Next Article in Journal
Tricuspid Regurgitation in the Era of Transcatheter Interventions: The Pivotal Role of Multimodality Imaging
Previous Article in Journal
Neural Correlates of Huntington’s Disease Based on Electroencephalography (EEG): A Mechanistic Review and Discussion of Excitation and Inhibition (E/I) Imbalance
Previous Article in Special Issue
Prognostic Factors for Mortality in Patients Aged 90 Years and Older with Proximal Femoral Fractures Undergoing Surgery: A Retrospective Study
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JCM
Volume 14
Issue 14
10.3390/jcm14145008
jcm-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Trends and Risk Factors for the Hospitalization of Older Adults Presenting to Emergency Departments After a Bed-Related Fall: A National Database Analysis

by
Andy Tom
1,
Sergio M. Navarro
1,2,
Grant M. Spears
3,
Adam Schluttenhofer
1,
Michelle Junker
1,
John Zietlow
1,
Roderick Davis
1,
Allyson K. Palmer
4,
Nathan K. LeBrasseur
5,
Fernanda Bellolio
6 and
Myung S. Park
1,*
1
Department of Surgery, Division of Trauma, Critical Care and General Surgery, Mayo Clinic, Rochester, MN 55905, USA
2
Department of Surgery, University of Minnesota, Minneapolis, MN 55455, USA
3
Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
4
Department of Medicine, Division of Hospital Internal Medicine, Mayo Clinic, Rochester, MN 55905, USA
5
Department of Physical Medicine and Rehabilitation, Mayo Clinic, Rochester, MN 55905, USA
6
Department of Emergency Medicine, Mayo Clinic, Rochester, MN 55905, USA
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2025, 14(14), 5008; https://doi.org/10.3390/jcm14145008
Submission received: 6 June 2025 / Revised: 27 June 2025 / Accepted: 10 July 2025 / Published: 15 July 2025
(This article belongs to the Special Issue Geriatric Fracture: Current Treatment and Future Options)
Browse Figures
Versions Notes

Abstract

Background/objectives: Falls are a leading cause of traumatic injury and hospitalization for adults over the age of 65. While common, bed-related falls are relatively understudied when compared to ambulatory falls. The aim of this study is to characterize the risk factors for the hospitalization of older adults presenting to U.S. emergency departments (EDs) after a fall from bed. Methods: This was a cross-sectional study using publicly available data from the U.S. Consumer Product Safety Commission’s National Electronic Injury Surveillance System (NEISS) from 2014 to 2023, including all adults over the age of 65 presenting to the NEISS’s participating EDs with bed-related fall injuries. We identified fall injuries using a keyword search of the NEISS narratives and determined how the fall occurred by manually reviewing a randomized 3% sample of the narratives. We summarized demographics and injury patterns with descriptive statistics. We constructed a multivariable logistic regression model to identify risk factors for hospitalization and used Poisson regression to assess temporal trends in fall incidence and hospital admissions. Results: An estimated average of 320,751 bed-related fall injuries presented to EDs annually from 2014 to 2023. ED visits increased by 2.85% per year, while hospital admissions rose by 5.67% per year (p < 0.001). The most common injury patterns were superficial injuries (contusions, abrasions, lacerations, avulsions, and punctures) (28.6%), fractures (21.7%), and internal injuries (including concussions) (21.6%). Most of the falls occurred while transitioning into or out of bed (34.4%) or falling out of bed (56.8%). Hospitalization was required in 34.1% of cases and was associated with male sex, medication use at time of injury, and fracture injuries. Conclusions: Bed-related falls and associated hospitalizations are increasing among older adults. ED providers should understand risk factors for hospitalization in these common injuries such as male sex, medication use at time of injury, and high-risk injury patterns. Additionally, prevention efforts should focus on helping older adults remain safely in bed and then assisting with transitions into or out of bed.

1. Introduction

Falls are the leading cause of injury-related morbidity and mortality in the U.S. for people over the age of 65 [1]. Older adults [defined as adults over the age of 65 are particularly susceptible to serious injury from low-energy falls due to age-related loss of bone mass and bone microstructure, reduced muscle mass, and balance impairment from other comorbidities such as diabetes [2,3,4]. Increased fall risk is also associated with an increased risk of 30-day mortality after an emergency department (ED) visit for any reason [5]. After falling, many older adults experience lasting impairment from loss of motor capacity, poor gait performance, and depression [6]. As the U.S. population ages, it is increasingly important to understand risk factors associated with falls in this patient population.
Fall injuries are also a significant burden on the finances of individual patients and society as a whole. In 2020, healthcare expenditures for fall injuries in older adults were estimated to exceed USD 80 billion, with the majority being paid by public insurers [7]. Additionally, the median cost to individuals experiencing a fall was USD 26,143, which could be a devastating blow to an already financially vulnerable demographic [8]. There are also additional costs associated with arranging long-term care or transport to follow-up appointments, as well as taking time from work for recovery.
An estimated 3 million falls presented to U.S. EDs in 2015 [9]. Many of these falls are related to consumer products that act as tripping hazards such as stairs, rugs, bathtubs, and toilets [10,11]. While some consumer product-related falls are well characterized in older adults [10,11,12], traumatic injuries secondary to falling from a bed are less well-studied. Bed-related falls have primarily been examined in a hospital or assisted living setting [13,14,15], but it is important to understand the number of these injuries that occur nationwide in the community setting. Additionally, traumatic falls from a bed may also present with injury patterns different from ambulatory falls that could guide evaluation in the ED. Lastly, it is important to understand how these injuries occur in the community setting to develop effective risk mitigation strategies.
In this study, we examined the injury patterns and demographic characteristics of older adults who presented to U.S. emergency departments following a bed-related fall from 2014 to 2023. To understand how these injuries occur and what factors lead to hospital admission, we manually reviewed a subset of narratives included in the NEISS data that provide a short description of the events preceding each injury. Additionally, we analyzed factors associated with an increased risk of hospitalization in these patients such as injury pattern, age, medication or alcohol use, and sex [16,17,18].

2. Materials and Methods

This was a cross-sectional descriptive study using publicly available online data from the U.S. Consumer Product Safety Commission’s National Electronic Injury Surveillance System (NEISS). The NEISS collects data on consumer product-related injuries from 100 EDs across the United States selected as a probability sample of all 5000+ U.S. hospitals. When patients with these injuries present to the ED, they are included in the NEISS database [19]. To generate national estimates, the NEISS assigns a weight to each case based on the hospital’s annual number of ED visits and geographic location [20].
At NEISS-participating hospitals, a professional coordinator collects standardized data including patient demographics (age and sex), diagnosis, body part injured, patient disposition, location where the injury occurred, and a brief narrative describing the incident that led to the injury. Additionally, the NEISS coordinator assigns each case a consumer product code that specifies a product associated with the injury [19]. Data from the NEISS has been used previously to characterize consumer product-related injury trends [10,11,12]. We adhere to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for reporting observational studies [21].
This study utilized publicly available data and was determined not to require Institutional Review Board (IRB) review or approval. The data used in this research is accessible to the public without any restrictions, ensuring that no identifiable information about individuals is included.
We queried the NEISS for product codes in the “Beds, mattresses, and pillows” category and included all patients ≥65 years old from 2014 to 2023 that presented after an injury to a NEISS-participating ED. We filtered for fall injuries by searching the narrative for keywords related to falls (i.e., fall, fell, etc.) and categorized bed-related injuries as “fall” or “non-fall.” We used the NEISS sampling weights to obtain national estimates and trends of bed-related fall injuries over the study period, as well as demographic and injury-related characteristics of these patients [20].
We randomly selected a sample of 3% (n = 2112) of the bed-related fall cohort for manual review. The 3% was based on the necessary sample size for estimating event proportions, based on a population of 70,391 cases, with a 2% margin of error and 95% confidence interval [22,23]. In the bed-related fall cohort, we reviewed the narrative for each injury and sorted injuries into one of the following categories:
  • Falling out of bed—incidents where the patient fell out of bed while they were already safely in bed.
  • Transitioning—falling during a transition into or out of bed.
  • During activity out of bed—falls that occurred when the patient was out of bed performing some activity (i.e., making the bed, vacuuming around the bed and tripped on bedframe, etc.).
  • Found down/other—patient was found down by someone else and unable to report mechanism or fall was due to a unique mechanism (i.e., trying to exercise sitting on edge of bed and fell).
  • Incorrectly labeled fall—injuries that were incorrectly identified as falls (n = 59, 2.8%). These were excluded from subsequent analyses.
We also reviewed the “body part” and “diagnosis” codes designated by the NEISS and combined them into the following “injury pattern” categories using a modified Barell matrix [24]: lower trunk fractures/dislocations, upper trunk fractures/dislocations, lower extremity fractures/dislocations, upper extremity fractures/dislocations, head/face/neck fractures, internal injuries (including hematomas and concussions), pain/weakness, contusions/abrasions/lacerations/avulsions/punctures, sprains/strains, and other/unspecified.
We report descriptive statistics as median (IQR) or frequency (%) and display the data three ways for comparison—as an unweighted summary of the full NEISS bed-related falls cohort, as weighted estimates for the full U.S. population based on the full cohort and each patient’s associated NEISS sample weight, and finally as an unweighted summary of the manually reviewed 3% subsample (after removing the incorrectly included non-fall injuries). To conduct a trend analysis of falls over time, within the full bed-related falls cohort, we fitted Poisson regression models to assess changes in the number of fall-related ED visits and resulting hospitalizations over time, with year as the independent variable. We adjusted these counts by dividing by the total number of ED visits per year among geriatric patients to account for annual fluctuations in total ED visits. We assessed the goodness of fit of the Poisson regression models with McFadden’s pseudo-R-squared.
Using the subsample of manually reviewed cases, we analyzed risk factors for hospitalization after presentation to the ED with logistic regression. We analyzed demographic and injury-related characteristics in a simple regression model and considered variables with a p-value less than 0.10 for inclusion in a multivariable regression model. Because data was only available from 2018 to 2023 regarding the contribution of medications to the fall incidents, we constructed a secondary multivariable model including this variable on data from that time frame. All statistical analyses were completed using R (version 4.3.0; R Core Team, 2024).

3. Results

Table 1 includes the demographics and injury characteristics of the cohort. From 2014 to 2023, a total of 70,391 patients with bed-related falls presented to NEISS-participating EDs, with an average of 7039 bed-related falls per year. This translated to a mean national estimate of 320,751 (SD 58,218) bed-related injuries per year. Over the study period, after accounting for sampling weights, the estimated median patient age was 81 (IQR 73–88), with 36.3% men and 63.7% women. An estimated 64.2% of injuries were treated and released from the ED, 35.1% required hospitalization, observation, or subsequent transfer, 0.5% left without being seen, and 0.1% resulted in mortality. Superficial injuries (contusions/abrasions/lacerations/avulsions) were the most common injury pattern with a frequency of 28.6%, fracture and dislocations accounted for 21.7%, and internal injuries (including concussions) accounted for 21.6%.
The number of ED visits for bed-related falls (as a fraction of total ED visits) increased by an average of 2.85% each year over the study period (95% CI 2.64–3.06%, p < 0.001), and hospital admissions due to falls (also as a fraction of total ED visits) increased by an average of 5.67% each year (95% CI 5.29–6.05, p < 0.001) (Figure 1 and Figure 2).
In the selected subsample, which was manually reviewed and categorized by mechanism of injury, a total of 56.8% of falls occurred while in bed, 34.4% occurred during a transition into or out of bed, 5.9% occurred during an activity out of the bed, and in 2.8%, the mechanism and involvement of the bed in the injury was unique or unable to be determined.
Results of the univariate logistic analyses are presented in Table 2. Men were more likely to require hospitalization and have injuries associated with medication use. Hospitalization rates were also significantly associated with injury patterns. Injury mechanism was borderline significant (p = 0.088), with hospitalization occurring most frequently in patients with injuries while transitioning in and out of bed. Age and alcohol use at time of injury did not meet our threshold for significance.
Based on the selection criteria (p < 0.10), sex, injury pattern, and injury mechanism were included in the multivariable model on all patients from 2014 to 2023 (Table 3). Men remained more likely to be admitted to the hospital compared to women (OR = 1.55, 95% CI: 1.26–1.91, p < 0.001). Among injury patterns, lower trunk fractures (OR = 10.04, 95% CI: 6.41–16.06 vs. patients diagnosed with pain/weakness) and lower extremity fractures (OR = 4.59, 95% CI: 2.71–7.88) were associated with the highest odds of hospitalization. Other significant injury patterns associated with increased hospitalization included other/unspecified injuries (OR = 2.52, 95% CI: 1.72–3.70), upper trunk fractures (OR = 2.23, 95% CI: 1.31–3.77), and head/neck/face fractures or dislocations (OR = 2.17, 95% CI: 1.01–4.61). Conversely, patients with superficial injuries (OR = 0.56, 95% CI: 0.39–0.80) and patients with sprains or strains (OR = 0.30, 95% CI: 0.12–0.66) were significantly less likely to be hospitalized compared to patients presenting with pain or weakness. Injury mechanism was not significantly associated with hospitalization in the multivariable model (p = 0.59). A secondary multivariable model (Table 4) was run on patients injured from 2018 to 2023, as data collected during this timeframe included information about whether medication/drug use contributed to each bed-related fall. In this model, falls caused by drug or medication use were associated with a higher likelihood of hospitalization (OR = 2.13, 95% CI: 1.20–3.80, p = 0.010).

4. Discussion

Our study investigated fall injuries involving a bed in older adults using a national database. Bed-related falls represent approximately 10% of the estimated 3 million falls in adults over 65 that presented to the ED in 2015 [9]. We found that bed-related fall injuries and hospitalizations have increased at a rate of 2.85% annually compared to a 3% annual increase for all consumer product-related injuries in this age group [19]. This increase mirrors a broader trend of rising fall injuries and mortality from falls [25], likely driven by increasing multimorbidity in the U.S. [26].
When examining bed-related injuries, we found that 12.8% of cases sub-sampled included ED presentations due to pain and general weakness, a category that is not part of the original NEISS coding. These cases do not fit well into a traditional injury classification scheme; however, older patients with generalized weakness are more likely to sustain an injury [27], and older patients who present to the ED with non-specific complaints have a high risk of hospitalization and 30-day mortality [28,29]. The 12.8% of patients presenting with pain/weakness in our cohort highlights the importance of a comprehensive assessment of older adults presenting to the ED after a fall and incorporating gait evaluation prior to dismissal to ensure safety [30]. Furthermore, falls in older adults represent a sentinel event and a marker for deterioration in their health [31]. Reducing fall-related injury begins with prehospital evaluations and “lift assist” calls and continues through risk assessments, screenings, and interventions that occur during and after emergency department visits [31].
A total of 36.7% of bed-related falls identified required subsequent hospitalization or transfer. Previous studies have shown that risk factors for hospitalization after a fall include diagnoses of Parkinson’s dementia and urinary tract infection, as well as low functional status and physical activity [32]. Our study found that certain injury patterns, medication use, and sex were risk factors for hospitalization. While women were more likely to present to the ED, men were more likely to require hospitalization. Men were also more likely to fall once hospitalized and are more likely to experience fall-related mortality, indicating that men may present to the hospital with more severe injuries relative to their health status than women [1,33]. Interestingly, age was not an independent risk factor for hospitalization in our cohort, indicating that other factors such as injury pattern and medication use are more important drivers of hospitalization. Other risk factors for hospitalization included fracture or dislocation of any type. These injuries were some of the most common injuries in our cohort, highlighting the need for a thorough musculoskeletal evaluation, including an evaluation for rib fractures after a bed-related fall. Injuries associated with prescription medications were also more likely to require hospitalization. The association between medications and hospitalization should prompt a medication review in older adults presenting to the ED after a fall. While internal injuries, most of which were head injuries, were not associated with an increased risk of admission in our cohort, these injuries have been associated with an increased risk of return to the ED within 90 days and should be followed up closely [34].
Polypharmacy, the use of high-risk medications like benzodiazepines or opioids [35,36], high comorbidity burden, visual and hearing impairments, orthostatic hypotension, home hazards, balance and gait abnormalities, sarcopenia, alcohol use disorder, and frailty are known risk factors for falls [37,38]. Identifying and addressing modifiable risk factors such as poor visual acuity, high-risk medication use, and gait abnormalities during ED and routine clinic visits prevent recurrent falls and promote safer mobility [39]. Measures of function collected at the index ED visit, such as the Timed Up and Go, are helpful in predicting clinical outcomes [40]. The American Geriatrics Society and the Geriatric Emergency Care Applied Research Network (GEAR) emphasize comprehensive fall risk assessments, including medication reviews and environmental evaluations, as part of their guidelines for geriatric emergency care [41].
The mechanisms we identified through our narrative analysis provide valuable insight for prevention efforts. Falling out of bed while already in bed accounted for 56.8% of injuries, while falls during a transition into or out of bed represented 34.4% of injuries. Interestingly, falls occurring while the patient was out of bed, such as tripping over the bed frame or falling while making the bed, were less frequent at 5.9% but still warrant attention as part of a comprehensive fall prevention strategy. Previous studies have demonstrated that falls are associated with decline in function in more than one third of the patients [37]. Our findings suggest that while fall mechanism was a significant risk factor for hospitalization in the univariate analysis, injury pattern, especially fractures, was the most important driver of hospitalization risk. This may be because certain fall mechanisms such as transitioning could lead to more severe injuries; thus, the mechanism and injury pattern may be dependent on one another.
While physical measures such as bed rails, compliant flooring, and low-level beds have not been shown to reduce bed-related fall injuries in hospital and long-term care settings, these interventions have not been evaluated in older adults living independently [13,14,15]. Exercise plans have been shown to reduce the risk of falling and risk of serious injury after a fall and could help reduce the risk of falling while transitioning into or out of bed [42]. Additionally, multifactorial plans that include an assessment (timed up and go test, gait speed test, Berg balance scale, etc.), a medication review, and one or more additional interventions based on individual risk factors have been shown to be effective at reducing falls, in general, but were not specifically targeted toward bed-related falls [42]. Our research suggests that interventions focused on helping older adults remain safely in their beds during rest and move in and out of their beds could have the greatest impact on reducing bed-related falls.
Our research had several limitations. The NEISS collects data from a nationally representative sample of EDs but does not include a validated injury severity score and does not account for non-emergent injuries treated at community clinics. As a result, the database likely underestimates the total incidence of bed-related fall injuries. Furthermore, the NEISS captures presentations to EDs from the community but may not capture bed-related fall injuries occurring during hospitalization. Additionally, while the narratives provide a useful source of supplementary information, they are not standardized and can vary in their content. The lack of standardized reporting in the narratives could potentially introduce non-differential misclassification bias, although the analysis of the narratives has been validated in case–control studies [43]. The coding structure of the NEISS can also lack specificity on the diagnoses [44], data quality and completeness rely on documentation available from the medical records, and the NEISS only includes drug and alcohol data starting in 2018. Lastly, the NEISS only records information during the initial presentation to the ED, precluding any long-term follow-up or analysis.
While our study provides a basic understanding of the epidemiology and risk factors for hospitalization for bed-related falls, more research is needed to find interventions effective at preventing these injuries. The current study highlights the need for targeted efforts to reduce fall-related injuries in the bed environment [42]. Additionally, ongoing research is primarily focused on preventing these injuries in hospital wards or assisted living settings [13,14,15]. Future research is needed on evaluating interventions for older adults who are at risk of falling but live independently in the community.

5. Conclusions

The incidence of bed-related falls and significant injuries requiring hospitalization has been increasing in the past decade. This trend will most likely continue as the U.S. population ages. Our study provides insight into the trends, mechanisms, and injury patterns of bed-related falls in the geriatric (≥65 years old) population. One third of patients who present to an ED following a bed-related fall will require subsequent hospitalization. Patients with nonspecific complaints such as pain and weakness were much more likely to require hospitalization than patients with superficial injuries, highlighting the need for comprehensive assessment in all patients presenting with falls, including those that have no evident injuries. Fracture injuries—particularly lower trunk and lower extremity fractures—alongside drug/medication use and male sex emerged as the strongest predictors of hospitalization after falls. Understanding these patterns and trends is vital to guiding patient counseling and future preventative efforts.

Author Contributions

Conceptualization, A.T., S.M.N., M.J., J.Z., R.D., M.S.P., F.B., A.K.P. and N.K.L.; methodology, A.T., S.M.N., M.J., J.Z., R.D., M.S.P., F.B., A.K.P. and N.K.L.; validation, A.T., A.S. and G.M.S.; formal analysis, A.T., S.M.N., A.S. and G.M.S.; investigation, A.T., S.M.N., A.S. and G.M.S.; data curation, A.T., A.S. and G.M.S.; writing—original draft preparation, A.T.; funding acquisition, S.M.N. and M.S.P.; writing—review and editing, A.T., S.M.N., A.S., G.M.S., M.J., J.Z., R.D., M.S.P., F.B., A.K.P. and N.K.L.; visualization, A.T., A.S. and G.M.S.; supervision, M.S.P.; project administration, S.M.N. All authors have read and agreed to the published version of the manuscript.

Funding

This project was supported by the Mayo Clinic Robert and Arlene Kogod Center on Aging and R01HL162729-02 (MSP), R38HL150086 Stimulating Access to Research in Residency (SMN), and UL1 TR002377 (MSP) from the National Center for Advancing Translational Sciences.

Institutional Review Board Statement

This study utilized publicly available data, which does not require IRB approval. The data used in this research is accessible to the public without any restrictions, ensuring that no identifiable information about individuals is included.

Informed Consent Statement

Patient consent was waived as the data used in this research is accessible to the public without any restrictions, ensuring that no identifiable information about individuals is included.

Data Availability Statement

The data supporting the conclusions of this article are available from https://www.cpsc.gov/cgibin/NEISSQuery/home.aspx (accessed on 24 October 2024).

Acknowledgments

This article is a revised and expanded version of a paper entitled “Increasing Incidence and Hospitalization of Bed-Related Fall Injuries in Older Adults”, which was presented at the American Geriatric Society 2025 Annual Meeting, Chicago, IL, USA, 8–10 May 2025 [45].

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Abbreviations

The following abbreviations are used in this manuscript:
NEISSNational electronic injury surveillance system
EDEmergency department
STROBEStrengthening the Reporting of Observational Studies in Epidemiology
IRBInstitutional Review Board
GEARGeriatric Emergency Care Applied Research Network

References

  1. Kakara, R.; Bergen, G.; Burns, E.; Stevens, M. Nonfatal and Fatal Falls Among Adults Aged ≥ 65 Years—United States, 2020-2021. MMWR Morb. Mortal. Wkly. Rep. 2023, 72, 938–943. [Google Scholar] [CrossRef] [PubMed]
  2. Reeve, J.; Loveridge, N. The fragile elderly hip: Mechanisms associated with age-related loss of strength and toughness. Bone 2014, 61, 138–148. [Google Scholar] [CrossRef] [PubMed]
  3. Yamada, M.; Kimura, Y.; Ishiyama, D.; Otobe, Y.; Suzuki, M.; Koyama, S.; Arai, H. Combined effect of lower muscle quality and quantity on incident falls and fall-related fractures in community-dwelling older adults: A 3-year follow-up study. Bone 2022, 162, 116474. [Google Scholar] [CrossRef] [PubMed]
  4. Wallace, C.; Reiber, G.E.; LeMaster, J.; Smith, D.G.; Sullivan, K.; Hayes, S.; Vath, C. Incidence of falls, risk factors for falls, and fall-related fractures in individuals with diabetes and a prior foot ulcer. Diabetes Care 2002, 25, 1983–1986. [Google Scholar] [CrossRef] [PubMed]
  5. Hamilton, M.P.; Bellolio, F.; Jeffery, M.M.; Bower, S.M.; Palmer, A.K.; Tung, E.E.; Mullan, A.F.; Carpenter, C.R.; Oliveira, J.E.S.L. Risk of falls is associated with 30-day mortality among older adults in the emergency department. Am. J. Emerg. Med. 2024, 79, 122–126. [Google Scholar] [CrossRef] [PubMed]
  6. Park, C.; Atique, M.M.U.; Mishra, R.; Najafi, B. Association between Fall History and Gait, Balance, Physical Activity, Depression, Fear of Falling, and Motor Capacity: A 6-Month Follow-Up Study. Int. J. Environ. Res. Public Health 2022, 19, 10785. [Google Scholar] [CrossRef] [PubMed]
  7. Haddad, Y.K.; Miller, G.F.; Kakara, R.; Florence, C.; Bergen, G.; Burns, E.R.; Atherly, A. Healthcare spending for non-fatal falls among older adults, USA. Inj. Prev. 2024, 30, 272–276. [Google Scholar] [CrossRef] [PubMed]
  8. Newgard, C.D.; Lin, A.; Caughey, A.B.; Eckstrom, E.; Bulger, E.M.; Staudenmayer, K.; Maughan, B.; Malveau, S.; Griffiths, D.; McConnell, K.J. The Cost of a Fall Among Older Adults Requiring Emergency Services. J. Am. Geriatr. Soc. 2021, 69, 389–398. [Google Scholar] [CrossRef] [PubMed]
  9. Moreland, B.L.; Kakara, R.; Haddad, Y.K.; Shakya, I.; Bergen, G. A Descriptive Analysis of Location of Older Adult Falls That Resulted in Emergency Department Visits in the United States, 2015. Am. J. Lifestyle Med. 2021, 15, 590–597. [Google Scholar] [CrossRef] [PubMed]
  10. Solaiman, R.H.; Irfanullah, E.; Navarro, S.M.; Keil, E.J.; Onizuka, N.; Tompkins, M.A.; Harmon, J.V., Jr. Rising incidence of stair-related upper extremity fractures among older adults in the United States: A 10-year nationwide analysis. Osteoporos. Int. 2023, 34, 1241–1248. [Google Scholar] [CrossRef] [PubMed]
  11. Shields, W.; Omaki, E.; Aassar, M.L.; Blue, T.; Brooks, B.; O’Hara, J.; Perona, R.Y. Structural housing elements associated with injuries in older adults in the USA. Inj. Prev. 2024, Aug 6:ip-2024-045252. [Google Scholar] [CrossRef] [PubMed]
  12. Orces, C.H. Emergency department visits for fall-related fractures among older adults in the USA: A retrospective cross-sectional analysis of the National Electronic Injury Surveillance System All Injury Program, 2001–2008. BMJ Open 2013, 3, e001722. [Google Scholar] [CrossRef] [PubMed]
  13. Capezuti, E.; Maislin, G.; Strumpf, N.; Evans, L.K. Side rail use and bed-related fall outcomes among nursing home residents. J. Am. Geriatr. Soc. 2002, 50, 90–96. [Google Scholar] [CrossRef] [PubMed]
  14. Mackey, D.C.; Lachance, C.C.; Wang, P.T.; Feldman, F.; Laing, A.C.; Leung, P.M.; Hu, X.J.; Robinovitch, S.N. The Flooring for Injury Prevention (FLIP) Study of compliant flooring for the prevention of fall-related injuries in long-term care: A randomized trial. PLoS Med. 2019, 16, e1002843. [Google Scholar] [CrossRef] [PubMed]
  15. Haines, T.P.; Bell, R.A.; Varghese, P.N. Pragmatic, cluster randomized trial of a policy to introduce low-low beds to hospital wards for the prevention of falls and fall injuries. J. Am. Geriatr. Soc. 2010, 58, 435–441. [Google Scholar] [CrossRef] [PubMed]
  16. Zaninotto, P.; Huang, Y.T.; Di Gessa, G.; Abell, J.; Lassale, C.; Steptoe, A. Polypharmacy is a risk factor for hospital admission due to a fall: Evidence from the English Longitudinal Study of Ageing. BMC Public Health 2020, 20, 1804. [Google Scholar] [CrossRef] [PubMed]
  17. Lo, A.X.; Flood, K.L.; Biese, K.; Platts-Mills, T.F.; Donnelly, J.P.; Carpenter, C.R. Factors Associated With Hospital Admission for Older Adults Receiving Care in U.S. Emergency Departments. J. Gerontol. A Biol. Sci. Med. Sci. 2017, 72, 1105–1109. [Google Scholar] [CrossRef] [PubMed]
  18. Schroeder, T.J.; Rodgers, G.B. Factors associated with the hospital admission of consumer product-related injuries treated in U.S. hospital emergency departments. Accid. Anal. Prev. 2013, 59, 566–573. [Google Scholar] [CrossRef] [PubMed]
  19. U.S. Consumer Product Safety Commission. National Electronic Injury Surveillance System. Available online: https://www.cpsc.gov/Research--Statistics/NEISS-Injury-Data/Neiss-Frequently-Asked-Questions (accessed on 12 November 2024).
  20. Schroeder, T.; Ault, K. The NEISS Sample (Design and Implementation) 1997 to Present; U.S. Consumer Product Safety Commission: Bethesda, MD, USA, 2001. [Google Scholar]
  21. von Elm, E.; Altman, D.G.; Egger, M.; Pocock, S.J.; Gøtzsche, P.C.; Vandenbroucke, J.P. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: Guidelines for reporting observational studies. Ann. Intern. Med. 2007, 147, 573–577. [Google Scholar] [CrossRef] [PubMed]
  22. Fox, N.; Hunn, A.; Mathers, N. Sampling and Sample Size Calculation. The NIHR RDS for the East Midlands/Yorkshire & the Humber 2007. Available online: https://www.researchgate.net/profile/Mansour-Abdullah-Alshehri/post/How_to_determine_population_and_sample_size_for_a_survey_research/attachment/5aa18dfc4cde266d58902255/AS%3A601990316974080%401520537084240/download/Sampling+Sample+Size+Calculation.pdf (accessed on 9 July 2025).
  23. Peduzzi, P.; Concato, J.; Kemper, E.; Holford, T.R.; Feinstein, A.R. A simulation study of the number of events per variable in logistic regression analysis. J. Clin. Epidemiol. 1996, 49, 1373–1379. [Google Scholar] [CrossRef] [PubMed]
  24. Barell, V.; Aharonson-Daniel, L.; Fingerhut, L.A.; Mackenzie, E.J.; Ziv, A.; Boyko, V.; Abargel, A.; Avitzour, M.; Heruti, R. An introduction to the Barell body region by nature of injury diagnosis matrix. Inj. Prev. 2002, 8, 91–96. [Google Scholar] [CrossRef] [PubMed]
  25. Kendrick, P.; Kelly, Y.O.; Baumann, M.M.; Kahn, E.; Compton, K.; Schmidt, C.; Sylte, D.O.; Li, Z.; La Motte-Kerr, W.; Daoud, F.; et al. Mortality due to falls by county, age group, race, and ethnicity in the USA, 2000–2019: A systematic analysis of health disparities. Lancet Public Health 2024, 9, e539–e550. [Google Scholar] [CrossRef] [PubMed]
  26. Bishop, N.J.; Haas, S.A.; Quiñones, A.R. Cohort Trends in the Burden of Multiple Chronic Conditions Among Aging U.S. Adults. J. Gerontol. B Psychol. Sci. Soc. Sci. 2022, 77, 1867–1879. [Google Scholar] [CrossRef] [PubMed]
  27. Middleton, R.; Poveda, J.L.; Orfila Pernas, F.; Martinez Laguna, D.; Diez Perez, A.; Nogués, X.; Carbonell Abella, C.; Reyes, C.; Prieto-Alhambra, D. Mortality, Falls, and Fracture Risk Are Positively Associated With Frailty: A SIDIAP Cohort Study of 890 000 Patients. J. Gerontol. A Biol. Sci. Med. Sci. 2022, 77, 148–154. [Google Scholar] [CrossRef] [PubMed]
  28. Erwander, K.; Ivarsson, K.; Olsson, M.L.; Agvall, B. Elderly patients with non-specific complaints at the emergency department have a high risk for admission and 30-days mortality. BMC Geriatr. 2024, 24, 5. [Google Scholar] [CrossRef] [PubMed]
  29. van Dam, C.S.; Peters, M.J.L.; Hoogendijk, E.O.; Nanayakkara, P.W.B.; Muller, M.; Trappenburg, M.C. Older patients with nonspecific complaints at the Emergency Department are at risk of adverse health outcomes. Eur. J. Intern. Med. 2023, 112, 86–92. [Google Scholar] [CrossRef] [PubMed]
  30. Sanford, A.M.; Morley, J.E.; Berg-Weger, M.; Lundy, J.; Little, M.O.; Leonard, K.; Malmstrom, T.K. High prevalence of geriatric syndromes in older adults. PLoS ONE 2020, 15, e0233857. [Google Scholar] [CrossRef] [PubMed]
  31. Carpenter, C.R.; Cameron, A.; Ganz, D.A.; Liu, S. Older Adult Falls in Emergency Medicine-A Sentinel Event. Clin. Geriatr. Med. 2018, 34, 355–367. [Google Scholar] [CrossRef] [PubMed]
  32. Abell, J.G.; Lassale, C.; Batty, G.D.; Zaninotto, P. Risk Factors for Hospital Admission After a Fall: A Prospective Cohort Study of Community-Dwelling Older People. J. Gerontol. A Biol. Sci. Med. Sci. 2021, 76, 666–674. [Google Scholar] [CrossRef] [PubMed]
  33. Berecki-Gisolf, J.; Rezaei-Darzi, E.; Natora, A.H. Gender differences in the incidence, characteristics and hospital admission outcomes of fall-related injuries in older adults in Victoria, Australia, over 5 years from 2018/19 to 2022/23. Front. Public Health 2024, 12, 1426726. [Google Scholar] [CrossRef] [PubMed]
  34. Southerland, L.T.; Stephens, J.A.; Robinson, S.; Falk, J.; Phieffer, L.; Rosenthal, J.A.; Caterino, J.M. Head Trauma from Falling Increases Subsequent Emergency Department Visits More Than Other Fall-Related Injuries in Older Adults. J. Am. Geriatr. Soc. 2016, 64, 870–874. [Google Scholar] [CrossRef] [PubMed]
  35. Babu, K.M.; Haddad, Y.K.; Causey, S.T.; Vargas-Torres, C.C.; Martinez, P.M.; Goldberg, E.M.; Dorfman, J.D.; Bleser, J.A.; Chapman, B.P.; Lai, J.T.; et al. Detection of ethanol, cannabinoids, benzodiazepines, and opioids in older adults evaluated for serious injuries from falls. Clin. Toxicol. 2024, 62, 661–668. [Google Scholar] [CrossRef] [PubMed]
  36. 2023 American Geriatrics Society Beers Criteria® Update Expert Panel. American Geriatrics Society 2023 updated AGS Beers Criteria® for potentially inappropriate medication use in older adults. J. Am. Geriatr. Soc. 2023, 71, 2052–2081. [Google Scholar] [CrossRef] [PubMed]
  37. Russell, M.A.; Hill, K.D.; Blackberry, I.; Day, L.L.; Dharmage, S.C. Falls risk and functional decline in older fallers discharged directly from emergency departments. J. Gerontol. A Biol. Sci. Med. Sci. 2006, 61, 1090–1095. [Google Scholar] [CrossRef] [PubMed]
  38. Carpenter, C.R.; Scheatzle, M.D.; D’Antonio, J.A.; Ricci, P.T.; Coben, J.H. Identification of fall risk factors in older adult emergency department patients. Acad. Emerg. Med. 2009, 16, 211–219. [Google Scholar] [CrossRef] [PubMed]
  39. Davenport, K.; Alazemi, M.; Sri-On, J.; Liu, S. Missed Opportunities to Diagnose and Intervene in Modifiable Risk Factors for Older Emergency Department Patients Presenting After a Fall. Ann. Emerg. Med. 2020, 76, 730–738. [Google Scholar] [CrossRef] [PubMed]
  40. Strauss, D.H.; Davoodi, N.M.; Resnik, L.J.; Keene, S.; Serina, P.T.; Goldberg, E.M. Emergency Department-Based Physical Function Measures for Falls in Older Adults and Outcomes: A Secondary Analysis of GAPcare. J. Geriatr. Phys. Ther. 2024, 47, 137–144. [Google Scholar] [CrossRef] [PubMed]
  41. Hammouda, N.; Carpenter, C.R.; Hung, W.W.; Lesser, A.; Nyamu, S.; Liu, S.; Gettel, C.J.; Malsch, A.; Castillo, E.M.; Forrester, S.; et al. Moving the needle on fall prevention: A Geriatric Emergency Care Applied Research (GEAR) Network scoping review and consensus statement. Acad. Emerg. Med. 2021, 28, 1214–1227. [Google Scholar] [CrossRef] [PubMed]
  42. Guirguis-Blake, J.M.; Perdue, L.A.; Coppola, E.L.; Bean, S.I. Interventions to Prevent Falls in Older Adults: Updated Evidence Report and Systematic Review for the US Preventive Services Task Force. JAMA 2024, 332, 58–69. [Google Scholar] [CrossRef] [PubMed]
  43. Graves, J.M.; Whitehill, J.M.; Hagel, B.E.; Rivara, F.P. Making the most of injury surveillance data: Using narrative text to identify exposure information in case-control studies. Injury 2015, 46, 891–897. [Google Scholar] [CrossRef] [PubMed]
  44. Min, J.; Gurka, K.K.; Kalesan, B.; Bian, J.; Prosperi, M. Injury Burden in the United States: Accurate, Reliable, and Timely Surveillance Using Electronic Health Care Data. Am. J. Public. Health 2019, 109, 1702–1706. [Google Scholar] [CrossRef] [PubMed]
  45. Tom, A.; Schuluttenhofer, A.; Navarro, S.M.; Junker, M.; Zietlow, J.; Davis, R.; Palmer, A.K.; LeBrasseur, N.K.; Bellolio, F.; Park, M. Increasing Incidence and Hospitalization of Bed-Related Fall Injuries in Older Adults. In Proceedings of the American Geriatric Society 2025 Annual Meeting, Chicago, IL, USA, 8–10 May 2025. [Google Scholar]
Jcm 14 05008 g001
Figure 1. Trends in national estimated ED visits for bed-related falls.
Figure 1. Trends in national estimated ED visits for bed-related falls.
Jcm 14 05008 g001
Jcm 14 05008 g002
Figure 2. Trends in hospitalization for bed-related falls.
Figure 2. Trends in hospitalization for bed-related falls.
Jcm 14 05008 g002
Table 1. Demographic and injury characteristics.
Table 1. Demographic and injury characteristics.
NEISS
Bed-Related Falls
Raw Total (n = 70,391)		Weighted U.S.
Estimate (n = 3,207,432)		Manually Reviewed Sample
Raw Total (n = 2053)
AgeMedian (IQR)81 (73, 88)81 (73, 88)81 (74-88)
SexMale25,677 (36.5%)1,163,366 (36.3%)735 (35.8%)
Female44,713 (63.5%)2,044,066 (63.7%)1318 (64.2%)
Intersex1 (0.0%)80 (0.0%)-
HospitalizedYes23,975 (34.1%)1,054,858 (32.9%)699 (34.0%)
No46,416 (65.9%)2,152,655 (67.1%)1354 (66.0%)
DispositionTreated and Released44,203 (62.8%)2,060,395 (64.2%)1284 (62.5%)
Treated and Transferred1072 (1.5%)67,590 (2.1%)28 (1.4%)
Treated and Hospitalized22,903 (32.5%)987,268 (30.8%)671 (32.7%)
Held for Observation1750 (2.5%)72,406 (2.3%)55 (2.7%)
Left without Being Seen368 (0.5%)16,096 (0.5%)10 (0.5%)
Fatality95 (0.1%)3757 (0.1%)5 (0.2%)
Alcohol 1Yes440 (1.1%)19,967 (1.1%)13 (1.1%)
No39,932 (98.9%)1,805,797 (98.9%)1177 (98.9%)
Drug/Medication Caused 1Yes1736 (4.3%)70,302 (3.9%)55 (4.6%)
No38,636 (95.7%)1,755,462 (96.1%)1135 (95.4%)
Injury PatternLower Trunk Fracture/Dislocation5965 (8.5%)271,325 (8.5%)170 (8.3%)
Lower Extremity Fracture/Dislocation2607 (3.7%)119,724 (3.7%)79 (3.8%)
Other/Unspecified7987 (11.3%)358,754 (11.2%)221 (10.8%)
Upper Trunk Fracture/Dislocation2282 (3.2%)104,235 (3.2%)79 (3.8%)
Head/Face/Neck Fracture/Dislocation1256 (1.8%)53,914 (1.7%)32 (1.6%)
Internal Injury (incl. Concussions)16,310 (23.2%)692,911 (21.6%)513 (25.0%)
Pain/Weakness9574 (13.6%)416,635 (13.0%)262 (12.8%)
Upper Extremity Fracture/Dislocation3081 (4.4%)145,776 (4.5%)106 (5.2%)
Superficial (Contusions/Abrasions/
Lacerations/Avulsions)		18,641 (26.5%)918,711 (28.6%)521 (25.4%)
Sprains/Strains2688 (3.8%)125,528 (3.9%)70 (3.4%)
Injury Mechanism 2In Bed (Fell Out/Rolled)--1167 (56.8%)
Transitioning--707 (34.4%)
Out of Bed (Walking/Activity)--122 (5.9%)
Other/Unclear--57 (2.8%)
1 Data only available for fall injuries from 2018–2023. 2 Data only available for manually reviewed sample.
Table 2. Univariate analysis identifying predictors of hospitalization.
Table 2. Univariate analysis identifying predictors of hospitalization.
VariableLevelN TotalN
Hospitalized		%
Hospitalized		OR95% CIp-Value
AgePer 10 years---1.070.97, 1.180.19
SexMale73528538.8%1.381.15, 1.67<0.001
Female131841431.4%1.00Ref
Alcohol 1Yes13753.8%2.000.66, 6.240.22
No117743436.9%1.00Ref
Drug/Medication Caused 1Yes552952.7%1.961.14, 3.390.015
No113541236.3%1.00Ref
Injury Pattern <0.001
Lower Trunk Fracture17013378.2%9.866.31, 15.72
Lower Extremity Fracture794860.8%4.252.52, 7.26
Other/Unspecified22112355.7%2.621.80, 3.85
Upper Trunk Fracture793544.3%2.181.29, 3.68
Head/Neck/Face
Fracture/Dislocation		321443.8%2.130.99, 4.51
Internal Injury (incl. Concussions)51316732.6%1.320.96, 1.85
Pain/Weakness2627026.7%1.00Ref
Upper Extremity Fracture1062725.5%0.940.55, 1.56
Superficial (Contusions/Abrasions/
Lacerations/Avulsions)		5219017.3%0.570.40, 0.82
Sprains/Strains70710.0%0.310.12, 0.66
Injury Mechanism 0.088
In Bed (Fell Out/Rolled)116738432.9%1.220.82, 1.86
Transitioning70726437.3%1.480.98, 2.28
Out of Bed (Walking/Activity)1243528.2%1.00Ref
Other/Unclear571628.1%0.970.47, 1.93
1 Data only available for fall injuries from 2018–2023. Bold used to denote p-values that are significant (<0.05).
Table 3. Multivariable logistic regression analysis of risk factors for hospital admission following falls using covariates with p < 0.10 in univariate analysis.
Table 3. Multivariable logistic regression analysis of risk factors for hospital admission following falls using covariates with p < 0.10 in univariate analysis.
PredictorLevelOR95% CIp-Value
SexMale1.551.26, 1.91<0.001
Female1.00Ref
Injury Pattern <0.001
Lower Trunk Fracture10.046.41, 16.06
Lower Extremity Fracture4.592.71, 7.88
Other/Unspecified2.521.72, 3.70
Upper Trunk Fracture2.231.31, 3.77
Head/Neck/Face
Fracture/Dislocation		2.171.01, 4.61
Internal Injury (incl. Concussions)1.300.94, 1.83
Pain/Weakness1.00Ref
Upper Extremity Fracture0.990.58, 1.64
Superficial (Contusions/Abrasions/
Lacerations/Avulsions)		0.560.39, 0.80
Sprains/Strains0.300.12, 0.66
Injury Mechanism 0.59
In Bed (Fell Out/Rolled)1.270.81, 2.02
Transitioning1.370.86, 2.20
Out of Bed (Walking/Activity)1.00Ref
Other/Unclear1.160.54, 2.43
Bold used to denote p-values that are significant (<0.05).
Table 4. Multivariable logistic regression analysis of risk factors for hospital admission following falls using covariates p < 0.10 in univariate analysis with only patients injured from 2018 to 2023 when medication data was available.
Table 4. Multivariable logistic regression analysis of risk factors for hospital admission following falls using covariates p < 0.10 in univariate analysis with only patients injured from 2018 to 2023 when medication data was available.
PredictorLevelOR95% CIp-Value
SexMale1.441.11, 1.880.007
Female1.00Ref
Drugs/MedicationsYes2.131.20, 3.800.010
No1.00Ref
Injury Pattern <0.001
Lower Trunk Fracture7.984.55, 14.43
Lower Extremity Fracture3.351.74, 6.58
Other/Unspecified2.051.29, 3.27
Upper Trunk Fracture1.540.77, 3.06
Head/Neck/Face Fracture/Dislocation1.430.53, 3.71
Internal Injury (incl. Concussions)0.900.59, 1.37
Pain/Weakness1.00Ref
Upper Extremity Fracture0.760.35, 1.55
Superficial (Contusions/Abrasions/
Lacerations/Avulsions)		0.570.36, 0.89
Sprains/Strains0.150.02, 0.52
Injury Mechanism 0.74
In Bed (Fell Out/Rolled)1.370.77, 2.53
Transitioning1.390.77, 2.59
Out of Bed (Walking/Activity)1.00Ref
Other/Unclear1.220.48, 3.03
Bold used to denote p-values that are significant (<0.05).
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Tom, A.; Navarro, S.M.; Spears, G.M.; Schluttenhofer, A.; Junker, M.; Zietlow, J.; Davis, R.; Palmer, A.K.; LeBrasseur, N.K.; Bellolio, F.; et al. Trends and Risk Factors for the Hospitalization of Older Adults Presenting to Emergency Departments After a Bed-Related Fall: A National Database Analysis. J. Clin. Med. 2025, 14, 5008. https://doi.org/10.3390/jcm14145008

AMA Style

Tom A, Navarro SM, Spears GM, Schluttenhofer A, Junker M, Zietlow J, Davis R, Palmer AK, LeBrasseur NK, Bellolio F, et al. Trends and Risk Factors for the Hospitalization of Older Adults Presenting to Emergency Departments After a Bed-Related Fall: A National Database Analysis. Journal of Clinical Medicine. 2025; 14(14):5008. https://doi.org/10.3390/jcm14145008

Chicago/Turabian Style

Tom, Andy, Sergio M. Navarro, Grant M. Spears, Adam Schluttenhofer, Michelle Junker, John Zietlow, Roderick Davis, Allyson K. Palmer, Nathan K. LeBrasseur, Fernanda Bellolio, and et al. 2025. "Trends and Risk Factors for the Hospitalization of Older Adults Presenting to Emergency Departments After a Bed-Related Fall: A National Database Analysis" Journal of Clinical Medicine 14, no. 14: 5008. https://doi.org/10.3390/jcm14145008

APA Style

Tom, A., Navarro, S. M., Spears, G. M., Schluttenhofer, A., Junker, M., Zietlow, J., Davis, R., Palmer, A. K., LeBrasseur, N. K., Bellolio, F., & Park, M. S. (2025). Trends and Risk Factors for the Hospitalization of Older Adults Presenting to Emergency Departments After a Bed-Related Fall: A National Database Analysis. Journal of Clinical Medicine, 14(14), 5008. https://doi.org/10.3390/jcm14145008

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop