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Article

Fall-Related Hospitalizations Among Older Adults in Los Angeles County: Differences by Dementia Status, 2016–2022

by
D’Artagnan M. Robinson
1,2,
Emiley Chang
1,3,4,5,
Dalia Regos-Stewart
1,
Mariana A. Reyes
1,
Tony Kuo
6,7,8 and
Noel C. Barragan
1,*
1
Division of Chronic Disease and Injury Prevention, Los Angeles County Department of Public Health, Los Angeles, CA 90010, USA
2
Division of Undergraduate Education, Campuswide Honors Collegium, University of California, Irvine, Irvine, CA 92697, USA
3
Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90024, USA
4
Section of Geriatrics and Palliative Care, Division of General Internal Medicine, Department of Medicine, Harbor-UCLA Medical Center, Torrance, CA 90509, USA
5
The Lundquist Institute for Biomedical Innovation, Harbor-UCLA Medical Center, Torrance, CA 90502, USA
6
Department of Family Medicine, David Geffen School Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
7
Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA 90095, USA
8
Population Health Program, Clinical and Translational Science Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA
*
Author to whom correspondence should be addressed.
J. Dement. Alzheimer's Dis. 2025, 2(4), 42; https://doi.org/10.3390/jdad2040042
Submission received: 13 June 2025 / Revised: 22 August 2025 / Accepted: 16 October 2025 / Published: 14 November 2025
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Abstract

Background/Objectives: Falls are a leading cause of hospitalization, injury, and healthcare spending among older adults. Surveillance data on local falls, especially for those associated with Alzheimer’s disease and related dementias (ADRD), are limited. We conducted a surveillance analysis to describe fall-related hospitalizations and their associations with ADRD in Los Angeles County (LAC). Methods: We analyzed countywide hospital discharge data for LAC residents aged 50+ from 2016–2022 (n = 3,520,927) to assess differences in fall-related hospitalizations by ADRD status and demographic characteristics. We used multivariable logistic regression to identify predictors of fall status and multinomial regression to examine associations between ADRD status and discharge disposition. Results: Of all hospitalizations, 6.8% were fall-related. Individuals hospitalized for falls had longer stays, higher charges, and were more frequently female, older, and White. Fall frequency peaks consistently occurred during winter months, with higher seasonal variation among those without ADRD. After adjustment, ADRD diagnosis was associated with increased odds of fall-related hospitalization (AOR = 1.14) and non-routine discharge, including transfer to a short-term hospital (AOR = 1.35), skilled nursing or other care facilities (AOR = 1.88), and home health care (AOR = 1.23). Conclusions: This study provides one of the most comprehensive local assessments of fall-related hospitalization among older adults in the United States. The findings highlight the increased risk and care complexity among patients with ADRD. As results are descriptive and reflect cross-sectional surveillance, temporality and causality cannot be inferred. Nevertheless, the findings underscore the need for better surveillance and integrated fall prevention, discharge planning, and post-hospital support strategies tailored to individuals with ADRD.

1. Introduction

Falls are a leading cause of injury, hospitalization, and mortality among older adults in the United States. Nationally, more than one in four adults aged 65 years or older report falling each year, resulting in approximately 3 million emergency department visits and nearly 1 million fall-related hospitalizations annually [1]. Fall events also impose a substantial economic burden, with estimated annual medical costs rising from $50 billion in 2015 to $80 billion in 2020—a figure expected to increase significantly as the general population ages [2]. Beyond their immediate clinical consequences, a history of falls frequently increases the risk of recurrent falls, long-term functional decline, loss of independence, and transitions to institutional care [3,4,5,6].
National public health agencies, including the Centers for Disease Control and Prevention and the Association of State and Territorial Health Officials, have called for expanded surveillance of older adult falls, including identification of both clinical and non-clinical risk factors, as well as subpopulations at increased risk [7,8]. One such subgroup is adults with Alzheimer’s disease and related dementias (ADRD); they often face heightened risk of falls due to deficits in executive function and judgment, gait stability, and spatial awareness [9,10]. Approximately 40–60% of older adults with dementia experience falls annually, with elevated rates of recurrence, severe injury, and fall-related mortality as compared to their peers without the condition [11]. This elevated risk is partially explained by the greater prevalence of fall-related risk factors such as visual impairment, confusion, incontinence, underuse of mobility aids, and polypharmacy [11,12]. Recent research also suggests a bidirectional relationship between falls and ADRD, in which not only does dementia increase fall risk, but falls themselves may contribute to an increased risk of future dementia diagnoses [10].
In addition to cognitive and clinical risk factors, seasonal and environmental influences also have been found to affect fall risk. For example, prior research suggests warmer weather is linked to increased risk of falls in older adults, potentially due to heat-related dehydration, dizziness, and orthostatic hypotension [13,14]. More recently, a national analysis of emergency department data identified a significant winter peak in fall-related visits among older adults, specifically driven by outdoor falls, underscoring how weather-related hazards, such as rain or snow, can contribute to seasonal variation in fall risk [14].
Hospitalization itself may exacerbate vulnerability to poor outcomes in older adults. Prolonged hospital stays are associated with functional decline and iatrogenic complications, including falls, infections, and deconditioning [11,15]. Older adults with ADRD may be particularly susceptible to hospital-acquired complications due to their increased dependency, difficulty understanding care instructions, and potential for delirium or behavioral disturbances in unfamiliar environments [11]. Post-fall complications—including longer hospitalizations, discharge to skilled nursing facilities, and readmissions—are common among patients with cognitive impairment [9].
To address knowledge gaps in fall surveillance and to support prevention efforts, particularly among individuals with ADRD, we conducted a population-based surveillance analysis using hospital discharge data for adults aged 50 years and older in Los Angeles County (LAC) from 2016 to 2022. The analyses aimed to (1) describe demographic, temporal, clinical, and hospitalization characteristics of fall-related discharges stratified by ADRD diagnosis; (2) estimate the adjusted association between ADRD diagnosis and the odds of fall-related hospitalization; and (3) estimate adjusted associations between ADRD diagnosis and discharge disposition among fall-related hospitalizations.

2. Materials and Methods

2.1. Data Source and Study Population

We examined hospitalizations using patient discharge data (PDD) from 2016–2022, obtained from the California Department of Health Care Access and Information (HCAI) [16]. HCAI maintains non-public, limited data sets containing patient-level inpatient discharge information collected through mandatory annual reporting from all state-licensed hospitals in California. These hospitals include general acute care, acute psychiatric, chemical dependency recovery, and psychiatric health facilities. PDD includes demographic, clinical, payer, and facility-level data for each inpatient record.
In accordance with the California Health and Human Services Agency’s Data De-Identification Guidelines, which align with the HIPAA Privacy Rule’s Expert Determination Method, PDD were de-identified by HCAI prior to being made available to researchers. This process included removing all direct identifiers (e.g., patient name, address, Social Security number) and, where feasible, generalizing or masking quasi-identifiers (e.g., truncating birth dates to month/year and/or replacing exact facility identifiers with coded values). In addition, geographic data were limited to broader units (e.g., county); rare diagnosis/procedure combinations were reviewed and later aggregated or suppressed; and date fields related to admission, discharge, and procedures were limited to year and month, with selected rare events recoded to protect privacy. HCAI also requires researchers to suppress small cell sizes (<11) and use additional statistical disclosure limitation techniques to minimize re-identification risk while preserving data utility for population-level analyses. As PDD are encounter-level data, they are cross-sectional in nature and contain no patient-level longitudinal linkage across encounters or care settings.
All hospitalizations among LAC residents aged 50 years and older at the time of admission were included in the analysis (n = 3,520,927), representing 45.8% of all hospitalization records during the study period (n = 7,680,080). The lower age threshold of 50 years was selected to capture individuals with early-onset or younger-onset dementia, defined as diagnosis before age 65. The analysis sample was stratified into mutually exclusive case and control groups based on fall status: individuals who (i) experienced a fall and (ii) did not experience a fall. Fall status was established based on the listing of an International Classification of Diseases, Tenth Revision, Clinical Modifications (ICD-10-CM) fall code (W00-W19) as an external cause of morbidity; i.e., the cited diagnosis that resulted in the most severe injury or health condition of the hospitalized patient [17]. Fall-related injuries were attributed to various circumstances such as falls on the same level from slipping, tripping and stumbling; falls on the same level due to collisions with another person; falls from a bed; falls from a chair; falls on and from stairs and steps; and other or unspecified falls.
A hospitalization was classified as involving “ADRD” if one or more relevant ICD-10-CM codes appeared as the principal diagnosis (i.e., the primary reason for admission) or as a secondary diagnosis present on admission or arising during the hospitalization due to its impact on patient management or length of stay. Notably, as this approach relies solely on documentation and coding within administrative hospital data, ADRD prevalence may be underestimated. A complete list of the ADRD codes used in the analysis are available in the Supplementary Table S1.
All statistical analyses were conducted using SAS Version 9.4 (SAS Institute Inc., Cary, NC, USA).

2.2. Variables

To examine differences in hospitalizations by fall status, descriptive and univariate statistics were generated for the following variables: ADRD diagnosis; age at admission in years; sex; race/ethnicity; other comorbid conditions or adverse events—depression, visual disorders, hearing loss, history of falls, and Parkinson’s disease; disposition—the outcome or event concluding the patient’s hospital stay; length of stay—the total number of days from admission to discharge; total charges—the total charges in U.S. dollars for services rendered based on the hospital’s full established rates; and year and month of admission. Where appropriate, bivariate associations between categorical variables and fall status were assessed using Pearson’s chi-square tests, while differences in continuous variables were evaluated using Mann–Whitney U tests due to non-normal distributions. Hypothesis tests were two-sided, with statistical significance defined as p < 0.05.
Age, length of stay, and total charges were analyzed as continuous variables. Race/ethnicity was categorized as White; Black; Hispanic; Asian, Native Hawaiian, and Pacific Islander (ANHPI); American Indian/Alaska Native; and Other (a composite group that comprises Other, Multiracial, Unknown, or Invalid).
To account for comorbid conditions or adverse events that could confound the association between ADRD and fall-related hospitalization, we selected a subset of high-priority conditions to balance our statistical adjustments with epidemiological relevance and model parsimony. Diagnosis of these conditions was established based on the presence of an ICD-10-CM code recorded as a principal or secondary diagnosis. Five binary comorbid indicators were included in these analyses: depression (F32–33); visual disorders (a composite of cataracts [H25–H26], glaucoma [H40], and macular degeneration [H35.3]); hearing loss (H90–91); history of falls (Z91.81); and Parkinson’s disease (G20). We chose these comorbidities based on their strong associations with both increased risk of falling and ADRD. Depression is a well-established predictor of both Alzheimer’s disease and injurious falls among older adults [18,19]. Similarly, visual disorders—including cataracts, glaucoma, and age-related macular degeneration—have been associated with heightened risk of falls and dementia [20,21,22,23]. Hearing loss, frequently underdiagnosed in older populations, has been shown to independently increase the risk of both falling and developing dementia [24,25,26,27,28]. A history of falls, which is among the strongest predictors of future fall-related injury, has also been associated with earlier dementia onset [10,29]. Lastly, Parkinson’s disease has a strong association with gait instability and recurrent falls and is also highly associated with developing dementia [30,31,32,33]. Several of these conditions reflect elements of age-related physical changes (e.g., sensory loss) that may overlap with domains often considered in frailty assessment, such as decreased mobility [34]. Frailty has been consistently associated with increased risk of falls, hospitalization after falls, and fall-related mortality [34].
We categorized discharge disposition in alignment with the Agency for Healthcare Research and Quality’s Healthcare Cost & Utilization Project classifications [35]. Discharge disposition included: routine discharge (to home or self-care); transfer to a short-term hospital (encompassing transfers to acute care hospitals or equivalent inpatient facilities, including those for specialized care); transfer to facilities other than a short-term hospital (inclusive of skilled nursing facilities [SNF], intermediate care facilities [ICF], inpatient rehabilitation units, and other types of facilities); home health care (including home hospice services); and leaving against medical advice or discontinuing care. Notably, death was not included as a category in this analysis because none of the fall-related hospitalization records were assigned this disposition.

2.3. Fall Counts Across Admission Years and Months

Annual total fall hospitalizations were visualized as stacked bar charts for each admission year, stratified by ADRD diagnosis. Monthly seasonality was visualized by plotting fall-related hospitalization counts by month of admission within each admission year and by ADRD status. To statistically evaluate seasonal variation, we conducted one-way chi-square goodness-of-fit tests of monthly fall counts within each year, separately by ADRD diagnosis status, under the null hypothesis of a uniform distribution across months (α = 0.05).

2.4. Fall Status

Using the LOGISTIC procedure, we constructed a multivariable logistic regression model to estimate the associations between patient demographics and clinical characteristics and the odds of fall-related hospitalization among older adults aged 50 years and older in LAC. The binary outcome variable, fall status (yes versus no), was regressed on ADRD diagnosis status, age category (50–54 years, 55–59 years, 60–64 years, 65–69 years, 70–74 years, 75–79 years, 80–84 years, 85+ years), sex, race/ethnicity, depression diagnosis, visual disorder diagnosis, hearing loss diagnosis, history of falls, and Parkinson’s disease diagnosis. Age was modeled as a categorical variable to satisfy the logistic regression assumption of linearity in the logit, based on a preliminary assessment of the relationship between continuous age and the log odds of falling. Age categories mirrored the HCAI age strata to preserve comparability. No interaction terms were included in the final model.
Model selection procedures included stepwise and backward elimination algorithms. Both approaches used the Akaike Information Criterion and Bayesian Information Criterion to assess model fit, with final inclusion of covariates also informed by theoretical relevance and clinical interpretability. All candidate variables were retained in the final model following the model selection process. Standard model diagnostics, including the Hosmer-Lemeshow goodness-of-fit test and area under the Receiver Operating Characteristic curve evaluation, were conducted to assess model adequacy. We examined measures of influence, including standardized residuals and leverage statistics, and did not exclude any observations. We assessed multicollinearity using variance inflation factors and detected no violations of model assumptions following variable transformation.
We excluded records with missing or invalid data for the outcome or any predictor variables from the analysis. The final analytic sample included n = 3,520,805 hospitalizations. Adjusted odds ratios (AORs), 95% confidence intervals (CIs), and p-values were reported for all variables used in the regression modeling, with statistical significance defined as p < 0.05.

2.5. Discharge Disposition

We constructed a multinomial logistic regression model to estimate associations between ADRD diagnosis and discharge disposition among older adults hospitalized for a fall using the LOGISTIC procedure with the generalized link logit option specified (LINK = GLOGIT). Discharge disposition was categorized into five mutually exclusive groups: routine discharge (referent category), transfer to a short-term hospital, transfer to facilities other than a short-term hospital, discharge with home health care, and leaving against medical advice or discontinuing care. The model included ADRD diagnosis status as a primary regressor and the following variables as covariates: age category (50–54 years, 55–59 years, 60–64 years, 65–69 years, 70–74 years, 75–79 years, 80–84 years, 85+ years), sex, race/ethnicity, depression diagnosis, visual disorder diagnosis, hearing loss diagnosis, history of falls, and Parkinson’s disease diagnosis. No interaction terms were used.
We performed a Pearson’s chi-square test to evaluate the unadjusted association between ADRD diagnosis and discharge disposition among fall-related hospitalizations to complement and support the robustness of the multinomial logistic regression model.
Model fit and predictor significance were assessed using a likelihood ratio test comparing the intercept-only model to the full model with the ADRD diagnosis variable and all covariates. A p-value < 0.05 was considered evidence of a significantly better fit for the full model. We performed standard diagnostic procedures to assess multicollinearity and model stability. Collinearity was evaluated through variance inflation factors, and influential observations were assessed using diagnostic influence statistics. We confirmed the absence of perfect separation by examining parameter estimates and standard errors for extreme or undefined values. We detected no violations of model assumptions following variable transformation.
We excluded records with missing or invalid data for discharge disposition or any of the model covariates from the analysis. The final analytic sample included n = 240,547 hospitalizations. AORs, 95% CIs, and p-values were reported for each discharge disposition category relative to the routine discharge referent, with statistical significance defined as p < 0.05.

3. Results

Between 2016 and 2022, a total of n = 3,520,927 hospitalizations among LAC residents aged 50 years and older were recorded. Of these, 240,547 (6.8%) were classified as being fall-related. Those with an ADRD diagnosis accounted for 20.5% of fall-related hospitalizations compared with 11.2% of non-fall-related hospitalizations. The median age at admission among those who fell was substantially higher (78 years) than among those who did not fall (69 years). Fall-related hospitalizations were more frequently observed among females (59.2%) than males (40.8%). Racial/ethnic patterns also differed by fall status. Among cases, 49.1% were White, 24.8% Hispanic, and 11.4% ANHPI. Diagnoses of depression, visual disorders, hearing loss, history of falls, and Parkinson’s disease were significantly more prevalent among cases of falls compared to controls. Routine discharge was markedly less common among cases (23.5%) than among controls (50.2%), while discharges to other facilities—including SNFs, ICFs, or assisted living—were more common (43.4% versus 19.4%). Fall-related hospitalizations were associated with a higher median length of stay (4 days versus 3 days) and higher median total hospital charges ($73,485 USD versus $59,122 USD) compared to non-fall-related hospitalizations (Table 1).
The absolute number of fall-related hospitalizations among patients without ADRD increased steadily from 2016 to 2019, declined in 2020 and 2021, and returned to 2018–2019 levels in 2022, while the proportion of falls among patients with ADRD remained relatively stable across all seven years (Figure 1). Monthly trends stratified by ADRD diagnosis status demonstrated consistent intra-annual variation in fall-related hospitalizations across all seven admission years (Figure 2). Variations in monthly fall counts were similar in both groups, although changes were more pronounced among patients without an ADRD diagnosis. Across most years, fall-related hospitalizations peaked in January and again in the late fall (October or November), particularly among individuals without an ADRD diagnosis. Conversely, the lowest numbers of fall-related hospitalizations were typically observed between late spring and the summer months (April through July), with an additional decline often visible in December. This seasonal pattern appeared relatively stable across the 2016–2022 period, with minimal year-to-year deviation in the shape of the monthly curves for both groups. One-way chi-square goodness-of-fit tests confirmed that within each admission year, the monthly distribution of fall-related hospitalizations differed significantly from a uniform distribution for both the ADRD and non-ADRD groups (all χ2 tests, p < 0.05).
Results from the multivariable logistic regression analysis (Table 2) indicate that, after adjusting for demographic and clinical factors, individuals with an ADRD diagnosis had 14% greater odds of fall-related hospitalization compared to those without ADRD (AOR = 1.14, 95% CI: 1.13–1.16, p < 0.0001). Increasing age was positively associated with fall-related hospitalization, with those aged 85 years and older having more than four times the odds of a fall compared to those aged 50–54 (AOR = 4.10, 95% CI: 4.01–4.19, p < 0.0001). Females had 29% higher odds of fall-related hospitalization than males (AOR = 1.29, 95% CI: 1.28–1.30, p < 0.0001). Black, Hispanic, ANHPI, and American Indian/Alaska Native patients had lower odds of fall-related hospitalization compared to White patients. In contrast, diagnoses of depression (AOR = 1.21, 95% CI: 1.20–1.23), visual disorders (AOR = 1.13, 95% CI: 1.11–1.15), hearing loss (AOR = 1.24, 95% CI: 1.21–1.27), a history of falls (AOR = 3.78, 95% CI: 3.72–3.84), and Parkinson’s disease (AOR = 1.20, 95% CI: 1.17–1.23) were all independently associated with increased odds of fall-related hospitalization (all p < 0.0001).
In bivariate analyses, ADRD status was significantly associated with non-routine discharge disposition among fall-related hospitalizations (χ2 = 6797.40, df = 4, p < 0.0001). The multinomial logistic regression analysis among fall-related hospitalizations found that individuals with an ADRD diagnosis had significantly elevated odds of being transferred to a short-term hospital (AOR = 1.35, 95% CI: 1.29–1.42); transferred to other facilities such as SNFs or ICFs (AOR = 1.87, 95% CI: 1.81–1.94); discharged with home health care (AOR = 1.23, 95% CI: 1.19–1.28); or leaving against medical advice or with discontinued care (AOR = 1.46, 95% CI: 1.34–1.59) compared to routine discharge. All associations were statistically significant at p < 0.0001 (Table 3).

4. Discussion

4.1. Study Considerations and Limitations

Although the study provides comprehensive surveillance of fall-related hospitalizations stratified by ADRD diagnosis status, it nevertheless contains notable limitations in spite of HCAI’s large datasets and sample sizes.
First, the analyses relied on deidentified administrative hospital discharge data, which may be subject to coding inaccuracies, precludes the tracking of readmissions, and may underreport diagnoses such as ADRD or outcomes including death [36,37,38,39]. In the case of the latter, in-hospital mortality was not an outcome in our analyses; differences in fall-related mortality rates between patients with and without ADRD were not compared and may warrant further investigation in a future study. However, to address the potential impact of ADRD underdiagnosis on our study results, we performed a sensitivity analysis using published sensitivity (30.2%) and specificity (94.0%) estimates for inpatient hospital claims-based dementia identification to explore the range of count variations [40]. For each fall status group, we applied the correction formula:
T r u e   A D R D   C o u n t =   O b s e r v e d   A D R D   C o u n t − 1 − S p e c i f i c i t y   ×   N S e n s i t i v i t y + S p e c i f i c i t y − 1
where N is the total number of hospitalizations in the group. This adjustment increased the estimated number of ADRD cases among fall-related hospitalizations from 49,205 to approximately 143,687, and among non-fall hospitalizations from 366,927 to approximately 702,910. Using these corrected counts, the adjusted odds ratio for the association between ADRD and fall status increased from 2.04 (observed) to 5.44 (corrected), indicating that misclassification of ADRD diagnosis in the administrative data may substantially underestimate the true strength of the association. Sensitivity for ADRD case identification increased substantially to 71% when hospital data are combined with other administrative and clinical data sources, such as Medicare claims [40]. Future studies should seek to link multiple data sources to improve ADRD case ascertainment and reduce potential for misclassification bias of fall-related hospitalizations [40].
Second, the HCAI data lacked information on patient medication use, living environments, or caregiver support—all of which may influence fall risk and outcomes [8,41,42,43,44,45]. Third, causal relationships cannot be inferred due to the cross-sectional nature of the data.
Fourth, numerous comorbid conditions have been shown to be associated with both ADRD and falls. Although we adjusted for five empirically supported comorbidities to preserve model parsimony, residual confounding likely remained as unseen, influential factors, since not all relevant conditions could be included in the model. Future studies, especially those focused on prediction or longitudinal endpoints, could compare index-based scores (e.g., Charlson/Elixhauser), propensity scoring, or high-dimensional approaches to better capture the effects of multimorbidity on key fall outcomes, including mortality or hospital readmission [46,47,48]. Because we could not fully account for residual confounding by other unmeasured clinical conditions, caution should be used in interpreting these associations.
Fifth, findings may not generalize beyond LAC or to populations underrepresented in hospitalization records (e.g., those who fall but do not seek care, people with dementia who have not received a diagnosis).
Finally, because the study period includes the first three years of the coronavirus disease 2019 (COVID-19) pandemic, overall hospitalizations rates may have been skewed, as access to emergency departments and hospitals for non-COVID-19-related conditions was likely more restricted compared to prior years [49,50].

4.2. Implications for Prevention and Practice

This study provides one of the most comprehensive local assessments of fall-related hospitalization among older adults in the United States, revealing notable patterns in fall risk, hospitalization burden, and discharge disposition. In LAC, fall-related hospitalizations accounted for nearly 7% of all inpatient admissions among adults aged 50 and older from 2016 to 2022 and were disproportionately associated with ADRD diagnoses, higher healthcare charges, and non-routine discharges. These findings underscore the complex and resource-intensive nature of fall events in aging populations, particularly those with cognitive impairment.
Fall-related hospitalizations were associated with significantly higher resource utilization, including longer median lengths of stay and greater total hospital charges. These findings reinforce national evidence on the financial burden of falls among older adults, with recent estimates placing annual spending on non-fatal falls above $80 billion [2]. Fall injuries, particularly those resulting in fractures, traumatic brain injuries, or loss of functional independence, often necessitate extended hospital care and complex discharge planning. These burdens are especially acute in patients with ADRD, who may require higher levels of monitoring, rehabilitation, or long-term institutional care to address these crises.
While annual patterns were consistent across the study period, a marked decrease in fall-related hospitalizations was observed in 2020 and 2021, coinciding with the beginning of the COVID-19 pandemic. The decline in fall-related hospitalizations was consistent with nationally reported drops in overall hospital admissions and emergency department-based admissions during the height of the COVID-19 pandemic [49,50]. The reductions observed likely reflect a combination of deferred or avoided care, reported more often among adults with multiple comorbidities or a disability [51,52]. Additionally, adults often deferred care due to perceptions of worsened quality of care or poorer outcomes for patients without COVID-19 infections during surges in the pandemic [51,52].
Monthly variations in counts of fall-related hospitalizations emerged as a consistent trend, with numbers peaking during the winter months and reaching their lowest levels in the summer. This potential temporal pattern aligns with prior studies showing increased fall risk in colder weather, likely driven by environmental hazards such as slippery surfaces, changes in footwear, and reduced daylight hours [53,54,55,56]. Seasonality may be further exacerbated by deconditioning due to decreased physical activity during colder months—particularly among individuals with visual or cognitive impairment [14]. Furthermore, the winter peak may reflect a higher burden of respiratory tract infections such as influenza, pneumonia, and COVID-19, which increase fall risk through mechanisms like weakness, delirium, and systemic inflammation [57,58].
In our analysis, ADRD was associated with higher odds of fall-related hospitalization after adjustment for demographic and clinical factors. While the proportion of falls was higher for older adults without ADRD (reflecting the larger non-ADRD population), the adjusted models indicate a consistent association between dementia and fall-related hospitalization. This finding aligns with prior research demonstrating that ADRD contributes to impaired gait, judgment, spatial awareness, and postural control, which may increase the likelihood of falls and their recurrence [59,60,61].
Studies have further identified that fall predictors differ based on dementia status, with factors such as vision impairment, cohabitation, and neighborhood deprivation influencing fall risk among those with cognitive impairment [61,62,63]. Research has also indicated that living settings and environmental barriers play a major role in modulating fall risk for individuals with dementia, with unique and overlapping risk factors identified across care contexts [64]. A recent minireview emphasized that individuals with Alzheimer’s disease are two to three times more likely to fall than older adults without cognitive impairment and are at greater risk of institutionalization following a fall [65]. Similarly, it was found that injurious falls may precede, coincide with, or follow dementia diagnosis, suggesting that fall surveillance could be a valuable tool for identifying individuals at high risk for ADRD [66]. The greater prevalence of visual impairment, urinary incontinence, confusion, and ambulatory aid use among hospitalized patients with dementia who fall reinforces the clinical complexity of this subgroup and the challenges in preventing recurrent falls [11].
Demographic patterns in fall-related hospitalization also revealed notable disparities. Females had significantly higher adjusted odds of fall-related admission than males, a trend supported by literature citing lower muscle mass, longer life expectancy, and higher prevalence of osteoporosis among females [67,68]. Furthermore, females may perceive fall risk and prevention strategies differently than males, influencing behavior and intervention uptake [68]. Evidence also suggests that fear of falling and physical activity restriction—both of which contribute to increased fall risk—are more prevalent among females, particularly those with depressive symptoms or prior fall experience [69,70].
Race and ethnicity were also strongly associated with fall risk: older adults identifying as Black, Hispanic, ANHPI, or American Indian/Alaska Native had lower adjusted odds of fall-related hospitalization compared to White individuals. These patterns are consistent with previous findings that fall prevalence is highest among White populations and lowest among Asian groups [71,72]. These differences may potentially reflect variation in caregiving arrangements, healthcare access, fall-related care-seeking behavior, or underdiagnosis [73,74]. For example, multi-generational households are more common among foreign-born and non-White populations [75]. Additionally, differences in caregiving resources, level of supervision, and household support across racial/ethnic groups may influence an individual’s exposure to environmental hazards that cause falls, thus influencing individual fall risk [74]. Furthermore, variations in access to care, such as insurance status, can delay emergency or hospital-based evaluation of fall-related events in particular racial/ethnic groups [74]. In practice, variation in fall prevalence is likely shaped by an interplay of factors, such as cultural attitudes and beliefs, overall health status, and sociodemographic characteristics. Although our study provides surveillance data and further insights into racial/ethnic differences among adults hospitalized with falls, the reasons for these differences are presently unclear and may require further study and explication.
Older age and confounding comorbidities were both strongly correlated with fall risk. Advancing age demonstrated a strong and graded association with fall status, consistent with dose–response patterns reported in prior studies [74,76]. This pattern may be attributable to continuous age-related declines in strength, balance, and neuromuscular function [74,76]. All selected comorbidities were also significantly associated with increased odds of fall-related hospitalization in the final adjusted model. These results were anticipated, as each condition was selected based on prior evidence linking it to both ADRD and fall risk [11,18,73]. The strong association observed for history of falls, in particular, aligns with earlier studies identifying it as one of the most robust predictors of future fall-related injury [10,29]. Depression, hearing loss, and visual disorders displayed modest associations with fall-related hospitalizations, suggesting that impaired balance, reduced environmental awareness, and diminished ability to respond to hazards are important pathways through which fall risk may be increased [19,20,21,22,23,24,25,26,27,28,29]. The association between Parkinson’s disease and fall status further indicates that gait instability and progressive motor decline are other notable factors which may influence fall-related admissions [30,31,32,33].
Our findings also showed that ADRD was associated with significantly higher odds of non-routine discharge following a fall-related hospitalization. Individuals with ADRD had increased likelihood of being transferred to short-term hospitals, skilled nursing or intermediate care facilities, or discharged with home health services compared to routine discharge. These findings align with research showing that older adults with dementia often experience complex post-acute care transitions and are at heightened risk of institutionalization, readmission, and long-term functional decline [62,77,78]. Previous studies have also found that patients with dementia are more likely to experience hospital-acquired complications such as delirium, dehydration, and confusion—factors that may lead to extended stays and more intensive discharge planning [6,11,79]. These trends reinforce the need for integrated discharge models that account for cognitive status and provide linkage to appropriate post-acute services.
Together, these findings emphasize the importance of tailoring fall prevention and post-hospital care strategies to reflect the multifaceted drivers of risk among older adults. The presence of ADRD, sex differences, racial disparities, and seasonal trends all contribute to distinct fall and discharge trajectories. Early recognition of dementia is especially critical given evidence that injurious falls begin to increase several years prior to formal diagnosis and peak during the year of diagnosis, suggesting that falls may serve as an early indicator of cognitive decline and warrant proactive, stage-specific prevention strategies [65,66,79,80]. Expanded use of evidence-based screening tools and protocols to reduce falls in older adults—particularly when integrated with dementia-informed discharge planning—could help mitigate fall-related hospitalizations and promote safer aging in place [42,80]. Public health entities should also consider implementing timely seasonal surveillance, enhancing post-discharge care coordination with hospitals and receiving agencies, and supporting family caregivers through public policies and linkages to community resources [8,43,44,45,46].

4.3. Recommendations for Action

Based on our findings, several actions and evidence-based strategies could be implemented to improve program planning and practice. First, the “screen-assess-intervene” workflow, developed by the Centers for Disease Control and Prevention’s Stopping Elderly Accidents, Deaths & Injuries (STEADI) initiative, could be adapted by hospitals for use during admission and at discharge to reduce post-discharge fall risk [41,42]. This workflow can be combined with early mobilization protocols and comprises a number of steps and activities, including screening for recent falls and gait unsteadiness; assessing current gait and balance using validated instruments and exams such as the Timed Up and Go, chair stand, or the 4-Stage Balance Test; monitoring for orthostatic hypotension; reviewing patient medications for polypharmacy and high-risk drug use; and assuring that older adults or adults with sensory deficits are equipped with proper vision-wear and footwear [41,42,81].
Second, early intervention with physical or occupational therapy should be prescribed for all adults who were either admitted after a fall or experienced a fall during hospitalization [41,42,82]. Hospital discharge plans should routinely ensure that warm handoff referrals be completed within 7–14 days post-discharge. These plans should also embed fall prevention education and resources for both the patient and their caregiver(s) as part of their timeline before and after discharge from the hospital [41,42,82,83]. All these interventions strongly align with the new Medicare Age Friendly Hospital measure domains of medication management, frailty screening and intervention (including cognition and mobility), and social vulnerability (e.g., isolation, caregiver stress, economic instability, healthcare access) [84].
And finally, seasonal intensification of community outreach (e.g., footwear/traction advice, outdoor hazard messaging, scheduling of preventive visits in response to risks created by the winter months) should be codified and promoted as standard practices in hospitals and in outpatient clinical settings [41,42].
Collectively, these recommended actions could help hospitals and other health-related agencies address falls and ADRD-specific needs in a more efficient, accountable way.

5. Conclusions

Falls remain a significant driver of hospitalization among older adults in LAC and elsewhere in the United States. Surveillance of these incidents is essential for characterizing and intervening on this injury, yet data on falls are often unavailable in a local context. Individuals with ADRD diagnoses face disproportionately higher risks of falling and more complex discharge outcomes. Our study findings emphasize the importance of surveillance, early risk identification, and targeted intervention strategies that account for cognitive status. Coordinated efforts across public health, hospital systems, and aging services are essential to reduce fall-related harm and support aging populations, especially among those experiencing cognitive decline or disability.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/jdad2040042/s1, Table S1: ICD-10-CM Codes used to identify Alzheimer’s disease and related dementias (ADRD).

Author Contributions

Conceptualization, D.M.R., N.C.B., E.C. and T.K.; Formal Analysis, D.M.R. and N.C.B.; Writing—Original Draft Preparation, D.M.R., M.A.R. and N.C.B.; Writing—Review and Editing, D.M.R., E.C., D.R.-S., M.A.R., T.K. and N.C.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

This analysis was considered exempt as non-human-subject research per 45 CFR 46.102(1) by the Los Angeles County Department of Public Health Institutional Review Board, Project No. 2025-02-003.

Informed Consent Statement

This project was a secondary data analysis of an existing, deidentified database. Informed consent was not applicable.

Data Availability Statement

Access to these data is restricted. The data were obtained from the California Department of Health Care Access and Information and are available at https://hcai.ca.gov/data-and-reports/request-data/ (accessed on 29 May 2025) to eligible hospitals and health departments, subject to approval by the California Department of Health Care Access and Information.

Acknowledgments

The findings and conclusions expressed in this article are those of the authors and do not necessarily reflect the views of any organization mentioned herein. The authors gratefully acknowledge the California Department of Health Care Access and Information for providing access to patient discharge data under a data use agreement.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Fall-related hospitalizations among adults aged 50 years and older across admission years by Alzheimer’s disease and related dementia (ADRD) diagnosis status, Los Angeles County, 2016–2022.
Figure 1. Fall-related hospitalizations among adults aged 50 years and older across admission years by Alzheimer’s disease and related dementia (ADRD) diagnosis status, Los Angeles County, 2016–2022.
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Figure 2. Fall-related hospitalizations among adults aged 50 and older across admission months by Alzheimer’s disease and related dementia (ADRD) diagnosis status, Los Angeles County, 2016–2022.
Figure 2. Fall-related hospitalizations among adults aged 50 and older across admission months by Alzheimer’s disease and related dementia (ADRD) diagnosis status, Los Angeles County, 2016–2022.
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Table 1. Hospitalizations of adults 50 years and older by fall status, Los Angeles County, 2016–2022.
Table 1. Hospitalizations of adults 50 years and older by fall status, Los Angeles County, 2016–2022.
CharacteristicsMedian (Range) or n (%) 1
Falls
(n = 240,547)		No Falls
(n = 3,280,380)		Combined Median (Range) or Total (%)
(n = 3,520,927)		p-Value 2
ADRD Diagnosis 49,205 (20.5)366,927 (11.2)416,132 (11.8)<0.0001
Age (years)78
50–120)		69
(50–120)		70
(50–120)		<0.0001
Sex
 Male98,234 (40.8)1,639,798 (49.9)1,738,032 (49.4)<0.0001
 Female142,302 (59.2)1,640,471 (50.1)1,782,773 (50.6)
Race/Ethnicity
 White118,116 (49.1)1,219,943 (37.2)1,338,059 (38.0)<0.0001
 Black19,195 (8.0)451,379 (13.8)470,574 (13.4)
 Hispanic59,614 (24.8)1,057,105 (32.2)1,116,719 (31.7)
 Asian, Native Hawaiian, or Pacific Islander27,537 (11.4)351,108 (10.7)378,645 (10.7)
 American Indian/Alaska Native5218 (2.2)71,489 (2.2)76,707 (2.2)
 Other 310,867 (4.5)129,356 (3.9)140,223 (4.0)
Depression Diagnosis32,207 (13.4)331,617 (10.1)363,824 (10.3)<0.0001
Visual Disorder Diagnosis 411,628 (4.8)100,045 (3.1)111,673 (3.2)<0.0001
Hearing Loss Diagnosis9094 (3.8)63,839 (2.0)72,933 (2.1)<0.0001
History of Falls Diagnosis22,445 (9.3)67,932 (2.1)90,377 (2.6)<0.0001
Parkinson’s Disease Diagnosis8048 (3.4)63,031 (1.9)71,079 (2.0)<0.0001
Disposition
 Routine56,605 (23.5)1,647,647 (50.2)1,704,252 (48.4)<0.0001
 Transfer to Short-Term Hospital16,472 (6.8)244,282 (7.5)260,754 (7.4)
 Transfer to Other 5104,306 (43.4)635,525 (19.4)739,831 (21.0)
 Home Health Care58,112 (24.2)675,232 (20.6)733,344 (20.8)
 Left Against Medical Advice or Discontinued Care4979 (2.1)76,671 (2.3)81,650 (2.3)
Length of Stay (days)4
(0–337)		3 (0–365)3 (0–365)<0.0001
Total Charges (US dollars)$73,485
($0–$9,999,999)		$59,122
($0–$10,005,739)		$60,098
($0–$10,005,739)		<0.0001
1 Percentages may not add to 100% due to rounding. Frequency counts for a given variable may not sum to a column total due to missing data or invalid variable codes. 2 p-values generated using chi-squared and Mann–Whitney U tests. 3 Includes Other, Multiracial, Unknown, Invalid, Missing. 4 Visual disorders represent a composite of cataracts, glaucoma, and macular degeneration. 5 Includes skilled nursing facility, intermediate care facility, other types of facilities.
Table 2. Multivariable logistic regression analysis: Fall status by patient characteristics, Los Angeles County, 2016–2022.
Table 2. Multivariable logistic regression analysis: Fall status by patient characteristics, Los Angeles County, 2016–2022.
Fall Status (Referent Group: No Fall)
CharacteristicsAdjusted Odds Ratio95% Confidence Intervalp-Value
ADRD Diagnosis (Referent: No ADRD) 11.141.13–1.16<0.0001
Age Category (Referent: 50–54 years)
  55–59 years1.151.12–1.18<0.0001
  60–64 years1.371.34–1.41<0.0001
  65–69 years1.631.60–1.67<0.0001
  70–74 years2.011.97–2.06<0.0001
  75–79 years2.502.45–2.56<0.0001
  80–84 years3.133.06–3.20<0.0001
  85+ years4.104.01–4.19<0.0001
Sex (Referent: Male)
  Female1.291.28–1.30<0.0001
Race-Ethnicity (Referent: White)
  Black0.550.54–0.56<0.0001
  Latino0.710.69–0.713<0.0001
  Asian, Native Hawaiian, or Pacific Islander0.780.77–0.79<0.0001
  American Indian/Alaska Native0.850.83–0.88<0.0001
  Other 20.970.95–0.990.0008
Depression Diagnosis (Referent: No Depression)1.211.20–1.23<0.0001
Visual Disorder Diagnosis (Referent: No Visual Disorder) 31.131.11–1.15<0.0001
Hearing Loss Diagnosis (Referent: No Hearing Loss)1.241.21–1.27<0.0001
History of Falls Diagnosis (Referent: No History of Falls)3.783.72–3.84<0.0001
Parkinson’s Disease Diagnosis (Referent: No Parkinson’s)1.201.17–1.23<0.0001
1 ADRD = Alzheimer’s disease and related dementias. 2 Includes Other, Multiracial. 3 Visual disorders represent a composite of cataracts, glaucoma, and macular degeneration.
Table 3. Multinomial logistic regression analysis: discharge disposition of fall admissions among adults aged 50 years and older by Alzheimer’s disease and related dementia (ADRD) diagnosis status, Los Angeles County, 2016–2022.
Table 3. Multinomial logistic regression analysis: discharge disposition of fall admissions among adults aged 50 years and older by Alzheimer’s disease and related dementia (ADRD) diagnosis status, Los Angeles County, 2016–2022.
Disposition (Referent: Routine Discharge)
ADRD Diagnosis (Referent: No ADRD)
Adjusted Odds Ratio95% Confidence Intervalp-Value
Transfer to Short-Term Hospital1.351.29–1.42<0.0001
Transfer Other 11.871.81–1.94<0.0001
Home Health Care1.231.19–1.28<0.0001
Left Against Medical Advice or Discontinued Care1.461.34–1.59<0.0001
1 Includes skilled nursing facility, intermediate care facility, other types of facilities.
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MDPI and ACS Style

Robinson, D.M.; Chang, E.; Regos-Stewart, D.; Reyes, M.A.; Kuo, T.; Barragan, N.C. Fall-Related Hospitalizations Among Older Adults in Los Angeles County: Differences by Dementia Status, 2016–2022. J. Dement. Alzheimer's Dis. 2025, 2, 42. https://doi.org/10.3390/jdad2040042

AMA Style

Robinson DM, Chang E, Regos-Stewart D, Reyes MA, Kuo T, Barragan NC. Fall-Related Hospitalizations Among Older Adults in Los Angeles County: Differences by Dementia Status, 2016–2022. Journal of Dementia and Alzheimer's Disease. 2025; 2(4):42. https://doi.org/10.3390/jdad2040042

Chicago/Turabian Style

Robinson, D’Artagnan M., Emiley Chang, Dalia Regos-Stewart, Mariana A. Reyes, Tony Kuo, and Noel C. Barragan. 2025. "Fall-Related Hospitalizations Among Older Adults in Los Angeles County: Differences by Dementia Status, 2016–2022" Journal of Dementia and Alzheimer's Disease 2, no. 4: 42. https://doi.org/10.3390/jdad2040042

APA Style

Robinson, D. M., Chang, E., Regos-Stewart, D., Reyes, M. A., Kuo, T., & Barragan, N. C. (2025). Fall-Related Hospitalizations Among Older Adults in Los Angeles County: Differences by Dementia Status, 2016–2022. Journal of Dementia and Alzheimer's Disease, 2(4), 42. https://doi.org/10.3390/jdad2040042

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