Direct Health Care Costs Associated With a Multicomponent COPD Exacerbation Intervention

BACKGROUND

Health care costs attributed to COPD have been estimated at $4.7 trillion globally in the next 30 years. With the global burden of COPD rising, identification of interventions that might lead to health care cost savings is an imperative. Although many studies report the effect of COPD self-management interventions on subject outcomes and health care utilization, few data describe their effect on health care costs.

METHODS

Using data linkage and established case-costing methods with provincial Canadian health databases, we established public health care costs (acute and community) for 12 months following randomization for the 462 participants enrolled in our randomized controlled trial of the Program of Integrated Care for Patients with COPD and Multiple Comorbidities.

RESULTS

Total median (interquartile range) in-hospital costs in the 12 months follow-up for all (intervention and control) 462 trial participants were CAD $4,769 ($417-16,834) (equivalent to US $3,566 [$312-12,588]). Total costs incurred in the community were higher at CAD $8,011 ($4,749-13,831) (equivalent to US $5,990 [$3,551-10,342]). Controlling for sex, income quintile, Johns Hopkins Aggregated Diagnosis Groups score, and living in an urban locality, we found lower community health care costs but no differences in acute care costs for participants receiving our multicomponent COPD exacerbation prevention management intervention compared to usual care.

CONCLUSIONS

Controlling for important confounders, we found lower public community health care costs but no difference in acute health care costs with our multicomponent COPD exacerbation prevention management intervention compared to usual care. Community health care costs were almost double those incurred compared to acute health care costs. Given this finding, although most COPD exacerbation management interventions generally focus on reducing the use of acute care, interventions that enable health care cost savings in the community require further exploration.

Adjusting for differential misclassification in matched case-control studies utilizing health administrative data

In epidemiological studies of secondary data sources, lack of accurate disease classifications often requires investigators to rely on diagnostic codes generated by physicians or hospital systems to identify case and control groups, resulting in a less-than-perfect assessment of the disease under investigation. Moreover, because of differences in coding practices by physicians, it is hard to determine the factors that affect the chance of an incorrectly assigned disease status. What results is a dilemma where assumptions of non-differential misclassification are questionable but, at the same time, necessary to proceed with statistical analyses. This paper develops an approach to adjust exposure-disease association estimates for disease misclassification, without the need of simplifying non-differentiality assumptions, or prior information about a complicated classification mechanism. We propose to leverage rich temporal information on disease-specific healthcare utilization to estimate each participant's probability of being a true case and to use these estimates as weights in a Bayesian analysis of matched case-control data. The approach is applied to data from a recent observational study into the early symptoms of multiple sclerosis (MS), where MS cases were identified from Canadian health administrative databases and matched to population controls that are assumed to be correctly classified. A comparison of our results with those from non-differentially adjusted analyses reveals conflicting inferences and highlights that ill-suited assumptions of non-differential misclassification can exacerbate biases in association estimates.

Bayesian analysis of pair-matched case-control studies subject to outcome misclassification

We examine the impact of nondifferential outcome misclassification on odds ratios estimated from pair-matched case-control studies and propose a Bayesian model to adjust these estimates for misclassification bias. The model relies on access to a validation subgroup with confirmed outcome status for all case-control pairs as well as prior knowledge about the positive and negative predictive value of the classification mechanism. We illustrate the model's performance on simulated data and apply it to a database study examining the presence of ten morbidities in the prodromal phase of multiple sclerosis.

Quebec Integrated Chronic Disease Surveillance System (QICDSS), an innovative approach

INTRODUCTION

With the growing burden of chronic diseases, surveillance will play an essential role in improving their prevention and control. The Institut national de santé publique du Québec has developed an innovative chronic disease surveillance system, the Quebec Integrated Chronic Disease Surveillance System (QICDSS). We discuss the primary features, strengths and limitations of this system in this report.

METHODS

The QICDSS was created by linking five health administrative databases. Updated annually, it currently covers the period from January 1, 1996, to March 31, 2012. The operational model comprises three steps: (1) extraction and linkage of health administrative data according to specific selection criteria; (2) analysis (validation of case definitions essentially) and production of surveillance measures; and (3) data interpretation, submission and dissemination of information. The QICDSS allows the surveillance of the following chronic diseases: diabetes, cardiovascular diseases, respiratory diseases, osteoporosis, osteoarticular diseases, mental disorders, Alzheimer's disease and related disorders. The system also lends itself to the analysis of multimorbidity and polypharmacy.

RESULTS

For 2011-2012, the QICDSS contained information on 7 995 963 Quebecers with an average age of 40.8 years. Of these, 95.3% met at least one selection criterion allowing the application of case definitions for chronic disease surveillance. The actual proportion varied with age, from 90.1% for those aged 19 years or less to 99.3% for those aged 65 years or over.

CONCLUSION

The QICDSS provides a way of producing population-based data on the chronic disease burden, health services and prescription drug uses. The system facilitates the integrated study of several diseases in combination, an approach rarely implemented until now in the context of population surveillance. The QICDSS possesses all the essential features of a surveillance system and supports the dissemination of information to public health decision-makers for future actions.

Identifying patients with physician-diagnosed asthma in health administrative databases

BACKGROUND

Asthma imposes a heavy and expensive burden on individuals and populations. A population-based surveillance and research program based on health administrative data could measure and study the burden of asthma; however, the validity of a health administrative data diagnosis of asthma must first be confirmed.

OBJECTIVE

To evaluate the accuracy of population-based provincial health administrative data in identifying adult patients with asthma for ongoing surveillance and research.

METHODS

Patients from randomly selected primary care practices were assigned to four categories according to their previous diagnoses: asthma, chronic obstructive pulmonary disease, related respiratory conditions and nonasthma conditions. In each practice, 10 charts from each category were randomly selected, abstracted, then reviewed by a blinded expert panel who identified them as asthma or nonasthma. These reference standard diagnoses were then linked to the patients' provincial records and compared with health administrative algorithms designed to identify asthma. Analyses were performed using the concepts of diagnostic test evaluation.

RESULTS

A total of 518 charts, including 160 from individuals with asthma, were reviewed. The algorithm of two or more ambulatory care visits and/or one or more hospitalization(s) for asthma in two years had a sensitivity of 83.8% (95% CI 77.1% to 89.1%) and a specificity of 76.5% (95% CI 71.8% to 80.8%).

CONCLUSION

Definitions of adult asthma using health administrative data are sensitive and specific for identifying adults with asthma. Using these definitions, cohorts of adults with asthma for ongoing population-based surveillance and research can be developed.