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.

Abstract

Objectives: To conduct a systematic review of studies reporting on the development or validation of comorbidity indices using administrative health data and compare their ability to predict outcomes related to comorbidity (ie, construct validity).

Study Design and Setting: We conducted a comprehensive literature search of MEDLINE and EMBASE, until September 2012. After title and abstract screen, relevant articles were selected for review by two independent investigators. Predictive validity and model fit were measured using c-statistic for dichotomous outcomes and R2 for continuous outcomes.

Results: Our review includes 76 articles. Two categories of comorbidity indices were identified: those identifying comorbidities based on diagnoses, using International Classification of Disease codes from hospitalization or outpatient data, and based on medications, using pharmacy data. The ability of indices studied to predict morbidity-related outcomes ranged from poor (C statistic 0.69) to excellent (C statisticO0.80) depending on the specific index, outcome measured, and study population. Diagnosis-based measures, particularly the Elixhauser Index and the Romano adaptation of the Charlson Index, resulted in higher ability to predict mortality outcomes. Medication-based indices, such as the Chronic Disease Score, demonstrated better performance for predicting health care utilization.

Conclusion: A number of valid comorbidity indices derived from administrative data are available. Selection of an appropriate index should take into account the type of data available, study population, and specific outcome of interest. 2015 Elsevier Inc. All rights reserved.

Abstract
Background
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.

Methods
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.

Results
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.

Conclusions
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.

BMC Geriatrics 2015, 15:24 (11 March 2015).

Abstract
Background: Though administrative databases are increasingly being used for research related to myocardial infarction (MI),
the validity of MI diagnoses in these databases has never been synthesized on a large scale.
Objective: To conduct the first systematic review of studies reporting on the validity of diagnostic codes for identifying MI
in administrative data.

Methods: MEDLINE and EMBASE were searched (inception to November 2010) for studies: (a) Using administrative data to
identify MI; or (b) Evaluating the validity of MI codes in administrative data; and (c) Reporting validation statistics (sensitivity,
specificity, positive predictive value (PPV), negative predictive value, or Kappa scores) for MI, or data sufficient for their
calculation. Additonal articles were located by handsearch (up to February 2011) of original papers. Data were extracted by
two independent reviewers; article quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool.

Results: Thirty studies published from 1984–2010 were included; most assessed codes from the International Classification
of Diseases (ICD)-9th revision. Sensitivity and specificity of hospitalization data for identifying MI in most [$50%] studies was
$86%, and PPV in most studies was $93%. The PPV was higher in the more-recent studies, and lower when criteria that do
not incorporate cardiac troponin levels (such as the MONICA) were employed as the gold standard. MI as a cause-of-death
on death certificates also demonstrated lower accuracy, with maximum PPV of 60% (for definite MI).

Conclusions: Hospitalization data has higher validity and hence can be used to identify MI, but the accuracy of MI as a
cause-of-death on death certificates is suboptimal, and more studies are needed on the validity of ICD-10 codes. When
using administrative data for research purposes, authors should recognize these factors and avoid using vital statistics data
if hospitalization data is not available to confirm deaths from MI.

Received September 29, 2013; Accepted February 20, 2014; Published March 28, 2014

Citation: McCormick N, Lacaille D, Bhole V, Avina-Zubieta JA (2014) Validity of Myocardial Infarction Diagnoses in Administrative Databases: A Systematic
Review. PLoS ONE 9(3): e92286. doi:10.1371/journal.pone.0092286

Abstract
Background
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.

Methods
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.

Results
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.

Conclusions
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.

BMC Geriatrics 2015, 15:24 (11 March 2015).

Abstract
Objective
To estimate the association between fine particulate (PM2.5) and nitrogen dioxide (NO2) pollution and systemic autoimmune rheumatic diseases (SARDs).

Methods
Associations between ambient air pollution (PM2.5 and NO2) and SARDs were assessed using land-use regression models for Calgary, Alberta and administrative health data (1993–2007). SARD case definitions were based on ≥2 physician claims, or ≥1 rheumatology billing code; or ≥1 hospitalization code (for systemic lupus, Sjogren’s Syndrome, scleroderma, polymyositis, dermatomyositis, or undifferentiated connective tissue disease). Bayesian hierarchical latent class regression models estimated the probability that each resident was a SARD case, based on these case definitions. The sum of individual level probabilities provided the estimated number of cases in each area. The latent class model included terms for age, sex, and an interaction term between age and sex. Bayesian logistic regression models were used to generate adjusted odds ratios (OR) for NO2 and PM2.5. pollutant models, adjusting for neighbourhood income, age, sex, and an interaction between age and sex. We also examined models stratified for First-Nations (FN) and non-FN subgroups.

Results
Residents that were female and/or aged >45 had a greater probability of being a SARD case, with the highest OR estimates for older females. Independently, the odds of being a SARDs case increased with PM2.5 levels, but the results were inconclusive for NO2. The results stratified by FN and non-FN groups were not distinctly different.

Conclusion
In this urban Canadian sample, adjusting for demographics, exposure to PM2.5 was associated with an increased risk of SARDs. The results for NO2 were inconclusive.

Environmental Research Volume 140, July 2015, Pages 474–478.

PUB MED Information on article. Can access it through your institution.

1. Rheumatol Int. 2014 Sep 26. [Epub ahead of print]

The prevalence of systemic autoimmune rheumatic diseases in Canadian pediatric populations: administrative database estimates.

Shiff NJ(1), Lix LM, Joseph L, Duffy C, Tucker LB, Svenson LW, Belisle P, Bernatsky S.

Author information:
(1)Department of Paediatrics, Royal University Hospital, University of Saskatchewan, 103 Hospital Drive, Saskatoon, SK, S7N 0W8, Canada, natalie.shiff@usask.ca.

To estimate systemic autoimmune rheumatic disease (SARD) prevalence using administrative data for pediatric populations in four Canadian provinces. Physician billing claims and inpatient hospitalizations from Alberta, Manitoba,
Quebec, and Saskatchewan were used to define cases aged ≤18 years with a SARD diagnosis code in: one or more hospitalization, two or more physician visits within 2 years and at least 2 months apart, or one or more physician visit to a rheumatologist. Estimates ranged from 15.9/100,000 in Quebec [95 % confidence interval (95 % CI) 14.1, 18.0] to 23.0/100,000 in Manitoba (95 % CI 17.9, 29.2). SARDs were more common in females than in males across all provinces. There was a slightly higher prevalence among those living in urban compared to rural areas of Alberta (rate difference 14.4, 95 % CI 8.6, 20.1) and Saskatchewan (rate difference 13.8, 95 % CI 1.0, 26.6). Our results provide population-based
prevalence estimates of pediatric SARDs in four Canadian provinces.

PMID: 25257764 [PubMed – as supplied by publisher]

ABSTRACT

Objective: We aimed to systematically review rheumatoid arthritis (RA) disease severity indices for use in administrative healthcare databases. We also provide an overview of alternative methods to control for RA disease severity in administrative database research.

Methods: We conducted a systematic review of studies that developed/validated an index for RA dis- ease severity using variables in administrative databases, and compared the convergent validity/reliability of the index with a standard measure of RA severity.

Results: After reviewing 539 articles, 2 studies were included. The claims-based index for RA severity (CIRAS) was developed in one study. Components of the CIRAS included tests for inflammatory markers, number of chemistry panels/platelet counts ordered, rheumatoid factor test, number of rehabilitation and rheumatology visits, and Felty’s syndrome. The CIRAS correlated moderately well with a previously validated RA medical records-based index of severity. The second study assessed whether current and lifetime treatment with disease-modifying anti-rheumatic drugs and/or biologics accurately predicted RA severity, as measured by the patient-reported Patient Activity Scale (PAS). Treatment variables did not fully distinguish patients in the highest and lowest quartiles of PAS scores (67.2% correctly classified).

Conclusion: Two claims-based indices of RA severity were identified but have some limitations for routine use. A concerted effort from experts in the field is needed to define, develop, and validate a widely applicable measure of RA disease severity for administrative database research.

(First Release Sept 15 2011; J Rheumatol 2011;38:2318–25; doi:10.3899/jrheum.110587)

Abstract

There is growing interest in developing tools
and methods for the surveillance of chronic rheumatic diseases, using existing resources such as administrative health databases. To illustrate how this might work, we used
population-based administrative data to estimate and compare the prevalence of systemic autoimmune rheumatic
diseases (SARDs) across three Canadian provinces, assessing for regional differences and the effects of demographic factors. Cases of SARDs (systemic lupus erythematosus, scleroderma, primary Sjogren’s, polymyositis/dermatomyositis) were ascertained from provincialphysician billing and hospitalization data. We combined information from three case definitions, using hierarchical Bayesian latent class regression models that account for the
imperfect nature of each case definition. Using methods that
account for the imperfect nature of both billing and hospitalization databases, we estimated the over-all prevalence of SARDs to be approximately 2–3 cases per 1,000 residents. Stratified prevalence estimates suggested similar demographic trends across provinces (i.e. greater prevalence in females-versus-males, and in persons of older age). The prevalence in older females approached or exceeded 1 in100, which may reflect the high burden of primary Sjogren’s syndrome in this group. Adjusting for demographics, there was a greater prevalence in urban-versus-rural settings. In our work, prevalence estimates had good face validity and provided useful information about potential regional and demographic variations. Our results suggest that surveillance of some rheumatic diseases using administrative data may indeed be feasible. Our work highlights the usefulness of using multiple data sources, adjusting for the error in each.