Excess short- and long-term mortality risk following any and hip fractures: a prospective matched cohort study using exposure density sampling

Excess short- and long-term mortality risk following any and hip fractures: a prospective matched cohort study using exposure density sampling | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Excess short- and long-term mortality risk following any and hip fractures: a prospective matched cohort study using exposure density sampling Yi Zhang, Kai Zhang, Lirong Chai, Xiaolin Hu, Weizheng Kong, Weijing Wang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6248768/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Evidence on the magnitude and duration of short- and long-term mortality following osteoporotic fractures was mixed. We aimed to examine the short- and long-term mortality risk after any and hip fractures. We conducted an “exposure density sampling” dynamically matched cohort study based on 363,884 adults of UK Biobank. A total of 19,163 any fracture and 3,114 hip fracture cases were included, and controls were matched 1:4 on age, sex, and frailty status. A piecewise Cox proportional hazards model was used to estimate mortality risk in different time periods from 30 days to 10 years after fractures. Among 86,685 any fracture cases and matched controls, the mean age (SD) was 58.3 (7.8) years, and 59.5% were women. During follow-up, men had a higher post-fracture mortality rate than women (15.85 vs 8.58 per 1,000 person-years). After adjustment, mortality risk peaked within the first 30 days following any fractures (HR = 23.31, 95% CI, 17.08–31.83), gradually declining but remaining elevated for up to 10 years (1.44, 1.08–1.91). Similar association patterns were observed for hip fractures. The above associations were consistently observed across various characteristics of the participants. This study underscores the importance of enhancing fracture prevention and early intervention, as well as long-term care. Health sciences/Health care/Fracture repair Health sciences/Health care/Geriatrics Health sciences/Health care/Quality of life Any fractures Hip fractures Mortality risk Long-term Short-term Time-dependent exposure Figures Figure 1 Figure 2 Figure 3 Introduction Osteoporotic fractures, a common disease affecting the health status and quality of life of middle-aged and older adults, are a growing public health problem in both developed and developing countries [ 1 , 2 ]. It has been reported that the residual lifetime risk of fractures in people over the age of 60 was 44% for women and 25% for men [ 3 ]. Moreover, fractures have been found to be a predictor of hospitalization, disability, and even death, posing a huge socio-economic and healthcare challenge [ 1 , 2 , 4 ], with the medical costs in the UK being approximately £1.8 billion in 2000 and expected to rise to £2.2 billion by 2025 [ 4 ]. As the population continues to age, the burden of fractures is expected to increase [ 5 ]. Currently, the increased short-term mortality following fractures is well recognized, with the majority of relevant research demonstrated the highest mortality risk within one-year [ 6 , 7 ] after fractures, and more specifically, several studies indicating the highest risk within 90 [ 4 ] or 30 days [ 8 ]. However, evidence on the magnitude and duration of the long-term mortality risk following fractures is mixed [ 1 , 9 ], with some studies showing the excess mortality post-fracture maintaining for up to 5 years [ 10 ], 10 years [ 1 ], and even as long as 20 years [ 11 ], while there are also studies that have found no elevated risk of long-term mortality [ 9 , 12 ]. When examining the post-fracture mortality risk, most studies have used matched designs with non-fracture or general population as controls, usually matched on age and sex. These studies mainly identified fractures only at one time point at baseline, ignoring the time-dependent nature of this exposure, with little research matching fractures as time-dependent variables; however, fracture events are dynamic in the course of follow-up. Besides, some analyses did not consider pre-fracture health status, making it hard to determine whether the increased post-fracture mortality was due to poor underlying health or to the fracture itself. In addition, necessary confounding factors, such as smoking status and alcohol consumption, were not adjusted for in some related epidemiological studies [ 7 , 8 ]. To fill in the research gap, based on the UK Biobank (UKB) cohort, we will use the “exposure density sampling (EDS)” approach [ 13 ] to dynamically match time-dependent exposures, avoiding time-varying bias [ 14 ] and underestimation of association estimates [ 13 , 15 ] caused by ignoring the effect of the temporal factors. To be specific, our research aims are as follows: (1) to examine the short- and long-term mortality risk following any and hip fractures, and how the magnitude of the associations changed over time after adjustment for sociodemographic characteristics and lifestyle factors; (2) to examine whether the associations are consistent across subpopulations of different age, sex, body mass index (BMI), frailty index (FI), and disease status. Methods Study population In this prospective cohort study, data were sourced from the UKB. UKB is a multi-center cohort study that enrolled 502,370 participants aged 40–69 years across 22 assessment centers in England, Scotland, and Wales between 2006 and 2010. In practice, some individuals outside the intended age range were also included, resulting in an actual participant age range of 38–72 years. After signing a written informed consent, each participant completed a baseline assessment, which included physical measurements, verbal interviews, and touch-screen questionnaires covering a broad spectrum of characteristics (including demographics, socioeconomics, lifestyle, environmental exposures, as well as health factors and medical history). UKB has been approved by the North West Multi-centre Research Ethics Committee (MREC) as a Research Tissue Bank (RTB). Additional details about the UKB can be accessed on its official website: https://www.ukbiobank.ac.uk/ . Ascertainment of exposures and outcomes We ascertained incident fractures from linkage to Hospital Episode Statistics (HES) using predefined the 10th revision of the International Classification of Diseases codes (ICD-10) categories. In the present study, we used the ICD-10 codes to classify fractures into any fracture (S12, S22, S32, S42, S52, S62, S72, S82, S92, T02, T08, T10, T12, T14.2, M48.4, M48.5, M49.5) and hip fracture (S72.0, S72.1, S72.2). The primary outcome was all-cause mortality, which in the UKB was obtained from death certificates held within the National Health Service Information Centre (England and Wales) and the National Health Service Central Register Scotland (Scotland). Median follow-up duration was 13.45 years (IQR 12.96–14.35 years). Assessment of covariates Covariates were collected at baseline, including sociodemographic characteristics (age, sex, ethnicity, education, employment status, and Townsend deprivation index), lifestyle factors (smoking status, alcohol consumption, physical activity, and BMI), and frailty status. In the present study, frailty status was assessed by the FI. We referred to a previous study by Williams DM and colleagues, who constructed the FI in UKB using 49 items (i.e., deficits) covering health (e.g., chest pain), presence of diseases and disabilities (e.g., diabetes and high blood pressure), and mental well-being (e.g., depressed feelings and sleep) [ 16 ]. Based on this, we omitted three items related to fracture exposures, namely falls, fractures and osteoporosis, and finally included 46 items. FI was calculated as the sum of non-missing deficits that the individual met divided by the total number of non-missing items. Referring to previous studies [ 17 – 19 ], we further classified participants into three categories: robust (FI ≤ 0.10), pre-frail (0.10 < FI < 0.25), and frail (FI ≥ 0.25). Details of the covariates, please see the Supplementary Text S1. Selection of the analysis cohort We sequentially excluded participants who had missing data on 9 or more (i.e., more than 20%) items for FI (n = 2,621); those with falls (n = 98,765), osteoporosis (n = 3,905) or fractures (n = 29,877) at baseline; those with date of fracture (i.e., any fracture and hip fracture) prior to the time of admission (n = 3,315); those with the same date of fracture and time of admission (n = 2); and those with date of birth later than the time of admission (n = 1) (Fig. 1 ). Of the remaining eligible participants (n = 363,884), we further used an “EDS” approach [ 13 ] to create a nested cohort comprising participants with and without fractures. EDS is an efficient risk set sampling approach designed for dynamic matching related to time-dependent exposures, enabling the estimation of the effect of exposure over time with minimal precision loss and unbiased results compared to a full cohort analysis [ 13 , 20 ]. Using the any fracture exposure as an example, a total of 19,163 participants with fractures were included in the matching process. Four participants individually matched to each exposed individual on time to exposure, baseline age groups (± 5 years), sex, and frailty status (robust, pre-frail, and frail) were selected from the risk set who were still at risk of death and not exposed at the time of any fracture (i.e., the index date) in the exposed individual, yielding 67,522 matched controls. During the EDS matching process, a fixed number of seeds ensured consistent results. To achieve unbiased estimates, controls were sampled with replacement, meaning the same individual could be sampled multiple times and served as a control for different exposed individuals [ 13 ]. The time of their first sampling as a control was regarded the left-truncated entry time. Besides, a sampled control may also have had any fractures and become exposed later. In this scenario, the first sampling time served as the entry time, and exposure was treated as a time-dependent variable [ 21 ]. The analysis cohort for hip fracture exposure was selected similarly as described above. Statistical analysis We summarized baseline characteristics of overall participants, any fracture cases, and matched controls, including sociodemographic characteristics, lifestyle factors, and frailty status. Values were expressed as means (SD) or percentages as appropriate. Analysis of variance was used for continuous variables and χ 2 test was used for categorical variables. In the present analyses, the follow-up time was determined from the index date to the date of death, loss to follow-up, or the end of the study period (i.e., 31 December 2022 in England, 31 August 2022 in Scotland, and 31 May 2022 in Wales), whichever came first. Sex-specific mortality rates for both fracture groups during follow-up were calculated as the number of post-fracture deaths divided by the total person-years. We used Kaplan-Meier survival curves to compare survival probabilities after any and hip fractures and stratified by age and sex groups, and differences were tested by log-rank statistics. We used a piecewise Cox proportional hazards model to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the effect of any and hip fractures on all-cause mortality over time periods. Multivariable models were adjusted for age, sex, ethnicity, education, employment status, Townsend deprivation index, smoking status, alcohol consumption, physical activity, and BMI. In the piecewise Cox proportional hazards models, time periods after the index date were split into 0–30 days, 31–90 days, 91 days to one year, and then yearly up to the tenth year after fractures. Because of small sample sizes in some time periods and subpopulations, time periods after the index date were combined as (0–1] year, (1–3] years, (3–6] years, and (6–10] years in the subgroup analyses. We further explored the variation in the association between fractures and mortality risk in different subpopulations: age at index date (< 60 or ≥ 60 years), sex (male or female), BMI (< 25 or ≥ 25kg/m 2 ), FI (< 0.25 or ≥ 0.25), and disease status of hypertension, diabetes, chronic obstructive pulmonary disease and cancer (presence or absence). The interactions between fractures and the above-mentioned factors were tested using likelihood-ratio tests by comparing models with and without cross-product interaction terms. In addition, to examine the robustness of our findings, sensitivity analyses for the fracture-mortality relationship were also performed by substituting seed number and matched controls after adjustment for all covariates. All analyses were done using Stata version 17.0 and R version 4.0.3. A two-sided P -value < 0.05 was considered statistically significant. Results Baseline characteristics of the study population in the nested analysis cohort of any fracture During follow-up, a total of 19,163 participants had any fractures, and 67,522 matched controls were obtained after EDS matching (Fig. 1 ). Of 86,685 any fracture cases and matched controls included in the nested analysis cohort, the mean (SD) age at baseline was 58.3 (7.8) years, 59.5% were women, and the mean (SD) value of Townsend deprivation index was − 1.4 (3.0). Although matched for baseline age groups, sex, and frailty status, compared with matched controls, any fracture cases were slightly older ( P < 0.0001) and frailer ( P = 0.025), as well as more often to be women ( P = 0.002). In addition, any fracture cases were less educated ( P < 0.0001), less likely to be working ( P < 0.0001), and more likely to be current smokers ( P < 0.0001) and underweight (BMI < 18.5 kg/m 2 ) ( P < 0.0001) than matched controls (Table 1 ). Table 1 Baseline characteristics of participants in the nested analysis cohorts of any fracture (n = 86,685) Overall Any fracture cases (n = 19,163) Matched controls (n = 67,522) P -value Sociodemographic characteristics Age at baseline, year (SD) 58.3 (7.8) 58.6 (7.8) 58.2 (7.8) < 0.0001 Age at index date, year (SD) 65.2 (8.9) 65.8 (9.0) 65.0 (8.9) < 0.0001 Women (%) 59.5 60.4 59.2 0.002 Ethnicity (%) < 0.0001 White 95.2 96.9 94.7 South Asian 1.7 1.3 1.8 East Asian 0.3 0.1 0.3 Black 1.3 0.6 1.5 Other/Mixed 1.2 0.9 1.3 Missing 0.3 0.3 0.3 Education (%) < 0.0001 College/University 30.7 30.3 30.8 A/AS Levels/Equivalent 10.5 10.1 10.7 O Levels/GCSEs/Equivalent 21.0 20.8 21.0 CSEs/Equivalent 4.7 4.5 4.8 NVQ/HND/HNC/Equivalent 6.4 6.2 6.4 Other professional qualifications 5.6 5.6 5.5 None of the above 19.2 20.4 18.8 Missing 2.0 2.1 2.0 Employment status (%) < 0.0001 Working 51.4 49.9 51.8 Retired 41.2 42.2 40.9 Other 7.1 7.6 6.9 Missing 0.3 0.3 0.4 Townsend deprivation index (SD) -1.4 (3.0) -1.3 (3.1) -1.5 (3.0) < 0.0001 Lifestyle factors Smoking status (%) < 0.0001 Never 54.7 52.3 55.3 Previous 35.7 36.2 35.5 Current 9.3 11.1 8.8 Missing 0.4 0.4 0.4 Alcohol consumption (%) < 0.0001 Never 8.0 8.1 7.9 Weekly 69.1 69.7 69.0 Monthly 22.8 22.0 23.0 Missing 0.1 0.2 0.1 Physical activity (%) < 0.0001 Low 14.6 14.7 14.6 Moderate 33.1 31.9 33.5 High 31.8 32.4 31.6 Missing 20.5 21.1 20.3 BMI (%) < 0.0001 Underweight 0.6 0.8 0.5 Normal weight 33.4 35.5 32.8 Overweight 42.2 40.4 42.7 Obesity 23.4 22.8 23.6 Missing 0.4 0.5 0.4 Health status Frailty status (%) 0.025 Robust 39.2 38.6 39.3 Pre-frail 52.7 52.9 52.7 Frail 8.1 8.5 8.0 BMI, body mass index; SD, standard deviation. Values are expressed as mean (SD) or percentage. Analysis of variance was used for continuous variables and the chi-square test was used for categorical variables. Any fracture and mortality risk During follow-up, the mortality risk was higher in any fracture cases than that in matched controls, with the relative risk (RR) of 2.93 (95% CI, 2.79–3.08). Among any fracture cases, there were 1,333 deaths in women and 1,567 in men during 155,453 and 98,886 person-years, yielding mortality rates (95% CI) per 1,000 person-years of 8.58 (8.11–9.04) and 15.85 (15.06–16.63) in women and men, respectively (Table 2 ). Compared with matched controls, any fracture cases had significantly increased mortality rates from the first month to the tenth year after fractures. The mortality rate (per 1,000 person-years) for any fracture cases within the first 30 days was 214.6 (Fig. 2 ). Based on the Kaplan-Meier curves of survival probability for participants during follow-up, regardless of age and sex groups, it was observed that the survival probability for any fracture cases was lower than that of the participants with no fracture ( P < 0.0001) (Supplementary Fig. S1 ). Table 2 Sex-specific post-fracture mortality rate and relative risk for any and hip fracture cases compared to matched controls Fracture cases Deaths Person-years Mortality rate /1,000 Person-years (95% CI) Relative risk (95% CI) a Overall Any fracture 19,163 2,900 254,339 11.40 (10.99–11.82) 2.93(2.79–3.08) Hip fracture 3,114 740 40,851 18.11 (16.81–19.42) 3.87(3.50–4.28) Female Any fracture 11,582 1,333 155,453 8.58 (8.11–9.04) 2.88(2.68–3.10) Hip fracture 1,949 408 25,778 15.83 (14.29–17.36) 4.34(3.77–4.99) Male Any fracture 7,581 1,567 98,886 15.85 (15.06–16.63) 3.04(2.84–3.25) Hip fracture 1,165 332 15,072 22.03 (19.66–24.40) 3.45(2.98–3.99) CI, confidence interval. a Relative risks were calculated using matched controls without any or hip fractures as the reference group. After adjustment for sociodemographic and lifestyle factors, compared with matched controls, the short- and long-term mortality risk was significantly increased in any fracture cases. The mortality risk for any fracture cases was highest in the first 30 days, followed by 31–90 days, with the HRs (95% CIs) of 23.31 (17.08–31.83) and 12.59 (9.92–15.98), respectively. Thereafter, the risk gradually decreased but remained significantly higher than matched controls until the tenth year, with an 44% increased risk of mortality (HR = 1.44, 95% CI, 1.08–1.91) (Fig. 2 ). Hip fracture and mortality risk During follow-up, hip fracture cases had a higher risk of mortality compared with matched controls, with the RR (95% CI) of 3.87 (3.50–4.28). Among hip fracture cases, 408 deaths were occurred in women and 332 in men over 25,778 and 15,072 person-years, resulting in mortality rates (95% CI) per 1,000 person-years of 15.83 (14.29–17.36) and 22.03 (19.66–24.40) in women and men, respectively (Table 2 ). As with any fractures, the mortality rates following hip fractures also were higher than in matched controls from the first month to the tenth year. The mortality rate (per 1,000 person-years) for hip fracture cases within the first 30 days was 265.2 (Fig. 3 ). The Kaplan-Meier survival curves showed that, hip fracture cases had the lowest survival probabilities in all age and sex groups, compared to participants with any fractures and those without fractures ( P < 0.0001) (Supplementary Fig. S1 ). After adjustment, compared with matched controls, hip fracture cases were associated with a significantly increased short- and long-term mortality risk. Compared to other time periods, the mortality risk for hip fracture cases elevated pronouncedly in the first 90 days, with the highest increase in 0–30 days (HR = 23.67, 95% CI, 12.17–46.01), and the next highest increase in 31–90 days (15.24, 9.84–23.60). Thereafter, a gradual decline in the risk was observed, but it remained significantly higher than matched controls until the tenth year, with a 115% excess mortality (HR = 2.15, 95% CI, 1.15-4.00) (Fig. 3 ). Sensitivity analyses and Subgroup analyses In the sensitivity analyses, the association patterns of any and hip fractures with mortality risk over 10 years remained generally unchanged (Supplementary Table S1 -S2). After replacing the number of seeds required in the EDS matching process, the risk after any fractures in the first 30 days was slightly reduced, with smaller changes in the remaining time periods; the risk after hip fractures was basically unaltered (Supplementary Table S1 ). Changing from 1:4 matching to 1:15 matching had little effect on the risk following any fractures; and resulted in a moderate reduction in the risk following hip fractures within the first 30 days and 31–90 days, with less change in the remaining time periods (Supplementary Table S2). The short- and long-term increased risk for mortality outcomes following any and hip fractures was seen across all subgroups, similar to the primary analyses (Supplementary Table S3-S4). Discussion In this large EDS matched cohort study nested within the UKB, we observed an elevated short- and long-term risk of all-cause mortality following any and hip fractures in middle-aged and older adults. During follow-up, men had a higher post-fracture mortality rate than women. The mortality risk was highest within the first 30 days after any fractures, increasing more than 20-fold, and then decreased gradually over time, with the high risk persisting for up to 10 years. The association pattern between hip fracture and mortality risk was similar. The above fracture-mortality relationships were consistently observed in all subgroups. In summary, this study emphasizes the importance of prevention, detection, and intervention for all types of osteoporotic fractures, especially within the first 30 days after fracture morbidity, as well as the concern for long-term health of middle-aged and older adults with fractures. The present study showed that hip fractures were associated with an elevated risk of short- and long-term (30 days − 10 years) all-cause mortality, and the high risk remained until the tenth year, which is largely consistent with existing studies [ 6 , 7 , 10 , 22 ]. A meta-analysis of more than 20 cohorts analyzing the short- and long-term (3-120 months) mortality risk after hip fractures, showed that the risk was highest in the first 3 months after hip fractures, with a 5- to 8-fold increase in the risk of all-cause mortality, with the HRs (95% CIs) of 7.95 (6.13–10.30) for men and 5.75 (4.94–6.67) for women. The high risk decreased over time but was consistently higher than in matched controls, and persisted until the tenth year after fractures, except for the seventh and eighth years in men [ 22 ]. However, the included cohorts in this meta-analysis varied considerably in sample size, duration of observation, selection of the control population, and ascertainment of death outcomes, leading to a high degree of heterogeneity. A matched nationwide register-based cohort study from the Danish national registers, with participants aged ≥ 60 years and controls matched 1:10 on age and sex, reported that the mortality risk after any and hip fractures was highest in the first 30 days and declined thereafter, but remained higher than the matched controls, with the high risk persisting up to 5 years later [ 8 ]. Two other studies also based on health care databases in Manitoba [ 10 ] and Taiwan [ 7 ] presented similar results, showing the highest short-term mortality after hip fractures within the first year, and the long-term excess risk persisted for up to 5 [ 10 ] and 10 [ 7 ] years, respectively. However, the three aforementioned literature based on health care databases lacked information on lifestyle confounders, such as smoking status, leaving possible confounding uncontrolled. Our study, after fully adjustment for potential confounders, demonstrated that fractures were associated with an elevated risk of short- and long-term mortality, but not all the existing analyses showed the long-term persistence of the post-fracture excess mortality [ 9 , 12 , 23 ]. Two matched cohort studies were conducted based on the Cardiovascular Health Study (CHS, age ≥ 65 years) and the Study of Osteoporotic Fractures (SOF, age ≥ 65 years), respectively, and followed for mortality outcome more than 15 years [ 9 , 12 ]. The CHS study, which included 1,513 hip fracture cases and matched controls, and matched for sex, age, race, recruitment period, as well as time since enrollment, reported that the risk of death was highest in the first month following hip fractures, and that the high risk remained for up to 4 years in women and only 6 months in men [ 9 ]. The SOF study, which included 5,580 hip fracture cases and age-matched controls, showed that among women, hip fracture cases had more than a 2-fold increase in mortality within the first year following fractures compared to controls, but the excess risk lasted only one year [ 12 ]. Whereas results from the Beijing Longitudinal Study of Aging, which followed up 3,257 Chinese adults aged ≥ 55 years for 8 years, showed no significant association between any fractures and long-term mortality risk (HR = 0.92, 95% CI, 0.75–1.14) [ 24 ]. Therefore, whether there is a long-term excess mortality following fractures, and if so, how long does it persist, warrant further confirmation given the inconsistencies between existing studies. To sum up, existing relevant literature has largely confirmed that fractures are associated with an increased risk of death, but the split of the time period of short-term mortality and the duration of excess mortality risk remain controversial. This may be due to differences in study populations, matching methods, and matching factors across analyses. Previous studies have mainly been conducted in adults aged 60 years and older and have not considered fractures as time-dependent exposures. In addition, matching factors generally included only demographic characteristics such as age, sex, and date of birth, but the impact of baseline health status on the association between fractures and mortality was not taken into account. Our study adds to the evidence by using EDS-dynamic matching method, which avoids potentially time-dependent bias by including fractures as time-dependent variables; and it also considers the effect of frailty status on the fracture-mortality relationship by including it as a matching factor. Our study found that men had a higher mortality rate than women after any and hip fractures in the course of follow-up, which is generally in line with previous studies [ 1 , 5 , 22 ]. In the Dubbo Osteoporosis Epidemiology Study of community-dwelling adults aged 60 years and older, during follow-up, the mortality rate (per 100 person-years) after all fractures was 7.78 (95% CI, 7.10–8.52) and 11.30 (9.82–12.99) for women and men, respectively, and after hip fractures was 15.42 (12.88–18.52) and 25.67 (19.46–33.87) for women and men, respectively [ 1 ]. Although most of the literature shows a higher risk of death after fractures in men than in women, this study and some previous studies have not observed sex differences between fracture-mortality association [ 25 – 27 ]. Therefore, additional research is required to explore the relationship between fractures and mortality risk in different sexes and to further elucidate the reasons. To our knowledge, the present study is the first large EDS dynamically matched cohort study to simultaneously examine the short- and long-term mortality risk following any and hip fractures, with a careful delineation of the post-fracture time period. The large sample size and long follow-up period of the UKB cohort ensured our statistical validity for the strength of association at different time periods from 30 days to 10 years after fractures. Besides, we address confounding bias due to frailty, sociodemographic characteristics, and lifestyle factors by matching as well as adjusting for potential confounders. In addition, various characteristics of the participants, and sex-specific mortality rates in both fracture groups, were also taken into account. Our study also had some limitations. First, UKB is not representative of the sampling population; there is a “healthy volunteer” selection bias [ 28 ]. The study population was predominantly white British, with comparatively lower levels of socio-economic deprivation than the UK average [ 29 ]. Therefore, caution is needed when extrapolating our results to the wider population. Second, some baseline characteristics relied on participant’s self-reporting and might be subject to information bias. Third, we assessed frailty status and collected information on lifestyle factors only at baseline. Hence, it was not possible to determine the potential impact of changes in these factors over time on the associations. Finally, lacking data on the causes of fractures, we cannot distinguish between fragility and traumatic fractures. However, there is evidence that most fractures in middle-aged and older adults are fragility fractures [ 30 ]. Conclusion This study demonstrated that any and hip fractures were associated with an increased risk of short- and long-term (30 days − 10 years) mortality. The mortality risk after fractures peaked within the first month, and then declined gradually, but was always higher than in matched controls, with the excess risk remaining until the tenth year. Therefore, prevention of all types of osteoporotic fractures, enhancing early intervention and treatment when detected, and long-term care, are important ways to mitigate fracture-related burden and consequently facilitating healthy population ageing. Declarations Journal name Scientific Reports Word count Abstract 199, Main text 3694 Conflict of interest The authors declare no competing interests. Consent for publication Not applicable. Ethics approval and consent to participate UK Biobank was approved by the North West Multi-Centre Research Ethics Committee (REC reference: 11/NW/03820) and all participants provided written informed consent to participate in the UK Biobank study. The study protocol is available online ( http://www.ukbiobank.ac.uk/ ). This work was conducted under the UK Biobank application number 95715. Funding UK Biobank was established by the Wellcome Trust medical charity, Medical Research Council, Department of Health, Scottish Government, and the Northwest Regional Development Agency and is funded by the Welsh Government and the British Heart Foundation. This work was supported by National Natural Science Foundation of China (82304226), Natural Science Foundation of Shandong Province (ZR2023QH188), China Postdoctoral Science Foundation (2023M731839), Mount Taishan Scholar Youth Program (No. tsqn202306179), Qingdao Postdoctoral Innovation Project (QDBSH20230102012), Qingdao University Scientific Research Startup Fund (DC2200002531). The funders had no role in the study design, data collection, data analysis and interpretation, writing of the report, or the decision to submit the article for publication. Author Contribution JF conceived and designed the study. YZ and JF analyzed the data. YZ drafted the manuscript. KZ, LC, XH, WK and WW helped to organize the information and review the data. JF and YZ helped the interpretation of the results. JF contributed to the critical revision of the manuscript for important intellectual content and approved the final version of the manuscript. All authors reviewed and approved the final manuscript. DZ is the guarantor. Acknowledgement We are grateful to UK Biobank participants. This research has been conducted using the UK Biobank Resource under Application Number 95715. Data Availability All UK Biobank information is available online on the webpage www.ukbiobank. Data access is available through applications. This research was conducted using the application number 95715. References Bliuc, D. et al. Mortality risk associated with low-trauma osteoporotic fracture and subsequent fracture in men and women. JAMA 301 (5), 513–521 (2009). Cummings, S. R. & Melton, L. J. Epidemiology and outcomes of osteoporotic fractures. Lancet 359 (9319), 1761–1767 (2002). Nguyen, N. D., Ahlborg, H. G., Center, J. R., Eisman, J. A. & Nguyen, T. V. Residual lifetime risk of fractures in women and men. J. Bone Min. Res. 22 (6), 781–788 (2007). Ravindrarajah, R. et al. Incidence and mortality of fractures by frailty level over 80 years of age: cohort study using UK electronic health records. BMJ Open. 8 (1), e018836 (2018). Tran, T. et al. Population-Wide Impact of Non-Hip Non-Vertebral Fractures on Mortality. J. Bone Min. Res. 32 (9), 1802–1810 (2017). Brown, J. P. et al. Mortality in older adults following a fragility fracture: real-world retrospective matched-cohort study in Ontario. BMC Musculoskelet. Disord . 22 (1), 105 (2021). Wang, C. B. et al. Excess mortality after hip fracture among the elderly in Taiwan: A nationwide population-based cohort study. Bone 56 (1), 147–153 (2013). Christensen, E. R. et al. Excess mortality following a first and subsequent osteoporotic fracture: a Danish nationwide register-based cohort study on the mediating effects of comorbidities. RMD Open. 9 (4), e003524 (2023). Robbins, J. A., Biggs, M. L. & Cauley, J. Adjusted Mortality After Hip Fracture: From the Cardiovascular Health Study. J. Am. Geriatr. Soc. 54 (12), 1885–1891 (2006). Morin, S. et al. Mortality rates after incident non-traumatic fractures in older men and women. Osteoporos. Int. 22 (9), 2439–2448 (2011). Vestergaard, P. R. L. & Mosekilde, L. Increased mortality in patients with a hip fracture-effect of pre-morbid conditions and post-fracture complications. Osteoporos. Int. 18 (12), 1583–1593 (2007). LeBlanc, E. S. et al. Hip fracture and increased short-term but not long-term mortality in healthy older women. Arch. Intern. Med. 171 (20), 1831–1837 (2011). Ohneberg, K., Beyersmann, J. & Schumacher, M. Exposure density sampling: Dynamic matching with respect to a time-dependent exposure. Stat. Med. 38 (22), 4390–4403 (2019). van Walraven, C., Davis, D., Forster, A. J. & Wells, G. A. Time-dependent bias was common in survival analyses published in leading clinical journals. J. Clin. Epidemiol. 57 (7), 672–682 (2004). Beyersmann, J., Wolkewitz, M. & Schumacher, M. The impact of time-dependent bias in proportional hazards modelling. Stat. Med. 27 (30), 6439–6454 (2008). Williams, D. M., Jylhävä, J., Pedersen, N. L. & Hägg, S. A Frailty Index for UK Biobank Participants. J. Gerontol. Biol. Sci. Med. Sci. 74 (4), 582–587 (2019). Hoogendijk, E. O. et al. Frailty: implications for clinical practice and public health. Lancet 394 (10206), 1365–1375 (2019). Song, X., Mitnitski, A. & Rockwood, K. Prevalence and 10-year outcomes of frailty in older adults in relation to deficit accumulation. J. Am. Geriatr. Soc. 58 (4), 681–687 (2010). Zhu, Y. et al. Agreement between the frailty index and phenotype and their associations with falls and overnight hospitalizations. Arch. Gerontol. Geriatr. 66 , 161–165 (2016). Wolkewitz, M., Beyersmann, J., Gastmeier, P. & Schumacher, M. Efficient risk set sampling when a time-dependent exposure is present: matching for time to exposure versus exposure density sampling. Methods Inf. Med. 48 (5), 438–443 (2009). Hu, Y. et al. Association between pneumonia hospitalisation and long-term risk of cardiovascular disease in Chinese adults: A prospective cohort study. EClinicalMedicine 55 , 101761 (2022). Haentjens, P. et al. Meta-analysis: excess mortality after hip fracture among older women and men. Ann. Intern. Med. 152 (6), 380–390 (2010). Tosteson, A. N., Gottlieb, D. J., Radley, D. C., Fisher, E. S. & Melton, L. J. 3rd Excess mortality following hip fracture: the role of underlying health status. Osteoporos. Int. 18 (11), 1463–1472 (2007). Fang, X. et al. Frailty in relation to the risk of falls, fractures, and mortality in older Chinese adults: results from the Beijing Longitudinal Study of Aging. J. Nutr. Health Aging . 16 (10), 903–907 (2012). Ioannidis, G. et al. Relation between fractures and mortality: results from the Canadian Multicentre Osteoporosis Study. CMAJ 181 (5), 265–271 (2009). Johnell, O. et al. Mortality after osteoporotic fractures. Osteoporos. Int. 15 (1), 38–42 (2004). Kanis, J. A., Oden, A., Johnell, O., De Laet, C. & Jonsson, B. Excess mortality after hospitalisation for vertebral fracture. Osteoporos. Int. 15 (2), 108–112 (2004). Fry, A. et al. Comparison of Sociodemographic and Health-Related Characteristics of UK Biobank Participants With Those of the General Population. Am. J. Epidemiol. 186 (9), 1026–1034 (2017). Hanlon, P. et al. Frailty and pre-frailty in middle-aged and older adults and its association with multimorbidity and mortality: a prospective analysis of 493 737 UK Biobank participants. Lancet Public. Health . 3 (7), e323–e32 (2018). Webster, J., Greenwood, D. C. & Cade, J. E. Risk of hip fracture in meat-eaters, pescatarians, and vegetarians: a prospective cohort study of 413,914 UK Biobank participants. BMC Med. 21 (1), 278 (2023). Additional Declarations No competing interests reported. Supplementary Files SupplementaryMateiralUKB.pdf Cite Share Download PDF Status: Published Journal Publication published 29 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 19 Aug, 2025 Reviews received at journal 18 Aug, 2025 Reviewers agreed at journal 18 Aug, 2025 Reviews received at journal 25 May, 2025 Reviewers agreed at journal 23 May, 2025 Reviewers invited by journal 11 Apr, 2025 Editor assigned by journal 11 Apr, 2025 Editor invited by journal 19 Mar, 2025 Submission checks completed at journal 18 Mar, 2025 First submitted to journal 17 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6248768","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":441590036,"identity":"8544e046-98fb-4082-950c-c847ec55598a","order_by":0,"name":"Yi Zhang","email":"","orcid":"","institution":"Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Zhang","suffix":""},{"id":441590037,"identity":"ede2b220-9114-4c77-ae85-bae36610900b","order_by":1,"name":"Kai Zhang","email":"","orcid":"","institution":"Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Zhang","suffix":""},{"id":441590039,"identity":"c123a878-828f-4835-aee3-2018f25e3b2f","order_by":2,"name":"Lirong Chai","email":"","orcid":"","institution":"Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Lirong","middleName":"","lastName":"Chai","suffix":""},{"id":441590040,"identity":"dcd2117e-9dc6-4173-895e-7cca5605da65","order_by":3,"name":"Xiaolin Hu","email":"","orcid":"","institution":"Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Xiaolin","middleName":"","lastName":"Hu","suffix":""},{"id":441590044,"identity":"602a4f5a-d4e1-49ab-9470-76eb9b3e27dd","order_by":4,"name":"Weizheng Kong","email":"","orcid":"","institution":"Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Weizheng","middleName":"","lastName":"Kong","suffix":""},{"id":441590046,"identity":"db789afc-2fe9-4661-8d66-ae63b1d5794b","order_by":5,"name":"Weijing Wang","email":"","orcid":"","institution":"Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Weijing","middleName":"","lastName":"Wang","suffix":""},{"id":441590047,"identity":"1cf41c93-9c60-492a-b311-10f0b1fb536a","order_by":6,"name":"Dongfeng Zhang","email":"","orcid":"","institution":"Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Dongfeng","middleName":"","lastName":"Zhang","suffix":""},{"id":441590050,"identity":"1bc3e5c7-2d2c-412a-9520-9c0f04a9c8e4","order_by":7,"name":"Junning Fan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIie2PsYrCQBCGJwQ2zS5pF5TkFTYseFr5KkkfbGwsjmMhkGdIocc9hVw5S8BKSWthET24SiHgC5wEc91tLIXbr/ln4P8YBsBieUI8BSBuSQFcxGbBg7BPofirkEQX27GM1ANKN8qS5YsE8M/yveftcM4+D8MX/0OU7J3HjnKPp71JobNYsu03nRR1rFdrPvOASJkalCmkQrK8pGKPiJc1nzuKkoFJof65U7RCtuSJwj6Fd1eqDDRTDylnEa3aKwR0seEyynp+oX46Epe8nIqqujbN61sQetnxy6TcIKINHt9311xvK3UbPvZXLRaL5X/yA4/xTwwOxqFiAAAAAElFTkSuQmCC","orcid":"","institution":"Qingdao University","correspondingAuthor":true,"prefix":"","firstName":"Junning","middleName":"","lastName":"Fan","suffix":""}],"badges":[],"createdAt":"2025-03-18 02:54:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6248768/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6248768/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-21718-8","type":"published","date":"2025-10-29T15:57:27+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80816418,"identity":"9e646aac-75b1-45e1-b870-9eda6979604a","added_by":"auto","created_at":"2025-04-17 11:13:40","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":362095,"visible":true,"origin":"","legend":"\u003cp\u003eSelection of the nested analysis cohort of any and hip fracture from the UKB cohort\u003c/p\u003e\n\u003cp\u003eAbbreviations: UKB, UK Biobank; FI, frailty index; EDS, exposure density sampling.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6248768/v1/2b58efce889e51eff17e5695.jpeg"},{"id":80815786,"identity":"71df5018-f62e-4161-93b2-2f3e8e4e18c1","added_by":"auto","created_at":"2025-04-17 11:05:40","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":512846,"visible":true,"origin":"","legend":"\u003cp\u003eShort- and long-term mortality risk after any fracture\u003c/p\u003e\n\u003cp\u003eAbbreviations: HR, hazard ratio; CI, confidence interval.\u003cstrong\u003e *\u003c/strong\u003eResults were expressed as HRs (95% CIs). \u003csup\u003ea\u003c/sup\u003e Values are expressed as number of events (Rate). Rates are equal to the number of events per 1,000 person-years. \u003csup\u003eb\u003c/sup\u003e Multivariable models were adjusted for age, sex, ethnicity, education, employment status, Townsend deprivation index, smoking status, alcohol consumption, physical activity and body mass index.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6248768/v1/a3d3359eb23d9453166a93df.jpeg"},{"id":80815368,"identity":"33272cda-2311-4d9c-862f-691e3143afff","added_by":"auto","created_at":"2025-04-17 10:57:40","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":506566,"visible":true,"origin":"","legend":"\u003cp\u003eShort- and long-term mortality risk after hip fracture\u003c/p\u003e\n\u003cp\u003eAbbreviations: HR, hazard ratio; CI, confidence interval.\u003cstrong\u003e *\u003c/strong\u003eResults were expressed as HRs (95% CIs). \u003csup\u003ea\u003c/sup\u003e Values are expressed as number of events (Rate). Rates are equal to the number of events per 1,000 person-years. \u003csup\u003eb\u003c/sup\u003e Multivariable models were adjusted for age, sex, ethnicity, education, employment status, Townsend deprivation index, smoking status, alcohol consumption, physical activity and body mass index.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6248768/v1/cfbc467b2e7771cb8392ac87.jpeg"},{"id":95039951,"identity":"18f225a7-be6c-4212-93e6-4ed2c5dd2815","added_by":"auto","created_at":"2025-11-03 16:06:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2401712,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6248768/v1/8fc515b8-674b-4478-9edc-b578e3d0f0c3.pdf"},{"id":80815785,"identity":"998403c1-2a61-47e5-a363-95419b6e6e79","added_by":"auto","created_at":"2025-04-17 11:05:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":263144,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMateiralUKB.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6248768/v1/f9b8ce9d1832c168450dd030.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Excess short- and long-term mortality risk following any and hip fractures: a prospective matched cohort study using exposure density sampling","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOsteoporotic fractures, a common disease affecting the health status and quality of life of middle-aged and older adults, are a growing public health problem in both developed and developing countries [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. It has been reported that the residual lifetime risk of fractures in people over the age of 60 was 44% for women and 25% for men [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Moreover, fractures have been found to be a predictor of hospitalization, disability, and even death, posing a huge socio-economic and healthcare challenge [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], with the medical costs in the UK being approximately \u0026pound;1.8\u0026nbsp;billion in 2000 and expected to rise to \u0026pound;2.2\u0026nbsp;billion by 2025 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. As the population continues to age, the burden of fractures is expected to increase [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrently, the increased short-term mortality following fractures is well recognized, with the majority of relevant research demonstrated the highest mortality risk within one-year [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] after fractures, and more specifically, several studies indicating the highest risk within 90 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] or 30 days [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, evidence on the magnitude and duration of the long-term mortality risk following fractures is mixed [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], with some studies showing the excess mortality post-fracture maintaining for up to 5 years [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], 10 years [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], and even as long as 20 years [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], while there are also studies that have found no elevated risk of long-term mortality [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhen examining the post-fracture mortality risk, most studies have used matched designs with non-fracture or general population as controls, usually matched on age and sex. These studies mainly identified fractures only at one time point at baseline, ignoring the time-dependent nature of this exposure, with little research matching fractures as time-dependent variables; however, fracture events are dynamic in the course of follow-up. Besides, some analyses did not consider pre-fracture health status, making it hard to determine whether the increased post-fracture mortality was due to poor underlying health or to the fracture itself. In addition, necessary confounding factors, such as smoking status and alcohol consumption, were not adjusted for in some related epidemiological studies [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo fill in the research gap, based on the UK Biobank (UKB) cohort, we will use the \u0026ldquo;exposure density sampling (EDS)\u0026rdquo; approach [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] to dynamically match time-dependent exposures, avoiding time-varying bias [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and underestimation of association estimates [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] caused by ignoring the effect of the temporal factors. To be specific, our research aims are as follows: (1) to examine the short- and long-term mortality risk following any and hip fractures, and how the magnitude of the associations changed over time after adjustment for sociodemographic characteristics and lifestyle factors; (2) to examine whether the associations are consistent across subpopulations of different age, sex, body mass index (BMI), frailty index (FI), and disease status.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eIn this prospective cohort study, data were sourced from the UKB. UKB is a multi-center cohort study that enrolled 502,370 participants aged 40\u0026ndash;69 years across 22 assessment centers in England, Scotland, and Wales between 2006 and 2010. In practice, some individuals outside the intended age range were also included, resulting in an actual participant age range of 38\u0026ndash;72 years. After signing a written informed consent, each participant completed a baseline assessment, which included physical measurements, verbal interviews, and touch-screen questionnaires covering a broad spectrum of characteristics (including demographics, socioeconomics, lifestyle, environmental exposures, as well as health factors and medical history). UKB has been approved by the North West Multi-centre Research Ethics Committee (MREC) as a Research Tissue Bank (RTB). Additional details about the UKB can be accessed on its official website: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ukbiobank.ac.uk/\u003c/span\u003e\u003cspan address=\"https://www.ukbiobank.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAscertainment of exposures and outcomes\u003c/h3\u003e\n\u003cp\u003eWe ascertained incident fractures from linkage to Hospital Episode Statistics (HES) using predefined the 10th revision of the International Classification of Diseases codes (ICD-10) categories. In the present study, we used the ICD-10 codes to classify fractures into any fracture (S12, S22, S32, S42, S52, S62, S72, S82, S92, T02, T08, T10, T12, T14.2, M48.4, M48.5, M49.5) and hip fracture (S72.0, S72.1, S72.2). The primary outcome was all-cause mortality, which in the UKB was obtained from death certificates held within the National Health Service Information Centre (England and Wales) and the National Health Service Central Register Scotland (Scotland). Median follow-up duration was 13.45 years (IQR 12.96\u0026ndash;14.35 years).\u003c/p\u003e\n\u003ch3\u003eAssessment of covariates\u003c/h3\u003e\n\u003cp\u003eCovariates were collected at baseline, including sociodemographic characteristics (age, sex, ethnicity, education, employment status, and Townsend deprivation index), lifestyle factors (smoking status, alcohol consumption, physical activity, and BMI), and frailty status. In the present study, frailty status was assessed by the FI. We referred to a previous study by Williams DM and colleagues, who constructed the FI in UKB using 49 items (i.e., deficits) covering health (e.g., chest pain), presence of diseases and disabilities (e.g., diabetes and high blood pressure), and mental well-being (e.g., depressed feelings and sleep) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Based on this, we omitted three items related to fracture exposures, namely falls, fractures and osteoporosis, and finally included 46 items. FI was calculated as the sum of non-missing deficits that the individual met divided by the total number of non-missing items. Referring to previous studies [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], we further classified participants into three categories: robust (FI\u0026thinsp;\u0026le;\u0026thinsp;0.10), pre-frail (0.10\u0026thinsp;\u0026lt;\u0026thinsp;FI\u0026thinsp;\u0026lt;\u0026thinsp;0.25), and frail (FI\u0026thinsp;\u0026ge;\u0026thinsp;0.25). Details of the covariates, please see the Supplementary Text S1.\u003c/p\u003e\n\u003ch3\u003eSelection of the analysis cohort\u003c/h3\u003e\n\u003cp\u003eWe sequentially excluded participants who had missing data on 9 or more (i.e., more than 20%) items for FI (n\u0026thinsp;=\u0026thinsp;2,621); those with falls (n\u0026thinsp;=\u0026thinsp;98,765), osteoporosis (n\u0026thinsp;=\u0026thinsp;3,905) or fractures (n\u0026thinsp;=\u0026thinsp;29,877) at baseline; those with date of fracture (i.e., any fracture and hip fracture) prior to the time of admission (n\u0026thinsp;=\u0026thinsp;3,315); those with the same date of fracture and time of admission (n\u0026thinsp;=\u0026thinsp;2); and those with date of birth later than the time of admission (n\u0026thinsp;=\u0026thinsp;1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOf the remaining eligible participants (n\u0026thinsp;=\u0026thinsp;363,884), we further used an \u0026ldquo;EDS\u0026rdquo; approach [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] to create a nested cohort comprising participants with and without fractures. EDS is an efficient risk set sampling approach designed for dynamic matching related to time-dependent exposures, enabling the estimation of the effect of exposure over time with minimal precision loss and unbiased results compared to a full cohort analysis [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Using the any fracture exposure as an example, a total of 19,163 participants with fractures were included in the matching process. Four participants individually matched to each exposed individual on time to exposure, baseline age groups (\u0026plusmn;\u0026thinsp;5 years), sex, and frailty status (robust, pre-frail, and frail) were selected from the risk set who were still at risk of death and not exposed at the time of any fracture (i.e., the index date) in the exposed individual, yielding 67,522 matched controls. During the EDS matching process, a fixed number of seeds ensured consistent results. To achieve unbiased estimates, controls were sampled with replacement, meaning the same individual could be sampled multiple times and served as a control for different exposed individuals [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The time of their first sampling as a control was regarded the left-truncated entry time. Besides, a sampled control may also have had any fractures and become exposed later. In this scenario, the first sampling time served as the entry time, and exposure was treated as a time-dependent variable [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The analysis cohort for hip fracture exposure was selected similarly as described above.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003e We summarized baseline characteristics of overall participants, any fracture cases, and matched controls, including sociodemographic characteristics, lifestyle factors, and frailty status. Values were expressed as means (SD) or percentages as appropriate. Analysis of variance was used for continuous variables and χ\u003csup\u003e2\u003c/sup\u003e test was used for categorical variables.\u003c/p\u003e \u003cp\u003eIn the present analyses, the follow-up time was determined from the index date to the date of death, loss to follow-up, or the end of the study period (i.e., 31 December 2022 in England, 31 August 2022 in Scotland, and 31 May 2022 in Wales), whichever came first. Sex-specific mortality rates for both fracture groups during follow-up were calculated as the number of post-fracture deaths divided by the total person-years. We used Kaplan-Meier survival curves to compare survival probabilities after any and hip fractures and stratified by age and sex groups, and differences were tested by log-rank statistics. We used a piecewise Cox proportional hazards model to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the effect of any and hip fractures on all-cause mortality over time periods. Multivariable models were adjusted for age, sex, ethnicity, education, employment status, Townsend deprivation index, smoking status, alcohol consumption, physical activity, and BMI. In the piecewise Cox proportional hazards models, time periods after the index date were split into 0\u0026ndash;30 days, 31\u0026ndash;90 days, 91 days to one year, and then yearly up to the tenth year after fractures. Because of small sample sizes in some time periods and subpopulations, time periods after the index date were combined as (0\u0026ndash;1] year, (1\u0026ndash;3] years, (3\u0026ndash;6] years, and (6\u0026ndash;10] years in the subgroup analyses.\u003c/p\u003e \u003cp\u003eWe further explored the variation in the association between fractures and mortality risk in different subpopulations: age at index date (\u0026lt;\u0026thinsp;60 or \u0026ge;\u0026thinsp;60 years), sex (male or female), BMI (\u0026lt;\u0026thinsp;25 or \u0026ge;\u0026thinsp;25kg/m\u003csup\u003e2\u003c/sup\u003e), FI (\u0026lt;\u0026thinsp;0.25 or \u0026ge;\u0026thinsp;0.25), and disease status of hypertension, diabetes, chronic obstructive pulmonary disease and cancer (presence or absence). The interactions between fractures and the above-mentioned factors were tested using likelihood-ratio tests by comparing models with and without cross-product interaction terms. In addition, to examine the robustness of our findings, sensitivity analyses for the fracture-mortality relationship were also performed by substituting seed number and matched controls after adjustment for all covariates.\u003c/p\u003e \u003cp\u003eAll analyses were done using Stata version 17.0 and R version 4.0.3. A two-sided \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics of the study population in the nested analysis cohort of any fracture\u003c/h2\u003e \u003cp\u003eDuring follow-up, a total of 19,163 participants had any fractures, and 67,522 matched controls were obtained after EDS matching (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Of 86,685 any fracture cases and matched controls included in the nested analysis cohort, the mean (SD) age at baseline was 58.3 (7.8) years, 59.5% were women, and the mean (SD) value of Townsend deprivation index was \u0026minus;\u0026thinsp;1.4 (3.0). Although matched for baseline age groups, sex, and frailty status, compared with matched controls, any fracture cases were slightly older (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and frailer (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025), as well as more often to be women (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002). In addition, any fracture cases were less educated (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), less likely to be working (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and more likely to be current smokers (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and underweight (BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) than matched controls (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of participants in the nested analysis cohorts of any fracture (n\u0026thinsp;=\u0026thinsp;86,685)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAny fracture cases\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;19,163)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMatched controls\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;67,522)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSociodemographic characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at baseline, year (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58.3 (7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58.6 (7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58.2 (7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at index date, year (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65.2 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.8 (9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.0 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth Asian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast Asian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther/Mixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege/University\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA/AS Levels/Equivalent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO Levels/GCSEs/Equivalent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSEs/Equivalent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNVQ/HND/HNC/Equivalent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther professional qualifications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone of the above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment status (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTownsend deprivation index (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.4 (3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.3 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.5 (3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLifestyle factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol consumption (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonthly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activity (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrailty status (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRobust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-frail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eBMI, body mass index; SD, standard deviation.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eValues are expressed as mean (SD) or percentage. Analysis of variance was used for continuous variables and the chi-square test was used for categorical variables.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAny fracture and mortality risk\u003c/h3\u003e\n\u003cp\u003eDuring follow-up, the mortality risk was higher in any fracture cases than that in matched controls, with the relative risk (RR) of 2.93 (95% CI, 2.79\u0026ndash;3.08). Among any fracture cases, there were 1,333 deaths in women and 1,567 in men during 155,453 and 98,886 person-years, yielding mortality rates (95% CI) per 1,000 person-years of 8.58 (8.11\u0026ndash;9.04) and 15.85 (15.06\u0026ndash;16.63) in women and men, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Compared with matched controls, any fracture cases had significantly increased mortality rates from the first month to the tenth year after fractures. The mortality rate (per 1,000 person-years) for any fracture cases within the first 30 days was 214.6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Based on the Kaplan-Meier curves of survival probability for participants during follow-up, regardless of age and sex groups, it was observed that the survival probability for any fracture cases was lower than that of the participants with no fracture (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSex-specific post-fracture mortality rate and relative risk for any and hip fracture cases compared to matched controls\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFracture cases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeaths\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePerson-years\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMortality rate\u003c/p\u003e \u003cp\u003e/1,000 Person-years\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRelative risk (95% CI)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny fracture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19,163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e254,339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.40 (10.99\u0026ndash;11.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.93(2.79\u0026ndash;3.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHip fracture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40,851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.11 (16.81\u0026ndash;19.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.87(3.50\u0026ndash;4.28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny fracture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11,582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e155,453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.58 (8.11\u0026ndash;9.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.88(2.68\u0026ndash;3.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHip fracture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25,778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.83 (14.29\u0026ndash;17.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.34(3.77\u0026ndash;4.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny fracture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7,581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98,886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.85 (15.06\u0026ndash;16.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.04(2.84\u0026ndash;3.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHip fracture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15,072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.03 (19.66\u0026ndash;24.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.45(2.98\u0026ndash;3.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eCI, confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ea\u003c/sup\u003e Relative risks were calculated using matched controls without any or hip fractures as the reference group.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAfter adjustment for sociodemographic and lifestyle factors, compared with matched controls, the short- and long-term mortality risk was significantly increased in any fracture cases. The mortality risk for any fracture cases was highest in the first 30 days, followed by 31\u0026ndash;90 days, with the HRs (95% CIs) of 23.31 (17.08\u0026ndash;31.83) and 12.59 (9.92\u0026ndash;15.98), respectively. Thereafter, the risk gradually decreased but remained significantly higher than matched controls until the tenth year, with an 44% increased risk of mortality (HR\u0026thinsp;=\u0026thinsp;1.44, 95% CI, 1.08\u0026ndash;1.91) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eHip fracture and mortality risk\u003c/h2\u003e \u003cp\u003eDuring follow-up, hip fracture cases had a higher risk of mortality compared with matched controls, with the RR (95% CI) of 3.87 (3.50\u0026ndash;4.28). Among hip fracture cases, 408 deaths were occurred in women and 332 in men over 25,778 and 15,072 person-years, resulting in mortality rates (95% CI) per 1,000 person-years of 15.83 (14.29\u0026ndash;17.36) and 22.03 (19.66\u0026ndash;24.40) in women and men, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). As with any fractures, the mortality rates following hip fractures also were higher than in matched controls from the first month to the tenth year. The mortality rate (per 1,000 person-years) for hip fracture cases within the first 30 days was 265.2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The Kaplan-Meier survival curves showed that, hip fracture cases had the lowest survival probabilities in all age and sex groups, compared to participants with any fractures and those without fractures (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAfter adjustment, compared with matched controls, hip fracture cases were associated with a significantly increased short- and long-term mortality risk. Compared to other time periods, the mortality risk for hip fracture cases elevated pronouncedly in the first 90 days, with the highest increase in 0\u0026ndash;30 days (HR\u0026thinsp;=\u0026thinsp;23.67, 95% CI, 12.17\u0026ndash;46.01), and the next highest increase in 31\u0026ndash;90 days (15.24, 9.84\u0026ndash;23.60). Thereafter, a gradual decline in the risk was observed, but it remained significantly higher than matched controls until the tenth year, with a 115% excess mortality (HR\u0026thinsp;=\u0026thinsp;2.15, 95% CI, 1.15-4.00) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analyses and Subgroup analyses\u003c/h2\u003e \u003cp\u003eIn the sensitivity analyses, the association patterns of any and hip fractures with mortality risk over 10 years remained generally unchanged (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-S2). After replacing the number of seeds required in the EDS matching process, the risk after any fractures in the first 30 days was slightly reduced, with smaller changes in the remaining time periods; the risk after hip fractures was basically unaltered (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Changing from 1:4 matching to 1:15 matching had little effect on the risk following any fractures; and resulted in a moderate reduction in the risk following hip fractures within the first 30 days and 31\u0026ndash;90 days, with less change in the remaining time periods (Supplementary Table S2). The short- and long-term increased risk for mortality outcomes following any and hip fractures was seen across all subgroups, similar to the primary analyses (Supplementary Table S3-S4).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large EDS matched cohort study nested within the UKB, we observed an elevated short- and long-term risk of all-cause mortality following any and hip fractures in middle-aged and older adults. During follow-up, men had a higher post-fracture mortality rate than women. The mortality risk was highest within the first 30 days after any fractures, increasing more than 20-fold, and then decreased gradually over time, with the high risk persisting for up to 10 years. The association pattern between hip fracture and mortality risk was similar. The above fracture-mortality relationships were consistently observed in all subgroups. In summary, this study emphasizes the importance of prevention, detection, and intervention for all types of osteoporotic fractures, especially within the first 30 days after fracture morbidity, as well as the concern for long-term health of middle-aged and older adults with fractures.\u003c/p\u003e \u003cp\u003eThe present study showed that hip fractures were associated with an elevated risk of short- and long-term (30 days \u0026minus;\u0026thinsp;10 years) all-cause mortality, and the high risk remained until the tenth year, which is largely consistent with existing studies [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. A meta-analysis of more than 20 cohorts analyzing the short- and long-term (3-120 months) mortality risk after hip fractures, showed that the risk was highest in the first 3 months after hip fractures, with a 5- to 8-fold increase in the risk of all-cause mortality, with the HRs (95% CIs) of 7.95 (6.13\u0026ndash;10.30) for men and 5.75 (4.94\u0026ndash;6.67) for women. The high risk decreased over time but was consistently higher than in matched controls, and persisted until the tenth year after fractures, except for the seventh and eighth years in men [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, the included cohorts in this meta-analysis varied considerably in sample size, duration of observation, selection of the control population, and ascertainment of death outcomes, leading to a high degree of heterogeneity.\u003c/p\u003e \u003cp\u003eA matched nationwide register-based cohort study from the Danish national registers, with participants aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years and controls matched 1:10 on age and sex, reported that the mortality risk after any and hip fractures was highest in the first 30 days and declined thereafter, but remained higher than the matched controls, with the high risk persisting up to 5 years later [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Two other studies also based on health care databases in Manitoba [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and Taiwan [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] presented similar results, showing the highest short-term mortality after hip fractures within the first year, and the long-term excess risk persisted for up to 5 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and 10 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] years, respectively. However, the three aforementioned literature based on health care databases lacked information on lifestyle confounders, such as smoking status, leaving possible confounding uncontrolled.\u003c/p\u003e \u003cp\u003eOur study, after fully adjustment for potential confounders, demonstrated that fractures were associated with an elevated risk of short- and long-term mortality, but not all the existing analyses showed the long-term persistence of the post-fracture excess mortality [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Two matched cohort studies were conducted based on the Cardiovascular Health Study (CHS, age\u0026thinsp;\u0026ge;\u0026thinsp;65 years) and the Study of Osteoporotic Fractures (SOF, age\u0026thinsp;\u0026ge;\u0026thinsp;65 years), respectively, and followed for mortality outcome more than 15 years [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The CHS study, which included 1,513 hip fracture cases and matched controls, and matched for sex, age, race, recruitment period, as well as time since enrollment, reported that the risk of death was highest in the first month following hip fractures, and that the high risk remained for up to 4 years in women and only 6 months in men [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The SOF study, which included 5,580 hip fracture cases and age-matched controls, showed that among women, hip fracture cases had more than a 2-fold increase in mortality within the first year following fractures compared to controls, but the excess risk lasted only one year [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Whereas results from the Beijing Longitudinal Study of Aging, which followed up 3,257 Chinese adults aged\u0026thinsp;\u0026ge;\u0026thinsp;55 years for 8 years, showed no significant association between any fractures and long-term mortality risk (HR\u0026thinsp;=\u0026thinsp;0.92, 95% CI, 0.75\u0026ndash;1.14) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Therefore, whether there is a long-term excess mortality following fractures, and if so, how long does it persist, warrant further confirmation given the inconsistencies between existing studies.\u003c/p\u003e \u003cp\u003eTo sum up, existing relevant literature has largely confirmed that fractures are associated with an increased risk of death, but the split of the time period of short-term mortality and the duration of excess mortality risk remain controversial. This may be due to differences in study populations, matching methods, and matching factors across analyses. Previous studies have mainly been conducted in adults aged 60 years and older and have not considered fractures as time-dependent exposures. In addition, matching factors generally included only demographic characteristics such as age, sex, and date of birth, but the impact of baseline health status on the association between fractures and mortality was not taken into account. Our study adds to the evidence by using EDS-dynamic matching method, which avoids potentially time-dependent bias by including fractures as time-dependent variables; and it also considers the effect of frailty status on the fracture-mortality relationship by including it as a matching factor.\u003c/p\u003e \u003cp\u003eOur study found that men had a higher mortality rate than women after any and hip fractures in the course of follow-up, which is generally in line with previous studies [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In the Dubbo Osteoporosis Epidemiology Study of community-dwelling adults aged 60 years and older, during follow-up, the mortality rate (per 100 person-years) after all fractures was 7.78 (95% CI, 7.10\u0026ndash;8.52) and 11.30 (9.82\u0026ndash;12.99) for women and men, respectively, and after hip fractures was 15.42 (12.88\u0026ndash;18.52) and 25.67 (19.46\u0026ndash;33.87) for women and men, respectively [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Although most of the literature shows a higher risk of death after fractures in men than in women, this study and some previous studies have not observed sex differences between fracture-mortality association [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Therefore, additional research is required to explore the relationship between fractures and mortality risk in different sexes and to further elucidate the reasons.\u003c/p\u003e \u003cp\u003eTo our knowledge, the present study is the first large EDS dynamically matched cohort study to simultaneously examine the short- and long-term mortality risk following any and hip fractures, with a careful delineation of the post-fracture time period. The large sample size and long follow-up period of the UKB cohort ensured our statistical validity for the strength of association at different time periods from 30 days to 10 years after fractures. Besides, we address confounding bias due to frailty, sociodemographic characteristics, and lifestyle factors by matching as well as adjusting for potential confounders. In addition, various characteristics of the participants, and sex-specific mortality rates in both fracture groups, were also taken into account.\u003c/p\u003e \u003cp\u003eOur study also had some limitations. First, UKB is not representative of the sampling population; there is a \u0026ldquo;healthy volunteer\u0026rdquo; selection bias [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The study population was predominantly white British, with comparatively lower levels of socio-economic deprivation than the UK average [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Therefore, caution is needed when extrapolating our results to the wider population. Second, some baseline characteristics relied on participant\u0026rsquo;s self-reporting and might be subject to information bias. Third, we assessed frailty status and collected information on lifestyle factors only at baseline. Hence, it was not possible to determine the potential impact of changes in these factors over time on the associations. Finally, lacking data on the causes of fractures, we cannot distinguish between fragility and traumatic fractures. However, there is evidence that most fractures in middle-aged and older adults are fragility fractures [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrated that any and hip fractures were associated with an increased risk of short- and long-term (30 days \u0026minus;\u0026thinsp;10 years) mortality. The mortality risk after fractures peaked within the first month, and then declined gradually, but was always higher than in matched controls, with the excess risk remaining until the tenth year. Therefore, prevention of all types of osteoporotic fractures, enhancing early intervention and treatment when detected, and long-term care, are important ways to mitigate fracture-related burden and consequently facilitating healthy population ageing.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eJournal name\u003c/h2\u003e \u003cp\u003eScientific Reports\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eWord count\u003c/strong\u003e \u003cp\u003eAbstract 199, Main text 3694\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e UK Biobank was approved by the North West Multi-Centre Research Ethics Committee (REC reference: 11/NW/03820) and all participants provided written informed consent to participate in the UK Biobank study. The study protocol is available online (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ukbiobank.ac.uk/\u003c/span\u003e\u003cspan address=\"http://www.ukbiobank.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This work was conducted under the UK Biobank application number 95715.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003e UK Biobank was established by the Wellcome Trust medical charity, Medical Research Council, Department of Health, Scottish Government, and the Northwest Regional Development Agency and is funded by the Welsh Government and the British Heart Foundation. This work was supported by National Natural Science Foundation of China (82304226), Natural Science Foundation of Shandong Province (ZR2023QH188), China Postdoctoral Science Foundation (2023M731839), Mount Taishan Scholar Youth Program (No. tsqn202306179), Qingdao Postdoctoral Innovation Project (QDBSH20230102012), Qingdao University Scientific Research Startup Fund (DC2200002531). The funders had no role in the study design, data collection, data analysis and interpretation, writing of the report, or the decision to submit the article for publication.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJF conceived and designed the study. YZ and JF analyzed the data. YZ drafted the manuscript. KZ, LC, XH, WK and WW helped to organize the information and review the data. JF and YZ helped the interpretation of the results. JF contributed to the critical revision of the manuscript for important intellectual content and approved the final version of the manuscript. All authors reviewed and approved the final manuscript. DZ is the guarantor.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe are grateful to UK Biobank participants. This research has been conducted using the UK Biobank Resource under Application Number 95715.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll UK Biobank information is available online on the webpage www.ukbiobank. Data access is available through applications. This research was conducted using the application number 95715.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBliuc, D. et al. Mortality risk associated with low-trauma osteoporotic fracture and subsequent fracture in men and women. \u003cem\u003eJAMA\u003c/em\u003e \u003cb\u003e301\u003c/b\u003e (5), 513\u0026ndash;521 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCummings, S. R. \u0026amp; Melton, L. J. 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Int.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e (1), 38\u0026ndash;42 (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKanis, J. A., Oden, A., Johnell, O., De Laet, C. \u0026amp; Jonsson, B. Excess mortality after hospitalisation for vertebral fracture. \u003cem\u003eOsteoporos. Int.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e (2), 108\u0026ndash;112 (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFry, A. et al. Comparison of Sociodemographic and Health-Related Characteristics of UK Biobank Participants With Those of the General Population. \u003cem\u003eAm. J. Epidemiol.\u003c/em\u003e \u003cb\u003e186\u003c/b\u003e (9), 1026\u0026ndash;1034 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHanlon, P. et al. Frailty and pre-frailty in middle-aged and older adults and its association with multimorbidity and mortality: a prospective analysis of 493 737 UK Biobank participants. \u003cem\u003eLancet Public. Health\u003c/em\u003e. \u003cb\u003e3\u003c/b\u003e (7), e323\u0026ndash;e32 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWebster, J., Greenwood, D. C. \u0026amp; Cade, J. E. Risk of hip fracture in meat-eaters, pescatarians, and vegetarians: a prospective cohort study of 413,914 UK Biobank participants. \u003cem\u003eBMC Med.\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e (1), 278 (2023).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Any fractures, Hip fractures, Mortality risk, Long-term, Short-term, Time-dependent exposure","lastPublishedDoi":"10.21203/rs.3.rs-6248768/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6248768/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEvidence on the magnitude and duration of short- and long-term mortality following osteoporotic fractures was mixed. We aimed to examine the short- and long-term mortality risk after any and hip fractures. We conducted an \u0026ldquo;exposure density sampling\u0026rdquo; dynamically matched cohort study based on 363,884 adults of UK Biobank. A total of 19,163 any fracture and 3,114 hip fracture cases were included, and controls were matched 1:4 on age, sex, and frailty status. A piecewise Cox proportional hazards model was used to estimate mortality risk in different time periods from 30 days to 10 years after fractures. Among 86,685 any fracture cases and matched controls, the mean age (SD) was 58.3 (7.8) years, and 59.5% were women. During follow-up, men had a higher post-fracture mortality rate than women (15.85 vs 8.58 per 1,000 person-years). After adjustment, mortality risk peaked within the first 30 days following any fractures (HR\u0026thinsp;=\u0026thinsp;23.31, 95% CI, 17.08\u0026ndash;31.83), gradually declining but remaining elevated for up to 10 years (1.44, 1.08\u0026ndash;1.91). Similar association patterns were observed for hip fractures. The above associations were consistently observed across various characteristics of the participants. This study underscores the importance of enhancing fracture prevention and early intervention, as well as long-term care.\u003c/p\u003e","manuscriptTitle":"Excess short- and long-term mortality risk following any and hip fractures: a prospective matched cohort study using exposure density sampling","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-17 10:57:35","doi":"10.21203/rs.3.rs-6248768/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-19T04:24:26+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-18T09:31:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"12586356119237039937815707522440951931","date":"2025-08-18T08:14:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-25T22:09:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"8479891075895018672603054547849519170","date":"2025-05-23T10:21:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-11T06:41:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-11T06:38:54+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-19T13:04:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-18T12:44:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-18T02:49:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"df9c9982-df31-4e5b-b1e2-c8b42393f045","owner":[],"postedDate":"April 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":47020150,"name":"Health sciences/Health care/Fracture repair"},{"id":47020151,"name":"Health sciences/Health care/Geriatrics"},{"id":47020152,"name":"Health sciences/Health care/Quality of life"}],"tags":[],"updatedAt":"2025-11-03T16:00:41+00:00","versionOfRecord":{"articleIdentity":"rs-6248768","link":"https://doi.org/10.1038/s41598-025-21718-8","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-10-29 15:57:27","publishedOnDateReadable":"October 29th, 2025"},"versionCreatedAt":"2025-04-17 10:57:35","video":"","vorDoi":"10.1038/s41598-025-21718-8","vorDoiUrl":"https://doi.org/10.1038/s41598-025-21718-8","workflowStages":[]},"version":"v1","identity":"rs-6248768","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6248768","identity":"rs-6248768","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}