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      Impact of a tuberculosis treatment adherence intervention versus usual care on treatment completion rates: results of a pragmatic cluster randomized controlled trial

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          Abstract

          Background

          With the global shortage of skilled health workers estimated at 7.2 million, outpatient tuberculosis (TB) care is commonly task-shifted to lay health workers (LHWs) in many low- and middle-income countries where the shortages are greatest. While shown to improve access to care and some health outcomes including TB treatment outcomes, lack of training and supervision limit the effectiveness of LHW programs. Our objective was to refine and evaluate an intervention designed to address common causes of non-adherence to TB treatment and LHW knowledge and skills training needs.

          Methods

          We employed a pragmatic cluster randomized controlled trial. Participants included 103 health centres (HCs) providing TB care in four districts in Malawi, randomized 1:1 stratified by district and HC funding (Ministry of Health, non-Ministry funded). At intervention HCs, a TB treatment adherence intervention was implemented using educational outreach, a point-of-care reminder tool, and a peer support network. Clusters in the control arm provided usual care. The primary outcome was the proportion of patients with successful TB treatment (i.e., cure or treatment completion). We used a generalized linear mixed model, with district as a fixed effect and HC as a random effect, to compare proportions of patients with treatment success, among the trial arms, with adjustment for baseline differences.

          Results

          We randomized 51 HCs to the intervention group and 52 HCs to the control group. Four intervention and six control HCs accrued no eligible patients, and 371 of 1169 patients had missing outcome, HC, or demographic data, which left 74 HCs and 798 patients for analysis. Randomization group was not related to missing outcome, however, district, age, and TB type were significantly related and included in the primary analysis model. Among the 1153 patients with HC and demographic data, 297/605 (49%) and 348/548 (64%) in the intervention and control arms, respectively, had treatment success. The intervention had no significant effect on treatment success (adjusted odds ratio 1.35 [95% confidence interval 0.93–1.98]).

          Conclusion

          We found no significant effect of the intervention on TB treatment outcomes with high variability in implementation quality, highlighting important challenges to both scale-up and sustainability.

          Trial registration

          ClinicalTrials.gov NCT02533089. Registered August 20, 2015.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s13012-020-01067-y.

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            Better reporting of interventions: template for intervention description and replication (TIDieR) checklist and guide

            Without a complete published description of interventions, clinicians and patients cannot reliably implement interventions that are shown to be useful, and other researchers cannot replicate or build on research findings. The quality of description of interventions in publications, however, is remarkably poor. To improve the completeness of reporting, and ultimately the replicability, of interventions, an international group of experts and stakeholders developed the Template for Intervention Description and Replication (TIDieR) checklist and guide. The process involved a literature review for relevant checklists and research, a Delphi survey of an international panel of experts to guide item selection, and a face to face panel meeting. The resultant 12 item TIDieR checklist (brief name, why, what (materials), what (procedure), who provided, how, where, when and how much, tailoring, modifications, how well (planned), how well (actual)) is an extension of the CONSORT 2010 statement (item 5) and the SPIRIT 2013 statement (item 11). While the emphasis of the checklist is on trials, the guidance is intended to apply across all evaluative study designs. This paper presents the TIDieR checklist and guide, with an explanation and elaboration for each item, and examples of good reporting. The TIDieR checklist and guide should improve the reporting of interventions and make it easier for authors to structure accounts of their interventions, reviewers and editors to assess the descriptions, and readers to use the information.
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              When and how should multiple imputation be used for handling missing data in randomised clinical trials – a practical guide with flowcharts

              Background Missing data may seriously compromise inferences from randomised clinical trials, especially if missing data are not handled appropriately. The potential bias due to missing data depends on the mechanism causing the data to be missing, and the analytical methods applied to amend the missingness. Therefore, the analysis of trial data with missing values requires careful planning and attention. Methods The authors had several meetings and discussions considering optimal ways of handling missing data to minimise the bias potential. We also searched PubMed (key words: missing data; randomi*; statistical analysis) and reference lists of known studies for papers (theoretical papers; empirical studies; simulation studies; etc.) on how to deal with missing data when analysing randomised clinical trials. Results Handling missing data is an important, yet difficult and complex task when analysing results of randomised clinical trials. We consider how to optimise the handling of missing data during the planning stage of a randomised clinical trial and recommend analytical approaches which may prevent bias caused by unavoidable missing data. We consider the strengths and limitations of using of best-worst and worst-best sensitivity analyses, multiple imputation, and full information maximum likelihood. We also present practical flowcharts on how to deal with missing data and an overview of the steps that always need to be considered during the analysis stage of a trial. Conclusions We present a practical guide and flowcharts describing when and how multiple imputation should be used to handle missing data in randomised clinical. Electronic supplementary material The online version of this article (10.1186/s12874-017-0442-1) contains supplementary material, which is available to authorized users.
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                Author and article information

                Contributors
                lisa.puchalskiritchie@utoronto.ca
                mvlettow@gmail.com
                a_makwakwa@yahoo.com
                esther_kip@yahoo.com
                sharon.straus@utoronto.ca
                h.kawonga@yahoo.co.uk
                jhamid@uottawa.ca
                Gerald.Lebovic@unityhealth.to
                kevin.thorpe@utoronto.ca
                merrick.zwarenstein@ices.on.ca
                mjs@ices.on.ca
                adrienne.chan@sunnybrook.ca
                Alexandra.Martiniuk@sydney.edu.au
                vanessavan3@gmail.com
                Journal
                Implement Sci
                Implement Sci
                Implementation Science : IS
                BioMed Central (London )
                1748-5908
                11 December 2020
                11 December 2020
                2020
                : 15
                : 107
                Affiliations
                [1 ]GRID grid.17063.33, ISNI 0000 0001 2157 2938, Department of Medicine, , University of Toronto, ; 6 Queen’s Park Crescent West, Third Floor, Toronto, ON M5S 3H2 Canada
                [2 ]GRID grid.415502.7, Li Ka Shing Knowledge Institute, , St. Michaels Hospital, ; St. Michael’s Hospital, 30 Bond St, Toronto, ON M5B 1W8 Canada
                [3 ]GRID grid.417184.f, ISNI 0000 0001 0661 1177, Department of Emergency Medicine, , University Health Network, Toronto General Hospital, ; 200 Elizabeth Street, RFE G-480, Toronto, M5G 2C4 Canada
                [4 ]GRID grid.17063.33, ISNI 0000 0001 2157 2938, Institute of Health Policy, Management and Evaluation, , University of Toronto, ; 155 College Street, Toronto, M5T 3M7 Canada
                [5 ]GRID grid.452470.0, Dignitas International, ; Zomba, Malawi
                [6 ]GRID grid.17063.33, ISNI 0000 0001 2157 2938, Dalla Lana School of Public Health, , University of Toronto, ; 155 College Street, Toronto, M5T 3M7 Canada
                [7 ]GRID grid.415722.7, National TB Program, , Ministry of Health, ; Lilongwe, Malawi
                [8 ]GRID grid.28046.38, ISNI 0000 0001 2182 2255, School of Epidemiology and Public Health, , University of Ottawa, ; Room 101, 600 Peter Morand Crescent, Ottawa, ON I1G 5Z3 Canada
                [9 ]GRID grid.415502.7, Applied Health Research Centre, Li Ka Shing Knowledge Institute, , St. Michael’s Hospital, ; 30 Bond St, Toronto, ON M5B 1W8 Canada
                [10 ]GRID grid.39381.30, ISNI 0000 0004 1936 8884, Department of Family Medicine, , Western University, ; London, ON Canada
                [11 ]GRID grid.39381.30, ISNI 0000 0004 1936 8884, Department of Family Medicine, Schulich School of Medicine & Dentistry, , Western University, ; 1151 Richmond St, London, ON N6A 5C1 Canada
                [12 ]GRID grid.17063.33, ISNI 0000 0001 2157 2938, Division of Infectious Diseases, Department of Medicine, Sunnybrook Health Sciences Center, , University of Toronto, ; c/o H2-66, 2075 Bayview Avenue, Toronto, ON M4N 3M5 Canada
                [13 ]GRID grid.498756.1, Dignitas International Toronto, ; C/O ICES attention Michael Schull, 2075 Bayview Avenue, G106, Toronto, ON M4N 3M5 Canada
                [14 ]GRID grid.415508.d, ISNI 0000 0001 1964 6010, George Institute for Global Health, ; Sydney, Australia
                [15 ]GRID grid.1013.3, ISNI 0000 0004 1936 834X, The University of Sydney, ; Edward Ford Building, Sydney, NSW Australia
                Author information
                http://orcid.org/0000-0002-1791-5368
                Article
                1067
                10.1186/s13012-020-01067-y
                7731739
                20a3cfbb-76af-4bb5-a7a1-44bcc3c9eb71
                © The Author(s) 2020

                Open AccessThis 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 February 2020
                : 1 December 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000024, Canadian Institutes of Health Research;
                Award ID: KAL-139700
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2020

                Medicine
                lay health workers,community health workers,educational outreach,reminders,peer support network,tuberculosis,cluster randomized trial

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