Calibration of FRAX ® 3.1 to the Dutch population with data on the epidemiology of hip fractures.

Abstract

The FRAX tool has been calibrated to the entire Dutch population, using nationwide (hip) fracture incidence rates and mortality statistics from the Netherlands. Data used for the Dutch model are described in this paper.
Risk communication and decision making about whether or not to treat with anti-osteoporotic drugs with the use of T-scores are often unclear for patients. The recently developed FRAX models use easily obtainable clinical risk factors to estimate an individual's 10-year probability of a major osteoporotic fracture and hip fracture that is useful for risk communication and subsequent decision making in clinical practice. As of July 1, 2010, the tool has been calibrated to the total Dutch population. This paper describes the data used to develop the current Dutch FRAX model and illustrates its features compared to other countries.
Age- and sex-stratified hip fracture incidence rates (LMR database) and mortality rates (Dutch national mortality statistics) for 2004 and 2005 were extracted from Dutch nationwide databases (patients aged 50+ years). For other major fractures, Dutch incidence rates were imputed, using Swedish ratios for hip to osteoporotic fracture (upper arm, wrist, hip, and clinically symptomatic vertebral) probabilities (age- and gender-stratified). The FRAX tool takes into account age, sex, body mass index (BMI), presence of clinical risk factors, and bone mineral density (BMD).
Fracture incidence rates increased with increasing age: for hip fracture, incidence rates were lowest among Dutch patients aged 50-54 years (per 10,000 inhabitants: 2.3 for men, 2.1 for women) and highest among the oldest subjects (95-99 years; 169 of 10,000 for men, 267 of 10,000 for women). Ten-year probability of hip or major osteoporotic fracture was increased in patients with a clinical risk factor, lower BMI, female gender, a higher age, and a decreased BMD T-score. Parental hip fracture accounted for the greatest increase in 10-year fracture probability.
The Dutch FRAX tool is the first fracture prediction model that has been calibrated to the total Dutch population, using nationwide incidence rates for hip fracture and mortality rates. It is based on the original FRAX methodology, which has been externally validated in several independent cohorts. Despite some limitations, the strengths make the Dutch FRAX tool a good candidate for implementation into clinical practice.

Full text

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ORIGINAL ARTICLE
Calibration of FRAX ® 3.1 to the Dutch population
with data on the epidemiology of hip fractures
A. Lalmohamed & P. M. J. Welsing & W. F. Lems &
J. W. G. Jacobs & J. A. Kanis & H. Johansson &
A. De Boer & F. De Vries
Received: 24 June 2011 /Accepted: 14 November 2011 /Published online: 26 November 2011
# The Author(s) 2011. This article is published with open access at Springerlink.com
Abstract
Summary The FRAX tool has been calibrated to the
entire Dutch population, using nationwide (hip) fracture
incidence rates and mortality statistics from the Netherlands.
Data used for the Dutch model are described in this
paper.
Introduction Riskcommunicationanddecisionmakingabout
whether or not to treat with anti-osteoporotic drugs with the
use of T-scores are often unclear for patients. The recently
developed FRAX models use easily obtainable clinical risk
factors to estimate an individual's 10-year probability of a
major osteoporotic fracture and hip fracture that is useful for
risk communication and subsequent decision making in
clinical practice. As of July 1, 2010, the tool has been
calibrated to the total Dutch population. This paper describes
the data used to develop the current Dutch FRAX model and
illustrates its features compared to other countries.
Methods Age- and sex-stratified hip fracture incidence rates
(LMR database) and mortality rates (Dutch national
mortality statistics) for 2004 and 2005 were extracted from
Dutch nationwide databases (patients aged 50+ years). For
other major fractures, Dutch incidence rates were imputed,
using Swedish ratios for hip to osteoporotic fracture (upper
arm, wrist, hip, and clinically symptomatic vertebral) proba-
bilities (age- and gender-stratified). The FRAX tool takes into
account age, sex, body mass index (BMI),presence ofclinical
risk factors, and bone mineral density (BMD).
Results Fracture incidence rates increased with increasing
age:forhipfracture,incidencerateswerelowestamongDutch
patients aged 50–54 years (per 10,000 inhabitants: 2.3 for
men, 2.1 for women) and highest among the oldest subjects
(95–99 years; 169 of 10,000 for men, 267 of 10,000 for
women). Ten-year probability of hip or major osteoporotic
fracture was increased in patients with a clinical risk factor,
lower BMI, female gender, a higher age, and a decreased
BMD T-score. Parental hip fracture accounted for the greatest
increase in 10-year fracture probability.
Conclusion The Dutch FRAX tool is the first fracture
prediction model that has been calibrated to the total Dutch
population, using nationwide incidence rates for hip
fracture and mortality rates. It is based on the original
A. Lalmohamed:A. De Boer:F. De Vries (*)
Division of Pharmacoepidemiology and Clinical Pharmacology,
Utrecht Institute for Pharmaceutical Sciences, Utrecht University,
Utrecht, The Netherlands
e-mail: f.devries@uu.nl
P. M. J. Welsing:J. W. G. Jacobs
Department of Rheumatology & Clinical Immunology,
University Medical Center Utrecht,
Utrecht, The Netherlands
P. M. J. Welsing
Julius Center for Health Sciences and Primary Care,
University Medical Center Utrecht,
Utrecht, The Netherlands
W. F. Lems
Department of Rheumatology, VU Medical Centre,
Amsterdam, The Netherlands
J. A. Kanis:H. Johansson
WHO Collaborating Centre for Metabolic Bone Diseases,
University of Sheffield Medical School,
Sheffield, UK
F. De Vries
MRC Epidemiology Resource Centre,
University of Southampton,
Southampton, UK
F. De Vries
Department of Clinical Pharmacy and Toxicology,
Maastricht University Medical Centre,
Maastricht, The Netherlands
Osteoporos Int (2012) 23:861–869
DOI 10.1007/s00198-011-1852-2

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FRAX methodology, which has been externally validated in
several independent cohorts. Despite some limitations, the
strengths make the Dutch FRAX tool a good candidate for
implementation into clinical practice.
Keywords FRAX.Hip fracture.Osteoporosis.
Osteoporotic fracture.10-year fracture probability
Introduction
Osteoporosis is a devastating disease resulting in substantial
health care costs and increased mortality. In Europe, osteopo-
rotic fractures affect one in two women and one in five men
aged 50 years and older [1]. In Europe, total health care costs
associated with these fractures have been estimated to be
around €30 billion [1]. In 2000, an estimated 5.8 million
disability-adjusted life years were caused by osteoporotic
fractures worldwide [2]. Among patients who have sustained
a hip fracture, one in five will die within the first year after
the fracture, whilst one in three of those surviving needs
assistance with walking [3, 4]. Because of this huge burden,
assessment of an individual's risk of fracture is important so
that a prophylactic intervention can be effectively targeted.
As of July 1, 2010, the FRAX® tool has been calibrated
to the total Dutch population (http://www.sheffield.ac.uk/
FRAX). FRAX uses easily obtainable clinical risk factors,
with or without femoral neck bone mineral density (BMD),
to estimate 10-year fracture probability [5]. It has been
constructed using primary data from nine population-based
cohorts around the world. The gradients of fracture risk
have been validated externally in 11 independent cohorts
withasimilargeographicdistribution[6]. FRAX is a platform
technology using Poisson models that integrate risk varia-
bles, fracture risk, and death risk over a 10-year interval.
Using the incidence rates of hip and osteoporotic fractures
and mortality rates, FRAX can be calibrated to create a
country-specific model [7]. With the introduction of the
online Dutch FRAX tool, it is important to understand the
origin of the data for further validation if needed. Further-
more, the possibilities of the Dutch FRAX tool and its
strengths/limitations compared to other Dutch models need
to be discussed. The objective of this paper is to describe the
data used to develop the current Dutch FRAX model.
Methods
Data sources
For the calibration of FRAX, we used two different sources
of data: (1) the national hospitalization registry of the
Netherlands and (2) the Dutch national mortality statistics.
Hip fractures in the Netherlands were identified using the
national hospitalization registry (“Landelijke Medische
Registratie, LMR”) [8]. The vast majority of patients who
sustain a hip fracture are recorded as inpatient hospital-
izations. The LMR is therefore the best option to estimate
national incidence rates of hip fractures in the Netherlands.
Up to 2004, the completeness of the LMR has been shown to
be very high (98.9% in 2004) [9], and the database has been
widely used for various research purposes [10–18]. Since
2005, however, the number of missing records in the LMR
has increased, probably as a result of the stepwise introduc-
tion of a new reimbursement system in hospitals. The
proportion of missing records was estimated at 3.3% in
2005, 10.5% in 2006, and 12.0% in 2007 [9]. The register is
held by several licensees; in this paper, we have used LMR
data from Statistics Netherlands for the years 2004/2005. The
reason for choosing 2004 and 2005 was that we considered a
1.1% rate of under-recording as acceptable, but not a >10%
(from 2005 on) missing rate. Data for 2004 were delivered in
an aggregated report by Statistics Netherlands.
In contrast to hip fractures, incidence of osteoporotic
fractures could not be determined using national registries
(including LMR), because a dedicated registry with
routinely recorded osteoporotic fractures does not exist in
the Netherlands. Therefore, the World Health Organization
Collaborating Centre for Metabolic Bone Disease used the
population of Sweden in order to impute incidence rates of
major osteoporotic fractures in the Netherlands [19, 20]. In
Malmö, radiography referrals are recorded for all fractures
that come to medical attention. For each age and sex
category, incidence rate ratios for major osteoporotic
fractures to hip fractures were calculated in this Swedish
population [20]. It was assumed that these age- and gender-
specific ratios found in Malmö are comparable to those in
the Netherlands. This assumption has also been used for
many of the FRAX models with incomplete epidemiolog-
ical information. Available information suggests that the
age- and gender-stratified pattern of fracture is very similar
in the Western world and Australia, although it should be
noted that incidence rates for vertebral fracture as judged by
vertebral morphometry may be underestimated in some of
these data sources [19].
Mortality rates were extracted using the national mortal-
ity registry, available from Statistics Netherlands. When a
patient dies, doctors and coroners are obliged to fill out a
death certificate. The national mortality registry has a high
degree of completeness because of the legal requirement.
Study population and outcomes
Using the LMR data from 2004/2005, Statistics Nether-
lands computed the age- and gender-specific incidence rates
of hip fracture (International Classification of Disease 9,
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ICD9) code 820. Cases were defined as patients (aged
50+ years) who were hospitalized for a hip fracture in
2004/2005 and who had not been hospitalized for a hip
fracture in the previous 5 years. Incidence rates were
estimated as follows: the number of men and women in
5-year age intervals with at least one hip fracture in
2004 and 2005 was divided by the age-and sex-specific
population of the Netherlands at the average midpoint
of 2004 and 2005. We included hip fracture cases of
persons who had been recorded in the national patient
register as a Dutch resident for the full calendar year.
We excluded those who had immigrated or emigrated
during 2004/2005 [21]. In order to estimate the incidence
of other osteoporotic fractures in the Netherlands, we used
Swedish population-based data (Malmö), as described
previously by Kanis et al. [19, 20]. First osteoporotic
fracture diagnoses were identified, using files at the
Department of Diagnostic Radiology in Malmö (1987–
1993). Osteoporotic fractures included those of the hip,
forearm, proximal humerus, and clinically symptomatic
vertebral fractures. Past records were examined to exclude
patients who had previously sustained a fracture of the
same type. Multiple osteoporotic fractures at different sites
were counted separately. Age- and gender-specific ratios
for osteoporotic fracture to hip fracture were calculated
and used to transform the Dutch hip fracture incidence
rates to those for osteoporotic fractures [7, 19]. Mortality
statistics for the year 2005 were retrieved from the website
of Statistics Netherlands (www.statline.nl).
Calibration
The development and validation of FRAX ® has been
extensively described by Kanis et al. and McCloskey et al.
[5, 22, 23]. The risk factors used were based on a
systematic set of meta-analyses of population-based cohorts
worldwide. For the construct of a FRAX model for the
Netherlands, data from the following sources are required:
(1) beta coefficients of the risk factors in the original FRAX
model and (2) incidence rates of hip fracture, and mortality
rates, for an individual country. The relative importance of
the beta coefficients for death and fracture was assumed to
be similar in the Netherlands, as has been shown across
several European countries [6]. However, absolute age-
specific fracture risk and mortality rates differ from country
to country [5]. Consequently, for each age category, the
hazard function was calibrated to match the mean risk (both
fracture risk and mortality rate) for that specific age group
in the Netherlands, without altering the relative importance
of the beta coefficients [5].
Comparison of Dutch hip fracture incidence rates
with those in other world regions
In order to compare Dutch hip fracture probabilities with
those of other regions of the world, the remaining lifetime
probability of hip fracture from the age of 50 years was
calculated for men and women, as described by Kanis et al.
and Johnell et al. [24, 25]. In the present analysis, values
for the Netherlands were compared with those of China
(with and without inclusion of Hong Kong), Mexico,
Portugal, Spain, France, UK, Turkey, USA, and Sweden.
Results
Table 1 shows 1-year age- and gender-stratified incidence
rates of hip fracture for the Netherlands (2004 and 2005), as
well as the incidence of osteoporotic fractures, based on the
Malmö transformation. Hip fracture incidence was lowest
in patients aged 50–54 years old (per 10,000 inhabitants:
2.3 for men and 2.1 for women) and highest among the
Table 1 Dutch age- and gender-
stratified 1-year incidence rates
of hip fracture (true data; 2004/
2005) and (imputed) osteopo-
rotic fracture (imputed using
Swedish data) per 10,000
inhabitants in 2004/2005 as
modeled in FRAX
Modeled Dutch incidence rates
for osteoporotic fractures im-
puted, using real-life Dutch in-
cidence rates for hip fractures,
and Swedish (age- and gender-
stratified) hip to osteoporotic
fracture incidence rate ratios
Age category (years)1-year incidence hip fracture by
FRAX (per 10,000 inhabitants)
1-year imputed incidence osteoporotic
fracture by FRAX (per 10,000 inhabitants)
Male Female Male Female
50–54
55–59
60–64
65–69
70–74
75–79
80–84
85–89
90–94
95–99
2.3
3.0
4.6
8.9
16.9
32.3
61.6
117.6
141.0
169.0
2.1
4.2
8.1
15.3
28.6
53.6
100.5
188.2
224.3
267.3
16.8
17.1
17.5
28.3
45.9
74.4
120.5
195.4
240.4
295.8
23.3
33.0
46.5
68.1
99.8
146.2
214.1
313.6
385.9
474.9
Osteoporos Int (2012) 23:861–869863

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oldest subjects (95–99 years) (169.0 of 10,000 and 267.3 of
10,000 for men and women, respectively). With increasing
age, there was a rise in proportion of all fractures primarily
accounted for by hip fractures, with the highest proportion
in the oldest patients (among osteoporotic fractures, 57.1%
were hip fractures in males, 56.3% in females).
Age- and gender-stratified mortality rates for the total
Dutch population are shown in Table 2. Mortality rates
increased with higher ages, with rates of 4,245 per 10,000
male inhabitants and 3,532 per 10,000 female residents in
the oldest age category (≥95 years).
In Table 3, 10-year probabilities of osteoporotic fractures
are shown for Dutch men and women per age and gender
category in the absence or presence of at least a single
clinical risk factor (each row), without entering information
on BMD, keeping BMI constant at 25 kg/m2. Parental
history of hip fracture was the strongest clinical risk factor in
the elderly: a 90-year-old woman with a BMI of 25 kg/m2,
and a parental hip fracture as single clinical risk factor, had a
26% 10-year probability of osteoporotic fracture (20% for
hip fracture), whilst the risk was only 13% for a female of
equal age and BMI without a parental hip fracture. When
compared to a male patient with the same clinical risk
factors, the 10-year probability of fracture was halved (13%
forosteoporotic fracture, 11% for hip fracture). In younger age
categories, much smaller differences between the two genders
wereobserved: the 10-yearprobability ofosteoporotic fracture
was 3.7% in a 50-year-old femalewith a BMI of25kg/m2and
a parental hip fracture as single clinical risk factor (0.2% for
hip fracture), as compared to 3.0% in a 50-year-old male with
comparable clinical risk factors (0.1% for hip fracture).
Tables 4 and 5 show the effect of BMD on the 10-year
probabilities of osteoporotic and hip fracture in men and
women aged 60 years old (Table 4) and aged 80 years old
(Table 5) with a BMI of 25 kg/m2, rheumatoid arthritis, and
a parental history of hip fracture. Fracture risk increased
with decreasing T-score. When BMD was entered into the
model, the difference in probabilities between men and
women became less marked than without BMD. There was
also a large range of probabilities noted as a function of the
T-score. Thus, probability was markedly underestimated in
individuals with low T-scores (for elderly patients, i.e.,
80 years old, only at T-scores below −2 SD), when
information on BMD was not used in the model.
Table 6 shows that Northern European countries (in-
cluding the Netherlands) yielded the highest lifetime
probabilities for hip fracture (with the highest rate seen in
Sweden) in individuals from the age of 50 years. In
contrast, much lower incidence rates were recorded in
China, Mexico, and Mediterranean countries.
Discussion
Inthis paper, we describe the FRAX® model developed for the
Netherlands, which can be used to assess individual 10-year
probabilitiesofhipfracture,aswellasanyosteoporoticfracture
in Dutch patients. It has been calibrated to the total Dutch
population,basedonnationwideincidenceratesforhipfracture
and mortality. The model became available in July 2010
at the FRAX® website (http://www.sheffield.ac.uk/FRAX).
Previous clinical risk scores in Holland have been
developed in cohorts that were representative for only a
small Dutch region, and these risk scores have not been
validated externally. Pluijm et al. proposed a clinical risk
score to estimate fracture risk in Dutch women, using
information from two different Dutch cohort studies [26].
Although the risk score is simple to use, there are some
limitations to the model. The cohorts included patients from
small regions and may therefore not be representative of the
country. Although one of the two models included multiple
cities throughout the country, the majority of fracture cases
originated from a specific area in the city of Rotterdam,
which is not comparable to patients from the general
population [26]. Furthermore, men had not been included in
these cohorts, limiting the use of the risk score to women
only. Finally, there may have been substantial under-
recording of several risk factors for fracture (such as
rheumatoid arthritis, smoking, alcohol intake, and oral
glucocorticoid use) in these GP-based cohorts. Compared
to pharmacy dispensing data (representative sample of the
total Dutch population, with a similar age), the prevalence
of oral glucocorticoid use was found to be 1.5–2.2-fold
lower in these GP-based registries (2%) [16]. More
recently, van Geel et al. developed a fracture risk model
in a cohort comprising postmenopausal women, inhabitants
of the southern part of the Netherlands [27]. This clinical
risk score is the simplest to use, as it only includes three
Table 2 Dutch age- and gender-stratified mortality rates (per 10,000
inhabitants) in 2005
Age category (years) Mortality rate (per 10,000 inhabitants)
MaleFemale
50–54
55–59
60–64
65–69
70–74
75–79
80–84
85–89
90–94
≥95
41.0
65.1
113.7
190.9
330.6
584.7
1,005.2
1,710.1
2,690.0
4,245.0
31.1
46.5
69.4
103.1
181.5
328.2
607.6
1,193.8
2,085.7
3,532.0
864Osteoporos Int (2012) 23:861–869

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risk factors in the final model. A major strength, compared
to the other Dutch fracture models, is the consideration of
the time window in which a prior fracture could have
occurred. Like the model described by Pluijm et al., the van
Geel model also is limited to women only and may not be
representative for the entire country. A third model,
introduced by the Dutch Institute for Healthcare Improve-
ment (CBO), aims to identify high-risk patients for fracture
by calculating a fracture risk score based on weighted
widely recognized risk factors [28]. However, in contrast to
the other Dutch fracture models, these weights are based on
expert opinion and have not been developed and validated
in clinical studies using Dutch patients' data. Therefore,
these estimated weights may not reflect real-life weights.
This CBO model is currently used in the national Dutch
guidelines for fracture prevention [28]. The use of FRAX in
these guidelines is limited: FRAX risk assessment is only
recommended in patients with multiple clinical risk factors
(CBO score ≥4), and a T-score between −2.0 SD and −2.5
SD, but without evidence of a recent fracture.
The importance of calibrating FRAX to an individual
country is illustrated by the marked differences in lifetime
risks of hip fracture in 50-year-old males and females
between countries worldwide. In line with previous reports,
we found much higher incidences for hip fracture in
European countries (including the Netherlands), as com-
pared to those in countries like China, Mexico, and those in
the Mediterranean area [29–31]. Possible explanations for
this decreased incidence rate in the latter countries as
compared to the Netherlands include lower life expectancy,
in particular in Latin America (as most hip fractures occur
after the age of 65 years) [30], variations in reversible
lifestyle factors, and genetics [32, 33]. High prevalence
rates in Scandinavian countries (including Sweden) may to
Table 3 Age- and gender-stratified 10-year probabilities (percent) of osteoporotic fracture in absence or presence of at least a single clinical risk
factor, without information on BMD
Males Females
Age (years)
Clinical risk factor
No risk factor
Previous fracture
Parental hip fracture
Current smoking
Glucocorticoid usea
Rheumatoid arthritis
Secondary osteoporosisb
Alcohol usec
5060
2.3
4.7
4.4
2.4
3.7
3.1
3.1
2.8
70 80 9050
1.8
4.1
3.7
2.0
3.1
2.5
2.5
2.2
607080
12
20
24
14
20
18
18
16
90
13
21
26
14
19
19
19
17
1.5
3.2
3.0
1.6
2.4
2.0
2.0
1.8
3.6
7.0
6.0
3.9
5.7
5.2
5.2
4.6
5.5
9.0
12
6.0
8.1
8.3
8.3
7.3
5.5
8.8
13
5.8
7.7
8.5
8.5
7.5
3.4
7.1
6.6
3.7
5.7
4.8
4.8
4.2
6.9
13
11
7.7
11
9.8
9.8
8.7
BMI is set at 25 kg/m2
aCurrent exposure to oral glucocorticoids or prior exposure for a period of at least 3 months at a daily dose of at least 5 mg prednisolone (or
equivalent doses of other glucocorticoids)
bIncludes patients diagnosed with diabetes mellitus type I, osteogenesis imperfecta, untreated long-standing hyperthyroidism, hypogonadism or
premature menopause (<45 years), chronic malnutrition or malabsorption, and chronic liver disease
cExposure to at least three units of alcohol daily (one unit equals 8–10 g alcohol)
Table 4 BMD- and gender-
stratified 10-year probabilities of
osteoporotic and hip fracture for
a 60-year-old patient with a BMI
of 25 kg/m2, rheumatoid arthri-
tis, and a parental history of
hip fracture
BMDMales Females
10-year probability (%) of10-year probability (%) of
T-scoreOsteoporotic fracture Hip fracture Osteoporotic fractureHip fracture
Not taken into account
1
0
−1
−2
−3
−4
5.9
4.5
5.2
6.6
9.5
15
28
0.8
0.1
0.3
0.7
2.2
6.5
18
8.9
6.1
6.9
8.1
11
17
29
1.3
0.1
0.2
0.5
1.6
5.0
15
Osteoporos Int (2012) 23:861–869865