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Background and aim Increased mortality during the COVID-19 pandemic is not explained exclusively by COVID-19 infection and its complications. We analysed non-COVID-19 causes of mortality in a population analysis based on data from the Spanish National Institute of Statistics.Methods Using monthly mortality data in Spain (January 2010–December 2020), we analysed deaths associated with cancer, blood, endocrine, mental, nervous, cardiovascular, respiratory and digestive diseases and explored the COVID-19 impact using a difference-in-difference strategy. We calculated monthly interannual variations in mortality and computed percentage change in terms of the log of deaths in month h of year t minus the log of deaths in month h in the previous year t−1.Results In 2020 in Spain, mortality increased 17.9% compared with 2019. COVID-19 was the leading cause of death (n=60 358), followed by ischaemic heart disease (n=29 654). Throughout 2020, monthly interannual variations in cardiovascular mortality showed an average upward trend of 1.7%, while digestive, cancer and blood diseases showed a downward trend.Conclusions During the COVID-19 pandemic in Spain in 2020, excess mortality was primarily related to cardiovascular mortality while mortality associated with digestive, cancer and blood diseases was reduced.

The primary objective of this study was to conduct a cost–utility analysis of proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors in real-world, comparing their use with standard care for managing cardiovascular disease. A multicenter prospective study was conducted across 12 Spanish hospitals from May 2020 to April 2022, involving 158 patients with hypercholesterolemia or atherosclerotic cardiovascular disease. This study assessed health-related quality of life (QoL) using the EQ-5D-3L questionnaire. The cost–utility analysis evaluated the economic impact of PCSK9 inhibitors when used with standard care compared to standard care alone, calculating the incremental cost–effectiveness ratio (ICER). This study included 158 patients with an average age of 61 years, male (66.5%). For patients initiating PCSK9 inhibitors, the treatment cost was EUR 13,633.39, while standard therapy cost EUR 3638.25 over two years. QoL for PCSK9 inhibitors stood at 1.6489 over two years, compared to 1.4548 for standard therapy. The results revealed favorable cost–utility outcomes, with an ICER of EUR 51,427.72. Significant improvements were observed in the domains of mobility, self-care, daily activities, pain/discomfort, and anxiety/depression (p < 0.001). This study presents the first real-world cost–utility analysis of PCSK9 inhibitors, supporting their economic rationale and highlighting their benefits in clinical practice. Healthcare decision-makers can use these results to inform their decisions and reimbursement policies concerning PCSK9 inhibitors. Trial Registration clinicaltrials.gov Identifier: NCT04319081.

Objectives The aim of the study was to answer whether the central government has been more efficient than the regional governments or vice versa. Likewise, through the analysis of the data, the aim was to shed light on whether decentralisation has had a positive impact on the efficiency of the hospital sector or not.Design In this paper, we have used data envelopment analysis to analyse the evolution of efficiency in the last 10 Autonomous Regions to receive healthcare competences at the end of 2001.Participants For this study, we have taken into account the number of beds and full-time workers as inputs and the calculation of basic care units as outputs to measure the efficiency of the Spanish public sector, private sector and jointly in the years 2002, 2007, 2012 and 2017 for the last Autonomous Regions receiving healthcare competences.Results Of the Autonomous Regions that received the transfers at the end of 2001, the following stand out for their higher efficiency growth: the Balearic Islands (81.44% improvement), the Madrid Autonomous Region, which practically reached absolute efficiency levels (having increased by 63.77%), and La Rioja which, together with the Balearic Islands which started from very low values, improved notably (46.13%).Conclusion In general, it can be observed that the transfer of responsibilities in the health sector has improved efficiency in the National Health Service.JEL classification C14; I18; H21.

Abstract Background The relative lack of flexibility of parametric models has led to the development of nonparametric regression techniques based on the family of generalized additive models. However, despite the potential advantages of using Generalized Additive Model (GAM) in practice many models have, until now, not been sufficiently explored in health economics problems. It could be interesting to calculate a new flexible hospital production function by means of a GAM including interactions and to compare it with the classic model Cobb-Douglas in the prediction of the behavior of productive factors. Method The flexible model considered has been the AM including the beds-facultative interaction. The covariates “Hospital”, being a categorical variable and “Year” being a continuous variable, have also been included in the model. Based on the estimation of the model penalized thin plate splines will be used to represent smoothed functions. In this configuration, the smoothed parameters will be estimated via REML. Results Cobb-douglas model fits well for the production functions of the more general clinical and surgical services, while the GAM adjusts better in the case of more specialized medical services. Conclusions Generalized Additive Models are more flexible than parametric models, providing a better fit in the presence of non-linear relationships and thus allowing more accurate prediction values. The results of this study suggest that AM is a promising technique for the areas of research and application in health economics.

Introduction. This study assesses the impact of an electronic physician-to-physician consultation program on the waiting list and the costs of a Pulmonology Unit. Materials and Methods. A prepost intervention study was conducted after a new ambulatory pulmonary care protocol was implemented and the capacity of the unit was adopted. In the new model, physicians at all levels of healthcare send electronic consultations to specialists. Results. In the preintervention year (2019), the Unit of Pulmonology attended 7,055 consultations (466 e-consultations and 6,589 first face-to-face visits), which decreased to 6,157 (3,934 e-consultations and 2,223 first face-to-face visits; 12.7% reduction) in the postintervention year (all were e-consultations). The mean wait time for the first appointment was 25.7 days in 2019 versus 3.2 days in 2021 (p

Objective The main objective of this study is to develop an eHealth adoption model based on patients’ perceptions of public value dimensions and empirically apply the model to understand the adoption of a governmental health app by Jordanian patients. The study attempts to contribute to overcoming the narrow focus of contemporary theories such as UTAUT and, ultimately, to designing more effective implementation strategies in order to address the current delays in global eHealth adoption. Methods We conducted a quantitative survey of 430 Jordanian patients, utilizing structural equation modeling (SEM) to process the empirical data. Specifically, we applied an SEM two-step approach that involved (a) evaluating the model-data fit through a review of the measurement model(s) and common method bias; and (b) analyzing the structural model. Results Our findings confirm that the proposed patients’ value scale is valid and reliable. Its five dimensions (hedonistic motivation, utilitarian motivation, social value, ethical public value, and public trust value) significantly correlate with patients’ public value, and the latter directly affects their use of eHealth apps, with habits mediating this relationship. Among the dimensions, hedonistic motivations tend to be prioritized over utilitarian ones. Ethical and trust values also play an essential role, particularly in how health technology handles patients’ data and upholds their dignity and self-esteem. Conclusions This study highlights the holistic nature of eHealth adoption by patients and the crucial role of public values in their use behavior. It provides useful insights for policymakers and developers.

Abstract Background Forecasting patient arrivals to hospital emergency departments is critical to dealing with surges and to efficient planning, management and functioning of hospital emerency departments. Objective We explore whether past mean values and past observations are useful to forecast daily patient arrivals in an Emergency Department. Material and methods We examine whether an integer-valued generalized autoregressive conditional heteroscedastic (INGARCH) model can yield a better conditional distribution fit and forecast of patient arrivals by using past arrival information and taking into account the dynamics of the volatility of arrivals. Results We document that INGARCH models improve both in-sample and out-of-sample forecasts, particularly in the lower and upper quantiles of the distribution of arrivals. Conclusion Our results suggest that INGARCH modelling is a useful model for short-term and tactical emergency department planning, e.g., to assign rotas or locate staff for unexpected surges in patient arrivals.

BACKGROUND: Although pulmonary rehabilitation (PR) is recommended in patients with chronic obstructive pulmonary disease (COPD), there is a scarcity of data demonstrating the cost-effectiveness and effectiveness of PR in reducing exacerbations. METHODS: A quasi-experimental study in 200 patients with COPD was conducted to determine the number of exacerbations 1 year before and after their participation in a PR program. Quality of life was measured using the COPD assessment test and EuroQol-5D. The costs of the program and exacerbations were assessed the year before and after participation in the PR program. The incremental cost-effectiveness ratio (ICER) was estimated in terms of quality-adjusted life years (QALYs). RESULTS: The number of admissions, length of hospital stay, and admissions to the emergency department decreased after participation in the PR program by 48.2%, 46.6%, and 42.5%, respectively (P < 0.001 for all). Results on quality of life tests improved significantly (P < 0.001 for the two tests). The cost of PR per patient and the cost of pre-PR and post-PR exacerbations were €1867.7 and €7895.2 and €4201.9, respectively. The PR resulted in a cost saving of €1826 (total, €365,200) per patient/year, and the gain in QALYs was+0.107. ICER was −€17,056. The total cost was

Objectives The goal of this study is to analyse hospital costs and length of stay of patients admitted to psychiatric units in hospitals in a European region of the Mediterranean Arc. The aim is to identify the effects of comorbidities and other variables in order to create an explanatory cost model.Methods In order to carry out the study, the Ministry of Health was asked to provide data on access to the mental health facilities of all hospitals in the region. Among other questions, this database identifies the most important diagnostic variables related to admission, like comorbidities, age and gender. The method used, based on the Manning–Mullahy algorithm, was linear regression. The results were measured by the statistical significance of the independent variables to determine which of them were valid to explain the cost of hospitalization.Results Psychiatric inpatients can be divided into three main groups (psychotic, organic and neurotic), which have statistically significant differences in costs. The independent variables that were statistically significant (p

Abstract Objective This article examines what the adoption and use of advanced medical technologies – computed tomography (CT) and magnetic resonance imaging (MRI) – by public hospitals depend on and to what extent. Methods From a sample of panel data for all public hospitals in the health service of Galicia (a subregion of the Galicia-North of Portugal Euroregion) for the 2010–2017 period, we grouped explanatory variables into inputs (resources), outputs (activities) and socio-demographic variables. Factor analysis was used to reduce as much as possible the number of analysed variables, discriminant analysis to examine the technologies adoption decision, and multiple regression analysis to investigate their use. Results Factor analysis identified motivators on adoption and use of CT and MRI medical technologies as follows: hospital inputs/outputs (Factor 1); radiology studies and adoption of CT by public hospitals (Factor 2); research/teaching role and big-ticket diagnostic and therapeutic (lithotripsy) technologies (Factor 3); number of transplants (Factor 4); cancer diagnosis/treatment (Factor 5); and catchment area geographical dispersion (Factor 6). Cronbach’s alpha of 0.881 indicated an acceptable degree of reliability of the factor variables. Regarding adoption of these technologies, Factor 1 is the most influential, explaining 37% of the variance and showing adequate global internal consistency, whereas Factor 2 is limited to 13% of the variance. In the discriminant analysis, values for Box’s M test and canonical correlations such as Wilks’s lambda for the two technologies underpin the reliability and predictive capacity of the discriminant equations. Finally, and according to the regression analysis, the factor with the greatest influence on CT and MRI use is Factor 2, followed by Factors 1 and 3 in the case of CT use, and Factors 3 and 5 in the case of MRI use. Conclusion CT and MRI adoption by public hospitals is mainly determined by hospital inputs and outputs. However, the use of both medical technologies is mainly influenced by conventional radiology studies and CT adoption. These results suggest that both choices – adoption and use of advanced medical technology – may be separate decisions as they are taken possibly by different people (the former by managers and policymakers and the latter by physicians).

Application of the Flusurge 2.0 methodology to predict the needs of conventional hospital treatment, intensive care, and respiratory support resources as a consequence of coronavirus disease (COVID-19) in several countries. Different countries of the following continents have been selected: Europe, Asia, Africa, North America, South America, and Oceania. Variables: Total population and age distribution; Number of COVID19 infections; Number of deaths from COVID19; Number of non-ICU hospital beds; Number of ICU beds; Number of ventilators. Method: The proposed possible scenario planning is based on the application of FluSurge 2.0 Software, developed by The Johns Hopkins Center for Health Security (CHS/CDC). Saturation of conventional hospitalization is expected in India, Uganda, Nepal, and Haiti; there is a forecast of saturation for ICU beds in all sample countries except Turkey. Ventilator saturation is expected in all countries of the sample except Argentina, Austria, Brazil, France, United Kingdom, Japan, South Korea, Norway, Poland, Turkey and the USA. The model shows, for a percentage greater than 50% of the countries, difficulties related to the saturation of their ICU units, and the use of ventilators.

In this paper, we first use the information we have on the patients of an oncology day hospital to distribute the treatment schedules they have in each of the visits to this centre. To do this, we propose a deterministic mathematical programming model in such a way that we minimise the duration of the waiting room stays of the total set of patients and taking into account the restrictions of the circuit. Secondly, we will look for a solution to the same problem under a stochastic approach. This model will explicitly consider the existing uncertainty in terms of the different times involved in the circuit, and this model also allows the reorganisation of the schedules of medical appointments with oncologists. The models are complemented by a tool that solves the problem of assigning nurses to patients. The work is motivated by the particular characteristics of a real hospital and the models are used and compared with data from this case.