Extending backcalculation to analyse BSE data.

We review the origins of backcalculation (or back projection) methods developed for the analysis of AIDS (acquired immunodeficiency syndrome) incidence data. These techniques have been used extensively for >15 years to deconvolute clinical case incidence, given knowledge of the incubation period distribution, to obtain estimates of past HIV (human immunodeficiency virus) infection incidence and short-term predictions of future AIDS incidence. Adaptations required for the analysis of bovine spongiform encephalopathy (BSE) incidence included: stratification of BSE incidence by age as well as birth cohort; allowance for incomplete survival between infection and the onset of clinical signs of disease; and decomposition of the age- and time-related infection incidence into a time-dependent feed risk component and an age-dependent exposure/susceptibility function. The most recent methodological developments focus on the incorporation of data from clinically unaffected cattle screened using recently developed tests for preclinical BSE infection. Backcalculation-based predictions of future BSE incidence obtained since 1996 are examined. Finally, future directions of epidemiological analysis of BSE epidemics are discussed taking into account ongoing developments in the science of BSE and possible changes in BSE-related policies

Validity of interpretation: A user validity perspective beyond the test score

This paper introduces the concept of user validity and provides a new perspective on the validity of interpretations from tests. Test interpretation is based on outputs such as test scores, profiles, reports, spreadsheets of multiple candidates' scores, etc. The user validity perspective focuses on the interpretations a test user makes given the purpose of the test and the information provided in the test output. This innovative perspective focuses on how user validity can be extended to content, criterion, and to some extent construct-related validity. It provides a basis for researching the validity of interpretations and an improved understanding of the appropriateness of different approaches to score interpretation, as well as how to design test outputs and assessments that are pragmatic and optimal

Multiple large clusters of tuberculosis in London: a cross-sectional analysis of molecular and spatial data

Large outbreaks of tuberculosis (TB) represent a particular threat to disease control because they reflect multiple instances of active transmission. The extent to which long chains of transmission contribute to high TB incidence in London is unknown. We aimed to estimate the contribution of large clusters to the burden of TB in London and identify risk factors. We identified TB patients resident in London notified between 2010 and 2014, and used 24-locus mycobacterial interspersed repetitive units-variable number tandem repeat strain typing data to classify cases according to molecular cluster size. We used spatial scan statistics to test for spatial clustering and analysed risk factors through multinomial logistic regression. TB isolates from 7458 patients were included in the analysis. There were 20 large molecular clusters (with n>20 cases), comprising 795 (11%) of all cases; 18 (90%) large clusters exhibited significant spatial clustering. Cases in large clusters were more likely to be UK born (adjusted odds ratio 2.93, 95% CI 2.28-3.77), of black-Caribbean ethnicity (adjusted odds ratio 3.64, 95% CI 2.23-5.94) and have multiple social risk factors (adjusted odds ratio 3.75, 95% CI 1.96-7.16). Large clusters of cases contribute substantially to the burden of TB in London. Targeting interventions such as screening in deprived areas and social risk groups, including those of black ethnicities and born in the UK, should be a priority for reducing transmission

Optimising spatial accessibility to inform rationalisation of specialist health services

BACKGROUND: In an era of budget constraints for healthcare services, strategies for provision of services that improve quality whilst saving costs are highly valued. A proposed means to achieve this is consolidation of services into fewer specialist centres, but this may lead to reduced spatial accessibility. We describe a methodology which includes implementing a combinatorial optimisation algorithm to derive combinations of services which optimise spatial accessibility in the context of service rationalisation, and demonstrate its use through the exemplar of tuberculosis clinics in London. METHODS: Our methodology involves (1) identifying the spatial distribution of the patient population using the service; (2) calculating patient travel times to each service location, and (3) using a combinatorial optimisation algorithm to identify subsets of locations that minimise overall travel time. We estimated travel times for tuberculosis patients notified in London between 2010 and 2013 to each of 29 clinics in the city. Travel time estimates were derived from the Transport for London Journey Planner service. We identified the subset of clinics that would provide the shortest overall travel time for each possible number of clinic subsets (1-28). RESULTS: Based on the 29 existing clinic locations, mean estimated travel time to clinics used by 12,061 tuberculosis patients in London was 33 min; and mean time to their nearest clinics was 28 min. Using optimum combinations of clinic locations, and assuming that patients attended their nearest clinics, a mean travel time of less than 45 min could be achieved with three clinics; of 34 min with ten clinics, and of less than 30 min with 18 clinics. CONCLUSIONS: We have developed a methodological approach to optimise spatial accessibility which can be used to inform rationalisation of health services. In urban conurbations, this may enable service reorganisation which increases quality and efficiency without substantially affecting spatial accessibility. This approach could be used to inform planning of service reorganisations, but may not be generalisable to rural areas or smaller urban centres

Methods for estimating the case fatality ratio for a novel, emerging infectious disease.

During the course of an epidemic of a potentially fatal disease, it is important that the case fatality ratio be well estimated. The authors propose a novel method for doing so based on the Kaplan-Meier survival procedure, jointly considering two outcomes (death and recovery), and evaluate its performance by using data from the 2003 epidemic of severe acute respiratory syndrome in Hong Kong, People's Republic of China. They compare this estimate obtained at various points in the epidemic with the case fatality ratio eventually observed; with two commonly quoted, naïve estimates derived from cumulative incidence and mortality statistics at single time points; and with estimates in which a parametric mixture model is used. They demonstrate the importance of patient characteristics regarding outcome by analyzing subgroups defined by age at admission to the hospital

Association of Minimal Residual Disease With Superior Survival Outcomes in Patients With Multiple Myeloma: A Meta-analysis

Importance: Numerous studies have evaluated the prognostic value of minimal residual disease (MRD) in patients with multiple myeloma (MM). Most studies were small and varied in terms of patient population, treatment, and MRD assessment methods. Objective: To evaluate the utility of MRD detection in patients with newly diagnosed MM. Data Sources: A Medline search was conducted for articles published in English between January 1990 and January 2016. Study Selection: Eligible studies reported MRD status and progression-free survival (PFS) or overall survival (OS) in 20 or more patients following treatment. Among 405 articles identified, 21 met the initial eligibility criteria and were included in the analysis. Data Extraction and Synthesis: Information on patient characteristics, treatment, MRD assessment, and outcomes were extracted using a standard form. Main Outcomes and Measures: The impact of MRD status on PFS and OS was assessed by pooling data from relevant trials. Data were adjusted to allow for different proportions of patients with MRD in different studies, and analyzed using the Peto method. Forest plots were created based on Cox model analysis. Other prespecified research questions were addressed qualitatively. Results: Fourteen studies (n = 1273) provided data on the impact of MRD on PFS, and 12 studies (n = 1100) on OS. Results were reported specifically in patients who had achieved conventional complete response (CR) in 5 studies for PFS (n = 574) and 6 studies for OS (n = 616). An MRD-negative status was associated with significantly better PFS overall (hazard ratio [HR], 0.41; 95% CI, 0.36-0.48; P < .001) and in studies specifically looking at CR patients (HR, 0.44; 95% CI, 0.34-0.56; P < .001). Overall survival was also favorable in MRD-negative patients overall (HR, 0.57; 95% CI, 0.46-0.71; P < .001) and in CR patients (HR, 0.47; 95% CI, 0.33-0.67; P < .001). Tests of heterogeneity found no significant differences among the studies for PFS and OS. Conclusions and Relevance: Minimal residual disease-negative status after treatment for newly diagnosed MM is associated with long-term survival. These findings provide quantitative evidence to support the integration of MRD assessment as an end point in clinical trials of MM