Low bone mineral density is common in older people and is a risk factor for osteoporotic fractures [1]. In the year 2000, an estimated nine million fractures occurred worldwide due to osteoporosis [2]. Known risk factors for sustaining osteoporotic fractures as well as falls include increasing age, female sex, Black ethnicity, geographical location, latitude, lack of physical activity, deficiency of calcium and vitamin D [3, 4]. The prevalence of osteoporosis and consequent incidence of fractures and falls has been increasing rapidly, along with the cost of treatment [5]. In the United States (US), the mean cost of hospitalisation in 2004 for an injurious fall in a person aged 65 years or older was US$ 17,483 [4]. The estimated cost of the incident and past fragility fractures (fracture from a simple fall such as from standing height or lower) in Europe was €37 billion in 2010 [6]. In the UK, during 2003–2013, the mean length of hospital stays for patients aged over 60 years with hip fracture was 20 days, which was highly correlated with the higher cost of hospitalisation [7].

Socioeconomic status (SES) can be defined as a relative term by which the social and economic situation of a community or person within the community can be described. Generally, proxy measures are used in place of precise estimations of socioeconomic status. Proxies include household income, educational attainment, employment status, homeownership, and difficulty accessing resources [8, 9]. However, the significance of these measures can vary across different populations or geographical locations [10].

It is well established that socioeconomic deprivation is associated with poor health [11]. Deprivation is associated with increased incidence and prevalence of many chronic illnesses, including cardiovascular diseases [12], asthma [13], cancer [14], and diabetes [15]. It is also associated with increased fracture incidence in older people [16]. A recent UK study which ran over the 14 years from 2001 reported a high burden of hip fracture incidence in men in the northeast region of the UK, where deprivation is more prominent [17]. The effect of deprivation on total bone health status has, however, not been well defined. Inconsistent findings characterise the association between material deprivation and bone health: some studies, for example, find a strong association between poor bone health and deprivation, while the results of others have been inconclusive [18,19,20]. A systematic review published in 2011 reported a lack of evidence supporting an association between SES and bone mineral density [21]. Availability of multiple composite deprivation indices, variation in these measures across countries, and use of inappropriate indices may produce inaccurate or varying results [20, 22]. In the UK, multiple composite indices are applied to define socioeconomic disadvantage, such as the Townsend deprivation index, the Index of Multiple Deprivation (IMD), the Carstairs index, and the Jarman underprivileged area index [23]. IMD which derived from administrative data has been used by the UK Government to measure deprivation. However, according to the UK Data Service, the Townsend Deprivation Index measure which is based on census data, has been widely used in research for health, education, and crime to establish whether relationships exist with deprivation. In a recent study (2017) Sanah Yousaf from the UK data service reported that unlike Townsend scores, the IMD scores are relative and based on administrative data for small areas (neighbourhoods) in England, but not validated for other parts of the UK. The Carstairs index is similar to the Townsend index, except that the unemployment component only considers males. In contrast, the Townsend measure is gender inclusive [23].

It is crucial to identify and mitigate risk factors for developing osteoporosis as well as address its consequences, including fractures and falls [5]. To examine the importance of socioeconomic deprivation as a risk factor for poor bone health status and falls, a cross-sectional study of the UK Biobank cohort using their baseline data was undertaken.


A cross-sectional study was conducted using baseline data from UK Biobank cohort participants. The details of the data collection, including the methods used, are described elsewhere [24]. Briefly, the UK Biobank is a UK-wide cohort study that recruited 502,682 participants (both men and women) during 2006–2010. The participants were middle-aged and older persons (40–69 years-old at recruitment). On recruitment, each participant was invited to one of 22 assessment centres distributed across Great Britain. Characteristics such as age, sex, body mass index, current residential address, ethnicity, lifestyle factors, and medication history were collected using touchscreen questionnaires, oral interviews, and physical measures. Northern Ireland is not included in the Biobank population. Participants gave informed consent before joining the UK Biobank study; additional information on consent and privacy can be found on the Biobank website [25].

The outcome variables were defined by four bone health status measures: 1) low heel bone mineral density at baseline(osteopenia), 2) bone fracture in the last 5 years, 3) fracture in the last 5 years due to a simple fall, and 4) fall in the last year. Low bone mineral density was determined by the T-score of the person’s heel ultrasound. The T-score represents the difference, measured in standard deviations between the observed bone density and the expected value for a healthy young adult of same sex. A T-score of -1 SD or below is deemed low bone mineral density [26]. Fracture in the last 5 years was based on a self-reported response to the touch screen questionnaire: "Have you fractured any bones in the last 5 years?”. Participants who responded with a positive answer were then asked: "Did the fracture result from a simple fall (for example, from standing height)?" which we refer to as “fracture from a simple fall in the last 5 years”. “Fall in the last year” was the response to: “In the last year, have you had any falls?".

The main study factor is socioeconomic deprivation. We used the Townsend deprivation index data of the UK Biobank, which is a proxy measure of the socioeconomic deprivation status of each participant at the area level. Among deprivation variables available throughout UK, the Townsend index data was selected as the most suitable because it applies to the whole of the UK, unlike other UK deprivation indices that consider only England or unemployment in males. The Townsend index data are aggregate area-based values of material deprivation from census data based on four indicators: the percentage unemployed among economically active 16–74-year-olds, regardless of gender; the percentage of households that are overcrowded; the percentage of households that do not own a vehicle; and the percentage of households that are rented or otherwise not owned [23]. Overcrowding is assessed by the census variable ‘persons per room’ where households with more occupants than available rooms are classified as overcrowded [23]. The Townsend index for the period just before participants joined the study was available in the Biobank database. It was assigned from national census output areas that included participants’ residential postcode. Townsend scores include both positive and negative values, and a value of zero represents the mean deprivation level of all UK census areas. Increasingly negative values represent lower deprivation levels while increasing positive values represent higher deprivation [23, 27] and the index has the national population mean of 0. We categorised the index into 5 quantiles.

Several covariates identified from existing literature and available in the UK Biobank were included in our statistical model. These were age, sex, body mass index, ethnicity, smoking and alcohol intake, oily fish consumption, and vitamin D supplementation, as well as days per week of moderate and vigorous physical activity [28]. Age was divided into 4 groups: less than 45, 45 to 54, 55 to 64, and 65 years or more. Sex was divided into 2 groups: female and male. Similarly, BMI was categorised as one of 4 groups: underweight (< 18.5 kg/m2), average (18.5–24.9 kg/m2), overweight (25–29.9 kg/m2) and obese (≥ 30 kg/m2). We categorised ethnicity into two subgroups from available multiple ethnic group: White and non-White. Smoking status was defined by never, current, and previous smoker. Consumption of alcohol was divided into 3 categories: never/special occasion/1–3 times a month, 1–4 times a week, and daily. To collect information about oily fish consumption, participants were asked the question, “How often do you eat oily fish? (e.g., sardines, salmon, mackerel, herring)”. Information about vitamin D supplementation was obtained from the question related to vitamin and mineral supplementation taken on a regular basis. We categorised two Biobank variables for physical activity: number of days per week of moderate and of vigorous physical activity lasting at least ten minutes. These were sorted into 3 groups: less than 2 days, 2–4 days, and more than 4 days per week. Vigorous physical activity was any activity causing sweating or hard breathing, such as fast cycling, aerobics, heavy lifting. Moderate activity included, for example, carrying light loads, or cycling at a normal pace, but not walking. The details of the questionnaires can be found in the UK Biobank website.

Statistical analysis

For descriptive analysis, the distribution (mean, standard deviation, median, frequency) and p values were calculated for each of the explanatory variables with each outcome. Univariable logistic regression models were fitted to select variables for the base models using a cut-off P-value of < 0.25. We adopted the backward elimination method (cut-off p-values were < 0.05) to come up with the final model after adjusting for potential confounders for all four outcomes separately. Deprivation, as the main exposure variable, was forced to remain in the model regardless of p value.

To check the trends of odds ratios across deprivation quantiles, logistic regression models were fitted using the midpoints of the deprivation quantiles as a continuous variable. Scatter plots were created to visualise trends, with the vertical axis denoting the log of the odds, and the horizontal axis indicating midpoints of the quantiles. All the statistical analyses were conducted using SAS 9.4 software.


Heel ultrasound was performed on 321,823 participants, of which 29% (94,448) had low bone density. A fall in the last year was reported by 20% (99,090/501585) of participants, and 10% (47,462/498700) reported a fracture in the last five years. Of fractures in the last five years, more than half (27,826 or 6% of total participants) were due to a simple fall. The mean and standard deviations of the Townsend deprivation index of the participants were -1.29 ± 3.09 and the population was skewed towards negative scores consistent with less deprivation than the overall UK population that has a mean score of 0 (Fig. 1).

Fig. 1
figure 1

Histogram of the Townsend deprivation index of the 501,740 Biobank cohort participants at recruitment

The distribution of bone health outcomes by each explanatory variable that describe the participant was examined (Supplementary information 1 for more details). There was very little difference in the proportion with low bone density across the deprivation categories. Compared with participants without low bone density, those with low bone density had a similar distribution of deprivation status of their area of residence, were more frequently female (66% versus 50%), aged 55 years or older (age 55–64: 47% versus 41%; age ≥ 65: 22% versus 17%), had average (37% versus 29%) or underweight (1% versus 0.5%) BMI categories, and the lowest frequency of vigorous physical activity (< 2 days/week: 60% versus 53%).

Compared with participants who had not fallen in the last year, those who had fallen were more frequently a resident in the most deprived area category (24% versus 19%), female (63% versus 52%), obese (30% versus 23%), and had the lowest frequency of physical activity (< 2 days/week: 58% versus 53%). Compared with participants who had a fracture in the last 5 years, those without a fracture were more frequently a resident of the most deprived area category (23% versus 20%), and female (59% versus 54%). In addition, fractures from simple falls in the last 5 years were more frequent in participants from the most deprived areas compared to people who had not had a fracture from a simple fall in the last 5 years (23% versus 20%).

Deprivation and bone health outcomes

Unadjusted and adjusted associations between deprivation and all four bone health outcomes were shown in Table 1. After adjusting for other covariates, the odds of low bone density in the most deprived group were 1.16 times (95% CI 1.13–1.19) higher than the least deprived group. The other covariates independently associated with higher odds of low bone density were older age (compared to < 45 years), female sex (compared to male), White ethnicity (compared to non-White), underweight BMI (compared to average BMI), ever smoking (compared to never) and having less than 2 days of vigorous physical activity (compared to more than 4 days). More details can be found in SI 4.

Table 1 Bone health outcomes: prevalence and unadjusted and adjusted odds ratios for deprivation quantiles

After adjusting for other covariates, being in the most deprived quantile was associated with 46% (AOR = 1.46 95% CI 1.42–1.49, p < 0.0001) higher odds of a fall in the last year compared to the least deprived quantile. Other factors that were independently associated with falls in the last year are shown in Table 2.

Table 2 Fall in the last year: prevalence and adjusted and unadjusted odds ratios (95% confidence interval) for factors considered

Fractures in the last five years and fractures from the simple fall

The adjusted odds of fractures in the last five years were 26% (95% CI 22%-30%) higher for people who were in the most deprived quantile than for the least deprived (Table 1). Other associated factors that were related to fractures in the last five years were shown in Table 3.

Table 3 Fracture in the last 5 years: prevalence and unadjusted and adjusted odds ratio (95% confidence interval) for factors considered

Similarly, the odds of fracture from a simple fall were 31% higher (95% CI 26%-36%) in the most deprived, compared to the least deprived, group (Table 1). For other risk factors, a dose–response relationship was observed between age and fracture from simple fall where odds of fracture were 89% higher than those in age group 65 and more (aOR 1.89; 95% CI 1.79–2.00), 60% higher among age group 55–64 years (aOR 1.61; 95% CI 1.53–1.70), and 12% higher in the age group 45–54 years (aOR 1.12; 95% CI 1.06–1.18) compared to those aged under 45 years. However, little or no evidence was found for the association between fracture in the last five years and age group 55–64 years (aOR 1.00; 95% CI 0.96–1.03) and 65 years or more (aOR 1.01; 95% CI 0.98–1.05). Additionally, females were almost twice as likely to have higher odds of sustaining a fracture from a simple fall compared to males (aOR 2.06; 95% CI 2.00–2.12; p < 0.0001) and low BMI was associated with higher odds of sustaining a fracture from a simple fall, the underweight individuals having the highest odds (OR 1.38; 95% CI 1.24–1.53) compared to the participants with average BMI. Moreover, 2–4 days of moderate or vigorous physical activity was more likely to be protective than more than 4 days (aOR 0.94; 95% CI 0.91–0.97 and aOR 0.86; 95% CI 0.83–0.90, respectively). Details can be found in SI 5.

The p-values from the logistic regression trend test for deprivation against all four bone health outcomes were < 0.0001. All outcomes show robust evidence for the presence of positive linear trends between deprivation and poor bone health (Figs. 2, 3 and SI 2,3).

Fig. 2
figure 2

Scatter plot of the test for linear trend between the logarithm of the odds of fall in the last year and deprivation (unadjusted estimate)

Fig. 3
figure 3

Scatter plot of the test for linear trend between the logarithm of the odds of fracture from a simple fall in the last five years and deprivation (unadjusted estimate)


This study examined the relationship between socioeconomic deprivation and four measures of bone health status including low bone mineral density, falls, and fractures, among the UK Biobank participants. We used the Townsend deprivation index as a proxy measure of deprivation. The participant population was skewed towards lower deprivation compared with the national population mean. We found that the odds of falls, fracture from a simple fall, and low bone mineral density were independently and positively associated with greater socioeconomic deprivation. The presence of a positive trend towards improving bone health with declining deprivation suggests a dose–response relationship.

The main strength of our study was the large sample size. To our knowledge, this is the first study to investigate the relationship between low bone mineral density, falls, and deprivation in such a large population in the UK. Additionally, we used Townsend deprivation index which is a very useful and validated tool [11] to measure socioeconomic deprivation across all over the UK.

Our result was consistent with a recent UK prospective study of 333 hospital patients with radiographically confirmed fractures of the distal radius that identified a positive ecological relationship between deprivation, measured by the index of multiple deprivation, and risk of fall [29]. However, while that study found no association between very low bone density and deprivation, the current study found a significant association. Differences in the methodological approach used to define low bone density could possibly explain the different results; for example, Johnson and colleagues used the Fracture Risk Assessment Tool (FRAX) whereas we used measured bone density T-score to define low bone density. Our finding also aligned with the findings of a systematic review on SES and bone mineral density in adults conducted by Brennan el al (2011). In the review, there was an increased risk of low bone mineral density found after exposure to measures related to SES including low educational levels. However, they reported that limited evidence exists for other parameters of SES such as income and unemployment in both genders [21]. In that context, our study has added significant value to address the notable research gap as the Townsend deprivation index includes unemployment status in both male and female. Also, ownership of car and housing information have played an important proxy measure for household earning and capital. Our results also supported the findings from a Canadian population-based study of more than 1.2 million subjects where lower wage earners had nearly double the risk of osteoporotic fracture risk compared to higher wage earners [30]. A similar result was found in the Geelong Osteoporosis study in Australia, where lower SES was associated with increased fracture incidence compared to higher SES [31].

Our study also confirmed that the risk of falls and low bone density rises with increasing age, female sex, and White ethnicity. This is consistent with the findings from other studies [4, 30]. Increasing age is related to low bone density and osteoporosis which causes muscle weakness, spinal deformities or reduced postural control leading to higher risk of falls [32]. This is especially evident in women with osteoporosis after menopause due to estrogen decline [32]. Ethnicity or race have also previously been associated with falls and fractures [4, 33]. In the UK, the fracture rate was higher in White populations, where the risk of fragility fracture was 4.7 times higher in White than in Black women aged 18 years and over [33]. Smoking is also a recognised risk factor where non-smokers and previous smokers were less likely to have a fracture than current smokers [4, 34].

We found that more frequent alcohol consumption was associated with improved bone health. This result is unsurprising because alcohol stimulates calcitonin secretion, which causes the new bone formation and resorption of bone [35, 36]. By contrast, Qiao et al. (2020) conducted a cross-sectional study with meta-analysis of 8475 participants (18–79 years) in China using data from the Henan Rural Cohort Study which found that moderate/heavy drinking in women increased the risk of osteoporosis, but no evidence was found for osteopenia either in women or men [8]. One reason for this conflicting result might be that most of the participants in our study were moderate alcohol drinkers, which might have a protective role. Another reason could be the age range of participants; the Biobank study recruited middle and older age groups, while the Chinese study included participants aged 18–79 years. Further study of the role of alcohol in bone health is required.

BMI is an important risk factor where underweight individuals are identified as being at higher risk of developing osteopenia or osteoporosis whereas overweight or obesity is protective compared to those of average weight [8]. The relationship between obesity and bone mineral density is quite complex and multifactorial that includes mechanical, hormonal and inflammatory factors [37]. One of the plausible explanations is increased mechanical loading and muscle strains are associated with increased body mass which directly affects bone geometry and modelling. Other factors include oestrogen, adipocytokines (leptin, adiponectin, resistin), ghrelin and cytokines (IL-6 and TNF-α). These metabolic factors are associated with maintaining skeletal homeostasis either by stimulating osteoblast formation or inhibiting osteoclastic activity except IL6 and TNF-α which promote osteoclastogenesis [37]. On the other hand, obesity has also been associated with the risk of fracture [36]. Consistent with this result, we found that despite higher BMI was related to lower odds of osteopenia, it also contributed to the increased odds of fracture compared to average weight. Also, obese participants had around twice the odds of a fall than those with an average BMI in our study. Similar result was found in a study involving 5681 community-dwelling individuals aged 65 years or over where the risk of falling was higher in people with obesity compared to those in the normal BMI category [38]. We also found that underweight individuals had nearly twice the risk of a fall than people with average BMI. This finding is also consistent with other study where low BMI or underweight was associated with increased falls risk when compared to normal weight individuals [39]. BMI abnormalities, including both high and low BMI in the elderly, correlate with other comorbidities, such as arthritis, diabetes, stroke, high blood pressure, and particular medication, eventually leading to falls and injurious falls [38, 40]. Another probable reason might be that low BMI is related to low bone mass, which causes osteoporosis to develop and results in falls and fractures. In contrast, other studies have reported no association between underweight and risk of falls and fracture [41, 42]. The inconsistency might be the omission of SES and physical activity level in their study. Mitchell et al. (2014) attributed to lack of physical activity, chronic diseases like diabetes, hypertension, osteoporosis, anxiety, depression, and some medications such as sleeping pills, tranquillisers, and anti-depressants as factors responsible for falls among older obese adults [38]. A meta-analysis of 22 prospective cohort studies reported an inverse association between physical activity and fracture risk [43], finding that physical activity was protective of and contributed to increase in skeletal muscle and neuromuscular function, hence, decreased fracture risk. Our study also supported with the finding that frequent physical activity decreased the odds of falls and fractures. However, when compared to more than 4 days, 2–4 days per week of moderate to vigorous physical activity (10 min or more) was protective against odds of fall and fracture from a simple fall. A possible explanation is that with more frequent occasions of physical exercise comes a higher risk of injury or falls.

There were several limitations to our study. First, a ‘healthy volunteer’ selection bias exists in this cohort. Assessing the representativeness of UK Biobank participants, Fry et al. (2017) found them to be generally healthier, less socially deprived, and to have a lower prevalence of obesity, smoking, and daily alcohol consumption than the general population. Nonetheless, the authors suggested that due to its large and heterogeneous sample the cohort is suitable for studying associations between exposures and outcomes but is not appropriate for population-based estimates of disease prevalence [44]. On the other hand, a study using the UK Biobank found a similar prevalence of musculoskeletal pain among the participants to that reported in population-based epidemiological studies in the UK [45]. Second, the available data did not include the volume of alcohol consumed by participants. However, 143,667 participants responded to the question regarding the amount of alcohol they consumed on a typical drinking day, which could be a proxy for the volume of alcohol consumed. Among those respondents, nearly 80% reported a maximum intake of 4 units of alcohol. From this response, we can assume that most drank alcohol moderately, which might have a protective role. Thirdly, in our study only bone related covariates were considered for the outcome fall in the last year. However, there are non-bone-related risk factors such as vertigo, Parkinson disease, antiepileptic drug use, visual impairment, and reduced physical functioning that could be considered in future studies.

Finally, in our study, we used the Townsend deprivation index as an ecological measure, rather than as an individual level measure, which may not reflect the individual’s situation [11]. According to Lee and colleagues, the area-level approach only helps to understand health inequalities in a relatively small number of people. Most families living in an underprivileged area were not underprivileged; only a minority of households were counted as ‘poor’ [11]. However, validation and reliability analyses were done to measure the validity and reliability of ten deprivation indices, including the Townsend deprivation index, one of the most reliable and valid area-based measures of deprivation [11]. According to Gordon [11], the census-based deprivation indices are extremely important because they provide key information and resource has been allocated according to that in both local government and health. Lee et al. [46] examined the reliability of 10 census-based deprivation indexes using classical test theory that comprises 1% sample of households (215,789 households) in Britain. In the analysis the correlation between the “True” Deprivation Score and the Index Score for the Townsend deprivation index was 0.65. They also conducted a validation test (Spearman’s Rank Correlation) using three validating variables including standardized illness ratio (illness), standardized mortality ratio (SMR 0–64), and the estimated average weekly earnings (mean earn) by ten deprivation indexes for the 10,500 electoral wards in Britain. They found that Spearman’s Rank Correlation Coefficient by Townsend deprivation index for SMR 0–64 was -0.71, standardized illness ratio was 0.76, and estimated average weekly earnings was 0.63. Based on these result Gordon concluded that the Townsend deprivation index is reasonably valid on all three criteria and reasonably reliable [11].

In conclusion, socioeconomic deprivation was significantly associated with poor bone health status. The research finding could be used to influence current public health guidelines to minimise social disparities in bone health. Investigating the causal pathway between deprivation and poor bone health to identify modifiable risk factors would be beneficial for the prevention of bone fragility and its consequences. A public health approach that targets disadvantaged groups such as promoting healthy lifestyle, a nutritious diet, tobacco control, reduced alcohol intake, regular physical activity, and bone health education including awareness-raising campaigns featuring peoples’ real-life experiences of consequences of poor bone health, could help to reduce the disability burden in the community.