What can we learn about breathlessness from population-based and administrative health data?

Abstract Background Previous research has shown that chronic disease case definitions constructed using population-based administrative health data may have low accuracy for ascertaining cases of episodic diseases such as rheumatoid arthritis, which are characterized by periods of good health followed by periods of illness. No studies have considered a dynamic approach that uses statistical (i.e., probability) models for repeated measures data to classify individuals into disease, non-disease, and indeterminate categories as an alternative to deterministic (i.e., non-probability) methods that use summary data for case ascertainment. The research objectives were to validate a model-based dynamic classification approach for ascertaining cases of juvenile arthritis (JA) from administrative data, and compare its performance with a deterministic approach for case ascertainment. Methods The study cohort was comprised of JA cases and non-JA controls 16 years or younger identified from a pediatric clinical registry in the Canadian province of Manitoba and born between 1980 and 2002. Registry data were linked to hospital records and physician billing claims up to 2018. Longitudinal discriminant analysis (LoDA) models and dynamic classification were applied to annual healthcare utilization measures. The deterministic case definition was based on JA diagnoses in healthcare use data anytime between birth and age 16 years; it required one hospitalization ever or two physician visits. Case definitions based on model-based dynamic classification and deterministic approaches were assessed on sensitivity, specificity, and positive and negative predictive values (PPV, NPV). Mean time to classification was also measured for the former. Results The cohort included 797 individuals; 386 (48.4 %) were JA cases. A model-based dynamic classification approach using an annual measure of any JA-related healthcare contact had sensitivity = 0.70 and PPV = 0.82. Mean classification time was 9.21 years. The deterministic case definition had sensitivity = 0.91 and PPV = 0.92. Conclusions A model-based dynamic classification approach had lower accuracy for ascertaining JA cases than a deterministic approach. However, the dynamic approach required a shorter duration of time to produce a case definition with acceptable PPV. The choice of methods to construct case definitions and their performance may depend on the characteristics of the chronic disease under investigation.

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Reproducibility, reliability and validity of population-based administrative health data for the assessment of cancer non-related comorbidities

PLoS ONE ◽

10.1371/journal.pone.0172814 ◽

2017 ◽

Vol 12 (3) ◽

pp. e0172814 ◽

Cited By ~ 29

Author(s):

Camille Maringe ◽

Helen Fowler ◽

Bernard Rachet ◽

Miguel Angel Luque-Fernandez

Keyword(s):

Reliability And Validity ◽

Population Based ◽

Health Data ◽

Administrative Health Data

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Who Doesn’t Authorize the Linking of Survey and Administrative Health Data? A General Population-based Investigation

Annals of Epidemiology ◽

10.1016/j.annepidem.2011.03.007 ◽

2011 ◽

Vol 21 (9) ◽

pp. 706-709 ◽

Cited By ~ 6

Author(s):

Timothy J. Beebe ◽

Jeanette Y. Ziegenfuss ◽

Sarah M. Jenkins ◽

Lindsey R. Haas ◽

Michael E. Davern

Keyword(s):

General Population ◽

Population Based ◽

Health Data ◽

Administrative Health Data

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Health Care Indicators of Moderate to Severe IBD and Subsequent IBD-Related Disability: A Longitudinal Study

Inflammatory Bowel Diseases ◽

10.1093/ibd/izz102 ◽

2019 ◽

Vol 25 (12) ◽

pp. 1996-2005 ◽

Cited By ~ 1

Author(s):

Leigh Anne Shafer ◽

John R Walker ◽

Tarun Chhibba ◽

Laura E Targownik ◽

Harminder Singh ◽

...

Keyword(s):

Health Care ◽

Longitudinal Study ◽

Health Care Utilization ◽

Severe Disease ◽

Population Based ◽

Health Data ◽

Care Utilization ◽

Administrative Health Data ◽

Health Care Indicators

Using administrative health data of a population based sample of persons with IBD we found that milestones of health care utilization suggesting moderate to severe disease (higher number of IBD-related hospitalizations, IBD-related surgeries, and corticosteroid or anti-TNF usage) predicted later development of IBD-related disability.

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High-dimensional propensity score adjustment in HIV research using linked administrative health data

International Journal for Population Data Science ◽

10.23889/ijpds.v4i3.1319 ◽

2019 ◽

Vol 4 (3) ◽

Author(s):

Taylor McLinden ◽

Rolando Barrios ◽

Robert Hogg

Keyword(s):

Propensity Score ◽

Administrative Data ◽

Population Data ◽

Population Based ◽

Health Data ◽

High Dimensional ◽

People Living With Hiv ◽

Administrative Health Data ◽

Hiv Research ◽

Linked Administrative Health Data

BackgroundDespite not being collected for research purposes, linked administrative health data are increasingly being used to conduct observational epidemiologic analyses. In the field of HIV research in British Columbia (BC), Canada, the Comparative Outcomes And Service Utilization Trends (COAST) Study is based on a linkage between HIV-related clinical data and several provincial administrative health datasets. Specifically, the BC Centre for Excellence in HIV/AIDS Drug Treatment Program, which manages antiretroviral therapy (ART) dispensation for all known people living with HIV (PLWH) in BC, is linked with several Population Data BC data holdings. Population Data BC is a repository that houses longitudinal administrative data for all BC residents. RationaleWhile the use of administrative data for research poses several challenges, bias due to confounding remains to be a key issue in this context. While randomized controlled trials of ART are common, an objective of COAST is to further examine the "real-world" impact of ART on health and clinical outcomes in a population-based sample of 13,907 PLWH in BC. Therefore, while longitudinal administrative data provide a unique opportunity to estimate the effect of ART on outcomes that are infrequently assessed in trials (e.g., chronic conditions), such data often lacks information on sociodemographic, socioeconomic, and behavioural confounders. ApproachIt has been shown that adjustment for large numbers of covariates, in the form of administrative codes (e.g., diagnostic ICD codes, procedure codes, drug identification numbers), allows for better control of confounding bias. Therefore, relying on an established methodology in pharmacoepidemiology, we will use the high-dimensional propensity score algorithm to select and prioritize covariates (codes) that collectively act as proxies for unmeasured confounders. The use of this causal inference methodology in COAST will enhance our ability to generate stronger evidence to inform strategies that may improve the health and wellbeing of PLWH in this setting.

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Wait times and patterns of care in the colorectal cancer diagnostic interval

International Journal for Population Data Science ◽

10.23889/ijpds.v1i1.211 ◽

2017 ◽

Vol 1 (1) ◽

Author(s):

Colleen Webber ◽

Jennifer Flemming ◽

Richard Birtwhistle ◽

Mark Rosenberg ◽

Patti Groome

Keyword(s):

Colorectal Cancer ◽

Cluster Analysis ◽

Control Charts ◽

Population Level ◽

Population Based ◽

Health Data ◽

Patterns Of Care ◽

Cross Sectional ◽

Administrative Health Data ◽

Diagnostic Interval

ABSTRACTObjectiveThere is concern that patients are waiting too long to be diagnosed with colorectal cancer (CRC) after presenting to the healthcare system. A prolonged time from first presentation to diagnosis, also known as the diagnostic interval, may be harmful to patients and indicate problems with the delivery of healthcare. The purpose of this study is to measure the length of the CRC diagnostic interval and describe variations in care that patients receive within the interval. ApproachThis is a population-based, cross-sectional study of CRC patients diagnosed in Ontario, Canada between 2009 and 2012 using data from the Institute for Clinical Evaluative Sciences (ICES). The diagnostic interval will be measured using physician billing, hospital discharge, emergency room and registry data. Patients’ healthcare encounters in the 18 months before diagnosis will be analyzed using control charts to identify the earliest cancer-related encounter. The diagnostic interval will be defined as the date of this first relevant healthcare encounter to the CRC diagnosis date. Cluster analysis will be used to identify and characterize groups of patients with similar diagnostic intervals, based on the care received within the interval. Analyses will examine factors associated with the length of the diagnostic interval and care received within the diagnostic interval. Results Analyses for this project are ongoing and will be complete by August 2016. Results from this study will describe the length of the CRC diagnostic interval and relevant sub-intervals, and variations in these intervals according to patient and clinical characteristics. Results will describe the care that patients received within the interval, including the number and types of tests received and physicians involved in the interval, and whether the care received in the interval was associated with how long patients wait for diagnosis. ConclusionThe findings from this study will advance our understanding of the CRC diagnostic interval. The control chart methodology used to identify CRC-related healthcare encounters from administrative health data is an improvement on previous research that has used arbitrary time periods and encounters which likely underestimate the length of the diagnostic interval. The cluster analysis method is a novel approach to characterizing the diagnostic interval that will identify common patterns of care and diagnostic pathways using administrative health data. This study will provide population-level estimates of how long patients are waiting to be diagnosed with CRC and provide an understanding of how patterns of care influence the length of the diagnostic interval.

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The SPOR-Canadian Data Platform: a national initiative to facilitate data rich multi-jurisdictional research

International Journal for Population Data Science ◽

10.23889/ijpds.v5i1.1374 ◽

2020 ◽

Vol 5 (1) ◽

Author(s):

Lindsey Todd Dahl ◽

Jennifer D Walker ◽

Michael Schull ◽

P. Alison Paprica ◽

James Ted McDonald ◽

...

Keyword(s):

Care Program ◽

Population Based ◽

Health Data ◽

Research Network ◽

Distributed Network ◽

Publicly Funded ◽

Administrative Health Data ◽

Canadian Data ◽

Data Platform ◽

National Initiative

Administrative health data is recognized for its value for conducting population-based research that has contributed to numerous improvements in health. In Canada, each province and territory is responsible for administering its own publicly funded health care program, which has resulted in multiple sets of administrative health data. Challenges to using these data within each of these jurisdictions have been identified, which are further amplified when the research involves more than one jurisdiction. The benefits to conducting multi-jurisdictional studies has been recognized by the Canadian Institutes of Health Research (CIHR), which issued a call in 2017 for proposals that address the challenges. The grant led to the creation of Health Data Research Network Canada (HDRN), with a vision is to establish a distributed network that facilitates and accelerates multi-jurisdictional research in Canada. HDRN received funding for seven years that will be used to support the objectives and activities of an initiative called the Strategy for Patient-Oriented Research Canadian Data Platform (SPOR-CDP). In this paper, we describe the challenges that researchers face while using, or considering using, administrative health data to conduct multi-jurisdictional research and the various ways that the SPOR-CDP will attempt to address them. Our objective is to assist other groups facing similar challenges associated with undertaking multi-jurisdictional research.

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