Development of fall prediction models in community-dwelling older adults: comparison of biological and multidomain models for any and injurious falls

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Existing fall prediction models vary widely in design and performance, and the added value of incorporating nonbiological factors remains unclear. This study aimed to evaluate the predictive performance of the biological, sociodemographic, behavioral, and environmental domains proposed by the World Health Organization (WHO) for discriminating fall risk in community-dwelling older adults. Methods : We conducted a cross-sectional analysis using baseline data from the INITIATE cohort of community-dwelling adults aged ≥ 65 years. LASSO regression with 10-fold cross-validation was used to select biological predictors for any fall and injurious fall outcomes, followed by multivariable logistic regression. Sequential models reflecting sociodemographic, behavioral, and environmental domains were constructed to assess incremental discriminative performance. Model performance was assessed using the area under the ROC curve (AUC), with DeLong’s test used for comparisons. Bootstrap validation (1,000 iterations) was used to assess internal validity. Results : A total of 433 participants were included (median age 76 years; 64% female). For any falls (n = 194, 44.8%) the reduced biological model (mobility limitation, balance, visual acuity, fear of falling) achieved an AUC of 0.64 (95% CI 0.58–0.69). For injurious falls (n = 40, 28.4%), the reduced biological model (Timed Up and Go [TUG] time, grip strength, executive function, global cognition, and pain interference) achieved an AUC of 0.73 (95% CI 0.64–0.82). Adding sociodemographic, behavioral, and environmental variables produced minimal, nonsignificant improvements for both outcomes (any falls: AUC 0.65, p = 0.46; injurious falls: AUC 0.78, p = 0.30). Conclusions : Parsimonious models based primarily on biological measures can provide clinically meaningful discrimination while remaining feasible for community and outpatient use. The distinct risk profiles for any falls and injurious falls highlight the need for outcome-specific screening approaches. Prospective evaluation and external validation are needed prior to clinical implementation. falls injurious falls fall risk assessment older adults community-dwelling prediction models LASSO regression biological predictors model performance Figures Figure 1 Background Each year, approximately 23–30% of community-dwelling older adults experience a fall ( 1 ), and approximately one-third of those fallers sustain injuries requiring medical attention ( 2 ). Globally, falls are the second leading cause of unintentional injury-related deaths and the leading cause of death among adults aged 70 years and older ( 3 ). In Canada alone, falls account for more than 65% of injury-related hospitalizations among adults aged 65–74 years and over 80% among those aged 75 years and older, resulting in an estimated $ 10.3 billion annually in direct and indirect healthcare costs ( 4 ). These individual and societal consequences underscore the need for early identification of high-risk individuals to guide effective prevention strategies and alleviate the burden on healthcare systems. Accurately predicting who will fall remains a challenge because fall risk is inherently multifactorial and shaped by biological, sociodemographic, behavioral, and environmental factors ( 5 ). Despite this multifactorial etiology, most fall risk prediction models have focused on a relatively narrow subset of predictors, largely emphasizing mobility- and performance-based biological measures, and may overlook the broader determinants that influence fall occurrence in real-world settings ( 6 , 7 ). This narrow focus is also evident in clinical practice and international guidelines, where fall risk assessment commonly prioritizes gait, balance, and functional performance ( 8 – 11 ). This focus is justified, as impairments in these capacities are strong and well-established risk factors for falls ( 6 , 7 ). However, fall risk profiles are not uniform across the older adult population. Among community-dwelling older adults, fall risk can be shaped not only by biological impairment but also by living circumstances, everyday behaviors, and exposure to potentially hazardous environments, which are often not observed during clinic-based assessments ( 12 ). Thus, while functional assessments remain essential, their predominant biological focus likely provides only a partial representation of fall risk. Importantly, contemporary frameworks such as the WHO fall risk model emphasize that biological risk extends beyond mobility alone and that social, behavioral, and environmental domains also contribute meaningfully to fall risk ( 5 , 11 ). While these broader constructs are increasingly recognized conceptually, relatively few studies have examined whether incorporating them into fall-risk prediction models provides meaningful additive value beyond biological predictors alone ( 6 ). Accordingly, the aim of this paper is to examine whether fall risk among community-dwelling older adults is influenced by factors beyond biological ones. Specifically, we aimed to (i) determine the extent to which biological predictors are associated with the retrospective 12-month incidence of any falls and injurious falls and (ii) examine whether the inclusion of sociodemographic, behavioral, and environmental factors improves discriminative performance compared with a biological-only model. Methods This study was reported in accordance with the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) Statement ( 13 ). The completed checklist is provided in Additional file 1. Study design and population This cross-sectional analysis used baseline data from the Initial Test for Fall Risk Assessment in the Elderly (INITIATE) cohort. Community-dwelling older adults aged ≥ 65 years were recruited between March 2020 and September 2023. Recruitment paused during the COVID-19 pandemic and subsequently resumed in March 2022. All baseline data collection was completed by October 2024. Baseline data used in this analysis were collected as part of the INITIATE prospective cohort using standardized questionnaires and clinical assessments administered by trained research staff, as previously described ( 14 ). Participants were eligible if they were community-dwelling, English speaking, able to walk 10 meters without physical assistance, and capable of providing informed consent. The exclusion criteria included severe visual or hearing impairments, insufficient English proficiency, or cognitive impairment precluding written consent. Participants completed a baseline assessment, during which retrospective fall data from the preceding 12 months were collected. Predictor and outcome data were collected at baseline via telephone or online surveys and a two-hour, in-person assessment of clinical and performance-based measures. The outcome assessors were not blinded to the participants’ retrospective fall status; however, they were trained in standardized assessment administration and followed predefined protocols. Ethical approval was obtained from the Hamilton Integrated Research Ethics Board (#7380). Outcomes Two dichotomous fall outcomes were defined based on self-reported falls in the 12 months preceding baseline: ( 1 ) any falls, indicating the occurrence of one or more falls, and ( 2 ) injurious falls, defined as a fall requiring medical attention from a health professional within 48 hours ( 15 ). As per the Prevention of Falls Network Europe recommendations ( 16 ), a fall was defined as “an unexpected event in which the individual comes to rest on the ground, floor, or lower level.” All eligible falls were retained, including recurrent falls and falls occurring during sports or recreational activities, as each fall has the potential to contribute to and perpetuate a cycle of increased fall risk ( 17 , 18 ). The outcomes were coded as 0 = no and 1 = yes. Predictors Candidate predictors were selected a priori to reflect the multifactorial nature of fall risk and to minimize selection bias ( 19 ). The selection of predictors was informed by existing literature ( 6 , 20 , 21 ), clinical applicability, feasibility of administration, and relevance to geriatric and rehabilitation practice. The predictors were grouped according to the WHO’s four-domain fall-risk model ( 5 ): Biological : frailty, multimorbidity, mobility limitation, Timed Up and Go (TUG) time, balance, grip strength, vision, hearing, sensory loss, global cognition, executive function, number of fall risk–increasing drugs (FRIDs), pain interference, fear of falling, and depression. Sociodemographic : education, living alone, social support. Behavioral : participation in strengthening exercise and nutritional risk. Environmental : neighborhood walkability. In total, 23 predictors, corresponding to 23 degrees of freedom, were available for model building. Operational definitions, measurement details, and questionnaire sources are provided in Additional file 2: Table S1. Sample size The sample size was determined by the primary objective of the parent study, the INITIATE cohort. No a priori sample size calculation was performed for the present analysis; however, concerns regarding events per variable (EPV) were mitigated by the use of LASSO penalization. All participants who completed the baseline assessment and provided retrospective data on falls over the preceding 12 months were eligible for inclusion in the analytic sample ( 14 ). Statistical analysis Sample characteristics were summarized by sex (female, male) using means and standard deviations (SDs) for normally distributed variables, medians and interquartile ranges (IQRs) for skewed variables, and frequencies and relative percentages for categorical variables (Table 1 ). Missing data were handled via complete case analysis, whereby only participants with complete predictor and outcome data were included in the models. To identify the most informative biological predictors, the least absolute shrinkage and selection operator (LASSO) was applied with 10-fold cross-validation ( 22 ). LASSO was chosen to address the high dimensionality of the predictor set, reduce multicollinearity, and mitigate overfitting by shrinking small coefficients toward zero ( 23 , 24 ). Predictors with nonzero coefficients were retained to form the biological base model for each outcome. Subsequent logistic regression models were then constructed to reflect the domains described by the WHO risk factor framework and evaluate the added discriminative contribution of nonbiological domains. LASSO retained biological predictors were entered into Tier 1, with age (continuous) ( 25 ) and sex (female/male) ( 26 ) forced into all models due to their known confounding effects. Sociodemographic variables were added in Tier 2, behavioral variables in Tier 3, and an environmental variable in Tier 4. Interaction terms were not examined due to the limited number of outcome events. The full list of variables considered at each tier is presented in Table 1 . For the final adjusted models, assumptions of logistic regression were assessed. The linearity of continuous predictors with the log-odds of the outcome was assessed visually. Multicollinearity was assessed via the calculation of variance inflation factors (VIFs); a VIF > 5 was regarded as indicating serious multicollinearity ( 27 ). Influential observations were identified using leverage and standardized residual plots. The results are presented as adjusted odds ratios (ORs) with 95% confidence intervals (CIs). Statistical significance was set at a two-sided p < 0.05. Model discrimination was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) ( 28 ), with higher values indicating better discrimination. AUC values were interpreted descriptively, with values around 0.6–0.7 reflecting poorer discrimination, 0.7–0.8 fair to acceptable discrimination, and higher values indicating stronger performance ( 29 , 30 ). AUC differences between models were tested using Stata’s ROCCOMP command, which applies DeLong’s method for comparing correlated ROC curves ( 31 ). Internal validation was performed using bootstrap resampling with 1,000 iterations, to assess the stability of model performance estimates and reduce optimism in classification results ( 32 ). Model calibration was assessed using the Brier score, with lower values indicating better calibration ( 33 ). All analyses were performed using Stata/SE 18.5 (StataCorp LLC, College Station, TX). Table 1 Design of tiered prediction models for falls and injurious falls Model Independent Variables Tier 1 Age, sex, biological predictors retained by LASSO Tier 2 Tier 1 + education, living alone, social support Tier 3 Tier 2 + participation in strengthening exercise, nutrition risk Tier 4 Tier 3 + neighborhood walkability Results Participant characteristics Among the 514 participants who consented to participate (median age = 76 years, IQR = 10; 63.8% female), the outcome data were complete for all participants. Eighty-one participants were excluded due to missing predictor data, yielding a final analytic sample of 433 participants (median age = 76 years, IQR = 10; 64.0% female). Missing predictor data were low overall, with no single variable exceeding 5% missingness (Additional file 3: Figure S1). The sociodemographic, lifestyle, and health-related characteristics of the analytic sample are shown in Table 2 . Nearly half of the participants (44.8%, n = 194) reported at least one fall in the preceding year. Among fallers, 28.4% ( n = 40) experienced an injurious fall in line with our study’s definition. Table 2 Sociodemographic and health-related characteristics of the participants Variables Female ( n = 277) Male ( n = 156) Total ( N = 433) Age, yr, median (IQR) 75 ( 10 ) 76 (9.5) 76 ( 10 ) White , n (%) 262 (94.6) 126 (80.8) 388 (89.6) Living alone , n (%) 120 (43.5) 31 (19.9) 151 (35.0) Education , n (%) <Secondary school 16 (5.8) 9 (5.7) 25 (5.8) Secondary school graduation but no post- secondary education 26 (9.4) 15 (9.6) 41 (9.5) Some post-secondary education 51 (18.4) 19 (12.2) 70 (16.2) Post-secondary degree/diploma 184 (66.4) 113 (72.4) 297 (68.6) Marital status , n (%) Single 23 (8.5) 4 (2.8) 27 (6.5) Married/living with a partner in common-law relationship 133 (49.1) 116 (80.6) 249 (60.0) Widowed 76 (28.0) 17 (11.8) 93 (22.4) Divorced or separated 39 (14.4) 7 (4.9) 46 (11.1) BMI, kg/m 2 , mean (SD) 28.0 (6.2) 28.1 (4.3) 28.0 (5.6) Systolic blood pressure, mmHg, mean (SD) 136.5 (20.2) 133.9 (17.5) 135.6 (19.3) Diastolic blood pressure, mmHg, mean (SD) 74.9 (10.1) 75.0 (11.0) 74.9 (10.4) Smoking status , n (%) Current 9 (3.3) 3 (1.9) 12 (2.8) Previous 120 (43.3) 79 (50.6) 199 (46.0) Never 148 (53.4) 74 (47.4) 222 (51.3) Current alcohol consumption , n (%) 202 (72.9) 126 (80.8) 328 (75.8) Self-reported health status , n (%) Excellent/very good/good 250 (92.3) 128 (88.9) 378 (91.1) Fair/poor 21 (7.7) 16 (11.1) 37 (8.9) Self-reported mental health status , n (%) Excellent/very good/good 254 (93.7) 132 (92.3) 386 (93.2) Fair/poor 17 (6.3) 11 (7.7) 28 (6.8) Note : Values are presented as the median (IQR), mean (SD), or n (%). Abbreviations : n, number; IQR, interquartile range; SD, standard deviation; BMI, body mass index; BP, blood pressure. Selection and discriminative performance of the biological prediction models LASSO regression was performed using all the predefined biological variables for each fall outcome. From the initial pool of fifteen candidate variables, along with the forced covariates of age and sex, the selection process identified four predictors for the any fall outcome: mobility limitation, balance, visual acuity, and fear of falling. For the injurious fall outcome, five predictors were selected: TUG time, grip strength, executive function, global cognition, and pain interference. None of the predictors overlapped across the models. The reduced biological model showed modest discrimination for any falls (AUC 0.64, 95% CI 0.58–0.69), comparable to the full biological model (AUC 0.65, 95% CI 0.60–0.70). For injurious falls, both the reduced (AUC 0.73, 95% CI 0.64–0.82) and full models (AUC 0.76, 95% CI 0.68–0.84) demonstrated acceptable discrimination. The differences in the AUCs between the reduced and full models were not statistically significant (any falls: p = 0.22; injurious falls: p = 0.19). Multivariate analysis of LASSO-selected biological predictors The retained variables were entered into multivariable logistic regression analyses. Assumptions of logistic regression were assessed for all final models, and no major violations were identified. In the adjusted logistic regression model for any falls, poorer visual acuity (OR 6.64, 95% CI 1.40–31.56) and greater fear of falling (OR 1.04, 95% CI 1.01–1.08) were significantly associated with increased fall risk. For injurious falls, greater pain interference was associated with lower odds of sustaining an injurious fall (OR 0.74, 95% CI 0.57–0.96). Table 3 presents the adjusted odds ratios following penalized LASSO selection for both outcomes. No other biological variables demonstrated statistically significant associations with either fall outcome after adjustment for covariates. Table 3. Adjusted associations between LASSO-selected biological predictors and fall outcomes Predictor Any falls model (n = 433) OR (95% CI) Injurious falls model (n = 141) OR (95% CI) Constant 1.51 (0.07–34.23) 0.11 (0.00–262.92) Age (years) 0.99 (0.95–1.02) 1.03 (0.95–1.12) Female sex (ref = male) 1.23 (0.81–1.86) 0.75 (0.23–2.38) Preclinical mobility limitation 1.07 (0.87–1.31) – Balance 0.98 (0.93–1.03) – Better-eye acuity-logMAR 6.64 (1.40–31.56) – Fear of falling 1.04 (1.01–1.08) – TUG (s) – 1.12 (0.99–1.28) Grip strength (kg) – 0.95 (0.89–1.01) Executive function – 1.01 (0.99–1.02) Global cognition – 0.97 (0.84–1.13) Pain interference – 0.74 (0.57–0.96) Note: Complete case analysis. Significant predictors are indicated in bold. Abbreviations: OR, odds ratio; CI, confidence interval; TUG, Timed Up and Go; logMAR, logarithm of the minimum angle of resolution. Discriminative performance of the extended models To evaluate whether additional WHO domains improved discrimination, the biological base models (Model 1) were sequentially extended to include sociodemographic (Model 2), behavioral (Model 3), and environmental (Model 4) predictors for each outcome. For any falls, model performance improved only minimally, from an AUC of 0.64 in Model 1 to 0.65 (95% CI 0.60–0.71) in the fully extended Model 4. For injurious falls, discrimination increased from 0.73 to 0.78 (95% CI 0.69–0.86). However, none of the incremental improvements in the AUCs were statistically significant (any falls: p = 0.46; injurious falls: p = 0.30). The AUC values for all the models are presented in Figure 1 and Additional file 4: Table S2. Given the lack of significant improvement with the inclusion of additional domains, Model 1 for each outcome was retained as the final model. The full prediction models are presented in Additional file 5: Table S3. Model validation Internal validation was performed using the bootstrap method with 1,000 resamples. For any falls, Model 1 yielded a Brier score of 0.23 (95% CI 0.22–0.24), identical to Model 4. For injurious falls, Models 1 and 4 achieved Brier scores of 0.17 (95% CI 0.14–0.21) and 0.16 (95% CI 0.13–0.20), respectively. All models showed acceptable overall calibration and model fit across outcomes. Full bootstrap validation results are presented in Additional file 6: Table S4. Discussion This study sought to identify key biological predictors of fall risk in community-dwelling older adults and to examine whether expanding fall risk prediction models beyond biological factors to include sociodemographic, behavioral, and environmental domains improved discriminative performance. Our findings showed that biological-only models performed comparably to models incorporating additional WHO risk factor domains for both any fall and injurious fall outcomes. Predictor profiles differed by outcome, indicating outcome-specific patterns in fall-risk prediction. Additionally, several predictors identified in this study, notably visual acuity and fear of falling, are not routinely emphasized in current screening approaches. Although the biological models demonstrated only modest discrimination, their performance was consistent with that of previously published models from community-based samples. A recent systematic review of any fall prediction models reported AUCs ranging from 0.49 to 0.87, with a narrower range of 0.62 to 0.69 among models evaluated in independent samples (34). Notably, many developed models lacked internal validation (34), which may contribute to optimistic performance estimates at the upper end of this range. In this context, the discrimination observed in the present study aligns with that typically reported for validated, community-based models and reflects the inherent challenge of predicting heterogeneous any fall outcomes. In the present study, the injurious fall models demonstrated modestly higher discrimination than the any fall models; however, the addition of sociodemographic, behavioral, and environmental predictors resulted in only minimal, nonsignificant improvements in performance for both outcomes, despite their theoretical relevance and emphasis in global guidelines (5, 9). This pattern is consistent with prior studies reporting modestly greater discrimination for models predicting injurious falls, with AUCs of 0.75 for women and 0.77 for men (35). This suggests that injurious falls may represent a more homogeneous and clinically distinct outcome and may therefore be more amenable to prediction than any fall outcome. Nonetheless, the overall modest discriminative performance observed here aligns with broader evidence indicating that fall risk prediction models rarely achieve high discrimination, even when incorporating multiple domains (6, 34). However, cross-study comparisons should be interpreted cautiously, as differences in study design, population, and the measurement or operationalization of risk factors may contribute to variability in discriminative performance. The limited incremental contribution of nonbiological domains in this study should not be interpreted as evidence that these factors lack predictive relevance for falls. Rather, their contribution likely reflects how these constructs were operationalized, the limited availability of well-validated measures for several nonbiological domains, and the characteristics of the cohort. Few predictors represented each nonbiological domain, and some factors in the INITIATE data set were difficult to operationalize for model inclusion. For instance, available environmental measures, such as home hazard assessments, are structured as checklist-based indicators rather than continuous or composite scores (36). In addition, the relatively healthy, well-educated, and homogeneous nature of the sample may have further limited the variability in sociodemographic and behavioral factors. Prior work suggests that individuals with higher educational attainment often have greater health literacy and access to supportive resources (37, 38), which may mitigate the influence of contextual risk factors observed in more socioeconomically diverse populations. In this context, the comparable performance of the developed models supports the value of parsimonious modeling approaches. From a clinical perspective, focusing on a smaller set of well-measured biological predictors may enhance feasibility, interpretability, and implementation of screening tools without compromising predictive accuracy. Beyond overall model performance, predictor profiles differed meaningfully by fall outcome, highlighting the importance of outcome definition in fall-risk modeling. For any falls, LASSO-selected predictors reflected a profile dominated by sensory function and psychological vulnerability (i.e., poorer visual acuity and greater fear of falling) whereas injurious falls were characterized by a more impairment-driven biological profile, including markers of reduced physical capacity, cognitive performance, and pain-related burden. Although both outcomes were biologically driven, the lack of overlap between predictor sets supports growing evidence that any falls and injurious falls represent related but distinct outcomes (39, 40). This distinction suggests that screening and prevention strategies may need to be tailored according to the specific fall outcome of interest rather than relying on a single, unified risk profile. Several predictors identified in this study diverged from those emphasized in commonly used screening tools. Performance-based measures that primarily assess physiological and functional aspects of mobility and postural stability, such as gait speed and balance, and are commonly prioritized in clinical practice and international guidelines (9, 41), were not independently associated with fall outcomes. Instead, visual acuity and fear of falling emerged as key predictors of any falls. While consistent with prior evidence linking sensory impairment and fear-related adaptations to fall risk (42, 43), these results suggest that mobility-focused screening alone may overlook important contributors to fall risk, particularly among active, community-dwelling older adults. This highlights the value of considering sensory and psychological dimensions alongside performance-based measures when evaluating the risk of any falls. Consistent with this broader interpretation, the association between visual acuity and any falls was characterized by a large odds ratio and a wide confidence interval, indicating substantial uncertainty around the effect estimate. This imprecision likely reflects a combination of limited representation at poorer levels of visual acuity, the scaling properties of the logMAR metric (where a one-unit change represents a clinically large decrement in vision) and unmeasured variability in the causes and functional consequences of vision impairment that are not captured by visual acuity alone. Accordingly, while the magnitude of the observed effect should be interpreted cautiously, the direction of the association aligns with prior evidence linking poorer vision to increased fall risk (42). Similarly, careful interpretation is warranted for the inverse association observed between pain interference and injurious falls. Although this finding diverges from studies reporting higher risk of injurious falls among older adults with pain (44, 45), it may reflect behavioral adaptation, whereby individuals experiencing greater pain modify or limit their participation in activities or environments associated with greater injury risk. Such adaptations, whether due to discomfort, reduced confidence, or functional limitation, may reduce exposure to hazardous situations and help explain why greater pain interference was associated with fewer injurious falls in this cohort. This pattern suggests that biological indicators may influence fall risk through accompanying behavioral and environmental pathways, reinforcing the value of multidomain assessment when evaluating fall risk. Strengths and limitations This study’s key strength is the comparison of fall risk prediction across the four WHO risk-factor domains, which, to the authors’ knowledge, has not been previously applied. Additional strengths include the use of penalized regression to reduce overfitting, a large community-based sample, standardized fall definitions, and the inclusion of a broad set of candidate predictors not commonly incorporated in prior models (46). Modeling both any falls and injurious falls, the latter of which is less frequently examined (34, 47), provides complementary insight into outcome-specific patterns of fall risk. Several limitations should be acknowledged. The cross-sectional design and retrospective fall reporting preclude causal inference and may introduce recall bias, while reliance on self-reported measures may be subject to measurement error. The representation of non-biological domains was constrained by data availability and challenges in operationalizing certain constructs. The relatively healthy, community-dwelling sample also limits generalizability to frailer or institutionalized populations. Findings should be extrapolated cautiously to differing socioeconomic and cultural contexts. Future research should prioritize prospective collection of fall outcomes, external validation in diverse populations, and the incorporation of a broader range of nonbiological constructs. Alternative measurement approaches, such as device-based and ecological momentary approaches, may enhance the ability to capture fall-related determinants and improve the prediction of any falls. Conclusion Falls remain a major health concern for older adults, highlighting the need for effective and feasible screening approaches. This study demonstrates that parsimonious, biologically-based models can meaningfully discriminate fall risk and perform comparably to more complex multidomain models in community settings. Given the differences in risk profiles by fall outcome, clinicians should recognize that a “one-size-fits-all” approach is unlikely to be adequate. Prevention strategies should be tailored to the fall type to enhance clinical relevance and impact. Declarations Ethics approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki. Ethics approval was obtained from the Hamilton Integrated Research Ethics Board (HiREB; #7380), and all participants provided informed consent prior to participation. Funding This work was supported by Canadian Institutes of Health Research grant number PJT-165844. Authors' contributions FE: conceptualized the study, conducted the statistical analyses, interpreted the data, and drafted the initial manuscript. SS: helped to conceptualize the study, acquired the data, interpreted the data, revised the manuscript, and co-supervised the first author. CD: helped to conceptualize the study, acquired the data, interpreted the data, revised the manuscript, and co-supervised the first author. 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Sex Differences in the Association Between Pain and Injurious Falls in Older Adults: A Population-Based Longitudinal Study. Am J Epidemiol. 2017;186(9):1049–56. Vatcheva KP, Lee M, McCormick JB, Rahbar MH. Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies. Epidemiol (Sunnyvale). 2016;6(2). Delacour H, Servonnet A, Perrot A, Vigezzi JF, Ramirez JM. [ROC (receiver operating characteristics) curve: principles and application in biology]. Ann Biol Clin (Paris). 2005;63(2):145–54. Çorbacıoğlu ŞK, Aksel G. Receiver operating characteristic curve analysis in diagnostic accuracy studies: A guide to interpreting the area under the curve value. Turk J Emerg Med. 2023;23(4):195–8. de Hond AAH, Steyerberg EW, van Calster B. Interpreting area under the receiver operating characteristic curve. Lancet Digit Health. 2022;4(12):e853–5. Zou L, Choi YH, Guizzetti L, Shu D, Zou J, Zou G. Extending the DeLong algorithm for comparing areas under correlated receiver operating characteristic curves with missing data. Stat Med. 2024;43(21):4148–62. Austin PC, Steyerberg EW. Events per variable (EPV) and the relative performance of different strategies for estimating the out-of-sample validity of logistic regression models. Stat Methods Med Res. 2017;26(2):796–808. Steyerberg EW, Vickers AJ, Cook NR, Gerds T, Gonen M, Obuchowski N, et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology. 2010;21(1):128–38. Dormosh N, van de Loo B, Heymans MW, Schut MC, Medlock S, van Schoor NM et al. A systematic review of fall prediction models for community-dwelling older adults: comparison between models based on research cohorts and models based on routinely collected data. Age Ageing. 2024;53(7). Ek S, Rizzuto D, Calderón-Larrañaga A, Franzén E, Xu W, Welmer AK. Predicting First-Time Injurious Falls in Older Men and Women Living in the Community: Development of the First Injurious Fall Screening Tool. J Am Med Dir Assoc. 2019;20(9):1163–e83. Prevention CfDCa. Check for Safety: A home fall prevention checklist for older adults. Centers for Disease Control and Prevention; 2015. Bayati T, Dehghan A, Bonyadi F, Bazrafkan L. Investigating the effect of education on health literacy and its relation to health-promoting behaviors in health center. J Educ Health Promot. 2018;7:127. Cho YI, Lee S-YD, Arozullah AM, Crittenden KS. Effects of health literacy on health status and health service utilization amongst the elderly. Soc Sci Med. 2008;66(8):1809–16. Ek S, Rizzuto D, Fratiglioni L, Johnell K, Xu W, Welmer AK. Risk Profiles for Injurious Falls in People Over 60: A Population-Based Cohort Study. J Gerontol Biol Sci Med Sci. 2018;73(2):233–9. Poh FJX, Shorey S. A Literature Review of Factors Influencing Injurious Falls. Clin Nurs Res. 2018;29(3):141–8. Additional Declarations No competing interests reported. Supplementary Files Additionalfiles.docx Cite Share Download PDF Status: Published Journal Publication published 06 Apr, 2026 Read the published version in BMC Geriatrics → Version 1 posted Editorial decision: Revision requested 03 Mar, 2026 Reviews received at journal 03 Mar, 2026 Reviews received at journal 26 Feb, 2026 Reviewers agreed at journal 23 Feb, 2026 Reviewers agreed at journal 22 Feb, 2026 Reviewers invited by journal 04 Feb, 2026 Editor assigned by journal 29 Jan, 2026 Editor invited by journal 28 Jan, 2026 Submission checks completed at journal 27 Jan, 2026 First submitted to journal 27 Jan, 2026 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. 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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-8681033","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587745881,"identity":"54f0bcd2-d4d3-4ff6-831d-3fab222ff987","order_by":0,"name":"Fajr Elbanna","email":"","orcid":"","institution":"McMaster University","correspondingAuthor":false,"prefix":"","firstName":"Fajr","middleName":"","lastName":"Elbanna","suffix":""},{"id":587745882,"identity":"72d67bf7-ee77-401b-9cca-d33b2b8e0c31","order_by":1,"name":"Stephanie Saunders","email":"","orcid":"","institution":"McMaster University","correspondingAuthor":false,"prefix":"","firstName":"Stephanie","middleName":"","lastName":"Saunders","suffix":""},{"id":587745883,"identity":"0a002499-7553-4674-86c2-bac03340a227","order_by":2,"name":"Cassandra D’Amore","email":"","orcid":"","institution":"University of British Columbia","correspondingAuthor":false,"prefix":"","firstName":"Cassandra","middleName":"","lastName":"D’Amore","suffix":""},{"id":587745884,"identity":"66244963-ca60-4d30-8c2b-c22bc5b07843","order_by":3,"name":"Marla K Beauchamp","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYBACAwglIUeyFgtjCJ1AvJaKxAaitZizNz+T+Jkjkb62/XTix58/bPIZ2A8/wKvFsueYmWTvNoncbWdyN0vzJKRZNvCkGeB32I0EM2lGkJYbvBukGRIOGzBIMBDQcv/5N5CWdLMbvJt//kj4D9TC/oGALTxgWxKAWrZJ8CQcAGrhwW+LZU9OsSXQL4ZAv2yz5klLNmDjySnAq8Wc/fjGGz+31cmbHT+7+eYPGzsDfvbjG/BqwQRsJKofBaNgFIyCUYAFAAB2o0JvgsoZSQAAAABJRU5ErkJggg==","orcid":"","institution":"McMaster University","correspondingAuthor":true,"prefix":"","firstName":"Marla","middleName":"K","lastName":"Beauchamp","suffix":""}],"badges":[],"createdAt":"2026-01-23 16:11:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8681033/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8681033/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12877-026-07417-7","type":"published","date":"2026-04-06T15:58:15+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":102238499,"identity":"63a153f8-2e2b-419d-8b89-9cc2b46d3535","added_by":"auto","created_at":"2026-02-09 16:40:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":22052,"visible":true,"origin":"","legend":"\u003cp\u003eParsimony plot for any fall and injurious fall prediction models\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8681033/v1/57259aa604a113396e84f139.png"},{"id":106809269,"identity":"39fa7d01-30a0-49d3-8329-bd491e70db49","added_by":"auto","created_at":"2026-04-13 16:08:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":975525,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8681033/v1/9a831a9e-0812-48e8-b96e-c5c0154b3600.pdf"},{"id":102238501,"identity":"2a4773fa-3d83-4055-bf21-5f888464aa5c","added_by":"auto","created_at":"2026-02-09 16:40:51","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":336269,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfiles.docx","url":"https://assets-eu.researchsquare.com/files/rs-8681033/v1/0310b921cd790faaa8e1f4c6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development of fall prediction models in community-dwelling older adults: comparison of biological and multidomain models for any and injurious falls","fulltext":[{"header":"Background","content":"\u003cp\u003eEach year, approximately 23\u0026ndash;30% of community-dwelling older adults experience a fall (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), and approximately one-third of those fallers sustain injuries requiring medical attention (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Globally, falls are the second leading cause of unintentional injury-related deaths and the leading cause of death among adults aged 70 years and older (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). In Canada alone, falls account for more than 65% of injury-related hospitalizations among adults aged 65\u0026ndash;74 years and over 80% among those aged 75 years and older, resulting in an estimated \u003cspan\u003e$\u003c/span\u003e10.3\u0026nbsp;billion annually in direct and indirect healthcare costs (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). These individual and societal consequences underscore the need for early identification of high-risk individuals to guide effective prevention strategies and alleviate the burden on healthcare systems.\u003c/p\u003e \u003cp\u003eAccurately predicting who will fall remains a challenge because fall risk is inherently multifactorial and shaped by biological, sociodemographic, behavioral, and environmental factors (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Despite this multifactorial etiology, most fall risk prediction models have focused on a relatively narrow subset of predictors, largely emphasizing mobility- and performance-based biological measures, and may overlook the broader determinants that influence fall occurrence in real-world settings (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis narrow focus is also evident in clinical practice and international guidelines, where fall risk assessment commonly prioritizes gait, balance, and functional performance (\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). This focus is justified, as impairments in these capacities are strong and well-established risk factors for falls (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). However, fall risk profiles are not uniform across the older adult population. Among community-dwelling older adults, fall risk can be shaped not only by biological impairment but also by living circumstances, everyday behaviors, and exposure to potentially hazardous environments, which are often not observed during clinic-based assessments (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Thus, while functional assessments remain essential, their predominant biological focus likely provides only a partial representation of fall risk.\u003c/p\u003e \u003cp\u003eImportantly, contemporary frameworks such as the WHO fall risk model emphasize that biological risk extends beyond mobility alone and that social, behavioral, and environmental domains also contribute meaningfully to fall risk (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). While these broader constructs are increasingly recognized conceptually, relatively few studies have examined whether incorporating them into fall-risk prediction models provides meaningful additive value beyond biological predictors alone (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Accordingly, the aim of this paper is to examine whether fall risk among community-dwelling older adults is influenced by factors beyond biological ones. Specifically, we aimed to (i) determine the extent to which biological predictors are associated with the retrospective 12-month incidence of any falls and injurious falls and (ii) examine whether the inclusion of sociodemographic, behavioral, and environmental factors improves discriminative performance compared with a biological-only model.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study was reported in accordance with the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) Statement (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The completed checklist is provided in Additional file 1.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and population\u003c/h2\u003e \u003cp\u003eThis cross-sectional analysis used baseline data from the Initial Test for Fall Risk Assessment in the Elderly (INITIATE) cohort. Community-dwelling older adults aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years were recruited between March 2020 and September 2023. Recruitment paused during the COVID-19 pandemic and subsequently resumed in March 2022. All baseline data collection was completed by October 2024. Baseline data used in this analysis were collected as part of the INITIATE prospective cohort using standardized questionnaires and clinical assessments administered by trained research staff, as previously described (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e Participants were eligible if they were community-dwelling, English speaking, able to walk 10 meters without physical assistance, and capable of providing informed consent. The exclusion criteria included severe visual or hearing impairments, insufficient English proficiency, or cognitive impairment precluding written consent.\u003c/p\u003e \u003cp\u003eParticipants completed a baseline assessment, during which retrospective fall data from the preceding 12 months were collected. Predictor and outcome data were collected at baseline via telephone or online surveys and a two-hour, in-person assessment of clinical and performance-based measures. The outcome assessors were not blinded to the participants\u0026rsquo; retrospective fall status; however, they were trained in standardized assessment administration and followed predefined protocols. Ethical approval was obtained from the Hamilton Integrated Research Ethics Board (#7380).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003eTwo dichotomous fall outcomes were defined based on self-reported falls in the 12 months preceding baseline: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) any falls, indicating the occurrence of one or more falls, and (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) injurious falls, defined as a fall requiring medical attention from a health professional within 48 hours (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). As per the Prevention of Falls Network Europe recommendations (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), a fall was defined as \u0026ldquo;an unexpected event in which the individual comes to rest on the ground, floor, or lower level.\u0026rdquo; All eligible falls were retained, including recurrent falls and falls occurring during sports or recreational activities, as each fall has the potential to contribute to and perpetuate a cycle of increased fall risk (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The outcomes were coded as 0\u0026thinsp;=\u0026thinsp;no and 1\u0026thinsp;=\u0026thinsp;yes.\u003c/p\u003e\n\u003ch3\u003ePredictors\u003c/h3\u003e\n\u003cp\u003eCandidate predictors were selected \u003cem\u003ea priori\u003c/em\u003e to reflect the multifactorial nature of fall risk and to minimize selection bias (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). The selection of predictors was informed by existing literature (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), clinical applicability, feasibility of administration, and relevance to geriatric and rehabilitation practice. The predictors were grouped according to the WHO\u0026rsquo;s four-domain fall-risk model (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e):\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eBiological\u003c/b\u003e: frailty, multimorbidity, mobility limitation, Timed Up and Go (TUG) time, balance, grip strength, vision, hearing, sensory loss, global cognition, executive function, number of fall risk\u0026ndash;increasing drugs (FRIDs), pain interference, fear of falling, and depression.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSociodemographic\u003c/b\u003e: education, living alone, social support.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eBehavioral\u003c/b\u003e: participation in strengthening exercise and nutritional risk.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eEnvironmental\u003c/b\u003e: neighborhood walkability.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eIn total, 23 predictors, corresponding to 23 degrees of freedom, were available for model building. Operational definitions, measurement details, and questionnaire sources are provided in Additional file 2: Table S1.\u003c/p\u003e\n\u003ch3\u003eSample size\u003c/h3\u003e\n\u003cp\u003eThe sample size was determined by the primary objective of the parent study, the INITIATE cohort. No \u003cem\u003ea priori\u003c/em\u003e sample size calculation was performed for the present analysis; however, concerns regarding events per variable (EPV) were mitigated by the use of LASSO penalization. All participants who completed the baseline assessment and provided retrospective data on falls over the preceding 12 months were eligible for inclusion in the analytic sample (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eSample characteristics were summarized by sex (female, male) using means and standard deviations (SDs) for normally distributed variables, medians and interquartile ranges (IQRs) for skewed variables, and frequencies and relative percentages for categorical variables (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Missing data were handled via complete case analysis, whereby only participants with complete predictor and outcome data were included in the models.\u003c/p\u003e \u003cp\u003eTo identify the most informative biological predictors, the least absolute shrinkage and selection operator (LASSO) was applied with 10-fold cross-validation (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). LASSO was chosen to address the high dimensionality of the predictor set, reduce multicollinearity, and mitigate overfitting by shrinking small coefficients toward zero (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Predictors with nonzero coefficients were retained to form the biological base model for each outcome. Subsequent logistic regression models were then constructed to reflect the domains described by the WHO risk factor framework and evaluate the added discriminative contribution of nonbiological domains. LASSO retained biological predictors were entered into Tier 1, with age (continuous) (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) and sex (female/male) (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) forced into all models due to their known confounding effects. Sociodemographic variables were added in Tier 2, behavioral variables in Tier 3, and an environmental variable in Tier 4. Interaction terms were not examined due to the limited number of outcome events. The full list of variables considered at each tier is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eFor the final adjusted models, assumptions of logistic regression were assessed. The linearity of continuous predictors with the log-odds of the outcome was assessed visually. Multicollinearity was assessed via the calculation of variance inflation factors (VIFs); a VIF\u0026thinsp;\u0026gt;\u0026thinsp;5 was regarded as indicating serious multicollinearity (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Influential observations were identified using leverage and standardized residual plots. The results are presented as adjusted odds ratios (ORs) with 95% confidence intervals (CIs). Statistical significance was set at a two-sided \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eModel discrimination was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), with higher values indicating better discrimination. AUC values were interpreted descriptively, with values around 0.6\u0026ndash;0.7 reflecting poorer discrimination, 0.7\u0026ndash;0.8 fair to acceptable discrimination, and higher values indicating stronger performance (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). AUC differences between models were tested using Stata\u0026rsquo;s ROCCOMP command, which applies DeLong\u0026rsquo;s method for comparing correlated ROC curves (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Internal validation was performed using bootstrap resampling with 1,000 iterations, to assess the stability of model performance estimates and reduce optimism in classification results (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Model calibration was assessed using the Brier score, with lower values indicating better calibration (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). All analyses were performed using Stata/SE 18.5 (StataCorp LLC, College Station, TX).\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\u003eDesign of tiered prediction models for falls and injurious falls\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndependent Variables\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTier 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge, sex, biological predictors retained by LASSO\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTier 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTier 1\u0026thinsp;+\u0026thinsp;education, living alone, social support\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTier 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTier 2\u0026thinsp;+\u0026thinsp;participation in strengthening exercise, nutrition risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTier 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTier 3\u0026thinsp;+\u0026thinsp;neighborhood walkability\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003eParticipant characteristics\u003c/h2\u003e\n\u003cp\u003eAmong the 514 participants who consented to participate (median age\u0026thinsp;=\u0026thinsp;76 years, IQR\u0026thinsp;=\u0026thinsp;10; 63.8% female), the outcome data were complete for all participants. Eighty-one participants were excluded due to missing predictor data, yielding a final analytic sample of 433 participants (median age\u0026thinsp;=\u0026thinsp;76 years, IQR\u0026thinsp;=\u0026thinsp;10; 64.0% female). Missing predictor data were low overall, with no single variable exceeding 5% missingness (Additional file 3: Figure S1). The sociodemographic, lifestyle, and health-related characteristics of the analytic sample are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Nearly half of the participants (44.8%, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;194) reported at least one fall in the preceding year. Among fallers, 28.4% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;40) experienced an injurious fall in line with our study\u0026rsquo;s definition.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eSociodemographic and health-related characteristics of the participants\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;277)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;156)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;433)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAge, yr, median (IQR)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e75 (\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e76 (9.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e76 (\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eWhite\u003c/strong\u003e, \u003cstrong\u003en\u003c/strong\u003e \u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e262 (94.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e126 (80.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e388 (89.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLiving alone\u003c/strong\u003e, \u003cstrong\u003en\u003c/strong\u003e \u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e120 (43.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e31 (19.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e151 (35.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e, \u003cstrong\u003en\u003c/strong\u003e \u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;Secondary school\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16 (5.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9 (5.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25 (5.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSecondary school graduation but no post- secondary education\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26 (9.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e15 (9.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41 (9.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSome post-secondary education\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51 (18.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e19 (12.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e70 (16.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePost-secondary degree/diploma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e184 (66.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e113 (72.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e297 (68.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e, \u003cstrong\u003en\u003c/strong\u003e \u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSingle\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23 (8.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4 (2.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27 (6.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMarried/living with a partner in common-law relationship\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e133 (49.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e116 (80.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e249 (60.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWidowed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e76 (28.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17 (11.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e93 (22.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDivorced or separated\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39 (14.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7 (4.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46 (11.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eBMI, kg/m\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e, \u003cstrong\u003emean (SD)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.0 (6.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e28.1 (4.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.0 (5.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSystolic blood pressure, mmHg, mean (SD)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e136.5 (20.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e133.9 (17.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e135.6 (19.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eDiastolic blood pressure, mmHg, mean (SD)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e74.9 (10.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e75.0 (11.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e74.9 (10.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSmoking status\u003c/strong\u003e, \u003cstrong\u003en\u003c/strong\u003e \u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCurrent\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9 (3.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3 (1.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (2.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrevious\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e120 (43.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e79 (50.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e199 (46.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNever\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e148 (53.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e74 (47.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e222 (51.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCurrent alcohol consumption\u003c/strong\u003e, \u003cstrong\u003en\u003c/strong\u003e \u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e202 (72.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e126 (80.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e328 (75.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSelf-reported health status\u003c/strong\u003e, \u003cstrong\u003en\u003c/strong\u003e \u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExcellent/very good/good\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e250 (92.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e128 (88.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e378 (91.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFair/poor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21 (7.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e16 (11.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37 (8.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSelf-reported mental health status\u003c/strong\u003e, \u003cstrong\u003en\u003c/strong\u003e \u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExcellent/very good/good\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e254 (93.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e132 (92.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e386 (93.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFair/poor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17 (6.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11 (7.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28 (6.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\"\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: Values are presented as the median (IQR), mean (SD), or n (%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: n, number; IQR, interquartile range; SD, standard deviation; BMI, body mass index; BP, blood pressure.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSelection and discriminative performance of the biological prediction models\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLASSO regression was performed using all the predefined biological variables for each fall outcome. From the initial pool of fifteen candidate variables, along with the forced covariates of age and sex, the selection process identified four predictors for the any fall outcome: mobility limitation, balance, visual acuity, and fear of falling. For the injurious fall outcome, five predictors were selected: TUG time, grip strength, executive function, global cognition, and pain interference. None of the predictors overlapped across the models.\u003c/p\u003e\n\u003cp\u003eThe reduced biological model showed modest discrimination for any falls (AUC 0.64, 95% CI 0.58\u0026ndash;0.69), comparable to the full biological model (AUC 0.65, 95% CI 0.60\u0026ndash;0.70). For injurious falls, both the reduced (AUC 0.73, 95% CI 0.64\u0026ndash;0.82) and full models (AUC 0.76, 95% CI 0.68\u0026ndash;0.84) demonstrated acceptable discrimination. The differences in the AUCs between the reduced and full models were not statistically significant (any falls: \u003cem\u003ep\u003c/em\u003e = 0.22; injurious falls: \u003cem\u003ep \u003c/em\u003e= 0.19).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultivariate analysis of LASSO-selected biological predictors \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe retained variables were entered into multivariable logistic regression analyses. Assumptions of logistic regression were assessed for all final models, and no major violations were identified. In the adjusted logistic regression model for any falls, poorer visual acuity (OR 6.64, 95% CI 1.40\u0026ndash;31.56) and greater fear of falling (OR 1.04, 95% CI 1.01\u0026ndash;1.08) were significantly associated with increased fall risk. For injurious falls, greater pain interference was associated with lower odds of sustaining an injurious fall (OR 0.74, 95% CI 0.57\u0026ndash;0.96). Table 3 presents the adjusted odds ratios following penalized LASSO selection for both outcomes. No other biological variables demonstrated statistically significant associations with either fall outcome after adjustment for covariates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. \u003c/strong\u003eAdjusted associations between LASSO-selected biological predictors and fall outcomes\u003c/p\u003e\n\u003ctable width=\"633\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003e\u003cstrong\u003ePredictor\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"187\"\u003e\n\u003cp\u003e\u003cstrong\u003eAny falls model (n = 433) \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e\u003cstrong\u003eInjurious falls model (n = 141) \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eConstant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"187\"\u003e\n\u003cp\u003e1.51 (0.07\u0026ndash;34.23)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e0.11 (0.00\u0026ndash;262.92)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eAge (years)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"187\"\u003e\n\u003cp\u003e0.99 (0.95\u0026ndash;1.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e1.03 (0.95\u0026ndash;1.12)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eFemale sex (ref = male)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"187\"\u003e\n\u003cp\u003e1.23 (0.81\u0026ndash;1.86)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e0.75 (0.23\u0026ndash;2.38)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003ePreclinical mobility limitation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"187\"\u003e\n\u003cp\u003e1.07 (0.87\u0026ndash;1.31)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e\u0026ndash;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eBalance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"187\"\u003e\n\u003cp\u003e0.98 (0.93\u0026ndash;1.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e\u0026ndash;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eBetter-eye acuity-logMAR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"187\"\u003e\n\u003cp\u003e\u003cstrong\u003e6.64 (1.40\u0026ndash;31.56)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e\u0026ndash;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eFear of falling\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"187\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.04 (1.01\u0026ndash;1.08)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e\u0026ndash;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eTUG (s)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"187\"\u003e\n\u003cp\u003e\u0026ndash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e1.12 (0.99\u0026ndash;1.28)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eGrip strength (kg)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"187\"\u003e\n\u003cp\u003e\u0026ndash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e0.95 (0.89\u0026ndash;1.01)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eExecutive function\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"187\"\u003e\n\u003cp\u003e\u0026ndash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e1.01 (0.99\u0026ndash;1.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eGlobal cognition\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"187\"\u003e\n\u003cp\u003e\u0026ndash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e0.97 (0.84\u0026ndash;1.13)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003ePain interference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"187\"\u003e\n\u003cp\u003e\u0026ndash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"214\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.74 (0.57\u0026ndash;0.96)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote: \u003c/strong\u003eComplete case analysis. Significant predictors are indicated in bold.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations: \u003c/strong\u003eOR, odds ratio; CI, confidence interval; TUG, Timed Up and Go; logMAR, logarithm of the minimum angle of resolution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscriminative performance of the extended models\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate whether additional WHO domains improved discrimination, the biological base models (Model 1) were sequentially extended to include sociodemographic (Model 2), behavioral (Model 3), and environmental (Model 4) predictors for each outcome.\u003c/p\u003e\n\u003cp\u003eFor any falls, model performance improved only minimally, from an AUC of 0.64 in Model 1 to 0.65 (95% CI 0.60\u0026ndash;0.71) in the fully extended Model 4. For injurious falls, discrimination increased from 0.73 to 0.78 (95% CI 0.69\u0026ndash;0.86). However, none of the incremental improvements in the AUCs were statistically significant (any falls: \u003cem\u003ep\u003c/em\u003e = 0.46; injurious falls: \u003cem\u003ep\u003c/em\u003e = 0.30). The AUC values for all the models are presented in Figure 1 and Additional file 4: Table S2. Given the lack of significant improvement with the inclusion of additional domains, Model 1 for each outcome was retained as the final model. The full prediction models are presented in Additional file 5: Table S3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel validation \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInternal validation was performed using the bootstrap method with 1,000 resamples. For any falls, Model 1 yielded a Brier score of 0.23 (95% CI 0.22\u0026ndash;0.24), identical to Model 4. For injurious falls, Models 1 and 4 achieved Brier scores of 0.17 (95% CI 0.14\u0026ndash;0.21) and 0.16 (95% CI 0.13\u0026ndash;0.20), respectively. All models showed acceptable overall calibration and model fit across outcomes. Full bootstrap validation results are presented in Additional file 6: Table S4.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study sought to identify key biological predictors of fall risk in community-dwelling older adults and to examine whether expanding fall risk prediction models beyond biological factors to include sociodemographic, behavioral, and environmental domains improved discriminative performance. Our findings showed that biological-only models performed comparably to models incorporating additional WHO risk factor domains for both any fall and injurious fall outcomes. Predictor profiles differed by outcome, indicating outcome-specific patterns in fall-risk prediction. Additionally, several predictors identified in this study, notably visual acuity and fear of falling, are not routinely emphasized in current screening approaches.\u003c/p\u003e\n\u003cp\u003eAlthough the biological models demonstrated only modest discrimination, their performance was consistent with that of previously published models from community-based samples. A recent systematic review of any fall prediction models reported AUCs ranging from 0.49 to 0.87, with a narrower range of 0.62 to 0.69 among models evaluated in independent samples (34). Notably, many developed models lacked internal validation (34), which may contribute to optimistic performance estimates at the upper end of this range. In this context, the discrimination observed in the present study aligns with that typically reported for validated, community-based models and reflects the inherent challenge of predicting heterogeneous any fall outcomes.\u003c/p\u003e\n\u003cp\u003eIn the present study, the injurious fall models demonstrated modestly higher discrimination than the any fall models; however, the addition of sociodemographic, behavioral, and environmental predictors resulted in only minimal, nonsignificant improvements in performance for both outcomes, despite their theoretical relevance and emphasis in global guidelines (5, 9). This pattern is consistent with prior studies reporting modestly greater discrimination for models predicting injurious falls, with AUCs of 0.75 for women and 0.77 for men (35). This suggests that injurious falls may represent a more homogeneous and clinically distinct outcome and may therefore be more amenable to prediction than any fall outcome. Nonetheless, the overall modest discriminative performance observed here aligns with broader evidence indicating that fall risk prediction models rarely achieve high discrimination, even when incorporating multiple domains (6, 34). However, cross-study comparisons should be interpreted cautiously, as differences in study design, population, and the measurement or operationalization of risk factors may contribute to variability in discriminative performance.\u003c/p\u003e\n\u003cp\u003eThe limited incremental contribution of nonbiological domains in this study should not be interpreted as evidence that these factors lack predictive relevance for falls. Rather, their contribution likely reflects how these constructs were operationalized, the limited availability of well-validated measures for several nonbiological domains, and the characteristics of the cohort. Few predictors represented each nonbiological domain, and some factors in the INITIATE data set were difficult to operationalize for model inclusion. For instance, available environmental measures, such as home hazard assessments, are structured as checklist-based indicators rather than continuous or composite scores (36). In addition, the relatively healthy, well-educated, and homogeneous nature of the sample may have further limited the variability in sociodemographic and behavioral factors. Prior work suggests that individuals with higher educational attainment often have greater health literacy and access to supportive resources (37, 38), which may mitigate the influence of contextual risk factors observed in more socioeconomically diverse populations. In this context, the comparable performance of the developed models supports the value of parsimonious modeling approaches. From a clinical perspective, focusing on a smaller set of well-measured biological predictors may enhance feasibility, interpretability, and implementation of screening tools without compromising predictive accuracy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBeyond overall model performance, predictor profiles differed meaningfully by fall outcome, highlighting the importance of outcome definition in fall-risk modeling. For any falls, LASSO-selected predictors reflected a profile dominated by sensory function and psychological vulnerability (i.e., poorer visual acuity and greater fear of falling) whereas injurious falls were characterized by a more impairment-driven biological profile, including markers of reduced physical capacity, cognitive performance, and pain-related burden. Although both outcomes were biologically driven, the lack of overlap between predictor sets supports growing evidence that any falls and injurious falls represent related but distinct outcomes (39, 40). This distinction suggests that screening and prevention strategies may need to be tailored according to the specific fall outcome of interest rather than relying on a single, unified risk profile.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSeveral predictors identified in this study diverged from those emphasized in commonly used screening tools. Performance-based measures that primarily assess physiological and functional aspects of mobility and postural stability, such as gait speed and balance, and are commonly prioritized in clinical practice and international guidelines (9, 41), were not independently associated with fall outcomes. Instead, visual acuity and fear of falling emerged as key predictors of any falls. While consistent with prior evidence linking sensory impairment and fear-related adaptations to fall risk (42, 43), these results suggest that mobility-focused screening alone may overlook important contributors to fall risk, particularly among active, community-dwelling older adults. This highlights the value of considering sensory and psychological dimensions alongside performance-based measures when evaluating the risk of any falls.\u003c/p\u003e\n\u003cp\u003eConsistent with this broader interpretation, the association between visual acuity and any falls was characterized by a large odds ratio and a wide confidence interval, indicating substantial uncertainty around the effect estimate. This imprecision likely reflects a combination of limited representation at poorer levels of visual acuity, the scaling properties of the logMAR metric (where a one-unit change represents a clinically large decrement in vision) and unmeasured variability in the causes and functional consequences of vision impairment that are not captured by visual acuity alone. Accordingly, while the magnitude of the observed effect should be interpreted cautiously, the direction of the association aligns with prior evidence linking poorer vision to increased fall risk (42).\u003c/p\u003e\n\u003cp\u003eSimilarly, careful interpretation is warranted for the inverse association observed between pain interference and injurious falls. Although this finding diverges from studies reporting higher risk of injurious falls among older adults with pain (44, 45), it may reflect behavioral adaptation, whereby individuals experiencing greater pain modify or limit their participation in activities or environments associated with greater injury risk. Such adaptations, whether due to discomfort, reduced confidence, or functional limitation, may reduce exposure to hazardous situations and help explain why greater pain interference was associated with fewer injurious falls in this cohort. This pattern suggests that biological indicators may influence fall risk through accompanying behavioral and environmental pathways, reinforcing the value of multidomain assessment when evaluating fall risk.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths and limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study’s key strength is the comparison of fall risk prediction across the four WHO risk-factor domains, which, to the authors’ knowledge, has not been previously applied. Additional strengths include the use of penalized regression to reduce overfitting, a large community-based sample, standardized fall definitions, and the inclusion of a broad set of candidate predictors not commonly incorporated in prior models (46). Modeling both any falls and injurious falls, the latter of which is less frequently examined (34, 47), provides complementary insight into outcome-specific patterns of fall risk.\u003c/p\u003e\n\u003cp\u003eSeveral limitations should be acknowledged. The cross-sectional design and retrospective fall reporting preclude causal inference and may introduce recall bias, while reliance on self-reported measures may be subject to measurement error. The representation of non-biological domains was constrained by data availability and challenges in operationalizing certain constructs. The relatively healthy, community-dwelling sample also limits generalizability to frailer or institutionalized populations. Findings should be extrapolated cautiously to differing socioeconomic and cultural contexts.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFuture research should prioritize prospective collection of fall outcomes, external validation in diverse populations, and the incorporation of a broader range of nonbiological constructs. Alternative measurement approaches, such as device-based and ecological momentary approaches, may enhance the ability to capture fall-related determinants and improve the prediction of any falls.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eFalls remain a major health concern for older adults, highlighting the need for effective and feasible screening approaches. This study demonstrates that parsimonious, biologically-based models can meaningfully discriminate fall risk and perform comparably to more complex multidomain models in community settings. Given the differences in risk profiles by fall outcome, clinicians should recognize that a “one-size-fits-all” approach is unlikely to be adequate. Prevention strategies should be tailored to the fall type to enhance clinical relevance and impact.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cu\u003eEthics approval and consent to participate\u003c/u\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki. Ethics approval was obtained from the Hamilton Integrated Research Ethics Board (HiREB; #7380), and all participants provided informed consent prior to participation.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eFunding\u003c/u\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis work was supported by Canadian Institutes of Health Research grant number PJT-165844.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAuthors' contributions\u003c/u\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFE: conceptualized the study, conducted the statistical analyses, interpreted the data, and drafted the initial manuscript.\u003c/p\u003e\n\u003cp\u003eSS: helped to conceptualize the study, acquired the data, interpreted the data, revised the manuscript, and co-supervised the first author.\u003c/p\u003e\n\u003cp\u003eCD: helped to conceptualize the study, acquired the data, interpreted the data, revised the manuscript, and co-supervised the first author.\u003c/p\u003e\n\u003cp\u003eMB: conceptualized the study, acquired the data, interpreted the data, revised the manuscript, and supervised the first author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSalari N, Darvishi N, Ahmadipanah M, Shohaimi S, Mohammadi M. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.publichealthontario.ca/en/About/News/2022/Fall-Prevention-Month-\u003c/span\u003e\u003cspan address=\"https://www.publichealthontario.ca/en/About/News/2022/Fall-Prevention-Month-\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgeing, WHO UL. WHO global report on falls prevention in older age. Geneva: WHO; 2008.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGade GV, J\u0026oslash;rgensen MG, Ryg J, Riis J, Thomsen K, Masud T, et al. Predicting falls in community-dwelling older adults: a systematic review of prognostic models. BMJ Open. 2021;11(5):e044170.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaunders S, D'Amore C, Hao Q, El-Moneim NA, Richardson J, Kuspinar A, et al. Risk Factors for Falls in Community-Dwelling Older Adults: An Umbrella Review. 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Definitions and methods of measuring and reporting on injurious falls in randomised controlled fall prevention trials: a systematic review. BMC Med Res Methodol. 2012;12:50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLamb SE, J\u0026oslash;rstad-Stein EC, Hauer K, Becker C. Development of a common outcome data set for fall injury prevention trials: the Prevention of Falls Network Europe consensus. J Am Geriatr Soc. 2005;53(9):1618\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChandrasekaran S, Hibino H, Gorniak SL, Layne CS, Johnston CA. Fear of Falling: Significant Barrier in Fall Prevention Approaches. Am J Lifestyle Med. 2021;15(6):598\u0026ndash;601.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaunders S, Mayhew A, Kirkwood R, Nguyen K, Kuspinar A, Vesnaver E, et al. Factors Influencing Mobility During the COVID-19 Pandemic in Community-Dwelling Older Adults. Arch Phys Med Rehabil. 2023;104(1):34\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoons KG, Kengne AP, Woodward M, Royston P, Vergouwe Y, Altman DG, et al. Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker. Heart. 2012;98(9):683\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLusardi MM, Fritz S, Middleton A, Allison L, Wingood M, Phillips E, et al. Determining Risk of Falls in Community Dwelling Older Adults: A Systematic Review and Meta-analysis Using Posttest Probability. J Geriatr Phys Ther. 2017;40(1):1\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTromp AM, Pluijm SM, Smit JH, Deeg DJ, Bouter LM, Lips P. Fall-risk screening test: a prospective study on predictors for falls in community-dwelling elderly. 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Age Ageing. 2016;45(6):789\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWelmer A-K, Rizzuto D, Calder\u0026oacute;n-Larra\u0026ntilde;aga A, Johnell K. Sex Differences in the Association Between Pain and Injurious Falls in Older Adults: A Population-Based Longitudinal Study. Am J Epidemiol. 2017;186(9):1049\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVatcheva KP, Lee M, McCormick JB, Rahbar MH. Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies. Epidemiol (Sunnyvale). 2016;6(2).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDelacour H, Servonnet A, Perrot A, Vigezzi JF, Ramirez JM. [ROC (receiver operating characteristics) curve: principles and application in biology]. Ann Biol Clin (Paris). 2005;63(2):145\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u0026Ccedil;orbacıoğlu ŞK, Aksel G. 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J Am Med Dir Assoc. 2019;20(9):1163\u0026ndash;e83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrevention CfDCa. Check for Safety: A home fall prevention checklist for older adults. Centers for Disease Control and Prevention; 2015.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBayati T, Dehghan A, Bonyadi F, Bazrafkan L. Investigating the effect of education on health literacy and its relation to health-promoting behaviors in health center. J Educ Health Promot. 2018;7:127.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCho YI, Lee S-YD, Arozullah AM, Crittenden KS. Effects of health literacy on health status and health service utilization amongst the elderly. Soc Sci Med. 2008;66(8):1809\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEk S, Rizzuto D, Fratiglioni L, Johnell K, Xu W, Welmer AK. Risk Profiles for Injurious Falls in People Over 60: A Population-Based Cohort Study. J Gerontol Biol Sci Med Sci. 2018;73(2):233\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePoh FJX, Shorey S. A Literature Review of Factors Influencing Injurious Falls. Clin Nurs Res. 2018;29(3):141\u0026ndash;8.\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":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"falls, injurious falls, fall risk assessment, older adults, community-dwelling, prediction models, LASSO regression, biological predictors, model performance","lastPublishedDoi":"10.21203/rs.3.rs-8681033/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8681033/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cb\u003eBackground\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eFalls are a leading cause of morbidity and loss of independence in older adults. Existing fall prediction models vary widely in design and performance, and the added value of incorporating nonbiological factors remains unclear. This study aimed to evaluate the predictive performance of the biological, sociodemographic, behavioral, and environmental domains proposed by the World Health Organization (WHO) for discriminating fall risk in community-dwelling older adults.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMethods\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eWe conducted a cross-sectional analysis using baseline data from the INITIATE cohort of community-dwelling adults aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years. LASSO regression with 10-fold cross-validation was used to select biological predictors for any fall and injurious fall outcomes, followed by multivariable logistic regression. Sequential models reflecting sociodemographic, behavioral, and environmental domains were constructed to assess incremental discriminative performance. Model performance was assessed using the area under the ROC curve (AUC), with DeLong\u0026rsquo;s test used for comparisons. Bootstrap validation (1,000 iterations) was used to assess internal validity.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eA total of 433 participants were included (median age 76 years; 64% female). For any falls (n\u0026thinsp;=\u0026thinsp;194, 44.8%) the reduced biological model (mobility limitation, balance, visual acuity, fear of falling) achieved an AUC of 0.64 (95% CI 0.58\u0026ndash;0.69). For injurious falls (n\u0026thinsp;=\u0026thinsp;40, 28.4%), the reduced biological model (Timed Up and Go [TUG] time, grip strength, executive function, global cognition, and pain interference) achieved an AUC of 0.73 (95% CI 0.64\u0026ndash;0.82). Adding sociodemographic, behavioral, and environmental variables produced minimal, nonsignificant improvements for both outcomes (any falls: AUC 0.65, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.46; injurious falls: AUC 0.78, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.30).\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusions\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eParsimonious models based primarily on biological measures can provide clinically meaningful discrimination while remaining feasible for community and outpatient use. The distinct risk profiles for any falls and injurious falls highlight the need for outcome-specific screening approaches. Prospective evaluation and external validation are needed prior to clinical implementation.\u003c/p\u003e","manuscriptTitle":"Development of fall prediction models in community-dwelling older adults: comparison of biological and multidomain models for any and injurious falls","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-09 16:40:46","doi":"10.21203/rs.3.rs-8681033/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-04T04:56:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-03T13:13:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-26T10:40:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"284664752897053770609088956044900749185","date":"2026-02-23T09:12:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"113155787793204327474273790276892963458","date":"2026-02-22T09:09:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-04T19:48:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-29T14:31:17+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-28T12:54:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-27T16:06:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Geriatrics","date":"2026-01-27T15:53:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"88f921e8-8bf0-451c-9ac6-7eae1dcf8226","owner":[],"postedDate":"February 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T16:05:10+00:00","versionOfRecord":{"articleIdentity":"rs-8681033","link":"https://doi.org/10.1186/s12877-026-07417-7","journal":{"identity":"bmc-geriatrics","isVorOnly":false,"title":"BMC Geriatrics"},"publishedOn":"2026-04-06 15:58:15","publishedOnDateReadable":"April 6th, 2026"},"versionCreatedAt":"2026-02-09 16:40:46","video":"","vorDoi":"10.1186/s12877-026-07417-7","vorDoiUrl":"https://doi.org/10.1186/s12877-026-07417-7","workflowStages":[]},"version":"v1","identity":"rs-8681033","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8681033","identity":"rs-8681033","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}