Unplanned hospitalisations represent a hazardous event for older persons. Timely identification of high-risk individuals using a prediction tool may facilitate preventive interventions. To develop and validate an easy-to-use prediction model for unplanned hospitalisations in community-dwelling older adults using readily available data to allow rapid bedside assessment by general practitioners. Retrospective study using general practice electronic health records of 243,129 community-dwelling adults aged ≥65 years linked with national administrative data. The dataset was geographically split into a development (58.7%) and validation (41.3%) sample to predict unplanned hospitalisations within 6 months. We evaluated the performance of three different models with increasingly smaller selections of candidate predictors (i.e. optimal, readily-available and easy-to-use model, respectively). We used logistic regression with backward selection for model development. The models were validated internally and externally. We assessed predictive performance by area under the curve (AUC) and calibration plots. In both samples, 7.6% had at least one unplanned hospitalisation within 6 months. The discriminative ability of the three models was comparable and remained stable after geographic validation. The easy-to-use model included age, sex, prior hospitalisations, pulmonary emphysema, heart failure and polypharmacy. Its discriminative ability after validation was AUC 0.72 [95% confidence interval: 0.72-0.71]. Calibration plots showed good calibration. Our models showed satisfactory predictive ability. Reducing the number of predictors and geographic validation did not impact predictive performance, demonstrating the robustness of the model. We developed an easy-to-use tool that may assist general practitioners in decision-making and targeted preventive interventions.

Background Generalist approaches can help address several challenges facing today's primary care. However, GPs report insufficient support to deliver advanced generalist medicine (AGM) in daily practice, struggling within a healthcare system that imposes strict adherence to single-disease focused guidelines. Aim We aimed to examine the professional and educational experiences of newly qualified GPs attending a course on AGM to understand how to redesign primary care systems to support their generalist work. Design and Setting Qualitative study focusing on AGM in UK general practice (England), conducted in the context of the research evaluation of an online career development programme on AGM. Method We conducted 36 interviews and 6 focus groups with newly qualified GPs attending an online career development programme on AGM, and analysed data using framework analysis. Results Three tensions experienced by the participants were identified: tension between realistic and idealistic practice; tension between different decision-making paradigms; tension in the formation of the GPs' professional identities. These were due to grey areas of practice deeply rooted in primary care systems - namely areas of work not adequately addressed by current education and service design. Conclusions Our findings have implications for tackling the general practice workforce crisis, highlighting that solutions targeting individual problems will not suffice by themselves. By making visible the grey areas of everyday general practice, we describe the changes needed to target tensions as described by the GPs in this study to ultimately enable, enhance and make visible the complex work of generalist medicine.

Background During the COVID-19 pandemic, global trends of reduced healthcare-seeking behaviour were observed. This raises concerns about the consequences of healthcare avoidance for population health. Aim To determine the association between healthcare avoidance during the early stages of the COVID-19 pandemic and all-cause mortality. Design and setting 32-month follow-up within the population-based Rotterdam Study, after sending a COVID-19 questionnaire at the onset of the pandemic in April 2020 to all non-institutionalised participants (response rate 73%). Method Cox proportional hazards models assessed the risk of all-cause mortality among respondents who avoided healthcare because of the COVID-19 pandemic. Mortality status was collected through municipality registries and medical records. Results Of 5656 respondents, one-fifth avoided healthcare due to the COVID-19 pandemic (N=1143). Compared to non-avoiders, those who avoided healthcare more often reported symptoms of depression (31.2% versus 12.3%) and anxiety (29.7% versus 12.2%), and more often valued their health as poor to fair (29.4% versus 10.1%). Healthcare avoiders had an increased adjusted risk of all-cause mortality (HR: 1.30; 95%CI 1.01-1.67), which remained nearly identical after adjustment for history of any non-communicable disease (1.20;0.93-1.54). However, this association attenuated after additional adjustment for mental and self-appreciated health factors (0.96;0.74-1.24). Conclusion We found an increased risk of all-cause mortality among individuals who avoided healthcare during COVID-19. These individuals were characterised by poor mental and physical self-appreciated health. Therefore, interventions should be targeted to these vulnerable individuals to safeguard their access to primary and specialist care in order to limit health disparities, inside and beyond healthcare crises.