Osteoporosis is a debilitating brittle bone disease characterized by low bone-mineral density and increased fracture risk. Osteoporotic fractures result in pain, disability, and even death.1,2 As the world's population ages, osteoporosis and the subsequent fractures will become increasingly common.3 Health care administrative databases are commonly used to measure fracture prevalence and to help determine osteoporosis drug effectiveness (eg, on femoral neck, radius/ulna, vertebral fractures) and safety (eg, atypical femoral fracture).4-8 These data are routinely collected and are generated from interactions with the health care system, including receiving a diagnosis, undergoing a procedure, or receiving prescription medication.9 Although administrative databases were primarily designed for billing purposes, such as pharmacy and medical reimbursement claims, they are also used extensively for research.9,10 Administrative data are a relatively inexpensive and efficient data source. Their use also circumvents challenges associated with primary data collection, such as participant recruitment and recall bias, and can allow for long-term follow-up.10 The availability of a high volume of health care administrative data has improved the ability to examine real-world drug safety and effectiveness.
Diagnoses and procedures are recorded in administrative databases by trained medical coders. However, once recorded, researchers can access these data and utilize different methods to define outcomes in observational studies.11,12 Indeed, differences in outcome definitions have led to calls for transparency and agreement on outcome definitions to improve reproducibility and rigor in real-world evidence.13 In an effort to inform fracture outcome definitions of fracture risk, we propose a scoping review to provide an exploratory description of how fractures are defined in observational osteoporosis effectiveness and safety studies that use health care administrative data. Experimental methods are used to provide evidence of drug efficacy prior to drug approval (ie, does the drug work under ideal conditions). To this end, outcomes are carefully adjudicated in randomized controlled trials. Once on the market, observational studies, such as cohort and case-control studies, are used to consider drug effectiveness and safety, such as fracture risk. Since data availability and fracture codes vary by jurisdiction, we will target observational studies using Canadian and American data. Furthermore, the exact codes used to identify an event, such as a diagnosis or procedure, can differ between jurisdictions. The United States and Canada use the International Classification of Diseases (ICD) system, while, for example, the United Kingdom uses the Read system (hip fracture ICD-9 code: 820.x, ICD-10: S72.1-S72.2, Read code: 7K1L400, S30.11).14 Our results will help inform fracture outcome definitions in future studies that leverage similar administrative data sources in Canada and the United States.
A preliminary search of PROSPERO, Open Science Framework, JBI Evidence Synthesis, MEDLINE, and the Cochrane Database of Systematic Reviews was conducted and no current or in-progress scoping reviews or systematic reviews on the topic were identified. Our objective is to describe fracture outcome definitions for observational studies.
A scoping review on this topic is needed for a number of reasons. First, although guidance on methods for defining fractures exists, no study has examined whether these methods have been implemented in primary research.11,12 In addition, our results can be used to lay the groundwork for future research and comparisons of fracture identification between definitions.
How do osteoporosis drug effects studies that use health care administrative data in Canada and the United States define fracture outcomes?
This review will consider adult populations (≥18 years) who are being treated for osteoporosis. Treatments for osteoporosis include alendronate, etidronate, risedronate, ibandonate, zoledronic acid, raloxifene, denosumab, calcitonin, teriparatide, strontium, romosozumab, and estradiol.
This review will consider studies that explore fractures as outcomes of osteoporosis pharmacotherapy and use health care administrative data to define fractures. Fractures must be the primary outcome and must be defined using health care administrative databases. We will include fracture sites included in osteoporosis drug effects studies of fracture risk. We are interested in how each study defines fractures as the outcome. Fracture outcome definitions may involve a combination of diagnosis and procedural codes within or without a prespecified time range, as well as a washout window.
This review will consider studies that are conducted using Canadian and American health care administrative data, due to similar data availability and coding systems. Since fracture definitions rely on data availability within regions, restricting inclusion to studies that leverage Canadian and American data would allow for future comparisons between fracture outcome definitions.
Types of sources
This review will consider published observational studies (eg, cohort, case-control, self-controlled) that investigate the effects of osteoporosis medication on fracture risk. Studies using data other than routinely collected health care administrative data, such as medical chart reviews and surveys, will be excluded. Database validation studies, methods papers, reviews, meta-analyses, editorials, commentaries, and letters to the editor will not be included. Gray literature will be considered for pharmacovigilance studies to inform our safety outcome.
The proposed scoping review will be conducted in accordance with JBI methodology for scoping reviews.15
The search strategy aims to locate published primary studies and gray literature. An initial limited search of MEDLINE (Ovid) and CINAHL (EBSCO) was undertaken to identify articles on the topic. We identified search concepts that align with our research question and increase the sensitivity of our search (observational research, administrative data, fractures, and osteoporosis pharmacotherapies). We constructed our search by referencing validated terms for our topics, such as a search from Larney and colleagues that yielded over 95% sensitivity for epidemiological publications.16,17 We also referenced search strategies for epidemiologic studies published by the University of Texas and used text mining to find the frequency of MeSH terms most associated with our key concepts. We also leveraged our previous work to generate search terms for fractures and osteoporosis pharmacotherapies concepts.18-21 We then performed two rounds of validation, each with 5 to 10 articles. In between validation rounds, we edited our terms to include articles that were not previously retrieved. After completing the first draft of our search, we scanned the first 50 titles for their degree of relevance to our study. Through this process, we broadened our terms (eg, adding exp medicare/ as a general term, and including claim∗ and data∗, which will identify articles that use all variations of these words). Text words contained in the titles and abstracts of relevant articles, index terms used to describe the articles, published literature, and validation were used to develop a full search strategy for MEDLINE (Ovid; see Appendix I). The search strategy, including all identified keywords and index terms, will be adapted for each data source.
Only articles published in English, the language of the reviewers, will be included. Articles published from 2000 to the present will be included; the first osteoporosis medications other than estradiol only became available in mid-1990s, making pharmacoepidemiologic osteoporosis outcomes studies uncommon before 2000.22
The databases to be searched include MEDLINE (Ovid), Embase (Ovid), and CINAHL (EBSCO). We will also search the US Food and Drug Administration (FDA), Health Canada websites (Public Health Agency of Canada, Canadian Institutes of Drugs and Technologies in Health), American Society of Bone and Mineral Research, National Osteoporosis Foundation, and Osteoporosis Canada websites to identify gray literature.
Study/source of evidence selection
Following the search, all identified records will be collated and uploaded into Endnote v.X9 (Clarivate Analytics, PA, USA), and duplicates removed. We will then conduct a pilot test of our screening process using JBI guidance. We will randomly select and screen 25 articles, address discrepancies in screening, and modify inclusion/exclusion criteria. We will only begin screening when at least 75% agreement is reached. Following this pilot test, titles and abstracts will then be screened by two independent reviewers for assessment against the inclusion criteria for the review. Potentially relevant papers will be retrieved in full and their citation details imported into the JBI System for the Unified Management, Assessment and Review of Information (JBI SUMARI; JBI, Adelaide, Australia).23 The full text of selected citations will be assessed in detail against the inclusion criteria by two independent reviewers. Reasons for exclusion of full-text papers that do not meet the inclusion criteria will be recorded and reported in the scoping review. Any disagreements that arise between reviewers at each stage of the selection process will be resolved through discussion or with a third reviewer. The results of the search will be reported in full in the final scoping review and presented in a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram.24
Data will be extracted from papers included in the scoping review by two independent reviewers using a data extraction tool developed by the reviewers. The data extracted will include specific details about the methods, including data sources and definitions used to define fractures relevant to the review question.15 A draft extraction tool is provided (Appendix II). The draft data extraction tool will be modified and revised as necessary during the process of extracting data from each included paper. Study data will be collected and managed using REDCap (Vanderbilt University, Nashville, USA) electronic data capture tools.25,26 Modifications will be detailed in the full scoping review. Any disagreements that arise between the reviewers will be resolved through discussion or with a third reviewer. Authors of papers will be contacted to request missing or additional data, where required.
Data analysis and presentation
Results will be presented in tabular format that aligns with the aim of this scoping review. Tables will report fracture type, definition (eg, number of diagnoses and procedural claims, time between claims), as well as the classification method and version (eg, ICD version 9 or 10), individual codes, and washout windows (see Appendix III). This table may be refined based on the data extracted. Graphical representations may also be used, such as histograms, line charts, and diagrams.
Dr. Sara Guilcher and Glyneva Bradley-Radout, MI, for their methodological guidance during the development of the search strategy.
NK and AR are supported by Canadian Institutes of Health Research (CIHR) Canada Graduate Scholarships Master Awards, the CIHR Drug Safety and Effectiveness Cross-Disciplinary Training program (DSECT) and the Dean's Graduate Entrance Scholarships from the Leslie Dan Faculty of Pharmacy, University of Toronto. NK is also supported by a Pfizer Fellowship for Health Outcomes Research. The opinions, results, and conclusions reported in this paper are those of the authors and are independent from the funding sources.
Appendix I: Search strategy
Search conducted on June 22, 2020
Appendix II: Draft data extraction instrument
Appendix III: Draft summary table
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