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The Cohort Study of Mobile Phone Use and Health (COSMOS) has repeatedly collected self-reported and operator-recorded data on mobile phone use. Assessing health effects using self-reported information is prone to measurement error, but operator data were available prospectively for only part of the study population and did not cover past mobile phone use. To optimize the available data and reduce bias, we evaluated different statistical approaches for constructing mobile phone exposure histories within COSMOS. We evaluated and compared the performance of 4 regression calibration (RC) methods (simple, direct, inverse, and generalized additive model for location, shape, and scale), complete-case analysis, and multiple imputation in a simulation study with a binary health outcome. We used self-reported and operator-recorded mobile phone call data collected at baseline (2007-2012) from participants in Denmark, Finland, the Netherlands, Sweden, and the United Kingdom. Parameter estimates obtained using simple, direct, and inverse RC methods were associated with less bias and lower mean squared error than those obtained with complete-case analysis or multiple imputation. We showed that RC methods resulted in more accurate estimation of the relationship between mobile phone use and health outcomes by combining self-reported data with objective operator-recorded data available for a subset of participants. [ABSTRACT FROM AUTHOR]

Background: Unplanned admissions to hospital represent a hazardous event for older people. Timely identification of high-risk individuals using a prediction tool may facilitate preventive interventions. Aim: To develop and validate an easy-to-use prediction model for unplanned admissions to hospital in community-dwelling older adults using readily available data to allow rapid bedside assessment by GPs. Design and setting: This was a retrospective study using the general practice electronic health records of 243 324 community-dwelling adults aged ≥65 years linked with national administrative data to predict unplanned admissions to hospital within 6 months. Method: The dataset was geographically split into a development (n = 142 791/243 324, 58.7%) and validation (n = 100 533/243 324, 41.3%) sample to predict unplanned admissions to hospital within 6 months. The performance of three different models was evaluated with increasingly smaller selections of candidate predictors (optimal, readily available, and easy-to-use models). Logistic regression was used with backward selection for model development. The models were validated internally and externally. Predictive performance was assessed by area under the curve (AUC) and calibration plots. Results: In both samples, 7.6% (development cohort: n = 10 839/142 791, validation cohort: n = 7675/100 533) had ≥1 unplanned hospital admission 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 admissions to hospital, pulmonary emphysema, heart failure, and polypharmacy. Its discriminative ability after validation was AUC 0.72 (95% confidence interval = 0.71 to 0.72). Calibration plots showed good calibration. Conclusion: The models showed satisfactory predictive ability. Reducing the number of predictors and geographic validation did not have an impact on predictive performance, demonstrating the robustness of the model. An easy-to-use tool has been developed in this study that may assist GPs in decision making and with targeted preventive interventions. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to assess the impact of on-demand versus continuous prescribing of proton pump inhibitors (PPIs) on symptom burden and health-related quality of life in patients with gastroesophageal reflux disease (GERD) presenting to primary care. Methods: Thirty-six primary care centres across Europe enrolled adult GERD patients from electronic health records. Participants were randomised to on-demand or continuous PPI prescriptions and were followed for 8 weeks. PPI intake, symptom burden, and quality of life were compared between the two groups using mixed-effect regression analyses. Spearman's correlation was used to assess the association between changes in PPI dose and patient-reported outcomes. Results: A total of 488 patients (median age 51years, 58% women) completed the initial visit, with 360 attending the follow-up visit. There was no significant difference in PPI use between the continuous and on-demand prescription groups (b=.57, 95%CI:0.40-1.53), although PPI use increased in both groups (b=1.33, 95%CI:0.65-2.01). Advice on prescribing strategy did not significantly affect patient-reported outcomes. Both symptom burden (Reflux Disease Questionnaire, b=-0.61, 95%CI:-0.73 - -0.49) and quality of life (12-item Short Form Survey physical score b=3.31, 95%CI:2.17-4.45) improved from baseline to follow-up in both groups. Increased PPI intake correlated with reduced reflux symptoms (n=347, ρ=-0.12, p=0.02) and improved quality of life (n=217, ρ=0.16, p=0.02). Conclusion: In real-world settings, both continuous and on-demand PPI prescriptions resulted in similar increases in PPI consumption with no difference in treatment effects. Achieving an adequate PPI dose to alleviate reflux symptom burden improves quality of life in GERD patients. EudraCT number 2014-001314-25. [ABSTRACT FROM AUTHOR]

Background: Many countries observed a sharp decline in the use of general practice services after the outbreak of the COVID-19 pandemic. However, research has not yet considered how changes in healthcare consumption varied among regions with the same restrictive measures but different COVID-19 prevalence. Aim: To investigate how the COVID-19 pandemic affected healthcare consumption in Dutch general practice during 2020 and 2021, among regions with known heterogeneity in COVID-19 prevalence, from a pre-pandemic baseline in 2019. Design & setting: Population-based cohort study using electronic health records. The study was undertaken in Dutch general practices involved in regional research networks. Method: An interrupted time-series analysis of changes in healthcare consumption from before to during the pandemic was performed. Descriptive statistics were used on the number of potential COVID-19-related contacts, reason for contact, and type of contact. Results: The study covered 3 595 802 contacts (425 639 patients), 3 506 637 contacts (433 340 patients), and 4 105 413 contacts (434 872 patients) in 2019, 2020, and 2021, respectively. Time-series analysis revealed a significant decrease in healthcare consumption after the outbreak of the pandemic. Despite interregional heterogeneity in COVID-19 prevalence, healthcare consumption decreased comparably over time in the three regions, before rebounding to a level significantly higher than baseline in 2021. Physical consultations transitioned to phone or digital over time. Conclusion: Healthcare consumption decreased irrespective of the regional prevalence of COVID-19 from the start of the pandemic, with the Delta variant triggering a further decrease. Overall, changes in care consumption appeared to reflect contextual factors and societal restrictions rather than infection rates. [ABSTRACT FROM AUTHOR]

Introduction: This paper provides insight into the development of the Dutch Dementia Care and Support Registry and the lessons that can be learned from it. The aim of this Registry was to contribute to quality improvement in dementia care and support. Methods: This paper describes how the Registry was set up in four stages, reflecting the four FAIR principles: the selection of data sources (Findability); obtaining access to the selected data sources (Accessibility); data linkage (Interoperability); and the reuse of data (Reusability). Results: The linkage of 16 different data sources, including national routine health and administrative data appeared to be technically and legally feasible. The linked data in the Registry offers rich information about (the use of) care for persons with dementia across various healthcare settings, including but not limited to primary care, secondary care, long-term care and medication use, that cannot be obtained from single data sources. Conclusions: A key lesson learned is that in order to reuse the data for quality improvement in practice, it is essential to involve healthcare professionals in setting up the Registry and to guide them in the interpretation of the data. [ABSTRACT FROM AUTHOR]