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      Self-sampling strategies (with/without digital innovations) in populations at risk of Chlamydia trachomatis and Neisseria gonorrhoeae: a systematic review and meta-analyses

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          Abstract

          Background

          Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (GC) resulted in over 200 million new sexually transmitted infections last year. Self-sampling strategies alone or combined with digital innovations (ie, online, mobile or computing technologies supporting self-sampling) could improve screening methods. Evidence on all outcomes has not yet been synthesised, so we conducted a systematic review and meta-analysis to address this limitation.

          Methods

          We searched three databases (period: 1 January 2000–6 January 2023) for reports on self-sampling for CT/GC testing. Outcomes considered for inclusion were: accuracy, feasibility, patient-centred and impact (ie, changes in linkage to care, first-time testers, uptake, turnaround time or referrals attributable to self-sampling).

          We used bivariate regression models to meta-analyse accuracy measures from self-sampled CT/GC tests and obtain pooled sensitivity/specificity estimates. We assessed quality with Cochrane Risk of Bias Tool-2, Newcastle–Ottawa Scale and Quality Assessment of Diagnostic Accuracy Studies-2 tool.

          Results

          We summarised results from 45 studies reporting self-sampling alone (73.3%; 33 of 45) or combined with digital innovations (26.7%; 12 of 45) conducted in 10 high-income (HICs; n=34) and 8 low/middle-income countries (LMICs; n=11). 95.6% (43 of 45) were observational, while 4.4% (2 of 45) were randomised clinical trials.

          We noted that pooled sensitivity (n=13) for CT/GC was higher in extragenital self-sampling (>91.6% (86.0%–95.1%)) than in vaginal self-sampling (79.6% (62.1%–90.3%)), while pooled specificity remained high (>99.0% (98.2%–99.5%)).

          Participants found self-sampling highly acceptable (80.0%–100.0%; n=24), but preference varied (23.1%–83.0%; n=16).

          Self-sampling reached 51.0%–70.0% (n=3) of first-time testers and resulted in 89.0%–100.0% (n=3) linkages to care. Digital innovations led to 65.0%–92% engagement and 43.8%–57.1% kit return rates (n=3).

          Quality of studies varied.

          Discussion

          Self-sampling had mixed sensitivity, reached first-time testers and was accepted with high linkages to care. We recommend self-sampling for CT/GC in HICs but additional evaluations in LMICs. Digital innovations impacted engagement and may reduce disease burden in hard-to-reach populations.

          PROSPERO registration number

          CRD42021262950.

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          Most cited references66

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          RoB 2: a revised tool for assessing risk of bias in randomised trials

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            How to perform a meta-analysis with R: a practical tutorial

            Meta-analysis is of fundamental importance to obtain an unbiased assessment of the available evidence. In general, the use of meta-analysis has been increasing over the last three decades with mental health as a major research topic. It is then essential to well understand its methodology and interpret its results. In this publication, we describe how to perform a meta-analysis with the freely available statistical software environment R, using a working example taken from the field of mental health. R package meta is used to conduct standard meta-analysis. Sensitivity analyses for missing binary outcome data and potential selection bias are conducted with R package metasens. All essential R commands are provided and clearly described to conduct and report analyses. The working example considers a binary outcome: we show how to conduct a fixed effect and random effects meta-analysis and subgroup analysis, produce a forest and funnel plot and to test and adjust for funnel plot asymmetry. All these steps work similar for other outcome types. R represents a powerful and flexible tool to conduct meta-analyses. This publication gives a brief glimpse into the topic and provides directions to more advanced meta-analysis methods available in R.
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              R: A language and environment for statistical computing

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                Author and article information

                Journal
                Sex Transm Infect
                Sex Transm Infect
                sextrans
                sti
                Sexually Transmitted Infections
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                1368-4973
                1472-3263
                September 2023
                29 March 2023
                : 99
                : 6
                : 420-428
                Affiliations
                [1 ] departmentCentre for Outcome Research and Evaluation , Ringgold_507266Research Institute of the McGill University Health Centre , Montreal, Quebec, Canada
                [2 ] departmentEpidemiology, Biostatistics, and Occupational Health , Ringgold_12367McGill University Faculty of Medicine , Montreal, Quebec, Canada
                [3 ] Ringgold_91635Foundation for Innovative New Diagnostics , Geneva, Switzerland
                [4 ] departmentSchool of Epidemiology and Public Health , Ringgold_6363University of Ottawa , Ottawa, Ontario, Canada
                [5 ] departmentFaculty of Medicine , Ringgold_12367McGill University , Montreal, Quebec, Canada
                Author notes
                [Correspondence to ] Dr Nitika Pant Pai, Research Institute of the McGill University Health Centre, H4A 3J1, H4A 3J1 Montreal, Canada; nitika.pai@ 123456mcgill.ca
                Author information
                http://orcid.org/0000-0001-8575-436X
                http://orcid.org/0000-0001-6431-4656
                http://orcid.org/0000-0002-4672-0500
                Article
                sextrans-2022-055557
                10.1136/sextrans-2022-055557
                10447399
                36990696
                bf070721-50bd-4103-a547-6ff67368ee37
                © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

                This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 10 August 2022
                : 24 February 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000156, Fonds de Recherche du Québec - Santé;
                Award ID: 324154
                Funded by: Foundation for Innovative New Diagnostics;
                Award ID: CNO: 8647
                Funded by: FundRef http://dx.doi.org/10.13039/501100000024, Canadian Institutes of Health Research;
                Award ID: PJT 153149
                Funded by: India-Canada Centre for Innovative Multidisciplinary Partnerships to Accelerate Community Transformation and Sustainability;
                Award ID: CNO: 3072
                Categories
                Systematic Review
                1506
                Custom metadata
                unlocked

                Sexual medicine
                systematic review,meta-analysis,neisseria gonorrhoeae,chlamydia infections,diagnosis

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