The impact of the use of multiple risk indicators for fracture on casefinding strategies: a mathematical approach
(2005) In Osteoporosis International 16(3). p.313318 Abstract
 The value of bone mineral density (BMD) measurements to stratify fracture probability can be enhanced in a casefinding strategy that combines BMD measurement with independent clinical risk indicators. Putative risk indicators include age and gender, BMI or weight, prior fracture, the use of corticosteroids, and possibly others. The aim of the present study was to develop a mathematical framework to quantify the impact of using combinations of risk indicators with BMD in case finding. Fracture probability can be expressed as a risk gradient, i.e. a relative risk (RR) of fracture per standard deviation (SD) change in BMD. With the addition of other continuous or categorical risk indicators a continuous distribution of risk indicators is... (More)
 The value of bone mineral density (BMD) measurements to stratify fracture probability can be enhanced in a casefinding strategy that combines BMD measurement with independent clinical risk indicators. Putative risk indicators include age and gender, BMI or weight, prior fracture, the use of corticosteroids, and possibly others. The aim of the present study was to develop a mathematical framework to quantify the impact of using combinations of risk indicators with BMD in case finding. Fracture probability can be expressed as a risk gradient, i.e. a relative risk (RR) of fracture per standard deviation (SD) change in BMD. With the addition of other continuous or categorical risk indicators a continuous distribution of risk indicators is obtained that approaches a normal distribution. It is then possible to calculate the risk of individuals compared with the average risk in the population, stratified by age and gender. A risk indicator with a gradient of fracture risk of 2 per SD identified 36% of the population as having a higher than average fracture risk. In individuals so selected, the risk was on average 1.7 times that of the general population. Where, through the combination of several risk indicators, the gradient of risk of the test increased to 4 per SD, a smaller proportion (24%) was identified as having a higher than average risk, but the average risk in this group was 3.1 times that of the population, which is a much better performance. At higher thresholds of risk, similar phenomena were found. We conclude that, whereas the change of the proportion of the population detected to be at high risk is small, the performance of a test is improved when the RR per SD is higher, indicated by the higher average risk in those identified to be at risk. Casefinding strategies that combine clinical risk indicators with BMD have increased efficiency, while having a modest impact on the number of individuals requiring treatment. Therefore, the costeffectiveness is enhanced. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/record/897380
 author
 De Laet, C; Oden, A; Johansson, H; Johnell, Olof ^{LU} ; Jonsson, B and Kanis, JA
 organization
 publishing date
 2005
 type
 Contribution to journal
 publication status
 published
 subject
 keywords
 case finding, risk, osteoporosis, fractures, mathematical model
 in
 Osteoporosis International
 volume
 16
 issue
 3
 pages
 313  318
 publisher
 Springer
 external identifiers

 wos:000227237700011
 pmid:15241584
 scopus:15044355611
 ISSN
 14332965
 DOI
 10.1007/s001980041689z
 language
 English
 LU publication?
 yes
 id
 910b4a057813408995ddde17c8155912 (old id 897380)
 date added to LUP
 20080111 13:59:34
 date last changed
 20180708 03:59:57
@article{910b4a057813408995ddde17c8155912, abstract = {The value of bone mineral density (BMD) measurements to stratify fracture probability can be enhanced in a casefinding strategy that combines BMD measurement with independent clinical risk indicators. Putative risk indicators include age and gender, BMI or weight, prior fracture, the use of corticosteroids, and possibly others. The aim of the present study was to develop a mathematical framework to quantify the impact of using combinations of risk indicators with BMD in case finding. Fracture probability can be expressed as a risk gradient, i.e. a relative risk (RR) of fracture per standard deviation (SD) change in BMD. With the addition of other continuous or categorical risk indicators a continuous distribution of risk indicators is obtained that approaches a normal distribution. It is then possible to calculate the risk of individuals compared with the average risk in the population, stratified by age and gender. A risk indicator with a gradient of fracture risk of 2 per SD identified 36% of the population as having a higher than average fracture risk. In individuals so selected, the risk was on average 1.7 times that of the general population. Where, through the combination of several risk indicators, the gradient of risk of the test increased to 4 per SD, a smaller proportion (24%) was identified as having a higher than average risk, but the average risk in this group was 3.1 times that of the population, which is a much better performance. At higher thresholds of risk, similar phenomena were found. We conclude that, whereas the change of the proportion of the population detected to be at high risk is small, the performance of a test is improved when the RR per SD is higher, indicated by the higher average risk in those identified to be at risk. Casefinding strategies that combine clinical risk indicators with BMD have increased efficiency, while having a modest impact on the number of individuals requiring treatment. Therefore, the costeffectiveness is enhanced.}, author = {De Laet, C and Oden, A and Johansson, H and Johnell, Olof and Jonsson, B and Kanis, JA}, issn = {14332965}, keyword = {case finding,risk,osteoporosis,fractures,mathematical model}, language = {eng}, number = {3}, pages = {313318}, publisher = {Springer}, series = {Osteoporosis International}, title = {The impact of the use of multiple risk indicators for fracture on casefinding strategies: a mathematical approach}, url = {http://dx.doi.org/10.1007/s001980041689z}, volume = {16}, year = {2005}, }