PURPOSEWe sought to identify and compare studies reporting the prevalence of multimorbidity and to suggest methodologic aspects to be considered in the conduct of such studies. METHODSWe searched the literature for English-and French-language articles published between 1980 and September 2010 that described the prevalence of multimorbidity in the general population, in primary care, or both. We assessed quality of included studies with a modifi ed version of the Strengthening the Reporting of Observational Studies in Epidemiology checklist. Results of individual prevalence studies were adjusted so that they could be compared graphically. RESULTSThe fi nal sample included 21 articles: 8 described studies conducted in primary care, 12 in the general population, and 1 in both. All articles were of good quality. The largest differences in prevalence of multimorbidity were observed at age 75 in both primary care (with prevalence ranging from 3.5% to 98.5% across studies) and the general population (with prevalence ranging from 13.1% to 71.8% across studies). Apart from differences in geographic settings, we identifi ed differences in recruitment method and sample size (primary care: 980-60,857 patients; general population: 1,099-316,928 individuals), data collection, and the operational defi nition of multimorbidity used, including the number of diagnoses considered (primary care: 5 to all; general population: 7 to all). This last aspect seemed to be the most important factor in estimating prevalence. CONCLUSIONSMarked variation exists among studies of the prevalence of multimorbidity with respect to both methodology and fi ndings. When undertaking such studies, investigators should carefully consider the specifi c diagnoses included and their number, as well as the operational defi nition of multimorbidity.
Engagement is a critical factor to consider when seeking to improve ITER use. Our articulation of external and internal influences on engagement provides a starting point for targeted interventions.
BackgroundThe increased use of electronic medical records (EMRs) in Canadian primary health care practice has resulted in an expansion of the availability of EMR data. Potential users of these data need to understand their quality in relation to the uses to which they are applied. Herein, we propose a basic model for assessing primary health care EMR data quality, comprising a set of data quality measures within four domains. We describe the process of developing and testing this set of measures, share the results of applying these measures in three EMR-derived datasets, and discuss what this reveals about the measures and EMR data quality. The model is offered as a starting point from which data users can refine their own approach, based on their own needs.MethodsUsing an iterative process, measures of EMR data quality were created within four domains: comparability; completeness; correctness; and currency. We used a series of process steps to develop the measures. The measures were then operationalized, and tested within three datasets created from different EMR software products.ResultsA set of eleven final measures were created. We were not able to calculate results for several measures in one dataset because of the way the data were collected in that specific EMR. Overall, we found variability in the results of testing the measures (e.g. sensitivity values were highest for diabetes, and lowest for obesity), among datasets (e.g. recording of height), and by patient age and sex (e.g. recording of blood pressure, height and weight).ConclusionsThis paper proposes a basic model for assessing primary health care EMR data quality. We developed and tested multiple measures of data quality, within four domains, in three different EMR-derived primary health care datasets. The results of testing these measures indicated that not all measures could be utilized in all datasets, and illustrated variability in data quality. This is one step forward in creating a standard set of measures of data quality. Nonetheless, each project has unique challenges, and therefore requires its own data quality assessment before proceeding.Electronic supplementary materialThe online version of this article (10.1186/s12911-019-0740-0) contains supplementary material, which is available to authorized users.
Background Databases derived from primary care electronic health records (EHRs) are ideally suited to study clinical influences on referral patterns. This is the first study outside the United Kingdom to use an EHR database to describe rates of referral per patient from family physicians to specialists.
Background.As the population ages, practice and policy need to be guided by accurate estimates of chronic disease burden in primary care.Objective.To produce a preliminary set of methodological considerations for cross-sectional and retrospective cohort studies of multi-morbidity in primary care using three studies as examples. Prevalence rate results from the three studies were re-estimated using identical age–sex groups.Methods.We compared the methods and results of three separate studies in primary care: (i) patients in the Saguenay region of Quebec, Canada (2005); (ii) a substudy of the BEACH (Bettering the Evaluation and Care of Health) programme in Australia (2008); and (iii) the DELPHI (Deliver Primary Health Care Information) project in South-western Ontario, Canada (2009). Areas where the methods of multi-morbidity studies may differ were identified. The percentage of patients with two or more chronic conditions was compared by age–sex groups.Results.Multi-morbidity prevalence varied by as much as 61%, where reported prevalence was 95% among females aged 45–64 in the Saguenay study, 46% in the BEACH substudy and 34% in the DELPHI study. Several aspects of the methods and study designs were identified as differing among the studies, including the sampling of frequent attenders, sampling period, source of data, and both the definition and count of chronic conditions.Conclusions.Understanding the differences among the methods used to produce prevalence data on multi-morbidity in primary care can help explain the varying results. Standardization of methods would allow for more valid inter-study comparisons.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.