Adaptation responses to climate change differ between global megacities

Urban areas are increasingly at risk from climate change, with negative impacts predicted for human health, the economy and ecosystems1, 2. These risks require responses from cities to improve their resilience. Policymakers need to understand current adaptation spend to plan comprehensively and effectively. Through the measurement of spend in the newly defined ‘adaptation economy’, we analyse current climate change adaptation efforts in ten megacities. In all cases, the adaptation economy remains a small part of the overall economy, representing a maximum of 0.33% of a city’s gross domestic product (here referred to as GDPc). Differences in total spend are significant between cities in developed, emerging and developing countries, ranging from £15 million to £1,600 million. Comparing key subsectors, we demonstrate the differences in adaptation profiles. Developing cities have higher proportional spend on health and agriculture, whereas developed cities have higher spend on energy and water. Spend per capita and percentage of GDPc comparisons more clearly show disparities between cities. Developing country cities spend half the proportion of GDPc and significantly less per capita, suggesting that adaptation spend is driven by wealth rather than the number of vulnerable people. This indicates that current adaptation activities are insufficient in major population centres in developing and emerging economies

A Guide to Handling Missing Data in Cost-Effectiveness Analysis Conducted Within Randomised Controlled Trials

The authors would like to thank Professor Adrian Grant and the team at the University of Aberdeen (Professor Craig Ramsay, Janice Cruden, Charles Boachie, Professor Marion Campbell and Seonaidh Cotton) who kindly allowed the REFLUX dataset to be used for this work, and Eldon Spackman for kindly sharing the Stata (R) code for calculating the probability that an intervention is cost effective following MI. The authors are grateful to the reviewers for their comments, which greatly improved this paper. M. G. is recipient of a Medical Research Council Early Career Fellowship in Economics of Health (grant number: MR/K02177X/1). I. R. W. was supported by the Medical Research Council [Unit Programme U105260558]. No specific funding was obtained to produce this paper. The authors declare no conflicts of interest.Missing data are a frequent problem in cost-effectiveness analysis (CEA) within a randomised controlled trial. Inappropriate methods to handle missing data can lead to misleading results and ultimately can affect the decision of whether an intervention is good value for money. This article provides practical guidance on how to handle missing data in within-trial CEAs following a principled approach: (i) the analysis should be based on a plausible assumption for the missing data mechanism, i.e. whether the probability that data are missing is independent of or dependent on the observed and/or unobserved values; (ii) the method chosen for the base-case should fit with the assumed mechanism; and (iii) sensitivity analysis should be conducted to explore to what extent the results change with the assumption made. This approach is implemented in three stages, which are described in detail: (1) descriptive analysis to inform the assumption on the missing data mechanism; (2) how to choose between alternative methods given their underlying assumptions; and (3) methods for sensitivity analysis. The case study illustrates how to apply this approach in practice, including software code. The article concludes with recommendations for practice and suggestions for future research.Medical Research Council Early Career Fellowship in Economics of Health MR/K02177X/1Medical Research Council UK (MRC) U105260558Medical Research Council UK (MRC) MC_U105260558 MR/K02177X/

Sensitivity Analysis for Not-at-Random Missing Data in Trial-Based Cost-Effectiveness Analysis : A Tutorial

Cost-effectiveness analyses (CEA) of randomised controlled trials are a key source of information for health care decision makers. Missing data are, however, a common issue that can seriously undermine their validity. A major concern is that the chance of data being missing may be directly linked to the unobserved value itself [missing not at random (MNAR)]. For example, patients with poorer health may be less likely to complete quality-of-life questionnaires. However, the extent to which this occurs cannot be ascertained from the data at hand. Guidelines recommend conducting sensitivity analyses to assess the robustness of conclusions to plausible MNAR assumptions, but this is rarely done in practice, possibly because of a lack of practical guidance. This tutorial aims to address this by presenting an accessible framework and practical guidance for conducting sensitivity analysis for MNAR data in trial-based CEA. We review some of the methods for conducting sensitivity analysis, but focus on one particularly accessible approach, where the data are multiply-imputed and then modified to reflect plausible MNAR scenarios. We illustrate the implementation of this approach on a weight-loss trial, providing the software code. We then explore further issues around its use in practice

Effectiveness and Cost-effectiveness of Outpatient Physiotherapy After Knee Replacement for Osteoarthritis: Study Protocol for a Randomised Controlled Trial

Background: Primary total knee replacement is a common operation that is performed to provide pain relief and restore functional ability. Inpatient physiotherapy is routinely provided after surgery to enhance recovery prior to hospital discharge. However, international variation exists in the provision of outpatient physiotherapy after hospital discharge. While evidence indicates that outpatient physiotherapy can improve short-term function, the longer term benefits are unknown. The aim of this randomised controlled trial is to evaluate the long-term clinical effectiveness and cost-effectiveness of a 6-week group-based outpatient physiotherapy intervention following knee replacement. Methods/design: Two hundred and fifty-six patients waiting for knee replacement because of osteoarthritis will be recruited from two orthopaedic centres. Participants randomised to the usual-care group (n = 128) will be given a booklet about exercise and referred for physiotherapy if deemed appropriate by the clinical care team. The intervention group (n = 128) will receive the same usual care and additionally be invited to attend a group-based outpatient physiotherapy class starting 6 weeks after surgery. The 1-hour class will be run on a weekly basis over 6 weeks and will involve task-orientated and individualised exercises. The primary outcome will be the Lower Extremity Functional Scale at 12 months post-operative. Secondary outcomes include: quality of life, knee pain and function, depression, anxiety and satisfaction. Data collection will be by questionnaire prior to surgery and 3, 6 and 12 months after surgery and will include a resource-use questionnaire to enable a trial-based economic evaluation. Trial participation and satisfaction with the classes will be evaluated through structured telephone interviews. The primary statistical and economic analyses will be conducted on an intention-to-treat basis with and without imputation of missing data. The primary economic result will estimate the incremental cost per quality-adjusted life year gained from this intervention from a National Health Services (NHS) and personal social services perspective. Discussion: This research aims to benefit patients and the NHS by providing evidence on the long-term effectiveness and cost-effectiveness of outpatient physiotherapy after knee replacement. If the intervention is found to be effective and cost-effective, implementation into clinical practice could lead to improvement in patients’ outcomes and improved health care resource efficiency

Utility of the Health of the Nation Outcome Scales (HoNOS) in Predicting Mental Health Service Costs for Patients with Common Mental Health Problems : Historical Cohort Study

BACKGROUND: Few countries have made much progress in implementing transparent and efficient systems for the allocation of mental health care resources. In England there are ongoing efforts by the National Health Service (NHS) to develop mental health 'payment by results' (PbR). The system depends on the ability of patient 'clusters' derived from the Health of the Nation Outcome Scales (HoNOS) to predict costs. We therefore investigated the associations of individual HoNOS items and the Total HoNOS score at baseline with mental health service costs at one year follow-up.METHODS: An historical cohort study using secondary care patient records from the UK financial year 2012-2013. Included were 1,343 patients with 'common mental health problems', represented by ICD-10 disorders between F32-48. Costs were based on patient contacts with community-based and hospital-based mental health services. The costs outcome was transformed into 'high costs' vs 'regular costs' in main analyses.RESULTS: After adjustment for covariates, 11 HoNOS items were not associated with costs. The exception was 'self-injury' with an odds ratio of 1.41 (95% CI 1.10-2.99). Population attributable fractions (PAFs) for the contribution of HoNOS items to high costs ranged from 0.6% (physical illness) to 22.4% (self-injury). After adjustment, the Total HoNOS score was not associated with costs (OR 1.03, 95% CI 0.99-1.07). However, the PAF (33.3%) demonstrated that it might account for a modest proportion of the incidence of high costs.CONCLUSIONS: Our findings provide limited support for the utility of the self-injury item and Total HoNOS score in predicting costs. However, the absence of associations for the remaining HoNOS items indicates that current PbR clusters have minimal ability to predict costs, so potentially contributing to a misallocation of NHS resources across England. The findings may inform the development of mental health payment systems internationally, especially since the vast majority of countries have not progressed past the early stages of this development. Discrepancies between our findings with those from Australia and New Zealand point to the need for further international investigations

Identifying and tracking key climate adaptation actors in the UK

To understand how climate adaptation planning and decision-making will progress, a better understanding is needed as to which organisations are expected to take on key responsibilities. Methodological challenges have impeded efforts to identify and track adaptation actors beyond the coarse scale of nation states. Yet, for effective adaptation to succeed, who do national governments need to engage, support and encourage? Using the UK as a case study, we conducted a systematic review of official government documents on climate adaptation, between 2006 and 2015, to understand which organisations are identified as key to future adaptation efforts and tracked the extent to which these organisations changed over time. Our unique longitudinal dataset found a very large number of organisations (n = 568). These organisations varied in size (small-medium enterprises to large multinationals), type (public, private and not-for-profit), sector (e.g. water, energy, transport and health), scale (local, national and international), and roles and responsibilities (policymaking, decision-making, knowledge production, retail). Importantly, our findings reveal a mismatch between official government policies that repeatedly call on private organisations to drive adaptation, on the one hand, and a clear dominance of the public sector on the other hand. Yet, the capacity of organisations to fulfil the roles and responsibilities assigned to them, particularly in the public sector, is diminishing. Unless addressed, climate adaptation actions could be assigned to those either unable, or unwilling, to implement them