Increasing incidence of and mortality from skin cancer are posing a large financial burden on the NHS in England. Information provided by cost-of-illness (CoI) studies are used in policy making and are particularly useful for measuring the potential savings from averting a case of disease. We estimate the cost of skin cancer in England, and model future costs up to 2020. We compare two costing approaches (top-down and bottom-up). We estimate that costs due to skin cancer were in the range of £106–£112 million in 2008. These figures are very closely related to those provided by the Department of Health (estimated to be £104.0 million in 2007–8 and £105.2 million 2008–9). The expected cost per case of malignant melanoma was estimated to be £2607 and £2560, using the bottom-up and top-down approaches, respectively. The mean cost per case of non-melanoma skin cancer was £889 and £1226, respectively. We estimate that the cost to the NHS due to skin cancer will amount to over £180 million in 2020. Effective prevention of skin cancer might not only reduce a significant burden of disease but it could also save considerable resources to the NHS.

Objective Complicated urinary tract infections (cUTIs) impose a high burden on healthcare systems and are a frequent cause of hospitalisation. The aims of this paper are to estimate the cost per episode of patients hospitalised due to cUTI and to explore the factors associated with cUTI-related healthcare costs in eight countries with high prevalence of multidrug resistance (MDR). Design This is a multinational observational, retrospective study. The mean cost per episode was computed by multiplying the volume of healthcare use for each patient by the unit cost of each item of care and summing across all components. Costs were measured from the hospital perspective. Patient-level regression analyses were used to identify the factors explaining variation in cUTI-related costs. Setting The study was conducted in 20 hospitals in eight countries with high prevalence of multidrug resistant Gram-negative bacteria (Bulgaria, Greece, Hungary, Israel, Italy, Romania, Spain and Turkey). Participants Data were obtained from 644 episodes of patients hospitalised due to cUTI. Results The mean cost per case was €5700, with considerable variation between countries (largest value €7740 in Turkey; lowest value €4028 in Israel), mainly due to differences in length of hospital stay. Factors associated with higher costs per patient were: type of admission, infection source, infection severity, the Charlson comorbidity index and presence of MDR. Conclusions The mean cost per hospitalised case of cUTI was substantial and varied significantly between countries. A better knowledge of the reasons for variations in length of stays could facilitate a better standardised quality of care for patients with cUTI and allow a more efficient allocation of healthcare resources. Urgent admissions, infections due to an indwelling urinary catheterisation, resulting in septic shock or severe sepsis, in patients with comorbidities and presenting MDR were related to a higher cost.

Objectives: Medical device companies are under growing pressure to provide health-economic evaluations of their products. Cost-effectiveness analyses are commonly undertaken as a one-off exercise at the late stage of development of new technologies; however, the benefits of an iterative use of economic evaluation during the development process of new products have been acknowledged in the literature. Furthermore, the use of Bayesian methods within health technology assessment has been shown to be of particular value in the dynamic framework of technology appraisal when new information becomes available in the life cycle of technologies. Methods: In this study, we set out a methodology to adapt these methods for their application to directly support investment decisions in a commercial setting from early stages of the development of new medical devices. Results and Conclusions: Starting with relatively simple analysis from the very early development phase and proceeding to greater depth of analysis at later stages, a Bayesian approach facilitates the incorporation of all available evidence and would help companies to make better informed choices at each decision point.

Background: The COVID-19 pandemic has hit both the Spanish economy and the population’s health hard. The result is an unprecedented economic and social crisis due to uncertainty about the remedy and the socioeconomic effects on people’s lives. Methods: We performed a retrospective analysis of the macroeconomic impact of the COVID-19 pandemic in 2020 using key indicators of the Spanish economy for the 17 Autonomous Communities (ACs) of the country. National statistics were examined in the search for impacts or anomalies occurring since the beginning of the pandemic. To estimate the strength of the impact on each of the indicators analyzed, we used Bayesian structural time series. We also calculated the correlation between the rate of GDP decline during 2020 and the cumulative incidence of COVID-19 cases per 100,000 inhabitants in the ACs. Results: In 2020, the cumulative impact on the gross domestic product was of −11.41% (95% credible interval: −13.46; −9.29). The indicator for business turnover changed by −9.37% (−12.71; −6.07). The Spanish employment market was strongly affected; our estimates showed a cumulative increase of 11.9% (4.27; 19.45) in the rate of unemployment during 2020. The worst indicators were recorded in the ACs most economically dependent on the services sector. There was no statistical association between the incidence of COVID-19 in 2020 and the fall in GDP in the ACs. Conclusions: Our estimates portray a dramatic situation in Spain, where the COVID-19 crisis has had more serious economic and health consequences than in other European countries. The productive system in Spain is too dependent on sectors vulnerable to the pandemic, and it is necessary to design and implement profound changes through the European Next Generation program.