New horizons in evidence synthesis for older adults.

Evidence synthesis, embedded within a systematic review of the literature, is a well-established approach for collating and combining all the relevant information on a particular research question. A robust synthesis can establish the evidence base, which underpins best practice guidance. Such endeavours are frequently used by policymakers and practitioners to inform their decision making. Traditionally, an evidence synthesis of interventions consisted of a meta-analysis of quantitative data comparing two treatment alternatives addressing a specific and focussed clinical question. However, as the methods in the field have evolved, especially in response to the increasingly complex healthcare questions, more advanced evidence synthesis techniques have been developed. These can deal with extended data structures considering more than two treatment alternatives (network meta-analysis) and complex multicomponent interventions. The array of questions capable of being answered has also increased with specific approaches being developed for different evidence types including diagnostic, prognostic and qualitative data. Furthermore, driven by a desire for increasingly up-to-date evidence summaries, living systematic reviews have emerged. All of these methods can potentially have a role in informing older adult healthcare decisions. The aim of this review is to increase awareness and uptake of the increasingly comprehensive array of newer synthesis methods available and highlight their utility for answering clinically relevant questions in the context of older adult research, giving examples of where such techniques have already been effectively applied within the field. Their strengths and limitations are discussed, and we suggest user-friendly software options to implement the methods described.

Data visualisation approaches for component network meta-analysis: visualising the data structure.

BACKGROUND: Health and social care interventions are often complex and can be decomposed into multiple components. Multicomponent interventions are often evaluated in randomised controlled trials. Across trials, interventions often have components in common which are given alongside other components which differ across trials. Multicomponent interventions can be synthesised using component NMA (CNMA). CNMA is limited by the structure of the available evidence, but it is not always straightforward to visualise such complex evidence networks. The aim of this paper is to develop tools to visualise the structure of complex evidence networks to support CNMA. METHODS: We performed a citation review of two key CNMA methods papers to identify existing published CNMA analyses and reviewed how they graphically represent intervention complexity and comparisons across trials. Building on identified shortcomings of existing visualisation approaches, we propose three approaches to standardise visualising the data structure and/or availability of data: CNMA-UpSet plot, CNMA heat map, CNMA-circle plot. We use a motivating example to illustrate these plots. RESULTS: We identified 34 articles reporting CNMAs. A network diagram was the most common plot type used to visualise the data structure for CNMA (26/34 papers), but was unable to express the complex data structures and large number of components and potential combinations of components associated with CNMA. Therefore, we focused visualisation development around representing the data structure of a CNMA more completely. The CNMA-UpSet plot presents arm-level data and is suitable for networks with large numbers of components or combinations of components. Heat maps can be utilised to inform decisions about which pairwise interactions to consider for inclusion in a CNMA model. The CNMA-circle plot visualises the combinations of components which differ between trial arms and offers flexibility in presenting additional information such as the number of patients experiencing the outcome of interest in each arm. CONCLUSIONS: As CNMA becomes more widely used for the evaluation of multicomponent interventions, the novel CNMA-specific visualisations presented in this paper, which improve on the limitations of existing visualisations, will be important to aid understanding of the complex data structure and facilitate interpretation of the CNMA results.

A multifaceted graphical display, including treatment ranking, was developed to aid interpretation of network meta-analysis.

OBJECTIVES: Network meta-analysis (NMA) is becoming a popular statistical tool for analyzing a network of evidence comparing more than two interventions. A particular advantage of NMA over pairwise meta-analysis is its ability to simultaneously compare multiple interventions including comparisons not previously trialed together, permitting intervention hierarchies to be created. Our aim was to develop a novel graphical display to aid interpretation of NMA to clinicians and decision-makers that incorporates ranking of interventions. STUDY DESIGN AND SETTING: Current literature was searched, scrutinized, and provided direction for developing the novel graphical display. Ranking results were often found to be misinterpreted when presented alone and, to aid interpretation and effective communication to inform optimal decision-making, need to be displayed alongside other important aspects of the analysis including the evidence networks and relative intervention effect estimates. RESULTS: Two new ranking visualizations were developed-the 'Litmus Rank-O-Gram' and the 'Radial SUCRA' plot-and embedded within a novel multipanel graphical display programmed within the MetaInsight application, with user feedback gained. CONCLUSION: This display was designed to improve the reporting, and facilitate a holistic understanding, of NMA results. We believe uptake of the display would lead to better understanding of complex results and improve future decision-making.

Double-counting of populations in evidence synthesis in public health: a call for awareness and future methodological development.

BACKGROUND: There is a growing interest in the inclusion of real-world and observational studies in evidence synthesis such as meta-analysis and network meta-analysis in public health. While this approach offers great epidemiological opportunities, use of such studies often introduce a significant issue of double-counting of participants and databases in a single analysis. Therefore, this study aims to introduce and illustrate the nuances of double-counting of individuals in evidence synthesis including real-world and observational data with a focus on public health. METHODS: The issues associated with double-counting of individuals in evidence synthesis are highlighted with a number of case studies. Further, double-counting of information in varying scenarios is discussed with potential solutions highlighted. RESULTS: Use of studies of real-world data and/or established cohort studies, for example studies evaluating the effectiveness of therapies using health record data, often introduce a significant issue of double-counting of individuals and databases. This refers to the inclusion of the same individuals multiple times in a single analysis. Double-counting can occur in a number of manners, such as, when multiple studies utilise the same database, when there is overlapping timeframes of analysis or common treatment arms across studies. Some common practices to address this include synthesis of data only from peer-reviewed studies, utilising the study that provides the greatest information (e.g. largest, newest, greater outcomes reported) or analysing outcomes at different time points. CONCLUSIONS: While common practices currently used can mitigate some of the impact of double-counting of participants in evidence synthesis including real-world and observational studies, there is a clear need for methodological and guideline development to address this increasingly significant issue.

Feasibility study for interactive reporting of network meta-analysis: experiences from the development of the MetaInsight COVID-19 app for stakeholder exploration, re-analysis and sensitivity analysis from living systematic reviews.

BACKGROUND: Network meta-analysis (NMA) has been increasingly adopted worldwide by Cochrane reviews, guideline developers and decision-making bodies to identify optimal treatment choices. However, NMA results are often produced statically, not allowing stakeholders to 'dig deeper' and interrogate with their own judgement. Additionally, amid the COVID-19 pandemic, unnecessary or duplicated reviews have been proposed which analyse from the same pool of evidence. We developed the 'MetaInsight COVID-19' app as a prototype for an interactive platform to eliminate such duplicated efforts, by empowering users to freely analyse the data and improve scientific transparency. METHODS: MetaInsight COVID-19 ( https://crsu.shinyapps.io/metainsightcovid/ ) was developed to conduct NMA with the evolving evidence on treatments for COVID-19. It was updated weekly between 19th May - 19th Oct 2020, incorporating new evidence identified from a living systematic review. RESULTS: The app includes embedded functions to facilitate study selection based on study characteristics, and displays the synthesised results in real time. It allows both frequentist and Bayesian NMA to be conducted as well as consistency and heterogeneity assessments. A demonstration of the app is provided and experiences of building such a platform are discussed. CONCLUSIONS: MetaInsight COVID-19 allows users to take control of the evidence synthesis using the analytic approach they deem appropriate to ascertain how robust findings are to alternative analysis strategies and study inclusion criteria. It is hoped that this app will help avoid many of the duplicated efforts when reviewing and synthesising the COVID-19 evidence, and, in addition, establish the desirability of an open platform format such as this for interactive data interrogation, visualisation, and reporting for any traditional or 'living' NMA.

Epidemiology of moderately severe and severe non-proliferative diabetic retinopathy in South West England.

AIMS: To estimate the incidence of early treatment diabetic retinopathy study (ETDRS) level 47 and 53 and progression to treatment with panretinal photocoagulation (PRP) for proliferative DR (PDR). METHODS: Log-linear regression was used to estimate the incidence of level 47-53 or worse for 33,009 people with diabetes (PWD) in Gloucestershire during 2013-2016 by calendar year and diabetes type, based on the first recording. Progression was analysed in Gloucestershire and Bristol with a parametric survival analysis examining the association of baseline and time-varying demographic and clinical factors on time to PRP after the first recording of level 47-53. RESULTS: Incidence decreased from 0.57 (95% confidence intervals (CI) 0.48-0.67) per 100 PWD in 2013 to 0.35 (95% CI 0.29-0.43) in 2016 (p < 0.001). For progression, 338 eligible PWD from Gloucestershire and 418 from Bristol were followed for a median of 1.4 years; 78 and 83% had Type 2 diabetes and a median (interquartile range) of 15 (10-22) and 17 (11-25) years duration of diagnosed diabetes respectively. Three years from the incident ETDRS 47-53, 18.9% and 17.2% had received PRP respectively. For Gloucestershire, severe IRMA and updated mean HbA1c were associated with an increase in the risk of initiating PRP (hazard ratio 3.14 (95% CI: 1.60-6.15) and 1.21 (95% CI: 1.06-1.38 per 10 mmol/mol) respectively). CONCLUSION: This study provides additional understanding of this population and shows that a high proportion of patients with ETDRS levels 47-53 need to be monitored as they are at high risk of progressing to PDR.

Prevalence and incidence of diabetic retinopathy (DR) in the UK population of Gloucestershire.

PURPOSE: To estimate prevalence and incidence of diabetic retinopathy (DR) in a UK region by severity between 2012 and 2016 and risk factors for progression to proliferative DR (PDR). METHODS: Electronic medical records from people with diabetes (PWD) ≥18 years seen at the Gloucestershire Diabetic Eye Screening Programme (GDESP) and the hospital eye clinic were analysed (HEC). Prevalence and incidence of DR per 100 PWD (%) by calendar year, grade and diabetes type were estimated using log-linear regression. Progression to PDR and associated risk factors were estimated using parametric survival analyses. RESULTS: Across the study period, 35 873 PWD had at least one DR assessment. They were aged 66 (56-75) years (median (interquartile range)), 57% male, 5 (1-10) years since diabetes diagnosis, 93% Type 2 diabetes. Prevalence of DR decreased from 38.9% (95% CI: 38.1%, 39.8%) in 2012 to 36.6% (95% CI: 35.9%, 37.3%) in 2016 (p < 0.001). Incidence of any DR decreased from 10.9% (95% CI: 10.4%, 11.5%) in 2013 to 8.5% (95% CI: 8.1%, 9.0%) in 2016 (p < 0.001). Prevalence of PDR decreased from 3.5% (95% CI: 3.3%, 3.8%) in 2012 to 3.1% (95% CI 2.9%, 3.3%) in 2016 (p = 0.008). Incidence of PDR did not change over time. HbA1c and bilateral moderate-severe NPDR were statistically significant risk factors associated with progression to PDR. CONCLUSIONS: Incidence and prevalence of DR decreased between 2012 and 2016 in this well-characterized population of the UK.

Following the science? Comparison of methodological and reporting quality of covid-19 and other research from the first wave of the pandemic.

BACKGROUND: Following the initial identification of the 2019 coronavirus disease (covid-19), the subsequent months saw substantial increases in published biomedical research. Concerns have been raised in both scientific and lay press around the quality of some of this research. We assessed clinical research from major clinical journals, comparing methodological and reporting quality of covid-19 papers published in the first wave (here defined as December 2019 to May 2020 inclusive) of the viral pandemic with non-covid papers published at the same time. METHODS: We reviewed research publications (print and online) from The BMJ, Journal of the American Medical Association (JAMA), The Lancet, and New England Journal of Medicine, from first publication of a covid-19 research paper (February 2020) to May 2020 inclusive. Paired reviewers were randomly allocated to extract data on methodological quality (risk of bias) and reporting quality (adherence to reporting guidance) from each paper using validated assessment tools. A random 10% of papers were assessed by a third, independent rater. Overall methodological quality for each paper was rated high, low or unclear. Reporting quality was described as percentage of total items reported. RESULTS: From 168 research papers, 165 were eligible, including 54 (33%) papers with a covid-19 focus. For methodological quality, 18 (33%) covid-19 papers and 83 (73%) non-covid papers were rated as low risk of bias, OR 6.32 (95%CI 2.85 to 14.00). The difference in quality was maintained after adjusting for publication date, results, funding, study design, journal and raters (OR 6.09 (95%CI 2.09 to 17.72)). For reporting quality, adherence to reporting guidelines was poorer for covid-19 papers, mean percentage of total items reported 72% (95%CI:66 to 77) for covid-19 papers and 84% (95%CI:81 to 87) for non-covid. CONCLUSIONS: Across various measures, we have demonstrated that covid-19 research from the first wave of the pandemic was potentially of lower quality than contemporaneous non-covid research. While some differences may be an inevitable consequence of conducting research during a viral pandemic, poor reporting should not be accepted.

Ethnicity and clinical outcomes in COVID-19: A systematic review and meta-analysis.

BACKGROUND: Patients from ethnic minority groups are disproportionately affected by Coronavirus disease (COVID-19). We performed a systematic review and meta-analysis to explore the relationship between ethnicity and clinical outcomes in COVID-19. METHODS: Databases (MEDLINE, EMBASE, PROSPERO, Cochrane library and MedRxiv) were searched up to 31st August 2020, for studies reporting COVID-19 data disaggregated by ethnicity. Outcomes were: risk of infection; intensive therapy unit (ITU) admission and death. PROSPERO ID: 180654. FINDINGS: 18,728,893 patients from 50 studies were included; 26 were peer-reviewed; 42 were from the United States of America and 8 from the United Kingdom. Individuals from Black and Asian ethnicities had a higher risk of COVID-19 infection compared to White individuals. This was consistent in both the main analysis (pooled adjusted RR for Black: 2.02, 95% CI 1.67-2.44; pooled adjusted RR for Asian: 1.50, 95% CI 1.24-1.83) and sensitivity analyses examining peer-reviewed studies only (pooled adjusted RR for Black: 1.85, 95%CI: 1.46-2.35; pooled adjusted RR for Asian: 1.51, 95% CI 1.22-1.88). Individuals of Asian ethnicity may also be at higher risk of ITU admission (pooled adjusted RR 1.97 95% CI 1.34-2.89) (but no studies had yet been peer-reviewed) and death (pooled adjusted RR/HR 1.22 [0.99-1.50]). INTERPRETATION: Individuals of Black and Asian ethnicity are at increased risk of COVID-19 infection compared to White individuals; Asians may be at higher risk of ITU admission and death. These findings are of critical public health importance in informing interventions to reduce morbidity and mortality amongst ethnic minority groups.