No abstract

BackgroundIt is well known that treatment variation exists in oral healthcare, but the consequences for oral health are unknown as the development of outcome measures is still in its infancy. The aim of this study was to identify and develop outcome measures for oral health and explore their performance using health insurance claims records and clinical data from general dental practices.MethodsThe Dutch healthcare insurance company Achmea collaborated with researchers, oral health experts, and general dental practitioners (GDPs) in a proof of practice study to test the feasibility of measures in general dental practices. A literature search identified previously described outcome measures for oral healthcare. Using a structured approach, identified measures were (i) prioritized, adjusted and added to after discussion and then (ii) tested for feasibility of data collection, their face validity and discriminative validity. Data sources were claims records from Achmea, clinical records from dental practices, and prospective, pre-determined clinical assessment data obtained during routine consultations.ResultsIn total eight measures (four on dental caries, one on tooth wear, two on periodontal health, one on retreatment) were identified, prioritized and tested. The retreatment measure and three measures for dental caries were found promising as data collection was feasible, they had face validity and discriminative validity. Deployment of these measures demonstrated variation in clinical practices of GDPs. Feedback of this data to GDPs led to vivid discussions on best practices and quality of care. The measure ‘tooth wear’ was not considered sufficiently responsive; ‘changes in periodontal health score’ was considered a controversial measure. The available data for the measures ‘percentage of 18-year-olds with no tooth decay’ and ‘improvement in gingival bleeding index at reassessment’ was too limited to provide accurate estimates per dental practice.ConclusionsThe evaluated measures ‘time to first restoration’, ‘distribution of risk categories for dental caries’, ‘filled-and-missing score’ and ‘retreatment after restoration’, were considered valid and relevant measures and a proxy for oral health status. As such, they improve the transparency of oral health services delivery that can be related to oral health outcomes, and with time may serve to improve these oral health outcomes.Electronic supplementary materialThe online version of this article (doi:10.1186/s12903-017-0410-5) contains supplementary material, which is available to authorized users.

Background Recent developments in machine learning have shown its potential impact for clinical use such as risk prediction, prognosis, and treatment selection. However, relevant data are often scattered across different stakeholders and their use is regulated, e.g. by GDPR or HIPAA. As a concrete use-case, hospital Erasmus MC and health insurance company Achmea have data on individuals in the city of Rotterdam, which would in theory enable them to train a regression model in order to identify high-impact lifestyle factors for heart failure. However, privacy and confidentiality concerns make it unfeasible to exchange these data. Methods This article describes a solution where vertically-partitioned synthetic data of Achmea and of Erasmus MC are combined using Secure Multi-Party Computation. First, a secure inner join protocol takes place to securely determine the identifiers of the patients that are represented in both datasets. Then, a secure Lasso Regression model is trained on the securely combined data. The involved parties thus obtain the prediction model but no further information on the input data of the other parties. Results We implement our secure solution and describe its performance and scalability: we can train a prediction model on two datasets with 5000 records each and a total of 30 features in less than one hour, with a minimal difference from the results of standard (non-secure) methods. Conclusions This article shows that it is possible to combine datasets and train a Lasso regression model on this combination in a secure way. Such a solution thus further expands the potential of privacy-preserving data analysis in the medical domain.

IntroductionPeriodontitis has been considered a sixth complication of diabetes. The aim of this study was to assess the impact of periodontal treatment on diabetes-related healthcare costs in patients with diabetes.Research design and methodsA retrospective analysis was done, exploiting unique and large-scale claims data of a Dutch health insurance company. Data were extracted for a cohort of adults who had been continuously insured with additional dental coverage for the years 2012–2018. Individuals with at least one diabetes-related treatment claim in 2012 were included for analysis. A series of panel data regression models with patient-level fixed effects were estimated to assess the impact of periodontal treatment on diabetes-related healthcare costs.ResultsA total of 41 598 individuals with diabetes (age range 18–100 years; 45.7% female) were included in the final analyses. The median diabetes-related healthcare costs per patient in 2012 were €38.45 per quarter (IQR €11.52–€263.14), including diagnoses, treatment, medication and hospitalization costs. The fixed effect models showed €12.03 (95% CI −€15.77 to −€8.29) lower diabetes-related healthcare costs per quarter of a year following periodontal treatment compared with no periodontal treatment.ConclusionsPeriodontitis, a possible complication of diabetes, should receive appropriate attention in diabetes management. The findings of this study provide corroborative evidence for reduced economic burdens due to periodontal treatment in patients with diabetes.

Original article 703Introduction ! Screening strategies that employ colonoscopy for the detection and removal of precursors of colorectal cancer (CRC) effectively reduce CRC-related mortality [1,2]. Apart from participation grades, the efficacy of population-based CRC screening depends on the quality of colonoscopy. Colonoscopy quality is ultimately reflected by a reduction in the incidence of CRC following colonoscopy. However, measurement of post-colonoscopy CRC is cumbersome and does not allow direct feedback. Several procedural indicators have been suggested for the monitoring of quality [3]. Two recent studies showed that the adenoma detection rate (ADR) of endoscopists was inversely associated with the risk of post-colonoscopy CRC [4,5] and related death [5]. In another study, patients dying from CRC were less likely to have undergone a previous complete colonoscopy than matched controls [6]. In addition, a substantial proportion of colorectal tumors originate from the right-sided colon [7], which underlines the importance of performing a complete colonoscopy, as measured by the cecal intubation rate (CIR). ADR and CIR have been reported to vary between hospitals depending on case mix and institutional or procedural factors [8 -10]. Case mix is determined by nonmodifiable patient characteristics, such as age, sex, co-morbidity, and indication for colonoscopy. In order to provide a useful incentive for hospitals to improve the quality of colonoscopy, a comparison of CIR and ADR between institutions should enable detection of differences that are independently affected by modifiable * The first two authors contributed equally to the work. ** Both senior authors contributed equally to the work. Background and study aims: Cecal intubation rate (CIR) and adenoma detection rate (ADR) have been found to be inversely associated with the occurrence of post-colonoscopy colorectal cancer. Depicting differences in CIR and ADR between hospitals could provide incentives for quality improvement. The aim of this study was to compare quality parameters of routine colonoscopies between seven hospitals in The Netherlands in order to determine the extent to which possible differences were attributable to procedural and institutional factors. Patients and methods: Consecutive patients undergoing colonoscopy were prospectively included between November 2012 and January 2013 at two academic and five nonacademic hospitals. Patients with inflammatory bowel disease or hereditary colorectal cancer syndromes were excluded. Main outcome measures were CIR and ADR. Results: A total of 3129 patients were included (mean age 59 ± 15 years; 45.5 % male). The major- Belderbos