[Show abstract][Hide abstract] ABSTRACT: Prescription medication use, which is common among long-term care facility (LTCF) residents, is routinely used to describe quality of care and predict health outcomes. Data sources that capture medication information, which include surveys, medical charts, administrative health databases, and clinical assessment records, may not collect concordant information, which can result in comparable prevalence and effect size estimates. The purpose of this research was to estimate agreement between two population-based electronic data sources for measuring use of several medication classes among LTCF residents: outpatient prescription drug administrative data and the Resident Assessment Instrument Minimum Data Set (RAI-MDS) Version 2.0.
Prescription drug and RAI-MDS data from the province of Saskatchewan, Canada (population 1.1 million) were linked for 2010/11 in this cross-sectional study. Agreement for anti-psychotic, anti-depressant, and anti-anxiety/hypnotic medication classes was examined using prevalence estimates, Cohen’s κ, and positive and negative agreement. Mixed-effects logistic regression models tested resident and facility characteristics associated with disagreement.
The cohort was comprised of 8,866 LTCF residents. In the RAI-MDS data, prevalence of anti-psychotics was 35.7%, while for anti-depressants it was 37.9% and for hypnotics it was 27.1%. Prevalence was similar in prescription drug data for anti-psychotics and anti-depressants, but lower for hypnotics (18.0%). Cohen’s κ ranged from 0.39 to 0.85 and was highest for the first two medication classes. Diagnosis of a mood disorder and facility affiliation was associated with disagreement for hypnotics.
Agreement between prescription drug administrative data and RAI-MDS assessment data was influenced by the type of medication class, as well as selected patient and facility characteristics. Researchers should carefully consider the purpose of their study, whether it is to capture medication that are dispensed or medications that are currently used by residents, when selecting a data source for research on LTCF populations.
[Show abstract][Hide abstract] ABSTRACT: sec> Objectives Electronic physician claims databases are widely used for chronic disease research and surveillance, but quality of the data may vary with a number of physician characteristics, including payment method. The objectives were to develop a prediction model for the number of prevalent diabetes cases in fee-for-service (FFS) electronic physician claims databases and apply it to estimate cases among non-FFS (NFFS) physicians, for whom claims data are often incomplete. Design A retrospective observational cohort design was adopted. Setting Data from the Canadian province of Newfoundland and Labrador were used to construct the prediction model and data from the province of Manitoba were used to externally validate the model. Participants A cohort of diagnosed diabetes cases was ascertained from physician claims, insured resident registry and hospitalisation records. A cohort of FFS physicians who were responsible for the diagnosis was ascertained from physician claims and registry data. Primary and secondary outcome measures A generalised linear model with a γ distribution was used to model the number of diabetes cases per FFS physician as a function of physician characteristics. The expected number of diabetes cases per NFFS physician was estimated. Results The diabetes case cohort consisted of 31 714 individuals; the mean cases per FFS physician was 75.5 (median=49.0). Sex and years since specialty licensure were significantly associated (p<0.05) with the number of cases per physician. Applying the prediction model to NFFS physician registry data resulted in an estimate of 18 546 cases; only 411 were observed in claims data. The model demonstrated face validity in an independent data set. Conclusions Comparing observed and predicted disease cases is a useful and generalisable approach to assess the quality of electronic databases for population-based research and surveillance. </sec
BMJ Open 08/2015; 5(8). DOI:10.1136/bmjopen-2014-006858 · 2.27 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: We investigated whether repeat BMD measurements in clinical populations are useful for fracture risk assessment. We report that repeat BMD measurements are a robust predictor of fracture in clinical populations; this is not affected by preceding BMD change or recent osteoporosis therapy.
In clinical practice, many patients selectively undergo repeat bone mineral density (BMD) measurements. We investigated whether repeat BMD measurements in clinical populations are useful for fracture risk assessment and whether this is affected by preceding change in BMD or recent osteoporosis therapy.
We identified women and men aged ≥50 years who had a BMD measurement during 1990-2009 from a large clinical BMD database for Manitoba, Canada (n = 50,215). Patient subgroups aged ≥50 years at baseline with repeat BMD measures were identified. Data were linked to an administrative data repository, from which osteoporosis therapy, fracture outcomes, and covariates were extracted. Using Cox proportional hazards models, we assessed covariate-adjusted risk for major osteoporotic fracture (MOF) and hip fracture according to BMD (total hip, lumbar spine, femoral neck) at different time points.
Prevalence of osteoporosis therapy increased from 18 % at baseline to 55 % by the fourth measurement. Total hip BMD was predictive of MOF at each time point. In the patient subgroup with two repeat BMD measurements (n = 13,481), MOF prediction with the first and second measurements was similar: adjusted-hazard ratio (HR) per SD 1.45 (95 % CI 1.34-1.56) vs. 1.64 (95 % CI 1.48-1.81), respectively. No differences were seen when the second measurement results were stratified by preceding change in BMD or osteoporosis therapy (both p-interactions >0.2). Similar results were seen for hip fracture prediction and when spine and femoral neck BMD were analyzed.
Repeat BMD measurements are a robust predictor of fracture in clinical populations; this is not affected by preceding BMD change or recent osteoporosis therapy.
Osteoporosis International 08/2015; DOI:10.1007/s00198-015-3259-y · 4.17 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Existing literature demonstrating the negative impact of delayed hip fracture surgery on mortality consists largely of observational studies prone to selection bias and may overestimate the negative effects of delay. We conducted an intervention study to assess initiatives aimed at meeting a 48-hour benchmark for hip fracture surgery to determine if the intervention achieved a reduction in time to surgery, and if a general reduction in time to surgery improved mortality and length of stay.
We compared time to surgery, length of stay and mortality between pre- and postintervention patients with a hip fracture using the Kaplan-Meier estimator and Cox proportional hazards model adjusting for age, sex, comorbidities, type of surgery and year.
We included 3525 pre- and 3007 postintervention patients aged 50 years or older. The proportion of patients receiving surgery within the benchmark increased from 66.8% to 84.6%, median length of stay decreased from 13.5 to 9.7 days, and crude in-hospital mortality decreased from 9.6% to 6.8% (all p < 0.001). Adjusted analyses revealed reduced mortality in hospital (hazard ratio [HR] 0.68, 95% confidence interval [CI] 0.57-0.81) and at 1 year (HR 0.87, 95%CI 0.79-0.96). Independent of the intervention period, having surgery within 48 hours demonstrated decreased adjusted risk of death in hospital (HR 0.51, 95%CI 0.41-0.63) and at 1 year postsurgery (HR 0.72, 95% CI 0.64-0.80).
Coordinated, region-wide efforts to improve timeliness of hip fracture surgery can successfully reduce time to surgery and appears to reduce length of stay and adjusted mortality in hospital and at 1 year.
Canadian journal of surgery. Journal canadien de chirurgie 08/2015; 58(3):257-63. DOI:10.1503/cjs.017714 · 1.51 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Although some individual and organizational contributors to person-centred care or quality of care have been studied, they have rarely been examined together. Our goal was to investigate the association of personal and organizational-environmental characteristics with self-reported person-centred behaviours in long-term residential care settings.
We asked 109 long-term care staff from two Canadian long-term care homes to complete scales assessing self-reported person-centred care, organizational support for person-centred care, beliefs about personhood in dementia, and burnout. Independent variables included four employee background characteristics (age, gender, occupation, and years of education), beliefs about personhood in dementia, burnout, and three aspects of organizational support for person-centred care (the physical environment of residents, collaboration on care, and support from management). Dependent variables included five aspects of person-centred care: autonomy, personhood, knowing the person, comfort care, and support for relationships .We used multiple linear regression analysis and changes in R(2) to test variable associations.
Including organizational variables in regression models resulted in statistically significant (p < .05) changes in R(2) for each of the five dependent variables. Including personal variables resulted in statistically significant changes in R(2) for some dependent variables, but not others. In particular, including employee background characteristics resulted in a statistically significant change in R(2) for comfort care, and including beliefs about personhood and burnout resulted in statistically significant changes in R(2) for personhood but not for other dependent variables.
Organizational characteristics are associated with several aspects of person-centred dementia care. Individual characteristics, including gender, beliefs about personhood, and burnout, appear to be more important to some aspects of person-centred dementia care (e.g., respect for personhood and comfort care) than others.
Aging and Mental Health 06/2015; DOI:10.1080/13607863.2015.1056771 · 1.75 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Conclusion
Across all analyses—estimated population based, matched, and propensity score—we found that smoking was
associated with an increased burden on the healthcare system. The dichotomous ever-versus-never-smoked
analyses consistently showed that individuals who reported having ever smoked used more health services than those who reported having never smoked. When we separated individuals into more refined categories we found that much of the healthcare burden was concentrated among individuals in three categories: current daily smokers, former daily smokers who quit daily smoking five years or less before survey date, and former daily smokers who quit daily smoking over five years before survey date.
A common perception is that while smokers may use more healthcare services, they die younger. It is believed that these individuals’ burden is truncated resulting in few or no additional costs in the long run. However, our results show that if smokers live to the age of 50, they typically live well into their late 70s and early 80s. Thus, not only did we find that smoking was associated with increased healthcare use; we also found that this increase was not curtailed by smokers dying young. Those in Manitoba who ever smoked use significantly more healthcare services and they do so over a considerably long period—into their late 70s and early 80s.
When we estimated the costs of those healthcare services used by those who ever smoked compared with those who never smoked we found that those who ever smoked cost the healthcare system more, even after adjusting for several confounding characteristics. Our estimates suggest that, on average, smoking costs the Manitoba healthcare system an additional $226,034,777 per year, plus another $18,342,017 for cancer-related costs.
First 05/2015; Winnipeg MB: Manitoba Centre for Health Policy., ISBN: ISBN 978-1-896489-77-3
[Show abstract][Hide abstract] ABSTRACT: As individuals experience changes in their health, they may alter the way they evaluate health and quality of life. The purpose of this study is to estimate the extent to which individuals with IBD change their rating of health over time because of response shift (RS).
This is a reanalysis of a population-based longitudinal study of IBD in Manitoba, Canada (n = 388). RS was examined using trajectories of the difference between observed and predicted health. Logistic regression and dual trajectories were used to identify predictors of RS.
Disease activity, vitality, pain, somatization, and physical and social function explained 51% of the variation in general health over two years with no evidence of RS in 82% of the sample. Negative RS was found for 8%, who initially rated health better than predicted; positive RS was found for 6%. The positive RS group was younger and had better baseline scores on measures of general health, hostility, pain, mental health and social and role function; less pain and better social function scores at baseline were predictors of negative RS.
In conclusion, the majority of people with IBD did not demonstrate a RS indicating that the health rating over time was stable in relation to that predicted by known time varying clinical variables. This adds to the evidence that the single question on self-rated health is useful for monitoring individuals over time.
Health and Quality of Life Outcomes 05/2015; 13(1):52. DOI:10.1186/s12955-015-0232-6 · 2.12 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Background:
With aging and obesity trends, the incidence and prevalence of osteoarthritis (OA) are expected to rise in Canada, increasing the demand for health resources. Resource planning to meet this increasing need requires estimates of the anticipated number of OA patients. Using administrative data from Alberta, we estimated OA incidence and prevalence rates and examined their sensitivity to alternative case definitions.
We identified cases in a linked dataset spanning 1993 to 2010 (Population Registry, Discharge Abstract Database, Physician Claims, Ambulatory Care Classification System and prescription drug data) using diagnostic codes and drug identification numbers. In the base case, incident cases were captured for patients with an OA diagnostic code for at least two physician visits within two years or any hospital admission. Seven alternative case definitions were applied and compared.
Age-sex standardized incidence and prevalence rates were estimated to be 8.6 and 80.3 cases/1000 population, respectively, in the base case. Physician Claims data alone captured 88% of OA cases. Prevalence rate estimates required 15 years of longitudinal data to plateau. Compared to base case, estimates are sensitive to alternative case definitions.
Administrative databases are a key source for estimating the burden and epidemiological trends of chronic diseases such as OA in Canada. Despite their limitations, these data provide valuable information for estimating disease burden and planning health services. Estimates of OA are mostly defined through Physician Claims data and require a long period of longitudinal data. This article is protected by copyright. All rights reserved.
[Show abstract][Hide abstract] ABSTRACT: Administrative health data have been used in hypertension surveillance using the 1H2P method: the International Classification of Disease (ICD) hypertension diagnosis codes were recorded in at least 1 hospitalization or 2 physician claims within 2 year-period. Accumulation of false positive cases over time using the 1H2P method could result in the overestimation of hypertension prevalence. In this study, we developed and validated a new reclassification method to define hypertension cases using regularized logistic regression with the age, sex, hypertension and comorbidities in physician claims, and diagnosis of hypertension in hospital discharge data as independent variables. A Bayesian method was then used to adjust the prevalence estimated from the reclassification method. We evaluated the hypertension prevalence in data from Alberta, Canada using the currently accepted 1H2P method and these newly developed methods. The reclassification method with Bayesian adjustment produced similar prevalence estimates as the 1H2P method. This supports the continued use of the 1H2P method as a simple and practical way to conduct hypertension surveillance using administrative health data
PLoS ONE 03/2015; 10(3). DOI:10.1371/journal.pone.0119186 · 3.23 Impact Factor