-infection in Liangshan Prefecture was 91.2%. Total delay (OR = 1.411, 95% CI: 1.015, 1.962), diagnostic delay (OR = 1.778, 95% CI: 1.261, 2.508), treatment delay (OR = 1.749, 95% CI: 1.146, 2.668) and health system delay (OR = 1.480 95% CI: (1.035, 2.118) were identified as risk factors for successful ATT outcome. Sensitivity analysis demonstrated the robustness of these findings.

conclusionsHIV-TB co-infection prevention and control policy in Liangshan Prefecture should prioritize early treatment for diagnosed HIV-TB co-infected patients. It is urgent to improve the health system in Liangshan Prefecture to reduce delays in diagnosis and treatment.

Indexed as

CoinfectionHIV InfectionsPropensity ScoreTuberculosisAdultAntitubercular AgentsChinaDelayed DiagnosisFemaleHumansMaleMiddle AgedTime-to-TreatmentTreatment OutcomeAntitubercular AgentsDelayHIVMultilevel modelPropensity scoreTreatmentTuberculosis

Identifiers

PMID38689228
PMCPMC11061920

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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.