Pediatric patients with chronic conditions frequently have unmet care needs, make extensive use of healthcare services, and often encounter fragmented, non-centered care. This study aimed to analyze the differences in the utilization of primary care (PC) and hospital care (HC) services by these patients according to sex, age, and complexity and to identify associated factors. A cross-sectional study was conducted in a basic health area of Madrid, including patients under 18 years. Among these patients, 15.7% had ≥1 chronic disease, 54.1% were male, the average age was 9.5 years, 3.5% had complexity, and 11.3% had multimorbidity. The mean number of contacts/year with the healthcare system was 9.1, including 8.3 contacts/year with PC (4.9 with pediatricians and 1.9 with nurses) and 0.8 contacts/year with HC (0.8 in external consultations and 0.01 hospitalizations). The factors associated with PC utilization were complexity; female sex; European origin; and diseases like asthma, epilepsy, stroke, recurrent urinary infection, attention deficit hyperactivity disorder, and anxiety, while older age was negatively associated. Thyroid disorders were significantly associated with HC utilization. These findings could help guide the design of optimized pediatric patient-centered care approaches to coordinate care across healthcare services and reduce high healthcare utilization, therefore improving the healthcare outcomes and quality of life for these patients.

Introduction: There is a need to develop harmonized procedures and a Minimum Data Set (MDS) for cross-border Multi Casualty Incidents (MCI) in medical emergency scenarios to ensure appropriate management of such incidents, regardless of place, language and internal processes of the institutions involved. That information should be capable of real-time communication to the command-and-control chain. It is crucial that the models adopted are interoperable between countries so that the rights of patients to cross-border healthcare are fully respected. Objective: To optimize management of cross-border Multi Casualty Incidents through a Minimum Data Set collected and communicated in real time to the chain of command and control for each incident. To determine the degree of agreement among experts. Method: We used the modified Delphi method supplemented with the Utstein technique to reach consensus among experts. In the first phase, the minimum requirements of the project, the profile of the experts who were to participate, the basic requirements of each variable chosen and the way of collecting the data were defined by providing bibliography on the subject. In the second phase, the preliminary variables were grouped into 6 clusters, the objectives, the characteristics of the variables and the logistics of the work were approved. Several meetings were held to reach a consensus to choose the MDS variables using a Modified Delphi technique. Each expert had to score each variable from 1 to 10. Non-voting variables were eliminated, and the round of voting ended. In the third phase, the Utstein Style was applied to discuss each group of variables and choose the ones with the highest consensus. After several rounds of discussion, it was agreed to eliminate the variables with a score of less than 5 points. In phase four, the researchers submitted the variables to the external experts for final assessment and validation before their use in the simulations. Data were analysed with SPSS Statistics (IBM, version 2) software. Results: Six data entities with 31 sub-entities were defined, generating 127 items representing the final MDS regarded as essential for incident management. The level of consensus for the choice of items was very high and was highest for the category ‘Incident’ with an overall kappa of 0.7401 (95% CI 0.1265–0.5812, p 0.000), a good level of consensus in the Landis and Koch model. The items with the greatest degree of consensus at ten were those relating to location, type of incident, date, time and identification of the incident. All items met the criteria set, such as digital collection and real-time transmission to the chain of command and control. Conclusions: This study documents the development of a MDS through consensus with a high degree of agreement among a group of experts of different nationalities working in different fields. All items in the MDS were digitally collected and forwarded in real time to the chain of command and control. This tool has demonstrated its validity in four large cross-border simulations involving more than eight countries and their emergency services.

Patients with multimorbidity have increased and more complex healthcare needs, posing their management a challenge for healthcare systems. This study aimed to describe their primary healthcare utilization and associated factors. A population-based cross-sectional study was conducted in a Spanish basic healthcare area including all patients with chronic conditions, differentiating between having multimorbidity or not. Sociodemographic, functional, clinical and service utilization variables were analyzed, stratifying the multimorbid population by the Adjusted Morbidity Groups (AMG) risk level, sex and age. A total of 6036 patients had multimorbidity, 64.2% being low risk, 28.5% medium risk and 7.3% high risk. Their mean age was 64.1 years and 63.5% were women, having on average 3.5 chronic diseases, and 25.3% were polymedicated. Their mean primary care contacts/year was 14.9 (7.8 with family doctors and 4.4 with nurses). Factors associated with primary care utilization were age (B-coefficient [BC] = 1.15;95% Confidence Interval [CI] = 0.30–2.01), female sex (BC = 1.04; CI = 0.30–1.78), having a caregiver (BC = 8.70; CI = 6.72–10.69), complexity (B-coefficient = 0.46; CI = 0.38–0.55), high-risk (B-coefficient = 2.29; CI = 1.26–3.32), numerous chronic diseases (B-coefficient = 1.20; CI = 0.37–2.04) and polypharmacy (B-coefficient = 5.05; CI = 4.00–6.10). This study provides valuable data on the application of AMG in multimorbid patients, revealing their healthcare utilization and the need for a patient-centered approach by primary care professionals. These results could guide in improving coordination among professionals, optimizing multimorbidity management and reducing costs derived from their extensive healthcare utilization.

Background: Patients scored as ‘high-risk’ and with chronic conditions in primary care (PC) often face medication-related problems. A significant but underexplored problem is the patient’s medication concern, which may also impact treatment decisions and outcomes. Aim: This study aimed to describe the characteristics of patients scored as high-risk with chronic conditions in Madrid, Spain who have documented medication concern and to analyse the associated factors. Design & setting: Descriptive cross-sectional study, conducted within the Innovative Medicines Initiative Horizon 2020 (IMI-H2020) European BEAMER project, including all patients scored as high-risk with chronic conditions in the Madrid region. Method: Patients were identified by the Adjusted Morbidity Groups (AMG) stratification tool in PC electronic clinical records. Variables included sociodemographic, clinical, and pharmacological, along with self-reported medication concerns. Univariate and bivariate analyses and logistic regression models were performed with medication concerns as the dependent variable. Results: In all, 40 776 (28.56%) reported medication concern. Patients with medication concern were older (84.6 versus 72.7 years), had more chronic conditions (7 versus 6), and medications (9.7 versus 8.7). They were more likely to live alone (8.5% versus 2.7%) and showed higher dependency. The following were associated with medication concern: older age odds ratio (OR) 11.0 (95% confidence interval (CI) = 5.4 to 22.3; P <0.001), polypharmacy OR 3.2 (95% CI = 3.1 to 3.3; P <0.001), living alone OR 1.5 (95% CI = 1.4 to 1.6; P <0.001), Parkinson’s OR 1.3 (95% CI = 1.2 to 1.4; P <0.001); chronic pain OR 1.6 (95% CI = 1.2 to 2.1; P = 0.001), and cardiovascular risk factors OR 1.2 (95% CI = 1.1 to 1.3; P = 0.001). Conclusion: Age, polypharmacy, multimorbidity, and social isolation are predictors of medication concern in patients scored as high-risk with chronic conditions in PC. Addressing psychological barriers, simplifying treatment, and strengthening social support through tailored interventions could decrease medication concerns.

Background: Dying in the preferred place is considered an indicator of the quality of end-of-life care. Advance care planning and home palliative care may increase the likelihood of dying at home, but it remains unclear whether the documentation of these preferences in clinical records is associated with the actual place of death. This study assessed whether the recording of patients’ preferred place of care or death was associated with the actual place of death among patients followed by home palliative care teams. Methods: We conducted a retrospective observational study with an exploratory approach that included adult patients who died in 2022 and were followed by a home palliative care team in Madrid, Spain. Data were extracted from electronic health records and analysed using bivariate tests and multivariable logistic regression. Results: A total of 464 patients were included (53% women; mean age 80.8 years). Overall, 82.5% of the patients died at home. Preferences were recorded for 64% of the patients; 97.6% preferred to die at home, and 89% died there. Documentation of preferences was not significantly associated with the place of death. Conclusions: Documentation alone was not independently associated with the place of death. These findings should be interpreted cautiously given the observational and exploratory design of the study and the limited explanatory power of the regression model. Notably, most patients expressed a preference to die at home, and most deaths also occurred at home. Recording preferences may still support advance care planning and patient-centred decision-making, but documentation should not be interpreted as sufficient in itself to influence place of death.

Background: Dying in the preferred place is considered an indicator of quality of end-of-life care. Advance care planning and home palliative care may increase the likelihood of dying at home. This study aimed to assess whether recording patients’ preferred place of care or death is associated with the actual place of death among patients followed by home palliative care teams. Methods: We conducted a retrospective observational study including adult patients who died in 2022 and were followed by a home palliative care team in Madrid, Spain. Sociodemographic and clinical variables, recorded preferred place of care or death, and actual place of death were extracted from electronic health records. Associations were analysed using bivariate tests and multivariable logistic regression. Results: A total of 464 patients were included (53% women; mean age 80.8 years). Overall, 82.5% died at home. A preferred place of care or death was recorded for 64% of patients; among them, 97.6% expressed a preference for home, and 89% of these patients died at home. In the multivariable analysis, older age and female sex were independently associated with death at home. Recording a preferred place of care or death was not independently associated with the place of death. Conclusions: Most patients followed by home palliative care teams died at home. Older age and female sex were associated with a higher probability of home death. Although most patients with a recorded preference for home died at home, recording preferences alone was not independently associated with the place of death. Systematic documentation of preferences may support advance care planning and patient-centred decision-making.