A Conceptual Framework for the Social Analysis of Reproductive Health

The dominant conceptual framework for understanding reproductive behaviour is highly individualistic. In this article, it is demonstrated that such a conceptualization is flawed, as behaviour is shaped by social relations and institutions. Using ethnographic evidence, the value of a social analysis of the local contexts of reproductive health is highlighted. A framework is set out for conducting such a social analysis, which is capable of generating data necessary to allow health programmes to assess the appropriate means of improving the responsiveness of service-delivery structures to the needs of the most vulnerable. Six key issues are identified in the framework for the analysis of social vulnerability to poor reproductive health outcomes. The key issues are: poverty and livelihood strategies, gender, health-seeking behaviour, reproductive behaviour, and access to services. The article concludes by briefly identifying the key interventions and strategies indicated by such an analysis

Journey Mapping: A New Approach to Extension Program Design and Evaluation

With origins in service and human-centered design and customer experience, journey mapping is a research and evaluation method that allows users to visualize the journey a person or group takes while engaging in a service, program, or system. Using this method, individuals provide feedback on their experience, highlighting successes and challenges along the way. Minnesota Extension educators have utilized journey mapping in program design and evaluation contexts and have found great value in both. This article highlights three use cases which provide insight into lessons learned during the process and how Extension staff may use the tool in the future

‘Arm-based’ parameterization for network meta-analysis

We present an alternative to the contrast‐based parameterization used in a number of publications for network meta‐analysis. This alternative “arm‐based” parameterization offers a number of advantages: it allows for a “long” normalized data structure that remains constant regardless of the number of comparators; it can be used to directly incorporate individual patient data into the analysis; the incorporation of multi‐arm trials is straightforward and avoids the need to generate a multivariate distribution describing treatment effects; there is a direct mapping between the parameterization and the analysis script in languages such as WinBUGS and finally, the arm‐based parameterization allows simple extension to treatment‐specific random treatment effect variances. We validated the parameterization using a published smoking cessation dataset. Network meta‐analysis using arm‐ and contrast‐based parameterizations produced comparable results (with means and standard deviations being within +/− 0.01) for both fixed and random effects models. We recommend that analysts consider using arm‐based parameterization when carrying out network meta‐analyses

Network meta-analysis on the log-hazard scale, combining count and hazard ratio statistics accounting for multi-arm trials: a tutorial.

BACKGROUND: Data on survival endpoints are usually summarised using either hazard ratio, cumulative number of events, or median survival statistics. Network meta-analysis, an extension of traditional pairwise meta-analysis, is typically based on a single statistic. In this case, studies which do not report the chosen statistic are excluded from the analysis which may introduce bias. METHODS: In this paper we present a tutorial illustrating how network meta-analyses of survival endpoints can combine count and hazard ratio statistics in a single analysis on the hazard ratio scale. We also describe methods for accounting for the correlations in relative treatment effects (such as hazard ratios) that arise in trials with more than two arms. Combination of count and hazard ratio data in a single analysis is achieved by estimating the cumulative hazard for each trial arm reporting count data. Correlation in relative treatment effects in multi-arm trials is preserved by converting the relative treatment effect estimates (the hazard ratios) to arm-specific outcomes (hazards). RESULTS: A worked example of an analysis of mortality data in chronic obstructive pulmonary disease (COPD) is used to illustrate the methods. The data set and WinBUGS code for fixed and random effects models are provided. CONCLUSIONS: By incorporating all data presentations in a single analysis, we avoid the potential selection bias associated with conducting an analysis for a single statistic and the potential difficulties of interpretation, misleading results and loss of available treatment comparisons associated with conducting separate analyses for different summary statistics

Evaluation of triage tests when existing test capacity is constrained: application to rapid diagnostic testing in COVID-19

Objectives: A triage test is used to determine which patients will undergo an existing or ‘reference’ test. This paper explores the potential value of triage tests before reference tests when capacity of the reference test is constrained. Methods: We developed a simple model with inputs: prevalence, sensitivity, specificity and reference test capacity. We included a case study of rapid diagnostic tests for recombinant SARS-CoV- 2antigens used as triage tests before a reference polymerase chain reaction test. Performance data is taken from evaluation by the Foundation for Innovative New Diagnostics. Results: When reference test capacity is constrained, the use of a triage test leads to a relative expansion of the population tested and cases identified, both are higher with a high specificity test. When reference test capacity is not constrained, the introduction of a triage test can be assessed using a standard cost-consequence or cost-utility framework balancing the benefit of the reduction in the number of reference tests required against the disbenefit of missed cases. In this case a test with high specificity leads to the greatest reduction in the number of reference tests required and to the greatest number of missed cases. In the constrained case, the advantage of a triage testing strategy in terms of population covered and cases identified reduces as prevalence increases. In the unconstrained case, the reduction in reference tests required is reduced and cases missed increase as prevalence rises. Conclusion: When availability of reference test is constrained, tests added in a triage position do not need high levels of accuracy to increase number of cases diagnosed. This has implications in many disease areas including COVID-19

A Conceptual Framework for the Social Analysis of Reproductive Health

The dominant conceptual framework for understanding reproductive behaviour is highly individualistic. In this article, it is demonstrated that such a conceptualization is flawed, as behaviour is shaped by social relations and institutions. Using ethnographic evidence, the value of a social analysis of the local contexts of reproductive health is highlighted. A framework is set out for conducting such a social analysis, which is capable of generating data necessary to allow health programmes to assess the ap-propriate means of improving the responsiveness of service-delivery structures to the needs of the most vulnerable. Six key issues are identified in the framework for the analysis of social vulnerability to poor reproductive health outcomes. The key issues are: poverty and livelihood strategies, gender, health-seeking behaviour, reproductive behaviour, and access to services. The article concludes by briefly identifying the key interventions and strategies indicated by such an analysis

Evaluation of the Economic Burden of Psoriatic Arthritis and the Relationship Between Functional Status and Healthcare Costs

Doce imágenes de un liposarcoma metastatizado situado en el cerebro de un paciente de 44 años.Twelve pictures of a metastasized liposarcoma located in the brain of a 44-year-old male patient

An Economic Evaluation of Preclinical Testing Strategies Compared to the Compulsory Scrapie Flock Scheme in the Control of Classical Scrapie

Cost-benefit is rarely combined with nonlinear dynamic models when evaluating control options for infectious diseases. The current strategy for scrapie in Great Britain requires that all genetically susceptible livestock in affected flocks be culled (Compulsory Scrapie Flock Scheme or CSFS). However, this results in the removal of many healthy sheep, and a recently developed pre-clinical test for scrapie now offers a strategy based on disease detection. We explore the flock level cost-effectiveness of scrapie control using a deterministic transmission model and industry estimates of costs associated with genotype testing, pre-clinical tests and the value of a sheep culled. Benefit was measured in terms of the reduction in the number of infected sheep sold on, compared to a baseline strategy of doing nothing, using Incremental Cost Effectiveness analysis to compare across strategies. As market data was not available for pre-clinical testing, a threshold analysis was used to set a unit-cost giving equal costs for CSFS and multiple pre-clinical testing (MT, one test each year for three consecutive years). Assuming a 40% within-flock proportion of susceptible genotypes and a test sensitivity of 90%, a single test (ST) was cheaper but less effective than either the CSFS or MT strategies (30 infected-sales-averted over the lifetime of the average epidemic). The MT strategy was slightly less effective than the CSFS and would be a dominated strategy unless preclinical testing was cheaper than the threshold price of £6.28, but may be appropriate for flocks with particularly valuable livestock. Though the ST is not currently recommended, the proportion of susceptible genotypes in the national flock is likely to continue to decrease; this may eventually make it a cost-effective alternative to the MT or CSFS

Oncology modeling for fun and profit! Key steps for busy analysts in health technology assessment

In evaluating new oncology medicines, two common modeling approaches are state transition (e.g., Markov and semi-Markov) and partitioned survival. Partitioned survival models have become more prominent in oncology health technology assessment processes in recent years. Our experience in conducting and evaluating models for economic evaluation has highlighted many important and practical pitfalls. As there is little guidance available on best practices for those who wish to conduct them, we provide guidance in the form of 'Key steps for busy analysts,' who may have very little time and require highly favorable results. Our guidance highlights the continued need for rigorous conduct and transparent reporting of economic evaluations regardless of the modeling approach taken, and the importance of modeling that better reflects reality, which includes better approaches to considering plausibility, estimating relative treatment effects, dealing with post-progression effects, and appropriate characterization of the uncertainty from modeling itself

A different animal? Identifying the features of health technology assessment for developers of medical technologies

Health technology assessment (HTA) conducted to inform developers of health technologies (development-focused HTA, DF-HTA) has a number of distinct features when compared to HTA conducted to inform usage decisions (use-focused HTA). To conduct effective DF-HTA, it is important that analysts are aware of its distinct features as analyses are often not published. We set out a framework of ten features, drawn from the literature and our own experience: a target audience of developers and investors; an underlying user objective to maximize return on investment; a broad range of decisions to inform; wide decision space; reduced evidence available; earlier timing of analysis; fluid business model; constrained resources for analysis; a positive stance of analysis; and a “consumer”-specific burden of proof. This paper presents a framework of ten features of DF-HTA intended to initiate debate as well as provide an introduction for analysts unfamiliar with the field