Multimorbidity as a predictor of health service utilization in primary care: a registry-based study of the Catalan population

Background: Multimorbidity is highly relevant for both service commissioning and clinical decision-making. Optimization of variables assessing multimorbidity in order to enhance chronic care management is an unmet need. To this end, we have explored the contribution of multimorbidity to predict use of healthcare resources at community level by comparing the predictive power of four different multimorbidity measures. Methods: A population health study including all citizens ≥18 years (n = 6,102,595) living in Catalonia (ES) on 31 December 2014 was done using registry data. Primary care service utilization during 2015 was evaluated through four outcome variables: A) Frequent attendants, B) Home care users, C) Social worker users, and, D) Polypharmacy. Prediction of the four outcome variables (A to D) was carried out with and without multimorbidity assessment. We compared the contributions to model fitting of the following multimorbidity measures: i) Charlson index; ii) Number of chronic diseases; iii) Clinical Risk Groups (CRG); and iv) Adjusted Morbidity Groups (GMA). Results: The discrimination of the models (AUC) increased by including multimorbidity as covariate into the models, namely: A) Frequent attendants (0.771 vs 0.853), B) Home care users (0.862 vs 0.890), C) Social worker users (0.809 vs 0.872), and, D) Polypharmacy (0.835 vs 0.912). GMA showed the highest predictive power for all outcomes except for polypharmacy where it was slightly below than CRG. Conclusions: We confirmed that multimorbidity assessment enhanced prediction of use of healthcare resources at community level. The Catalan population-based risk assessment tool based on GMA presented the best combination of predictive power and applicability

Risk and temporal order of disease diagnosis of comorbidities in patients with COPD: a population health perspective

Introduction: Comorbidities in patients with chronic obstructive pulmonary disease (COPD) generate a major burden on ealthcare. Identification of costeffective strategies aiming at preventing and enhancing management of comorbid conditions in patients with COPD requires deeper knowledge on epidemiological patterns and on shared biological pathways xplaining cooccurrence of diseases. Methods: The study assesses the co-occurrence of several chronic conditions in patients with COPD using two different datasets: Catalan Healthcare Surveillance System (CHSS) (ES, 1.4 million registries) and Medicare (USA, 13 million registries). Temporal order of disease diagnosis was analysed in the CHSS dataset. Results The results demonstrate higher prevalence of most of the diseases, as comorbid conditions, in elderly (>65) patients with COPD compared with non-COPD subjects, an effect observed in both CHSS and Medicare datasets. Analysis of temporal order of disease diagnosis showed that comorbid conditions in elderly patients with COPD tend to appear after the diagnosis of the obstructive disease, rather than before it. Conclusion: The results provide a population health perspective of the comorbidity challenge in patients with COPD, indicating the increased risk of developing comorbid conditions in these patients. The research reinforces the need for novel approaches in the prevention and management of comorbidities in patients with COPD to effectively reduce the overall burden of the disease on these patients

Informe sobre les característiques sociodemogràfiques, clíniques i els factors pronòstics dels pacients amb el diagnòstic de COVID-19 a Catalunya: resum executiu

Coronavirus SARS-CoV-2; COVID-19; 2019-nCoV; Característiques sociodemogràfiques; Característiques clíniques; Factors pronòstics; PacientsCoronavirus SARS-CoV-2; COVID-19; 2019-nCoV; Características sociodemográficas; Características clínicas; Factores pronósticos; PacientesCoronavirus SARS-CoV-2; COVID-19; 2019-nCoV; Sociodemographic characteristics; Clinical features; Prognostic factors; PatientsInforme que descriu quines són les característiques de les persones afectades per la COVID-19 i quins són els factors que condicionen el seu pitjor pronòstic. Aquests factors són importants actualment, per exemple, per a definir el grup considerat vulnerable i al que s’ha de protegir amb l’ús de les mesures de confinament/distanciament físic que s’estan implementant per part dels governs

Changes in Treatment Patterns and Costs for Lung Cancer Have Not Resulted in Relevant Improvements in Survival : A Population-Based Observational Study in Catalonia

Real-world data collect clinical and economic information from daily clinical practice and can support decisions in the context of health evaluation and management. The aim of our retrospective cohort study was to describe the different approaches used for treating lung cancer in Catalonia in 2014 and 2018 and to assess the associated cost and impact on patient survival until December 2021. Treatment patterns for lung cancer changed in younger patients, and all costs of treatment increased significantly. These changes, mainly related to the use of several novel drugs, such as immunotherapy and targeted therapy, were not associated with an increase in the overall patient survival in the period of time under evaluation. Objective: Few published studies have described multidisciplinary therapeutic strategies for lung cancer. This study aims to describe the different approaches used for treating lung cancer in Catalonia in 2014 and 2018 and to assess the associated cost and impact on patient survival. A retrospective observational cohort study using data of patients with lung cancer from health care registries in Catalonia was carried out. We analyzed change in treatment patterns, costs and survival according to the year of treatment initiation (2014 vs. 2018). The Kaplan-Meier method was used to estimate survival, with the follow-up until 2021. From 2014 to 2018, the proportion of patients undergoing surgery increased and treatments for unresectable tumors decreased, mainly in younger patients. Immunotherapy increased by up to 9% by 2018. No differences in patient survival were observed within treatment patterns. The mean cost per patient in the first year of treatment increased from EUR 14,123 (standard deviation [SD] 4327) to EUR 14,550 (SD 3880) in surgical patients, from EUR 4655 (SD 3540) to EUR 5873 (SD 6455) in patients receiving curative radiotherapy and from EUR 4723 (SD 7003) to EUR 6458 (SD 10,116) in those treated for unresectable disease. From 2014 to 2018, surgical approaches increased in younger patients. The mean cost of treating patients increased, especially in pharmaceutical expenditure, mainly related to the use of several biomarker-targeted treatments. While no differences in overall patient survival were observed, it seems reasonable to expect improvements in this outcome in upcoming years as more patients receive innovative treatments

Trends in mortality in septic patients according to the different organ failure during 15 years

Sepsis syndrome; Epidemiologic methods; Septic shockSíndrome séptico; Métodos epidemiológicos; Choque sépticoSíndrome sèptic; Mètodes epidemiològics; Xoc sèpticBackground The incidence of sepsis can be estimated between 250 and 500 cases/100.000 people per year and is responsible for up to 6% of total hospital admissions. Identified as one of the most relevant global health problems, sepsis is the condition that generates the highest costs in the healthcare system. Important changes in the management of septic patients have been included in recent years; however, there is no information about how changes in the management of sepsis-associated organ failure have contributed to reduce mortality. Methods A retrospective analysis was conducted from hospital discharge records from the Minimum Basic Data Set Acute-Care Hospitals (CMBD-HA in Catalan language) for the Catalan Health System (CatSalut). CMBD-HA is a mandatory population-based register of admissions to all public and private acute-care hospitals in Catalonia. Sepsis was defined by the presence of infection and at least one organ dysfunction. Patients hospitalized with sepsis were detected, according ICD-9-CM (since 2005 to 2017) and ICD-10-CM (2018 and 2019) codes used to identify acute organ dysfunction and infectious processes. Results Of 11.916.974 discharges from all acute-care hospitals during the study period (2005–2019), 296.554 had sepsis (2.49%). The mean annual sepsis incidence in the population was 264.1 per 100.000 inhabitants/year, and it increased every year, going from 144.5 in 2005 to 410.1 in 2019. Multiorgan failure was present in 21.9% and bacteremia in 26.3% of cases. Renal was the most frequent organ failure (56.8%), followed by cardiovascular (24.2%). Hospital mortality during the study period was 19.5%, but decreases continuously from 25.7% in 2005 to 17.9% in 2019 (p < 0.0001). The most important reduction in mortality was observed in cases with cardiovascular failure (from 47.3% in 2005 to 31.2% in 2019) (p < 0.0001). In the same way, mean mortality related to renal and respiratory failure in sepsis was decreased in last years (p < 0.0001). Conclusions The incidence of sepsis has been increasing in recent years in our country. However, hospital mortality has been significantly reduced. In septic patients, all organ failures except liver have shown a statistically significant reduction on associated mortality, with cardiovascular failure as the most relevant.The study is part of a project that has received a grant from the "Fundació Marató TV3", entitled: "Sepsis Training, Analysis and Feedback (STAF) strategy for the implementation of Sepsis Code" (Id Num: 201836_10)

The adjusted morbidity groups (GMA): an exhaustive and severity-balanced tool for risk assessment

Grups morbiditat ajustada; GMA; Eina d'estratificació; Avaluació de riscosGrupos morbilidad ajustada; GMA; Herramienta de estratificación; Evaluación de riesgosAdjusted morbidity groups; GMA; Stratification tool; Risk assessmentEls GMA consisteixen en una eina que permet avaluar el risc en salut a partir de les característiques demogràfiques dels pacients, les seves malalties cròniques i aquelles situacions o malalties agudes que puguin tenir-hi impacte. Aquesta eina proporciona un índex de risc que es pot utilitzar com a factor d’ajust en models específics d’una determinada malaltia i a la vegada actua com un agrupament per estratificar la població en diferents nivells de risc.Los GMA consisten en una herramienta que permite evaluar el riesgo en salud a partir de las características demográficas de los pacientes, sus enfermedades crónicas y aquellas situaciones o enfermedades agudas que puedan tener impacto. Esta herramienta proporciona un índice de riesgo que se puede utilizar como factor de ajuste en modelos específicos de una determinada enfermedad y al mismo tiempo actúa como un agrupamiento para estratificar la población en diferentes niveles de riesgo.GMAs are a tool that assesses health risk based on the demographic characteristics of patients, their chronic diseases and those situations or acute diseases that may have an impact. This tool provides a risk index that can be used as an adjustment factor in specific models of a given disease and at the same time acts as a grouping to stratify the population at different levels of risk

Prevention of Unplanned Hospital Admissions in Multimorbid Patients Using Computational Modeling: Observational Retrospective Cohort Study

Background: Enhanced management of multimorbidity constitutes a major clinical challenge. Multimorbidity shows well-established causal relationships with the high use of health care resources and, specifically, with unplanned hospital admissions. Enhanced patient stratification is vital for achieving effectiveness through personalized postdischarge service selection. Objective: The study has a 2-fold aim: (1) generation and assessment of predictive models of mortality and readmission at 90 days after discharge; and (2) characterization of patients' profiles for personalized service selection purposes. Methods: Gradient boosting techniques were used to generate predictive models based on multisource data (registries, clinical/functional and social support) from 761 nonsurgical patients admitted in a tertiary hospital over 12 months (October 2017 to November 2018). K-means clustering was used to characterize patient profiles. Results: Performance (area under the receiver operating characteristic curve, sensitivity, and specificity) of the predictive models was 0.82, 0.78, and 0.70 and 0.72, 0.70, and 0.63 for mortality and readmissions, respectively. A total of 4 patients' profiles were identified. In brief, the reference patients (cluster 1; 281/761, 36.9%), 53.7% (151/281) men and mean age of 71 (SD 16) years, showed 3.6% (10/281) mortality and 15.7% (44/281) readmissions at 90 days following discharge. The unhealthy lifestyle habit profile (cluster 2; 179/761, 23.5%) predominantly comprised males (137/179, 76.5%) with similar age, mean 70 (SD 13) years, but showed slightly higher mortality (10/179, 5.6%) and markedly higher readmission rate (49/179, 27.4%). Patients in the frailty profile (cluster 3; 152/761, 19.9%) were older (mean 81 years, SD 13 years) and predominantly female (63/152, 41.4%, males). They showed medical complexity with a high level of social vulnerability and the highest mortality rate (23/152, 15.1%), but with a similar hospitalization rate (39/152, 25.7%) compared with cluster 2. Finally, the medical complexity profile (cluster 4; 149/761, 19.6%), mean age 83 (SD 9) years, 55.7% (83/149) males, showed the highest clinical complexity resulting in 12.8% (19/149) mortality and the highest readmission rate (56/149, 37.6%). Conclusions: The results indicated the potential to predict mortality and morbidity-related adverse events leading to unplanned hospital readmissions. The resulting patient profiles fostered recommendations for personalized service selection with the capacity for value generation

Health outcomes from home hospitalization: multisource predictive modeling

Background: Home hospitalization is widely accepted as a cost-effective alternative to conventional hospitalization for selected patients. A recent analysis of the home hospitalization and early discharge (HH/ED) program at Hospital Clínic de Barcelona over a 10-year period demonstrated high levels of acceptance by patients and professionals, as well as health value-based generation at the provider and health-system levels. However, health risk assessment was identified as an unmet need with the potential to enhance clinical decision making. Objective: The objective of this study is to generate and assess predictive models of mortality and in-hospital admission at entry and at HH/ED discharge. Methods: Predictive modeling of mortality and in-hospital admission was done in 2 different scenarios: at entry into the HH/ED program and at discharge, from January 2009 to December 2015. Multisource predictive variables, including standard clinical data, patients' functional features, and population health risk assessment, were considered. Results: We studied 1925 HH/ED patients by applying a random forest classifier, as it showed the best performance. Average results of the area under the receiver operating characteristic curve (AUROC; sensitivity/specificity) for the prediction of mortality were 0.88 (0.81/0.76) and 0.89 (0.81/0.81) at entry and at home hospitalization discharge, respectively; the AUROC (sensitivity/specificity) values for in-hospital admission were 0.71 (0.67/0.64) and 0.70 (0.71/0.61) at entry and at home hospitalization discharge, respectively. Conclusions: The results showed potential for feeding clinical decision support systems aimed at supporting health professionals for inclusion of candidates into the HH/ED program, and have the capacity to guide transitions toward community-based care at HH discharge

Inequalities by Income in the Prevalence of Cardiovascular Disease and Its Risk Factors in the Adult Population of Catalonia

Background Understanding the magnitude of cardiovascular disease (CVD) inequalities is the first step toward addressing them. The linkage of socioeconomic and clinical data in universal health care settings provides critical information to characterize CVD inequalities. Methods and Results We employed a prospective cohort design using electronic health records data from all residents of Catalonia aged 18+ between January and December of 2019 (N=6 332 228). We calculated age-adjusted sex-specific prevalence of 5 CVD risk factors (diabetes, hypertension, hyperlipidemia, obesity, and smoking), and 4 CVDs (coronary heart disease, cerebrovascular disease, atrial fibrillation, and heart failure). We categorized income into high, moderate, low, and very low according to individual income (tied to prescription copayments) and receipt of welfare support. We found large inequalities in CVD and CVD risk factors among men and women. CVD risk factors with the largest inequalities were diabetes, smoking, and obesity, with prevalence rates 2- or 3-fold higher for those with very low (versus high) income. CVDs with the largest inequalities were cerebrovascular disease and heart failure, with prevalence rates 2 to 4 times higher for men and women with very low (versus high) income. Inequalities varied by age, peaking at midlife (30-50 years) for most diseases, while decreasing gradually with age for smoking. Conclusions We found wide and heterogeneous inequalities by income in 5 CVD risk factors and 4 CVD. Our findings in a region with a high-quality public health care system and universal coverage stress that strong equity-promoting policies are necessary to reduce disparities in CVD

Population-based analysis of patients with COPD in Catalonia: a cohort study with implications for clinical management

BACKGROUND: Clinical management of patients with chronic obstructive pulmonary disease (COPD) shows potential for improvement provided that patients' heterogeneities are better understood. The study addresses the impact of comorbidities and its role in health risk assessment. OBJECTIVE: To explore the potential of health registry information to enhance clinical risk assessment and stratification. DESIGN: Fixed cohort study including all registered patients with COPD in Catalonia (Spain) (7.5 million citizens) at 31 December 2014 with 1-year (2015) follow-up. METHODS: A total of 264 830 patients with COPD diagnosis, based on the International Classification of Diseases (Ninth Revision) coding, were assessed. Performance of multiple logistic regression models for the six main dependent variables of the study: mortality, hospitalisations (patients with one or more admissions; all cases and COPD-related), multiple hospitalisations (patients with at least two admissions; all causes and COPD-related) and users with high healthcare costs. Neither clinical nor forced spirometry data were available. RESULTS: Multimorbidity, assessed with the adjusted morbidity grouper, was the covariate with the highest impact in the predictive models, which in turn showed high performance measured by the C-statistics: (1) mortality (0.83), (2 and 3) hospitalisations (all causes: 0.77; COPD-related: 0.81), (4 and 5) multiple hospitalisations (all causes: 0.80; COPD-related: 0.87) and (6) users with high healthcare costs (0.76). Fifteen per cent of individuals with highest healthcare costs to year ratio represented 59% of the overall costs of patients with COPD. CONCLUSIONS: The results stress the impact of assessing multimorbidity with the adjusted morbidity grouper on considered health indicators, which has implications for enhanced COPD staging and clinical management