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.
Background Routinely recorded electronic health records (EHRs) from general practitioners (GPs) are increasingly available and provide valuable data for estimating incidence and prevalence rates of diseases in the population. This paper describes how we developed an algorithm to construct episodes of illness based on EHR data to calculate morbidity rates. Objective The goal of the research was to develop a simple and uniform algorithm to construct episodes of illness based on electronic health record data and develop a method to calculate morbidity rates based on these episodes of illness. Methods The algorithm was developed in discussion rounds with two expert groups and tested with data from the Netherlands Institute for Health Services Research Primary Care Database, which consisted of a representative sample of 219 general practices covering a total population of 867,140 listed patients in 2012. Results All 685 symptoms and diseases in the International Classification of Primary Care version 1 were categorized as acute symptoms and diseases, long-lasting reversible diseases, or chronic diseases. For the nonchronic diseases, a contact-free interval (the period in which it is likely that a patient will visit the GP again if a medical complaint persists) was defined. The constructed episode of illness starts with the date of diagnosis and ends at the time of the last encounter plus half of the duration of the contact-free interval. Chronic diseases were considered irreversible and for these diseases no contact-free interval was needed. Conclusions An algorithm was developed to construct episodes of illness based on routinely recorded EHR data to estimate morbidity rates. The algorithm constitutes a simple and uniform way of using EHR data and can easily be applied in other registries.
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Background: Routinely recorded electronic health records (EHRs) from general practi-
To investigate the exact nature of anorectal continence, a manometric evaluation was performed in 88 healthy test persons with a two-balloon catheter. The pressure in the anal canal (Pac) and the rectal ampulla at rest (Par), the maximum squeeze pressure (Pac max) and pressure rise in the anal canal after dilatation of the rectal ampulla (ΔPAC) were evaluated separately for men and women, and the test persons were divided into 3 age groups (group I: under 40 years, group II: 40–60 years, group III: over 60 years). Pac showed a statistically significant difference between men and women and a constant decrease with age – statistically significant for men and for women between groups II and III –whereas the statistical significance of the 3 other values had to be restricted to a constant decrease with advance in years. Anorectal continence is characterized by a Pac at rest of over 40 mm Hg, a PAc max reaching at least 80 mm Hg, a pressure gradient between rectal ampulla and anal canal of over 20 mm Hg and a ΔPAc of more than 10 mm Hg. These values as well as the differences depending on age and sex should be kept in mind for the evaluation of the continence function.
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