A non-parametric approach for jointly combining evidence on progression free and overall survival time in network meta-analysis.

Randomised controlled trials of cancer treatments typically report progression free survival (PFS) and overall survival (OS) outcomes. Existing methods to synthesise evidence on PFS and OS either rely on the proportional hazards assumption or make parametric assumptions which may not capture the diverse survival curve shapes across studies and treatments. Furthermore, PFS and OS are not independent; OS is the sum of PFS and post-progression survival (PPS). Our aim was to develop a non-parametric approach for jointly synthesising evidence from published Kaplan-Meier survival curves of PFS and OS without assuming proportional hazards. Restricted mean survival times (RMST) are estimated by the area under the survival curves (AUCs) up to a restricted follow-up time. The correlation between AUCs due to the constraint that OS > PFS is estimated using bootstrap re-sampling. Network meta-analysis models are given for RMST for PFS and PPS and ensure that OS = PFS + PPS. Both additive and multiplicative network meta-analysis models are presented to obtain relative treatment effects as either differences or ratios of RMST. The methods are illustrated with a network meta-analysis of treatments for stage IIIA-N2 non-small cell lung cancer. The approach has implications for health economic models of cancer treatments, which require estimates of the mean time spent in the PFS and PPS health-states. The methods can be applied to a single time-to-event outcome, and so have wide applicability in any field where time-to-event outcomes are reported, the proportional hazards assumption is in doubt, and survival curve shapes differ across studies and interventions.

Population Adjustment Methods for Indirect Comparisons: A Review of National Institute for Health and Care Excellence Technology Appraisals.

OBJECTIVES: Indirect comparisons via a common comparator (anchored comparisons) are commonly used in health technology assessment. However, common comparators may not be available, or the comparison may be biased due to differences in effect modifiers between the included studies. Recently proposed population adjustment methods aim to adjust for differences between study populations in the situation where individual patient data are available from at least one study, but not all studies. They can also be used when there is no common comparator or for single-arm studies (unanchored comparisons). We aim to characterise the use of population adjustment methods in technology appraisals (TAs) submitted to the United Kingdom National Institute for Health and Care Excellence (NICE). METHODS: We reviewed NICE TAs published between 01/01/2010 and 20/04/2018. RESULTS: Population adjustment methods were used in 7 percent (18/268) of TAs. Most applications used unanchored comparisons (89 percent, 16/18), and were in oncology (83 percent, 15/18). Methods used included matching-adjusted indirect comparisons (89 percent, 16/18) and simulated treatment comparisons (17 percent, 3/18). Covariates were included based on: availability, expert opinion, effective sample size, statistical significance, or cross-validation. Larger treatment networks were commonplace (56 percent, 10/18), but current methods cannot account for this. Appraisal committees received results of population-adjusted analyses with caution and typically looked for greater cost effectiveness to minimise decision risk. CONCLUSIONS: Population adjustment methods are becoming increasingly common in NICE TAs, although their impact on decisions has been limited to date. Further research is needed to improve upon current methods, and to investigate their properties in simulation studies.

The natural history of Chlamydia trachomatis infection in women: a multi-parameter evidence synthesis.

BACKGROUND AND OBJECTIVES: The evidence base supporting the National Chlamydia Screening Programme, initiated in 2003, has been questioned repeatedly, with little consensus on modelling assumptions, parameter values or evidence sources to be used in cost-effectiveness analyses. The purpose of this project was to assemble all available evidence on the prevalence and incidence of Chlamydia trachomatis (CT) in the UK and its sequelae, pelvic inflammatory disease (PID), ectopic pregnancy (EP) and tubal factor infertility (TFI) to review the evidence base in its entirety, assess its consistency and, if possible, arrive at a coherent set of estimates consistent with all the evidence. METHODS: Evidence was identified using 'high-yield' strategies. Bayesian Multi-Parameter Evidence Synthesis models were constructed for separate subparts of the clinical and population epidemiology of CT. Where possible, different types of data sources were statistically combined to derive coherent estimates. Where evidence was inconsistent, evidence sources were re-interpreted and new estimates derived on a post-hoc basis. RESULTS: An internally coherent set of estimates was generated, consistent with a multifaceted evidence base, fertility surveys and routine UK statistics on PID and EP. Among the key findings were that the risk of PID (symptomatic or asymptomatic) following an untreated CT infection is 17.1% [95% credible interval (CrI) 6% to 29%] and the risk of salpingitis is 7.3% (95% CrI 2.2% to 14.0%). In women aged 16-24 years, screened at annual intervals, at best, 61% (95% CrI 55% to 67%) of CT-related PID and 22% (95% CrI 7% to 43%) of all PID could be directly prevented. For women aged 16-44 years, the proportions of PID, EP and TFI that are attributable to CT are estimated to be 20% (95% CrI 6% to 38%), 4.9% (95% CrI 1.2% to 12%) and 29% (95% CrI 9% to 56%), respectively. The prevalence of TFI in the UK in women at the end of their reproductive lives is 1.1%: this is consistent with all PID carrying a relatively high risk of reproductive damage, whether diagnosed or not. Every 1000 CT infections in women aged 16-44 years, on average, gives rise to approximately 171 episodes of PID and 73 of salpingitis, 2.0 EPs and 5.1 women with TFI at age 44 years. CONCLUSIONS AND RESEARCH RECOMMENDATIONS: The study establishes a set of interpretations of the major studies and study designs, under which a coherent set of estimates can be generated. CT is a significant cause of PID and TFI. CT screening is of benefit to the individual, but detection and treatment of incident infection may be more beneficial. Women with lower abdominal pain need better advice on when to seek early medical attention to avoid risk of reproductive damage. The study provides new insights into the reproductive risks of PID and the role of CT. Further research is required on the proportions of PID, EP and TFI attributable to CT to confirm predictions made in this report, and to improve the precision of key estimates. The cost-effectiveness of screening should be re-evaluated using the findings of this report. FUNDING: The Medical Research Council grant G0801947.

Symptom lead times in lung and colorectal cancers: what are the benefits of symptom-based approaches to early diagnosis?

BACKGROUND: Individuals with undiagnosed lung and colorectal cancers present with non-specific symptoms in primary care more often than matched controls. Increased access to diagnostic services for patients with symptoms generates more early-stage diagnoses, but the mechanisms for this are only partially understood. METHODS: We re-analysed a UK-based case-control study to estimate the Symptom Lead Time (SLT) distribution for a range of potential symptom criteria for investigation. Symptom Lead Time is the time between symptoms caused by cancer and eventual diagnosis, and is analogous to Lead Time in a screening programme. We also estimated the proportion of symptoms in lung and colorectal cancer cases that are actually caused by the cancer. RESULTS: Mean Symptom Lead Times were between 4.1 and 6.0 months, with medians between 2.0 and 3.2 months. Symptom Lead Time did not depend on stage at diagnosis, nor which criteria for investigation are adopted. Depending on the criteria, an estimated 27-48% of symptoms in individuals with as yet undiagnosed lung cancer, and 12-32% with undiagnosed colorectal cancer are not caused by the cancer. CONCLUSIONS: In most cancer cases detected by a symptom-based programme, the symptoms are caused by cancer. These cases have a short lead time and benefit relatively little. However, in a significant minority of cases cancer detection is serendipitous. This group experiences the benefits of a standard screening programme, a substantial mean lead time and a higher probability of early-stage diagnosis.

Simultaneous multioutcome synthesis and mapping of treatment effects to a common scale.

OBJECTIVES: A new method is presented for both synthesizing treatment effects on multiple outcomes subject to measurement error and estimating coherent mapping coefficients between all outcomes. It can be applied to sets of trials reporting different combinations of patient- or clinician-reported outcomes, including both disease-specific measures and generic health-related quality-of-life measures. It is underpinned by a structural equation model that includes measurement error and latent common treatment effect factor. Treatment effects can be expressed on any of the test instruments that have been used. METHODS: This is illustrated in a synthesis of eight placebo-controlled trials of TNF-α inhibitors in ankylosing spondylitis, each reporting treatment effects on between two and five of a total six test instruments. RESULTS: The method has advantages over other methods for synthesis of multiple outcome data, including standardization and multivariate normal synthesis. Unlike standardization, it allows synthesis of treatment effect information from test instruments sensitive to different underlying constructs. It represents a special case of previously proposed multivariate normal models for evidence synthesis, but unlike the former, it also estimates mappings. Combining synthesis and mapping as a single operation makes more efficient use of available data than do current mapping methods and generates treatment effects that are consistent with the mappings. A limitation, however, is that it can only generate mappings to and from those instruments on which some trial data exist. CONCLUSIONS: The method should be assessed in a wide range of data sets on different clinical conditions, before it can be used routinely in health technology assessment.

Incidence of Chlamydia trachomatis infection in women in England: two methods of estimation.

Information on the incidence of Chlamydia trachomatis (CT) is essential for models of the effectiveness and cost-effectiveness of screening programmes. We developed two independent estimates of CT incidence in women in England: one based on an incidence study, with estimates 'recalibrated' to the general population using data on setting-specific relative risks, and allowing for clearance and re-infection during follow-up; the second based on UK prevalence data, and information on the duration of CT infection. The consistency of independent sources of data on incidence, prevalence and duration, validates estimates of these parameters. Pooled estimates of the annual incidence rate in women aged 16-24 and 16-44 years for 2001-2005 using all these data were 0·05 [95% credible interval (CrI) 0·035-0·071] and 0·021 (95% CrI 0·015-0·028), respectively. Although, the estimates apply to England, similar methods could be used in other countries. The methods could be extended to dynamic models to synthesize, and assess the consistency of data on contact and transmission rates.

Risk of pelvic inflammatory disease following Chlamydia trachomatis infection: analysis of prospective studies with a multistate model.

Our objective in this study was to estimate the probability that a Chlamydia trachomatis (CT) infection will cause an episode of clinical pelvic inflammatory disease (PID) and the reduction in such episodes among women with CT that could be achieved by annual screening. We reappraised evidence from randomized controlled trials of screening and controlled observational studies that followed untreated CT-infected and -uninfected women to measure the development of PID. Data from these studies were synthesized using a continuous-time Markov model which takes into account the competing risk of spontaneous clearance of CT. Using a 2-step piecewise homogenous Markov model that accounts for the distinction between prevalent and incident infections, we investigated the possibility that the rate of PID due to CT is greater during the period immediately following infection. The available data were compatible with both the homogenous and piecewise homogenous models. Given a homogenous model, the probability that a CT episode will cause clinical PID was 0.16 (95% credible interval (CrI): 0.06, 0.25), and annual screening would prevent 61% (95% CrI: 55, 67) of CT-related PID in women who became infected with CT. Assuming a piecewise homogenous model with a higher rate during the first 60 days, corresponding results were 0.16 (95% CrI: 0.07, 0.26) and 55% (95% CrI: 32, 72), respectively.

Evidence synthesis for decision making 4: inconsistency in networks of evidence based on randomized controlled trials.

Inconsistency can be thought of as a conflict between "direct" evidence on a comparison between treatments B and C and "indirect" evidence gained from AC and AB trials. Like heterogeneity, inconsistency is caused by effect modifiers and specifically by an imbalance in the distribution of effect modifiers in the direct and indirect evidence. Defining inconsistency as a property of loops of evidence, the relation between inconsistency and heterogeneity and the difficulties created by multiarm trials are described. We set out an approach to assessing consistency in 3-treatment triangular networks and in larger circuit structures, its extension to certain special structures in which independent tests for inconsistencies can be created, and describe methods suitable for more complex networks. Sample WinBUGS code is given in an appendix. Steps that can be taken to minimize the risk of drawing incorrect conclusions from indirect comparisons and network meta-analysis are the same steps that will minimize heterogeneity in pairwise meta-analysis. Empirical indicators that can provide reassurance and the question of how to respond to inconsistency are also discussed.

Randomized controlled trial of a primary care-based screening program to identify older women with prevalent osteoporotic vertebral fractures: Cohort for Skeletal Health in Bristol and Avon (COSHIBA).

Approximately 12% of postmenopausal women have osteoporotic vertebral fractures (VFs); these are associated with excess morbidity and mortality and a high risk of future osteoporotic fractures. Despite this, less than one-third come to clinical attention, partly due to lack of clear clinical triggers for referral for spinal radiographs. The aim of this study was to investigate whether a novel primary care-based screening tool could be used to identify postmenopausal women with osteoporotic VFs and increase appropriate management of osteoporosis. A randomized controlled trial was undertaken in 15 general practices within the Bristol area of the UK. A total of 3200 women aged 65 to 80 years were enrolled, with no exclusion criteria. A simple screening tool was carried out by a nurse in primary care to identify women at high risk of osteoporotic VFs. All identified high-risk women were offered a diagnostic thoracolumbar radiograph. Radiographs were reported using standard National Health Service (NHS) reporting, with results sent back to each participant's general practitioner (GP). Participants in the control arm did not receive the screening tool or radiographs. The main outcome measure was self-reported prescription of medication for osteoporosis at 6 months with a random 5% subsample verified against electronic GP records. Secondary outcome was self-reported incidence of new fractures. Results showed that allocation to screening increased prescription of osteoporosis medications by 124% (odds ratio [OR] for prescription 2.24 at 6 months; 95% confidence interval [CI], 1.16 to 4.33). Allocation to screening also reduced fracture incidence at 12-month follow-up (OR for new fracture 0.60; 95% CI, 0.35-1.03; p = 0.063), although this did not reach statistical significance. This study supports the use of a simple screening tool administered in primary care to increase appropriate prescription of medications for osteoporosis in postmenopausal women in the UK.

Consistency and inconsistency in network meta-analysis: concepts and models for multi-arm studies.

Meta-analyses that simultaneously compare multiple treatments (usually referred to as network meta-analyses or mixed treatment comparisons) are becoming increasingly common. An important component of a network meta-analysis is an assessment of the extent to which different sources of evidence are compatible, both substantively and statistically. A simple indirect comparison may be confounded if the studies involving one of the treatments of interest are fundamentally different from the studies involving the other treatment of interest. Here, we discuss methods for addressing inconsistency of evidence from comparative studies of different treatments. We define and review basic concepts of heterogeneity and inconsistency, and attempt to introduce a distinction between 'loop inconsistency' and 'design inconsistency'. We then propose that the notion of design-by-treatment interaction provides a useful general framework for investigating inconsistency. In particular, using design-by-treatment interactions successfully addresses complications that arise from the presence of multi-arm trials in an evidence network. We show how the inconsistency model proposed by Lu and Ades is a restricted version of our full design-by-treatment interaction model and that there may be several distinct Lu-Ades models for any particular data set. We introduce novel graphical methods for depicting networks of evidence, clearly depicting multi-arm trials and illustrating where there is potential for inconsistency to arise. We apply various inconsistency models to data from trials of different comparisons among four smoking cessation interventions and show that models seeking to address loop inconsistency alone can run into problems. Copyright © 2012 John Wiley & Sons, Ltd.

Evaluating novel agent effects in multiple-treatments meta-regression.

Multiple-treatments meta-analyses are increasingly used to evaluate the relative effectiveness of several competing regimens. In some fields which evolve with the continuous introduction of new agents over time, it is possible that in trials comparing older with newer regimens the effectiveness of the latter is exaggerated. Optimism bias, conflicts of interest and other forces may be responsible for this exaggeration, but its magnitude and impact, if any, needs to be formally assessed in each case. Whereas such novelty bias is not identifiable in a pair-wise meta-analysis, it is possible to explore it in a network of trials involving several treatments. To evaluate the hypothesis of novel agent effects and adjust for them, we developed a multiple-treatments meta-regression model fitted within a Bayesian framework. When there are several multiple-treatments meta-analyses for diverse conditions within the same field/specialty with similar agents involved, one may consider either different novel agent effects in each meta-analysis or may consider the effects to be exchangeable across the different conditions and outcomes. As an application, we evaluate the impact of modelling and adjusting for novel agent effects for chemotherapy and other non-hormonal systemic treatments for three malignancies. We present the results and the impact of different model assumptions to the relative ranking of the various regimens in each network. We established that multiple-treatments meta-regression is a good method for examining whether novel agent effects are present and estimation of their magnitude in the three worked examples suggests an exaggeration of the hazard ratio by 6 per cent (2-11 per cent).

Checking consistency in mixed treatment comparison meta-analysis.

Pooling of direct and indirect evidence from randomized trials, known as mixed treatment comparisons (MTC), is becoming increasingly common in the clinical literature. MTC allows coherent judgements on which of the several treatments is the most effective and produces estimates of the relative effects of each treatment compared with every other treatment in a network.We introduce two methods for checking consistency of direct and indirect evidence. The first method (back-calculation) infers the contribution of indirect evidence from the direct evidence and the output of an MTC analysis and is useful when the only available data consist of pooled summaries of the pairwise contrasts. The second more general, but computationally intensive, method is based on 'node-splitting' which separates evidence on a particular comparison (node) into 'direct' and 'indirect' and can be applied to networks where trial-level data are available. Methods are illustrated with examples from the literature. We take a hierarchical Bayesian approach to MTC implemented using WinBUGS and R.We show that both methods are useful in identifying potential inconsistencies in different types of network and that they illustrate how the direct and indirect evidence combine to produce the posterior MTC estimates of relative treatment effects. This allows users to understand how MTC synthesis is pooling the data, and what is 'driving' the final estimates.We end with some considerations on the modelling assumptions being made, the problems with the extension of the back-calculation method to trial-level data and discuss our methods in the context of the existing literature.

Synthesis of survival and disease progression outcomes for health technology assessment of cancer therapies.

Studies of clinical efficacy commonly report more than one clinical endpoint. For example, randomized controlled trials of treatments for cancer will normally report time to disease progression as well as overall survival. It is likely that disease progression will be associated with higher mortality rates. Disease progression rates will also have consequences for the societal economic burden of the disease. Economic evaluation of the cost-effectiveness of different treatment regimes therefore requires the joint estimation of both disease progression and mortality. We describe a model to combine evidence from studies reporting time to event summaries for disease progression and/or mortality, motivated by a systematic review of 1st-line treatment for advanced breast cancer to provide inputs for an economic evaluation as part of the National Institute for Health and Clinical Excellence (NICE) clinical guideline on treatment of advanced breast cancer in England and Wales. The review identified a network of treatment comparisons, which provides the basis for indirect comparison. A variety of outcomes were reported: overall survival, time to progression (overall and responders only), and the proportion of responder, stable, progressive disease, and non-assessable patients. There were only five trials, and not all trials reported all outcomes. The scarcity of the available evidence required us to make strong assumptions in order to identify model parameters. However, this evidence structure often occurs in health technology assessment (HTA) of treatments for cancer. We discuss the validity of the assumptions made, and the potential to assess their validity in other applications of HTA of cancer therapies. Copyright © 2011 John Wiley & Sons, Ltd.

Bayesian methods for evidence synthesis in cost-effectiveness analysis.

Recently, health systems internationally have begun to use cost-effectiveness research as formal inputs into decisions about which interventions and programmes should be funded from collective resources. This process has raised some important methodological questions for this area of research. This paper considers one set of issues related to the synthesis of effectiveness evidence for use in decision-analytic cost-effectiveness (CE) models, namely the need for the synthesis of all sources of available evidence, although these may not 'fit neatly' into a CE model. Commonly encountered problems include the absence of head-to-head trial evidence comparing all options under comparison, the presence of multiple endpoints from trials and different follow-up periods. Full evidence synthesis for CE analysis also needs to consider treatment effects between patient subpopulations and the use of nonrandomised evidence. Bayesian statistical methods represent a valuable set of analytical tools to utilise indirect evidence and can make a powerful contribution to the decision-analytic approach to CE analysis. This paper provides a worked example and a general overview of these methods with particular emphasis on their use in economic evaluation.

Evidence synthesis, parameter correlation and probabilistic sensitivity analysis.

Over the last decade or so, there have been many developments in methods to handle uncertainty in cost-effectiveness studies. In decision modelling, it is widely accepted that there needs to be an assessment of how sensitive the decision is to uncertainty in parameter values. The rationale for probabilistic sensitivity analysis (PSA) is primarily based on a consideration of the needs of decision makers in assessing the consequences of decision uncertainty. In this paper, we highlight some further compelling reasons for adopting probabilistic methods for decision modelling and sensitivity analysis, and specifically for adopting simulation from a Bayesian posterior distribution. Our reasoning is as follows. Firstly, cost-effectiveness analyses need to be based on all the available evidence, not a selected subset, and the uncertainties in the data need to be propagated through the model in order to provide a correct analysis of the uncertainties in the decision. In many--perhaps most--cases the evidence structure requires a statistical analysis that inevitably induces correlations between parameters. Deterministic sensitivity analysis requires that models are run with parameters fixed at 'extreme' values, but where parameter correlation exists it is not possible to identify sets of parameter values that can be considered 'extreme' in a meaningful sense. However, a correct probabilistic analysis can be readily achieved by Monte Carlo sampling from the joint posterior distribution of parameters. In this paper, we review some evidence structures commonly occurring in decision models, where analyses that correctly reflect the uncertainty in the data induce correlations between parameters. Frequently, this is because the evidence base includes information on functions of several parameters. It follows that, if health technology assessments are to be based on a correct analysis of all available data, then probabilistic methods must be used both for sensitivity analysis and for estimation of expected costs and benefits.

Comparison of methods for analysing cluster randomized trials: an example involving a factorial design.

BACKGROUND: Studies involving clustering effects are common, but there is little consistency in their analysis. Various analytical methods were compared for a factorial cluster randomized trial (CRT) of two primary care-based interventions designed to increase breast screening attendance. METHODS: Three cluster-level and five individual-level options were compared in respect of log odds ratios of attendance and their standard errors (SE), for the two intervention effects and their interaction. Cluster-level analyses comprised: (C1) unweighted regression of practice log odds; (C2) regression of log odds weighted by their inverse variance; (C3) random-effects meta-regression of log odds with practice as a random effect. Individual-level analyses comprised: (I1) standard logistic regression ignoring clustering; (I2) robust SE; (I3) generalized estimating equations; (I4) random-effects logistic regression; (I5) Bayesian random-effects logistic regression. Adjustments for stratification and baseline variables were investigated. RESULTS: As expected, method I1 was highly anti-conservative. The other, valid, methods exhibited considerable differences in parameter estimates and standard errors, even between the various random-effects methods based on the same statistical model. Method I4 was particularly sensitive to between-cluster variation and was computationally stable only after controlling for baseline uptake. CONCLUSIONS: Commonly used methods for the analysis of CRT can give divergent results. Simulation studies are needed to compare results from different methods in situations typical of cluster trials but when the true model parameters are known.

Screening for Down's syndrome: effects, safety, and cost effectiveness of first and second trimester strategies.

OBJECTIVE: To compare the effects, safety, and cost effectiveness of antenatal screening strategies for Down's syndrome. DESIGN: Analysis of incremental cost effectiveness. SETTING: United Kingdom. MAIN OUTCOME MEASURES: Number of liveborn babies with Down's syndrome, miscarriages due to chorionic villus sampling or amniocentesis, health care costs of screening programme, and additional costs and additional miscarriages per additional affected live birth prevented by adopting a more effective strategy. RESULTS: Compared with no screening, the additional cost per additional liveborn baby with Down's syndrome prevented was 22 000 pound sterling for measurement of nuchal translucency. The cost of the integrated test was 51 000 pound sterling compared with measurement of nuchal translucency. All other strategies were more costly and less effective, or cost more per additional affected baby prevented. Depending on the cost of the screening test, the first trimester combined test and the quadruple test would also be cost effective options. CONCLUSIONS: The choice of screening strategy should be between the integrated test, first trimester combined test, quadruple test, or nuchal translucency measurement depending on how much service providers are willing to pay, the total budget available, and values on safety. Screening based on maternal age, the second trimester double test, and the first trimester serum test was less effective, less safe, and more costly than these four options.

Human T cell leukaemia/lymphoma virus infection in pregnant women in the United Kingdom: population study.

OBJECTIVE: To assess the prevalence of human T cell leukaemia/lymphoma virus (HTLV) infection in pregnant women in the United Kingdom. DESIGN: Population study. SUBJECTS: Guthrie card samples from babies born in 1997-8. Samples were linked to data on mother's age and ethnic status and parents' country of birth and then anonymised. SETTING: North Thames Regional Health Authority. MAIN OUTCOME MEASURES: Presence of antibodies against HTLV in eluates tested by gelatin particle agglutination assay and results confirmed by immunoblot. RESULTS: Of 126 010 samples tested, 67 had confirmed antibodies to HTLV (59 HTLV-I, 2 HTLV-II, 6 untyped) and six had indeterminate results. Seroprevalence was 17.0 per 1000 (95% confidence interval 9.2 to 28.3) in infants whose mothers were born in the Caribbean, 3.2/1000 (1.5 to 5.9) with mothers born in west and central Africa, and 6.8/1000 (3.1 to 12.9) in infants of black Caribbean mothers born in non-endemic regions. In infants with no known risk (both parents born in non-endemic regions and mother not black Caribbean) seroprevalence was 0.06-0.12 per 1000. Mother's country of birth, father's country of birth, and mother's ethnic status were all independently associated with neonatal seroprevalence. An estimated 223 (95% confidence interval 110 to 350) of the 720 000 pregnant women each year in the United Kingdom are infected with HTLV. CONCLUSIONS: The prevalence of HTLV and HIV infections in pregnant women in the United Kingdom are comparable. The cost effectiveness of antenatal HTLV screening should be evaluated, and screening of blood donations should be considered.

Cost effectiveness analysis of antenatal HIV screening in United Kingdom.

OBJECTIVE: To assess the cost effectiveness of universal antenatal HIV screening compared with selective screening in the United Kingdom. DESIGN: Incremental cost effectiveness analysis relating additional costs of screening to life years gained. Maternal and paediatric costs and life years were combined. SETTING: United Kingdom. MAIN OUTCOME MEASURES: Number of districts for which universal screening would be cost effective compared with selective screening under various conditions. RESULTS: On base case assumptions, a new diagnosis of a pregnant woman with HIV results in a gain of 6.392 life years and additional expenditure of 14 833 pounds. If decision makers are prepared to pay up to 10 000 pounds an additional life year, this would imply a net benefit of 49 090 pounds (range 12 300 pounds- 59 000 pounds), which would be available to detect each additional infected woman in an antenatal screening programme. In London, universal antenatal screening would be cost effective compared with a selective screening under any reasonable assumptions about screening costs. Outside London, universal screening with uptake above 90% would be cost effective with a 0.60 pounds HIV antibody test cost and up to 3.5 minutes for pretest discussion. Cost effectiveness of universal testing is lower if selective testing can achieve high uptake among those at higher risk. A universal strategy with only 50% uptake may not be less cost effective in low prevalence districts and may cost more and be less effective than a well run selective strategy. CONCLUSIONS: Universal screening with pretest discussion should be adopted throughout the United Kingdom as part of routine antenatal care as long as test costs can be kept low and uptake high.