Network meta-analysis of diagnostic test accuracy studies identifies and ranks the optimal diagnostic tests and thresholds for healthcare policy and decision making

Objective: Network meta-analyses have extensively been used to compare the effectiveness of multiple interventions for healthcare policy and decision-making. However, methods for evaluating the performance of multiple diagnostic tests are less established. In a decision-making context, we are often interested in comparing and ranking the performance of multiple diagnostic tests, at varying levels of test thresholds, in one simultaneous analysis. Study design and setting: Motivated by an example of cognitive impairment diagnosis following stroke, we synthesized data from 13 studies assessing the efficiency of two diagnostic tests: Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA), at two test thresholds: MMSE <25/30 and <27/30, and MoCA <22/30 and <26/30. Using Markov Chain Monte Carlo (MCMC) methods, we fitted a bivariate network meta-analysis model incorporating constraints on increasing test threshold, and accounting for the correlations between multiple test accuracy measures from the same study. Results: We developed and successfully fitted a model comparing multiple tests/threshold combinations while imposing threshold constraints. Using this model, we found that MoCA at threshold <26/30 appeared to have the best true positive rate, whilst MMSE at threshold <25/30 appeared to have the best true negative rate. Conclusion: The combined analysis of multiple tests at multiple thresholds allowed for more rigorous comparisons between competing diagnostics tests for decision making

Diagnostic test accuracy for COVID-19: systematic review and meta-analysis protocol.

Objective Accurate diagnosis of COVID-19 infection is paramount to initiating appropriate measures for reducing spread. We aim to conduct a systematic review and meta-analysis, augmented by linked electronic health records, to assess the diagnostic test accuracy for COVID-19. Approach We will search the following databases from November 2019 to February 2022: MEDLINE (PubMed), Embase, and Scopus, as well as reference lists of eligible studies and review articles. Keywords will relate to COVID-19 and diagnostic testing. Eligible studies will use an appropriate study design (e.g. prospective and retrospective cohort and case-control) to assess the accuracy of any COVID-19 diagnostic test (including thoracic imaging, mass spectrometry, and serological tests) in all healthcare and community settings. Studies of participants under 18 will be excluded. Data will be extracted using a piloted extraction form and bias will be assessed using the QUADAS-2 tool. Results Main outcomes will include frequency statistics, sensitivity and specificity, and positive and negative predictive value. Paired forest plots will be used to illustrate sensitivity and specificity across studies. We will pool data on sensitivity and specificity in a Bayesian framework using a bivariate random-effects logistic regression model, where appropriate. Uncertainty in the estimates will be represented using 95% credible intervals. A comparative framework will be developed to allow assessment of the comparative accuracy of diagnostic tests. Subgroup analyses will be undertaken for time since onset of symptoms, setting (including community and secondary care testing), and reference standard, where appropriate. Conclusion Results of this review will be combined with routinely collected electronic health records from the DECOVID database to inform relationships between tests and subgroups for healthcare decision-making. New methodology developed as part of this review will be generalizable to the evaluation of diagnostic test accuracy in other diseases

The Hospital Frailty Risk Score (HFRS) applied to primary data: protocol for a systematic review

Introduction Frailty is characterised by vulnerability to adverse health outcomes and increases with age. Many frailty risk scores have been developed. One important example is the Hospital Frailty Risk Score (HFRS) which has the potential to be widely used and automatically calculated which will provide accurate assessment of frailty in a time/cost-effective manner. This systematic review, therefore, seeks to describe the HFRS use since its publication in 2018. Methods and analysis The proposed systematic review will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We will include published original peer-reviewed articles, preprints, conference proceedings and letters to the editor reporting primary data where there is an English language abstract available from 1 January 2018 to 30 June 2022. Databases to be searched are MEDLINE, EMBASE and Web of Science. Additional studies from, for example, the reference of the included studies will be identified and assessed for potential inclusion. Two independent reviewers will perform and assess the following: (1) eligibility of the included studies, (2) critical appraisal using the Cochrane Risk of Bias in Non-randomized Studies of Interventions tool, and (3) data extraction using a predefined form. Disagreements will be resolved through discussions or by involvement of a third reviewer. It may be possible to undertake a meta-analysis if there are sufficient studies reporting effect measures in homogenous populations and/or settings. Effect sizes will be calculated using meta-analysis methods and expressed as risk ratios or ORs with 95% CIs. Ethics and dissemination No ethical approval is required for this systematic review as it will use secondary data only. The results of the systematic review will be submitted for publication in recognised peer-reviewed journals related to frailty and geriatric care and will be widely disseminated through conferences, congresses, seminars, symposia and scientific meetings

Informant-based screening tools for dementia: an overview of systematic reviews

Background: Informant-based questionnaires may have utility for cognitive impairment or dementia screening. Reviews describing the accuracy of respective questionnaires are available, but their focus on individual questionnaires precludes comparisons across tools. We conducted an overview of systematic reviews to assess the comparative accuracy of informant questionnaires and identify areas where evidence is lacking. Methods: We searched six databases to identify systematic reviews describing diagnostic test accuracy of informant questionnaires for cognitive impairment or dementia. We pooled sensitivity and specificity data for each questionnaire and used network approaches to compare accuracy estimates across the differing tests. We used grading of recommendations, assessment, development and evaluation (GRADE) to evaluate the overall certainty of evidence. Finally, we created an evidence ‘heat-map’, describing the availability of accurate data for individual tests in different populations and settings. Results: We identified 25 reviews, consisting of 93 studies and 13 informant questionnaires. Pooled analysis (37 studies; 11 052 participants) ranked the eight-item interview to ascertain dementia (AD8) highest for sensitivity [90%; 95% credible intervals (CrI) = 82–95; ‘best-test’ probability = 36]; while the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) was most specific (81%; 95% CrI = 66–90; ‘best-test’ probability = 29%). GRADE-based evaluation of evidence suggested certainty was ‘low’ overall. Our heat-map indicated that only AD8 and IQCODE have been extensively evaluated and most studies have been in the secondary care settings. Conclusions: AD8 and IQCODE appear to be valid questionnaires for cognitive impairment or dementia assessment. Other available informant-based cognitive screening questionnaires lack evidence to justify their use at present. Evidence on the accuracy of available tools in primary care settings and with specific populations is required

Variations in COVID-19 vaccination uptake among people in receipt of psychotropic drugs: cross-sectional analysis of a national population-based prospective cohort

BackgroundCoronavirus disease 2019 (COVID-19) has disproportionately affected people with mental health conditions.AimsWe investigated the association between receiving psychotropic drugs, as an indicator of mental health conditions, and COVID-19 vaccine uptake.MethodWe conducted a cross-sectional analysis of a prospective cohort of the Northern Ireland adult population using national linked primary care registration, vaccination, secondary care and pharmacy dispensing data. Univariable and multivariable logistic regression analyses investigated the association between anxiolytic, antidepressant, antipsychotic, and hypnotic use and COVID-19 vaccination status, accounting for age, gender, deprivation and comorbidities. Receiving any COVID-19 vaccine was the primary outcome.ResultsThere were 1 433 814 individuals, of whom 1 166 917 received a COVID-19 vaccination. Psychotropic medications were dispensed to 267 049 people. In univariable analysis, people who received any psychotropic medication had greater odds of receiving COVID-19 vaccination: odds ratio (OR) = 1.42 (95% CI 1.41–1.44). However, after adjustment, psychotropic medication use was associated with reduced odds of vaccination (ORadj = 0.90, 95% CI 0.89–0.91). People who received anxiolytics (ORadj = 0.63, 95% CI 0.61–0.65), antipsychotics (ORadj = 0.75, 95% CI 0.73–0.78) and hypnotics (ORadj = 0.90, 95% CI 0.87–0.93) had reduced odds of being vaccinated. Antidepressant use was not associated with vaccination (ORadj = 1.02, 95% CI 1.00–1.03).ConclusionsWe found significantly lower odds of vaccination in people who were receiving treatment with anxiolytic and antipsychotic medications. There is an urgent need for evidence-based, tailored vaccine support for people with mental health conditions

Trends in SARS-CoV-2 infection and vaccination in school staff, students and their household members from 2020 to 2022 in Wales, UK: an electronic cohort study

Objectives We investigated SARS-CoV-2 infection trends, risk of SARS-CoV-2 infection and COVID-19 vaccination uptake among school staff, students and their household members in Wales, UK. Design Seven-day average of SARS-CoV-2 infections and polymerase chain reaction tests per 1000 people daily, cumulative incidence of COVID-19 vaccination uptake and multi-level Poisson models with time-varying covariates. Setting National electronic cohort between September 2020 and May 2022 when several variants were predominant in the UK (Alpha, Delta and Omicron). Participants School students aged 4 to 10/11 years (primary school and younger middle school, n = 238,163), and 11 to 15/16 years (secondary school and older middle school, n = 182,775), school staff in Wales (n = 47,963) and the household members of students and staff (n = 697,659). Main outcome measures SARS-CoV-2 infection and COVID-19 vaccination uptake. Results School students had a sustained period of high infection rates compared with household members after August 2021. Primary schedule vaccination uptake was highest among staff (96.3%) but lower for household members (72.2%), secondary and older middle school students (59.8%), and primary and younger middle school students (3.3%). Multi-level Poisson models showed that vaccination was associated with a lower risk of SARS-CoV-2 infection. The Delta variant posed a greater infection risk for students than the Alpha variant. However, Omicron was a larger risk for staff and household members. Conclusions Public health bodies should be informed of the protection COVID-19 vaccines afford, with more research being required for younger populations. Furthermore, schools require additional support in managing new, highly transmissible variants. Further research should examine the mechanisms between child deprivation and SARS-CoV-2 infection

COVID-19 booster vaccination uptake and infection breakthrough amongst health care workers in Wales:A national prospective cohort study

Background: From September 2021, Health Care Workers (HCWs) in Wales began receiving a COVID-19 booster vaccination. This is the first dose beyond the primary vaccination schedule. Given the emergence of new variants, vaccine waning vaccine, and increasing vaccination hesitancy, there is a need to understand booster vaccine uptake and subsequent breakthrough in this high-risk population. Methods: We conducted a prospective, national-scale, observational cohort study of HCWs in Wales using anonymised, linked data from the SAIL Databank. We analysed uptake of COVID-19 booster vaccinations from September 2021 to February 2022, with comparisons against uptake of the initial primary vaccination schedule. We also analysed booster breakthrough, in the form of PCR-confirmed SARS-Cov-2 infection, comparing to the second primary dose. Cox proportional hazard models were used to estimate associations for vaccination uptake and breakthrough regarding staff roles, socio-demographics, household composition, and other factors. Results: We derived a cohort of 73,030 HCWs living in Wales (78% female, 60% 18–49 years old). Uptake was quickest amongst HCWs aged 60 + years old (aHR 2.54, 95%CI 2.45–2.63), compared with those aged 18–29. Asian HCWs had quicker uptake (aHR 1.18, 95%CI 1.14–1.22), whilst Black HCWs had slower uptake (aHR 0.67, 95%CI 0.61–0.74), compared to white HCWs. HCWs residing in the least deprived areas were slightly quicker to have received a booster dose (aHR 1.12, 95%CI 1.09–1.16), compared with those in the most deprived areas. Strongest associations with breakthrough infections were found for those living with children (aHR 1.52, 95%CI 1.41–1.63), compared to two-adult only households. HCWs aged 60 + years old were less likely to get breakthrough infections, compared to those aged 18–29 (aHR 0.42, 95%CI 0.38–0.47). Conclusion: Vaccination uptake was consistently lower among black HCWs, as well as those from deprived areas. Whilst breakthrough infections were highest in households with children

Maternal mental health and children's problem behaviours: a bi-directional relationship?

Transactional theory and the coercive family process model have illustrated how the parent-child relationship is reciprocal. Emerging research using advanced statistical methods has examined these theories, but further investigations are necessary. In this study, we utilised linked health data on maternal mental health disorders and explored their relationship with child problem behaviours via the Strengths and Difficulties Questionnaire for over 13 years. We accessed data from the Millennium Cohort Study, linked to anonymised individual-level population-scale health and administrative data within the Secure Anonymised Information Linkage (SAIL) Databank. We used Bayesian Structural Equation Modelling, specifically Random-Intercept Cross-Lagged Panel Models, to analyse the relationships between mothers and their children. We then explored these models with the addition of time-invariant covariates. We found that a mother’s mental health was strongly associated over time, as were children’s problem behaviours. We found mixed evidence for bi-directional relationships, with only emotional problems showing bi-directional associations in mid to late childhood. Only child-to-mother pathways were identified for the overall problem behaviour score and peer problems; no associations were found for conduct problems or hyperactivity. All models had strong between-effects and clear socioeconomic and sex differences. We encourage the use of whole family-based support for mental health and problem behaviours, and recommend that socioeconomic, sex and wider differences should be considered as factors in tailoring family-based interventions and support

Effect on life expectancy of temporal sequence in a multimorbidity cluster of psychosis, diabetes, and congestive heart failure among 1·7 million individuals in Wales with 20-year follow-up : a retrospective cohort study using linked data

Funding: This work was supported by Health Data Research UK (HDRUK) Measuring and Understanding Multimorbidity using Routine Data in the UK (MUrMuRUK; award numbers HDR-9006 and CFC0110). HDRUK is funded by the UK Medical Research Council (MRC), Engineering and Physical Sciences Research Council, Economic and Social Research Council, NIHR (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation, and Wellcome Trust. This work also was co-funded by the MRC and NIHR (grant number MR/S027750/1). The work was supported by the Administrative Data Research (ADR) Wales programme of work, part of the Economic and Social Research Council (part of UK Research and Innovation) funded ADR UK (grant ES/S007393/1). RKO is supported by a Springboard award (SBF006\1122) funded by the Academy of Medical Sciences, Wellcome Trust, Government Department of Business, Energy and Industrial Strategy, British Heart Foundation, and Diabetes UK. SS is part funded by the NIHR Applied Research Collaboration West Midlands, the NIHR Health Protection Research Unit (HPRU) in Gastrointestinal Infections, and the NIHR HPRU in Genomics and Enabling Data.Background To inform targeted public health strategies, it is crucial to understand how coexisting diseases develop over time and their associated impacts on patient outcomes and health-care resources. This study aimed to examine how psychosis, diabetes, and congestive heart failure, in a cluster of physical–mental health multimorbidity, develop and coexist over time, and to assess the associated effects of different temporal sequences of these diseases on life expectancy in Wales. Methods In this retrospective cohort study, we used population-scale, individual-level, anonymised, linked, demographic, administrative, and electronic health record data from the Wales Multimorbidity e-Cohort. We included data on all individuals aged 25 years and older who were living in Wales on Jan 1, 2000 (the start of follow-up), with follow-up continuing until Dec 31, 2019, first break in Welsh residency, or death. Multistate models were applied to these data to model trajectories of disease in multimorbidity and their associated effect on all-cause mortality, accounting for competing risks. Life expectancy was calculated as the restricted mean survival time (bound by the maximum follow-up of 20 years) for each of the transitions from the health states to death. Cox regression models were used to estimate baseline hazards for transitions between health states, adjusted for sex, age, and area-level deprivation (Welsh Index of Multiple Deprivation [WIMD] quintile). Findings Our analyses included data for 1 675 585 individuals (811 393 [48·4%] men and 864 192 [51·6%] women) with a median age of 51·0 years (IQR 37·0–65·0) at cohort entry. The order of disease acquisition in cases of multimorbidity had an important and complex association with patient life expectancy. Individuals who developed diabetes, psychosis, and congestive heart failure, in that order (DPC), had reduced life expectancy compared with people who developed the same three conditions in a different order: for a 50-year-old man in the third quintile of the WIMD (on which we based our main analyses to allow comparability), DPC was associated with a loss in life expectancy of 13·23 years (SD 0·80) compared with the general otherwise healthy or otherwise diseased population. Congestive heart failure as a single condition was associated with mean a loss in life expectancy of 12·38 years (0·00), and with a loss of 12·95 years (0·06) when preceded by psychosis and 13·45 years (0·13) when followed by psychosis. Findings were robust in people of older ages, more deprived populations, and women, except that the trajectory of psychosis, congestive heart failure, and diabetes was associated with higher mortality in women than men. Within 5 years of an initial diagnosis of diabetes, the risk of developing psychosis or congestive heart failure, or both, was increased. Interpretation The order in which individuals develop psychosis, diabetes, and congestive heart failure as combinations of conditions can substantially affect life expectancy. Multistate models offer a flexible framework to assess temporal sequences of diseases and allow identification of periods of increased risk of developing subsequent conditions and death.Publisher PDFPeer reviewe

Effect on life expectancy of temporal sequence in a multimorbidity cluster of psychosis, diabetes, and congestive heart failure among 1·7 million individuals in Wales with 20-year follow-up: a retrospective cohort study using linked data

BACKGROUND: To inform targeted public health strategies, it is crucial to understand how coexisting diseases develop over time and their associated impacts on patient outcomes and health-care resources. This study aimed to examine how psychosis, diabetes, and congestive heart failure, in a cluster of physical-mental health multimorbidity, develop and coexist over time, and to assess the associated effects of different temporal sequences of these diseases on life expectancy in Wales. METHODS: In this retrospective cohort study, we used population-scale, individual-level, anonymised, linked, demographic, administrative, and electronic health record data from the Wales Multimorbidity e-Cohort. We included data on all individuals aged 25 years and older who were living in Wales on Jan 1, 2000 (the start of follow-up), with follow-up continuing until Dec 31, 2019, first break in Welsh residency, or death. Multistate models were applied to these data to model trajectories of disease in multimorbidity and their associated effect on all-cause mortality, accounting for competing risks. Life expectancy was calculated as the restricted mean survival time (bound by the maximum follow-up of 20 years) for each of the transitions from the health states to death. Cox regression models were used to estimate baseline hazards for transitions between health states, adjusted for sex, age, and area-level deprivation (Welsh Index of Multiple Deprivation [WIMD] quintile). FINDINGS: Our analyses included data for 1 675 585 individuals (811 393 [48·4%] men and 864 192 [51·6%] women) with a median age of 51·0 years (IQR 37·0-65·0) at cohort entry. The order of disease acquisition in cases of multimorbidity had an important and complex association with patient life expectancy. Individuals who developed diabetes, psychosis, and congestive heart failure, in that order (DPC), had reduced life expectancy compared with people who developed the same three conditions in a different order: for a 50-year-old man in the third quintile of the WIMD (on which we based our main analyses to allow comparability), DPC was associated with a loss in life expectancy of 13·23 years (SD 0·80) compared with the general otherwise healthy or otherwise diseased population. Congestive heart failure as a single condition was associated with mean a loss in life expectancy of 12·38 years (0·00), and with a loss of 12·95 years (0·06) when preceded by psychosis and 13·45 years (0·13) when followed by psychosis. Findings were robust in people of older ages, more deprived populations, and women, except that the trajectory of psychosis, congestive heart failure, and diabetes was associated with higher mortality in women than men. Within 5 years of an initial diagnosis of diabetes, the risk of developing psychosis or congestive heart failure, or both, was increased. INTERPRETATION: The order in which individuals develop psychosis, diabetes, and congestive heart failure as combinations of conditions can substantially affect life expectancy. Multistate models offer a flexible framework to assess temporal sequences of diseases and allow identification of periods of increased risk of developing subsequent conditions and death. FUNDING: Health Data Research UK