Evidence map›Paper›PMID 39247655›Full record

ArticleJournal of applied statistics2024

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

Sedigheh Omidvar, Mohammad Jafari Jozani, Nader Nematollahi, Wiliam D Leslie

Abstract read
In one paragraph

Article in Journal of applied statistics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Sedigheh OmidvarDepartment of Statistics, Allameh Tabataba'i University, Tehran, Iran.
Mohammad Jafari JozaniDepartment of Statistics, University of Manitoba, Winnipeg, Canada.
Nader NematollahiDepartment of Medicine, University of Manitoba, Winnipeg, Canada.
Wiliam D LeslieDepartment of Statistics, University of Manitoba, Winnipeg, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Osteoporosis 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.

Indexed as

Bone mineral densityEM algorithmfinite mixture modelosteoporosisstratified samplingunbalanced ranked set sampling

Identifiers

PMID39247655
PMCPMC11378117

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.