Impact of Medical and Social Determinants of Health on Survival Following Fall-Induced Hip Fractures: A Retrospective Analysis

Impact of Medical and Social Determinants of Health on Survival Following Fall-Induced Hip Fractures: A Retrospective Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Impact of Medical and Social Determinants of Health on Survival Following Fall-Induced Hip Fractures: A Retrospective Analysis Atta Taseh, Evan Sirls, Surbhi Srinivas, Abhinav Bhamidipati, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7490385/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose Hip fractures are more than isolated injuries; they can lead to long-term disability, complications, and reduced survival. Managing these outcomes may depend on both medical and socioeconomic factors. This study examines the association of social determinants of health (SDOH) and key medical indicators with survival rates and postoperative hospital length of stay (LOS) in patients with hip fractures. Materials and Methods A retrospective study included patients aged ≥ 60 years with a history of fall-induced hip fracture. The collected variables included demographics, Charlson Comorbidity Index (CCI), fracture type, frequency and mechanism of falls, Fracture Risk Assessment Tool (FRAX), LOS, first-year readmissions, Social Vulnerability Index (SVI), Area Deprivation Index (ADI), and insurance type. Cox proportional hazard models were used to evaluate mid- (1 and 3 years) and long-term (5 years) survival rates. Results We included 409 patients (81.99 ± 8.31 years; 68.9% female). Reduced 1-year survival was associated with prolonged LOS and a CCI > 6. At 3 years, age, sex, and CCI, and at 5 years, only a CCI > 6 predicted decreased survival. Conclusion This study highlights the role of age, sex, LOS, and CCI as key predictors of hip‑fracture survival, while SDOH did not show an effect. These findings underscore the need for a larger study to capture risk factors in diverse patient populations for better long-term prediction. Falling injuries Health Disparity Fracture Mortality Multimorbidity Figures Figure 1 Figure 2 1 Introduction Hip fractures are a serious health concern, leading to approximately 300,000 hospitalizations each year and an annual mortality rate of 22%.[ 1 ], [ 2 ] Reduced survival rates have been linked to various clinical risk factors and may persist for up to nine years following surgery.[ 3 ] Numerous studies have identified patient characteristics such as age, sex, and comorbidities as strong predictors of postoperative survival.[ 4 ], [ 5 ] Additional clinical variables, including fracture type, time to surgery, and post-operative length of stay (LOS), have also been shown to impact outcomes.[ 4 ], [ 6 ], [ 7 ] Since more than 90% of hip fractures in older adults are caused by falls, falling circumstances and the frequency of previous falls are increasingly recognized as important contributors to survival rates.[ 8 ] More recently, attention has turned to the Fracture Risk Assessment Tool (FRAX), which estimates the 10-year probability of hip fracture. While some studies, such as that by Sezgin et al., report higher one-year mortality rates in patients categorized as high risk by FRAX, others, including Baklaci et al., found no added predictive value.[ 9 ], [ 10 ] These mixed findings suggest that clinical and medical factors alone may not fully capture the complexity of survival outcomes, highlighting the need to explore additional predictors beyond the traditional biomedical model. Growing evidence underscores the significant impact of social factors on postoperative outcomes and mortality following hip fracture.[ 11 ] These factors are collectively called social determinants of health (SDOH), encompassing various elements such as education, income, employment, and socioeconomic status.[ 11 ] Studies have demonstrated that individual components of SDOH can influence survival. For example, Petrelli et al. reported higher one-year mortality among individuals with lower educational attainment (OR 1.27, 95% CI: 1.12–1.44).[ 12 ], [ 13 ] Insurance status has also been associated with mortality risk, with privately insured patients experiencing lower one-year mortality compared to those with public insurance.[ 14 ] Moreover, public insurance has been linked to longer LOS, which may negatively affect recovery and long-term outcomes.[ 15 ] However, to capture the complex interplay among these social factors, some researchers are focused on area-based composite measures of SDOH. In the UK, Quah et al. found higher survival rates in the most deprived quintile using the English Indices of Multiple Deprivation ( P = .03).[ 16 ] However, most of these studies have been conducted outside the United States, and limited research has explored the prognostic utility of US-based composite indices, such as the Social Vulnerability Index (SVI) and the Area Deprivation Index (ADI), in predicting long-term survival after hip fracture surgery. Although clinical and demographic factors are well-established predictors of survival following hip fracture, the influence of broader SDOH remains insufficiently explored within the U.S. population. This study aims to fill that gap by evaluating the association between mid- and long-term postoperative survival, area-level social disadvantage, and clinical and fall-related risk factors. 2 Material and Methods 2.1 Study Design and Population We conducted a retrospective case-control study through a tertiary institution in Boston, Massachusetts. Patientswere identified from the institutional data repository using hip fracture Current Procedural Terminology (CPT) codes (27125, 27130, 27226, 27228, 27235, 27236, 27244, 27245, and 27248) between January 2010 and December 2019. Inclusion criteria consisted of adults aged ≥ 60 years who sustained a fall-induced hip fracture, including intra- and extra-capsular femur fractures. The study excluded individuals whose medical charts lacked documentation of the fall characteristics or enough data for the calculation of their FRAX score (n = 87), whose falls were due to violent encounters, animal attacks, significant external forces, high-impact sports, or who sustained acetabular fractures (n = 14). This was done to minimize the impact of confounding injuries. 2.2 Data Collection and Variables The initial data was cross-referenced with patients’ electronic health records to confirm patients' eligibility. The demographic data included age, sex, race, and Body Mass Index (BMI). Given the higher number of White participants, race was classified into two categories: White participants and participants from other racial groups, including American Indian or Alaska Native, Asian, Black or African American, and Native Hawaiian or Other Pacific Islander. Clinical variables of interest extracted for each patient included vital status, current smoking status, current alcohol use, number of previous falls, fracture type (intra- vs extra-capsular), mechanism of fall (categorized as same-level, or multi-level), time to surgery (admission to surgery), FRAX score for hip fracture, the Charlson Comorbidity Index (CCI), LOS, insurance type (public vs private) and post-operation first-year readmission.[ 17 ] Insurance types classified as public included government-funded programs that provide coverage primarily for specific populations, such as older adults, individuals with disabilities, and those with low income. This category encompassed Medicare (including all variants such as “Medicare A + B & Blue Cross Federal”, “Medicare with Medex Supplement”, and “Medicare/Blue Cross Blue Shield”), Medicaid, MassHealth Commonwealth Care Alliance, United Health Care Senior Care Options, and combined coverage plans such as Blue Cross Blue Shield and MassHealth. Records with missing or ambiguous insurance information, including those labeled as Self-Pay, Free Care, or Hospice, were excluded from analysis to reduce potential misclassification bias and maintain the integrity of comparisons between insurance types. All remaining insurance entries were categorized as private, encompassing commercially funded plans typically obtained through employers or individual purchase. The CCI is a weighted index of 19 chronic conditions ( e.g. , heart disease, diabetes, renal disease, malignancy, etc.), originally developed to predict 1-year mortality in hospitalized patients.[ 18 ] FRAX calculates the 10-year probability of hip fractures or major osteoporotic fractures, including various clinical risk factors ( e.g. , previous fragility fractures, use of oral corticosteroids, and secondary causes of osteoporosis).[ 19 ] Proxies for SDOH included ADI, which focuses on socioeconomic factors such as income and education, and SVI, which emphasizes vulnerability to health emergencies.[ 20 ], [ 21 ] The ADI was calculated using participants’ zip codes through a tool developed by the Center for Health Disparities Research at the University of Wisconsin. At the same time, the SVI was determined using the Centers for Disease Control and Prevention’s official calculation tool.[ 22 ], [ 23 ] FRAX and CCI scores were also calculated using their respective online calculation tools.[ 24 ], [ 25 ] Primary outcomes included the association of the above variables with mid-term (1- and 3-year) and long-term (5-year) survival.[ 26 ] For deceased individuals, survival time was calculated from surgery to death and categorized into 1-, 3-, and 5-year intervals. The effect of different variables on LOS was also investigated as a secondary outcome. 2.3 Statistical Analysis All analyses were performed using the Statistical Package for the Social Sciences (IBM SPSS Version 28.0.0.0). Descriptive statistics were reported for baseline characteristics, with continuous variables expressed as mean ± standard deviation (SD), and categorical variables as percentages. Cox Proportional Hazards Models were used to evaluate overall survival, with results reported as Hazard Ratios (HR) and corresponding 95% confidence intervals (CI). Additionally, regression and Receiver Operating Characteristic (ROC) curve analyses were performed to assess the impact of significant variables on 1-, 3-, and 5-year survival. Results included Odds Ratios (OR) and Area Under the Curve (AUC) values with their respective 95% CIs. Collinearity statistics were evaluated using the Variance Inflation Factor (VIF) with a cutoff of 3, showing remarkable collinearity.[ 27 ] Lastly, a multivariable regression analysis further assessed the influence of clinical and social factors on LOS. Statistical significance was defined as a P -value less than 0.05. 3 Results The study included 409 patients (mean age: 81.99 ± 8.31 years; 68.9% female). An overview of the baseline characteristics of the patients is provided in Table 1 . Table 1 Baseline characteristics of the study cohort Variables * Values (Mean ± SD or Percentage) Age (in years, n = 409) 81.99 ± 8.31 Sex (n = 408) 68.9% (female) Body Mass Index (in kg/m 2 , n = 397) 23.17 ± 5.10 Race (n = 409) 94.1% (White) Current Smoker (n = 396) 6.1% Current Alcohol (n = 374) 27.1% Fracture Type (n = 409) 54.5% (extracapsular) Insurance (n = 371) 81.6% (public) Social Vulnerability Index (n = 409) 0.48 ± 0.24 Area Deprivation Index (n = 403) 15.33 ± 11.20 Previous Falls 53.1% Fracture Risk Assessment Score, Hip Fracture (n = 409) 11.20 ± 9.32 Level of fall (Same-level) 80.2% Post-op Length of Stay (in days, n = 405) 4.88 ± 4.50 Charlson Comorbidity Index 6.50 ± 2.71 Time to Surgery (in days, n = 406) 1.58 ± 2.32 First Year Readmission (n = 409) 6.4% *The total number of records included in the analysis for each variable is presented in the parenthesis The mortality rates were calculated to be 13.7% (1-year), 22.2% (3-year), and 30.3% (5-year). The Cox Proportional Hazard Models demonstrated that age, sex, LOS, and CCI significantly impacted overall mortality rates (Fig. 1 ). However, proxies for SDOH, including the SVI (SVI; HR = 1.49, 95% CI: 0.61, 3.63) and ADI (HR = 0.99, 95% CI: 0.97, 1.01), did not significantly influence survival. Further analysis revealed that among the significant variables, CCI > 6 and LOS affected survival within the first year (Table 2 ). For 3-year survival, all significant predictors except LOS retained their influence, whereas for the fifth year, only CCI > 6 significantly contributed to survival. The age trend for each sex over follow-up points is depicted in Fig. 2 . Table 2 The effect of significant variables on survival stratified by year. Multivariate logistic regression statistics are presented as odds ratios (OR) with their corresponding 95% confidence intervals (CI). Predictor Variables Year 1 Year 3 Year 5 OR (95% CI) P -Value OR (95% CI) P -Value OR (95% CI) P -Value Age 1.03 (0.99, 1.07) .12 1.07 (1.01, 1.13) .007 0.98 (0.94, 1.02) .45 Sex (Male) 1.40 (0.75, 2.60) .30 2.53 (1.20, 5.40) .01 1.77 (0.81, 3.83) .15 Charlson Comorbidity Index (> 6) 2.18 (1.20, 3.96) .01 2.53 (1.20, 5.3) .01 2.12 (1.01, 4.43) .04 Post-op LOS * 1.11 (1.03, 1.20) .004 1.06 (0.96, 1.18) .22 0.95 (0.82, 1.10) .56 AUC (95% CI) ** 0.68 (0.61, 0.75) 0.73 (0.65, 0.80) 0.63 (0.53, 0.73) *Post-operative Length of Hospital Stay ** Area Under the Receiver Operating Characteristics Curve None of the variables were a significant predictor of LOS. Although insurance type was positively associated with LOS, with a marginally significant P -value [B = 0.71, 95% CI: -0.01 to 1.45, P = .05; Table 3 ]. None of the predictors showed VIF values above the accepted cutoff of 3 (Table 3 ). Table 3 The effect of clinical and social determinants of health on post-operative length of hospital stay using multivariate linear regression. Predictor Variables B Coefficient (95% CI) P -value Collinearity statistics Tolerance Variance Inflation Factor (Constant) 4.19 (0.57, 7.81) .02 N/A N/A Social Vulnerability Index -0.14 (-1.40, 1.12) .82 0.87 1.14 Previous Falls (Yes) -0.12 (-0.72, 0.48) .69 0.92 1.08 Level of fall (Multi) 0.51 (-0.24, 1.28) .18 0.92 1.08 Time to Surgery -0.01 (-0.18, 0.16) .88 0.93 1.07 Age 0.002 (-0.03, 0.04) .90 0.83 1.19 Sex (Male) 0.42 (-0.25, 1.11) .21 0.84 1.18 Body Mass Index -0.01 (-0.07, 0.045) .60 0.87 1.14 Race (White) 0.41 (-0.96, 1.79) .55 0.94 1.05 Current Smoker (Yes) -0.22 (-1.45, 1.00) .71 0.91 1.09 Current Alcohol (Yes) -0.28 (-0.94, 0.37) .40 0.92 1.08 Charlson Comorbidity Index 0.04 (-0.06, 0.14) .45 0.97 1.03 Fracture Type (Extracapsular) -0.12 (-0.73, 0.48) .68 0.90 1.10 Insurance (Private) 0.71 (-0.01, 1.45) .05 0.98 1.02 Area Deprivation Index -0.006 (-0.03, 0.02) .66 0.90 1.10 Fracture Risk Assessment Score (FRAX) -0.01 (-0.05, 0.01) .29 0.71 1.40 4 Discussion This study highlights the significant impact of demographic and medical determinants, including age, sex, CCI, and LOS, on mid- and long-term survival following hip fractures. Conversely, social determinants, as measured by the SVI and ADI, did not demonstrate a statistically significant association with survival outcomes. Previous studies have shown that 45%-60% of hip fracture patients have at least one comorbid condition that significantly impacts survival outcomes.[ 28 ] Lunde et al, investigating a cohort of 38,126 hip fracture patients, reported a 39% decrease in the 6-year survival of patients with CCI > 3 versus 17% of those with no comorbidity.[ 28 ] Their findings, along with several others, highlighted the significant role of comorbidity, aligning with our results.[ 29 ] Although many studies have emphasized the influence of CCI on mortality, relatively few have investigated how these associations evolve longitudinally. Notably, Jürisson et al., studying a cohort of 8,298 hip fracture patients, found that while higher CCI scores were associated with an increased risk of excess mortality in the short-term, this effect diminished progressively and, after 5–7 years, was even lower than that observed in patients with a CCI of 0.[ 30 ] This trend is consistent with our findings, where the impact of high CCI scores was attenuated at the 5-year follow-up ( P = .04 vs P = .01 at 1 and 3 years). This observation may, in part, reflect the limitations of conventional survival analyses, which typically incorporate only fixed baseline risk factors and do not account for the dynamic nature of their effects over time, as suggested by Dekker et al.[ 31 ] These findings suggest that long-term care planning should incorporate ongoing clinical reassessment to more accurately guide treatment decisions. Age and sex exert subtle, time-dependent influences on hip fracture mortality. LeBlanc et al. found that women aged 65–79 had elevated mortality risk primarily within the first year post-fracture, subsequently returning to baseline levels in those ≥ 70 years old, whereas Forsén et al. demonstrated persistently higher mortality risks among men irrespective of age.[ 26 ] Our study highlights these sex-specific dynamics with men showing excess mortality while maintaining a stable mean age at death at both 1- and 3-year intervals (Fig. 2 ). Women who died within three years post-fracture were, on average, seven years older than men, consistent with higher female life expectancy, underscoring the heightened vulnerability of men during the first three years, necessitating comprehensive post-fracture care strategies.[ 2 ], [ 32 ], [ 33 ] Additionally, prolonged LOS and delayed surgical intervention, markers often associated with perioperative complications and discharge difficulties, have been implicated in increased short-term mortality.[ 7 ] Huette et al. reported that surgical delays modestly increased 1-year mortality hazard by 5% (HR: 1.05), whereas Lari et al. identified LOS itself as an independent predictor of 1-year mortality (OR: 1.08).[ 34 ], [ 35 ] In our analysis, longer LOS decreased 1-year survival, though this effect dissipated by 3- and 5-year follow-ups, and time to surgery had no measurable impact, likely due to high adherence (87%) to recommended American Academy of Orthopaedic Surgeons (AAOS) surgical timelines (≤ 48 hours).[ 36 ] Further analysis revealed no significant predictors of LOS aside from private insurance status, which trended toward longer hospitalization. However, interpretation requires caution given the substantial insurance imbalance (81.6% publicly insured) within our cohort. Although prior research has underscored the influence of social support and individual-level socioeconomic status on mortality following hip fracture, relatively few studies have examined the impact of area-level deprivation indices on these outcomes.[ 37 ] Consistent with our findings, Lee et al. found no significant association between the ADI and 1-year mortality among a cohort of 1,150 hip fracture patients.[ 38 ] However, the relatively high home values within Boston could obscure true deprivation levels, given the strong correlation between median home value and overall ADI (r = 0.98).[ 38 ] Although Boston contains pockets of vulnerability, most of its census tracts cluster near the middle range of national distributions for both SVI and ADI, resulting in limited between-group variability. Spangler et al. previously noted that the median SVI for census tracts in the New England region was 30.7, positioning approximately half of these tracts within the lowest third nationally for social vulnerability.[ 39 ] The Neighborhood Atlas reveals a similar pattern for ADI, with Massachusetts block groups skewing toward lower national deprivation percentiles. Such restricted variability attenuates regression coefficients and broadens confidence intervals, consequently diminishing statistical power and complicating the detection of true associations. Previous studies have highlighted the impact of intra- versus extracapsular fractures on short-term mortality outcomes.[ 40 ], [ 41 ] However, in our cohort, no significant association was found between fracture type and mid- or long-term mortality, suggesting that the influence of fracture location may diminish over extended follow-up periods. Regarding fracture risk assessment, a study from Brazil identified high-risk FRAX categories as predictors of hip fracture mortality.[ 42 ] In our study, the mean FRAX score was 11.20 ± 9.32, placing the majority of patients in the very high-risk category according to the National Osteoporosis Guideline Group (NOGG) thresholds.[ 43 ] This homogeneity in elevated risk may have limited the ability to detect significant associations, as the lack of variability reduces statistical power to discern differences. Additionally, while some studies have emphasized the role of fall-related factors, such as the circumstances and mechanics of the fall, in influencing hip fracture survival, our analysis did not reveal any significant associations between these factors and long-term mortality.[ 8 ], [ 44 ] This discrepancy may be due to differences in study populations, methodologies, or the multifactorial nature of fall-related injuries, underscoring the need for further research to elucidate these relationships. A key limitation of this study is its single-center design, which may limit the generalizability of the findings. Conducted at a tertiary academic hospital, the patient population may not accurately reflect the clinical or demographic diversity observed in other settings, such as community hospitals or rural regions. Additionally, limiting the risk factors to fixed baseline values and categorizing survival outcomes into discrete 1-, 3-, and 5-year intervals may obscure the dynamic nature of survival and limit insight into temporal trends. 5 Conclusion This study identifies age, sex, LOS, and CCI as significant predictors of post-hip-fracture mortality, whereas proxies for SDOH, such as the SVI and ADI, did not demonstrate statistical significance. These findings underscore the importance of comorbidity burden and perioperative care efficiency in shaping mid- and long-term mortality outcomes. Nonetheless, they also highlight the need for prospective, multicenter studies to validate these findings across diverse patient populations and to account for evolving risk profiles, ultimately improving the accuracy of long-term prognostication. Declarations Ethics Statement This retrospective study was conducted in accordance with the principles outlined in the Declaration of Helsinki and was approved by the Institutional Review Board (IRB). Given the retrospective nature of the study, the requirement for informed consent was waived by the IRB. Competing Interests The authors of this manuscript have no competing interests to declare. Funding The authors have no relevant financial or non-financial interests to disclose. Data Availability Statement The corresponding author can provide data upon reasonable request by the journal contingent upon IRB permission. Declaration of AI use During the preparation of this manuscript, the authors utilized OpenAI’s ChatGPT (GPT-4) to assist with language refinement and improving overall readability. 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De Mutsert, P. C. Van Dijk, C. Zoccali, and K. J. Jager, “Survival analysis: time-dependent effects and time-varying risk factors,” Kidney Int. , vol. 74, no. 8, pp. 994–997, Oct. 2008, doi: 10.1038/ki.2008.328. E. Arias, J. Xu, T.-V. Betzaida, and B. Bastian, “Arias, E., Xu, J., Tejada-Vera, B., & Bastian, B. (2024). U.S. State Life Tables, 2021. National Vital Statistics Reports, 73(7). Centers for Disease Control and Prevention | CDC (.gov).” National Vital Statistics Reports, Aug. 21, 2024. [Online]. Available: www.cdc.gov/nchs/data/nvsr/nvsr73/nvsr73-07.pdf F. A. Miralles-Muñoz et al. , “Change in 1-year mortality after hip fracture surgery over the last decade in a European population,” Arch. Orthop. Trauma Surg. , vol. 143, no. 7, pp. 4173–4179, Jul. 2023, doi: 10.1007/s00402-022-04719-4. P. Huette et al. , “Risk factors and mortality of patients undergoing hip fracture surgery: a one-year follow-up study,” Sci. Rep. , vol. 10, no. 1, Jun. 2020, doi: 10.1038/s41598-020-66614-5. A. Lari, A. Haidar, Y. AlRumaidhi, M. Awad, and O. AlMutairi, “Predictors of mortality and length of stay after hip fractures – A multicenter retrospective analysis,” J. Clin. Orthop. Trauma , vol. 28, p. 101853, May 2022, doi: 10.1016/j.jcot.2022.101853. Y. J. Seong, W. C. Shin, N. H. Moon, and K. T. Suh, “Timing of Hip-fracture Surgery in Elderly Patients: Literature Review and Recommendations,” Hip Pelvis , vol. 32, no. 1, pp. 11–16, Mar. 2020, doi: 10.5371/hp.2020.32.1.11. M. Auais, F. Al-Zoubi, A. Matheson, K. Brown, J. Magaziner, and S. D. French, “Understanding the role of social factors in recovery after hip fractures: A structured scoping review,” Health Soc. Care Community , vol. 27, no. 6, pp. 1375–1387, Nov. 2019, doi: 10.1111/hsc.12830. C. Lee, E. S. McConnell, S. Wei, T. (Michelle) Xue, H. Tsumura, and W. Pan, “Effect of Race/ethnicity, Insurance Status, and Area Deprivation on Hip Fracture Outcomes Among Older Adults in the United States,” Clin. Nurs. Res. , vol. 31, no. 3, pp. 541–552, Mar. 2022, doi: 10.1177/10547738211061216. K. R. Spangler, J. Manjourides, A. H. Lynch, and G. A. Wellenius, “Characterizing Spatial Variability of Climate-Relevant Hazards and Vulnerabilities in the New England Region of the United States,” GeoHealth , vol. 3, no. 4, pp. 104–120, Apr. 2019, doi: 10.1029/2018GH000179. K. B. Björkelund, A. Hommel, K.-G. Thorngren, D. Lundberg, and S. Larsson, “Factors at admission associated with 4 months outcome in elderly patients with hip fracture,” AANA J. , vol. 77, no. 1, pp. 49–58, Feb. 2009. M. D. Neuman, J. H. Silber, M. R. Passarella, and R. M. Werner, “Comparing the Contributions of Acute and Postacute Care Facility Characteristics to Outcomes After Hospitalization for Hip Fracture,” Med. Care , vol. 55, no. 4, pp. 411–420, Apr. 2017, doi: 10.1097/mlr.0000000000000664. T. Freitas et al. , “Fracture Risk Assessment Tool (FRAX®) Scores Predict Mortality in Community-Dwelling Elderlies,” Arthritis Rheumatol , no. 76, 2024. C. L. Gregson et al. , “UK clinical guideline for the prevention and treatment of osteoporosis,” Arch. Osteoporos. , vol. 17, no. 1, Dec. 2022, doi: 10.1007/s11657-022-01061-5. S. W. Burm et al. , “Fall Patterns Predict Mortality After Hip Fracture in Older Adults, Independent of Age, Sex, and Comorbidities,” Calcif. Tissue Int. , vol. 109, no. 4, pp. 372–382, Oct. 2021, doi: 10.1007/s00223-021-00846-z. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-7490385","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":512123857,"identity":"ae48f288-9f61-49f2-8b5d-b306dea39abb","order_by":0,"name":"Atta Taseh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYLACxgYbBgYJBjYoN4GBgQefcjawljTStRwmQQv//ObHHz7uOB8tH93A9uDnnsPy5u0JjA/etuHWInGMzUxy5pnbuRvvHGA37Hl22HDOmQfMhnPxaGE4xmDGzNsG1DIjgU2C50Aa4wyJBDZpXjxa5I+xf/78t+0cWIvknwNp9kAt7L/xaTE4xmMgzdh2IHc+yHCeAzaJIFuY8WkxPJZTJtnblpy7QSKxTVrmgE3yDJ6HzZJzzuHWInf4+OYPP9vscufPSD4m+eaAhO0M9uSDH96U4fE+3IUHGBugTDiDAJAnUt0oGAWjYBSMQAAAz9xVO7KM/ugAAAAASUVORK5CYII=","orcid":"","institution":"Massachusetts General Hospital","correspondingAuthor":true,"prefix":"","firstName":"Atta","middleName":"","lastName":"Taseh","suffix":""},{"id":512123859,"identity":"415f5a6f-89e7-4ed6-9322-60eef6ce96b4","order_by":1,"name":"Evan Sirls","email":"","orcid":"","institution":"Massachusetts General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Evan","middleName":"","lastName":"Sirls","suffix":""},{"id":512123860,"identity":"67d7b70e-d8c0-4f58-bfca-29cf00118cb6","order_by":2,"name":"Surbhi Srinivas","email":"","orcid":"","institution":"Massachusetts General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Surbhi","middleName":"","lastName":"Srinivas","suffix":""},{"id":512123861,"identity":"e34d5217-7322-486d-b15a-bfe9bc6471f1","order_by":3,"name":"Abhinav Bhamidipati","email":"","orcid":"","institution":"Massachusetts General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Abhinav","middleName":"","lastName":"Bhamidipati","suffix":""},{"id":512123862,"identity":"82faf9fe-e4bf-46c6-a50d-567aef263247","order_by":4,"name":"Jonathan F. Bean","email":"","orcid":"","institution":"VA Boston Healthcare System","correspondingAuthor":false,"prefix":"","firstName":"Jonathan","middleName":"F.","lastName":"Bean","suffix":""},{"id":512123863,"identity":"43084d36-389c-418f-b5c5-e2bb510ebf32","order_by":5,"name":"Job N. Doornberg","email":"","orcid":"","institution":"University of Groningen, University Medical Center Groningen","correspondingAuthor":false,"prefix":"","firstName":"Job","middleName":"N.","lastName":"Doornberg","suffix":""},{"id":512123864,"identity":"22ff2349-3319-4efc-b381-8671d0a35bc8","order_by":6,"name":"Ara Nazarian","email":"","orcid":"","institution":"Musculoskeletal Translational Innovation Initiative, Beth Israel Deaconess Medical Center, Harvard Medical School","correspondingAuthor":false,"prefix":"","firstName":"Ara","middleName":"","lastName":"Nazarian","suffix":""},{"id":512123865,"identity":"b4e79e6c-c3e7-439a-be31-8581d15f49b6","order_by":7,"name":"Mitchel B. Harris","email":"","orcid":"","institution":"Massachusetts General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mitchel","middleName":"B.","lastName":"Harris","suffix":""},{"id":512123866,"identity":"08b95a1f-ef62-4e00-8586-32b722b4dc6f","order_by":8,"name":"Soheil Ashkani-Esfahani","email":"","orcid":"","institution":"Massachusetts General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Soheil","middleName":"","lastName":"Ashkani-Esfahani","suffix":""}],"badges":[],"createdAt":"2025-08-29 16:38:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7490385/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7490385/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91200287,"identity":"2e5fc454-dcad-4348-8c3f-1d20d454c659","added_by":"auto","created_at":"2025-09-12 15:23:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":124778,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot depicting the 5-year survival analysis in patients with fall-induced hip fractures, showing the hazard ratios (HR) of study variables with their corresponding 95% confidence intervals (CI)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7490385/v1/45ea847cbd71ca6cc7fadf84.png"},{"id":91200288,"identity":"1a905117-bbc5-4358-ae04-df22f73b7a8b","added_by":"auto","created_at":"2025-09-12 15:23:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":335327,"visible":true,"origin":"","legend":"\u003cp\u003eMortality age trends for each sex over the study timeline. Age values are presented as mean, and the error bar represent standard deviation.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7490385/v1/c234bad80875da79c59db5c4.png"},{"id":101205681,"identity":"c7b82a78-259b-4659-a584-c9be31d20586","added_by":"auto","created_at":"2026-01-27 09:50:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1326435,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7490385/v1/046f0cae-3977-4349-88cf-7dcfead4c838.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of Medical and Social Determinants of Health on Survival Following Fall-Induced Hip Fractures: A Retrospective Analysis","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eHip fractures are a serious health concern, leading to approximately 300,000 hospitalizations each year and an annual mortality rate of 22%.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] Reduced survival rates have been linked to various clinical risk factors and may persist for up to nine years following surgery.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] Numerous studies have identified patient characteristics such as age, sex, and comorbidities as strong predictors of postoperative survival.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] Additional clinical variables, including fracture type, time to surgery, and post-operative length of stay (LOS), have also been shown to impact outcomes.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] Since more than 90% of hip fractures in older adults are caused by falls, falling circumstances and the frequency of previous falls are increasingly recognized as important contributors to survival rates.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] More recently, attention has turned to the Fracture Risk Assessment Tool (FRAX), which estimates the 10-year probability of hip fracture. While some studies, such as that by Sezgin et al., report higher one-year mortality rates in patients categorized as high risk by FRAX, others, including Baklaci et al., found no added predictive value.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] These mixed findings suggest that clinical and medical factors alone may not fully capture the complexity of survival outcomes, highlighting the need to explore additional predictors beyond the traditional biomedical model.\u003c/p\u003e\u003cp\u003eGrowing evidence underscores the significant impact of social factors on postoperative outcomes and mortality following hip fracture.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] These factors are collectively called social determinants of health (SDOH), encompassing various elements such as education, income, employment, and socioeconomic status.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] Studies have demonstrated that individual components of SDOH can influence survival. For example, Petrelli et al. reported higher one-year mortality among individuals with lower educational attainment (OR 1.27, 95% CI: 1.12\u0026ndash;1.44).[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] Insurance status has also been associated with mortality risk, with privately insured patients experiencing lower one-year mortality compared to those with public insurance.[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] Moreover, public insurance has been linked to longer LOS, which may negatively affect recovery and long-term outcomes.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] However, to capture the complex interplay among these social factors, some researchers are focused on area-based composite measures of SDOH. In the UK, Quah et al. found higher survival rates in the most deprived quintile using the English Indices of Multiple Deprivation (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.03).[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] However, most of these studies have been conducted outside the United States, and limited research has explored the prognostic utility of US-based composite indices, such as the Social Vulnerability Index (SVI) and the Area Deprivation Index (ADI), in predicting long-term survival after hip fracture surgery.\u003c/p\u003e\u003cp\u003eAlthough clinical and demographic factors are well-established predictors of survival following hip fracture, the influence of broader SDOH remains insufficiently explored within the U.S. population. This study aims to fill that gap by evaluating the association between mid- and long-term postoperative survival, area-level social disadvantage, and clinical and fall-related risk factors.\u003c/p\u003e"},{"header":"2 Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Design and Population\u003c/h2\u003e\u003cp\u003eWe conducted a retrospective case-control study through a tertiary institution in Boston, Massachusetts. Patientswere identified from the institutional data repository using hip fracture Current Procedural Terminology (CPT) codes (27125, 27130, 27226, 27228, 27235, 27236, 27244, 27245, and 27248) between January 2010 and December 2019. Inclusion criteria consisted of adults aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years who sustained a fall-induced hip fracture, including intra- and extra-capsular femur fractures. The study excluded individuals whose medical charts lacked documentation of the fall characteristics or enough data for the calculation of their FRAX score (n\u0026thinsp;=\u0026thinsp;87), whose falls were due to violent encounters, animal attacks, significant external forces, high-impact sports, or who sustained acetabular fractures (n\u0026thinsp;=\u0026thinsp;14). This was done to minimize the impact of confounding injuries.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Data Collection and Variables\u003c/h2\u003e\u003cp\u003eThe initial data was cross-referenced with patients\u0026rsquo; electronic health records to confirm patients' eligibility. The demographic data included age, sex, race, and Body Mass Index (BMI). Given the higher number of White participants, race was classified into two categories: White participants and participants from other racial groups, including American Indian or Alaska Native, Asian, Black or African American, and Native Hawaiian or Other Pacific Islander.\u003c/p\u003e\u003cp\u003eClinical variables of interest extracted for each patient included vital status, current smoking status, current alcohol use, number of previous falls, fracture type (intra- vs extra-capsular), mechanism of fall (categorized as same-level, or multi-level), time to surgery (admission to surgery), FRAX score for hip fracture, the Charlson Comorbidity Index (CCI), LOS, insurance type (public vs private) and post-operation first-year readmission.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] Insurance types classified as public included government-funded programs that provide coverage primarily for specific populations, such as older adults, individuals with disabilities, and those with low income. This category encompassed Medicare (including all variants such as \u0026ldquo;Medicare A\u0026thinsp;+\u0026thinsp;B \u0026amp; Blue Cross Federal\u0026rdquo;, \u0026ldquo;Medicare with Medex Supplement\u0026rdquo;, and \u0026ldquo;Medicare/Blue Cross Blue Shield\u0026rdquo;), Medicaid, MassHealth Commonwealth Care Alliance, United Health Care Senior Care Options, and combined coverage plans such as Blue Cross Blue Shield and MassHealth. Records with missing or ambiguous insurance information, including those labeled as Self-Pay, Free Care, or Hospice, were excluded from analysis to reduce potential misclassification bias and maintain the integrity of comparisons between insurance types. All remaining insurance entries were categorized as private, encompassing commercially funded plans typically obtained through employers or individual purchase.\u003c/p\u003e\u003cp\u003eThe CCI is a weighted index of 19 chronic conditions (\u003cem\u003ee.g.\u003c/em\u003e, heart disease, diabetes, renal disease, malignancy, etc.), originally developed to predict 1-year mortality in hospitalized patients.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] FRAX calculates the 10-year probability of hip fractures or major osteoporotic fractures, including various clinical risk factors (\u003cem\u003ee.g.\u003c/em\u003e, previous fragility fractures, use of oral corticosteroids, and secondary causes of osteoporosis).[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eProxies for SDOH included ADI, which focuses on socioeconomic factors such as income and education, and SVI, which emphasizes vulnerability to health emergencies.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] The ADI was calculated using participants\u0026rsquo; zip codes through a tool developed by the Center for Health Disparities Research at the University of Wisconsin. At the same time, the SVI was determined using the Centers for Disease Control and Prevention\u0026rsquo;s official calculation tool.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] FRAX and CCI scores were also calculated using their respective online calculation tools.[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e\u003cp\u003ePrimary outcomes included the association of the above variables with mid-term (1- and 3-year) and long-term (5-year) survival.[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] For deceased individuals, survival time was calculated from surgery to death and categorized into 1-, 3-, and 5-year intervals. The effect of different variables on LOS was also investigated as a secondary outcome.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Statistical Analysis\u003c/h2\u003e\u003cp\u003eAll analyses were performed using the Statistical Package for the Social Sciences (IBM SPSS Version 28.0.0.0). Descriptive statistics were reported for baseline characteristics, with continuous variables expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), and categorical variables as percentages. Cox Proportional Hazards Models were used to evaluate overall survival, with results reported as Hazard Ratios (HR) and corresponding 95% confidence intervals (CI). Additionally, regression and Receiver Operating Characteristic (ROC) curve analyses were performed to assess the impact of significant variables on 1-, 3-, and 5-year survival. Results included Odds Ratios (OR) and Area Under the Curve (AUC) values with their respective 95% CIs. Collinearity statistics were evaluated using the Variance Inflation Factor (VIF) with a cutoff of 3, showing remarkable collinearity.[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] Lastly, a multivariable regression analysis further assessed the influence of clinical and social factors on LOS. Statistical significance was defined as a \u003cem\u003eP\u003c/em\u003e-value less than 0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cp\u003eThe study included 409 patients (mean age: 81.99\u0026thinsp;\u0026plusmn;\u0026thinsp;8.31 years; 68.9% female). An overview of the baseline characteristics of the patients is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of the study cohort\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\u003eVariables\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eValues (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or Percentage)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (in years, n\u0026thinsp;=\u0026thinsp;409)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81.99\u0026thinsp;\u0026plusmn;\u0026thinsp;8.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex (n\u0026thinsp;=\u0026thinsp;408)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68.9% (female)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBody Mass Index (in kg/m\u003csup\u003e2\u003c/sup\u003e, n\u0026thinsp;=\u0026thinsp;397)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.17\u0026thinsp;\u0026plusmn;\u0026thinsp;5.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace (n\u0026thinsp;=\u0026thinsp;409)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e94.1% (White)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent Smoker (n\u0026thinsp;=\u0026thinsp;396)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent Alcohol (n\u0026thinsp;=\u0026thinsp;374)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27.1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFracture Type (n\u0026thinsp;=\u0026thinsp;409)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54.5% (extracapsular)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInsurance (n\u0026thinsp;=\u0026thinsp;371)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81.6% (public)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial Vulnerability Index (n\u0026thinsp;=\u0026thinsp;409)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArea Deprivation Index (n\u0026thinsp;=\u0026thinsp;403)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.33\u0026thinsp;\u0026plusmn;\u0026thinsp;11.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious Falls\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53.1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFracture Risk Assessment Score, Hip Fracture (n\u0026thinsp;=\u0026thinsp;409)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.20\u0026thinsp;\u0026plusmn;\u0026thinsp;9.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLevel of fall (Same-level)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80.2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePost-op Length of Stay (in days, n\u0026thinsp;=\u0026thinsp;405)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.88\u0026thinsp;\u0026plusmn;\u0026thinsp;4.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharlson Comorbidity Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.50\u0026thinsp;\u0026plusmn;\u0026thinsp;2.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime to Surgery (in days, n\u0026thinsp;=\u0026thinsp;406)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.58\u0026thinsp;\u0026plusmn;\u0026thinsp;2.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirst Year Readmission (n\u0026thinsp;=\u0026thinsp;409)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e*The total number of records included in the analysis for each variable is presented in the parenthesis\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe mortality rates were calculated to be 13.7% (1-year), 22.2% (3-year), and 30.3% (5-year). The Cox Proportional Hazard Models demonstrated that age, sex, LOS, and CCI significantly impacted overall mortality rates (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, proxies for SDOH, including the SVI (SVI; HR\u0026thinsp;=\u0026thinsp;1.49, 95% CI: 0.61, 3.63) and ADI (HR\u0026thinsp;=\u0026thinsp;0.99, 95% CI: 0.97, 1.01), did not significantly influence survival.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFurther analysis revealed that among the significant variables, CCI\u0026thinsp;\u0026gt;\u0026thinsp;6 and LOS affected survival within the first year (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For 3-year survival, all significant predictors except LOS retained their influence, whereas for the fifth year, only CCI\u0026thinsp;\u0026gt;\u0026thinsp;6 significantly contributed to survival. The age trend for each sex over follow-up points is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe effect of significant variables on survival stratified by year. Multivariate logistic regression statistics are presented as odds ratios (OR) with their corresponding 95% confidence intervals (CI).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePredictor Variables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eYear 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eYear 3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eYear 5\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-Value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.03 (0.99, 1.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.07 (1.01, 1.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e.007\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.98 (0.94, 1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex (Male)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.40 (0.75, 2.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.53 (1.20, 5.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.77 (0.81, 3.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCharlson Comorbidity Index (\u0026gt;\u0026thinsp;6)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.18 (1.20, 3.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.53 (1.20, 5.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.12 (1.01, 4.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e.04\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePost-op LOS\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.11 (1.03, 1.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e.004\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.06 (0.96, 1.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.95 (0.82, 1.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAUC (95% CI)\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.68 (0.61, 0.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.73 (0.65, 0.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.63 (0.53, 0.73)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e*Post-operative Length of Hospital Stay\u003c/p\u003e\u003cp\u003e** Area Under the Receiver Operating Characteristics Curve\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eNone of the variables were a significant predictor of LOS. Although insurance type was positively associated with LOS, with a marginally significant \u003cem\u003eP\u003c/em\u003e-value [B\u0026thinsp;=\u0026thinsp;0.71, 95% CI: -0.01 to 1.45, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.05; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e]. None of the predictors showed VIF values above the accepted cutoff of 3 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe effect of clinical and social determinants of health on post-operative length of hospital stay using multivariate linear regression.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePredictor Variables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eB Coefficient (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eCollinearity statistics\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTolerance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVariance Inflation Factor\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e(Constant)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.19 (0.57, 7.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSocial Vulnerability Index\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.14 (-1.40, 1.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePrevious Falls (Yes)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.12 (-0.72, 0.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLevel of fall (Multi)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.51 (-0.24, 1.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTime to Surgery\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.01 (-0.18, 0.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.002 (-0.03, 0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex (Male)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.42 (-0.25, 1.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBody Mass Index\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.01 (-0.07, 0.045)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRace (White)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.41 (-0.96, 1.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCurrent Smoker (Yes)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.22 (-1.45, 1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCurrent Alcohol (Yes)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.28 (-0.94, 0.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCharlson Comorbidity Index\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.04 (-0.06, 0.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFracture Type (Extracapsular)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.12 (-0.73, 0.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInsurance (Private)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.71 (-0.01, 1.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eArea Deprivation Index\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.006 (-0.03, 0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFracture Risk Assessment Score (FRAX)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.01 (-0.05, 0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study highlights the significant impact of demographic and medical determinants, including age, sex, CCI, and LOS, on mid- and long-term survival following hip fractures. Conversely, social determinants, as measured by the SVI and ADI, did not demonstrate a statistically significant association with survival outcomes.\u003c/p\u003e\u003cp\u003ePrevious studies have shown that 45%-60% of hip fracture patients have at least one comorbid condition that significantly impacts survival outcomes.[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] Lunde et al, investigating a cohort of 38,126 hip fracture patients, reported a 39% decrease in the 6-year survival of patients with CCI\u0026thinsp;\u0026gt;\u0026thinsp;3 versus 17% of those with no comorbidity.[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] Their findings, along with several others, highlighted the significant role of comorbidity, aligning with our results.[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] Although many studies have emphasized the influence of CCI on mortality, relatively few have investigated how these associations evolve longitudinally. Notably, J\u0026uuml;risson et al., studying a cohort of 8,298 hip fracture patients, found that while higher CCI scores were associated with an increased risk of excess mortality in the short-term, this effect diminished progressively and, after 5\u0026ndash;7 years, was even lower than that observed in patients with a CCI of 0.[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] This trend is consistent with our findings, where the impact of high CCI scores was attenuated at the 5-year follow-up (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.04 vs \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.01 at 1 and 3 years). This observation may, in part, reflect the limitations of conventional survival analyses, which typically incorporate only fixed baseline risk factors and do not account for the dynamic nature of their effects over time, as suggested by Dekker et al.[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] These findings suggest that long-term care planning should incorporate ongoing clinical reassessment to more accurately guide treatment decisions.\u003c/p\u003e\u003cp\u003eAge and sex exert subtle, time-dependent influences on hip fracture mortality. LeBlanc et al. found that women aged 65\u0026ndash;79 had elevated mortality risk primarily within the first year post-fracture, subsequently returning to baseline levels in those\u0026thinsp;\u0026ge;\u0026thinsp;70 years old, whereas Fors\u0026eacute;n et al. demonstrated persistently higher mortality risks among men irrespective of age.[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] Our study highlights these sex-specific dynamics with men showing excess mortality while maintaining a stable mean age at death at both 1- and 3-year intervals (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Women who died within three years post-fracture were, on average, seven years older than men, consistent with higher female life expectancy, underscoring the heightened vulnerability of men during the first three years, necessitating comprehensive post-fracture care strategies.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] Additionally, prolonged LOS and delayed surgical intervention, markers often associated with perioperative complications and discharge difficulties, have been implicated in increased short-term mortality.[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] Huette et al. reported that surgical delays modestly increased 1-year mortality hazard by 5% (HR: 1.05), whereas Lari et al. identified LOS itself as an independent predictor of 1-year mortality (OR: 1.08).[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] In our analysis, longer LOS decreased 1-year survival, though this effect dissipated by 3- and 5-year follow-ups, and time to surgery had no measurable impact, likely due to high adherence (87%) to recommended American Academy of Orthopaedic Surgeons (AAOS) surgical timelines (\u0026le;\u0026thinsp;48 hours).[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] Further analysis revealed no significant predictors of LOS aside from private insurance status, which trended toward longer hospitalization. However, interpretation requires caution given the substantial insurance imbalance (81.6% publicly insured) within our cohort.\u003c/p\u003e\u003cp\u003eAlthough prior research has underscored the influence of social support and individual-level socioeconomic status on mortality following hip fracture, relatively few studies have examined the impact of area-level deprivation indices on these outcomes.[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] Consistent with our findings, Lee et al. found no significant association between the ADI and 1-year mortality among a cohort of 1,150 hip fracture patients.[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] However, the relatively high home values within Boston could obscure true deprivation levels, given the strong correlation between median home value and overall ADI (r\u0026thinsp;=\u0026thinsp;0.98).[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] Although Boston contains pockets of vulnerability, most of its census tracts cluster near the middle range of national distributions for both SVI and ADI, resulting in limited between-group variability. Spangler et al. previously noted that the median SVI for census tracts in the New England region was 30.7, positioning approximately half of these tracts within the lowest third nationally for social vulnerability.[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] The Neighborhood Atlas reveals a similar pattern for ADI, with Massachusetts block groups skewing toward lower national deprivation percentiles. Such restricted variability attenuates regression coefficients and broadens confidence intervals, consequently diminishing statistical power and complicating the detection of true associations.\u003c/p\u003e\u003cp\u003ePrevious studies have highlighted the impact of intra- versus extracapsular fractures on short-term mortality outcomes.[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] However, in our cohort, no significant association was found between fracture type and mid- or long-term mortality, suggesting that the influence of fracture location may diminish over extended follow-up periods. Regarding fracture risk assessment, a study from Brazil identified high-risk FRAX categories as predictors of hip fracture mortality.[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] In our study, the mean FRAX score was 11.20\u0026thinsp;\u0026plusmn;\u0026thinsp;9.32, placing the majority of patients in the very high-risk category according to the National Osteoporosis Guideline Group (NOGG) thresholds.[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] This homogeneity in elevated risk may have limited the ability to detect significant associations, as the lack of variability reduces statistical power to discern differences. Additionally, while some studies have emphasized the role of fall-related factors, such as the circumstances and mechanics of the fall, in influencing hip fracture survival, our analysis did not reveal any significant associations between these factors and long-term mortality.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] This discrepancy may be due to differences in study populations, methodologies, or the multifactorial nature of fall-related injuries, underscoring the need for further research to elucidate these relationships.\u003c/p\u003e\u003cp\u003eA key limitation of this study is its single-center design, which may limit the generalizability of the findings. Conducted at a tertiary academic hospital, the patient population may not accurately reflect the clinical or demographic diversity observed in other settings, such as community hospitals or rural regions. Additionally, limiting the risk factors to fixed baseline values and categorizing survival outcomes into discrete 1-, 3-, and 5-year intervals may obscure the dynamic nature of survival and limit insight into temporal trends.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis study identifies age, sex, LOS, and CCI as significant predictors of post-hip-fracture mortality, whereas proxies for SDOH, such as the SVI and ADI, did not demonstrate statistical significance. These findings underscore the importance of comorbidity burden and perioperative care efficiency in shaping mid- and long-term mortality outcomes. Nonetheless, they also highlight the need for prospective, multicenter studies to validate these findings across diverse patient populations and to account for evolving risk profiles, ultimately improving the accuracy of long-term prognostication.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study was conducted in accordance with the principles outlined in the Declaration of Helsinki and was approved by the Institutional Review Board (IRB). Given the retrospective nature of the study, the requirement for informed consent was waived by the IRB.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors of this manuscript have no competing interests to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe corresponding author can provide data upon reasonable request by the journal contingent upon IRB permission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of AI use\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this manuscript, the authors utilized OpenAI\u0026rsquo;s ChatGPT (GPT-4) to assist with language refinement and improving overall readability. The authors carefully reviewed, edited, and verified all content generated with the assistance of this tool and accept full responsibility for the integrity and accuracy of the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eN. Veronese and S. Maggi, \u0026ldquo;Epidemiology and social costs of hip fracture,\u0026rdquo; \u003cem\u003eInjury\u003c/em\u003e, vol. 49, no. 8, pp. 1458\u0026ndash;1460, Aug. 2018, doi: 10.1016/j.injury.2018.04.015.\u003c/li\u003e\n\u003cli\u003eC. Downey, M. Kelly, and J. F. Quinlan, \u0026ldquo;Changing trends in the mortality rate at 1-year post hip fracture - a systematic review,\u0026rdquo; \u003cem\u003eWorld J. Orthop.\u003c/em\u003e, vol. 10, no. 3, pp. 166\u0026ndash;175, Mar. 2019, doi: 10.5312/wjo.v10.i3.166.\u003c/li\u003e\n\u003cli\u003eM. H. L. 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Gregson \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;UK clinical guideline for the prevention and treatment of osteoporosis,\u0026rdquo; \u003cem\u003eArch. Osteoporos.\u003c/em\u003e, vol. 17, no. 1, Dec. 2022, doi: 10.1007/s11657-022-01061-5.\u003c/li\u003e\n\u003cli\u003eS. W. Burm \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Fall Patterns Predict Mortality After Hip Fracture in Older Adults, Independent of Age, Sex, and Comorbidities,\u0026rdquo; \u003cem\u003eCalcif. Tissue Int.\u003c/em\u003e, vol. 109, no. 4, pp. 372\u0026ndash;382, Oct. 2021, doi: 10.1007/s00223-021-00846-z.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Falling injuries, Health Disparity, Fracture Mortality, Multimorbidity","lastPublishedDoi":"10.21203/rs.3.rs-7490385/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7490385/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eHip fractures are more than isolated injuries; they can lead to long-term disability, complications, and reduced survival. Managing these outcomes may depend on both medical and socioeconomic factors. This study examines the association of social determinants of health (SDOH) and key medical indicators with survival rates and postoperative hospital length of stay (LOS) in patients with hip fractures.\u003c/p\u003e\u003ch2\u003eMaterials and Methods\u003c/h2\u003e\u003cp\u003eA retrospective study included patients aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years with a history of fall-induced hip fracture. The collected variables included demographics, Charlson Comorbidity Index (CCI), fracture type, frequency and mechanism of falls, Fracture Risk Assessment Tool (FRAX), LOS, first-year readmissions, Social Vulnerability Index (SVI), Area Deprivation Index (ADI), and insurance type. Cox proportional hazard models were used to evaluate mid- (1 and 3 years) and long-term (5 years) survival rates.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eWe included 409 patients (81.99\u0026thinsp;\u0026plusmn;\u0026thinsp;8.31 years; 68.9% female). Reduced 1-year survival was associated with prolonged LOS and a CCI\u0026thinsp;\u0026gt;\u0026thinsp;6. At 3 years, age, sex, and CCI, and at 5 years, only a CCI\u0026thinsp;\u0026gt;\u0026thinsp;6 predicted decreased survival.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study highlights the role of age, sex, LOS, and CCI as key predictors of hip‑fracture survival, while SDOH did not show an effect. These findings underscore the need for a larger study to capture risk factors in diverse patient populations for better long-term prediction.\u003c/p\u003e","manuscriptTitle":"Impact of Medical and Social Determinants of Health on Survival Following Fall-Induced Hip Fractures: A Retrospective Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-12 15:15:51","doi":"10.21203/rs.3.rs-7490385/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"32cc6bf4-8eb7-4554-ad61-4a2222adfbf0","owner":[],"postedDate":"September 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-25T21:38:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-12 15:15:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7490385","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7490385","identity":"rs-7490385","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}