Overall Vertical Transmission of Hepatitis C Virus, Transmission Net of Clearance, and Timing of Transmission.

BACKGROUND: It is widely accepted that the risk of hepatitis C virus (HCV) vertical transmission (VT) is 5%-6% in monoinfected women, and that 25%-40% of HCV infection clears spontaneously within 5 years. However, there is no consensus on how VT rates should be estimated, and there is a lack of information on VT rates "net" of clearance. METHODS: We reanalyzed data on 1749 children in 3 prospective cohorts to obtain coherent estimates of overall VT rate and VT rates net of clearance at different ages. Clearance rates were used to impute the proportion of uninfected children who had been infected and then cleared before testing negative. The proportion of transmission early in utero, late in utero, and at delivery was estimated from data on the proportion of HCV RNA positive within 3 days of birth, and differences between elective cesarean and nonelective cesarean deliveries. RESULTS: Overall VT rates were 7.2% (95% credible interval [CrI], 5.6%-8.9%) in mothers who were human immunodeficiency virus (HIV) negative and 12.1% (95% CrI, 8.6%-16.8%) in HIV-coinfected women. The corresponding rates net of clearance at 5 years were 2.4% (95% CrI, 1.1%-4.1%), and 4.1% (95% CrI, 1.7%-7.3%). We estimated that 24.8% (95% CrI, 12.1%-40.8%) of infections occur early in utero, 66.0% (95% CrI, 42.5%-83.3%) later in utero, and 9.3% (95% CrI, 0.5%-30.6%) during delivery. CONCLUSIONS: Overall VT rates are about 24% higher than previously assumed, but the risk of infection persisting beyond age 5 years is about 38% lower. The results can inform design of trials of interventions to prevent or treat pediatric HCV infection, and strategies to manage children exposed in utero.

Modelling the potential effectiveness of hepatitis C screening and treatment strategies during pregnancy in Egypt and Ukraine.

BACKGROUND & AIMS: HCV test and treat campaigns currently exclude pregnant women. Pregnancy offers a unique opportunity for HCV screening and to potentially initiate direct-acting antiviral treatment. We explored HCV screening and treatment strategies in two lower middle-income countries with high HCV prevalence, Egypt and Ukraine. METHODS: Country-specific probabilistic decision models were developed to simulate a cohort of pregnant women. We compared five strategies: S0, targeted risk-based screening and deferred treatment (DT) to after pregnancy/breastfeeding; S1, World Health Organization (WHO) risk-based screening and DT; S2, WHO risk-based screening and targeted treatment (treat women with risk factors for HCV vertical transmission [VT]); S3, universal screening and targeted treatment during pregnancy; S4, universal screening and treatment. Maternal and infant HCV outcomes were projected. RESULTS: S0 resulted in the highest proportion of women undiagnosed: 59% and 20% in Egypt and Ukraine, respectively, with 0% maternal cure by delivery and VT estimated at 6.5% and 7.9%, respectively. WHO risk-based screening and DT (S1) increased the proportion of women diagnosed with no change in maternal cure or VT. Universal screening and treatment during pregnancy (S4) resulted in the highest proportion of women diagnosed and cured by delivery (65% and 70%, respectively), and lower levels of VT (3.4% and 3.6%, respectively). CONCLUSIONS: This is one of the first models to explore HCV screening and treatment strategies in pregnancy, which will be critical in informing future care and policy as more safety/efficacy data emerge. Universal screening and treatment in pregnancy could potentially improve both maternal and infant outcomes. IMPACT AND IMPLICATIONS: In the context of two lower middle-income countries with high HCV burdens (Egypt and Ukraine), we designed a decision analytic model to explore five different HCV testing and treatment strategies for pregnant women, with the assumption that treatment was safe and efficacious for use in pregnancy. Assuming direct-acting antiviral treatment during pregnancy would reduce vertical transmission, our findings indicate that the provision of universal (rather than risk-based targeted) screening and treatment would provide the greatest maternal and infant benefits. While future trials are needed to assess the safety and efficacy of direct-acting antivirals in pregnancy and their impact on vertical transmission, there is increasing recognition that the elimination of HCV cannot leave entire subpopulations of pregnant women and young children behind. Our findings will be critical for policymakers when developing improved screening and treatment recommendations for pregnant women.

Intravenous immunoglobulin for severe sepsis and septic shock: clinical effectiveness, cost-effectiveness and value of a further randomised controlled trial.

INTRODUCTION: Prior to investing in a large, multicentre randomised controlled trial (RCT), the National Institute for Health Research in the UK called for an evaluation of the feasibility and value for money of undertaking a trial on intravenous immunoglobulin (IVIG) as an adjuvant therapy for severe sepsis/septic shock. METHODS: In response to this call, this study assessed the clinical and cost-effectiveness of IVIG (using a decision model), and evaluated the value of conducting an RCT (using expected value of information (EVI) analysis). The evidence informing such assessments was obtained through a series of systematic reviews and meta-analyses. Further primary data analyses were also undertaken using the Intensive Care National Audit & Research Centre Case Mix Programme Database, and a Scottish Intensive Care Society research study. RESULTS: We found a large degree of statistical heterogeneity in the clinical evidence on treatment effect, and the source of such heterogeneity was unclear. The incremental cost-effectiveness ratio of IVIG is within the borderline region of estimates considered to represent value for money, but results appear highly sensitive to the choice of model used for clinical effectiveness. This was also the case with EVI estimates, with maximum payoffs from conducting a further clinical trial between £ 137 and £ 1,011 million. CONCLUSIONS: Our analyses suggest that there is a need for a further RCT. Results on the value of conducting such research, however, were sensitive to the clinical effectiveness model used, reflecting the high level of heterogeneity in the evidence base.

An overview of models used in economic analyses of biologic therapies for arthritis--from current diversity to future consensus.

A number of cost-effectiveness models have been developed with the aim of providing guidance for decision making on biologic therapies for the management of inflammatory joint disease. The findings of these analyses can differ markedly, and these differences can undermine the credibility of such models if unexplained. To allow differences between models to be identified more easily, we define six components common to all models-initial response, longer term disease progression, mortality, quality-adjusted life year estimation, resource use and the selection and interpretation of data. We give examples of divergent approaches taken by model structures to the same issue, and explore the impact of divergence on model results, with particular focus on two models that have reported substantially different estimates for the cost-effectiveness of third-line etanercept vs conventional DMARD. The sensitivity of results to a particular assumption made in a model will depend on the decision problem and assumptions made elsewhere in the model, highlighting the importance of guidance throughout model development. To some extent, guidance from bodies such as the National Institute of Health and Clinical Excellence can be used to determine which approach should be preferred where models differ. However, there is a pressing need for clinical input and guidance before consensus can be reached on the most credible model(s) to use for decision support.

Are head-to-head trials of biologics needed? The role of value of information methods in arthritis research.

Reimbursement decisions are typically based on cost-effectiveness analyses. While a cost-effectiveness analysis can identify the optimum strategy, there is usually some degree of uncertainty around this decision. Sources of uncertainty include statistical sampling error in treatment efficacy measures, underlying baseline risk, utility measures and costs, as well as uncertainty in the structure of the model. The optimal strategy is therefore only optimal on average, and a decision to adopt this strategy might still be the wrong decision if all uncertainty could be eliminated. This means that there is a quantifiable expected (average) loss attaching to decisions made under uncertainty, and hence a value in collecting information to reduce that uncertainty. Value of information (VOI) analyses can be used to provide guidance on whether more research would be cost-effective, which particular model inputs (parameters) have the most bearing on decision uncertainty, and can also help with the design and sample size of further research. Here, we introduce the key concepts in VOI analyses, and highlight the inputs required to calculate it. The adoption of the new biologic treatments for RA and PsA tends to be based on placebo-controlled trials. We discuss the possible role of VOI analyses in deciding whether head-to-head comparisons of the biologic therapies should be carried out, illustrating with examples from other fields. We emphasize the need for a model of the natural history of RA and PsA, which reflects a consensus view.

Indirect and mixed treatment comparisons in arthritis research.

Evidence for the efficacy of biologic therapies in inflammatory arthritis comes overwhelmingly from placebo-controlled trials. Increasingly, however, authorities responsible for purchasing and reimbursement have tried to determine whether there are differences between these powerful new therapies, which would lead them to recommend some in preference to others, either on grounds of efficacy or cost-effectiveness. In the absence of head-to-head trial comparisons, indirect comparisons may be used. Furthermore, network meta-analysis, also known as mixed treatment comparisons can combine information from trials in a connected network. These methods allow inferences about head-to-head comparisons even when there is little or no head-to-head evidence, which has caused some concern. In this article we briefly review these methodologies and describe results from recent applications to inflammatory arthritis in the clinical literature. We then focus on how the methodologies are used in decision making, taking as an illustration some recent technology appraisals conducted by the National Institute for Health and Clinical Excellence in the UK. We conclude that, in practice, the key decisions have been based on results from placebo-controlled trials.

Accuracy of UK Rapid Test Consortium (UK-RTC) "AbC-19 Rapid Test" for detection of previous SARS-CoV-2 infection in key workers: test accuracy study.

OBJECTIVE: To assess the accuracy of the AbC-19 Rapid Test lateral flow immunoassay for the detection of previous severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. DESIGN: Test accuracy study. SETTING: Laboratory based evaluation. PARTICIPANTS: 2847 key workers (healthcare staff, fire and rescue officers, and police officers) in England in June 2020 (268 with a previous polymerase chain reaction (PCR) positive result (median 63 days previously), 2579 with unknown previous infection status); and 1995 pre-pandemic blood donors. MAIN OUTCOME MEASURES: AbC-19 sensitivity and specificity, estimated using known negative (pre-pandemic) and known positive (PCR confirmed) samples as reference standards and secondly using the Roche Elecsys anti-nucleoprotein assay, a highly sensitive laboratory immunoassay, as a reference standard in samples from key workers. RESULTS: Test result bands were often weak, with positive/negative discordance by three trained laboratory staff for 3.9% of devices. Using consensus readings, for known positive and negative samples sensitivity was 92.5% (95% confidence interval 88.8% to 95.1%) and specificity was 97.9% (97.2% to 98.4%). Using an immunoassay reference standard, sensitivity was 94.2% (90.7% to 96.5%) among PCR confirmed cases but 84.7% (80.6% to 88.1%) among other people with antibodies. This is consistent with AbC-19 being more sensitive when antibody concentrations are higher, as people with PCR confirmation tended to have more severe disease whereas only 62% (218/354) of seropositive participants had had symptoms. If 1 million key workers were tested with AbC-19 and 10% had actually been previously infected, 84 700 true positive and 18 900 false positive results would be projected. The probability that a positive result was correct would be 81.7% (76.8% to 85.8%). CONCLUSIONS: AbC-19 sensitivity was lower among unselected populations than among PCR confirmed cases of SARS-CoV-2, highlighting the scope for overestimation of assay performance in studies involving only PCR confirmed cases, owing to "spectrum bias." Assuming that 10% of the tested population have had SARS-CoV-2 infection, around one in five key workers testing positive with AbC-19 would be false positives. STUDY REGISTRATION: ISRCTN 56609224.

Clinical Outcomes of a Zika Virus Mother-Child Pair Cohort in Spain.

BACKGROUND: Zika virus (ZIKV) infection has been associated with congenital microcephaly and other neurodevelopmental abnormalities. There is little published research on the effect of maternal ZIKV infection in a non-endemic European region. We aimed to describe the outcomes of pregnant travelers diagnosed as ZIKV-infected in Spain, and their exposed children. METHODS: This prospective observational cohort study of nine referral hospitals enrolled pregnant women (PW) who travelled to endemic areas during their pregnancy or the two previous months, or those whose sexual partners visited endemic areas in the previous 6 months. Infants of ZIKV-infected mothers were followed for about two years. RESULTS: ZIKV infection was diagnosed in 163 PW; 112 (70%) were asymptomatic and 24 (14.7%) were confirmed cases. Among 143 infants, 14 (9.8%) had adverse outcomes during follow-up; three had a congenital Zika syndrome (CZS), and 11 other potential Zika-related outcomes. The overall incidence of CZS was 2.1% (95%CI: 0.4-6.0%), but among infants born to ZIKV-confirmed mothers, this increased to 15.8% (95%CI: 3.4-39.6%). CONCLUSIONS: A nearly 10% overall risk of neurologic and hearing adverse outcomes was found in ZIKV-exposed children born to a ZIKV-infected traveler PW. Longer-term follow-up of these children is needed to assess whether there are any later-onset manifestations.

Correction: Sera selected from national STI surveillance system shows Chlamydia trachomatis PgP3 antibody correlates with time since infection and number of previous infections.

[This corrects the article DOI: 10.1371/journal.pone.0208652.].

Methods for Population-Adjusted Indirect Comparisons in Health Technology Appraisal.

Standard methods for indirect comparisons and network meta-analysis are based on aggregate data, with the key assumption that there is no difference between the trials in the distribution of effect-modifying variables. Methods which relax this assumption are becoming increasingly common for submissions to reimbursement agencies, such as the National Institute for Health and Care Excellence (NICE). These methods use individual patient data from a subset of trials to form population-adjusted indirect comparisons between treatments, in a specific target population. Recently proposed population adjustment methods include the Matching-Adjusted Indirect Comparison (MAIC) and the Simulated Treatment Comparison (STC). Despite increasing popularity, MAIC and STC remain largely untested. Furthermore, there is a lack of clarity about exactly how and when they should be applied in practice, and even whether the results are relevant to the decision problem. There is therefore a real and present risk that the assumptions being made in one submission to a reimbursement agency are fundamentally different to-or even incompatible with-the assumptions being made in another for the same indication. We describe the assumptions required for population-adjusted indirect comparisons, and demonstrate how these may be used to generate comparisons in any given target population. We distinguish between anchored and unanchored comparisons according to whether a common comparator arm is used or not. Unanchored comparisons make much stronger assumptions, which are widely regarded as infeasible. We provide recommendations on how and when population adjustment methods should be used, and the supporting analyses that are required to provide statistically valid, clinically meaningful, transparent and consistent results for the purposes of health technology appraisal. Simulation studies are needed to examine the properties of population adjustment methods and their robustness to breakdown of assumptions.

Sera selected from national STI surveillance system shows Chlamydia trachomatis PgP3 antibody correlates with time since infection and number of previous infections.

BACKGROUND: Seroprevalence surveys of Chlamydia trachomatis (CT) antibodies are promising for estimating age-specific CT cumulative incidence, however accurate estimates require improved understanding of antibody response to CT infection. METHODS: We used GUMCAD, England's national sexually transmitted infection (STI) surveillance system, to select sera taken from female STI clinic attendees on the day of or after a chlamydia diagnosis. Serum specimens were collected from laboratories and tested anonymously on an indirect and a double-antigen ELISA, both of which are based on the CT-specific Pgp3 antigen. We used cross-sectional and longitudinal descriptive analyses to explore the relationship between seropositivity and a) cumulative number of chlamydia diagnoses and b) time since most recent chlamydia diagnosis. RESULTS: 919 samples were obtained from visits when chlamydia was diagnosed and 812 during subsequent follow-up visits. Pgp3 seropositivity using the indirect ELISA increased from 57.1% (95% confidence interval: 53.2-60.7) on the day of a first-recorded chlamydia diagnosis to 89.6% (95%CI: 79.3-95.0) on the day of a third or higher documented diagnosis. With the double-antigen ELISA, the increase was from 61.1% (95%CI: 53.2-60.7) to 97.0% (95%CI: 88.5-99.3). Seropositivity decreased with time since CT diagnosis on only the indirect assay, to 49.3% (95%CI: 40.9-57.7) two or more years after a first diagnosis and 51.9% (95%CI: 33.2-70.0) after a repeat diagnosis. CONCLUSION: Seropositivity increased with cumulative number of infections, and decreased over time after diagnosis on the indirect ELISA, but not on the double-antigen ELISA. This is the first study to demonstrate the combined impact of number of chlamydia diagnoses, time since diagnosis, and specific ELISA on Pgp3 seropositivity. Our findings are being used to inform models estimating age-specific chlamydia incidence over time using serial population-representative serum sample collections, to enable accurate public health monitoring of chlamydia.

Extrapolation of Survival Curves from Cancer Trials Using External Information.

BACKGROUND: Estimates of life expectancy are a key input to cost-effectiveness analysis (CEA) models for cancer treatments. Due to the limited follow-up in Randomized Controlled Trials (RCTs), parametric models are frequently used to extrapolate survival outcomes beyond the RCT period. However, different parametric models that fit the RCT data equally well may generate highly divergent predictions of treatment-related gain in life expectancy. Here, we investigate the use of information external to the RCT data to inform model choice and estimation of life expectancy. METHODS: We used Bayesian multi-parameter evidence synthesis to combine the RCT data with external information on general population survival, conditional survival from cancer registry databases, and expert opinion. We illustrate with a 5-year follow-up RCT of cetuximab plus radiotherapy v. radiotherapy alone for head and neck cancer. RESULTS: Standard survival time distributions were insufficiently flexible to simultaneously fit both the RCT data and external data on general population survival. Using spline models, we were able to estimate a model that was consistent with the trial data and all external data. A model integrating all sources achieved an adequate fit and predicted a 4.7-month (95% CrL: 0.4; 9.1) gain in life expectancy due to cetuximab. CONCLUSIONS: Long-term extrapolation using parametric models based on RCT data alone is highly unreliable and these models are unlikely to be consistent with external data. External data can be integrated with RCT data using spline models to enable long-term extrapolation. Conditional survival data could be used for many cancers and general population survival may have a role in other conditions. The use of external data should be guided by knowledge of natural history and treatment mechanisms.

The relative responsiveness of test instruments can be estimated using a meta-analytic approach: an illustration with treatments for depression.

OBJECTIVES: We present a meta-analytic method that combines information on treatment effects from different instruments from a network of randomized trials to estimate instrument relative responsiveness. STUDY DESIGN AND SETTING: Five depression-test instruments [Beck Depression Inventory (BDI I/II), Patient Health Questionnaire (PHQ9), Hamilton Rating for Depression 17 and 24 items, Montgomery-Asberg Depression Rating] and three generic quality of life measures [EuroQoL (EQ-5D), SF36 mental component summary (SF36 MCS), and physical component summary (SF36 PCS)] were compared. Randomized trials of treatments for depression reporting outcomes on any two or more of these instruments were identified. Information on the within-trial ratios of standardized treatment effects was pooled across the studies to estimate relative responsiveness. RESULTS: The between-instrument ratios of standardized treatment effects vary across trials, with a coefficient of variation of 13% (95% credible interval: 6%, 25%). There were important differences between the depression measures, with PHQ9 being the most responsive instrument and BDI the least. Responsiveness of the EQ-5D and SF36 PCS was poor. SF36 MCS performed similarly to depression instruments. CONCLUSION: Information on relative responsiveness of several test instruments can be pooled across networks of trials reporting at least two outcomes, allowing comparison and ranking of test instruments that may never have been compared directly.

Applying Multiple Criteria Decision Analysis to Comparative Benefit-Risk Assessment: Choosing among Statins in Primary Prevention.

Decision makers in different health care settings need to weigh the benefits and harms of alternative treatment strategies. Such health care decisions include marketing authorization by regulatory agencies, practice guideline formulation by clinical groups, and treatment selection by prescribers and patients in clinical practice. Multiple criteria decision analysis (MCDA) is a family of formal methods that help make explicit the tradeoffs that decision makers accept between the benefit and risk outcomes of different treatment options. Despite the recent interest in MCDA, certain methodological aspects are poorly understood. This paper presents 7 guidelines for applying MCDA in benefit-risk assessment and illustrates their use in the selection of a statin drug for the primary prevention of cardiovascular disease. We provide guidance on the key methodological issues of how to define the decision problem, how to select a set of nonoverlapping evaluation criteria, how to synthesize and summarize the evidence, how to translate relative measures to absolute ones that permit comparisons between the criteria, how to define suitable scale ranges, how to elicit partial preference information from the decision makers, and how to incorporate uncertainty in the analysis. Our example on statins indicates that fluvastatin is likely to be the most preferred drug by our decision maker and that this result is insensitive to the amount of preference information incorporated in the analysis.

Accounting for Heterogeneity in Relative Treatment Effects for Use in Cost-Effectiveness Models and Value-of-Information Analyses.

Cost-effectiveness analysis (CEA) models are routinely used to inform health care policy. Key model inputs include relative effectiveness of competing treatments, typically informed by meta-analysis. Heterogeneity is ubiquitous in meta-analysis, and random effects models are usually used when there is variability in effects across studies. In the absence of observed treatment effect modifiers, various summaries from the random effects distribution (random effects mean, predictive distribution, random effects distribution, or study-specific estimate [shrunken or independent of other studies]) can be used depending on the relationship between the setting for the decision (population characteristics, treatment definitions, and other contextual factors) and the included studies. If covariates have been measured that could potentially explain the heterogeneity, then these can be included in a meta-regression model. We describe how covariates can be included in a network meta-analysis model and how the output from such an analysis can be used in a CEA model. We outline a model selection procedure to help choose between competing models and stress the importance of clinical input. We illustrate the approach with a health technology assessment of intravenous immunoglobulin for the management of adult patients with severe sepsis in an intensive care setting, which exemplifies how risk of bias information can be incorporated into CEA models. We show that the results of the CEA and value-of-information analyses are sensitive to the model and highlight the importance of sensitivity analyses when conducting CEA in the presence of heterogeneity. The methods presented extend naturally to heterogeneity in other model inputs, such as baseline risk.

Symptom lead time distribution in lung cancer: natural history and prospects for early diagnosis.

BACKGROUND: Before their diagnosis, patients with cancer present in primary care more frequently than do matched controls. This has raised hopes that earlier investigation in primary care could lead to earlier stage at diagnosis. METHODS: We re-analysed primary care symptom data collected from 247 lung cancer cases and 1235 matched controls in Devon, UK. We identified the most sensitive and specific definition of symptoms, and estimated its incidence in cases and controls prior to diagnosis. We estimated the symptom lead time (SLT) distribution (the time between symptoms attributable to cancer and diagnosis), taking account of the investigations already carried out in primary care. The impact of route of diagnosis on stage at diagnosis was also examined. RESULTS: Symptom incidence in cases was higher than in controls 2 years before diagnosis, accelerating markedly in the last 6 months. The median SLT was under 3 months, with mean 5.3 months [95% credible interval (CrI) 4.5-6.1] and did not differ by stage at diagnosis. An earlier stage at diagnosis was observed in patients identified through chest X-ray originated in primary care. CONCLUSIONS: Most symptoms preceded clinical diagnosis by only a few months. Symptom-based investigation would lengthen lead times and result in earlier stage at diagnosis in a small proportion of cases, but would be far less effective than standard screening targeted at smokers.

Expected value of sample information for multi-arm cluster randomized trials with binary outcomes.

Expected value of sample information (EVSI) measures the anticipated net benefit gained from conducting new research with a specific design to add to the evidence on which reimbursement decisions are made. Cluster randomized trials raise specific issues for EVSI calculations because 1) a hierarchical model is necessary to account for between-cluster variability when incorporating new evidence and 2) heterogeneity between clusters needs to be carefully characterized in the cost-effectiveness analysis model. Multi-arm trials provide parameter estimates that are correlated, which needs to be accounted for in EVSI calculations. Furthermore, EVSI is computationally intensive when the net benefit function is nonlinear, due to the need for an inner-simulation step. We develop a method for the computation of EVSI that avoids the inner simulation step for cluster randomized multi-arm trials with a binary outcome, where the net benefit function is linear in the probability of an event but nonlinear in the log-odds ratio parameters. We motivate and illustrate the method with an example of a cluster randomized 2 × 2 factorial trial for interventions to increase attendance at breast screening in the UK, using a previously reported cost-effectiveness model. We highlight assumptions made in our approach, extensions to individually randomized trials and inclusion of covariates, and areas for further developments. We discuss computation time, the research-design space, and the ethical implications of an EVSI approach. We suggest that EVSI is a practical and appropriate tool for the design of cluster randomized trials.