Peter Sargious

Association of socio-economic status with diabetes prevalence and utilization of diabetes care services

BackgroundLow income appears to be associated with a higher prevalence of diabetes and diabetes related complications, however, little is known about how income influences access to diabetes care. The objective of the present study was to determine whether income is associated with referral to a diabetes centre within a universal health care system.MethodsData on referral for diabetes care, diabetes prevalence and median household income were obtained from a regional Diabetes Education Centre (DEC) database, the Canadian National Diabetes Surveillance System (NDSS) and the 2001 Canadian Census respectively. Diabetes rate per capita, referral rate per capita and proportion with diabetes referred was determined for census dissemination areas. We used Chi square analyses to determine if diabetes prevalence or population rates of referral differed across income quintiles, and Poisson regression to model diabetes rate and referral rate in relation to income while controlling for education and age.ResultsThere was a significant gradient in both diabetes prevalence (χ2 = 743.72, p < 0.0005) and population rates of referral (χ2 = 168.435, p < 0.0005) across income quintiles, with the lowest income quintiles having the highest rates of diabetes and referral to the DEC. Referral rate among those with diabetes, however, was uniform across income quintiles. Controlling for age and education, Poisson regression models confirmed a significant socio-economic gradient in diabetes prevalence and population rates of referral.ConclusionLow income is associated with a higher prevalence of diabetes and a higher population rate of referral to this regional DEC. After accounting for diabetes prevalence, however, the equal proportions referred to the DEC across income groups suggest that there is no access bias based on income.

Enrolment in primary care networks: impact on outcomes and processes of care for patients with diabetes

Background: Primary care networks are a newer model of primary care that focuses on improved access to care and the use of multidisciplinary teams for patients with chronic disease. We sought to determine the association between enrolment in primary care networks and the care and outcomes of patients with diabetes. Methods: We used administrative health care data to study the care and outcomes of patients with incident and prevalent diabetes separately. For patients with prevalent diabetes, we compared those whose care was managed by physicians who were or were not in a primary care network using propensity score matching. For patients with incident diabetes, we studied a cohort before and after primary care networks were established. Each cohort was further divided based on whether or not patients were cared for by physicians enrolled in a network. Our primary outcome was admissions to hospital or visits to emergency departments for ambulatory care sensitive conditions specific to diabetes. Results: Compared with patients whose prevalent diabetes is managed outside of primary care networks, patients in primary care networks had a lower rate of diabetes-specific ambulatory care sensitive conditions (adjusted incidence rate ratio 0.81, 95% confidence interval [CI] 0.75 to 0.87), were more likely to see an ophthalmologist or optometrist (risk ratio 1.19, 95% CI 1.17 to 1.21) and had better glycemic control (adjusted mean difference −0.067, 95% CI −0.081 to −0.052). Interpretation: Patients whose diabetes was managed in primary care networks received better care and had better clinical outcomes than patients whose condition was not managed in a network, although the differences were very small.

Methods for identifying 30 chronic conditions: application to administrative data

BackgroundMultimorbidity is common and associated with poor clinical outcomes and high health care costs. Administrative data are a promising tool for studying the epidemiology of multimorbidity. Our goal was to derive and apply a new scheme for using administrative data to identify the presence of chronic conditions and multimorbidity.MethodsWe identified validated algorithms that use ICD-9 CM/ICD-10 data to ascertain the presence or absence of 40 morbidities. Algorithms with both positive predictive value and sensitivity ≥70% were graded as “high validity”; those with positive predictive value ≥70% and sensitivity <70% were graded as “moderate validity”. To show proof of concept, we applied identified algorithms with high to moderate validity to inpatient and outpatient claims and utilization data from 574,409 people residing in Edmonton, Canada during the 2008/2009 fiscal year.ResultsOf the 40 morbidities, we identified 30 that could be identified with high to moderate validity. Approximately one quarter of participants had identified multimorbidity (2 or more conditions), one quarter had a single identified morbidity and the remaining participants were not identified as having any of the 30 morbidities.ConclusionsWe identified a panel of 30 chronic conditions that can be identified from administrative data using validated algorithms, facilitating the study and surveillance of multimorbidity. We encourage other groups to use this scheme, to facilitate comparisons between settings and jurisdictions.

Comorbidity as a driver of adverse outcomes in people with chronic kidney disease

Chronic kidney disease (CKD) is associated with poor outcomes, perhaps due to a high burden of comorbidity. Most studies of CKD populations focus on concordant comorbidities, which cause CKD (such as hypertension and diabetes) or often accompany CKD (such as heart failure or coronary disease). Less is known about the burden of mental health conditions and discordant conditions (those not concordant but still clinically relevant, like dementia or cancer). Here we did a retrospective population-based cohort study of 530,771 adults with CKD residing in Alberta, Canada between 2003 and 2011. Validated algorithms were applied to data from the provincial health ministry to assess the presence/absence of 29 chronic comorbidities. Linkage between comorbidity burden and adverse clinical outcomes (mortality, hospitalization or myocardial infarction) was examined over median follow-up of 48 months. Comorbidities were classified into three categories: concordant, mental health/chronic pain, and discordant. The median number of comorbidities was 1 (range 0-15) but a substantial proportion of participants had 3 and more, or 5 and more comorbidities (25 and 7%, respectively). Concordant comorbidities were associated with excess risk of hospitalization, but so were discordant comorbidities and mental health conditions. Thus, discordant comorbidities and mental health conditions as well as concordant comorbidities are important independent drivers of the adverse outcomes associated with CKD.

Clinical and medication profiles stratified by household income in patients referred for diabetes care.

BackgroundLow income individuals with diabetes are at particularly high risk for poor health outcomes. While specialized diabetes care may help reduce this risk, it is not currently known whether there are significant clinical differences across income groups at the time of referral. The objective of this study is to determine if the clinical profiles and medication use of patients referred for diabetes care differ across income quintiles.MethodsThis cross-sectional study was conducted using a Canadian, urban, Diabetes Education Centre (DEC) database. Clinical information on the 4687 patients referred to the DEC from May 2000 – January 2002 was examined. These data were merged with 2001 Canadian census data on income. Potential differences in continuous clinical parameters across income quintiles were examined using regression models. Differences in medication use were examined using Chi square analyses.ResultsMultivariate regression analysis indicated that income was negatively associated with BMI (p < 0.0005) and age (p = 0.023) at time of referral. The highest income quintiles were found to have lower serum triglycerides (p = 0.011) and higher HDL-c (p = 0.008) at time of referral. No significant differences were found in HBA1C, LDL-c or duration of diabetes. The Chi square analysis of medication use revealed that despite no significant differences in HBA1C, the lowest income quintiles used more metformin (p = 0.001) and sulfonylureas (p < 0.0005) than the wealthy. Use of other therapies were similar across income groups, including lipid lowering medications. High income patients were more likely to be treated with diet alone (p < 0.0005).ConclusionOur findings demonstrate that low income patients present to diabetes clinic older, heavier and with a more atherogenic lipid profile than do high income patients. Overall medication use was higher among the lower income group suggesting that differences in clinical profiles are not the result of under-treatment, thus invoking lifestyle factors as potential contributors to these findings.

Association between participation in a brief diabetes education programme and glycaemic control in adults with newly diagnosed diabetes.

To determine the association between participation in a brief introductory didactic diabetes education programme and change in HbA1c among individuals with newly diagnosed diabetes.

How a hobby can shape cognition: visual word recognition in competitive Scrabble players

Competitive Scrabble is an activity that involves extraordinary word recognition experience. We investigated whether that experience is associated with exceptional behavior in the laboratory in a classic visual word recognition paradigm: the lexical decision task (LDT). We used a version of the LDT that involved horizontal and vertical presentation and a concreteness manipulation. In Experiment 1, we presented this task to a group of undergraduates, as these participants are the typical sample in word recognition studies. In Experiment 2, we compared the performance of a group of competitive Scrabble players with a group of age-matched nonexpert control participants. The results of a series of cognitive assessments showed that the Scrabble players and control participants differed only in Scrabble-specific skills (e.g., anagramming). Scrabble expertise was associated with two specific effects (as compared to controls): vertical fluency (relatively less difficulty judging lexicality for words presented in the vertical orientation) and semantic deemphasis (smaller concreteness effects for word responses). These results suggest that visual word recognition is shaped by experience, and that with experience there are efficiencies to be had even in the adult word recognition system.

Evidence-based medicine and the real world: understanding the controversy.

Controversy has surrounded the ‘paradigm’ of evidence-based medicine since its introduction in 1992 as a new approach to the teaching and practice of medicine. Here, we address two questions: (1) is evidence-based medicine a good thing?; and (2) why has so much controversy arisen? In addressing these questions, we propose that the discussion surrounding evidence-based medicine should no longer be about whether the application of evidence in clinical practice is a good thing, because it obviously is. Instead, the debate ought to focus on the more difficult question of how to enhance its acceptability among busy clinicians practising in the ‘real world’. For the future, we optimistically anticipate an enhanced adoption of evidence-based medicine, as clinicians will become increasingly capable of efficiently accessing existing and forthcoming evidence resources.

This is your brain on Scrabble: Neural correlates of visual word recognition in competitive Scrabble players as measured during task and resting-state

Competitive Scrabble players devote considerable time to studying words and practicing Scrabble-related skills (e.g., anagramming). This training is associated with extraordinary performance in lexical decision, the standard visual word recognition task (Hargreaves, Pexman, Zdrazilova & Sargious, 2012). In the present study we investigated the neural consequences of this lexical expertise. Using both event-related and resting-state fMRI, we compared brain activity and connectivity in 12 competitive Scrabble experts with 12 matched non-expert controls. Results showed that when engaged in the lexical decision task (LDT), Scrabble experts made use of brain regions not generally associated with meaning retrieval in visual word recognition, but rather those associated with working memory and visual perception. The analysis of resting-state data also showed group differences, such that a different network of brain regions was associated with higher levels of Scrabble-related skill in experts than in controls.

Seeing the forests and the trees—innovative approaches to exploring heterogeneity in systematic reviews of complex interventions to enhance health system decision-making: a protocol

BackgroundTo improve quality of care and patient outcomes, health system decision-makers need to identify and implement effective interventions. An increasing number of systematic reviews document the effects of quality improvement programs to assist decision-makers in developing new initiatives. However, limitations in the reporting of primary studies and current meta-analysis methods (including approaches for exploring heterogeneity) reduce the utility of existing syntheses for health system decision-makers. This study will explore the role of innovative meta-analysis approaches and the added value of enriched and updated data for increasing the utility of systematic reviews of complex interventions.Methods/DesignWe will use the dataset from our recent systematic review of 142 randomized trials of diabetes quality improvement programs to evaluate novel approaches for exploring heterogeneity. These will include exploratory methods, such as multivariate meta-regression analyses and all-subsets combinatorial meta-analysis. We will then update our systematic review to include new trials and enrich the dataset by surveying authors of all included trials. In doing so, we will explore the impact of variables not, reported in previous publications, such as details of study context, on the effectiveness of the intervention. We will use innovative analytical methods on the enriched and updated dataset to identify key success factors in the implementation of quality improvement interventions for diabetes. Decision-makers will be involved throughout to help identify and prioritize variables to be explored and to aid in the interpretation and dissemination of results.DiscussionThis study will inform future systematic reviews of complex interventions and describe the value of enriching and updating data for exploring heterogeneity in meta-analysis. It will also result in an updated comprehensive systematic review of diabetes quality improvement interventions that will be useful to health system decision-makers in developing interventions to improve outcomes for people with diabetes.Systematic review registrationPROSPERO registration no. CRD42013005165