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Aims To validate a multivariable risk prediction model (Cohorts for Heart and Aging Research in Genomic Epidemiology model for atrial fibrillation (CHARGE-AF)) for 5-year risk of atrial fibrillation (AF) in routinely collected primary care data and to assess CHARGE-AF’s potential for automated, low-cost selection of patients at high risk for AF based on routine primary care data.Methods We included patients aged ≥40 years, free of AF and with complete CHARGE-AF variables at baseline, 1 January 2014, in a representative, nationwide routine primary care database in the Netherlands (Nivel-PCD). We validated CHARGE-AF for 5-year observed AF incidence using the C-statistic for discrimination, and calibration plot and stratified Kaplan-Meier plot for calibration. We compared CHARGE-AF with other predictors and assessed implications of using different CHARGE-AF cut-offs to select high-risk patients.Results Among 111 475 patients free of AF and with complete CHARGE-AF variables at baseline (17.2% of all patients aged ≥40 years and free of AF), mean age was 65.5 years, and 53% were female. Complete CHARGE-AF cases were older and had higher AF incidence and cardiovascular comorbidity rate than incomplete cases. There were 5264 (4.7%) new AF cases during 5-year follow-up among complete cases. CHARGE-AF’s C-statistic for new AF was 0.74 (95% CI 0.73 to 0.74). The calibration plot showed slight risk underestimation in low-risk deciles and overestimation of absolute AF risk in those with highest predicted risk. The Kaplan-Meier plot with categories 5% predicted 5-year risk was highly accurate. CHARGE-AF outperformed CHA2DS2-VASc (Cardiac failure or dysfunction, Hypertension, Age >=75 [Doubled], Diabetes, Stroke [Doubled]-Vascular disease, Age 65-74, and Sex category [Female]) and age alone as predictors for AF. Dichotomisation at cut-offs of 2.5%, 5% and 10% baseline CHARGE-AF risk all showed merits for patient selection in AF screening efforts.Conclusion In patients with complete baseline CHARGE-AF data through routine Dutch primary care, CHARGE-AF accurately assessed AF risk among older primary care patients, outperformed both CHA2DS2-VASc and age alone as predictors for AF and showed potential for automated, low-cost patient selection in AF screening.

A comprehensive and representative nationwide general practice database was explored to study associations between atopic disorders and prescribed medication in children.All children aged 0-18 years listed in the NIVEL Primary Care Database in 2014 were selected. Atopic children with atopic eczema, asthma and allergic rhinitis (AR) were matched with controls (not diagnosed with any of these disorders) within the same general practice on age and gender. Logistic regression analyses were performed to study the differences in prescribed medication between both groups by calculating odds ratios (OR); 93 different medication groups were studied.A total of 45,964 children with at least one atopic disorder were identified and matched with controls. Disorder-specific prescriptions seem to reflect evidence-based medicine guidelines for atopic eczema, asthma and AR. However, these disorder-specific prescriptions were also prescribed for children who were not registered as having that specific disorder. For eczema-related medication, about 3.7-8.4% of the children with non-eczematous atopic morbidity received these prescriptions, compared to 1.4-3.5% of the non-atopic children. The same pattern was observed for anti-asthmatics (having non-asthmatic atopic morbidity: 0.8-6.2% vs. controls: 0.3-2.1%) and AR-related medication (having non-AR atopic morbidity: 4.7-12.5% vs. controls: 2.8-3.1%). Also, non-atopic related medication, such as laxatives and antibiotics were more frequently prescribed for atopic children.The present study shows that atopic children received more prescriptions, compared to non-atopic children. Non-atopic controls frequently received specific prescriptions for atopic disorders. This indicates that children with atopic disorders need better monitoring by their GP.

Abstract Background Cardiometabolic diseases (CMD) are the major cause of death worldwide and are associated with a lower quality of life and high healthcare costs. To prevent a further rise in CMD and related healthcare costs, early detection and adequate management of individuals at risk could be an effective preventive strategy. The objective of this study was to determine long-term cost-effectiveness of stepwise CMD risk assessment followed by individualized treatment if indicated compared to care as usual. A computer-based simulation model was used to project long-term health benefits and cost-effectiveness, assuming the prevention program was implemented in Dutch primary care. Methods A randomized controlled trial in a primary care setting in which 1934 participants aged 45–70 years without recorded CMD or CMD risk factors participated. The intervention group was invited for stepwise CMD risk assessment through a risk score (step 1), additional risk assessment at the practice in case of increased risk (step 2) and individualized follow-up treatment if indicated (step 3). The control group was not invited for risk assessment, but completed a health questionnaire. Results of the effectiveness analysis on systolic blood pressure (− 2.26 mmHg; 95% CI − 4.01: − 0.51) and total cholesterol (− 0.15 mmol/l; 95% CI − 0.23: − 0.07) were used in this analysis. Outcome measures were the costs and benefits after 1-year follow-up and long-term (60 years) cost-effectiveness of stepwise CMD risk assessment compared to no assessment. A computer-based simulation model was used that included data on disability weights associated with age and disease outcomes related to CMD. Analyses were performed taking a healthcare perspective. Results After 1 year, the average costs in the intervention group were 260 Euro higher than in the control group and differences were mainly driven by healthcare costs. No meaningful change was found in EQ 5D-based quality of life between the intervention and control groups after 1-year follow-up (− 0.0154; 95% CI − 0.029: 0.004). After 60 years, cumulative costs of the intervention were 41.4 million Euro and 135 quality-adjusted life years (QALY) were gained. Despite improvements in blood pressure and cholesterol, the intervention was not cost-effective (ICER of 306,000 Euro/QALY after 60 years). Scenario analyses did not allow for a change in conclusions with regard to cost-effectiveness of the intervention. Conclusions Implementation of this primary care-based CMD prevention program is not cost-effective in the long term. Implementation of this program in primary care cannot be recommended. Trial registration Dutch Trial Register NTR4277 , registered on 26 November 2013

Abstract Background Incidence rates and prevalence proportions are commonly used to express the populations health status. Since there are several methods used to calculate these epidemiological measures, good comparison between studies and countries is difficult. This study investigates the impact of different operational definitions of numerators and denominators on incidence rates and prevalence proportions. Methods Data from routine electronic health records of general practices contributing to NIVEL Primary Care Database was used. Incidence rates were calculated using different denominators (person-years at-risk, person-years and midterm population). Three different prevalence proportions were determined: 1 year period prevalence proportions, point-prevalence proportions and contact prevalence proportions. Results One year period prevalence proportions were substantially higher than point-prevalence (58.3 - 206.6%) for long-lasting diseases, and one year period prevalence proportions were higher than contact prevalence proportions (26.2 - 79.7%). For incidence rates, the use of different denominators resulted in small differences between the different calculation methods (-1.3 - 14.8%). Using person-years at-risk or a midterm population resulted in higher rates compared to using person-years. Conclusions All different operational definitions affect incidence rates and prevalence proportions to some extent. Therefore, it is important that the terminology and methodology is well described by sources reporting these epidemiological measures. When comparing incidence rates and prevalence proportions from different sources, it is important to be aware of the operational definitions applied and their impact.

Abstract Background The value of joint ultrasonography (US) in the prediction of clinical arthritis in individuals at risk of developing rheumatoid arthritis (RA) is still a point of debate, due to varying scanning protocols and different populations. We investigated whether US abnormalities assessed with a standard joint protocol can predict development of arthritis in seropositive patients with arthralgia. Methods Anti-citrullinated protein antibodies and/or rheumatoid factor positive patients with arthralgia, but without clinical arthritis were included. US was performed at baseline in 16 joints: bilateral metacarpophalangeal 2–3, proximal interphalangeal 2–3, wrist and metatarsophalangeal (MTP) joints 2–3 and 5. Images were scored semi-quantitatively for synovial thickening and for positive signs on power Doppler (PD). Association between US abnormalities and arthritis development at the joint and at the patient level was evaluated. Also, we investigated the added value of US over clinical parameters. Results Out of 163 patients who underwent US examination, 51 (31%) developed clinical arthritis after a median follow-up time of 12 (interquartile range 5–24) months, of which 44 (86%) satisfied the 2010 American College of Rheumatology/European League Against Rheumatism classification criteria for RA. US revealed synovial thickening and PD in at least one joint in 49 patients (30%) and 7 patients (4%), respectively. Synovial thickening was associated with both development and timing of clinical arthritis in any joint (patient level) when MTP joints were excluded from the US assessment (odds ratio 6.6, confidence interval (CI) 1.9–22), and hazard ratio 3.4, CI 1.6–6.8, respectively, with a mean time to arthritis of 23 versus 45 months when synovial thickening was present versus not present). There was no association between US and arthritis development at the joint level. Predictive capacity was highest in the groups with an intermediate and high risk of developing arthritis based on a prediction rule with clinical parameters. Conclusions Synovial thickening on US predicted clinical arthritis development at the patient level in seropositive patients with arthralgia when MTPs were excluded from the US assessment. Positive PD signs were infrequently seen in these at-risk individuals and was not predictive. In patients at intermediate risk of RA, US may help to identify those at higher risk of developing arthritis.

Atopic disorders: Clinical information improves prevalence estimates The prevalence of atopic disorders in children can be more reliably calculated by incorporating clinical information with diagnosis data. Researchers in the Netherlands, led by David Pols from the Erasmus University Medical Center Rotterdam, examined the electronic health records of more than 660,000 children, aged 0 to 18, from a Dutch primary care database to determine the number of cases of atopic eczema, asthma, and allergic rhinitis. They looked for diagnosed children who also had at least two relevant clinical consultations and at least two relevant prescriptions. This strategy helps correct for the problem of overestimation, because it doesnot assume that a child, once diagnosed, will have an atopic disorder for life. However, other methods are still needed to identify cases that are missed or misclassified in the health database.