Osteoporosis is a ‘silent disease’ which often has no immediate symptoms but gradually weakens bones and makes them more likely to break. A bone fracture caused by osteoporosis in people over the age of 50 is linked to long-term health decline and in some cases, even early death. However, poor communication of the mortality risk to patients has led to a low uptake of treatment, resulting in a crisis of osteoporosis management.

The impact of a fracture on life expectancy is typically conveyed to patients and the public in terms of probability (how likely something is to occur) or the relative risk of death compared to other groups. However, statements such as “Your risk of death over the next 10 years is 5% if you have suffered from a bone fracture” can be difficult to comprehend and can lead to patients underestimating the gravity of the risk.

With the aim of devising a new way of conveying risks to patients, Tran et al. analyzed the relationship between fracture and lifespan in over 1.6 million individuals who were 50 years of age or older. The findings showed that one fracture was associated with losing up to 7 years of life, depending on gender, age and fracture site. Based on this finding, Tran et al. proposed the idea of 'skeletal age' as a new metric for quantifying the impact of a fracture on life expectancy.

Skeletal age is the sum of the chronological age of a patient and the estimated number of years of life lost following a fracture. For example, a 60-year-old man with a hip fracture is predicted to lose an estimated 6 years of life, resulting in a skeletal age of around 66. Therefore, this individual has the same life expectancy as a 66-year-old person that has not experienced a fracture.

Skeletal age can also be used to quantify the benefit of osteoporosis treatments. Some approved treatments substantially reduce the likelihood of post-fracture death and translating this into skeletal age could help communicate this to patients. For instance, telling patients that “This treatment will reduce your skeletal age by 2 years” is easier to understand than “This treatment will reduce your risk of death by 25%”.

Given the current crisis of osteoporosis management, adopting skeletal age as a new measure of how the skeleton declines after a fracture could enhance doctor-patient communication regarding treatment options and fracture risk assessment. Tran et al. are now developing an online tool called ‘BONEcheck.org’ to enable health care professionals and the public to calculate skeletal age. Future work should investigate the effectiveness of this new metric in conveying risk to patients, compared with current methods.

Fragility fracture is a direct consequence of osteoporosis, just as stroke is a consequence of hypertension. Fracture, especially hip fracture, is associated with an increased risk of mortality. Indeed, patients with a fragility fracture have on average a twofold increase in the risk of mortality (Center et al., 1999). Between 22% (Brauer et al., 2009) and 58% (Rapp et al., 2008) of patients with a hip fracture die within 12 months post fracture. The identification of high-risk individuals for early intervention is a major priority in the control of osteoporotic fractures in the general community. In randomized controlled trials, treating high-risk individuals reduces the risk of fracture (Black et al., 2020), though whether the reduction translates into reduced mortality risk remains contentious (Bolland et al., 2010; Cummings et al., 2019). However, the treatment uptake has been low, with only 40% of hip fracture patients in the United States being given osteoporosis treatment within 1 month after discharge in 2002, which was then halved in 2011 (Solomon et al., 2014). This undertreatment is considered a crisis in the management of osteoporosis globally.

Despite the excess mortality after fracture, mortality is not part of doctor-patient communication about treatment or risk assessment, because there is a lack of an intuitive method of conveying risk. Traditionally, relative risk (e.g. "the risk of mortality is increased by twofold") has been used as a metric of excess risk, but this metric often gives an exaggerated impression (Trevena et al., 2013), and is perceived as larger than the absolute risk (Akl et al., 2011). On the other hand, absolute risk in terms of probability over a period of time is much harder to understand (Zipkin et al., 2014), even for doctors and patients (Gigerenzer et al., 2007). The underappreciation of post-fracture mortality’s gravity has caused patients to be hesitant towards treatment and prevention, contributing to the crisis of osteoporosis management. Thus, there is an urgent need for a more informative metric to internalize the combined risks of fracture and mortality in a way that patients and doctors can comprehend easily.

The concept of ‘Effective Age’ (Spiegelhalter, 2016) is useful here. In engineering, effective age is the age of a structure based on its current conditions; whereas in medicine, the effective age of an individual is the age of a typical healthy person who matches the specific risk profile of this individual (Spiegelhalter, 2016). Depending on whether the risk profile is not healthy (i.e. presence of risk factors) or healthy (i.e. absence of risk factors), the effective age is older or younger, respectively than the chronological age (Spiegelhalter, 2016). The best-known effective age in medicine is ‘Heart Age’ or ‘Vascular Age’ (NHS Health Check, 2022) and ‘Lung Age’ (Morris and Temple, 1985). The use of heart age and lung age has resulted in a better clinical impact than the traditional absolute risk metric (Kulendrarajah et al., 2020).

We consider that patients with osteoporosis and the general public are not sufficiently informed about the risk of post-fracture mortality, and this might have contributed to the current crisis of under management of osteoporosis (Roux and Briot, 2020; Beaudoin et al., 2019). In an effort to improve the management of osteoporosis, we advance the idea of the ‘Skeletal Age’ which is conceptually defined as the age of one’s skeleton as a consequence of fragility fracture. This new metric is not only one that quantifies the impact of fracture on mortality but also one that captures the risk of fracture and the risk of post-fracture mortality.

The aim of this study was to analyze the relationship between fracture and mortality, and then translate this relationship into the ‘Skeletal Age’ for each fracture site by leveraging data from the Danish National Hospital Discharge Registry (NHDR). The NHDR data are ideal for this analysis because, apart from comorbidities at the individual patient level, it has documented the incidence of fractures and post-fracture mortality for the entire Danish population.

It has been well established that fracture, especially hip fracture, is associated with an increased risk of mortality, and this excess risk is commonly expressed in terms of the relative risk metric. Here, we proposed a new effective age metric called ‘Skeletal Age’ to quantify the impact of fracture on mortality. Using data from a nationwide cohort of 1.7 million adults aged 50+ years old in Denmark, we showed that almost all types of fracture were associated with a loss of years of life, indicating that the skeletal age of individuals suffering from a fracture is greater than their chronological age. This finding has important implications that we discuss below.

Our finding confirmed the previous studies (Tran et al., 2018; Bliuc et al., 2009; Browner et al., 1996; Melton et al., 2013; Haentjens et al., 2010; Tran et al., 2017) that patients with a fragility fracture were associated with a significantly greater risk of mortality than their similarly aged and gender counterparts without fracture who had the same comorbidity profile. Our finding is also consistent with an earlier Danish study demonstrating reduced life expectancy in patients, with or without fractures, at the time of beginning osteoporosis treatment (Abrahamsen et al., 2015). However, the magnitude of the association between individual sites of fracture observed in our study is slightly lower than that documented in several previous cohort studies (Bliuc et al., 2009; Browner et al., 1996; Melton et al., 2013; Haentjens et al., 2010; Tran et al., 2017), with hip fractures being associated with a 1.7-to-6-fold increased risk of death (Haentjens et al., 2010). This discrepancy could be due to the difference in study populations. While cohort studies usually recruit healthy participants, our national registry-based study was able to document all individuals in Denmark. As a result, our control group (i.e. those without a fracture) included individuals in poor health conditions with multiple comorbidities who are usually unable to be recruited in a cohort study. Whether the association between fracture and mortality is causal or not is a matter of contention. It is commonly assumed that the association is confounded by comorbidities, but multiple studies have found that comorbidities contributed little to the excess risk of mortality among fractured patients (Vestergaard et al., 2007; Chen et al., 2018). In the present analysis, we have also adjusted for comorbidities, and the significant fracture – mortality association remained unchanged, suggesting that the association is unlikely due to comorbidities.

Regardless of the nature of the association, mortality is not a component of doctor-patient communication. Existing fracture risk assessment models such as Garvan (Nguyen et al., 2007) and FRAX (Kanis et al., 2007) provide the estimated probability (i.e. absolute risk) of fracture over a 10 year period without any information on mortality. However, doctors and patients have difficulty in understanding and interpreting probability (Gigerenzer et al., 2007). Only a fifth of a sample of highly educated American adults could understand one in 1000 is equivalent to 0.1% (Lipkus et al., 2001). Conveying a low, though clinically significant absolute risk (e.g. ‘Your risk of hip fracture over the next 10 years is 5%’) might provide a false peace of mind and underestimated risk perception, leading to increasing possibilities of refusing the recommended treatment (Trevena et al., 2013). Thus, poor communication about fracture risk and mortality consequences might have contributed to the global crisis of undertreatment of osteoporosis.

Based on the concept of effective age, we proposed the idea of ‘Skeletal Age’ as a new metric for communicating the risk of mortality following a fracture. Unlike the current fracture risk assessment tools (Beaudoin et al., 2019) which estimate the probability of fracture over time using probability-based metrics, such as relative risk and absolute risk, skeletal age quantifies the consequence of a fracture using a natural frequency metric. A natural frequency metric has been consistently shown to be easier and more friendly to doctors and patients than the probability-based metrics (Akl et al., 2011; Gigerenzer et al., 2007; Ahmed et al., 2012). It is not straightforward to appreciate the importance of the twofold increased risk of death (i.e. relative risk = 2.0) without knowing the background risk (i.e. twofolds of 1% would remarkably differ from twofolds of 10%). By contrast, for the same twofold mortality risk of hip fracture, telling a 60 year man with a hip fracture that his skeletal age would be 66 years old, equivalent to a 6 year loss of life, is more intuitive. The skeletal age of 66 years old can also be interpreted as the individual being in the same risk category as a 66-year-old with ‘favorable risk factors’ or at least the ones that are potentially modifiable. Hence, an older skeletal age also means a greater risk of fracture. In addition, the skeletal age can be also used to convey the possible benefit of treatment, providing an alternative metric to the conventional metrics such as relative risk or relative risk reduction. For instance, a patient might find the statement ‘Zoledronic acid treatment helps a patient with a hip fracture gain 3 years of life’ much easier to understand and probably more persuasive than ‘Zoledronic acid treatment reduced the risk of death by 28%’ (Lyles et al., 2007).

Skeletal age is an addition to the already available metrics such as ‘Heart Age,’ ‘Lung Age,’ and more recently ‘Covid Age’ which have been demonstrated to be superior to the conventional risk metrics (Grover et al., 2007; Lowensteyn et al., 1998; Lopez-Gonzalez et al., 2015; Bonner et al., 2015; Svendsen et al., 2020) in terms of positive behavioral changes. For instance, compared with usual care, individuals who were given heart age had a greater smoking cessation rate (Lopez-Gonzalez et al., 2015; Bonner et al., 2015), substantial improvement in weight, body mass index, and physical activity (Lopez-Gonzalez et al., 2015), and a greater proportion of high-risk patients returning for a follow-up appointment (Lowensteyn et al., 1998). The use of heart age also led to significantly greater improvement at 12 months in metabolic parameters (Lopez-Gonzalez et al., 2015) or lipid profiles (Grover et al., 2007). A recent cluster randomized controlled trial in Norway also found that heart age was a good way to communicate cardiovascular risk and to motivate individuals to reduce cardiovascular risk factors (Svendsen et al., 2020). Similarly, the use of lung age could also lead to a clinically significant increase in the reported smoking cessation rates (Kaminsky et al., 2011; Takagi et al., 2017; Parkes et al., 2008). Collectively, these data suggest that effective age metrics can help patients better understand their risk and lead to preventive changes. A web-based calculator ‘BONEcheck.org’ has been developed to assist healthcare professionals and the public to estimate the risk of fracture and skeletal age for an individual with specific risk profiles, potentially improving doctor-patient risk communication.

Our findings should be interpreted within the context of their strengths and limitations. First, we included a whole-country population with long follow-up and robust diagnostic data, minimizing potential selection bias and misclassification (Frank, 2000), and allowing us to examine mortality risk following individual fracture sites. Second, the current study analyzed both fracture and covariates, such as aging and the presence of comorbidities in a time-dependent manner, making it statistically rigorous in minimizing an immortal time bias and sufficiently accounting for confounding effects. The analysis thus provided accurate estimates of the association between specific fracture sites and mortality risk as it was able to control for confounding effects at both the study entry and the time of fracture.

However, the use of registry-based data, originally documented and coded for administrative or reimbursement purposes is prone to variable data accuracy, the lack of specific clinical information, and non-medical factors (Mazzali and Duca, 2015). Fortunately, all essential study variables (i.e. comorbidities, fracture, and death) were systematically obtained from the Danish NHDR that includes excellent, complete medical records and precise diagnoses for all individuals living in Denmark since 1995 (Andersen et al., 1999; Vestergaard and Mosekilde, 2002). The use of diagnosis-based comorbidities, though possibly associated with lower sensitivity than the self-reported ones (Lujic et al., 2017) is able to capture the severe health disorders that prompt the participants to seek for medical assistance. Second, the analyses could not make adjustments for lifestyle factors and physical activity, which are usually obtained in a cohort study. As they are closely related to aging and the presence of comorbidities which were already accounted for in our analysis, making further adjustments for these lifestyle factors is unlikely to substantially modify the findings. Third, mild, asymptomatic fractures or chronic diseases that did not require medical attention might not be registered in the Danish NHDR. It is nevertheless unlikely that these unregistered fractures and comorbidities would modify the findings significantly, as previous studies have indicated a high concordance between self-reported fractures and those registered in the NHDR (Hundrup et al., 2004), and a high positive predictive value of the self-reported comorbidities (Lujic et al., 2017). Finally, our analysis did not include the examination of the effect of bone metabolism-affecting medications on the assessment of skeletal health. However, these medications were prescribed to only a few individuals aged 50 years or over in Denmark (Vestergaard et al., 2005), even for those with a hip fracture (Roerholt et al., 2009), and there is no strong evidence from randomized controlled trials of their impact on post-fracture mortality (Cummings et al., 2019).

In conclusion, we advanced the concept of skeletal age as the age of an individual’s skeleton resulting from a fragility fracture. Unlike existing metrics (e.g. relative risk, probability of fracture), skeletal age combines the risk that an individual will sustain a fracture and the risk of mortality once a fracture has occurred, making the doctor-patient communication more intuitive and possibly more effective. Given the evidence of the successful implication of similar effective age metrics, skeletal age is expected to improve risk communication and ultimately improve treatment uptake among patients who are indicated for treatment. A randomized controlled trial aiming to compare the use of skeletal age and the current metrics in fracture risk communication is warranted.

The study used data from the Danish National Patient Register which individual patient data cannot be shared without authorised access, as per Danish Legislation (https://www.dst.dk/en/TilSalg/Forskningsservice/Dataadgang). To be able to access data interested researchers need to be employees of Danish university, university hospital or other specified Danish organizations (https://www.dst.dk/ext/594574631/0/forskning/Regler-for-adgang-til-pseudonymiserede-mikrodata-under-Danmarks-Statistiks-forskerordninger--pdf). Non-Danish citizens may obtain access if employed at said institutions. The interested researchers will need to be registered as an authorized research group under Statistics Denmark (https://www.dst.dk/da/TilSalg/Forskningsservice/Dataadgang/Autorisering) and fulfill the criteria mentioned above. The research proposal expected to present the specific aims and the feasibility is examined and approved by the Danish Data Protection Agency and the Danish Health Data Authority [https://sundhedsdatastyrelsen.dk/da/forskerservice/ansog-om-data]. However, commercial research cannot be performed (https://www.dst.dk/ext/594574631/0/forskning/Regler-for-adgang-til-pseudonymiserede-mikrodata-under-Danmarks-Statistiks-forskerordninger--pdf). Deidentified data may not be removed from the server (https://www.dst.dk/da/TilSalg/Forskningsservice/hjemtagelse-af-analyseresultater). Aggregated data can only be presented if no individuals can be identified (e.g., counts in a contingency table must not be below five). Supplementary File 1 provides the ICD-10 codes from the Danish National Patient Discharge Register used to define fractures and comorbidities in this analysis. The aggregated data and the R analysis codes utilized to estimate skeletal age for each individual fracture site and to generate the graphs and charts in the manuscript (Skeletal-Age.html), and the analysis output (Skeletal Age_Rmarkdown.pdf) are publicly accessible (https://github.com/ThachSTran/Skeletal-Age.git, copy archived at swh:1:rev:db2c39ebf11f55612a745140ea078d7afa279bb0).

1. 1. Abrahamsen B
   2. Osmond C
   3. Cooper C

   (2015) Life expectancy in patients treated for osteoporosis: observational cohort study using national danish prescription data

   Journal of Bone and Mineral Research 30:1553–1559.

   https://doi.org/10.1002/jbmr.2478

   • PubMed
   • Google Scholar
2. 1. Ahmed H
   2. Naik G
   3. Willoughby H
   4. Edwards AGK

   (2012) Communicating risk

   BMJ 344:e3996.

   https://doi.org/10.1136/bmj.e3996

   • PubMed
   • Google Scholar
3. 1. Akl EA
   2. Oxman AD
   3. Herrin J
   4. Vist GE
   5. Terrenato I
   6. Sperati F
   7. Costiniuk C
   8. Blank D
   9. Schünemann H
   10. Cochrane Consumers and Communication Group

   (2011) Using alternative statistical formats for presenting risks and risk reductions

   The Cochrane Database of Systematic Reviews 2011:CD006776.

   https://doi.org/10.1002/14651858.CD006776.pub2

   • PubMed
   • Google Scholar
4. 1. Andersen TF
   2. Madsen M
   3. Jørgensen J
   4. Mellemkjoer L
   5. Olsen JH

   (1999)

   The Danish national Hospital register. A valuable source of data for modern health sciences

   Danish Medical Bulletin 46:263–268.

   • PubMed
   • Google Scholar
5. 1. Beaudoin C
   2. Moore L
   3. Gagné M
   4. Bessette L
   5. Ste-Marie LG
   6. Brown JP
   7. Jean S

   (2019) Performance of predictive tools to identify individuals at risk of non-traumatic fracture: a systematic review, meta-analysis, and meta-regression

   Osteoporosis International 30:721–740.

   https://doi.org/10.1007/s00198-019-04919-6

   • PubMed
   • Google Scholar
6. 1. Black DM
   2. Bauer DC
   3. Vittinghoff E
   4. Lui L-Y
   5. Grauer A
   6. Marin F
   7. Khosla S
   8. de Papp A
   9. Mitlak B
   10. Cauley JA
   11. McCulloch CE
   12. Eastell R
   13. Bouxsein ML
   14. Foundation for the National Institutes of Health Bone Quality Project

   (2020) Treatment-Related changes in bone mineral density as a surrogate biomarker for fracture risk reduction: meta-regression analyses of individual patient data from multiple randomised controlled trials

   The Lancet. Diabetes & Endocrinology 8:672–682.

   https://doi.org/10.1016/S2213-8587(20)30159-5

   • PubMed
   • Google Scholar
7. 1. Bliuc D
   2. Nguyen ND
   3. Milch VE
   4. Nguyen TV
   5. Eisman JA
   6. Center JR

   (2009) Mortality risk associated with low-trauma osteoporotic fracture and subsequent fracture in men and women

   JAMA 301:513–521.

   https://doi.org/10.1001/jama.2009.50

   • PubMed
   • Google Scholar
8. 1. Bolland MJ
   2. Grey AB
   3. Gamble GD
   4. Reid IR

   (2010) Effect of osteoporosis treatment on mortality: a meta-analysis

   The Journal of Clinical Endocrinology and Metabolism 95:1174–1181.

   https://doi.org/10.1210/jc.2009-0852

   • PubMed
   • Google Scholar
9. 1. Bonner C
   2. Jansen J
   3. Newell BR
   4. Irwig L
   5. Teixeira-Pinto A
   6. Glasziou P
   7. Doust J
   8. McKinn S
   9. McCaffery K

   (2015) Is the `` heart age'' concept helpful or harmful compared to absolute cardiovascular disease risk? an experimental study

   Medical Decision Making 35:967–978.

   https://doi.org/10.1177/0272989X15597224

   • PubMed
   • Google Scholar
10. 1. Brauer CA
    2. Coca-Perraillon M
    3. Cutler DM
    4. Rosen AB

    (2009) Incidence and mortality of hip fractures in the United States

    JAMA 302:1573–1579.

    https://doi.org/10.1001/jama.2009.1462

    • PubMed
    • Google Scholar
11. 1. Brenner H
    2. Gefeller O
    3. Greenland S

    (1993) Risk and rate advancement periods as measures of exposure impact on the occurrence of chronic diseases

    Epidemiology 4:229–236.

    https://doi.org/10.1097/00001648-199305000-00006

    • PubMed
    • Google Scholar
12. 1. Browner WS
    2. Pressman AR
    3. Nevitt MC
    4. Cummings SR

    (1996) Mortality following fractures in older women: the study of osteoporotic fractures

    Archives of Internal Medicine 156:1521–1525.

    https://doi.org/10.1001/archinte.1996.00440130053006

    • PubMed
    • Google Scholar
13. 1. Center JR
    2. Nguyen TV
    3. Schneider D
    4. Sambrook PN
    5. Eisman JA

    (1999) Mortality after all major types of osteoporotic fracture in men and women: an observational study

    Lancet 353:878–882.

    https://doi.org/10.1016/S0140-6736(98)09075-8

    • PubMed
    • Google Scholar
14. 1. Chen W
    2. Simpson JM
    3. March LM
    4. Blyth FM
    5. Bliuc D
    6. Tran T
    7. Nguyen TV
    8. Eisman JA
    9. Center JR

    (2018) Comorbidities only account for a small proportion of excess mortality after fracture: a record linkage study of individual fracture types

    Journal of Bone and Mineral Research 33:795–802.

    https://doi.org/10.1002/jbmr.3374

    • PubMed
    • Google Scholar
15. 1. Cummings SR
    2. Lui L-Y
    3. Eastell R
    4. Allen IE

    (2019) Association between drug treatments for patients with osteoporosis and overall mortality rates

    JAMA Internal Medicine 179:1491.

    https://doi.org/10.1001/jamainternmed.2019.2779

    • PubMed
    • Google Scholar
16. 1. Frank L

    (2000) Epidemiology. When an entire country is a cohort

    Science 287:2398–2399.

    https://doi.org/10.1126/science.287.5462.2398

    • PubMed
    • Google Scholar
17. 1. Gigerenzer G
    2. Gaissmaier W
    3. Kurz-Milcke E
    4. Schwartz LM
    5. Woloshin S

    (2007) Helping doctors and patients make sense of health statistics

    Psychological Science in the Public Interest 8:53–96.

    https://doi.org/10.1111/j.1539-6053.2008.00033.x

    • PubMed
    • Google Scholar
18. 1. Gompertz B

    (1825) XXIV. on the nature of the function expressive of the law of human mortality, and on a new mode of determining the value of life contingencies. in a letter to francis baily, esq. f. r. s. &c

    Philosophical Transactions of the Royal Society of London 115:513–583.

    https://doi.org/10.1098/rstl.1825.0026

    • Google Scholar
19. 1. Grover SA
    2. Lowensteyn I
    3. Joseph L
    4. Kaouache M
    5. Marchand S
    6. Coupal L
    7. Boudreau G
    8. Cardiovascular Health Evaluation to Improve Compliance and Knowledge Among Uninformed Patients (CHECK-UP) Study Group

    (2007) Patient knowledge of coronary risk profile improves the effectiveness of dyslipidemia therapy: the CHECK-UP study: a randomized controlled trial

    Archives of Internal Medicine 167:2296–2303.

    https://doi.org/10.1001/archinte.167.21.2296

    • PubMed
    • Google Scholar
20. 1. Haentjens P
    2. Magaziner J
    3. Colón-Emeric CS
    4. Vanderschueren D
    5. Milisen K
    6. Velkeniers B
    7. Boonen S

    (2010) Meta-Analysis: excess mortality after hip fracture among older women and men

    Annals of Internal Medicine 152:380–390.

    https://doi.org/10.7326/0003-4819-152-6-201003160-00008

    • PubMed
    • Google Scholar
21. 1. Henderson R
    2. Keiding N

    (2005) Individual survival time prediction using statistical models

    Journal of Medical Ethics 31:703–706.

    https://doi.org/10.1136/jme.2005.012427

    • PubMed
    • Google Scholar
22. 1. Hundrup YA
    2. Høidrup S
    3. Obel EB
    4. Rasmussen NK

    (2004) The validity of self-reported fractures among Danish female nurses: comparison with fractures registered in the Danish national Hospital register

    Scandinavian Journal of Public Health 32:136–143.

    https://doi.org/10.1080/14034940310017490

    • PubMed
    • Google Scholar
23. 1. Kaminsky DA
    2. Marcy T
    3. Dorwaldt A
    4. Pinckney R
    5. DeSarno M
    6. Solomon L
    7. Hughes JR

    (2011) Motivating smokers in the hospital pulmonary function laboratory to quit smoking by use of the lung age concept

    Nicotine & Tobacco Research 13:1161–1166.

    https://doi.org/10.1093/ntr/ntr096

    • PubMed
    • Google Scholar
24. 1. Kanis JA
    2. Oden A
    3. Johnell O
    4. Johansson H
    5. De Laet C
    6. Brown J
    7. Burckhardt P
    8. Cooper C
    9. Christiansen C
    10. Cummings S
    11. Eisman JA
    12. Fujiwara S
    13. Glüer C
    14. Goltzman D
    15. Hans D
    16. Krieg MA
    17. La Croix A
    18. McCloskey E
    19. Mellstrom D
    20. Melton LJ
    21. Pols H
    22. Reeve J
    23. Sanders K
    24. Schott AM
    25. Silman A
    26. Torgerson D
    27. van Staa T
    28. Watts NB
    29. Yoshimura N

    (2007) The use of clinical risk factors enhances the performance of BMD in the prediction of hip and osteoporotic fractures in men and women

    Osteoporosis International 18:1033–1046.

    https://doi.org/10.1007/s00198-007-0343-y

    • PubMed
    • Google Scholar
25. 1. Kulendrarajah B
    2. Grey A
    3. Nunan D

    (2020) How effective are `` age'' tools at changing patient behaviour? a rapid review

    BMJ Evidence-Based Medicine 25:1–2.

    https://doi.org/10.1136/bmjebm-2019-111244

    • PubMed
    • Google Scholar
26. 1. Kulinskaya E
    2. Gitsels LA
    3. Bakbergenuly I
    4. Wright NR

    (2020) Calculation of changes in life expectancy based on proportional hazards model of an intervention

    Insurance 93:27–35.

    https://doi.org/10.1016/j.insmatheco.2020.04.006

    • Google Scholar
27. 1. Lipkus IM
    2. Samsa G
    3. Rimer BK

    (2001) General performance on a numeracy scale among highly educated samples

    Medical Decision Making 21:37–44.

    https://doi.org/10.1177/0272989X0102100105

    • PubMed
    • Google Scholar
28. 1. Lopez-Gonzalez AA
    2. Aguilo A
    3. Frontera M
    4. Bennasar-Veny M
    5. Campos I
    6. Vicente-Herrero T
    7. Tomas-Salva M
    8. De Pedro-Gomez J
    9. Tauler P

    (2015) Effectiveness of the heart age tool for improving modifiable cardiovascular risk factors in a southern European population: a randomized trial

    European Journal of Preventive Cardiology 22:389–396.

    https://doi.org/10.1177/2047487313518479

    • PubMed
    • Google Scholar
29. 1. Lowensteyn I
    2. Joseph L
    3. Levinton C
    4. Abrahamowicz M
    5. Steinert Y
    6. Grover S

    (1998) Can computerized risk profiles help patients improve their coronary risk? the results of the coronary health assessment study (CHAS)

    Preventive Medicine 27:730–737.

    https://doi.org/10.1006/pmed.1998.0351

    • PubMed
    • Google Scholar
30. 1. Lujic S
    2. Simpson JM
    3. Zwar N
    4. Hosseinzadeh H
    5. Jorm L

    (2017) Multimorbidity in australia: comparing estimates derived using administrative data sources and survey data

    PLOS ONE 12:e0183817.

    https://doi.org/10.1371/journal.pone.0183817

    • PubMed
    • Google Scholar
31. 1. Lyles KW
    2. Colón-Emeric CS
    3. Magaziner JS
    4. Adachi JD
    5. Pieper CF
    6. Mautalen C
    7. Hyldstrup L
    8. Recknor C
    9. Nordsletten L
    10. Moore KA
    11. Lavecchia C
    12. Zhang J
    13. Mesenbrink P
    14. Hodgson PK
    15. Abrams K
    16. Orloff JJ
    17. Horowitz Z
    18. Eriksen EF
    19. Boonen S
    20. HORIZON Recurrent Fracture Trial

    (2007) Zoledronic acid and clinical fractures and mortality after hip fracture

    The New England Journal of Medicine 357:1799–1809.

    https://doi.org/10.1056/NEJMoa074941

    • PubMed
    • Google Scholar
32. 1. Mazzali C
    2. Duca P

    (2015) Use of administrative data in healthcare research

    Internal and Emergency Medicine 10:517–524.

    https://doi.org/10.1007/s11739-015-1213-9

    • PubMed
    • Google Scholar
33. 1. Melton LJ
    2. Achenbach SJ
    3. Atkinson EJ
    4. Therneau TM
    5. Amin S

    (2013) Long-term mortality following fractures at different skeletal sites: a population-based cohort study

    Osteoporosis International 24:1689–1696.

    https://doi.org/10.1007/s00198-012-2225-1

    • PubMed
    • Google Scholar
34. 1. Morris JF
    2. Temple W

    (1985) Spirometric `` lung age'' estimation for motivating smoking cessation

    Preventive Medicine 14:655–662.

    https://doi.org/10.1016/0091-7435(85)90085-4

    • PubMed
    • Google Scholar
35. 1. Nguyen ND
    2. Frost SA
    3. Center JR
    4. Eisman JA
    5. Nguyen TV

    (2007) Development of a nomogram for individualizing hip fracture risk in men and women

    Osteoporosis International 18:1109–1117.

    https://doi.org/10.1007/s00198-007-0362-8

    • PubMed
    • Google Scholar
36. Website

    1. NHS Health Check

    (2022) NHS Choices- Heart age calculator

    Accessed January 15, 2022.

    https://www.nhs.uk/conditions/nhs-health-check/check-your-heart-age-tool/2015
37. 1. Parkes G
    2. Greenhalgh T
    3. Griffin M
    4. Dent R

    (2008) Effect on smoking quit rate of telling patients their lung age: the step2quit randomised controlled trial

    BMJ 336:598–600.

    https://doi.org/10.1136/bmj.39503.582396.25

    • PubMed
    • Google Scholar
38. 1. Quan H
    2. Li B
    3. Couris CM
    4. Fushimi K
    5. Graham P
    6. Hider P
    7. Januel J-M
    8. Sundararajan V

    (2011) Updating and validating the charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries

    American Journal of Epidemiology 173:676–682.

    https://doi.org/10.1093/aje/kwq433

    • PubMed
    • Google Scholar
39. 1. Rapp K
    2. Becker C
    3. Lamb SE
    4. Icks A
    5. Klenk J

    (2008) Hip fractures in institutionalized elderly people: incidence rates and excess mortality

    Journal of Bone and Mineral Research 23:1825–1831.

    https://doi.org/10.1359/jbmr.080702

    • PubMed
    • Google Scholar
40. Software

    1. R Development Core Team

    (2020) R: A language and environment for statistical computing

    R Foundation for Statistical Computing, Vienna, Austria.

    https://www.R-project.org
41. 1. Roerholt C
    2. Eiken P
    3. Abrahamsen B

    (2009) Initiation of anti-osteoporotic therapy in patients with recent fractures: a nationwide analysis of prescription rates and persistence

    Osteoporosis International 20:299–307.

    https://doi.org/10.1007/s00198-008-0651-x

    • PubMed
    • Google Scholar
42. 1. Roux C
    2. Briot K

    (2020) The crisis of inadequate treatment in osteoporosis

    The Lancet Rheumatology 2:e110–e119.

    https://doi.org/10.1016/S2665-9913(19)30136-5

    • Google Scholar
43. 1. Solomon DH
    2. Johnston SS
    3. Boytsov NN
    4. McMorrow D
    5. Lane JM
    6. Krohn KD

    (2014) Osteoporosis medication use after hip fracture in U.S. patients between 2002 and 2011

    Journal of Bone and Mineral Research 29:1929–1937.

    https://doi.org/10.1002/jbmr.2202

    • PubMed
    • Google Scholar
44. 1. Spiegelhalter D

    (2016) How old are you, really? communicating chronic risk through `` effective age'' of your body and organs

    BMC Medical Informatics and Decision Making 16:104.

    https://doi.org/10.1186/s12911-016-0342-z

    • PubMed
    • Google Scholar
45. 1. Suissa S

    (2008) Immortal time bias in pharmacoepidemiology

    American Journal of Epidemiology 167:492–499.

    https://doi.org/10.1093/aje/kwm324

    • PubMed
    • Google Scholar
46. 1. Svendsen K
    2. Jacobs DR
    3. Mørch-Reiersen LT
    4. Garstad KW
    5. Henriksen HB
    6. Telle-Hansen VH
    7. Retterstøl K

    (2020) Evaluating the use of the heart age tool in community pharmacies: a 4-week cluster-randomized controlled trial

    European Journal of Public Health 30:1139–1145.

    https://doi.org/10.1093/eurpub/ckaa048

    • PubMed
    • Google Scholar
47. 1. Takagi H
    2. Morio Y
    3. Ishiwata T
    4. Shimada K
    5. Kume A
    6. Miura K
    7. Kuwasaki E
    8. Kato M
    9. Seyama K
    10. Takahashi K

    (2017) Effect of telling patients their `` spirometric-lung-age'' on smoking cessation in Japanese smokers

    Journal of Thoracic Disease 9:5052–5060.

    https://doi.org/10.21037/jtd.2017.11.06

    • PubMed
    • Google Scholar
48. 1. Tran T
    2. Bliuc D
    3. van Geel T
    4. Adachi JD
    5. Berger C
    6. van den Bergh J
    7. Eisman JA
    8. Geusens P
    9. Goltzman D
    10. Hanley DA
    11. Josse RG
    12. Kaiser SM
    13. Kovacs CS
    14. Langsetmo L
    15. Prior JC
    16. Nguyen TV
    17. Center JR

    (2017) Population-Wide impact of non-hip non-vertebral fractures on mortality

    Journal of Bone and Mineral Research 32:1802–1810.

    https://doi.org/10.1002/jbmr.3118

    • PubMed
    • Google Scholar
49. 1. Tran T
    2. Bliuc D
    3. Hansen L
    4. Abrahamsen B
    5. van den Bergh J
    6. Eisman JA
    7. van Geel T
    8. Geusens P
    9. Vestergaard P
    10. Nguyen TV
    11. Center JR

    (2018) Persistence of excess mortality following individual nonhip fractures: a relative survival analysis

    The Journal of Clinical Endocrinology and Metabolism 103:3205–3214.

    https://doi.org/10.1210/jc.2017-02656

    • PubMed
    • Google Scholar
50. 1. Trevena LJ
    2. Zikmund-Fisher BJ
    3. Edwards A
    4. Gaissmaier W
    5. Galesic M
    6. Han PKJ
    7. King J
    8. Lawson ML
    9. Linder SK
    10. Lipkus I
    11. Ozanne E
    12. Peters E
    13. Timmermans D
    14. Woloshin S

    (2013) Presenting quantitative information about decision outcomes: a risk communication primer for patient decision aid developers

    BMC Medical Informatics and Decision Making 13 Suppl 2:S7.

    https://doi.org/10.1186/1472-6947-13-S2-S7

    • PubMed
    • Google Scholar
51. 1. Vaupel JW

    (2010) Biodemography of human ageing

    Nature 464:536–542.

    https://doi.org/10.1038/nature08984

    • PubMed
    • Google Scholar
52. 1. Vestergaard P
    2. Mosekilde L

    (2002) Fracture risk in patients with celiac disease, Crohn’s disease, and ulcerative colitis: a nationwide follow-up study of 16,416 patients in Denmark

    American Journal of Epidemiology 156:1–10.

    https://doi.org/10.1093/aje/kwf007

    • PubMed
    • Google Scholar
53. 1. Vestergaard P
    2. Rejnmark L
    3. Mosekilde L

    (2005) Osteoporosis is markedly underdiagnosed: a nationwide study from Denmark

    Osteoporosis International 16:134–141.

    https://doi.org/10.1007/s00198-004-1680-8

    • PubMed
    • Google Scholar
54. 1. Vestergaard P
    2. Rejnmark L
    3. Mosekilde L

    (2007) Increased mortality in patients with a hip fracture-effect of pre-morbid conditions and post-fracture complications

    Osteoporosis International 18:1583–1593.

    https://doi.org/10.1007/s00198-007-0403-3

    • PubMed
    • Google Scholar
55. 1. Zipkin DA
    2. Umscheid CA
    3. Keating NL
    4. Allen E
    5. Aung K
    6. Beyth R
    7. Kaatz S
    8. Mann DM
    9. Sussman JB
    10. Korenstein D
    11. Schardt C
    12. Nagi A
    13. Sloane R
    14. Feldstein DA

    (2014) Evidence-based risk communication: a systematic review

    Annals of Internal Medicine 161:270–280.

    https://doi.org/10.7326/M14-0295

    • PubMed
    • Google Scholar

Author details

1. Thach Tran

   1. School of Biomedical Engineering, University of Technology Sydney, Sydney, Australia
   2. Garvan Institute of Medical Research, Sydney, Australia
   3. Faculty of Medicine, UNSW Sydney, New South Wales, Australia

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Validation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   SonThach.Tran@uts.edu.au

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6454-124X

2. Thao Ho-Le

   School of Biomedical Engineering, University of Technology Sydney, Sydney, Australia

   Contribution

   Conceptualization, Formal analysis, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

3. Dana Bliuc

   1. Garvan Institute of Medical Research, Sydney, Australia
   2. Faculty of Medicine, UNSW Sydney, New South Wales, Australia

   Contribution

   Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

4. Bo Abrahamsen

   1. Department of Medicine, Holbæk Hospital, Holbæk, Denmark
   2. Department of Clinical Research, Odense Patient Data Explorative Network, University of Southern Denmark, Odense, Denmark
   3. Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences University of Oxford, Oxford, United Kingdom

   Contribution

   Data curation, Writing – review and editing

   Competing interests

   has received institutional research grants from UCB, Kyowa-Kirin and Pharmacosmos; consulting fees from UCB and Kyowa-Kirin; and fees for lectures from Amgen, Gedeon-Richter, Kyowa-Kirin, Eli Lily and Pharmacosmos. The author is also on the Advisory Board for UCB and is president of European Calcified Tissue Society. The author has no other competing interests to declare

5. Louise Hansen

   Kontraktenheden, North Denmark Region, Denmark, Denmark

   Contribution

   Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4400-2732

6. Peter Vestergaard

   1. Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
   2. Department of Endocrinology, Aalborg University Hospital, Aalborg, Denmark
   3. Steno Diabetes Center North Jutland, Aalborg, Denmark

   Contribution

   Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

7. Jacqueline R Center

   1. Garvan Institute of Medical Research, Sydney, Australia
   2. Faculty of Medicine, UNSW Sydney, New South Wales, Australia
   3. School of Medicine Sydney, University of Notre Dame Australia, Sydney, Australia

   Contribution

   Data curation, Writing – review and editing

   Competing interests

   has received honoraria for educational talks and Advisory boards from Amgen and honoraria for an Advisory board from Bayer. The author has no other competing interests to declare

   "This ORCID iD identifies the author of this article:" 0000-0002-5278-4527

8. Tuan V Nguyen

   1. School of Biomedical Engineering, University of Technology Sydney, Sydney, Australia
   2. School of Medicine Sydney, University of Notre Dame Australia, Sydney, Australia
   3. School of Population Health, UNSW Medicine, UNSW Sydney, Kensington, Australia

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   TuanVan.Nguyen@uts.edu.au

   Competing interests

   has received grants from Australian National Health and Medical Research Council and Amgen Competitive Grant Program; fees for lectures from Amgen, Bridge Health Care (VN), DKSH Pharma, MSD and VT Health Care (VN); and support from Amgen for attending the annual meeting of APCO and from VT Health Care (VN) for attending the Vietnam Osteoporosis Society Annual Scientific Meeting. The author is also Chair of the Research Committee for Australian and New Zealand Bone and Mineral Society, a Senior Advisor for Vietnam Osteoporosis Society, and an Executive Member of Asia Pacific Consortium on Osteoporosis. The author has no other competing interests to declare

   "This ORCID iD identifies the author of this article:" 0000-0002-3246-6281

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