Estimating the prevalence of osteoporosis using ranked-based methodologies and Manitoba's population-based BMD registry

Abstract

AbstractOsteoporosis is a metabolic bone disorder that is characterized by reduced bone mineral density (BMD) and deterioration of bone microarchitecture. Osteoporosis is highly prevalent among women over 50, leading to skeletal fragility and risk of fracture. Early diagnosis and treatment of those at high risk for fracture is very important in order to avoid morbidity, mortality and economic burden from preventable fractures. The province of Manitoba established a BMD testing program in 1997. The Manitoba BMD registry is now the largest population-based BMD registry in the world, and has detailed information on fracture outcomes and other covariates for over 160,000 BMD assessments. In this paper, we develop a number of methodologies based on ranked-set type sampling designs to estimate the prevalence of osteoporosis among women of age 50 and older in the province of Manitoba. We use a parametric approach based on finite mixture models, as well as the usual approaches using simple random and stratified sampling designs. Results are obtained under perfect and imperfect ranking scenarios while the sampling and ranking costs are incorporated into the study. We observe that rank-based methodologies can be used as cost-efficient methods to monitor the prevalence of osteoporosis.Keywords: Bone mineral densityEM algorithmfinite mixture modelosteoporosisstratified samplingunbalanced ranked set sampling Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingMohammad Jafari Jozani gratefully acknowledges the research support of the Natural Sciences and Engineering Research Council of Canada (NSERC). We express our gratitude to two anonymous reviewers and an associate editor for their valuable and constructive comments