Comorbidity and household income as mediators of gender inequalities in dementia risk: a real-world data population study.

BACKGROUND: Low household income (HI), comorbidities and female sex are associated with an increased risk of dementia. The aim of this study was to measure the mediating effect of comorbidity and HI on the excess risk due to gender in relation to the incidence and prevalence of dementia in the general population. METHODS: A retrospective and observational study using real-world data analysed all people over 60 who were registered with the Basque Health Service in Gipuzkoa. The study measured HI level, the Charlson comorbidity index (CCI), age and sex. The prevalence and incidence of dementia were analysed using logistic regression and Poisson regression models, respectively, adjusted by HI, sex, comorbidity and age. We estimated the combined mediation effect of HI and comorbidity on the prevalence of dementia associated with gender. RESULTS: Of the 221,777 individuals, 3.85% (8,549) had a diagnosis of dementia as of 31 December 2021. Classification by the CCI showed a gradient with 2.90% in CCI 0-1, 10.60% in CCI 2-3 and 18.01% in CCI > 3. Both low HI and gender were associated with a higher crude prevalence of dementia. However, in the CCI-adjusted model, women had an increased risk of dementia, while HI was no longer statistically significant. The incidence analysis produced similar results, although HI was not significant in any model. The CCI was significantly higher for men and for people with low HI. The mediation was statistically significant, and the CCI and HI explained 79% of the gender effect. CONCLUSIONS: Comorbidity and low HI act as mediators in the increased risk of dementia associated with female sex. Given the difference in the prevalence of comorbidities by HI, individual interventions to control comorbidities could not only prevent dementia but also reduce inequalities, as the risk is greater in the most disadvantaged population.

Using Age-Specific Rates for Parametric Survival Function Estimation in Simulation Models.

PURPOSE: To describe a procedure for incorporating parametric functions into individual-level simulation models to sample time to event when age-specific rates are available but not the individual data. METHODS: Using age-specific event rates, regression analysis was used to parametrize parametric survival distributions (Weibull, Gompertz, log-normal, and log-logistic), select the best fit using the R2 statistic, and apply the corresponding formula to assign random times to events in simulation models. We used stroke rates in the Spanish population to illustrate our procedure. RESULTS: The 3 selected survival functions (Gompertz, Weibull, and log-normal) had a good fit to the data up to 85 y of age. We selected Gompertz distribution as the best-fitting distribution due to its goodness of fit. CONCLUSIONS: Our work provides a simple procedure for incorporating parametric risk functions into simulation models without individual-level data. HIGHLIGHTS: We describe the procedure for sampling times to event for individual-level simulation models as a function of age from parametric survival functions when age-specific rates are available but not the individual dataWe used linear regression to estimate age-specific hazard functions, obtaining estimates of parameter uncertainty.Our approach allows incorporating parameter (second-order) uncertainty in individual-level simulation models needed for probabilistic sensitivity analysis in the absence of individual-level survival data.

Cost-Effectiveness Analysis of Vaccines for COVID-19 According to Sex, Comorbidity and Socioeconomic Status: A Population Study.

BACKGROUND AND OBJECTIVE: Coronavirus disease 2019 (COVID-19) vaccines are extremely effective in preventing severe disease, but their real-world cost effectiveness is still an open question. We present an analysis of the cost-effectiveness and economic impact of the initial phase of the COVID-19 vaccination rollout in the Basque Country, Spain. METHODS: To calculate costs and quality-adjusted life years for the entire population of the Basque Country, dynamic modelling and a real-world data analysis were combined. Data on COVID-19 infection outcomes (cases, hospitalisations, intensive care unit admissions and deaths) and population characteristics (age, sex, socioeconomic status and comorbidity) during the initial phase of the vaccination rollout, from January to June of 2021, were retrieved from the Basque Health Service database. The outcomes in the alternative scenario (without vaccination) were estimated with the dynamic model used to guide public health authority policies, from February to December 2020. Individual comorbidity-adjusted life expectancy and costs were estimated. RESULTS: By averting severe disease-related outcomes, COVID-19 vaccination resulted in monetary savings of €26.44 million for the first semester of 2021. The incremental cost-effectiveness ratio was €707/quality-adjusted life year considering official vaccine prices and dominant real prices. While the analysis by comorbidity showed that vaccines were considerably more cost effective in individuals with pre-existing health conditions, this benefit was lower in the low socioeconomic status group. CONCLUSIONS: The incremental cost-effectiveness ratio of the vaccination programme justified the policy of prioritising high-comorbidity patients. The initial phase of COVID-19 vaccination was dominant from the perspective of the healthcare payer.

Cost-utility analysis of the UPRIGHT intervention promoting resilience in adolescents.

BACKGROUND: As mental health in adulthood is related to mental status during adolescence, school-based interventions have been proposed to improve resilience. The objective of this study was to build a simulation model representing the natural history of mental disorders in childhood, adolescence and youth to estimate the cost-effectiveness of the UPRIGHT school-based intervention in promoting resilience and mental health in adolescence. METHODS: We built a discrete event simulation model fed with real-world data (cumulative incidence disaggregated into eight clusters) from the Basque Health Service database (609,381 individuals) to calculate utilities (quality-adjusted life years [QALYs]) and costs for the general population in two scenarios (base case and intervention). The model translated changes in the wellbeing of adolescents into different risks of mental illnesses for a time horizon of 30 years. RESULTS: The number of cases of anxiety was estimated to fall by 5,125 or 9,592 and those of depression by 1,269 and 2,165 if the effect of the intervention lasted 2 or 5 years respectively. From a healthcare system perspective, the intervention was cost-effective for all cases considered with incremental cost-utility ratios always lower than €10,000/QALY and dominant for some subgroups. The intervention was always dominant when including indirect and non-medical costs (societal perspective). CONCLUSIONS: Although the primary analysis of the trial did not did not detect significant differences, the UPRIGHT intervention promoting positive mental health was dominant in the economic evaluation from the societal perspective. Promoting resilience was more cost-effective in the most deprived group. Despite a lack of information about the spillover effect in some sectors, the economic evaluation framework developed principally for pharmacoeconomics can be applied to interventions to promote resilience in adolescents. As prevention of mental health disorders is even more necessary in the post-coronavirus disease-19 era, such evaluation is essential to assess whether investment in mental health promotion would be good value for money by avoiding costs for healthcare providers and other stakeholders.

Excess healthcare costs of mental disorders in children, adolescents and young adults in the Basque population registry adjusted for socioeconomic status and sex.

BACKGROUND: Mental illnesses account for a considerable proportion of the global burden of disease. Economic evaluation of public policies and interventions aimed at mental health is crucial to inform decisions and improve the provision of healthcare services, but experts highlight that nowadays the cost implications of mental illness are not properly quantified. The objective was to measure the costs of excess use of all healthcare services by 1- to 30-year-olds in the Basque population as a function of whether or not they had a mental disorder diagnosis. METHODS: A real-world data study was used to identify diagnoses of mental disorders and to measure resource use in the Basque Health Service Registry in 2018. Diagnoses were aggregated into eight diagnostic clusters: anxiety, attention deficit hyperactivity disorder, conduct disorders, mood disorders, substance use, psychosis and personality disorders, eating disorders, and self-harm. We calculated the costs incurred by each individual by multiplying the resource use by the unit costs. Annual costs for each cluster were compared with those for individuals with no diagnosed mental disorders through entropy balancing and two-part models which adjusted for socioeconomic status (SES). RESULTS: Of the 609,381 individuals included, 96,671 (15.9%) had ≥ 1 mental disorder diagnosis. The annual cost per person was two-fold higher in the group diagnosed with mental disorders (€699.7) than that with no diagnoses (€274.6). For all clusters, annual excess costs associated with mental disorders were significant. The adjustment also evidenced a social gradient in healthcare costs, individuals with lower SES consuming more resources than those with medium and higher SES across all clusters. Nonetheless, the effect of being diagnosed with a mental disorder had a greater impact on the mean and excess costs than SES. CONCLUSIONS: Results were consistent in showing that young people with mental disorders place a greater burden on healthcare services. Excess costs were higher for severe mental disorders like self-harm and psychoses, and lower SES individuals incurred, overall, more than twice the costs per person with no diagnoses. A socioeconomic gradient was notable, excess costs being higher in low SES individuals than those with a high-to-medium SES. Differences by sex were also statistically significant but their sizes were smaller than those related to SES.

Assessment of the Effectiveness, Socio-Economic Impact and Implementation of a Digital Solution for Patients with Advanced Chronic Diseases: The ADLIFE Study Protocol.

Due to population ageing and medical advances, people with advanced chronic diseases (ACD) live longer. Such patients are even more likely to face either temporary or permanent reduced functional reserve, which typically further increases their healthcare resource use and the burden of care on their caregiver(s). Accordingly, these patients and their caregiver(s) may benefit from integrated supportive care provided via digitally supported interventions. This approach may either maintain or improve their quality of life, increase their independence, and optimize the healthcare resource use from early stages. ADLIFE is an EU-funded project, aiming to improve the quality of life of older people with ACD by providing integrated personalized care via a digitally enabled toolbox. Indeed, the ADLIFE toolbox is a digital solution which provides patients, caregivers, and health professionals with digitally enabled, integrated, and personalized care, supporting clinical decisions, and encouraging independence and self-management. Here we present the protocol of the ADLIFE study, which is designed to provide robust scientific evidence on the assessment of the effectiveness, socio-economic, implementation, and technology acceptance aspects of the ADLIFE intervention compared to the current standard of care (SoC) when applied in real-life settings of seven different pilot sites across six countries. A quasi-experimental trial following a multicenter, non-randomized, non-concurrent, unblinded, and controlled design will be implemented. Patients in the intervention group will receive the ADLIFE intervention, while patients in the control group will receive SoC. The assessment of the ADLIFE intervention will be conducted using a mixed-methods approach.

Incidence of mental disorders in the general population aged 1-30 years disaggregated by gender and socioeconomic status.

PURPOSE: The objective of this study was to estimate the incidence and age of onset of mental disorders diagnosed by gender and socioeconomic status (SES) in children, adolescents, and young adults up to 30 years of age in the whole population of the Basque Country (Spain). METHODS: All mental health diagnoses documented in Basque Health Service records from 1 January 2003 to 31 December 2018, were classified into eight clusters: anxiety, attention deficit hyperactivity disorder (ADHD), conduct disorders, depression, psychosis/personality disorders, substance use, eating disorders, and self-harm. We calculated incidence and cumulative incidence for each cluster, disaggregated by gender, and socioeconomic status (SES). Poisson regression analyses were performed. RESULTS: Overall, 9,486,853 person-years of observation were available for the 609,281 individuals included. ADHD and conduct disorders were diagnosed in the first decade, anxiety and depression disorders in the second and third decades, and psychosis/personality and substance use in the third. The cumulative incidence at 18 years of age for any type of disorder was 15.5%. The group with low SES had a statistically significantly higher incidence of all eight clusters. The incidence of ADHD, conduct disorders, depression, psychosis/personality disorders, and substance use was higher in males and that of anxiety, eating disorders and self-harm was higher in females. CONCLUSIONS: The incidence of mental disorders is high among children, adolescents, and young adults in the Basque Country underlining the need for preventive interventions. Marked differences by gender and SES highlight mental health inequalities, especially for depression and psychosis in low SES males.

Budget impact analysis of the use of Souvenaid in patients with prodromal Alzheimer's Disease in Spain.

INTRODUCTION: The effectiveness, safety, and cost-effectiveness of the use of Souvenaid for Alzheimer's disease (AD) have been previously evidenced. To complete the economic analysis, there is a need to assess whether society can afford it. The objective of this study was to carry out a budget impact analysis of the use of Souvenaid in Spain under the conditions of the LipiDidiet clinical trial from a societal perspective. METHODS: We built a population model that took into account all the cohorts of individuals with AD, their individual progression, and the potential impact of Souvenaid treatment on their trajectories. Patient progression data were obtained from mixed models. The target population was estimated based on the population forecast for 2020-2035 and the incidence of dementia. Individual progression to dementia measured by the Clinical Dementia Rating-Sum of Boxes was reproduced using mixed models. Besides the costs of treatment and diagnosis, direct costs of medical and non-medical care and indirect costs were included. RESULTS: The epidemiological indicators and the distribution of life expectancy by stages validated the model. From the third year (2022), the differences in the cost of dementia offset the incremental cost of diagnosis and treatment. The costs of dependency reached €500 million/year while those of the intervention were limited to €40 million. CONCLUSIONS: Souvenaid, with modest effectiveness in delaying dementia associated with AD, achieved a positive economic balance between costs and savings. Its use in the treatment of prodromal AD would imply an initial cost that would be ongoing, but this would be offset by savings in the care system for dependency associated with dementia from the third year. These results were based on adopting a societal perspective taking into account the effect of treatment on the use of health, social, and family resources.

Changes in Social and Clinical Determinants of COVID-19 Outcomes Achieved by the Vaccination Program: A Nationwide Cohort Study.

BACKGROUND: The objective of this study was to assess changes in social and clinical determinants of COVID-19 outcomes associated with the first year of COVID-19 vaccination rollout in the Basque population. METHODS: A retrospective study was performed using the complete database of the Basque Health Service (n = 2,343,858). We analyzed data on age, sex, socioeconomic status, the Charlson comorbidity index (CCI), hospitalization and intensive care unit (ICU) admission, and COVID-19 infection by Cox regression models and Kaplan-Meier curves. RESULTS: Women had a higher hazard ratio (HR) of infection (1.1) and a much lower rate of hospitalization (0.7). With older age, the risk of infection fell, but the risks of hospitalization and ICU admission increased. The higher the CCI, the higher the risks of infection and hospitalization. The risk of infection was higher in high-income individuals in all periods (HR = 1.2-1.4) while their risk of hospitalization was lower in the post-vaccination period (HR = 0.451). CONCLUSION: Despite the lifting of many control measures during the second half of 2021, restoring human mobility patterns, the situation could not be defined as syndemic, clinical determinants seeming to have more influence than social ones on COVID-19 outcomes, both before and after vaccination program implementation.

Estimation of the epidemiology of dementia and associated neuropsychiatric symptoms by applying machine learning to real-world data.

INTRODUCTION: Incidence rates of dementia-related neuropsychiatric symptoms (NPS) are not known and this hampers the assessment of their population burden. The objective of this study was to obtain an approximate estimate of the population incidence and prevalence of both dementia and NPS. METHODS: Given the dynamic nature of the population with dementia, a retrospective study was conducted within the database of the Basque Health Service (real-world data) at the beginning and end of 2019. Validated random forest models were used to identify separately depressive and psychotic clusters according to their presence in the electronic health records of all patients diagnosed with dementia. RESULTS: Among the 631,949 individuals over 60 years registered, 28,563 were diagnosed with dementia, of whom 15,828 (55.4%) showed psychotic symptoms and 19,461 (68.1%) depressive symptoms. The incidence of dementia in 2019 was 6.8/1000 person-years. Most incident cases of depressive (72.3%) and psychotic (51.9%) NPS occurred in cases of incident dementia. The risk of depressive-type NPS grows with years since dementia diagnosis, living in a nursing home, and female sex, but falls with older age. In the psychotic cluster model, the effects of male sex, and older age are inverted, both increasing the probability of this type of symptoms. CONCLUSIONS: The stigmatization factor conditions the social and attitudinal environment, delaying the diagnosis of dementia, preventing patients from receiving adequate care and exacerbating families' suffering. This study evidences the synergy between big data and real-world data for psychiatric epidemiological research.

An Integrated Care Platform System (C3-Cloud) for Care Planning, Decision Support, and Empowerment of Patients With Multimorbidity: Protocol for a Technology Trial.

BACKGROUND: There is an increasing need to organize the care around the patient and not the disease, while considering the complex realities of multiple physical and psychosocial conditions, and polypharmacy. Integrated patient-centered care delivery platforms have been developed for both patients and clinicians. These platforms could provide a promising way to achieve a collaborative environment that improves the provision of integrated care for patients via enhanced information and communication technology solutions for semiautomated clinical decision support. OBJECTIVE: The Collaborative Care and Cure Cloud project (C3-Cloud) has developed 2 collaborative computer platforms for patients and members of the multidisciplinary team (MDT) and deployed these in 3 different European settings. The objective of this study is to pilot test the platforms and evaluate their impact on patients with 2 or more chronic conditions (diabetes mellitus type 2, heart failure, kidney failure, depression), their informal caregivers, health care professionals, and, to some extent, health care systems. METHODS: This paper describes the protocol for conducting an evaluation of user experience, acceptability, and usefulness of the platforms. For this, 2 "testing and evaluation" phases have been defined, involving multiple qualitative methods (focus groups and surveys) and advanced impact modeling (predictive modeling and cost-benefit analysis). Patients and health care professionals were identified and recruited from 3 partnering regions in Spain, Sweden, and the United Kingdom via electronic health record screening. RESULTS: The technology trial in this 4-year funded project (2016-2020) concluded in April 2020. The pilot technology trial for evaluation phases 3 and 4 was launched in November 2019 and carried out until April 2020. Data collection for these phases is completed with promising results on platform acceptance and socioeconomic impact. We believe that the phased, iterative approach taken is useful as it involves relevant stakeholders at crucial stages in the platform development and allows for a sound user acceptance assessment of the final product. CONCLUSIONS: Patients with multiple chronic conditions often experience shortcomings in the care they receive. It is hoped that personalized care plan platforms for patients and collaboration platforms for members of MDTs can help tackle the specific challenges of clinical guideline reconciliation for patients with multimorbidity and improve the management of polypharmacy. The initial evaluative phases have indicated promising results of platform usability. Results of phases 3 and 4 were methodologically useful, yet limited due to the COVID-19 pandemic. TRIAL REGISTRATION: ClinicalTrials.gov NCT03834207; https://clinicaltrials.gov/ct2/show/NCT03834207. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/21994.

Estimation of the epidemiology of dementia and associated neuropsychiatric symptoms by applying machine learning to real-world data

Introduction: Incidence rates of dementia-related neuropsychiatric symptoms (NPS) are not known and this hampers the assessment of their population burden. The objective of this study was to obtain an approximate estimate of the population incidence and prevalence of both dementia and NPS.Methods: Given the dynamic nature of the population with dementia, a retrospective study was conducted within the database of the Basque Health Service (real-world data) at the beginning and end of 2019. Validated random forest models were used to identify separately depressive and psychotic clusters according to their presence in the electronic health records of all patients diagnosed with dementia.Results: Among the 631,949 individuals over 60 years registered, 28,563 were diagnosed with dementia, of whom 15,828 (55.4%) showed psychotic symptoms and 19,461 (68.1%) depressive symptoms. The incidence of dementia in 2019 was 6.8/1000 person-years. Most incident cases of depressive (72.3%) and psychotic (51.9%) NPS occurred in cases of incident dementia. The risk of depressive-type NPS grows with years since dementia diagnosis, living in a nursing home, and female sex, but falls with older age. In the psychotic cluster model, the effects of male sex, and older age are inverted, both increasing the probability of this type of symptoms.Conclusions: The stigmatization factor conditions the social and attitudinal environment, delaying the diagnosis of dementia, preventing patients from receiving adequate care and exacerbating families’ suffering. This study evidences the synergy between big data and real-world data for psychiatric epidemiological research. (AU)

Introducción: Se desconocen las tasas de incidencia de los síntomas neuropsiquiátricos (SN) asociados a la demencia, lo cual dificulta la evaluación de su carga para la población. El objetivo de este estudio fue obtener una estimación aproximada de la incidencia y prevalencia en la población tanto de la demencia como de los SN.Métodos: Dada la naturaleza dinámica de la población con demencia, se realizó un estudio dentro de la base de datos del Servicio Vasco de Salud (datos del mundo real) a comienzos y finales de 2019. Se utilizaron modelos de bosques aleatorios validados para identificar por separado los clústeres depresivos y psicóticos, con arreglo a su presencia en los registros sanitarios electrónicos de todos los pacientes con diagnóstico de demencia.Resultados: Entre los 631.949 individuos mayores de 60 años registrados, 28.563 fueron diagnosticados de demencia, de los cuales 15.828 (55,4%) mostraron síntomas psicóticos y 19.461 (68,1%) síntomas depresivos. La incidencia de la demencia en 2019 fue de 6,8/1.000 personas-años. Muchos de los casos incidentes de SN depresivos (72,3%) y psicóticos (51,9%) se produjeron en casos de demencia incidente. El riesgo de SN de tipo depresivo se incrementa con factores tales como los años transcurridos desde que se diagnostica la demencia, la residencia en un sanatorio, y el sexo femenino, pero desciende con la edad avanzada. En el modelo de clúster psicótico, los efectos del sexo masculino y la edad avanzada se invierten, incrementando ambos la probabilidad de este tipo de síntomas.Conclusiones: El factor de estigmatización condiciona el entorno social y actitudinal, demorando el diagnóstico de la demencia, impidiendo que los pacientes reciban los cuidados adecuados, y exacerbando el sufrimiento de las familias. Este estudio evidencia la sinergia entre los grandes datos y los datos del mundo real para la investigación epidemiológica psiquiátrica. (AU)

Health care costs of breast, prostate, colorectal and lung cancer care by clinical stage and cost component / Costes de la atención de los cánceres de mama, próstata, colorrectal y pulmón por estadio clínico y componente de coste

Objective: To measure 3-year care costs of breast, prostate, colorectal and lung cancers disaggregated by site and clinical stage. Method: A retrospective observational design was employed to investigate care costs of cases recorded in the Registry of the Basque Country between 2010 and 2015. Data gathered included TNM stage and demographic, clinical and resource use variables. Total costs per patient with stage IV disease were calculated by combining generalized linear models with parametric survival analysis. Unit costs were obtained from the analytical accounting system of the Basque Health Service. Results: The sample comprised 23,782 cancer cases (7801 colorectal, 5530 breast, 4802 prostate and 5649 lung cancer). The mean 3-year costs per patient with stage I to III disease were €11,323, €13,727, €8,651 and €12,023 for colorectal, breast, prostate and lung cancer, respectively. The most important cost components were surgery and chemotherapy. Total survival-adjusted costs until death for patients with stage IV disease (€27,568, €26,296, €16,151 and €15,931 for breast, colorectal, lung and prostate cancer, respectively) were higher than the 3-year costs for those with earlier-stage disease. Conclusions: This study quantitatively shows the pattern of changes in the economic burden of cancer throughout its natural history and the great magnitude of this burden for the health system. The use of indicators based on real-world data from each regional health service would allow cancer care in each region to be tailored to local population needs. (AU)

Objetivo: Determinar el coste del tratamiento de los cánceres de mama, próstata, colorrectal y pulmón según la localización y el estadio clínico. Método: Se utilizó un diseño observacional retrospectivo con los casos del Registro de Euskadi entre 2010 y 2015. Los datos incluyeron el estadio TNM, variables demográficas y clínicas, y uso de recursos. Los costes totales por paciente en estadio IV se calcularon combinando modelos lineales generalizados con el análisis paramétrico de supervivencia. Los costes unitarios se obtuvieron del sistema de contabilidad analítica del Servicio Vasco de Salud. Resultados: La muestra estuvo compuesta por 23.782 casos (7801 colorrectal, 5530 de mama, 4802 de próstata y 5649 de pulmón). Los costes medios por paciente a 3 años en estadio I a III fueron 11.323 €, 13.727 €, 8651 € y 12.023 € para los cánceres colorrectal, de mama, de próstata y de pulmón, respectivamente. Los costes para el estadio IV (27.568 €, 26.296 €, 16.151 € y 15.931 € para los cánceres de mama, colorrectal, de pulmón y de próstata, respectivamente) fueron mayores que en los estadios iniciales. Conclusiones: Este estudio muestra cuantitativamente el cambio en la carga económica del cáncer a lo largo de su evolución y la gran carga que supone para el sistema de salud. El uso de datos del mundo real de cada servicio de salud permitiría adaptar la atención del cáncer a las necesidades de la población local. (AU)

Stratified cost-utility analysis of total hip arthroplasty in displaced femoral neck fracture / Análisis de coste-utilidad estratificado de la prótesis total de cadera en la fractura de cuello de fémur desplazada

Objective: To conduct a stratified cost-utility analysis of total versus partial hip arthroplasty as a function of clinical subtype. Method: All cases of this type of intervention were analysed between 2010 and 2016 in the Basque Health Service, gathering data on clinical outcomes and resource use to calculate the cost and utility in quality-adjusted life years (QALYs) at individual level. The statistical analysis included applying the propensity score to balance the groups, and seemingly unrelated regression models to calculate the incremental cost-utility ratio and plot the cost-effectiveness plane. The interaction between age group and American Society of Anesthesiologists (ASA) risk class was assessed in the multivariate analysis. Results: The study identified 5867 patients diagnosed with femoral neck fracture, of whom 1307 and 4560 were treated with total and partial hip arthroplasty, respectively. In the cost-utility analysis based on the seemingly unrelated regression, total hip arthroplasty was found to have a higher cost and higher utility (2465€ and 0.42 QALYs). Considering a willingness-to-pay threshold of €22,000 per QALY, total hip arthroplasty was cost-effective in the under-80-year-old subgroup. Among patients above this age, hemiarthroplasty was cost-effective in ASA class I-II patients and dominant in ASA class III-IV patients. Conclusions: Subgroup analysis supports current daily clinical practice in displaced femoral neck fractures, namely, using partial replacement in most patients and reserving total replacement for younger patients. (AU)

Objetivo: Realizar un análisis de coste-utilidad de la prótesis total de cadera frente a la prótesis parcial. Método: Se analizaron todos los casos intervenidos desde 2010 hasta 2016 en el Servicio Vasco de Salud, recogiendo resultados clínicos y uso de recursos para calcular individualmente el coste y la utilidad en años de vida ajustados por calidad (AVAC). El análisis estadístico incluyó el pareamiento por puntaje de propensión para balancear los grupos y modelos de regresión aparentemente no relacionados para calcular la razón de coste-utilidad incremental y el plano de coste-efectividad. La interacción de grupo de edad y riesgo según la American Society of Anesthesiologists (ASA) se incluyó en el análisis multivariante. Resultados: Se identificaron 1307 pacientes con prótesis total y 4560 con prótesis parcial. Al hacer el análisis de coste-utilidad con modelos de regresión aparentemente no relacionados el resultado fue mayor coste y mayor utilidad para la prótesis total (2465 € y 0,42 AVAC). Para un umbral de 22.000 € por AVAC, la prótesis total fue coste-efectiva en el subgrupo de menores de 80 años. En el grupo de mayores de 80 años la parcial fue coste-efectiva en los casos con riesgo ASA I-II y dominante en los ASA III-IV. Conclusiones: El análisis de subgrupos ratifica la práctica clínica habitual en las fracturas de cuello de fémur desplazadas de intervenir a la mayoría de los pacientes mediante prótesis parcial y reservar la prótesis total para los pacientes más jóvenes. (AU)

Descriptive and predictive analysis identify centenarians' characteristics from the Basque population.

Background: Centenarians exhibit extreme longevity and have been postulated, by some researchers, as a model for healthy aging. The identification of the characteristics of centenarians might be useful to understand the process of human aging. Methods: In this retrospective study, we took advantage of demographic, clinical, biological, and functional data of deceased individuals between 2014 and 2020 in Guipúzcoa (Basque Country, Spain) taken from the Basque Health Service electronic health records data lake. Fifty characteristics derived from demographic, clinical, pharmaceutical, biological, and functional data were studied in the descriptive analysis and compared through differences in means tests. Twenty-seven of them were used to build machine learning models in the predictive analysis and their relevance for classifying centenarians was assessed. Results: Most centenarians were women and lived in nursing homes. Importantly, they developed fewer diseases, took fewer drugs, and required fewer medical attendances. They also showed better biological profiles, exhibiting lower levels of glucose, hemoglobin, glycosylated hemoglobin, and triglycerides in blood analysis compared with non-centenarians. In addition, machine learning analyses revealed the main characteristics of the profiles associated with centenarians' status as being women, having fewer consultations, having fewer diagnoses of neoplasms, and having lower levels of hemoglobin. Conclusions: Our results revealed the main characteristics linked to centenarians in the Basque Country using Computational Biology programs. These results expand the knowledge on the characterization of the centenarian population and hence of human longevity.

Stratified cost-utility analysis of total hip arthroplasty in displaced femoral neck fracture.

OBJECTIVE: To conduct a stratified cost-utility analysis of total versus partial hip arthroplasty as a function of clinical subtype. METHOD: All cases of this type of intervention were analysed between 2010 and 2016 in the Basque Health Service, gathering data on clinical outcomes and resource use to calculate the cost and utility in quality-adjusted life years (QALYs) at individual level. The statistical analysis included applying the propensity score to balance the groups, and seemingly unrelated regression models to calculate the incremental cost-utility ratio and plot the cost-effectiveness plane. The interaction between age group and American Society of Anesthesiologists (ASA) risk class was assessed in the multivariate analysis. RESULTS: The study identified 5867 patients diagnosed with femoral neck fracture, of whom 1307 and 4560 were treated with total and partial hip arthroplasty, respectively. In the cost-utility analysis based on the seemingly unrelated regression, total hip arthroplasty was found to have a higher cost and higher utility (2465€ and 0.42 QALYs). Considering a willingness-to-pay threshold of €22,000 per QALY, total hip arthroplasty was cost-effective in the under-80-year-old subgroup. Among patients above this age, hemiarthroplasty was cost-effective in ASA class I-II patients and dominant in ASA class III-IV patients. CONCLUSIONS: Subgroup analysis supports current daily clinical practice in displaced femoral neck fractures, namely, using partial replacement in most patients and reserving total replacement for younger patients.

Health care costs of breast, prostate, colorectal and lung cancer care by clinical stage and cost component.

OBJECTIVE: To measure 3-year care costs of breast, prostate, colorectal and lung cancers disaggregated by site and clinical stage. METHOD: A retrospective observational design was employed to investigate care costs of cases recorded in the Registry of the Basque Country between 2010 and 2015. Data gathered included TNM stage and demographic, clinical and resource use variables. Total costs per patient with stage IV disease were calculated by combining generalized linear models with parametric survival analysis. Unit costs were obtained from the analytical accounting system of the Basque Health Service. RESULTS: The sample comprised 23,782 cancer cases (7801 colorectal, 5530 breast, 4802 prostate and 5649 lung cancer). The mean 3-year costs per patient with stage I to III disease were €11,323, €13,727, €8,651 and €12,023 for colorectal, breast, prostate and lung cancer, respectively. The most important cost components were surgery and chemotherapy. Total survival-adjusted costs until death for patients with stage IV disease (€27,568, €26,296, €16,151 and €15,931 for breast, colorectal, lung and prostate cancer, respectively) were higher than the 3-year costs for those with earlier-stage disease. CONCLUSIONS: This study quantitatively shows the pattern of changes in the economic burden of cancer throughout its natural history and the great magnitude of this burden for the health system. The use of indicators based on real-world data from each regional health service would allow cancer care in each region to be tailored to local population needs.

Development and Validation of a Discrete Event Simulation Model to Evaluate the Cardiovascular Impact of Population Policies for Obesity.

INTRODUCTION: Our aim was to describe the development and validation of an obesity model representing the cardiovascular risks associated with different body mass index (BMI) categories, through simulation, designed to evaluate the epidemiological and economic impact of population policies for obesity. METHODS: A discrete event simulation model was built in R considering the risk of cardiovascular events (heart failure, stroke, coronary heart disease, and diabetes) associated with BMI categories in the Spanish population. The main parameters included in the model were estimated from Spanish hospital discharge records and the Spanish Health Survey and allowed both first-order and second-order (probabilistic sensitivity analysis) uncertainty to be programmed into the model. The simulation yielded the incidence and prevalence of cardiovascular events as validation outputs. To illustrate the capacity of the model, we estimated the reduction in cardiovascular events and cost-utility (incremental cost/incremental quality-adjusted life-years [QALYs]) of a hypothetical intervention that fully eliminated the cardiovascular risks associated with obesity and overweight. RESULTS: The Validation Status of Health-Economic decision models (AdViSHE) tool was applied. Internal validation plots showed adequate goodness of fit for the Spanish population. External validation was achieved by comparing the simulated and real incidence by age group for stroke, acute myocardial infarction, and heart failure. The intervention reduced the population hazard ratios of stroke, acute myocardial infarction, and heart failure to 0.81, 0.74, and 0.78, respectively, and added 0.74 QALYs to the whole population. CONCLUSIONS: This obesity simulation model evidenced good properties for estimating the long-term epidemiological and economic impact of policies to tackle obesity in Spain. The conceptual model could be implemented for other counties using country-specific input data.

Estimation of the epidemiology of dementia and associated neuropsychiatric symptoms by applying machine learning to real-world data.

INTRODUCTION: Incidence rates of dementia-related neuropsychiatric symptoms (NPS) are not known and this hampers the assessment of their population burden. The objective of this study was to obtain an approximate estimate of the population incidence and prevalence of both dementia and NPS. METHODS: Given the dynamic nature of the population with dementia, a retrospective study was conducted within the database of the Basque Health Service (real-world data) at the beginning and end of 2019. Validated random forest models were used to identify separately depressive and psychotic clusters according to their presence in the electronic health records of all patients diagnosed with dementia. RESULTS: Among the 631,949 individuals over 60 years registered, 28,563 were diagnosed with dementia, of whom 15,828 (55.4%) showed psychotic symptoms and 19,461 (68.1%) depressive symptoms. The incidence of dementia in 2019 was 6.8/1000 person-years. Most incident cases of depressive (72.3%) and psychotic (51.9%) NPS occurred in cases of incident dementia. The risk of depressive-type NPS grows with years since dementia diagnosis, living in a nursing home, and female sex, but falls with older age. In the psychotic cluster model, the effects of male sex, and older age are inverted, both increasing the probability of this type of symptoms. CONCLUSIONS: The stigmatization factor conditions the social and attitudinal environment, delaying the diagnosis of dementia, preventing patients from receiving adequate care and exacerbating families' suffering. This study evidences the synergy between big data and real-world data for psychiatric epidemiological research.