Authors: Lisa Lix, James Ayles, Sharon Bartholomew, Charmaine Cooke, Joellyn Ellison, Valerie Emond, Naomi Hamm, Heather Hannah, Sonia Jean, Shannon LeBlanc, J. Michael Paterson, Catherine Pelletier, Karen Phillips, Rolf Puchtinger, Kim Reimer, Cynthia Robitaille, Mark Smith, Lawrence Svenson, Karen Tu, Linda VanTil, Sean Waits, Louise Pelletier [Canadian Chronic Disease Surveillance System (CCDSS)]

The Canadian Chronic Disease Surveillance System (CCDSS) uses administrative data to estimate prevalence of multiple chronic diseases, including arthritis.

Chronic diseases have a major impact on populations and healthcare systems worldwide. Administrative health data are an ideal resource for chronic disease surveillance because they are population-based and routinely collected. For multi-jurisdictional surveillance, a distributed model is advantageous because it does not require individual-level data to be shared across jurisdictional boundaries. Our objective is to describe the process, structure, benefits, and challenges of a distributed model for chronic disease surveillance across all Canadian provinces and territories (P/Ts) using linked administrative data.

Abstract
Purpose of Review
One justification for using expensive biologic therapy in rheumatoid arthritis (RA) has been that it can reduce future healthcare utilization such as joint surgeries and physician visits. However, the evidence to support this assertion is unclear. We conducted a review of the literature for studies which have analyzed the trends in resource use of RA patients, and then undertook a retrospective observational analysis of a Canadian administrative database using instrumental variable methods.

Recent Findings
Our review found a trend in reduced resource utilization prior to the introduction of biologics and no evidence that biologic therapies have specifically contributed to this reduction. Our observational analysis, which overcame some of the epidemiological challenges with determining the influence of biologics on resource utilization, found a possible reduction in other medications but possible increases rather than decreases in physician visits and hospitalizations. However, our sample was not sufficiently large to make definitive conclusions.

Summary
Over 15 years since the introduction of biologics for RA, no evidence exists supporting the assumption that biologic therapies reduce future healthcare utilization. While such a question is challenging to generate evidence for, and so an absence of evidence does not suggest that the hypothesis is incorrect, an instrumental variable analysis using sufficient data could provide definitive evidence.

The advent and increased use of targeted therapies in rheumatology have stimulated the establishment of clinical drug registers. Such registers have evaluated a broad spectrum of outcomes in patients exposed to these uniquely designed, potent and expensive drugs.1–8 Although the main focus of most drug registers in rheumatology is drug safety, other important issues include drug usage, real-life effectiveness and economic consequences.

ABSTRACT
Objective: Measuring the incidence of healthcare associated infections (HAI) is of increasing importance in current healthcare delivery systems.
Administrative data algorithms, including (combinations of ) diagnosis codes, are commonly used to determine the occurrence of HAI, either to support within-hospital surveillance programmes or as free-standing quality indicators. We conducted a systematic review evaluating the diagnostic accuracy of administrative data for the detection of HAI.
Methods: Systematic search of Medline, Embase, CINAHL and Cochrane for relevant studies (1995–2013). Methodological quality assessment was performed using QUADAS-2 criteria; diagnostic accuracy estimates were stratified by HAI type and key study characteristics.
Results: 57 studies were included, the majority aiming to detect surgical site or bloodstream infections. Study designs were very diverse regarding the specification of their administrative data algorithm (code selections, follow-up) and definitions of HAI
presence. One-third of studies had important methodological limitations including differential or incomplete HAI ascertainment or lack of blinding of assessors. Observed sensitivity and positive predictive values of administrative data algorithms for HAI detection were very heterogeneous and generally modest at best, both for within-hospital algorithms and for formal quality indicators; accuracy was particularly poor for the identification of device-associated HAI such as central line associated bloodstream infections. The large heterogeneity in study designs across the included studies precluded formal calculation of summary diagnostic accuracy estimates in most instances.
Conclusions: Administrative data had limited and highly variable accuracy for the detection of HAI, and their judicious use for internal surveillance efforts and external quality assessment is recommended. If hospitals and policymakers choose to rely on administrative data for HAI surveillance, continued improvements to existing algorithms and their robust validation are imperative.

Routinely collected health data, obtained for administrative and clinical purposes without specific a priori research goals, are increasingly used for research. The rapid evolution and availability of these data have revealed issues not addressed by existing reporting guidelines, such as Strengthening the Reporting of Observational Studies in Epidemiology (STROBE). The REporting of studies Conducted using Observational Routinely collected health Data (RECORD) statement was created to fill these gaps. RECORD was created as an extension to the STROBE statement to address reporting items specific to observational studies using routinely collected health data. RECORD consists of a checklist of 13 items related to the title, abstract, introduction, methods, results, and discussion section of articles, and other information required for inclusion in such research reports. This document contains the checklist and explanatory and elaboration information to enhance the use of the checklist. Examples of good reporting for each RECORD checklist item are also included herein. This document, as well as the accompanying website and message board (http://www.record-statement.org), will enhance the implementation and understanding of RECORD. Through implementation of RECORD, authors, journals editors, and peer reviewers can encourage transparency of research reporting.

Purpose: Currently, there are no reliable and validated tools that health service decision-makers can use to inform system level policy decisions. To address this need, we worked with health administrators, clinicians and researchers to create and validate a decision-support tool that service planners can use to achieve a sustainable, integrated care system for hip and knee osteoarthritis (OA).

Methods: The tool is based on a system dynamics (SD) model of patient flow across the continuum of care, including self-directed, primary, rheumatologic and orthopaedic specialist, acute, and surgical follow-up care. The model was developed in four phases: phase 1 focused on demand and flow rates, phase 2 on resource use and costs, phase 3 on geographical stratification, and phase 4 on adding feedback loops. We populated the model with data from several sources, including Alberta Health & Wellness (e.g. physician claims, inpatient, and ambulatory data), Statistics Canada (e.g. the Survey of Living with Chronic Diseases in Canada and population projections), and the Alberta Bone and Joint Health Institute (clinical/surgical data). Using established principles of SD modeling and an iterative, integrated knowledge translation process involving multiple workshops with front-line clinical staff and administrators, we defined the problem, determined the care process, modeled the system as a series of stock and flows, tested, validated and calibrated the model.

Results: We have developed the full SD model, for two key applications. First, it can help identify flow, resource use and cost variations in current practice, which may benefit from further exploration. For example, variations in practice patterns, particularly surgery rates and resource use, were observed among the health zones reflecting regional differences. Second, it can be used to explore the effects of various ‘what if’ scenarios that can demonstrate system wide and long-term effects that may result from changes in care processes. For example, two scenarios examined were: “What would happen if 1) primary care providers could manage more patients medically, ultimately referring fewer patients to specialists; and 2) primary care providers in all health zones adopted one zone’s rheumatologist referral patterns for OA patients?” Such scenarios change the pathways through which simulated patients flow, the results of which can provide insight into intended and unintended effects on resource use and costs across the continuum of care over a lengthy time horizon.

Conclusions: Our SD model can be used as a decision-support tool to estimate changes in health care demands, resource requirements and costs over time and as a result of ‘what if’ scenarios. It is critically important to involve clinicians and decision-makers in the development of such tools to ensure they are appropriate representations of the system and to facilitate their adoption and continued use to inform decision making.

Purpose: The prevalence of osteoarthritis (OA) is increasing with the aging population; correspondingly, the demand for OA care, including hip and knee replacement surgery, is increasing. Simultaneously, combined with patients seeking surgery at younger ages and more revision surgeries, there is an increasing burden on the healthcare system. In systems with limited surgical capacity, such as Canada’s, this is raising concerns about lengthy surgical wait times. Policy makers are being called upon to identify means of managing these anticipated future demands in order to meet benchmark targets in a way that is sustainable.

Abstract
Background
The time between symptom onset and physician diagnosis is a period when people with osteoarthritis can make lifestyle changes to reduce pain, improve function and delay disability.
Data and methods
This study analyses data for a nationally representative sample of 4,565 Canadians aged 20 or older who responded to the Arthritis component of the 2009 Survey on Living with Chronic Diseases in Canada. Descriptive statistics are used to report the prevalence of hip and knee osteoarthritis; the mean age of symptom onset and diagnosis; medication use; and contacts with health professionals during the previous year.
Results
Among people with a physician diagnosis of arthritis, 37% reported osteoarthritis. Of these, 70% experienced pain in the hip(s), knee(s), or hip(s) and knee(s). Close to half (48%) of these people experienced symptoms the same year they were diagnosed; 42% experienced symptoms at least a year before the diagnosis; and 10% experienced
symptoms after the diagnosis. Among those who had symptoms before diagnosis, the average time between symptom onset and diagnosis was 7.7 years.
Interpretation
Individuals with osteoarthritis may experience symptoms for several years before they obtain a physician diagnosis.