0
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

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

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          HIV-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.

          Methods

          We 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).

          Results

          The 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.

          Conclusions

          HIV-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.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12879-024-09328-7.

          Related collections

          Most cited references41

          • Record: found
          • Abstract: not found
          • Article: not found

          The central role of the propensity score in observational studies for causal effects

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Sensitivity Analysis in Observational Research: Introducing the E-Value.

            Sensitivity analysis is useful in assessing how robust an association is to potential unmeasured or uncontrolled confounding. This article introduces a new measure called the "E-value," which is related to the evidence for causality in observational studies that are potentially subject to confounding. The E-value is defined as the minimum strength of association, on the risk ratio scale, that an unmeasured confounder would need to have with both the treatment and the outcome to fully explain away a specific treatment-outcome association, conditional on the measured covariates. A large E-value implies that considerable unmeasured confounding would be needed to explain away an effect estimate. A small E-value implies little unmeasured confounding would be needed to explain away an effect estimate. The authors propose that in all observational studies intended to produce evidence for causality, the E-value be reported or some other sensitivity analysis be used. They suggest calculating the E-value for both the observed association estimate (after adjustments for measured confounders) and the limit of the confidence interval closest to the null. If this were to become standard practice, the ability of the scientific community to assess evidence from observational studies would improve considerably, and ultimately, science would be strengthened.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              DAGitty: a graphical tool for analyzing causal diagrams.

                Bookmark

                Author and article information

                Contributors
                duliang0606@vip.sina.com
                scdxzhangtao@163.com
                Journal
                BMC Infect Dis
                BMC Infect Dis
                BMC Infectious Diseases
                BioMed Central (London )
                1471-2334
                30 April 2024
                30 April 2024
                2024
                : 24
                : 457
                Affiliations
                [1 ]GRID grid.13291.38, ISNI 0000 0001 0807 1581, 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
                [2 ]Department of Epidemiology and Health Statistics, West China School of Public Health, West China Fourth Hospital, Sichuan University, ( https://ror.org/011ashp19) Chengdu, 610041 Sichuan People’s Republic of China
                [3 ]Sichuan Center for Disease Control and Prevention, ( https://ror.org/05nda1d55) Chengdu, 610041 Sichuan People’s Republic of China
                [4 ]Liangshan Center for Disease Control and Prevention, ( https://ror.org/02yr91f43) Xichang, 615000 Sichuan People’s Republic of China
                [5 ]Editorial department of Journal of Sichuan University (Medical Sciences), Sichuan University, ( https://ror.org/011ashp19) Chengdu, CN People’s Republic of China
                Article
                9328
                10.1186/s12879-024-09328-7
                11061920
                38689228
                3d36c3d1-b72e-4b04-aad8-70ae87869ae5
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 12 March 2024
                : 16 April 2024
                Funding
                Funded by: Sichuan Science and Technology Program
                Award ID: 2022YFS0229
                Award ID: 2020YFS0015, 2020YFS0091, 2021YFS0001-LH
                Funded by: Key Research and development Project of Liangshan Prefecture Science and Technology Plan
                Award ID: 22ZDYF0125
                Funded by: Health Commission of Sichuan province
                Award ID: 20PJ092
                Funded by: National Natural Science Foundation of China
                Award ID: 81602935
                Funded by: Chongqing Science and Technology Program
                Award ID: cstc2020jscx-cylhX0003
                Funded by: FundRef http://dx.doi.org/10.13039/501100011935, Sichuan University Education Foundation;
                Award ID: 2018hhf-26
                Funded by: Central government funding items
                Award ID: 2021zc02
                Funded by: Liangshan Yi autonomous prefecture Center for Disease Control and Prevention
                Award ID: H210322
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2024

                Infectious disease & Microbiology
                tuberculosis,hiv,treatment,delay,propensity score,multilevel model
                Infectious disease & Microbiology
                tuberculosis, hiv, treatment, delay, propensity score, multilevel model

                Comments

                Comment on this article