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Background We aimed to investigate the longitudinal impact of COVID-19 and the effects of clinical and psychosocial factors, accounting for post-COVID conditions (PCC), on the mental and physical aspect of health-related quality of life (HRQoL) of patients diagnosed with COVID-19. Methods Data from the Nivel Corona Cohort were used, which includes individuals with an established SARS-CoV-2 infection that received four questionnaires over a year’s time with questions regarding HRQoL (SF-12), symptoms and social characteristics. PCC was determined based on questionnaire data. Data on medical history and healthcare utilization were obtained from electronic health records from general practice. A repeated measures linear mixed model was used to explore associations between clinical and social characteristics, and the course of mental and physical HRQoL after a SARS-CoV-2 infection, taking PCC into account. Results One hundred fifty-eight individuals of whom it was possible to determine whether they had PCC or not were included in this study. Seventy-six (48.1%) developed PCC, which was associated with a persistent reduction in both physical and mental HRQoL. Hospitalization during the acute phase of the infection had a negative impact on the physical HRQoL, which decreased over time. Females, people older than 53, and those with increased resilience and mental HRQoL before infection were more likely to report a more positive mental HRQoL over time. Conclusion The negative association PCC has with both mental and physical HRQoL for at least six months, calls for more research to support patients with PCC.

Introduction: Harmonized neuropsychological assessment for neurocognitive disorders, an international priority for valid and reliable diagnostic procedures, has been achieved only in specific countries or research contexts.Methods: To harmonize the assessment of mild cognitive impairment in Europe, a workshop (Geneva, May 2018) convened stakeholders, methodologists, academic, and non-academic clinicians and experts from European, US, and Australian harmonization initiatives.Results: With formal presentations and thematic working-groups we defined a standard battery consistent with the U.S. Uniform DataSet, version 3, and homogeneous methodology to obtain consistent normative data across tests and languages. Adaptations consist of including two tests specific to typical Alzheimer's disease and behavioral variant frontotemporal dementia. The methodology for harmonized normative data includes consensus definition of cognitively normal controls, classification of confounding factors (age, sex, and education), and calculation of minimum sample sizes.Discussion: This expert consensus allows harmonizing the diagnosis of neurocognitive disorders across European countries and possibly beyond.

The use of routinely recorded electronic health record (EHR) data is increasingly common, especially in epidemiological research. However, data must be processed and prepared for secondary use, and decisions made during this process could significantly impact research outcomes. A demonstration of the extent of these consequences is necessary. The aim of this study was to investigate the influence of data processing steps on research outcomes derived from the secondary use of EHR data. EHR data from 8 Dutch general practices from 2019 were used. These practices contributed data to 2 research databases: the Academic General Practitioner Development Network registry and the Nivel Primary Care Database. Data were extracted and processed through distinct extraction, transformation, and loading (ETL) pipelines, allowing the evaluation of the impact of different ETL methods by comparing the 2 datasets in three steps: (1) patient demographics, (2) epidemiology of concordant patients, and (3) health service use of patients with 3 diagnoses. A number of similarity indicators, including the number of contacts, regular consultations and visits, prescriptions, and episodes, were compared between the 2 databases. The outcomes were compared by performing paired samples t tests using 99% CIs. Prevalence, number of prescriptions, and number of regular consultations and visits per 1000 patient years were calculated and compared for 3 diagnoses (diabetes mellitus, urinary tract infection, and cough). These outcomes were compared using the SD. Differences were observed between the datasets in the number of enrolled patients (Academic General Practitioner Development Network registry: n=47,517; Nivel Primary Care Database: n=44,247). Despite this, patient demographics were similar. All indicator outcomes of the concordant patients showed significant differences between the databases, that is, the number of contacts, prescriptions, and episodes per patient, and the number of regular consultations and visits. Differences in the indicator outcomes for the 3 diagnosis groups varied greatly in SD, however, none of the differences were deemed significant. The findings highlight the importance of routine health data users' awareness of different ETL steps involved. Transparency and shared knowledge about these processes are critical, and making them available for research is necessary. Data processors should share their knowledge regarding their choices, and researchers and policy makers should invest in their knowledge of this type of metadata. Transparency and shared knowledge are particularly important in light of the European Health Data Space and the ever-increasing secondary use of routinely recorded health data. Future research should focus on the role of transparency, joint decision-making, and the minimization of effects of ETL steps, and on the insight into the individual influence of ETL steps on research outcomes. This could stimulate standardized approaches among data processors and researchers, resulting in increased data interoperability.

Some of those infected with SARS-CoV-2 suffer from post-COVID syndrome (PCS). However, an uniform definition of PCS is lacking, causing uncertainty about the prevalence and nature of this syndrome. We aimed to improve understanding of PCS by operationalizing different classifications and to explore clinical subtypes. We used data from Nivel Primary Care database from 2019-2020 which consists of electronic health records (EHR) from general practices (GPs) combined with sociodemographic data for n = 10,313 individuals infected with the SARS-CoV-2. In addition, data from n = 276 individuals who had been infected with the SARS-CoV-2 in 2021, collected via a longitudinal survey, was used. In the GP-EHR data, we operationalized two classifications of PCS (based on symptoms and diagnosis recorded in GP-EHR data and healthcare utilization 3-12 months after acute infection) to calculate frequency and characteristics and compared this to the survey results. In a subgroup of the EHR data we conducted community detection analyses to explore clinical subtypes of PCS. The frequency of PCS was 15% with on average 4.6 symptoms for which the GP was consulted using the narrow definition and 32% with on average 6.8 symptoms for the broad definition. Across all methods and classifications, the mean age of individuals with PCS was around 53 years and they were more often female. There were small sex differences in the type of symptoms and overall symptoms were persistent for 6 months. The community detection analysis revealed three possible clinical subtypes. We showed that frequency rates of PCS differ between methods and data sources, but characteristics of the affected individuals are relatively stable. Overall, PCS is a heterogeneous syndrome affecting a substantial group of individuals who need adequate care. Future studies should focus on care trajectories and qualitative measures such as quality of life of individuals living with PCS.

Background Policymakers have embraced substitution of hospital care to more affordable primary care as a means to contain rising healthcare costs and provide care closer to home. Health insurers play an important role in the extent to which substitution of care takes place. This study explores the perspective of Dutch health insurers on barriers and facilitators to facilitate a shift from hospitals to general practice in the current healthcare system. Methods Semi-structured group interviews were conducted with healthcare purchasers from various health insurers, involving fifteen participants from seven insurers representing 76.5% of the market. Thematic analysis was used to identify perceived facilitators and barriers for effective substitution of care. Results Long-term contracts that enable strategic planning and collaboration between general practices and hospitals, as well as strong organizational structures in general practice and long waiting times in hospitals are reported to facilitate substitution. Uncertainties around collaboration under the Competition Act, inadequate compensation through the risk equalization model, complex billing codes for innovative initiatives, a rigid national budgetary framework and strong bargaining power of hospitals as opposed to insurers and general practices are stated to hinder the shift towards general practice. Conclusions Key areas for improvement to facilitate substitution, as reported by healthcare purchasers, include clear guidelines on insurer collaboration, adjustments to the risk equalization model, strengthening the bargaining power of general practices, and promoting long-term contracts. This study provides insights into the perceived barriers and facilitators for care substitution from the payer’s perspective. Addressing these barriers is essential for facilitating the shift from hospital to general practice care. Also, potential discrepancies between perceptions and current regulations highlight areas where enhanced dialogue and collaboration between policy makers and health insurers could improve mutual understanding and regulatory compliance.

Background There is an urgent need for novel, noninvasive biomarkers to diagnose Alzheimer’s disease (AD) in the predementia stages and to predict the rate of decline. Therefore, we set up the European Medical Information Framework for Alzheimer’s Disease Multimodal Biomarker Discovery (EMIF-AD MBD) study. In this report we describe the design of the study, the methods used and the characteristics of the participants. Methods Participants were selected from existing prospective multicenter and single-center European studies. Inclusion criteria were having normal cognition (NC) or a diagnosis of mild cognitive impairment (MCI) or AD-type dementia at baseline, age above 50 years, known amyloid-beta (Aβ) status, availability of cognitive test results and at least two of the following materials: plasma, DNA, magnetic resonance imaging (MRI) or cerebrospinal fluid (CSF). Targeted and untargeted metabolomic and proteomic analyses were performed in plasma, and targeted and untargeted proteomics were performed in CSF. Genome-wide SNP genotyping, next-generation sequencing and methylation profiling were conducted in DNA. Visual rating and volumetric measures were assessed on MRI. Baseline characteristics were analyzed using ANOVA or chi-square, rate of decline analyzed by linear mixed modeling. Results We included 1221 individuals (NC n  = 492, MCI n  = 527, AD-type dementia n  = 202) with a mean age of 67.9 (SD 8.3) years. The percentage Aβ+ was 26% in the NC, 58% in the MCI, and 87% in the AD-type dementia groups. Plasma samples were available for 1189 (97%) subjects, DNA samples for 929 (76%) subjects, MRI scans for 862 (71%) subjects and CSF samples for 767 (63%) subjects. For 759 (62%) individuals, clinical follow-up data were available. In each diagnostic group, the APOE ε4 allele was more frequent amongst Aβ+ individuals ( p  < 0.001). Only in MCI was there a difference in baseline Mini Mental State Examination (MMSE) score between the A groups ( p  < 0.001). Aβ+ had a faster rate of decline on the MMSE during follow-up in the NC ( p  < 0.001) and MCI ( p  < 0.001) groups. Conclusions The characteristics of this large cohort of elderly subjects at various cognitive stages confirm the central roles of Aβ and APOE ε4 in AD pathogenesis. The results of the multimodal analyses will provide new insights into underlying mechanisms and facilitate the discovery of new diagnostic and prognostic AD biomarkers. All researchers can apply for access to the EMIF-AD MBD data by submitting a research proposal via the EMIF-AD Catalog.

Alzheimer’s disease (AD) is the most frequent neurodegenerative disease with an increasing prevalence in industrialized, ageing populations. AD susceptibility has an established genetic basis which has been the focus of a large number of genome-wide association studies (GWAS) published over the last decade. Most of these GWAS used dichotomized clinical diagnostic status, i.e. case vs. control classification, as outcome phenotypes, without the use of biomarkers. An alternative and potentially more powerful study design is afforded by using quantitative AD-related phenotypes as GWAS outcome traits, an analysis paradigm that we followed in this work. Specifically, we utilized genotype and phenotype data from n=931 individuals collected under the auspices of the European Medical Information Framework for Alzheimer’s Disease Multimodal Biomarker Discovery (EMIF-AD MBD) study to perform a total of 19 separate GWAS analyses. As outcomes we used five magnetic resonance imaging (MRI) traits and seven cognitive performance traits. For the latter, longitudinal data from at least two timepoints were available in addition to cross-sectional assessments at baseline. Our GWAS analyses revealed several genome-wide significant associations for the neuropsychological performance measures, in particular those assayed longitudinally. Among the most noteworthy signals were associations in or near EHBP1 (EH domain binding protein 1; on chromosome 2p15) and CEP112 (centrosomal protein 112; 17q24.1) with delayed recall in a memory performance test. On the X chromosome, which is often excluded in other GWAS, we identified a genome-wide significant signal near IL1RAPL1 (interleukin 1 receptor accessory protein like 1; Xp21.3). While polygenic score (PGS) analyses showed the expected strong associations with SNPs highlighted in relevant previous GWAS on hippocampal volume and cognitive function, they did not show noteworthy associations with recent AD risk GWAS findings. In summary, our study highlights the power of using quantitative endophenotypes as outcome traits in AD-related GWAS analyses and nominates several new loci not previously implicated in cognitive decline.

Background Aggregation of amyloid β into plaques in the brain is one of the earliest pathological events in Alzheimer’s disease (AD). The exact pathophysiology leading to dementia is still uncertain, but the apolipoprotein E (APOE) ε4 genotype plays a major role. We aimed to identify the molecular pathways associated with amyloid β aggregation using cerebrospinal fluid (CSF) proteomics and to study the potential modifying effects of APOE ε4 genotype. Methods We tested 243 proteins and protein fragments in CSF comparing 193 subjects with AD across the cognitive spectrum (65% APOE ε4 carriers, average age 75 ± 7 years) against 60 controls with normal CSF amyloid β, normal cognition, and no APOE ε4 allele (average age 75 ± 6 years). Results One hundred twenty-nine proteins (53%) were associated with aggregated amyloid β. APOE ε4 carriers with AD showed altered concentrations of proteins involved in the complement pathway and glycolysis when cognition was normal and lower concentrations of proteins involved in synapse structure and function when cognitive impairment was moderately severe. APOE ε4 non-carriers with AD showed lower expression of proteins involved in synapse structure and function when cognition was normal and lower concentrations of proteins that were associated with complement and other inflammatory processes when cognitive impairment was mild. Repeating analyses for 114 proteins that were available in an independent EMIF-AD MBD dataset ( n  = 275) showed that 80% of the proteins showed group differences in a similar direction, but overall, 28% effects reached statistical significance (ranging between 6 and 87% depending on the disease stage and genotype), suggesting variable reproducibility. Conclusions These results imply that AD pathophysiology depends on APOE genotype and that treatment for AD may need to be tailored according to APOE genotype and severity of the cognitive impairment.

Background Genome-wide association studies (GWAS) of Alzheimer’s disease (AD) have identified several risk loci, but many remain unknown. Cerebrospinal fluid (CSF) biomarkers may aid in gene discovery and we previously demonstrated that six CSF biomarkers (β-amyloid, total/phosphorylated tau, NfL, YKL-40, and neurogranin) cluster into five principal components (PC), each representing statistically independent biological processes. Here, we aimed to (1) identify common genetic variants associated with these CSF profiles, (2) assess the role of associated variants in AD pathophysiology, and (3) explore potential sex differences. Methods We performed GWAS for each of the five biomarker PCs in two multi-center studies (EMIF-AD and ADNI). In total, 973 participants ( n  = 205 controls, n  = 546 mild cognitive impairment, n  = 222 AD) were analyzed for 7,433,949 common SNPs and 19,511 protein-coding genes. Structural equation models tested whether biomarker PCs mediate genetic risk effects on AD, and stratified and interaction models probed for sex-specific effects. Results Five loci showed genome-wide significant association with CSF profiles, two were novel (rs145791381 [inflammation] and GRIN2D [synaptic functioning]) and three were previously described ( APOE , TMEM106B , and CHI3L1 ). Follow-up analyses of the two novel signals in independent datasets only supported the GRIN2D locus, which contains several functionally interesting candidate genes. Mediation tests indicated that variants in APOE are associated with AD status via processes related to amyloid and tau pathology, while markers in TMEM106B and CHI3L1 are associated with AD only via neuronal injury/inflammation. Additionally, seven loci showed sex-specific associations with AD biomarkers. Conclusions These results suggest that pathway and sex-specific analyses can improve our understanding of AD genetics and may contribute to precision medicine.

During the COVID-19 pandemic, chronic disease management programs (CDMP) for Dutch type 2 diabetes patients by general practitioners (GP) were scaled down. These programs aim to improve diabetes prognosis through appropriate interventions and avoid hospital treatment. However, it remains unknown whether downsizing CDMP increased care in other settings. Therefore, we examined the changes in healthcare utilization for type 2 diabetes patients during the COVID-19 pandemic including CDMP, GP out-of-hours care, hospital care, and regular GP care. Routine healthcare data from electronic patient records of GPs, participating in Nivel Primary Care Database, of 15,247 Dutch type 2 diabetes patients enrolled in CDMP, were linked to GP out-of-hours registration data and hospital claims data. Regression analyses compared healthcare utilization in 2020 and 2021 (pandemic) to 2019 (non-pandemic). For most quarters of 2020 and 2021, care through CDMP was significantly lower, down to 38% in Q2 of 2020 compared to 2019. In Q1 of 2020, type 2 diabetes patient visits to out-of-hours GP services rose notably, but decreased in Q1 of 2021, compared to 2019. Hospital care for diabetes showed a significant increase in Q2 of 2021 (+11.3%), compared to Q2 2019 and regular GP care increased from Q1 2021 (up to +11.1% in Q3 2021). Although no significant differences were observed in other quarters, there were different trends visible. Reduced CDMP contacts in 2020 were significantly associated with increased regular GP care in 2021. Moreover, reduced CDMP in early 2021 was significantly associated with more regular GP care and hospital care later in 2021. Downscaling CDMP care for type 2 diabetes patients during the COVID-19 pandemic was associated with temporary increases in hospital care for diabetes and regular GP care at various times during the pandemic. These findings may contribute to making informed decisions regarding measures during future pandemics, and, therefore, the pandemic provided a unique learning opportunity for the healthcare system in delivering appropriate care through CDMP. In future pandemics, it will be essential to implement adaptations such as telemedicine to mitigate health deterioration and alleviate pressure on other healthcare services.