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The first edition of Applied Health Economics did an expert job of showing how the availability of large scale data sets and the rapid advancement of advanced econometric techniques can help health economists and health professionals make sense of information better than ever before.The book draws on key sources of information such as the European Community Household Panel (ECHP) and the WHO Multi-Country Survey Study (WHO-MCS) and assumes a familiarity with the computer programme Stata, now in an eleventh version. The book has been fully updated to reflect the enhancements to this key package.In addition to methodology, the book also contains a brand new chapter on regression models for health care costs, thus broadening the book's readership to those working on risk adjustment and health technology appraisal. The text also fully reflects the very latest advances in the health economics field and the key journal literature.

Objectives Stroke is a leading cause for disability and morbidity associated with increased economic burden due to treatment and post-stroke care (PSC). The aim of our study is to provide information on resource consumption for PSC, to identify relevant cost drivers, and to discuss potential information gaps. Methods A systematic literature review on economic studies reporting PSC-associated data was performed in PubMed/MEDLINE, Scopus/Elsevier and Cochrane databases, Google Scholar and gray literature ranging from January 2000 to August 2016. Results for post-stroke interventions (treatment and care) were systematically extracted and summarized in evidence tables reporting study characteristics and economic outcomes. Economic results were converted to 2015 US Dollars, and the total cost of PSC per patient month (PM) was calculated. Results We included 42 studies. Overall PSC costs (inpatient/outpatient) were highest in the USA ($4850/PM) and lowest in Australia ($752/PM). Studies assessing only outpatient care reported the highest cost in the United Kingdom ($883/PM), and the lowest in Malaysia ($192/PM). Fifteen different segments of specific services utilization were described, in which rehabilitation and nursing care were identified as the major contributors. Conclusion The highest PSC costs were observed in the USA, with rehabilitation services being the main cost driver. Due to diversity in reporting, it was not possible to conduct a detailed cost analysis addressing different segments of services. Further approaches should benefit from the advantages of administrative and claims data, focusing on inpatient/outpatient PSC cost and its predictors, assuring appropriate resource allocation.

Health economic evaluations are comparative analyses of alternative courses of action in terms of their costs and consequences. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement, published in 2013, was created to ensure health economic evaluations are identifiable, interpretable, and useful for decision making. It was intended as guidance to help authors report accurately which health interventions were being compared and in what context, how the evaluation was undertaken, what the findings were, and other details that may aid readers and reviewers in interpretation and use of the study. The new CHEERS 2022 statement replaces previous CHEERS reporting guidance. It reflects the need for guidance that can be more easily applied to all types of health economic evaluation, new methods and developments in the field, as well as the increased role of stakeholder involvement including patients and the public. It is also broadly applicable to any form of intervention intended to improve the health of individuals or the population, whether simple or complex, and without regard to context (such as health care, public health, education, social care, etc.). This summary article presents the new CHEERS 2022 28-item checklist and recommendations for each item. The CHEERS 2022 statement is primarily intended for researchers reporting economic evaluations for peer reviewed journals as well as the peer reviewers and editors assessing them for publication. However, we anticipate familiarity with reporting requirements will be useful for analysts when planning studies. It may also be useful for health technology assessment bodies seeking guidance on reporting, as there is an increasing emphasis on transparency in decision making.

IntroductionStated preference studies are a valuable tool to elicit respondents’ willingness to pay (WTP) for goods or services, especially in situations where no market valuation exists. Contingent valuation (CV) is a widely used approach among stated-preference techniques for eliciting WTP if prices do not exist or do not reflect actual costs, for example, when services are covered by insurance. This review aimed to provide an overview of relevant factors determining WTP for health services to support variable selection.MethodsA comprehensive systematic literature search and review of CV studies assessing determinants of WTP for health services was conducted, including 11 electronic databases. Two of the authors made independent decisions on the eligibility of studies. We extracted all determinants used and related p values for the effect sizes (e.g. reported in regression models with WTP for a health service as outcome variable). Determinants were summarised in systematic evidence tables and structured by thematic domains.ResultsWe identified 2082 publications, of which 202 full texts were checked for eligibility. We included 62 publications on 61 studies in the review. Across all studies, we identified 22 WTP determinants and other factors from 5 thematic domains: sociodemographic characteristics, perceived threat, perceived benefit, perceived barriers, and other information.ConclusionOur review provides evidence on 22 relevant determinants of WTP for health services, which may be used for variable selection and as guidance for planning CV surveys. Endogeneity should be carefully considered before interpreting these determinants as causal factors and potential intervention targets.

When algorithms use data generated by human beings, they inherit the errors stemming from human biases, which likely diminishes their performance. We examine the design and value of a bias-aware linear classification algorithm that accounts for bias in input data, using breast cancer diagnosis as our specific setting. In this context, a referring physician makes a follow-up recommendation to a patient based on two inputs: the patient’s clinical-risk information and the radiologist’s mammogram assessment. Critically, the radiologist’s assessment could be biased by the clinical-risk information, which in turn can negatively affect the referring physician’s performance. Thus, a bias-aware algorithm has the potential to be of significant value if integrated into a clinical decision support system used by the referring physician. We develop and show that a bias-aware algorithm can eliminate the adverse impact of bias if the error in the mammogram assessment due to radiologist’s bias has no variance. On the other hand, in the presence of error variance, the adverse impact of bias can be mitigated, but not eliminated, by the bias-aware algorithm. The bias-aware algorithm assigns less (more) weight to the clinical-risk information (radiologist’s mammogram assessment) when the mean error increases (decreases), but the reverse happens when the error variance increases. Using point estimates obtained from mammography practice and the medical literature, we show that the bias-aware algorithm can significantly improve the expected patient life years or the accuracy of decisions based on mammography. The online appendix is available at https://doi.org/10.1287/isre.2018.0789 .

Health information exchanges (HIEs) are entities that have emerged in healthcare delivery markets across the United States. By providing an interorganizational information system (IOIS) and governance over use of this system and the information exchanged through it, HIEs enable more routine and efficient electronic sharing of patient information between disparate and fragmented healthcare providers. This should result in improved quality and efficiency of care. However, significant questions persist about the extent to which HIEs produce these benefits in practice, particularly in terms of reducing healthcare spending. We use transaction cost economics (TCE) to theorize that HIEs establish a quasi-hierarchy that decreases frictions associated with information sharing in ways that reduce healthcare spending, and that the magnitude of reductions is greater when (1) insurer and provider incentives align, and (2) HIE capabilities mature. We can test these conjectures because HIEs, unlike other efforts that provide IOIS, are typically confined to regional markets and develop heterogeneously between these markets, introducing variation in insurer-provider incentive alignment and HIE maturity. Leveraging a unique national panel data set, we evaluate whether HIEs reduce spending for the largest insurer in the United States, i.e., Medicare, and whether incentives and HIE maturity modify the magnitude of reductions. We find significant spending reductions in healthcare markets that have established operational HIEs, with an average reduction of $139 per Medicare beneficiary per year (1.4% decrease) or a $3.12 billion annual reduction in spending if HIEs were nationally implemented in 2015. We also find that these reductions occur disproportionately in healthcare markets where providers have financial incentives to use an HIE to reduce spending and when HIEs are more mature. Our results inform an important open empirical question in the healthcare domain related to the value of HIEs, while also joining perspectives from TCE with the IOIS literature to understand the factors that may be relevant to IOIS value creation more generally.

\"Obesity is a global ticking time-bomb with huge potential negative economic and health impacts, especially for the poor. Countries and global partners need to act urgently to address this ensuing epidemic with emphasis highlighting interventions that require corrective public action rather than one of individual responsibility\"--

BackgroundAround 2 million people in the UK suffer from Long COVID (LC). Of concern is the disease impact on productivity and informal care burden. This study aimed to quantify and value productivity losses and informal care receipt in a sample of LC patients in the UK.MethodsThe target population comprised LC patients referred to LC specialist clinics. The questionnaires included a health economics questionnaire (HEQ) measuring productivity impacts, informal care receipt and service utilisation, EQ-5D-5L, C19-YRS LC condition-specific measure, and sociodemographic and COVID-19 history variables. Outcomes were changes from the incident infection resulting in LC to the month preceding the survey in paid work status/h, work income, work performance and informal care receipt. The human capital approach valued productivity losses; the proxy goods method valued caregiving hours. The values were extrapolated nationally using published prevalence data. Multilevel regressions, nested by region, estimated associations between the outcomes and patient characteristics.Results366 patients responded to HEQ (mean LC duration 449.9 days). 51.7% reduced paid work hours relative to the pre-infection period. Mean monthly work income declined by 24.5%. The average aggregate value of productivity loss since incident infection was £10,929 (95% bootstrap confidence interval £8,844-£13,014) and £5.7 billion (£3.8-£7.6 billion) extrapolated nationally. The corresponding values for informal caregiving were £8,726 (£6,247-£11,204) and £4.8 billion (£2.6-£7.0 billion). Multivariate analyses found significant associations between each outcome and health utility and C19-YRS subscale scores.ConclusionLC significantly impacts productivity losses and provision of informal care, exacerbated by high national prevalence of LC.

According to the most traditional economic evaluation manuals, all \"relevant\" costs should be included in the economic analysis, taking into account factors such as the patient population, setting, location, year, perspective and time horizon. However, cost information may be designed for other purposes. Health care organisations may lack sophisticated accounting systems and consequently, health economists may be unfamiliar with cost accounting terminology, which may lead to discrepancy in terms used in the economic evaluation literature and management accountancy. This paper identifies new tendencies in costing methodologies in health care and critically comments on each included article. For better clarification of terminology, a pragmatic glossary of terms is proposed. A scoping review of English and Spanish language literature (2005–2018) was conducted to identify new tendencies in costing methodologies in health care. The databases PubMed, Scopus and EconLit were searched. A total of 21 studies were included yielding 43 costing analysis. The most common analysis was top-down micro-costing (49%), followed by top-down gross-costing (37%) and bottom-up micro-costing (14%). Resource data were collected prospectively in 12 top-down studies (32%). Hospital database was the most common way of collection of resource data (44%) in top-down gross-costing studies. In top-down micro-costing studies, the most resource use data collection was the combination of several methods (38%). In general, substantial inconsistencies in the costing methods were found. The convergence of top-down and bottom-up methods may be an important topic in the next decades.

Aim The EQ-5D is a generic measure of health that is widely applied for health economic and non-economic purposes. Population norms can be used to facilitate the interpretation of EQ-5D data. The objective of this study was to develop a set of pooled normative EQ-5D-3L values for the five largest European economies (EUR5). Methods EQ-5D-3L index values based on the time trade-off (TTO) were available for all EUR5 countries (n = 21,425): France, Germany, Italy, Spain, and the United Kingdom (UK). Country-specific data sets were aggregated and weighted to facilitate the derivation of norms for gender and age groups. Analyses included equal weighting and weighting by population and economy size. Norms were also calculated using the European visual analog scale-based value set (European VAS), the EQ VAS and separately by dimension. Results Pooled mean (SD) population weighted TTO values for males/females were 0.967 (0.122)/0.959 (0.118) for ages 18-24; 0.965 (0.096)/0.954 (0.117) for ages 25-34; 0.943 (0.165)/0.936 (0.169) for ages 35-44; 0.934 (0.150)/0.921 (0.157) for ages 45-54; 0.896 (0.188)/0.875 (0.197) for ages 55-64; 0.900 (0.158)/0.839 (0.218) for ages 65-74; and 0.830 (0.234)/0.756 (0.291) for ages 75 and older. Mean values decreased and variance increased with age; females had slightly lower mean values than males across all age bands. The unequal weighting approaches produced similar point estimates with smaller variances. Mean values for the European VAS were slightly lower than those for the TTO-based index. Discussion Normative EQ-5D-3L values can be used to benchmark the outcomes of treated patients against the health of the general population. EUR5 norms may be useful in research applications inferring to Europe or the European Union as a whole, particularly when sample size precludes analysis at the country level.