Association Between Prehip Fracture Antidepressant Use and Posthip Fracture Length of Hospital Stay in Medicare Beneficiaries and Assessing Sex Differences.
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Association Between Pre-Hip Fracture Antidepressant Use and Post-Hip Fracture Length of Hospital Stay in Medicare Beneficiaries and Assessing Sex Differences - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Search Log in Primary site navigation Search Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Am J Geriatr Psychiatry . Author manuscript; available in PMC: 2026 Jun 1. Published in final edited form as: Am J Geriatr Psychiatry. 2024 Nov 14;33(6):654–663. doi: 10.1016/j.jagp.2024.11.001 Association Between Pre-Hip Fracture Antidepressant Use and Post-Hip Fracture Length of Hospital Stay in Medicare Beneficiaries and Assessing Sex Differences Rhea Mehta 1, * , Jason R Falvey 1, 2 , Chixiang Chen 1 , Yu Dong 3 , Michelle D Shardell 1, 4 , Takashi Yamashita 5 , Denise L Orwig 1 Author information Article notes Copyright and License information 1 Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD, USA 2 Department of Physical Therapy and Rehabilitation Science, University of Maryland School of Medicine, Baltimore, Maryland, USA 3 Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA 4 Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, USA 5 Department of Sociology, Anthropology, and Public Health, University of Maryland Baltimore County, Baltimore County, MD, USA * Corresponding Author : Rhea Mehta; Howard Hall, 660 W. Redwood Street, University of Maryland School of Medicine, Baltimore, MD 21201; rhea.mehta@som.umaryland.edu . ✉ Author Contributions: The corresponding author confirms that all individual who contributed to this manuscript have been accounted for and their contributions are noted below. Study concept and design: All; Acquisition of subjects and/or data: RM, JF;, Analysis of data: RM, JF, CC; Interpretation of data: All; Preparation of manuscript: All. Issue date 2025 Jun. PMC Copyright notice PMCID: PMC12005975 NIHMSID: NIHMS2035505 PMID: 39609242 The publisher's version of this article is available at Am J Geriatr Psychiatry Abstract Objective: Antidepressants are the first-line treatment for depression among older adults. While antidepressants are associated with increased risk of falls and fractures in older adults, their effect on outcomes after fall-related injuries such as hip fracture, and whether these effects differ by sex, is unknown. Thus, the purpose of this study was to examine the association between pre-fracture antidepressant use and hospital length of stay (LOS) among hip fracture survivors, and related sex differences. Methods: Participants included 17,936 community-dwelling Medicare fee-for-service beneficiaries with depression and hospitalization claim for hip fracture surgery between 2010 and 2017. Ordinal logistic regression estimated the association between pre-fracture antidepressant use and hospital LOS in days, categorized into three groups (1-4, 5-8, and 8+ days) during the 30-day post-fracture period, adjusting for demographic, medical, facility, and geographic factors. A sex-by-antidepressant use interaction term was included to examine effect heterogeneity by sex. Results: Pre-fracture antidepressant users (47%, n=8,350) were more likely to be younger, White females. The adjusted ordinal logistic regression showed beneficiaries who used antidepressants had 8% higher odds of being in a shorter hospital LOS category compared to non-users (OR=1.08; 95% CI=1.02, 1.14; p=0.01). The sex-by-antidepressant use interaction was not statistically significant (p=0.92). Conclusions: Among older adults with depression who subsequently experienced a hip fracture, antidepressant use of >30 days in the 6 months prior to fracture was associated with a shorter hospital LOS. These findings indicate that use of antidepressants does not prolong early recovery from hip fracture and may be protective. Keywords: Hip-fracture, Antidepressants, Depression, Sex Difference, Medicare INTRODUCTION Depression is a prevalent mental health condition, particularly among older adults. Managing depression effectively in older adults can improve overall quality of life, mental health, and functional abilities. 1 , 2 Perioperative depression prevalence specifically ranges between 6% and 52% in older adults and may reduce rehabilitation participation post-surgery. 3 Antidepressants are used as the first-line depression treatment among older adults 4 , 5 , especially among females, 6 and a better understanding of the risks and benefits of this medication in older populations is warranted. While antidepressants are crucial in managing depression, as a group, they can have side effects such as dizziness and sedation. 7 , 8 These side effects are especially pronounced in older adults, 9 who are already vulnerable to falls and fractures due to age-related declines in balance 10 and bone and muscle strength. 11 , 12 Hip fracture is a serious public health concern, with related medical costs reaching over $6 billion 13 for nearly 300,000 incident cases annually in the US. 14 Though historically more common in females, 15 recent literature projects an increase in hip fractures among males, 16 and older males have been noted to have poorer clinical outcomes after hip fracture, including longer hospital stays, and higher risk of delirium and mortality. 17 - 19 Existing literature has focused on post-hip fracture depression onset, which is common among older adults and can result in sequalae such as cognitive decline and delirium. 3 , 20 , 21 Hospital length of stay (LOS) is an important clinical outcome to consider among hip fracture patients with depression. Pre-operative depression is linked to longer hospital LOS, 22 , 23 which can result in declining mental and physical health among older adults, 24 , 25 including loss of memory and executive function, 26 poor mobility, and decreased engagement in activities of daily living after discharge. 27 Longer durations of hospital stay among hip fracture patients specifically is associated with higher odds of mortality post-discharge. 28 Hospitalized adults using antidepressants have longer LOS compared to non-users, 29 , 30 but existing literature has only explored LOS in older patients who were taking antidepressants during hospitalization, and not the potential influence of medication use prior to hospital admission. This may be due to a lack of data and knowledge surrounding patient characteristics prior to hospitalization. Additionally, although sex differences in hip fracture, depression, and antidepressant use are noted, they have not been clearly understood in the context of hospital LOS after hip fracture. These existing research gaps justify exploring antidepressant use among hip fracture survivors with depression and may reveal whether they confer benefit during recovery, informing the way providers coordinate and manage post-fracture medical care. Thus, the objective of this study was to assess the association between pre-fracture antidepressant use and hospital LOS among older hip fracture survivors with a depression diagnosis prior to fracture, and to determine whether this relationship presents differently in males compared to females. Based on existing literature, we hypothesized that those who used antidepressants pre-fracture would have a longer hospital LOS than non-users, and the magnitude of the association would be greater in older females compared to males. METHODS Sample The retrospective study sample consisted of community-dwelling, male and female fee-for-service Medicare beneficiaries aged 65+ years who had a hospitalization claim for hip fracture surgery between July 1, 2010 and December 31, 2017. All included beneficiaries had a diagnosis of depression (ICD-9 codes: 296.2, 296.3, 296.5, 300.4, 311, 301.12, 309.0, 309.1; ICD-10 codes: F20.4, F31.3-F31.5, F32.x, F33.x, F34.1, F41.2, F43.2), which was diagnosed prior to the 6 months preceding the index hip fracture. Those who were continuously enrolled in Medicare Parts A and B for 6 months prior to and 12 months after the index fracture were included. Participants were also required to be continuously enrolled in Medicare Part D in the 6 months prior to the index fracture. Beneficiaries who died during hospitalization, had Medicare Advantage, or were discharged against medical advice or to hospice were excluded. For beneficiaries who had multiple hip fractures within the study period, only the first index hospitalization was included. Hospital stay data was ascertained from the Medicare Provider and Analysis Review (MEDPAR) file, and medication data was identified using the Part D Event (PDE) and Part D Drug Characteristics files. Beneficiaries’ demographic information was obtained from the Master Beneficiary Summary Base File (MBSF). Measures Primary Exposure The primary exposure was antidepressant use, where users were defined as beneficiaries with a hip fracture and diagnosis of depression prior to the 6 months before index fracture. Users also had a total days’ supply of more than 30 days of any antidepressant medication filled within the 6 months prior to hip fracture. This definition was chosen to identify those who had more than one medication fill during the lookback period. Examples of classes of antidepressant medication that were included are SSRIs, serotonin-norepinephrine reuptake inhibitors (SNRIs), tricyclics (TCAs), monoamine oxidase inhibitors (MAOIs), and serotonin modulators. Primary Outcome The primary outcome was hospital LOS in days, measured as an ordinal categorical outcome during the 30-day post-fracture period. Based upon clinical perspectives and existing literature on hospital LOS after hip fracture surgery, 28 , 31 , 32 the LOS outcome variable was classified into three groups: 1 to 4 days, 5 to 8 days, and more than 8 days. Covariates The biopsychosocial framework 33 and relevant literature informed the selection of study covariates. Bivariate analyses were conducted to examine the distribution of hospital LOS among the covariates of interest to explore significant differences. Demographic and socioeconomic factors included age at the time of hip fracture (years), sex (male/female), self-reported race and ethnicity (White, Black, Hispanic, Asian, North American Native, Other), and dual Medicaid eligibility (yes/no). The Elixhauser Comorbidity Index captured 31 comorbidities at the time of hospital admission, which were ascertained and combined into a single composite score using ICD-9 and ICD-10 codes from claims data. 34 Our calculation excluded depression as a comorbidity, as the entire sample had depression, resulting in a range of 30 comorbid conditions. Alzheimer’s Disease and related dementias prior to hospital admission for hip fracture (yes/no) was included as a medical complexity that may influence LOS. County hospital bed size and rurality (ascertained using US Department of Agriculture’s rural-urban continuum codes) in tertiles were included as facility and geographical characteristics, respectively, that may impact LOS. Statistical Analysis Descriptive characteristics of the study sample were compared by pre-fracture antidepressant use using frequencies and percentages for categorical variables and means and standard deviations for continuous variables. Between-group differences in covariates at baseline were evaluated using chi-square and t-tests, respectively. Ordinal logistic regression was the primary method of analysis used to obtain odds ratios (OR) to examine the effect of pre-fracture antidepressant use on hospital LOS among hip fracture patients with depression. This method was utilized to best handle the ordinal nature of the categorical outcome. The fully adjusted model controlled for age, sex, race and ethnicity, Medicaid dual eligibility, Elixhauser score, prior Alzheimer’s Disease and/or dementia, hospital bed size, and rurality. To examine sex differences, we included a sex-by-antidepressant use interaction term in the adjusted model. Since missing data did not exceed 1% for any covariate, a complete case analysis approach was utilized. 35 - 37 Statistical significance was defined as p < 0.05, and all analyses were performed in SAS Studio version 9.4 (SAS Institute Inc., Cary, NC). Exploratory Analysis After conducting the primary analyses, an approximate E-value 38 was calculated to evaluate the potential impact of unmeasured confounding. No distinct cut-off values were pre-specified, as these depend on the data, exposure, and outcome of interest; however, a larger E-value is generally indicative of greater resilience to unmeasured confounding. 39 , 40 RESULTS The final analytic sample included 17,936 Medicare beneficiaries, of which 8,350 (47%) were users of antidepressant medication ( Figure 1 ). Descriptive statistics of the study participants are presented in Table 1 by pre-fracture antidepressant user status. Users of antidepressant medications were more likely to be younger, White, and female. Nearly 28% of users had fills for 2 or more different antidepressant medications, and SSRIs were the most filled antidepressant ( Table 2 ). Figure 1. Open in a new tab Study sample flow diagram. Table 1. Descriptive characteristics of Medicare beneficiaries by pre-fracture antidepressant use. Antidepressant Nonusers (n= 9,586) Antidepressant Users 2 (n=8,350) Variables 1 Mean (SD) Mean (SD) Age (years, 65+) * 80.8 (10.3) 79.4 (10.5) Elixhauser score * 3.3 (1.9) 3.5 (1.9) N (%) N (%) Sex * Male 2,123 (56.4%) 1,643 (43.6%) Female 7,463 (52.6%) 6,707 (47.3%) Race * White 8,717 (52.4%) 7,908 (47.6%) Black 405 (71.3%) 163 (28.7%) Hispanic 218 (65.7%) 114 (34.3%) Asian 109 (65.3%) 58 (34.7%) North American Native 53 (58.9%) 37 (41.1%) Other 63 (57.8%) 46 (42.2%) Dual eligible 2,996 (53.0%) 3,656 (46.9%) Prior Alzheimer's/dementia 2,802 (50.9%) 2,696 (49.0%) Intensive care unit stay 717 (52.2%) 657 (47.8%) For profit hospital 1,420 (54.6%) 1,179 (45.4%) Number of primary care providers per 100,000 patients <59 3,284 (53.3%) 2,873 (46.7%) 59-84 3,209 (54.7%) 2,663 (43.4%) >84 3,063 (52.4%) 2,782 (47.6%) Hospital bed size 0-230 beds 3,079 (52.6%) 2,774 (47.4%) 231-424 beds 3,202 (54.0%) 2,725 (45.9%) 425-2449 beds 3,303 (53.7%) 2,850 (46.3%) Nursing home bed size 0-423 beds 3,143 (54.8%) 2,589 (45.2%) 424-658 beds 3,093 (52.3%) 2,819 (47.7%) 425-2449 beds 3,275 (53.2%) 2,879 (46.8%) Teaching hospital 4,652 (53.1%) 4,240 (46.2%) % Living below poverty line by county 0-12.5% 2,975 (52.1%) 2,735 (47.9%) 12.5-16.9% 3,107 (53.2%) 2,729 (46.8%) 16.9-51.9% 3,497 (54.8%) 2,884 (45.2%) Rurality Urban 7,511 (53.9%) 6,420 (46.1%) Rural, urban adjacent 1,331 (51.4%) 1,259 (48.6%) Rural, not urban adjacent 737 (52.4%) 669 (47.6%) Open in a new tab 1 Notes: Race data was missing for 45, profit status data missing for 6, primary care provider data missing for 62, hospital bed size missing for 3, nursing home bed size missing for 138, poverty data missing for 9, and rurality data missing for 9 beneficiaries. 2 Notes: number of different antidepressants filled for users were as follows: 1 (n=5,924, 70.9%), 2 (n=1,960, 23.5%), 3 (n=392, 4.7%), and 4+ (n=74, 0.8%). * p < 0.0001. Table 2. Antidepressant use among users by class. Antidepressants 1 % of Use 2 SSRIs 57.4% Citalopram hydrobromide 16.2% Escitalopram oxalate 10.4% Fluoxetine HCL 6.3% Fluvoxamine maleate 0.2% Paroxetine HCL 6.1% Sertraline HCL 18.2% SNRIs 11.8% Desvenlafaxine 0.6% Duloxetine HCL 6.3% Venlafaxine HCL 4.9% TCAs 17.3% Amitriptyline HCL 4.2% Doxepin HCL 1.0% Imipramine HCL 0.4% Maprotiline HCL 10.6% Nortriptyline HCL 1.1% Serotonin modulators 13.2% Trazodone HCL 12.7% Vilazodone HCL 0.3% Vortioxetine hydrobromide 0.2% Open in a new tab 1 Notes: The following antidepressants are not shown due to small sample sizes: levomilnacipran HCL, clomipramine HCL, desipramine, protriptyline, nefazodone HCL, and tranylcypromine. 2 The average days filled with one or more antidepressants over the 6-month look back period was 128.5 days (SD: 45.8) 3 % of use may include beneficiaries who were prescribed multiple medications During the 6-month look back period, antidepressant users had an average of 128.5 days (standard deviation [SD]=45.8) of one or more antidepressant medication fills, and almost 9% of users had fills during the entire look back period. Approximately 53% of non-users and 46% of users spent between 1-4 days and 5-8 days in the hospital after hip fracture surgery, respectively. Among male and female beneficiaries, the distribution of hospital LOS was similar between non-users and users of antidepressants ( Figure 2a and 2b ). In the adjusted regression model, antidepressant medication users had approximately 8% higher odds of having a shorter LOS compared to non-users (odds ratio [OR]=1.08; 95% confidence interval [CI]=1.02, 1.14; p=0.01). The sex-by-antidepressant use interaction term was not statistically significant (p=0.92). Figure 2. Open in a new tab a and 2b. Percent of male (2a) and female (2b) beneficiaries in each hospital length of stay category by pre-fracture antidepressant use status (user/non-user). Exploratory Analysis An approximate E-value was calculated based upon the OR estimate obtained from the adjusted ordinal logistic regression model, 1.08. The formula 41 for calculating an E-value for an adjusted OR greater than 1 is: E − value approx = OR + √ ( √ OR x ( √ OR − 1 ) ) The corresponding E-value was ~1.35, suggesting that an unmeasured confounder would need to be associated with both antidepressant use and hospital LOS with an OR of at least 1.35, after accounting for any measured confounders, for the observed association to be nullified. DISCUSSION In this large sample of Medicare beneficiaries, antidepressant users had higher odds of shorter hospital LOS compared to non-users, and there was no evidence of sex differences in the association between pre-fracture antidepressant use and LOS. These differences persisted after a robust adjustment for demographics, medical complexity, and social determinants of health. The present study suggests that antidepressant use among older adults with depression does not impede early recovery after hip fracture and may even be protective against prolonged hospital stays. The current findings align with existing literature that reports functional benefits of antidepressant treatment for depression in older adults. Antidepressants have been shown to be efficacious among older adults with depression, 42 , 43 including reducing mortality risk 44 , 45 and suicidal ideations. 46 Antidepressant use among older adults with other musculoskeletal conditions, like arthritis, is associated with decreased depression symptomology and pain 47 and increased quality of life. 48 Although there has been a significant increase in antidepressant prescription among older adults and their use has increased across all age groups, 49 , 50 deprescribing these medications in older adults has been proposed. 51 - 53 Managing depression and use of antidepressants in older adults is complex, 9 and this new finding underscores the importance of a balanced approach in prescribing antidepressants. Our results did not demonstrate significant sex differences, which is consistent with some existing literature. Hospital LOS increases with age, regardless of sex, 54 and examination of the clinical efficacy of antidepressants among older adults cites no notable differences attributable to sex. 55 - 57 Furthermore, while sex differences in depression diagnoses remain prevalent, a review of multiple large-scale clinical trials examining responses to antidepressants among US adults showed that baseline depression severity did not differ by sex. 58 Thus, it is plausible other biopsychosocial factors may influence sex differences in the response to antidepressant drugs. For example, sex differences in hormone and stress levels 59 , 60 and genetics 61 have been shown to be associated with antidepressant drug response, both of which were not captured in the current study. Decreasing hospital LOS among older hip fracture survivors may have a direct, beneficial effect on patients. Extant literature has found longer durations of hospital stay to be associated with poor mental and physical health among older adults, 24 , 25 including decreased engagement in activities of daily living and mobility, 27 and declines in cognitive function. 26 In addition to these impairments, longer hospital LOS can lead to a loss of independence and increased caregiver burdens and exposure to infections, 62 all of which may impact patient recovery. Importantly, longer LOS among older adults is associated with an increased risk of mortality within 1 and 6 months, and 2 years post-discharge. 63 It may be especially important to consider those admitted to the hospital for hip fracture because hospitalized older adults who are at risk for functional decline are noted to have longer hospital stays, 64 and longer LOS is linked to increased risk of falls. 65 A higher risk of falls among older hip fracture survivors may lead to a secondary fracture, which often results in significant impairments in mobility and a higher mortality risk. 66 - 68 There are several limitations and strengths with this work. For one, there may be unmeasured confounding by other covariates not included in our study, but this was acknowledged by calculating an approximate E-value. The E-value of ~1.35 indicates some degree of resilience against unmeasured confounding and that another factor would need to be associated with both the exposure and outcome by an OR of at least 1.35 to explain away the observed association between antidepressant use and LOS. Additionally, there may be some presence of misclassification, where antidepressant users are defined as non-users due to the 6-month lookback period that was used to define the exposure. Finally, although this study was particularly interested in examining the effects of pre-fracture antidepressant use, it is important to acknowledge that it is unknown whether antidepressants were used throughout the acute hospitalization period. However, this work helps to address an important research gap surrounding the effects of antidepressant medications on older adults. To our knowledge, this is the first study to specifically examine the association between pre-fracture antidepressant use and hospital LOS among hip fracture survivors. Given the increased attention surrounding deprescription of antidepressants among older adults, these findings present an opposing idea, suggesting a potential benefit of antidepressant use prior to hip fracture in terms of hospital time. Finally, the use of a large, nationally representative sample of Medicare beneficiaries is particularly advantageous, as it provides additional details on patient characteristics prior to the event of interest and enhances generalizability. In sum, pre-hip fracture antidepressant use was associated with shorter hospital LOS in this sample of Medicare beneficiaries with a depression diagnosis who survived a hip fracture. These results may help inform decisions surrounding pre-fracture antidepressant prescription among hip fracture survivors with depression. Exploring differences in LOS by antidepressant class or dose may be an important next step, as well as examining the effect of pre-hip fracture antidepressant use on additional health outcomes post-discharge. Highlights. What is the primary question addressed by this study? —This work addresses whether pre-hip fracture antidepressant use is associated with hospital length of stay among Medicare beneficiaries after hip fracture surgery. What is the main finding of this study? —Beneficiaries who used antidepressant medication pre-fracture had higher odds of a shorter hospital length of stay compared to non-medication users. What is the meaning of the finding? —The results suggest a benefit of antidepressant use in terms of length of stay prior to hip fracture hospitalization. ACKNOWLEDGMENTS Funding sources: This work was supported by grants from the National Institutes of Health (T32 AG000262, P30 AG028747, RF1 NS128360, R01 AG048069, R01 AG079854, K76AG074926-01). Footnotes Conflict of Interest: The authors report no conflicts with any product mentioned or concept discussed in this article. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Data Statement The data has not been previously presented orally or by poster at scientific meetings. References 1. Gitlin LN, Harris LF, Mccoy MC, et al. A home-based intervention to reduce depressive symptoms and improve quality of life in older African Americans: A randomized trial. Ann Intern Med. 2013;159(4):243–252. doi: 10.7326/0003-4819-159-4-201308200-00005/ASSET/IMAGES/5TT4.JPG [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 2. Unützer J, Katon W, Callahan CM, et al. Collaborative Care Management of Late-Life Depression in the Primary Care Setting: A Randomized Controlled Trial. JAMA. 2002;288(22):2836–2845. doi: 10.1001/JAMA.288.22.2836 [ DOI ] [ PubMed ] [ Google Scholar ] 3. Srifuengfung M, Abraham J, Avidan MS, Lenze EJ. Perioperative Anxiety and Depression in Older Adults: Epidemiology and Treatment. Am J Geriatr Psychiatry. 2023;31(11):996–1008. doi: 10.1016/J.JAGP.2023.07.002 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 4. American Psychological Association. Depression Treatments for Older Adults. American Psychological Association. 2019. Accessed December 5, 2022. https://www.apa.org/depression-guideline/older-adults [ Google Scholar ] 5. Mulsant BH, Blumberger DM, Ismail Z, Rabheru K, Rapoport MJ. A systematic approach to pharmacotherapy for geriatric major depression. Clin Geriatr Med. 2014;30(3):517–534. doi: 10.1016/J.CGER.2014.05.002 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 6. Elflein J. Antidepressant use by age and gender U.S. 2019 ∣ Statista. Statista. July 17, 2020. Accessed January 1, 2023. https://www.statista.com/statistics/1133598/antidepressants-usage-by-age-gender-us/ [ Google Scholar ] 7. Hanlon JT, Zhao X, Naples JG, et al. Central Nervous System Medication Burden and Serious Falls in Older Nursing Home Residents. J Am Geriatr Soc. 2017;65(6):1183. doi: 10.1111/JGS.14759 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 8. Jung YS, Suh D, Choi HS, Park HD, Jung SY, Suh DC. Risk of Fall-Related Injuries Associated with Antidepressant Use in Elderly Patients: A Nationwide Matched Cohort Study. Int J Environ Res Public Health. 2022;19(4). doi: 10.3390/ijerph19042298 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 9. van Poelgeest EP, Pronk AC, Rhebergen D, van der Velde N. Depression, antidepressants and fall risk: therapeutic dilemmas—a clinical review. Eur Geriatr Med. 2021;12(3):585. doi: 10.1007/S41999-021-00475-7 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 10. Granacher U, Muehlbauer T, Gruber M. A qualitative review of balance and strength performance in healthy older adults: Impact for testing and training. J Aging Res. 2012;2012. doi: 10.1155/2012/708905 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 11. Becerikli M, Jaurich H, Schira J, et al. Age- dependent alterations in osteoblast and osteoclast activity in human cancellous bone. J Cell Mol Med. 2017;21(11):2773. doi: 10.1111/JCMM.13192 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 12. Padilla Colón CJ, Molina-Vicenty IL, Frontera-Rodríguez M, et al. Muscle and Bone Mass Loss in the Elderly Population: Advances in diagnosis and treatment HHS Public Access. J Biomed (Syd). 2018;3:40–49. doi: 10.7150/jbm.23390 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 13. Adeyemi A, Delhougne G. Incidence and Economic Burden of Intertrochanteric Fracture: A Medicare Claims Database Analysis. JBJS Open Access. 2019;4(1). doi: 10.2106/JBJS.OA.18.00045 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 14. Swenning T, Leighton J, Nentwig M, Dart B. Hip fracture care and national systems. OTA Int. 2020;3(1):e073. doi: 10.1097/OI9.0000000000000073 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 15. Sullivan KJ, Husak LE, Altebarmakian M, Brox WT. Demographic factors in hip fracture incidence and mortality rates in California, 2000-2011. J Orthop Surg Res. 2016;11(1):1–10. doi: 10.1186/S13018-015-0332-3/FIGURES/10 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 16. Stevens JA, Rudd RA. The impact of decreasing U.S. hip fracture rates on future hip fracture estimates. Osteoporos Int. 2013;24(10):2725–2728. doi: 10.1007/S00198-013-2375-9 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 17. Bajracharya R, Guralnik JM, Shardell MD, et al. Long-term sex differences in all-cause and infection-specific mortality post hip fracture. J Am Geriatr Soc. 2022;70(7):2107–2114. doi: 10.1111/JGS.17800 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 18. Oh ES, Sieber FE, Leoutsakos JM, Inouye SK, Lee HB. Sex Differences in Hip Fracture Surgery: Preoperative Risk Factors for Delirium and Postoperative Outcomes HHS Public Access. J Am Geriatr Soc. 2016;64(8):1616–1621. doi: 10.1111/jgs.14243 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 19. Orwig DL, Abraham DS, Hochberg MC, et al. Sex Differences in Recovery Across Multiple Domains Among Older Adults With Hip Fracture. The Journals of Gerontology: Series A. 2022;77(7):1463–1471. doi: 10.1093/GERONA/GLAB271 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 20. Burns A, Younger J, Morris J, et al. Outcomes following hip fracture surgery: a 2-year prospective study. Am J Geriatr Psychiatry. 2014;22(8):838–844. doi: 10.1016/J.JAGP.2013.01.047 [ DOI ] [ PubMed ] [ Google Scholar ] 21. Oughli HA, Chen G, Philip Miller J, et al. Cognitive improvement in older adults in the year after hip fracture: implications for brain resilience in advanced aging. Am J Geriatr Psychiatry. 2018;26(11):1119. doi: 10.1016/J.JAGP.2018.07.001 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 22. Krampe H, Barth-Zoubairi A, Schnell T, Salz AL, Kerper LF, Spies CD. Social Relationship Factors, Preoperative Depression, and Hospital Length of Stay in Surgical Patients. Int J Behav Med. 2018;25(6):658–668. doi: 10.1007/S12529-018-9738-8 [ DOI ] [ PubMed ] [ Google Scholar ] 23. Kerper LF, Spies CD, Buspavanich P, et al. Preoperative depression and hospital length of stay in surgical patients. Minerva Anestesiol. 2014;80(9):984–991. Accessed December 16, 2023. https://pubmed.ncbi.nlm.nih.gov/24280816/ [ PubMed ] [ Google Scholar ] 24. Bo M, Fonte G, Pivaro F, et al. Prevalence of and factors associated with prolonged length of stay in older hospitalized medical patients. Geriatr Gerontol Int. 2016;16(3):314–321. doi: 10.1111/GGI.12471 [ DOI ] [ PubMed ] [ Google Scholar ] 25. Barra BJ, Varela LF, Maldonado JR, et al. Clinical Profile and Length of Hospital Stay in a Sample of Psychogeriatric Patients Referred to Consultation Liaison Psychiatric Unit. Medicina (B Aires). 2021;57(3). doi: 10.3390/MEDICINA57030256 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 26. Wilson RS, Hebert LE, Scherr PA, Dong X, Leurgens SE, Evans DA. Cognitive decline after hospitalization in a community population of older persons. Neurology. 2012;78(13):950–956. doi: 10.1212/WNL.0B013E31824D5894 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 27. van Vliet M, Huisman M, Deeg DJH. Decreasing Hospital Length of Stay: Effects on Daily Functioning in Older Adults. J Am Geriatr Soc. 2017;65(6):1214–1221. doi: 10.1111/JGS.14767 [ DOI ] [ PubMed ] [ Google Scholar ] 28. Nikkel LE, Kates SL, Schreck M, Maceroli M, Mahmood B, Elfar JC. Length of hospital stay after hip fracture and risk of early mortality after discharge in New York state: retrospective cohort study. The BMJ. 2015;351. doi: 10.1136/BMJ.H6246 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 29. Hengartner MP, Passalacqua S, Andreae A, et al. Antidepressant use during acute inpatient care is associated with an increased risk of psychiatric rehospitalisation over a 12-month follow-up after discharge. Front Psychiatry. 2019;10(FEB). doi: 10.3389/fpsyt.2019.00079 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 30. Jones JF, Le J, Lee KC. Effect of antidepressant use on length of hospitalization in patients on anti-infective therapy. J Psychiatr Res. 2021;134:97–101. doi: 10.1016/J.JPSYCHIRES.2020.12.050 [ DOI ] [ PubMed ] [ Google Scholar ] 31. National Hospital Discharge Survey. Average Length of Hospital Stay for Persons with Hip Fractures by Year. Centers for Disease Control and Prevention. March 11, 2005. Accessed November 21, 2023. https://www.cdc.gov/mmwr/preview/mmwrhtml/mm5409a4.htm [ Google Scholar ] 32. Gleason LJ, Benton EA, Alvarez-Nebreda ML, Weaver MJ, Harris MB, Javedan H. FRAIL Questionnaire Screening Tool and Short-Term Outcomes in Geriatric Fracture Patients. J Am Med Dir Assoc. 2017;18(12):1082. doi: 10.1016/J.JAMDA.2017.07.005 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 33. Engel GL. The need for a new medical model: a challenge for biomedicine. Science. 1977;196(4286):129–136. doi: 10.1126/SCIENCE.847460 [ DOI ] [ PubMed ] [ Google Scholar ] 34. Quan H, Sundararajan V, Halfon P, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005;43(11):1130–1139. doi: 10.1097/01.MLR.0000182534.19832.83 [ DOI ] [ PubMed ] [ Google Scholar ] 35. Bennett DA. How can I deal with missing data in my study? Aust N Z J Public Health. 2001;25(5):464–469. doi: 10.1111/J.1467-842X.2001.TB00294.X [ DOI ] [ PubMed ] [ Google Scholar ] 36. Schafer JL. Multiple imputation: a primer. Stat Methods Med Res. 1999;8(1):3–15. doi: 10.1177/096228029900800102 [ DOI ] [ PubMed ] [ Google Scholar ] 37. Schafer JL, Graham JW. Missing Data: Our View of the State of the Art. Psychol Methods. 2002;7(2):147–177. doi: 10.1037/1082-989X.7.2.147 [ DOI ] [ PubMed ] [ Google Scholar ] 38. Vanderweele TJ, Ding P. Sensitivity analysis in observational research: Introducing the E-Value. Ann Intern Med. 2017;167(4):268–274. doi: 10.7326/M16-2607 [ DOI ] [ PubMed ] [ Google Scholar ] 39. Vanderweele TJ, Mathur MB. Commentary: Developing best-practice guidelines for the reporting of E-values. Int J Epidemiol. 2020;49(5):1495. doi: 10.1093/IJE/DYAA094 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 40. Gaster T, Eggertsen CM, Støvring H, Ehrenstein V, Petersen I. Quantifying the impact of unmeasured confounding in observational studies with the E value. BMJ Medicine. 2023;2(1):e000366. doi: 10.1136/BMJMED-2022-000366 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 41. VanderWeele TJ. On a square-root transformation of the odds ratio for a common outcome. Epidemiology. 2017;28(6):e58. doi: 10.1097/EDE.0000000000000733 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 42. Hvidberg MF. Are antidepressants effective in the treatment of depression in the elderly? A critical umbrella review on reviews, methods, and future perspectives. Mental Health Science. 2023;1(2):85–103. doi: 10.1002/MHS2.14 [ DOI ] [ Google Scholar ] 43. Gutsmiedl K, Krause M, Bighelli I, Schneider-Thoma J, Leucht S. How well do elderly patients with major depressive disorder respond to antidepressants: a systematic review and single-group meta-analysis. BMC Psychiatry. 2020;20(1). doi: 10.1186/S12888-020-02514-2 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 44. Kollhorst B, Jobski K, Krappweis J, Schink T, Garbe E, Schmedt N. Antidepressants and the risk of death in older patients with depression: A population-based cohort study. PLoS One. 2019;14(4). doi: 10.1371/JOURNAL.PONE.0215289 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 45. Gallo JJ, Morales KH, Bogner HR, et al. Long term effect of depression care management on mortality in older adults: follow-up of cluster randomized clinical trial in primary care. BMJ. 2013;346(7911). doi: 10.1136/BMJ.F2570 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 46. Gibbons RD, Brown CH, Hur K, Davis JM, Mann JJ. Suicidal thoughts and behavior with antidepressant treatment: reanalysis of the randomized placebo-controlled studies of fluoxetine and venlafaxine. Arch Gen Psychiatry. 2012;69(6):580–587. doi: 10.1001/ARCHGENPSYCHIATRY.2011.2048 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 47. Wohlreich MM, Sullivan MD, Mallinckrodt CH, et al. Duloxetine for the Treatment of Recurrent Major Depressive Disorder in Elderly Patients: Treatment Outcomes in Patients With Comorbid Arthritis. Psychosomatics. 2009;50(4):402–412. doi: 10.1176/APPI.PSY.50.4.402 [ DOI ] [ PubMed ] [ Google Scholar ] 48. Lin EHB, Katon W, Von Korff M, et al. Effect of improving depression care on pain and functional outcomes among older adults with arthritis: a randomized controlled trial. JAMA. 2003;290(18):2428–2434. doi: 10.1001/JAMA.290.18.2428 [ DOI ] [ PubMed ] [ Google Scholar ] 49. Pratt LA, Brody DJ, Gu Q. Antidepressant Use in Persons Aged 12 and Over: United States, 2005–2008. Centers for Disease Control and Prevention. 2011. Accessed March 7, 2023. https://www.cdc.gov/nchs/products/databriefs/db76.htm [ Google Scholar ] 50. Iaboni A, Seitz DP, Fischer HD, Diong CC, Rochon PA, Flint AJ. Initiation of Antidepressant Medication After Hip Fracture in Community-Dwelling Older Adults. Am J Geriatr Psychiatry. 2015;23(10):1007–1015. doi: 10.1016/J.JAGP.2014.10.002 [ DOI ] [ PubMed ] [ Google Scholar ] 51. Romdhani A, Lehmann S, Schlatter J. Discontinuation of Antidepressants in Older Adults: A Literature Review. Ther Clin Risk Manag. 2023;19:291–299. doi: 10.2147/TCRM.S395449 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 52. Mangin D, Siu HYH. Deprescribing Antidepressants in Older People - Breaking Up Can Be Hard to Do. Sr Care Pharm. 2021;36(5):228–237. doi: 10.4140/TCP.N.2021.228 [ DOI ] [ PubMed ] [ Google Scholar ] 53. Liacos M, Page AT, Etherton-Beer C. Deprescribing in older people. Aust Prescr. 2020;43(4):120. doi: 10.18773/AUSTPRESCR.2020.033 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 54. Healthcare Cost and Utilization Project (HCUP). HCUP Facts and Figures: Statistics on Hospital-Based Care in the United States, 2009 .; 2011. [ PubMed ] [ Google Scholar ] 55. Thorlund K, Druyts E, Wu P, Balijepalli C, Keohane D, Mills E. Comparative efficacy and safety of selective serotonin reuptake inhibitors and serotonin-norepinephrine reuptake inhibitors in older adults: a network meta-analysis. J Am Geriatr Soc. 2015;63(5):1002–1009. doi: 10.1111/JGS.13395 [ DOI ] [ PubMed ] [ Google Scholar ] 56. Felice D, O’Leary OF, Cryan JF, et al. When ageing meets the blues: Are current antidepressants effective in depressed aged patients? Neurosci Biobehav Rev. 2015;55:478–497. doi: 10.1016/J.NEUBIOREV.2015.06.005 [ DOI ] [ PubMed ] [ Google Scholar ] 57. Tham A, Jonsson U, Andersson G, Söderlund A, Allard P, Bertilsson G. Efficacy and tolerability of antidepressants in people aged 65 years or older with major depressive disorder - A systematic review and a meta-analysis. J Affect Disord. 2016;205:1–12. doi: 10.1016/J.JAD.2016.06.013 [ DOI ] [ PubMed ] [ Google Scholar ] 58. Strawn JR, Mills JA, Suresh V, et al. The impact of age on antidepressant response: A mega-analysis of individuals with major depressive disorder. J Psychiatr Res. 2023;159:266–273. doi: 10.1016/J.JPSYCHIRES.2023.01.043 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 59. Sramek JJ, Murphy MF, Cutler NR. Sex differences in the psychopharmacological treatment of depression. Dialogues Clin Neurosci. 2016;18(4):447. doi: 10.31887/DCNS.2016.18.4/NCUTLER [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 60. LeGates TA, Kvarta MD, Thompson SM. Sex differences in antidepressant efficacy. Neuropsychopharmacology. 2019;44(1):140. doi: 10.1038/S41386-018-0156-Z [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 61. Porcelli S, Drago A, Fabbri C, Gibiino S, Calati R, Serretti A. Pharmacogenetics of antidepressant response. J Psychiatry Neurosci. 2011;36(2):87. doi: 10.1503/JPN.100059 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 62. Kaboli PJ, Go OT, Hockenberry J, et al. Associations between reduced hospital length of stay and 30-day readmission rate and mortality: 14-year experience in 129 Veterans Affairs hospitals. Ann Intern Med. 2012;157(12):837–845. doi: 10.7326/0003-4819-157-12-201212180-00003 [ DOI ] [ PubMed ] [ Google Scholar ] 63. Han TS, Murray P, Robin J, Wilkinson P, Fluck D, Fry CH. Evaluation of the association of length of stay in hospital and outcomes. International Journal for Quality in Health Care. 2022;34(2):1–9. doi: 10.1093/INTQHC/MZAB160 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 64. Buurman BM, Hoogerduijn JG, van Gemert EA, de Haan RJ, Schuurmans MJ, de Rooij SE. Clinical Characteristics and Outcomes of Hospitalized Older Patients with Distinct Risk Profiles for Functional Decline: A Prospective Cohort Study. PLoS One. 2012;7(1). doi: 10.1371/JOURNAL.PONE.0029621 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 65. Najafpour Z, Godarzi Z, Arab M, Yaseri M. Risk Factors for Falls in Hospital In-Patients: A Prospective Nested Case Control Study. Int J Health Policy Manag. 2019;8(5):300. doi: 10.15171/IJHPM.2019.11 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 66. Solou K, Tyllianakis M, Kouzelis A, Lakoumentas J, Panagopoulos A. Morbidity and Mortality After Second Hip Fracture With and Without Nursing Care Program. Cureus. 2022;14(3). doi: 10.7759/CUREUS.23373 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 67. Berry SD, Samelson EJ, Hannan MT, et al. Second Hip Fracture in Older Men and Women: The Framingham Study. Arch Intern Med. 2007;167(18):1971–1976. doi: 10.1001/ARCHINTE.167.18.1971 [ DOI ] [ PubMed ] [ Google Scholar ] 68. Trevisan C, Bedogni M, Pavan S, et al. The impact of second hip fracture on rehospitalization and mortality in older adults. Arch Gerontol Geriatr. 2020;90. doi: 10.1016/J.ARCHGER.2020.104175 [ DOI ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Data Availability Statement The data has not been previously presented orally or by poster at scientific meetings. 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