I am pleased that our paper on the fracture attribution has recently been published by the Journal of Clinical Endocrinology and Metablism. We found that in men and women aged 60 yr and older only 20% of all fractures were attributable to osteoporosis. I think that this finding has important implication for the prevention of fracture in the general population. I have also had a chance to discuss our work with Endocrinology Today here.
The underlying idea of this work was actually derived from the axiom articulated by Goeffrey Rose more than 30 years ago. In an influential essay, “Sick individuals and sick population” (Int J Epidemiol 1985), Rose postulated that when the relationship between a risk factor and disease is continuous, a truncation of the distribution of the risk factor into “low risk” versus “high risk” group will result in the majority of disease cases in the low risk group rather in the high risk group.
In this study, my student (Ha Mai), my colleagues and I sought to test the Rose’s axiom by examining the relationship between bone mineral density (BMD) and fracture. We all know that people with lower BMD have higher risk of fracture, and that the relationship between BMD and fracture is continuous. However, in 1994, a WHO expert panel recommended that those with BMD T-score ≤ -2.5 be diagnosed as having “osteoporosis”.
I just would like to make a note here that at that time, some prominent experts were not impressed with the idea of T-score dichotomization. In a commentary in Clinical Rheumatology (1), Dr. Richard Wasnich writes that “What are the issues surrounding the use of T-scores, as recommended by the WHO panel? On the one side, they seemingly offer simplicity, which is sorely needed. However they are not readily translated into interventional guidelines. The opposing viewpoint is that T-scores are a major step backwards into the realm of fracture thresholds.” He continues: “Fundamental to this debate is the fact that bone density is a risk factor, and not a diagnostic test. So making a ‘diagnosis’ of osteoporosis based on the presence of a single risk factor, at a single point in time, is already a tenuous concept.” He concludes that “what we need is an estimate of absolute fracture rate […] There is no need to obscure this useful information by inventing a new statistic, e.g. the “T-score.“
At the time, I was a relatively young fellow who had just entered the field for only 5 years. However, those words of Dr. Wasnich have haunted me for a long time. I thought he was on something, but I could not have time to work it out. Then, I came across a Goeffrey Rose’s book, The Strategy of Preventive Medicine, which was an eye-opening reading for me. So, I was determined to pursue the idea proposed by Wasnich and Rose.
Back to to the T-score and osteoporosis, in subsequent years, epidemiologic studies showed that among post-menopausal women, the prevalence of osteoporosis was around 20%. More importantly, many RCTs showed that treating women with osteoporosis reduced their risk of fracture by about 50%. However, according to Rose’s reasoning, we can predict that the majority of fractures would be occurred in those without osteoporosis, and treating those with osteoporosis will do little to reduce the burden of fracture in the general population.
In this study, we used data from the famous Dubbo Osteoporosis Epidemiology Study that involved 3700 men and women, all aged 60 yr and above at baseline. Fracture was prospectively ascertained using X-ray reports. We used a statistical method called “heuristic population attributable risk” (Hanley, 2001) to work out the proportion of fractures that is attributable to osteoporosis and advancing age. We defined “advancing age” as those aged 70 yr and older.
So, what did we find? Well, we found many things, but some key findings can be summarized as follows:
- the prevalence of osteoporosis (using the WHO criteria) was 21% in women and 11% in men;
- the risk of fracture among people with osteoporosis was increased by 80% (compared with those without osteoporosis), but the magnitude of association progressively declined with time;
- approximately 20% of total fractures were attributable to osteoporosis within the first year of follow-up, decreasing to 17% by year 5, further decreasing to 14% by year 10 and rising to 19% by year 20.
- by accounting for advancing age, we found that ~35% of total fractures in women and men were attributable to advancing age and/or osteoporosis.
Those findings support the Rose’s axiom: a large majority (two-thirds) of fractures was not attributable to either osteoporosis or advancing age. From a population prevention point of view, this finding suggests that if we focus on treating those with osteoporosis, we would reduce a very small proportion of fractures in the general population.
I thought the finding therefore has important public health implication. I submitted the manuscript to BMJ, but they politely rejected our work, because — they said — it would be more suitable to a specialist journal. The manuscript was then submitted to JCEM where it received a more receptive review. One reviewer complained about the word “attributable“, because the reviewer thought that the word implies causation whereas all we can say is an association. Of course, the reviewer is correct, but the problem is the “population attributable risk” expression has been well established in the lexicon of epidemiology.
Another reviewer suggested to change the title to something more interesting. Our original title was “Fractures and fracture-associated mortality attributable to low bone mineral density and advancing age: a time-variant analysis“. However, the reviewer thought that the title did not capture the key finding of the study. We then came up with a declarative title: “Two-thirds of all fractures are not attributable to osteoporosis and advancing age: implication for fracture prevention“. The reviewer seemed happy with the change. I would like to take this opportunity to thank both reviewers for their helpful and really constructive comments.
So, what is next? Well, in the Endocrinology Today interview, I said that the clinical implication is that we can’t predict who will fracture from a measurement of bone mineral density alone. Therefore, the assessment of fracture risk for an individual should move beyond bone mineral density to take into account — among others — important factors such as a history of fracture, prior falls, smoking habit, and co-morbidities. Tools such as the Garvan Fracture Risk Calculator and FRAX can help doctors to evaluate a patient’s risk of fracture based on multiple risk factors. I also touches on the use of genomics in fracture prediction. Fracture is partly due to hereditary factors, and over the past ten years or so, we have identified many genes that were associated with fracture risk. So, the next step is to develop a test that combines the information of these genes to identify high risk individuals much earlier, even at birth.
I should mention that in our study, we also wanted to answer the following question: how many deaths are attributable to advancing age, osteoporosis and fracture? The answer is: almost 80% of mortality were attributable to advancing age, osteoporosis and fracture; however, most of the attributable proportion was accounted for advancing age. The important implication of this finding is that patients with a fracture have reduced survival. At present, there are effective anti-osteoporosis therapies that can reduce the risk of mortality among patients with a fracture. However, the reality is that most (approximately 70%) patients with fracture do not receive any treatment, and that is a crisis. Our finding implies that many lives can be saved if doctors initiate treatment early.
(2) Wasnich RD, Clin Rheumatol 1997;16:337-339.