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Supplemental material, sj-docx-6-bmi-10.1177_11772719221144459 for What is the Diagnostic Accuracy of Novel Urine Biomarkers for Urinary Tract Infection? by George Edwards, Anna Seeley, Adam Carter, Maia Patrick Smith, Elizabeth LA Cross, Kathryn Hughes, Ann Van den Bruel, Martin J Llewelyn, Jan Y Verbakel and Gail Hayward in Biomarker Insights

Supplemental material, sj-docx-4-bmi-10.1177_11772719221144459 for What is the Diagnostic Accuracy of Novel Urine Biomarkers for Urinary Tract Infection? by George Edwards, Anna Seeley, Adam Carter, Maia Patrick Smith, Elizabeth LA Cross, Kathryn Hughes, Ann Van den Bruel, Martin J Llewelyn, Jan Y Verbakel and Gail Hayward in Biomarker Insights

Supplemental material, sj-docx-1-bmi-10.1177_11772719221144459 for What is the Diagnostic Accuracy of Novel Urine Biomarkers for Urinary Tract Infection? by George Edwards, Anna Seeley, Adam Carter, Maia Patrick Smith, Elizabeth LA Cross, Kathryn Hughes, Ann Van den Bruel, Martin J Llewelyn, Jan Y Verbakel and Gail Hayward in Biomarker Insights

Background\ud Accurate rapid diagnostic tests for SARS‐CoV‐2 infection would be a useful tool to help manage the COVID‐19 pandemic. Testing strategies that use rapid antigen tests to detect current infection have the potential to increase access to testing, speed detection of infection, and inform clinical and public health management decisions to reduce transmission. This is the second update of this review, which was first published in 2020.\ud \ud Objectives\ud To assess the diagnostic accuracy of rapid, point‐of‐care antigen tests for diagnosis of SARS‐CoV‐2 infection. We consider accuracy separately in symptomatic and asymptomatic population groups. Sources of heterogeneity investigated included setting and indication for testing, assay format, sample site, viral load, age, timing of test, and study design.\ud \ud Search methods\ud We searched the COVID‐19 Open Access Project living evidence database from the University of Bern (which includes daily updates from PubMed and Embase and preprints from medRxiv and bioRxiv) on 08 March 2021. We included independent evaluations from national reference laboratories, FIND and the Diagnostics Global Health website. We did not apply language restrictions.\ud \ud Selection criteria\ud We included studies of people with either suspected SARS‐CoV‐2 infection, known SARS‐CoV‐2 infection or known absence of infection, or those who were being screened for infection. We included test accuracy studies of any design that evaluated commercially produced, rapid antigen tests. We included evaluations of single applications of a test (one test result reported per person) and evaluations of serial testing (repeated antigen testing over time). Reference standards for presence or absence of infection were any laboratory‐based molecular test (primarily reverse transcription polymerase chain reaction (RT‐PCR)) or pre‐pandemic respiratory sample.\ud \ud Data collection and analysis\ud We used standard screening procedures with three people. Two people independently carried out quality assessment (using the QUADAS‐2 tool) and extracted study results. Other study characteristics were extracted by one review author and checked by a second. We present sensitivity and specificity with 95% confidence intervals (CIs) for each test, and pooled data using the bivariate model. We investigated heterogeneity by including indicator variables in the random‐effects logistic regression models. We tabulated results by test manufacturer and compliance with manufacturer instructions for use and according to symptom status.\ud \ud Main results\ud We included 155 study cohorts (described in 166 study reports, with 24 as preprints). The main results relate to 152 evaluations of single test applications including 100,462 unique samples (16,822 with confirmed SARS‐CoV‐2). Studies were mainly conducted in Europe (101/152, 66%), and evaluated 49 different commercial antigen assays. Only 23 studies compared two or more brands of test.\ud \ud Risk of bias was high because of participant selection (40, 26%); interpretation of the index test (6, 4%); weaknesses in the reference standard for absence of infection (119, 78%); and participant flow and timing 41 (27%). Characteristics of participants (45, 30%) and index test delivery (47, 31%) differed from the way in which and in whom the test was intended to be used. Nearly all studies (91%) used a single RT‐PCR result to define presence or absence of infection.\ud \ud The 152 studies of single test applications reported 228 evaluations of antigen tests. Estimates of sensitivity varied considerably between studies, with consistently high specificities. Average sensitivity was higher in symptomatic (73.0%, 95% CI 69.3% to 76.4%; 109 evaluations; 50,574 samples, 11,662 cases) compared to asymptomatic participants (54.7%, 95% CI 47.7% to 61.6%; 50 evaluations; 40,956 samples, 2641 cases). Average sensitivity was higher in the first week after symptom onset (80.9%, 95% CI 76.9% to 84.4%; 30 evaluations, 2408 cases) than in the second week of symptoms (53.8%, 95% CI 48.0% to 59.6%; 40 evaluations, 1119 cases). For those who were asymptomatic at the time of testing, sensitivity was higher when an epidemiological exposure to SARS‐CoV‐2 was suspected (64.3%, 95% CI 54.6% to 73.0%; 16 evaluations; 7677 samples, 703 cases) compared to where COVID‐19 testing was reported to be widely available to anyone on presentation for testing (49.6%, 95% CI 42.1% to 57.1%; 26 evaluations; 31,904 samples, 1758 cases). Average specificity was similarly high for symptomatic (99.1%) or asymptomatic (99.7%) participants.\ud \ud We observed a steady decline in summary sensitivities as measures of sample viral load decreased.\ud \ud Sensitivity varied between brands. When tests were used according to manufacturer instructions, average sensitivities by brand ranged from 34.3% to 91.3% in symptomatic participants (20 assays with eligible data) and from 28.6% to 77.8% for asymptomatic participants (12 assays). For symptomatic participants, summary sensitivities for seven assays were 80% or more (meeting acceptable criteria set by the World Health Organization (WHO)). The WHO acceptable performance criterion of 97% specificity was met by 17 of 20 assays when tests were used according to manufacturer instructions, 12 of which demonstrated specificities above 99%. For asymptomatic participants the sensitivities of only two assays approached but did not meet WHO acceptable performance standards in one study each; specificities for asymptomatic participants were in a similar range to those observed for symptomatic people.\ud \ud At 5% prevalence using summary data in symptomatic people during the first week after symptom onset, the positive predictive value (PPV) of 89% means that 1 in 10 positive results will be a false positive, and around 1 in 5 cases will be missed. At 0.5% prevalence using summary data for asymptomatic people, where testing was widely available and where epidemiological exposure to COVID‐19 was suspected, resulting PPVs would be 38% to 52%, meaning that between 2 in 5 and 1 in 2 positive results will be false positives, and between 1 in 2 and 1 in 3 cases will be missed.\ud \ud Authors' conclusions\ud Antigen tests vary in sensitivity. In people with signs and symptoms of COVID‐19, sensitivities are highest in the first week of illness when viral loads are higher. Assays that meet appropriate performance standards, such as those set by WHO, could replace laboratory‐based RT‐PCR when immediate decisions about patient care must be made, or where RT‐PCR cannot be delivered in a timely manner. However, they are more suitable for use as triage to RT‐PCR testing. The variable sensitivity of antigen tests means that people who test negative may still be infected. Many commercially available rapid antigen tests have not been evaluated in independent validation studies.\ud \ud Evidence for testing in asymptomatic cohorts has increased, however sensitivity is lower and there is a paucity of evidence for testing in different settings. Questions remain about the use of antigen test‐based repeat testing strategies. Further research is needed to evaluate the effectiveness of screening programmes at reducing transmission of infection, whether mass screening or targeted approaches including schools, healthcare setting and traveller screening.

BACKGROUND: Recognising patients who need immediate hospital treatment for sepsis while simultaneously limiting unnecessary referrals is challenging for GPs. AIM: To develop and validate a sepsis prediction model for adult patients in primary care. DESIGN AND SETTING: This was a prospective cohort study in four out-of-hours primary care services in the Netherlands, conducted between June 2018 and March 2020. METHOD: Adult patients who were acutely ill and received home visits were included. A total of nine clinical variables were selected as candidate predictors, next to the biomarkers C-reactive protein, procalcitonin, and lactate. The primary endpoint was sepsis within 72 hours of inclusion, as established by an expert panel. Multivariable logistic regression with backwards selection was used to design an optimal model with continuous clinical variables. The added value of the biomarkers was evaluated. Subsequently, a simple model using single cut-off points of continuous variables was developed and externally validated in two emergency department populations. RESULTS: A total of 357 patients were included with a median age of 80 years (interquartile range 71-86), of which 151 (42%) were diagnosed with sepsis. A model based on a simple count of one point for each of six variables (aged >65 years; temperature >38°C; systolic blood pressure ≤110 mmHg; heart rate >110/min; saturation ≤95%; and altered mental status) had good discrimination and calibration (C-statistic of 0.80 [95% confidence interval = 0.75 to 0.84]; Brier score 0.175). Biomarkers did not improve the performance of the model and were therefore not included. The model was robust during external validation. CONCLUSION: Based on this study's GP out-of-hours population, a simple model can accurately predict sepsis in acutely ill adult patients using readily available clinical parameters. ispartof: BRITISH JOURNAL OF GENERAL PRACTICE vol:72 issue:719 pages:E437-E445 ispartof: location:England status: published

Background COVID-19 illness is highly variable, ranging from infection with no symptoms through to pneumonia and life-threatening consequences. Symptoms such as fever, cough, or loss of sense of smell (anosmia) or taste (ageusia), can help flag early on if the disease is present. Such information could be used either to rule out COVID-19 disease, or to identify people who need to go for COVID-19 diagnostic tests. This is the second update of this review, which was first published in 2020. Objectives To assess the diagnostic accuracy of signs and symptoms to determine if a person presenting in primary care or to hospital outpatient settings, such as the emergency department or dedicated COVID-19 clinics, has COVID-19. Search methods We undertook electronic searches up to 10 June 2021 in the University of Bern living search database. In addition, we checked repositories of COVID-19 publications. We used artificial intelligence text analysis to conduct an initial classification of documents. We did not apply any language restrictions. Selection criteria Studies were eligible if they included people with clinically suspected COVID-19, or recruited known cases with COVID-19 and also controls without COVID-19 from a single-gate cohort. Studies were eligible when they recruited people presenting to primary care or hospital outpatient settings. Studies that included people who contracted SARS-CoV-2 infection while admitted to hospital were not eligible. The minimum eligible sample size of studies was 10 participants. All signs and symptoms were eligible for this review, including individual signs and symptoms or combinations. We accepted a range of reference standards. Data collection and analysis Pairs of review authors independently selected all studies, at both title and abstract, and full-text stage. They resolved any disagreements by discussion with a third review author. Two review authors independently extracted data and assessed risk of bias using the QUADAS-2 checklist, and resolved disagreements by discussion with a third review author. Analyses were restricted to prospective studies only. We presented sensitivity and specificity in paired forest plots, in receiver operating characteristic (ROC) space and in dumbbell plots. We estimated summary parameters using a bivariate random-effects meta-analysis whenever five or more primary prospective studies were available, and whenever heterogeneity across studies was deemed acceptable. Main results We identified 90 studies; for this update we focused on the results of 42 prospective studies with 52,608 participants. Prevalence of COVID-19 disease varied from 3.7% to 60.6% with a median of 27.4%. Thirty-five studies were set in emergency departments or outpatient test centres (46,878 participants), three in primary care settings (1230 participants), two in a mixed population of in- and outpatients in a paediatric hospital setting (493 participants), and two overlapping studies in nursing homes (4007 participants). The studies did not clearly distinguish mild COVID-19 disease from COVID-19 pneumonia, so we present the results for both conditions together. Twelve studies had a high risk of bias for selection of participants because they used a high level of preselection to decide whether reverse transcription polymerase chain reaction (RT-PCR) testing was needed, or because they enrolled a non-consecutive sample, or because they excluded individuals while they were part of the study base. We rated 36 of the 42 studies as high risk of bias for the index tests because there was little or no detail on how, by whom and when, the symptoms were measured. For most studies, eligibility for testing was dependent on the local case definition and testing criteria that were in effect at the time of the study, meaning most people who were included in studies had already been referred to health services based on the symptoms that we are evaluating in this review. The applicability of the results of this review iteration improved in comparison with the previous reviews. This version has more studies of people presenting to ambulatory settings, which is where the majority of assessments for COVID-19 take place. Only three studies presented any data on children separately, and only one focused specifically on older adults. We found data on 96 symptoms or combinations of signs and symptoms. Evidence on individual signs as diagnostic tests was rarely reported, so this review reports mainly on the diagnostic value of symptoms. Results were highly variable across studies. Most had very low sensitivity and high specificity. RT-PCR was the most often used reference standard (40/42 studies). Only cough (11 studies) had a summary sensitivity above 50% (62.4%, 95% CI 50.6% to 72.9%)); its specificity was low (45.4%, 95% CI 33.5% to 57.9%)). Presence of fever had a sensitivity of 37.6% (95% CI 23.4% to 54.3%) and a specificity of 75.2% (95% CI 56.3% to 87.8%). The summary positive likelihood ratio of cough was 1.14 (95% CI 1.04 to 1.25) and that of fever 1.52 (95% CI 1.10 to 2.10). Sore throat had a summary positive likelihood ratio of 0.814 (95% CI 0.714 to 0.929), which means that its presence increases the probability of having an infectious disease other than COVID-19. Dyspnoea (12 studies) and fatigue (8 studies) had a sensitivity of 23.3% (95% CI 16.4% to 31.9%) and 40.2% (95% CI 19.4% to 65.1%) respectively. Their specificity was 75.7% (95% CI 65.2% to 83.9%) and 73.6% (95% CI 48.4% to 89.3%). The summary positive likelihood ratio of dyspnoea was 0.96 (95% CI 0.83 to 1.11) and that of fatigue 1.52 (95% CI 1.21 to 1.91), which means that the presence of fatigue slightly increases the probability of having COVID-19. Anosmia alone (7 studies), ageusia alone (5 studies), and anosmia or ageusia (6 studies) had summary sensitivities below 50% but summary specificities over 90%. Anosmia had a summary sensitivity of 26.4% (95% CI 13.8% to 44.6%) and a specificity of 94.2% (95% CI 90.6% to 96.5%). Ageusia had a summary sensitivity of 23.2% (95% CI 10.6% to 43.3%) and a specificity of 92.6% (95% CI 83.1% to 97.0%). Anosmia or ageusia had a summary sensitivity of 39.2% (95% CI 26.5% to 53.6%) and a specificity of 92.1% (95% CI 84.5% to 96.2%). The summary positive likelihood ratios of anosmia alone and anosmia or ageusia were 4.55 (95% CI 3.46 to 5.97) and 4.99 (95% CI 3.22 to 7.75) respectively, which is just below our arbitrary definition of a 'red flag', that is, a positive likelihood ratio of at least 5. The summary positive likelihood ratio of ageusia alone was 3.14 (95% CI 1.79 to 5.51). Twenty-four studies assessed combinations of different signs and symptoms, mostly combining olfactory symptoms. By combining symptoms with other information such as contact or travel history, age, gender, and a local recent case detection rate, some mu ltivariable prediction scores reached a sensitivity as high as 90%. Authors' conclusions Most individual symptoms included in this review have poor diagnostic accuracy. Neither absence nor presence of symptoms are accurate enough to rule in or rule out the disease. The presence of anosmia or ageusia may be useful as a red flag for the presence of COVID-19. The presence of cough also supports further testing. There is currently no evidence to support further testing with PCR in any individuals presenting only with upper respiratory symptoms such as sore throat, coryza or rhinorrhoea. Combinations of symptoms with other readily available information such as contact ortravel history, or the local recent case detection rate may prove more useful and should be further investigated in an unselected population presenting to primary care or hospital outpatient settings. The diagnostic accuracy of symptoms for COVID-19 is moderate to low and anytesting strategy using symptoms as selection mechanism will result in both large numbers of missed cases and large numbers of people requiring testing. Which one of these is minimised, is determined by the goal of COVID-19 testing strategies, that is, controlling the epidemic by isolating every possible case versus identifying those with clinically important disease so that they can be monitored or treated to optimise their prognosis. The former will require a testing strategy that uses very few symptoms as entry criterion for testing, the latter could focus on more specific symptoms such as fever and anosmia.

Additional file 2. “Table: Incidence rates of cystitis, pyelonephritis and urine testing with 95% confidence intervals per age and gender (2020)”. Table including the estimated incidence rates of cystitis, pyelonephritis and urine testing rate per age and gender.

Additional file 1: Supplementary Table 1. Full analysis. Supplementary Table 2. Full analysis by number of children.

Additional file 4. “Figure: Incidence rates of laboratory urine tests per cystitis episode per age group from 2000 to 2020”. Figure showing the incidence rates of laboratory urine tests per cystitis episode per age group from 2000 to 2020.

Additional file 5. “Table: Results of the autoregressive moving average time series analysis of trends of antibiotic prescriptions and urine testing in children with cystitis from 2000-2020”. Table presenting the results of the autoregressive moving average time series analysis of trends of antibiotic prescriptions and urine testing in children with cystitis from 2000-2020.

Additional file 3. “Figure: Number of cystitis episodes* per month (2018-2020) *standardized for the variation in yearly population”. Figure showing number of cystitis episodes per month, for the last 3 years available.

Additional file 1. “Table: Number of general practices and population of children per year”. Table including the number of practices per year, participating in the Intego project; the yearly contact group, % boys, % girls, and estimated practice population.

Additional file 3: Table S3. List of excluded studies with reasons why (full text screening) (n = 277).

Additional file 2: Table S2. Electronic search strategy (Embase).

Additional file 4: Table S4. Characteristics of included studies.

Additional file 6: Figures S22–23. Risk of bias and applicability assessment.

Additional file 1: Table S1. PRISMA - Diagnostic test Accuracy Studies checklist.

BACKGROUND: The respiratory illness caused by SARS-CoV-2 infection continues to present diagnostic challenges. Early research showed thoracic (chest) imaging to be sensitive but not specific in the diagnosis of coronavirus disease 2019 (COVID-19). However, this is a rapidly developing field and these findings need to be re-evaluated in the light of new research. This is the first update of this 'living systematic review'. This update focuses on people suspected of having COVID-19 and excludes studies with only confirmed COVID-19 participants. OBJECTIVES: To evaluate the diagnostic accuracy of thoracic imaging (computed tomography (CT), X-ray and ultrasound) in people with suspected COVID-19. SEARCH METHODS: We searched the COVID-19 Living Evidence Database from the University of Bern, the Cochrane COVID-19 Study Register, The Stephen B. Thacker CDC Library, and repositories of COVID-19 publications through to 22 June 2020. We did not apply any language restrictions. SELECTION CRITERIA: We included studies of all designs that recruited participants of any age group suspected to have COVID-19, and which reported estimates of test accuracy, or provided data from which estimates could be computed. When studies used a variety of reference standards, we retained the classification of participants as COVID-19 positive or negative as used in the study. DATA COLLECTION AND ANALYSIS: We screened studies, extracted data, and assessed the risk of bias and applicability concerns using the QUADAS-2 domain-list independently, in duplicate. We categorised included studies into three groups based on classification of index test results: studies that reported specific criteria for index test positivity (group 1); studies that did not report specific criteria, but had the test reader(s) explicitly classify the imaging test result as either COVID-19 positive or negative (group 2); and studies that reported an overview of index test findings, without explicitly classifying the imaging test as either COVID-19 positive or negative (group 3). We presented the results of estimated sensitivity and specificity using paired forest plots, and summarised in tables. We used a bivariate meta-analysis model where appropriate. We presented uncertainty of the accuracy estimates using 95% confidence intervals (CIs). MAIN RESULTS: We included 34 studies: 30 were cross-sectional studies with 8491 participants suspected of COVID-19, of which 4575 (54%) had a final diagnosis of COVID-19; four were case-control studies with 848 cases and controls in total, of which 464 (55%) had a final diagnosis of COVID-19. Chest CT was evaluated in 31 studies (8014 participants, 4224 (53%) cases), chest X-ray in three studies (1243 participants, 784 (63%) cases), and ultrasound of the lungs in one study (100 participants, 31 (31%) cases). Twenty-six per cent (9/34) of all studies were available only as preprints. Nineteen studies were conducted in Asia, 10 in Europe, four in North America and one in Australia. Sixteen studies included only adults, 15 studies included both adults and children and one included only children. Two studies did not report the ages of participants. Twenty-four studies included inpatients, four studies included outpatients, while the remaining six studies were conducted in unclear settings. The majority of included studies had a high or unclear risk of bias with respect to participant selection, index test, reference standard, and participant flow. For chest CT in suspected COVID-19 participants (31 studies, 8014 participants, 4224 (53%) cases) the sensitivity ranged from 57.4% to 100%, and specificity ranged from 0% to 96.0%. The pooled sensitivity of chest CT in suspected COVID-19 participants was 89.9% (95% CI 85.7 to 92.9) and the pooled specificity was 61.1% (95% CI 42.3 to 77.1). Sensitivity analyses showed that when the studies from China were excluded, the studies from other countries demonstrated higher specificity compared to the overall included studies. When studies that did not classify index tests as positive or negative for COVID-19 (group 3) were excluded, the remaining studies (groups 1 and 2) demonstrated higher specificity compared to the overall included studies. Sensitivity analyses limited to cross-sectional studies, or studies where at least two reverse transcriptase polymerase chain reaction (RT-PCR) tests were conducted if the first was negative, did not substantively alter the accuracy estimates. We did not identify publication status as a source of heterogeneity. For chest X-ray in suspected COVID-19 participants (3 studies, 1243 participants, 784 (63%) cases) the sensitivity ranged from 56.9% to 89.0% and specificity from 11.1% to 88.9%. The sensitivity and specificity of ultrasound of the lungs in suspected COVID-19 participants (1 study, 100 participants, 31 (31%) cases) were 96.8% and 62.3%, respectively. We could not perform a meta-analysis for chest X-ray or ultrasound due to the limited number of included studies. AUTHORS' CONCLUSIONS: Our findings indicate that chest CT is sensitive and moderately specific for the diagnosis of COVID-19 in suspected patients, meaning that CT may have limited capability in differentiating SARS-CoV-2 infection from other causes of respiratory illness. However, we are limited in our confidence in these results due to the poor study quality and the heterogeneity of included studies. Because of limited data, accuracy estimates of chest X-ray and ultrasound of the lungs for the diagnosis of suspected COVID-19 cases should be carefully interpreted. Future diagnostic accuracy studies should pre-define positive imaging findings, include direct comparisons of the various modalities of interest on the same participant population, and implement improved reporting practices. Planned updates of this review will aim to: increase precision around the accuracy estimates for chest CT (ideally with low risk of bias studies); obtain further data to inform accuracy of chest X-rays and ultrasound; and obtain data to further fulfil secondary objectives (e.g. 'threshold' effects, comparing accuracy estimates across different imaging modalities) to inform the utility of imaging along different diagnostic pathways. ispartof: COCHRANE DATABASE OF SYSTEMATIC REVIEWS vol:11 issue:11 ispartof: location:England status: published

Background The diagnostic challenges associated with the COVID‐19 pandemic resulted in rapid development of diagnostic test methods for detecting SARS‐CoV‐2 infection. Serology tests to detect the presence of antibodies to SARS‐CoV‐2 enable detection of past infection and may detect cases of SARS‐CoV‐2 infection that were missed by earlier diagnostic tests. Understanding the diagnostic accuracy of serology tests for SARS‐CoV‐2 infection may enable development of effective diagnostic and management pathways, inform public health management decisions and understanding of SARS‐CoV‐2 epidemiology. Objectives To assess the accuracy of antibody tests, firstly, to determine if a person presenting in the community, or in primary or secondary care has current SARS‐CoV‐2 infection according to time after onset of infection and, secondly, to determine if a person has previously been infected with SARS‐CoV‐2. Sources of heterogeneity investigated included: timing of test, test method, SARS‐CoV‐2 antigen used, test brand, and reference standard for non‐SARS‐CoV‐2 cases. Search methods The COVID‐19 Open Access Project living evidence database from the University of Bern (which includes daily updates from PubMed and Embase and preprints from medRxiv and bioRxiv) was searched on 30 September 2020. We included additional publications from the Evidence for Policy and Practice Information and Co‐ordinating Centre (EPPI‐Centre) ‘COVID‐19: Living map of the evidence’ and the Norwegian Institute of Public Health ’NIPH systematic and living map on COVID‐19 evidence’. We did not apply language restrictions. Selection criteria We included test accuracy studies of any design that evaluated commercially produced serology tests, targeting IgG, IgM, IgA alone, or in combination. Studies must have provided data for sensitivity, that could be allocated to a predefined time period after onset of symptoms, or after a positive RT‐PCR test. Small studies with fewer than 25 SARS‐CoV‐2 infection cases were excluded. We included any reference standard to define the presence or absence of SARS‐CoV‐2 (including reverse transcription polymerase chain reaction tests (RT‐PCR), clinical diagnostic criteria, and pre‐pandemic samples). Data collection and analysis We use standard screening procedures with three reviewers. Quality assessment (using the QUADAS‐2 tool) and numeric study results were extracted independently by two people. Other study characteristics were extracted by one reviewer and checked by a second. We present sensitivity and specificity with 95% confidence intervals (CIs) for each test and, for meta‐analysis, we fitted univariate random‐effects logistic regression models for sensitivity by eligible time period and for specificity by reference standard group. Heterogeneity was investigated by including indicator variables in the random‐effects logistic regression models. We tabulated results by test manufacturer and summarised results for tests that were evaluated in 200 or more samples and that met a modification of UK Medicines and Healthcare products Regulatory Agency (MHRA) target performance criteria. Main results We included 178 separate studies (described in 177 study reports, with 45 as pre‐prints) providing 527 test evaluations. The studies included 64,688 samples including 25,724 from people with confirmed SARS‐CoV‐2; most compared the accuracy of two or more assays (102/178, 57%). Participants with confirmed SARS‐CoV‐2 infection were most commonly hospital inpatients (78/178, 44%), and pre‐pandemic samples were used by 45% (81/178) to estimate specificity. Over two‐thirds of studies recruited participants based on known SARS‐CoV‐2 infection status (123/178, 69%). All studies were conducted prior to the introduction of SARS‐CoV‐2 vaccines and present data for naturally acquired antibody responses. Seventy‐nine percent (141/178) of studies reported sensitivity by week after symptom onset and 66% (117/178) for convalescent phase infection. Studies evaluated enzyme‐linked immunosorbent assays (ELISA) (165/527; 31%), chemiluminescent assays (CLIA) (167/527; 32%) or lateral flow assays (LFA) (188/527; 36%). Risk of bias was high because of participant selection (172, 97%); application and interpretation of the index test (35, 20%); weaknesses in the reference standard (38, 21%); and issues related to participant flow and timing (148, 82%). We judged that there were high concerns about the applicability of the evidence related to participants in 170 (96%) studies, and about the applicability of the reference standard in 162 (91%) studies. Average sensitivities for current SARS‐CoV‐2 infection increased by week after onset for all target antibodies. Average sensitivity for the combination of either IgG or IgM was 41.1% in week one (95% CI 38.1 to 44.2; 103 evaluations; 3881 samples, 1593 cases), 74.9% in week two (95% CI 72.4 to 77.3; 96 evaluations, 3948 samples, 2904 cases) and 88.0% by week three after onset of symptoms (95% CI 86.3 to 89.5; 103 evaluations, 2929 samples, 2571 cases). Average sensitivity during the convalescent phase of infection (up to a maximum of 100 days since onset of symptoms, where reported) was 89.8% for IgG (95% CI 88.5 to 90.9; 253 evaluations, 16,846 samples, 14,183 cases), 92.9% for IgG or IgM combined (95% CI 91.0 to 94.4; 108 evaluations, 3571 samples, 3206 cases) and 94.3% for total antibodies (95% CI 92.8 to 95.5; 58 evaluations, 7063 samples, 6652 cases). Average sensitivities for IgM alone followed a similar pattern but were of a lower test accuracy in every time slot. Average specificities were consistently high and precise, particularly for pre‐pandemic samples which provide the least biased estimates of specificity (ranging from 98.6% for IgM to 99.8% for total antibodies). Subgroup analyses suggested small differences in sensitivity and specificity by test technology however heterogeneity in study results, timing of sample collection, and smaller sample numbers in some groups made comparisons difficult. For IgG, CLIAs were the most sensitive (convalescent‐phase infection) and specific (pre‐pandemic samples) compared to both ELISAs and LFAs (P < 0.001 for differences across test methods). The antigen(s) used (whether from the Spike‐protein or nucleocapsid) appeared to have some effect on average sensitivity in the first weeks after onset but there was no clear evidence of an effect during convalescent‐phase infection. Investigations of test performance by brand showed considerable variation in sensitivity between tests, and in results between studies evaluating the same test. For tests that were evaluated in 200 or more samples, the lower bound of the 95% CI for sensitivity was 90% or more for only a small number of tests (IgG, n = 5; IgG or IgM, n = 1; total antibodies, n = 4). More test brands met the MHRA minimum criteria for specificity of 98% or above (IgG, n = 16; IgG or IgM, n = 5; total antibodies, n = 7). Seven assays met the specified criteria for both sensitivity and specificity. In a low‐prevalence (2%) setting, where antibody testing is used to diagnose COVID‐19 in people with symptoms but who have had a negative PCR test, we would anticipate that 1 (1 to 2) case would be missed and 8 (5 to 15) would be falsely positive in 1000 people undergoing IgG or IgM testing in week three after onset of SARS‐CoV‐2 infection. In a seroprevalence survey, where prevalence of prior infection is 50%, we would anticipate that 51 (46 to 58) cases would be missed and 6 (5 to 7) would be falsely positive in 1000 people having IgG tests during the convalescent phase (21 to 100 days post‐symptom onset or post‐positive PCR) of SARS‐CoV‐2 infection. Authors' conclusions Some antibody tests could be a useful diagnostic tool for those in whom molecular‐ or antigen‐based tests have failed to detect the SARS‐CoV‐2 virus, including in those with ongoing symptoms of acute infection (from week three onwards) or those presenting with post‐acute sequelae of COVID‐19. However, antibody tests have an increasing likelihood of detecting an immune response to infection as time since onset of infection progresses and have demonstrated adequate performance for detection of prior infection for sero‐epidemiological purposes. The applicability of results for detection of vaccination‐induced antibodies is uncertain. Plain language summary What is the diagnostic accuracy of antibody tests for the detection of infection with the COVID‐19 virus? Background COVID‐19 is an infectious disease caused by the SARS‐CoV‐2 virus that spreads easily between people in a similar way to the common cold or ‘flu’. Most people with COVID‐19 have a mild‐to‐moderate respiratory illness, and some may have no symptoms (asymptomatic infection). Others experience severe symptoms and need specialist treatment and intensive care. In response to COVID‐19 infection, the immune system develops proteins called antibodies that can attack the virus as it circulates in their blood. People who have been vaccinated against COVID‐19 also produce these antibodies against the virus. Tests are available to detect antibodies in peoples' blood, which may indicate that they currently have COVID‐19 or have had it previously, or it may indicate that they have been vaccinated (although this group was not the focus of this review). Why are accurate tests important? Accurate testing allows identification of people who need to isolate themselves to prevent the spread of infection, or who might need treatment for their infection. Failure of diagnostic tests to detect infection with COVID‐19 when it is present (a false negative result) may delay treatment and risk further spread of infection to others. Incorrect diagnosis of COVID‐19 when it is not present (a false positive result) may lead to unnecessary further testing, treatment, and isolation of the person and close contacts. Accurate identification of people who have previously had COVID‐19 is important in measuring disease spread and assessing the success of public health interventions. To determine the accuracy of an antibody test in identifying COVID‐19, test results are compared in people known to have (or have had) COVID‐19 and in people known not to have (or have had) COVID‐19. The criteria used to determine whether people are known or not known to have COVID‐19 is called the ‘reference standard’. Many studies use a test called reverse transcriptase polymerase chain reaction (RT‐PCR) as the reference standard, with samples taken from the nose and throat. Additional tests that can be used include measuring symptoms, like coughing or high temperature, or ‘imaging’ tests like chest X‐rays. People known not to have COVID‐19 are sometimes identified from stored blood samples taken before COVID‐19 existed, or from patients with symptoms confirmed to be caused by other diseases. What did the review study? We wanted to find out whether antibody tests: ‐ are able to diagnose infection in people with or without symptoms of COVID‐19, and ‐ can be used to find out if someone has already had COVID‐19. The studies we included in our review looked at three types of antibodies. Most commonly, antibody tests measure two types known as IgG and IgM, but some tests only measure a single type of antibody or different combinations of the three types of antibodies (IgA, IgG, IgM). What did we do? We looked for studies that measured the diagnostic accuracy of antibody tests to detect current or past COVID‐19 infection and compared them with reference standard criteria. Since there are many antibody tests available, we included studies assessing any antibody test compared with any reference standard. People could be tested in hospital or in the community. The people tested may have been confirmed to have, or not to have, COVID‐19 infection, or they may be suspected of having COVID‐19. Study characteristics We found 178 relevant studies. Studies took place in Europe (94), Asia (45), North America (35), Australia (2), and South America (2). Seventy‐eight studies included people who were in hospital with suspected or confirmed COVID‐19 infection and 14 studies included people in community settings. Several studies included people from multiple settings (35) or did not report where the participants were recruited from (39). One hundred and forty‐one studies included recent infection cases (mainly week 1 to week 3 after onset of symptoms), and many also included people tested later (from day 21 onwards after infection) (117). Main results In participants that had COVID‐19 and were tested one week after symptoms developed, antibody tests detected only 27% to 41% of infections. In week 2 after first symptoms, 64% to 79% of infections were detected, rising to 78% to 88% in week 3. Tests that specifically detected IgG or IgM antibodies were the most accurate and, when testing people from 21 days after first symptoms, they detected 93% of people with COVID‐19. Tests gave false positive results for 1% of those without COVID‐19. Below we illustrate results for two different scenarios. If 1000 people were tested for IgG or IgM antibodies during the third week after onset of symptoms and only 20 (2%) of them actually had COVID‐19: ‐ 26 people would test positive. Of these, 8 people (31%) would not have COVID‐19 (false positive result). ‐ 974 people would test negative. Of these, 2 people (0.2%) would actually have COVID‐19 (false negative result). If 1000 people with no symptoms for COVID‐19 were tested for IgG antibodies and 500 (50%) of them had previously had COVID‐19 infection more than 21 days previously: ‐ 455 people would test positive. Of these, 6 people (1%) would not have been infected (false positive result). ‐ 545 people would test negative. Of these, 51 (9%) would actually have had a prior COVID‐19 infection (false negative result). How reliable were the results of the studies of this review? We have limited confidence in the evidence for several reasons. The number of samples contributed by studies for each week post‐symptom onset was often small, and there were sometimes problems with how studies were conducted. Participants included in the studies were often hospital patients who were more likely to have experienced severe symptoms of COVID‐19. The accuracy of antibody tests for detecting COVID‐19 in these patients may be different from the accuracy of the tests in people with mild or moderate symptoms. It is not possible to identify by how much the test results would differ in other populations. Who do the results of this review apply to? A high percentage of participants were in hospital with COVID‐19, so were likely to have more severe disease than people with COVID‐19 who were not hospitalised. Only a small number of studies assessed these tests in people with no symptoms. The results of the review may therefore be more applicable to those with severe disease than people with mild symptoms. Studies frequently did not report whether participants had symptoms at the time samples were taken for testing making it difficult to fully separate test results for early‐phase infection as opposed to later‐phase infections. The studies in our review assessed several test methods across a global population, therefore it is likely that test results would be similar in most areas of the world. What are the implications of this review? The review shows that antibody tests could have a useful role in detecting if someone has had COVID‐19, but the timing of test use is important. Some antibody tests may help to confirm COVID‐19 infection in people who have had symptoms for more than two weeks but who have been unable to confirm their infection using other methods. This is particularly useful if they are experiencing potentially serious symptoms that may be due to COVID‐19 as they may require specific treatment. Antibody tests may also be useful to determine how many people have had a previous COVID‐19 infection. We could not be certain about how well the tests work for people who have milder disease or no symptoms, or for detecting antibodies resulting from vaccination. How up‐to‐date is this review? This review updates our previous review. The evidence is up‐to‐date to September 2020. [Infectious Diseases] Abstract - Background The diagnostic challenges associated with the COVID‐19 pandemic resulted in rapid development of diagnostic test methods for detecting SARS‐CoV‐2 infection. Serology tests to detect the presence of antibodies to SARS‐CoV‐2 enable detection of past infection and may detect cases of SARS‐CoV‐2 infection that were missed by earlier diagnostic tests. Understanding the diagnostic accuracy of serology tests for SARS‐CoV‐2 infection may enable development of effective diagnostic and management pathways, inform public health management decisions and understanding of SARS‐CoV‐2 epidemiology. Objectives To assess the accuracy of antibody tests, firstly, to determine if a person presenting in the community, or in primary or secondary care has current SARS‐CoV‐2 infection according to time after onset of infection and, secondly, to determine if a person has previously been infected with SARS‐CoV‐2. Sources of heterogeneity investigated included: timing of test, test method, SARS‐CoV‐2 antigen used, test brand, and reference standard for non‐SARS‐CoV‐2 cases. Search methods The COVID‐19 Open Access Project living evidence database from the University of Bern (which includes daily updates from PubMed and Embase and preprints from medRxiv and bioRxiv) was searched on 30 September 2020. We included additional publications from the Evidence for Policy and Practice Information and Co‐ordinating Centre (EPPI‐Centre) ‘COVID‐19: Living map of the evidence’ and the Norwegian Institute of Public Health ’NIPH systematic and living map on COVID‐19 evidence’. We did not apply language restrictions. Selection criteria We included test accuracy studies of any design that evaluated commercially produced serology tests, targeting IgG, IgM, IgA alone, or in combination. Studies must have provided data for sensitivity, that could be allocated to a predefined time period after onset of symptoms, or after a positive RT‐PCR test. Small studies with fewer than 25 SARS‐CoV‐2 infection cases were excluded. We included any reference standard to define the presence or absence of SARS‐CoV‐2 (including reverse transcription polymerase chain reaction tests (RT‐PCR), clinical diagnostic criteria, and pre‐pandemic samples). Data collection and analysis We use standard screening procedures with three reviewers. Quality assessment (using the QUADAS‐2 tool) and numeric study results were extracted independently by two people. Other study characteristics were extracted by one reviewer and checked by a second. We present sensitivity and specificity with 95% confidence intervals (CIs) for each test and, for meta‐analysis, we fitted univariate random‐effects logistic regression models for sensitivity by eligible time period and for specificity by reference standard group. Heterogeneity was investigated by including indicator variables in the random‐effects logistic regression models. We tabulated results by test manufacturer and summarised results for tests that were evaluated in 200 or more samples and that met a modification of UK Medicines and Healthcare products Regulatory Agency (MHRA) target performance criteria. Main results We included 178 separate studies (described in 177 study reports, with 45 as pre‐prints) providing 527 test evaluations. The studies included 64,688 samples including 25,724 from people with confirmed SARS‐CoV‐2; most compared the accuracy of two or more assays (102/178, 57%). Participants with confirmed SARS‐CoV‐2 infection were most commonly hospital inpatients (78/178, 44%), and pre‐pandemic samples were used by 45% (81/178) to estimate specificity. Over two‐thirds of studies recruited participants based on known SARS‐CoV‐2 infection status (123/178, 69%). All studies were conducted prior to the introduction of SARS‐CoV‐2 vaccines and present data for naturally acquired antibody responses. Seventy‐nine percent (141/178) of studies reported sensitivity by week after symptom onset and 66% (117/178) for convalescent phase infection. Studies evaluated enzyme‐linked immunosorbent assays (ELISA) (165/527; 31%), chemiluminescent assays (CLIA) (167/527; 32%) or lateral flow assays (LFA) (188/527; 36%). Risk of bias was high because of participant selection (172, 97%); application and interpretation of the index test (35, 20%); weaknesses in the reference standard (38, 21%); and issues related to participant flow and timing (148, 82%). We judged that there were high concerns about the applicability of the evidence related to participants in 170 (96%) studies, and about the applicability of the reference standard in 162 (91%) studies. Average sensitivities for current SARS‐CoV‐2 infection increased by week after onset for all target antibodies. Average sensitivity for the combination of either IgG or IgM was 41.1% in week one (95% CI 38.1 to 44.2; 103 evaluations; 3881 samples, 1593 cases), 74.9% in week two (95% CI 72.4 to 77.3; 96 evaluations, 3948 samples, 2904 cases) and 88.0% by week three after onset of symptoms (95% CI 86.3 to 89.5; 103 evaluations, 2929 samples, 2571 cases). Average sensitivity during the convalescent phase of infection (up to a maximum of 100 days since onset of symptoms, where reported) was 89.8% for IgG (95% CI 88.5 to 90.9; 253 evaluations, 16,846 samples, 14,183 cases), 92.9% for IgG or IgM combined (95% CI 91.0 to 94.4; 108 evaluations, 3571 samples, 3206 cases) and 94.3% for total antibodies (95% CI 92.8 to 95.5; 58 evaluations, 7063 samples, 6652 cases). Average sensitivities for IgM alone followed a similar pattern but were of a lower test accuracy in every time slot. Average specificities were consistently high and precise, particularly for pre‐pandemic samples which provide the least biased estimates of specificity (ranging from 98.6% for IgM to 99.8% for total antibodies). Subgroup analyses suggested small differences in sensitivity and specificity by test technology however heterogeneity in study results, timing of sample collection, and smaller sample numbers in some groups made comparisons difficult. For IgG, CLIAs were the most sensitive (convalescent‐phase infection) and specific (pre‐pandemic samples) compared to both ELISAs and LFAs (P < 0.001 for differences across test methods). The antigen(s) used (whether from the Spike‐protein or nucleocapsid) appeared to have some effect on average sensitivity in the first weeks after onset but there was no clear evidence of an effect during convalescent‐phase infection. Investigations of test performance by brand showed considerable variation in sensitivity between tests, and in results between studies evaluating the same test. For tests that were evaluated in 200 or more samples, the lower bound of the 95% CI for sensitivity was 90% or more for only a small number of tests (IgG, n = 5; IgG or IgM, n = 1; total antibodies, n = 4). More test brands met the MHRA minimum criteria for specificity of 98% or above (IgG, n = 16; IgG or IgM, n = 5; total antibodies, n = 7). Seven assays met the specified criteria for both sensitivity and specificity. In a low‐prevalence (2%) setting, where antibody testing is used to diagnose COVID‐19 in people with symptoms but who have had a negative PCR test, we would anticipate that 1 (1 to 2) case would be missed and 8 (5 to 15) would be falsely positive in 1000 people undergoing IgG or IgM testing in week three after onset of SARS‐CoV‐2 infection. In a seroprevalence survey, where prevalence of prior infection is 50%, we would anticipate that 51 (46 to 58) cases would be missed and 6 (5 to 7) would be falsely positive in 1000 people having IgG tests during the convalescent phase (21 to 100 days post‐symptom onset or post‐positive PCR) of SARS‐CoV‐2 infection. Authors' conclusions Some antibody tests could be a useful diagnostic tool for those in whom molecular‐ or antigen‐based tests have failed to detect the SARS‐CoV‐2 virus, including in those with ongoing symptoms of acute infection (from week three onwards) or those presenting with post‐acute sequelae of COVID‐19. However, antibody tests have an increasing likelihood of detecting an immune response to infection as time since onset of infection progresses and have demonstrated adequate performance for detection of prior infection for sero‐epidemiological purposes. The applicability of results for detection of vaccination‐induced antibodies is uncertain. Plain language summary What is the diagnostic accuracy of antibody tests for the detection of infection with the COVID‐19 virus? Background COVID‐19 is an infectious disease caused by the SARS‐CoV‐2 virus that spreads easily between people in a similar way to the common cold or ‘flu’. Most people with COVID‐19 have a mild‐to‐moderate respiratory illness, and some may have no symptoms (asymptomatic infection). Others experience severe symptoms and need specialist treatment and intensive care. In response to COVID‐19 infection, the immune system develops proteins called antibodies that can attack the virus as it circulates in their blood. People who have been vaccinated against COVID‐19 also produce these antibodies against the virus. Tests are available to detect antibodies in peoples' blood, which may indicate that they currently have COVID‐19 or have had it previously, or it may indicate that they have been vaccinated (although this group was not the focus of this review). Why are accurate tests important? Accurate testing allows identification of people who need to isolate themselves to prevent the spread of infection, or who might need treatment for their infection. Failure of diagnostic tests to detect infection with COVID‐19 when it is present (a false negative result) may delay treatment and risk further spread of infection to others. Incorrect diagnosis of COVID‐19 when it is not present (a false positive result) may lead to unnecessary further testing, treatment, and isolation of the person and close contacts. Accurate identification of people who have previously had COVID‐19 is important in measuring disease spread and assessing the success of public health interventions. To determine the accuracy of an antibody test in identifying COVID‐19, test results are compared in people known to have (or have had) COVID‐19 and in people known not to have (or have had) COVID‐19. The criteria used to determine whether people are known or not known to have COVID‐19 is called the ‘reference standard’. Many studies use a test called reverse transcriptase polymerase chain reaction (RT‐PCR) as the reference standard, with samples taken from the nose and throat. Additional tests that can be used include measuring symptoms, like coughing or high temperature, or ‘imaging’ tests like chest X‐rays. People known not to have COVID‐19 are sometimes identified from stored blood samples taken before COVID‐19 existed, or from patients with symptoms confirmed to be caused by other diseases. What did the review study? We wanted to find out whether antibody tests: ‐ are able to diagnose infection in people with or without symptoms of COVID‐19, and ‐ can be used to find out if someone has already had COVID‐19. The studies we included in our review looked at three types of antibodies. Most commonly, antibody tests measure two types known as IgG and IgM, but some tests only measure a single type of antibody or different combinations of the three types of antibodies (IgA, IgG, IgM). What did we do? We looked for studies that measured the diagnostic accuracy of antibody tests to detect current or past COVID‐19 infection and compared them with reference standard criteria. Since there are many antibody tests available, we included studies assessing any antibody test compared with any reference standard. People could be tested in hospital or in the community. The people tested may have been confirmed to have, or not to have, COVID‐19 infection, or they may be suspected of having COVID‐19. Study characteristics We found 178 relevant studies. Studies took place in Europe (94), Asia (45), North America (35), Australia (2), and South America (2). Seventy‐eight studies included people who were in hospital with suspected or confirmed COVID‐19 infection and 14 studies included people in community settings. Several studies included people from multiple settings (35) or did not report where the participants were recruited from (39). One hundred and forty‐one studies included recent infection cases (mainly week 1 to week 3 after onset of symptoms), and many also included people tested later (from day 21 onwards after infection) (117). Main results In participants that had COVID‐19 and were tested one week after symptoms developed, antibody tests detected only 27% to 41% of infections. In week 2 after first symptoms, 64% to 79% of infections were detected, rising to 78% to 88% in week 3. Tests that specifically detected IgG or IgM antibodies were the most accurate and, when testing people from 21 days after first symptoms, they detected 93% of people with COVID‐19. Tests gave false positive results for 1% of those without COVID‐19. Below we illustrate results for two different scenarios. If 1000 people were tested for IgG or IgM antibodies during the third week after onset of symptoms and only 20 (2%) of them actually had COVID‐19: ‐ 26 people would test positive. Of these, 8 people (31%) would not have COVID‐19 (false positive result). ‐ 974 people would test negative. Of these, 2 people (0.2%) would actually have COVID‐19 (false negative result). If 1000 people with no symptoms for COVID‐19 were tested for IgG antibodies and 500 (50%) of them had previously had COVID‐19 infection more than 21 days previously: ‐ 455 people would test positive. Of these, 6 people (1%) would not have been infected (false positive result). ‐ 545 people would test negative. Of these, 51 (9%) would actually have had a prior COVID‐19 infection (false negative result). How reliable were the results of the studies of this review? We have limited confidence in the evidence for several reasons. The number of samples contributed by studies for each week post‐symptom onset was often small, and there were sometimes problems with how studies were conducted. Participants included in the studies were often hospital patients who were more likely to have experienced severe symptoms of COVID‐19. The accuracy of antibody tests for detecting COVID‐19 in these patients may be different from the accuracy of the tests in people with mild or moderate symptoms. It is not possible to identify by how much the test results would differ in other populations. Who do the results of this review apply to? A high percentage of participants were in hospital with COVID‐19, so were likely to have more severe disease than people with COVID‐19 who were not hospitalised. Only a small number of studies assessed these tests in people with no symptoms. The results of the review may therefore be more applicable to those with severe disease than people with mild symptoms. Studies frequently did not report whether participants had symptoms at the time samples were taken for testing making it difficult to fully separate test results for early‐phase infection as opposed to later‐phase infections. The studies in our review assessed several test methods across a global population, therefore it is likely that test results would be similar in most areas of the world. What are the implications of this review? The review shows that antibody tests could have a useful role in detecting if someone has had COVID‐19, but the timing of test use is important. Some antibody tests may help to confirm COVID‐19 infection in people who have had symptoms for more than two weeks but who have been unable to confirm their infection using other methods. This is particularly useful if they are experiencing potentially serious symptoms that may be due to COVID‐19 as they may require specific treatment. Antibody tests may also be useful to determine how many people have had a previous COVID‐19 infection. We could not be certain about how well the tests work for people who have milder disease or no symptoms, or for detecting antibodies resulting from vaccination. How up‐to‐date is this review? This review updates our previous review. The evidence is up‐to‐date to September 2020. ispartof: Cochrane Database of Systematic Reviews vol:11 issue:11 ispartof: location:England status: Published online