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Abstract Background Guidelines recommend to prescribe a laxative with an opioid to prevent constipation. We aimed to determine the adherence by general practitioners (GPs) to this recommendation and to explore which GP- and patient related factors were associated with it from the perspective of the GP. Methods We conducted an observational study using GPs’ prescription data from the Nivel Primary Care Database combined with a questionnaire asking for reasons of non-adherence. The proportion of first opioid prescriptions prescribed together with a laxative was determined as primary outcome. Possible explanatory factors such as the quality of registration, the level of collaboration with the pharmacy, familiarity with the recommendation and use of a clinical decision support system were explored, as were the self-reported reasons for non-adherence (classified as either GP-related or patient-related). We assessed the association of factors with the primary outcome using univariable multilevel logistic regression analysis. Results The recommendation was measured in 195 general practices. The median proportion of first opioid prescriptions prescribed together with a laxative in these practices was 54% (practice range 18–88%). None of the determinants was consistently associated with the primary outcome. GPs from 211 practices filled out the questionnaire and the most frequently mentioned reason not to prescribe a laxative was that the patient has laxatives in stock, followed by that the patient doesn’t want a laxative; both were patient-related factors. Conclusion There was room for improvement in following the guideline on laxative prescribing in opioid use. A main reason seemed to be that the patient refuses a laxative. Improvement measures should therefore focus on communication between GPs and patients on the relevance of co-using a laxative with opioids. Future studies need to establish the effect of such improvement measures, and determine whether reasons for non-adherence to the guideline changed over time.

Abstract Background The growing volume of health data provides new opportunities for medical research. By using existing registries, large populations can be studied over a long period of time. Patient-level linkage of registries leads to even more detailed and extended information per patient, but brings challenges regarding responsibilities, privacy and security, and quality of data linkage. In this paper we describe how we dealt with these challenges when creating the Primary Secondary Cancer Care Registry (PSCCR)- Breast Cancer. Methods The PSCCR – Breast Cancer was created by linking two existing registries containing data on 1) diagnosis, tumour and treatment characteristics of all Dutch breast cancer patients (NCR), and 2) consultations and diagnoses from primary care electronic health records of about 10% of Dutch GP practices (Nivel-PCD). The existing registry governance structures and privacy regulations were incorporated in those of the new registry. Privacy and security risks were reassessed. Data were restricted to females and linked using postal code and date of birth. The breast cancer diagnosis was verified in both registries and for a subsample of 44 patients with the GP as well. Results A collaboration agreement was signed in which the organisations retained data responsibility and accountability for ‘their’ registry. A Trusted Third Party performed the record linkage. Ten percent of the patients with breast cancer could be linked to the primary care registry, as was expected based on the coverage of Nivel-PCD, and finally 7 % could be included. The breast cancer diagnosis was verified by the GP in 42 of the 44 patients. Conclusions We developed and validated a procedure for patient-level linkage of health data registries without a unique identifier, while preserving the integrity and privacy of the original registries. The method described may help researchers wishing to link existing health data registries.

Abstract Background General practitioners (GPs) can be considered the designated professionals to identify high fall risk and to guide older people to fall preventive care. Currently it is not exactly known how GPs treat this risk. This study aims to investigate GPs’ daily practice regarding fall preventive care for frail older patients. Methods Sixty-five GPs from 32 Dutch practices participated in this study for a period of 12 months. When a GP entered specific International Classification of Primary Care-codes related to frailty and/or high fall risk in their Electronic Health Records, a pop-up appeared asking “Is this patient frail?”. If the GP confirmed this, the GP completed a short questionnaire about patient’s fall history and fear of falling (FOF), and the fall preventive care provided. Results The GPs completed questionnaires regarding 1394 frail older patients aged ≥75. Of 20% of these patients, the GPs did not know whether they had experienced a fall or not. The GPs did not know whether a FOF existed in even more patients (29%). Of the patients with a fall history and/or a FOF (N = 726), 37% (N = 271) received fall preventive care. Two main reasons for not offering fall preventive care to these patients were: I) the patient finds treatment too intensive or too much of a hassle (37%), and II) the GP identified a high fall risk but the patient did not acknowledge this (14%). When patients were treated for high fall risk, the GP and the physiotherapist were the most frequently involved health care providers. The involved health care providers most often treated mobility limitations, cardiovascular risk factors, and FOF. Conclusions The results from this study show that GPs were frequently not aware of their frail patient’s fall history and/or FOF and that the majority of the frail older patients with a fall history and/or FOF did not receive fall preventive care. Developing systematic screening strategies for the primary care setting enhancing the identification of high fall risk and the provision of fall preventive care may improve patients’ quality of life and reduce health care costs.

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 Shared decision-making (SDM) in the management of metastatic breast cancer care is associated with positive patient outcomes. In daily clinical practice, however, SDM is not fully integrated yet. Initiatives to improve the implementation of SDM would be helpful. The aim of this review was to assess the availability and effectiveness of tools supporting SDM in metastatic breast cancer care. Methods Literature databases were systematically searched for articles published since 2006 focusing on the development or evaluation of tools to improve information-provision and to support decision-making in metastatic breast cancer care. Internet searches and experts identified additional tools. Data from included tools were extracted and the evaluation of tools was appraised using the GRADE grading system. Results The literature search yielded five instruments. In addition, two tools were identified via internet searches and consultation of experts. Four tools were specifically developed for supporting SDM in metastatic breast cancer, the other three tools focused on metastatic cancer in general. Tools were mainly applicable across the care process, and usable for decisions on supportive care with or without chemotherapy. All tools were designed for patients to be used before a consultation with the physician. Effects on patient outcomes were generally weakly positive although most tools were not studied in well-designed studies. Conclusions Despite its recognized importance, only two tools were positively evaluated on effectiveness and are available to support patients with metastatic breast cancer in SDM. These tools show promising results in pilot studies and focus on different aspects of care. However, their effectiveness should be confirmed in well-designed studies before implementation in clinical practice. Innovation and development of SDM tools targeting clinicians as well as patients during a clinical encounter is recommended.

Abstract Background Morbidity estimates between different GP registration networks show large, unexplained variations. This research explores the potential of modeling differences between networks in distinguishing new (incident) cases from existing (prevalent) cases in obtaining more reliable estimates. Methods Data from five Dutch GP registration networks and data on four chronic diseases (chronic obstructive pulmonary disease [COPD], diabetes, heart failure, and osteoarthritis of the knee) were used. A joint model (DisMod model) was fitted using all information on morbidity (incidence and prevalence) and mortality in each network, including a factor for misclassification of prevalent cases as incident cases. Results The observed estimates vary considerably between networks. Using disease modeling including a misclassification term improved the consistency between prevalence and incidence rates, but did not systematically decrease the variation between networks. Osteoarthritis of the knee showed large modeled misclassifications, especially in episode of care-based registries. Conclusion Registries that code episodes of care rather than disease generally provide lower estimates of the prevalence of chronic diseases requiring low levels of health care such as osteoarthritis. For other diseases, modeling misclassification rates does not systematically decrease the variation between registration networks. Using disease modeling provides insight in the reliability of estimates.