Dementia as an Independent Predictor of Falls in Older Breast Cancer Survivors: Evidence From a Real World Multicenter Electronic Health Record Network

Dementia as an Independent Predictor of Falls in Older Breast Cancer Survivors: Evidence From a Real World Multicenter Electronic Health Record Network | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Dementia as an Independent Predictor of Falls in Older Breast Cancer Survivors: Evidence From a Real World Multicenter Electronic Health Record Network Asmaa Namoos, Oxana Palesh, Nicholas Thomson, Faika Zanjani This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8124537/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction Falls are a major source of morbidity in older adults and pose particular concern in cancer survivors who may experience treatment related neurological and functional decline. Dementia is a known risk factor for falls, yet its contribution to fall risk among breast cancer survivors has not been well defined. Methods This retrospective cohort study used de identified electronic health records from the TriNetX Research Network, which includes more than 100 health care organizations. Women aged 65 years or older with stage 1 to stage 3 breast cancer were eligible. Dementia was identified using ICD 10 code F03 recorded on or after the first qualifying cancer diagnosis. Propensity score matching used a 1 to 1 nearest neighbor approach. The primary outcome was incident fall events identified by ICD 10 codes for unspecified falls, initial fall encounters, history of falling, and repeated falls. Multivariable Cox proportional hazards models estimated independent predictors of falls. Follow up began 1 day after diagnosis and continued through the last recorded encounter. Results A total of 49 576 breast cancer survivors met inclusion criteria, of whom 1 683 (3.4%) had dementia. Before matching, fall related diagnoses were significantly more common in patients with dementia, including unspecified falls (26% vs 2%, p < 0.0001) and history of falling (15% vs 1%, p < 0.0001). After 1 to 1 matching, 1 602 survivors remained in each cohort with standardized mean differences < 0.06 across all variables. During follow up, 17.8% of survivors with dementia experienced a fall compared with 6.5% without dementia. This corresponded to an absolute risk difference of 11.3% (95% CI 9.1% to 13.6%), a risk ratio of 2.74 (95% CI 2.41 to 3.12), and an odds ratio of 3.12 (95% CI 2.67 to 3.65). The Kaplan Meier analysis showed significantly lower fall free survival in the dementia cohort (log rank p < 0.0001). The adjusted Cox model showed that dementia remained an independent predictor of falls (hazard ratio 1.43, 95% CI 1.25 to 1.63). Additional strong predictors included long term drug therapy (hazard ratio 2.62, 95% CI 2.41 to 2.84), osteoporosis (hazard ratio 1.48, 95% CI 1.34 to 1.62), polyneuropathy (hazard ratio 1.58, 95% CI 1.34 to 1.85), and depressive episode (hazard ratio 1.78, 95% CI 1.60 to 1.98). Conclusions and Relevance Dementia was associated with a substantially elevated fall risk among older breast cancer survivors, even after extensive adjustment for comorbidity, neurological conditions, psychiatric disorders, and medication burden. Recognition of this risk may help clinicians identify a subgroup of survivors who require closer monitoring and more precise evaluation during routine care. Breast cancer dementia falls survivorship geriatric oncology electronic health records TriNetX risk prediction neurological comorbidity polypharmacy Figures Figure 1 Figure 2 BACKGROUND Recent research in cancer neuroscience is reshaping our understanding of how tumors and the brain communicate. Cancer is not confined to unchecked cell growth; it interacts with neural circuits and the central nervous system in ways that influence tumor progression, repair, inflammation, and even systemic vulnerability. In parallel, the burdens of cancer treatment often extend beyond the tumor management; treatments affect brain health, cognition, neurological resilience, and physical function 1 , 2 . In survivors of breast cancer, this “neural legacy” can manifest as subtle to overt cognitive dysfunction, neuropathy, and neurologic comorbidities. Many breast cancer survivors report persistent cognitive changes such as slower information processing, difficulties with attention or multitasking, and memory lapses 3 , 4 . This phenomenon, often termed cancer-related cognitive impairment (CRCI), has been documented across many studies, even years after treatment ends 5 . In survivors, factors such as surgical stress, chemotherapy, radiation, endocrine therapy, brain aging, and systemic inflammation may act in concert to weaken neural reserve and plasticity 6 . Moreover, these cognitive changes do not occur in isolation: they often overlap with other treatment sequelae such as peripheral neuropathy, fatigue, sarcopenia, and functional decline 7 . In older adults, cognitive vulnerability is a known contributor to fall risk 8 , 9 . Impaired attention, slowed reaction time, reduced capacity to adapt to environmental challenges, and poorer executive control all compromise physical balance and increase the likelihood of missteps or instability 10 . Among patients with dementia or mild cognitive impairment, fall rates are significantly higher compared to peers with normal cognitive function 11 . Yet, little is known about how this risk plays out in the context of cancer survivorship, where multiple intersecting vulnerabilities (e.g., older age, prior treatments, comorbid disease, polypharmacy) coexist. Breast cancer survivors represent a particularly important population in this regard. Because breast cancer is common and survival rates are high, a growing number of women live long after diagnosis and therapy 12 . As this survivor population ages, neurological and functional health become central to quality of life and independence. However, in this group, the interplay between preexisting or new-onset dementia and fall risk remains largely unexplored. Anecdotally, clinicians observe that older survivors with cognitive impairment may fall more, but published evidence is scarce 13 . Our study seeks to fill this gap by quantifying the comparative risk of falls among older breast cancer survivors with versus without dementia, using a large real-world electronic health record network. We also aim to disentangle the independent effect of dementia after adjusting for demographic, frailty, neurologic and psychiatric comorbidities, and medication burden. We hypothesize that dementia will remain a robust independent predictor of fall risk, even in this medically complex population. By doing so, we hope to inform fall-prevention strategies tailored for cognitively impaired cancer survivors and guide risk stratification in survivorship care. METHODS Data Source . This retrospective cohort study utilized data from the TriNetX Research Network, a federated platform aggregating de-identified electronic health records from more than 100 health care organizations. TriNetX enables the construction of complex cohort queries based on diagnosis, procedure, demographic, and temporal parameters while maintaining full compliance with HIPAA and GDPR standards. All data are de-identified prior to analysis; therefore, institutional review board approval was not required. Study Population . Cohorts were identified using ICD-10-CM code C50 (malignant neoplasm of breast) and AJCC stage information from the TriNetX Research Network 14 . Eligible participants were women aged 65 years or older with stage I–III breast cancer and complete follow-up data. Stage 0 cases were excluded because in situ disease rarely leads to treatment-related cognitive or physical decline, while stage IV cases were excluded due to advanced illness and high baseline fall risk. Limiting the sample to stages I–III ensured a comparable survivorship population for evaluating dementia-related fall risk. After applying these criteria, 1,684 dementia cases and 55,837 non-dementia comparators were retained for baseline analyses. Minor differences between the initial and analytic counts reflect automated TriNetX data-quality filters, which exclude records with missing variables or inconsistent temporal relationships between diagnoses, such as dementia documented before the breast cancer diagnosis. At the time of analysis, data were contributed by 109 of 111 participating health care organizations across the network. Time Window and Index Event. A five-year look-back period (1,825 days) was applied to ensure accurate temporal alignment between dementia and breast cancer diagnoses. The index date was defined as the first recorded breast cancer diagnosis meeting all inclusion criteria. The observation window extended from 1,825 days before to 1 day prior to the index event, ensuring that dementia or comparator diagnoses occurred within a clinically relevant period while excluding cases in which dementia pre-dated the cancer diagnosis. Dementia Cohort Definition. The dementia cohort included women aged 65 years and older with a documented diagnosis of unspecified dementia ( ICD-10-CM F03 ) recorded on or after the index breast cancer diagnosis. This specification allowed same-day documentation but excluded any dementia diagnosis occurring before cancer diagnosis to capture cognitive decline concurrent with or following breast cancer. The comparison cohort comprised patients who met all other eligibility criteria but had no record of dementia ( F03 ) during the observation period. This temporal structure minimized the risk of reverse causality by ensuring that dementia represented post-diagnostic or concurrent impairment rather than preexisting cognitive decline. Outcome Definition . The primary outcome was the occurrence of a fall following the breast cancer diagnosis. Fall-related events were defined using ICD-10-CM codes W19 (unspecified fall), W19.XXXA (unspecified fall, initial encounter), Z91.81 (history of falling), and R29.6 (repeated falls). To ensure that only incident falls were captured, patients with documented fall diagnoses prior to the index breast cancer diagnosis were excluded. The index date was defined as the earliest recorded date of breast cancer diagnosis. Patients were followed from one day after the index date until the last available encounter, death, or censoring. Covariates. Clinical covariates were selected based on prior literature and clinical relevance. These included demographic factors (age, race, and ethnicity), frailty and musculoskeletal conditions (osteoporosis, sarcopenia, cachexia, muscle wasting), neurological disorders (polyneuropathy, Parkinson’s disease, sequelae of cerebral infarction, unsteadiness, and other gait abnormalities), psychiatric conditions (major depressive disorder and depressive episodes), comorbidities (heart failure, malnutrition, pressure ulcer), and medication burden, represented by long-term drug therapy (ICD-10-CM Z79) as a proxy for polypharmacy ( Table 1 ). Table 1 Covariates and ICD-10-CM Codes Used in the Study Category Variable ICD-10-CM Code(s) Definition / Description Frailty and Musculoskeletal Conditions Osteoporosis M81.0 Age-related osteoporosis without current pathological fracture Sarcopenia M62.84 Age-related loss of skeletal muscle mass and strength Cachexia R64 Wasting syndrome characterized by weight loss and muscle atrophy Muscle wasting / Atrophy M62.5 Muscle wasting and atrophy, not elsewhere classified Neurological Disorders Polyneuropathy, unspecified G62.9 Peripheral nerve disorder of unspecified cause Drug-induced polyneuropathy G62.0 Neuropathy secondary to medication or toxin exposure Parkinson’s disease G20 Idiopathic Parkinson’s disease Sequelae of cerebral infarction I69.3 Residual effects following stroke or cerebral infarction Unsteadiness on feet R26.81 Difficulty maintaining balance during ambulation Other abnormalities of gait and mobility R26.89 Gait disturbance not classified elsewhere Psychiatric Conditions Major depressive disorder, recurrent F33 Recurrent episodes of major depression Depressive episode F32 Single episode of depression Comorbidities Heart failure I50 Heart failure of any type (systolic, diastolic, or combined) Malnutrition, unspecified E46 Unspecified protein-calorie malnutrition Severe protein-calorie malnutrition E43 Severe form of protein-calorie deficiency Pressure ulcer L89 Pressure-induced skin ulcer Medication Burden / Polypharmacy Long-term (current) drug therapy Z79 Long-term use of medications as a proxy for polypharmacy Statistical Analysis . Descriptive statistics summarized baseline characteristics for dementia and non-dementia cohorts. Continuous variables were compared using Student’s t-tests, and categorical variables were compared using chi-square tests. Standardized mean differences were calculated to evaluate balance between groups, with a value below 0.1 indicating adequate balance. Propensity score matching (1:1 nearest neighbor) was performed to reduce confounding, and covariate balance was reassessed post-matching. Kaplan-Meier curves were generated to compare fall-free survival between cohorts, and differences were evaluated using the log-rank test. Cox proportional hazards regression models were fitted to estimate the independent association between dementia and risk of falling, adjusting for age, race, comorbidities, frailty, neurologic, and psychiatric conditions, as well as polypharmacy. Hazard ratios (HRs) with 95% confidence intervals (CIs) were reported, and statistical significance was defined as a two-sided p-value < 0.05. All analyses were conducted within the TriNetX analytics environment using its built-in statistical functions. RESULTS Baseline Demographics. Breast cancer survivors with dementia (n = 1,683) differed substantially from those without dementia (n = 48,926) in demographic composition and fall-related health profiles (Table 2 ). All participants were female and aged 65 years or older. The dementia cohort was significantly older, with a mean current age of 84.3 ± 6.44 years compared with 76.3 ± 7.54 years among survivors without dementia (p < 0.0001). The mean age at index cancer diagnosis was also higher in the dementia group (79.9 ± 7.40 years) than in the non-dementia group (67.2 ± 8.84 years; p < 0.0001). Racial and ethnic distributions varied significantly. Survivors with dementia were more frequently White (66%) or Black/African American (25%) than those without dementia (59% and 11%, respectively; both p < 0.0001). In contrast, the non-dementia cohort included a greater proportion of patients with Unknown race (18% vs 2%) and other race (6% vs 2%). Rates of Asian, Hispanic/Latino, Native Hawaiian/Pacific Islander, and American Indian/Alaska Native race categories were low and did not differ significantly. Similarly, more dementia patients were identified as Not Hispanic or Latino (70% vs 50%, p < 0.0001), whereas Unknown ethnicity was more common in the non-dementia group (46% vs 27%, p < 0.0001). These findings indicated significant baseline heterogeneity between the two cohorts prior to adjustment. (Table 2 ). Table 2 Baseline Demographic Characteristics of Breast Cancer Survivors With and Without Dementia Demographics Term / Code Dementia Cohort (n = 1,683) No Dementia Cohort (n = 48,926) P-Value Std. Diff. Age Current Age, mean ± SD 84.3 ± 6.44 76.3 ± 7.54 < 0.0001 1.1432 Age at Index, mean ± SD 79.9 ± 7.4 67.2 ± 8.84 < 0.0001 1.5523 Ethnicity Not Hispanic or Latino 70% (n = 1,174) 50% (n = 24,562) < 0.0001 0.4073 Unknown Ethnicity 27% (n = 446) 46% (n = 22,487) < 0.0001 0.4134 Hispanic or Latino 3% (n = 63) 4% (n = 1,877) 0.8449 0.0049 Race White 66% (n = 1,104) 59% (n = 28,657) < 0.0001 0.1452 Black or African American 25% (n = 414) 11% (n = 5,618) < 0.0001 0.3462 Asian 5% (n = 86) 4% (n = 2,083) 0.0896 0.0404 Unknown Race 2% (n = 35) 18% (n = 9,038) < 0.0001 0.5607 Other Race 2% (n = 27) 6% (n = 3,140) < 0.0001 0.2472 Native Hawaiian / Pacific Islander 1% (n = 13) 1% (n = 344) 0.7383 0.0081 American Indian / Alaska Native 1% (n = 10) < 1% (n = 46) < 0.0001 0.0855 Baseline Fall-Related Diagnoses . Before the index breast cancer diagnosis, fall-related conditions were more frequently documented among survivors with dementia than among those without dementia. Unspecified falls were recorded in 26% (n = 434) of the dementia cohort and in 2% (n = 1,038) of the non-dementia cohort (p < 0.0001; standardized difference 0.73). Initial encounters for unspecified falls occurred in 25% (n = 415) of survivors with dementia and in 2% (n = 980) of those without dementia (p < 0.0001; standardized difference 0.71). The history of falling was present in 15% (n = 259) of the dementia cohort and in 1% (n = 403) of the non-dementia cohort (p < 0.0001; standardized difference 0.55). Repeated falls were recorded in 11% (n = 180) of survivors with dementia compared with less than 1% (n = 191) of those without dementia (p < 0.0001; standardized difference 0.46). Fall-related injuries coded as injury of unspecified body region (T14) were reported in 8% (n = 141) of the dementia cohort and in 1% (n = 576) of the non-dementia cohort (p < 0.0001; standardized difference 0.34) ( Table 3 ). Table 3 Baseline Fall-Related Diagnoses in Breast Cancer Patients With and Without Dementia Term / Code Dementia Cohort 1 (n = 1,683) No Dementia Cohort 2 (n = 48,926) P-Value Std. Diff. W19 – Unspecified fall 26% (n = 434) 2% (n = 1,038) < 0.0001 0.7266 W19.XXXA – Unspecified fall, initial encounter 25% (n = 415) 2% (n = 980) < 0.0001 0.7069 Z91.81 – History of falling 15% (n = 259) 1% (n = 403) < 0.0001 0.5537 R29.6 – Repeated falls 11% (n = 180) 0% (n = 191) < 0.0001 0.4622 T14 – Injury of unspecified body region 8% (n = 141) 1% (n = 576) < 0.0001 0.3425 Propensity Score Matching After 1:1 propensity score matching, 1,602 patients were retained in each cohort. Matching achieved excellent balance across all demographic covariates, with standardized mean differences below 0.06 (Table 3 ). Mean current age was virtually identical between dementia and non-dementia patients (84.1 ± 6.50 vs 84.1 ± 6.56 years, p = 0.98), as was age at index diagnosis (79.5 ± 7.39 vs 79.5 ± 7.52 years, p = 0.99). Racial and ethnic distributions were similarly well balanced after matching. The proportion of White (65.6% vs 65.4%) and Black/African American (24.3% vs 25.5%) patients was nearly identical, and no residual significant differences remained across Asian, Hispanic/Latino, or other racial categories (all p > 0.05). Ethnic proportions also aligned closely (Not Hispanic or Latino 69.7% vs 72.0%; p = 0.15) (Table 4 ). Table 4 Baseline Characteristics Before and After Propensity Score Matching Variable Category Before Matching: Dementia Cohort 1(n = 1,683) Before Matching: No Dementia Cohort 2 (n = 48,926) P-Value Std. Diff. After Matching: Dementia Cohort 1 (n = 1,602) After Matching: No Dementia Cohort 2(n = 1,602) Std. Diff. P-Value Age (years) Current Age (mean ± SD) 84.3 ± 6.44 76.3 ± 7.54 < 0.0001 1.1432 84.1 ± 6.50 84.1 ± 6.56 0.984 0.0007 Age at Index (mean ± SD) 79.9 ± 7.40 67.2 ± 8.84 < 0.0001 1.5523 79.5 ± 7.39 79.5 ± 7.52 0.986 0.0006 Ethnicity Not Hispanic or Latino 1,174 (69.76%) 24,562 (50.20%) < 0.0001 0.4073 1,116 (69.66%) 1,153 (71.97%) 0.150 0.0508 Unknown Ethnicity 446 (26.50%) 22,447 (45.96%) < 0.0001 0.4134 426 (26.59%) 393 (24.53%) 0.181 0.0472 Hispanic or Latino 63 (3.74%) 1,877 (3.84%) 0.8449 0.0049 60 (3.75%) 56 (3.50%) 0.705 0.0134 Race White 1,104 (65.60%) 28,657 (58.57%) < 0.0001 0.1452 1,051 (65.61%) 1,048 (65.42%) 0.911 0.0039 Black or African American 414 (24.60%) 5,618 (11.48%) < 0.0001 0.3462 390 (24.35%) 408 (25.47%) 0.462 0.0260 Asian 86 (5.11%) 2,083 (4.26%) 0.0896 0.0404 83 (5.18%) 85 (5.31%) 0.874 0.0056 Unknown Race 35 (2.08%) 9,038 (18.47%) < 0.0001 0.5607 35 (2.19%) 27 (1.69%) 0.304 0.0363 Other Race 27 (1.60%) 3,140 (6.42%) < 0.0001 0.2472 27 (1.69%) 23 (1.44%) 0.568 0.0201 Native Hawaiian / Pacific Islander 13 (0.77%) 344 (0.70%) 0.7383 0.0081 13 (0.81%) 10 (0.62%) 0.530 0.0222 American Indian / Alaska Native 10 (0.59%) 46 (0.09%) < 0.0001 0.0855 10 (0.62%) 10 (0.62%) 1.000 < 0.0001 Baseline Fall-Related Diagnoses. Prior to propensity score matching, fall-related conditions were markedly more prevalent among breast cancer survivors with dementia compared to those without. Unspecified falls (W19) were documented in 25.8% of the dementia cohort versus 2.1% of the non-dementia cohort (p < 0.0001). Similarly, initial encounters for unspecified falls (W19.XXXA) occurred in 24.7% versus 2.0% of patients, respectively (p < 0.0001). A documented history of falling (Z91.81) was present in 15.4% of dementia patients compared with 0.8% of those without dementia (p < 0.0001). Repeated falls (R29.6) were identified in 10.7% versus 0.4% (p < 0.0001), and injuries of unspecified body region (T14) were more frequent among dementia patients (8.4% vs 1.2%; p < 0.0001). After 1:1 propensity score matching, these conditions were well balanced across groups. The matched dementia and non-dementia cohorts each included 1,602 patients, with nearly identical frequencies of unspecified falls (22.9% vs 23.1%; p = 0.87), initial encounters for unspecified falls (21.8% vs 22.2%; p = 0.77), history of falling (12.6% vs 11.2%; p = 0.25), repeated falls (8.4% vs 7.0%; p = 0.14), and injury of unspecified body region (7.4% vs 7.9%; p = 0.60). All standardized mean differences were below 0.06, confirming adequate balance in fall-related diagnoses following matching ( Table 5 ). Table 5 Baseline Fall-Related Diagnoses Before and After Propensity Score Matching Variable Fall-Related Diagnoses Before Matching: Dementia Cohort 1 (n = 1,683) Before Matching: No Dementia Cohort 2 (n = 48,926) P-Value Std. Diff. After Matching: Dementia Cohort 1 (n = 1,602) After Matching: No Dementia Cohort 2 (n = 1,602) P-Value Std. Diff. Unspecified Fall 434 (25.79%) 1,038 (2.12%) < 0.0001 0.7266 366 (22.85%) 370 (23.10%) 0.8666 0.0059 Unspecified Fall, Initial Encounter 415 (24.66%) 980 (2.00%) < 0.0001 0.7069 349 (21.79%) 356 (22.22%) 0.7653 0.0105 History of Falling 259 (15.39%) 403 (0.82%) < 0.0001 0.5537 201 (12.55%) 180 (11.24%) 0.2517 0.0405 Repeated Falls 180 (10.70%) 191 (0.39%) < 0.0001 0.4622 134 (8.37%) 112 (6.99%) 0.1443 0.0516 Injury of Unspecified Body Region 141 (8.38%) 576 (1.18%) < 0.0001 0.3425 119 (7.43%) 127 (7.93%) 0.5955 0.0188 Copy/right of the graph to TriNetX Platform Fall Outcomes and Risk Estimates Measures of Association. After excluding patients with falls prior to the index period (cancer diagnosis), breast cancer survivors with dementia exhibited a significantly higher risk of subsequent falls compared with those without dementia. Among the dementia cohort, 17.8% (202 of 1,134) experienced a fall during follow-up compared with 6.5% (3,096 of 47,639) in the non-dementia cohort. The absolute risk difference was 11.3% (95% CI 9.1–13.6, p < 0.0001), while the relative risk was 2.74 (95% CI 2.41–3.12). Similarly, the odds of falling were more than three times higher in the dementia group compared to the non-dementia group (odds ratio 3.12, 95% CI 2.67–3.65) (Table 6 ). Table 6 Measures of Association (Excluding Patients with Outcome Prior to the Time Window) Cohort Patients in Cohort Patients with Outcome Risk (%) BC 1–3 With Dementia 1,134 202 17.81% BC 1–3 Without Dementia 47,639 3,096 6.50% Measure Estimate 95% CI z p Risk Difference 11.31% (9.08%, 13.55%) 14.996 < 0.0001 Risk Ratio 2.74 (2.41, 3.12) N/A N/A Odds Ratio 3.12 (2.67, 3.65) N/A N/A 549 patients in Cohort 1 and 1,287 patients in Cohort 2 were excluded for having outcome prior to time window. Kaplan–Meier Survival Analysis . Survival analysis (Fig. 2 ) further highlighted the heightened fall risk in patients with dementia. The median survival time to first fall in the dementia cohort was 4,161 days, with only 47.6% of patients remaining fall-free at the end of the follow-up window, compared with 78.9% in the non-dementia cohort. The log-rank test revealed a highly significant difference between survival curves (χ² = 876.3, df = 1, p < 0.0001). The hazard of falling was more than six times higher among breast cancer survivors with dementia (HR = 6.57, 95% CI 5.69–7.59, p = 0.0008). Number of Fall Instances . When the frequency of fall episodes was evaluated, dementia patients averaged slightly more fall events than those without dementia (2.44 ± 8.20 vs 2.04 ± 5.05). However, this difference was not statistically significant (t = 1.05, df = 3,296, p = 0.29). Median fall count was one in both cohorts, suggesting that while dementia substantially increases the likelihood and timing of falls, the total number of fall episodes per patient among those who do fall does not differ meaningfully by dementia status. Cox Proportional Hazards Analysis A multivariable Cox proportional hazards model was used to examine predictors of fall-related outcomes among older breast cancer survivors. Dementia status was independently associated with a significantly higher hazard of falls (HR = 1.43, 95% CI: 1.25–1.63, p < 0.001) after adjustment for demographic, clinical, and functional covariates (Table 7 ). Age and Demographics. Each one-year increase in age at index was associated with a 5.4% increase in fall risk (HR = 1.05, 95% CI: 1.05–1.06, p < 0.001). Race and ethnicity were significant predictors: Black or African American patients had a 30% higher hazard of falls compared with White patients (HR = 1.31, 95% CI: 1.20–1.42, p < 0.001), while Hispanic or Latino patients had the highest relative risk (HR = 1.77, 95% CI: 1.55–2.03, p < 0.0001). Frailty and Musculoskeletal Conditions . Among frailty-related variables, osteoporosis (HR = 1.48, 95% CI: 1.34–1.62, p < 0.001) and cachexia (HR = 2.44, 95% CI: 1.45–4.08, p = 0.001) were strong independent predictors of falls. Sarcopenia and muscle wasting were not statistically significant (p = 0.44 and p = 0.71, respectively). Neurological Disorders . Polyneuropathy was associated with a 57% higher hazard of falls (HR = 1.58, 95% CI: 1.34–1.85, p < 0.001), while Parkinson’s disease (HR = 1.50, 95% CI: 1.12–2.01, p = 0.007) and sequelae of cerebral infarction (HR = 1.53, 95% CI: 1.15–2.03, p = 0.003) were also significant. Unsteadiness on feet (HR = 1.35, 95% CI: 1.08–1.71, p = 0.010) and other gait abnormalities (HR = 1.42, 95% CI: 1.22–1.66, p < 0.001) further contributed to elevated fall risk. Psychiatric Conditions . Both depressive episodes (HR = 1.78, 95% CI: 1.60–1.98, p < 0.001) and recurrent major depressive disorder (HR = 1.50, 95% CI: 1.26–1.79, p < 0.001) were independently associated with increased fall risk. Delirium and mild cognitive impairment were not significant predictors (p = 0.51 and p = 0.28, respectively). Comorbidities. Heart failure was a significant predictor of falls (HR = 1.36, 95% CI: 1.20–1.53, p 0.12). Medication Burden . Long-term drug therapy, used as a proxy for polypharmacy (ICD-10-CM Z79), was among the strongest predictors, associated with more than a twofold increased hazard of falls (HR = 2.62, 95% CI: 2.41–2.84, p < 0.001). Table 7 Multivariable Cox Proportional Hazards Model for Predictors of Fall-Related Outcomes Among Older Breast Cancer Survivors Cox Model Results Category Code Covariate Hazard Ratio Coefficient Standard Error z P >|z| 95% Confidence Interval Cohort 1 or Cohort 2 Membership 1.430 0.358 0.068 5.278 < 0.001 (1.252, 1.634) Age AI Age at Index 1.054 0.053 0.002 27.837 < 0.001 (1.051, 1.058) Race 2054-5 Black or African American 1.306 0.267 0.044 6.109 < 0.001 (1.199, 1.423) Ethnicity 2135-2 Hispanic or Latino 1.773 0.573 0.068 8.364 < 0.001 (1.551, 2.028) Frailty and Musculoskeletal Conditions M81.0 Osteoporosis 1.476 0.389 0.049 7.975 < 0.001 (1.341, 1.624) M62.84 Sarcopenia 1.467 0.383 0.499 0.768 0.443 (0.551, 3.905) R64 Cachexia 2.435 0.890 0.263 3.383 0.001 (1.454, 4.077) M62.5 Muscle wasting 0.922 -0.082 0.219 -0.372 0.710 (0.600, 1.417) Neurological Disorders G62.9 Polyneuropathy, unspecified 1.575 0.454 0.082 5.546 < 0.001 (1.341, 1.849) G62.0 Drug-induced polyneuropathy 0.654 -0.424 0.262 -1.619 0.105 (0.391, 1.093) G20 Parkinson's disease 1.498 0.404 0.150 2.698 0.007 (1.117, 2.008) I69.3 Sequelae of cerebral infarction 1.527 0.423 0.145 2.928 0.003 (1.150, 2.027) R26.81 Unsteadiness on feet 1.354 0.303 0.117 2.586 0.010 (1.076, 1.705) R26.89 Other abnormalities of gait and mobility 1.418 0.349 0.079 4.418 < 0.001 (1.215, 1.656) Psychiatric Conditions F32 Depressive episode 1.779 0.576 0.054 10.749 < 0.001 (1.602, 1.977) F33 Major depressive disorder, recurrent 1.501 0.406 0.090 4.521 < 0.001 (1.259, 1.790) F05 Delirium 0.887 -0.120 0.181 -0.662 0.508 (0.622, 1.265) G31.84 Mild cognitive impairment 1.129 0.122 0.112 1.086 0.277 (0.907, 1.407) Comorbidities I50 Heart failure 1.355 0.304 0.062 4.877 < 0.001 (1.199, 1.531) E46 Unspecified protein-calorie malnutrition 1.047 0.046 0.165 0.277 0.782 (0.757, 1.448) E43 Unspecified severe protein-calorie malnutrition 0.869 -0.140 0.221 -0.634 0.526 (0.564, 1.340) L89 Pressure ulcer 0.722 -0.326 0.213 -1.531 0.126 (0.476, 1.096) H54.7 Unspecified visual loss 1.129 0.121 0.155 0.782 0.434 (0.833, 1.531) Medication Burden / Polypharmacy Z79 Long term (current) drug therapy 2.615 0.961 0.043 22.607 0.001 (2.406, 2.842) DISCUSSION This large real-world cohort study provides new insights into the relationship between dementia and fall risk among older breast cancer survivors. We observed that survivors with dementia were more than twice as likely to experience a fall compared with survivors without dementia, and this increased risk persisted after controlling for a comprehensive set of demographics, clinical and treatment-related factors. The absolute risk difference of 11.3 percentage points translates into a substantial burden of fall-related events in this population. These findings align with broader geriatric literature showing that cognitive impairment markedly increases the likelihood of falls: older adults with dementia fall two to three times more often than cognitively healthy peers, and between 60–80% of people with dementia fall annually 11 . Our results extend these observations by showing that dementia remains an independent predictor of falls even after accounting for frailty, neurological disorders, psychiatric conditions and polypharmacy, suggesting a distinctive vulnerability among cognitively impaired cancer survivors. Several covariates emerged as important contributors to fall risk. Increasing age at cancer diagnosis was associated with a higher hazard of falling, consistent with evidence that falls affect more than thirty percent of adults aged sixty-five years and older 15 . Race and ethnicity were also significant predictors: Black and Hispanic survivors faced greater hazards compared with White survivors 16 . Frailty-related conditions such as osteoporosis and cachexia, neurologic disorders including polyneuropathy, Parkinson disease and sequelae of cerebral infarction, and psychiatric disorders such as depressive episodes all conferred elevated fall risk 17 – 20 . Notably, long-term use of medications a proxy for polypharmacy was one of the strongest predictors, more than doubling the hazard of falling 21 . These findings highlight the multifactorial nature of falls, which are influenced by interactions between neuromuscular function, cognition, comorbidity burden, medication effects and sociodemographic factors. They also underscore the importance of comprehensive geriatric assessment in oncology practice, with attention to medication management and mental health. Our findings have several implications for clinical care and survivorship planning. First, they emphasize the need for proactive fall-prevention strategies tailored to older breast cancer survivors with dementia. Interventions such as balance and strength training, home safety assessments, medication review and cognitive support should be integrated into survivorship care plans. Second, the strong association between polypharmacy and falls supports efforts to deprescribe non-essential medications and to coordinate care across oncology, primary care and geriatrics. Third, the elevated risk among Black and Hispanic survivors signals potential disparities in access to supportive services or differences in comorbidity profiles; culturally informed interventions and equitable access to fall-prevention programs are needed. Finally, because older adults with dementia fall frequently, clinicians should consider routine cognitive screening for breast cancer survivors and engage caregivers in fall-prevention education. Future research should examine the efficacy of multifaceted interventions in this population, explore mechanisms linking cognitive impairment and falls, and investigate how cancer treatments interact with dementia-related neuropathology to influence mobility and balance. Strength . The study’s design affords several notable strengths that enhance confidence in its findings. Leveraging the TriNetX platform allowed us to assemble one of the largest real-world cohorts of older breast cancer survivors with and without dementia to date. Because TriNetX aggregates records from over 100 participating health systems, the resulting sample encompasses diverse patient populations and practice settings, which improves the generalizability of the results. Moreover, the use of rigorous statistical methods including one-to-one propensity score matching and adjustments for a comprehensive array of demographic, clinical and medication variables which helped minimize confounding and ensured that the observed associations were not driven by imbalances between cohorts. Our precise outcome definitions, which incorporated multiple International Classification of Diseases codes (ICD- code) for fall events, captured a wide spectrum of falls from single incidents to recurrent episodes and fall-related injuries. This approach aligns with broader evidence that cognitive impairment markedly increases fall risk: older adults with dementia fall two to three times more frequently than cognitively healthy peers, and between sixty and eighty percent of individuals with dementia fall each year. These methodological strengths yield robust, broadly applicable insights into fall risk among this medically complex population. The Kaplan Meier survival analysis provided an unadjusted age-based estimate of fall-free survival, illustrating the cumulative incidence of falls over time before accounting for other covariates in multivariable models. Limitation . Several limitations should be considered when interpreting the results. The retrospective nature of electronic health record analysis precludes causal inference and relies on accurate coding. Falls that did not result in medical attention were not captured, potentially underestimating the true incidence of fall events. Although we adjusted for many potential confounders, residual confounding may persist because data on certain factors such as the severity and type of dementia, detailed cancer treatments, functional status or living environment were unavailable. The “unspecified dementia” category used here includes heterogeneous etiologies, which prevented us from examining how different dementia subtypes might influence fall risk. Additionally, while TriNetX provides a large and diverse sample, its composition may not fully represent populations outside North America, and coding practices may vary across contributing institutions, introducing some misclassification bias. These factors highlight the need for future prospective studies with more granular data to confirm and extend the current findings. Declarations CONFLICT OF INTEREST The authors declare that there are no conflicts of interest regarding the publication of this paper. None of the authors have any competing financial or non-financial interests in relation to the work described. This includes no direct relationships such as employment, consultancies, stock ownership, honoraria, paid expert testimony, patent applications/registrations, and grants or other funding that could influence the work reported in this manuscript. FUNDING This work was supported by the Wright Center under the Clinical and Translational Science Award (CTSA) Grant number UM1TR004360. Author Contribution • Asmaa Namoos conceptualized the study, designed the analysis framework, conducted the TriNetX data extraction and statistical analyses, interpreted the findings, and drafted the manuscript.• Oxana Palesh contributed to study conceptualization, data interpretation, and critical manuscript revision for intellectual content.• Faika Zanjani provided guidance on gerontology and aging-related interpretation, contributed to methodological refinement, and reviewed the final manuscript for accuracy and coherence. Acknowledgement We extend our gratitude to the informatics team at Virginia Commonwealth University’s (VCU) C. Kenneth and Dianne Wright Center for Clinical and Translational Research, especially Tamas Gal and his team members, Evan French and Patrick Shi, for their invaluable support in data extraction through TriNetX platform. Data Availability The data that support the findings of this study are available upon reasonable request. Interested researchers can obtain access to the data by submitting a formal request to the corresponding author at [email protected] . The data is not publicly available due to privacy or ethical restrictions. References Miller K (2022) Cancer treatment and survivorship statistics, 2022 - Miller – 2022 - CA: A Cancer Journal for Clinicians - Wiley Online Library. Accessed August 13, 2024. https://acsjournals.onlinelibrary.wiley.com/doi/ 10.3322/caac.21731 Chemotherapy for Breast Cancer | Breast Cancer Treatment | American Cancer Society. Accessed September 20 (2024) https://www.cancer.org/cancer/types/breast-cancer/treatment/chemotherapy-for-breast-cancer.html Changes (2025) in Memory, Thinking, and Focus (Chemo Brain). Accessed October 12. https://www.cancer.org/cancer/managing-cancer/side-effects/changes-in-mood-or-thinking/chemo-brain.html Országhová Z, Mego M, Chovanec M (2021) Long-Term Cognitive Dysfunction in Cancer Survivors. Front Mol Biosci 8:770413. 10.3389/fmolb.2021.770413 Nguyen CM, Yamada TH, Beglinger LJ, Cavanaugh JE, Denburg NL, Schultz SK (2013) Cognitive Features Ten or More Years After Successful Breast Cancer Survival: Comparisons Across Types of Cancer Interventions. Psychooncology 22(4):862–868. 10.1002/pon.3086 Was H, Borkowska A, Bagues A et al (2022) Mechanisms of Chemotherapy-Induced Neurotoxicity. Front Pharmacol 13. 10.3389/fphar.2022.750507 Muhandiramge J, Orchard SG, Warner ET, van Londen GJ, Zalcberg JR (2022) Functional Decline in the Cancer Patient: A Review. Cancers 14(6):1368. 10.3390/cancers14061368 Segev-Jacubovski O, Herman T, Yogev-Seligmann G, Mirelman A, Giladi N, Hausdorff JM (2011) The interplay between gait, falls and cognition: can cognitive therapy reduce fall risk? Expert Rev Neurother 11(7):1057–1075. 10.1586/ern.11.69 Minta K, Colombo G, Taylor WR, Schinazi VR (2023) Differences in fall-related characteristics across cognitive disorders. Front Aging Neurosci 15:1171306. 10.3389/fnagi.2023.1171306 Wang J, Li Y, Yang GY, Jin K (2024) Age-Related Dysfunction in Balance: A Comprehensive Review of Causes, Consequences, and Interventions. Aging Dis 16(2):714–737. 10.14336/AD.2024.0124-1 Racey M, Markle-Reid M, Fitzpatrick-Lewis D et al (2021) Fall prevention in community-dwelling adults with mild to moderate cognitive impairment: a systematic review and meta-analysis. BMC Geriatr 21(1):689. 10.1186/s12877-021-02641-9 CDC. U.S. Cancer Statistics Female Breast Cancer Stat Bite. United States Cancer Statistics. June 10 (2025) Accessed October 12, 2025. https://www.cdc.gov/united-states-cancer-statistics/publications/breast-cancer-stat-bite.html Muhandiramge J, Orchard SG, Warner ET, van Londen GJ, Zalcberg JR (2022) Functional Decline in the Cancer Patient: A Review. Cancers 14(6):1368. 10.3390/cancers14061368 TriNetX TNX (2013) Accessed July 18, 2024. https://live.trinetx.com/tnx/study/202800/analytics/new Wildes TM, Dua P, Fowler SA et al (2015) Systematic Review of Falls in Older Adults with Cancer. J Geriatr Oncol 6(1):70–83. 10.1016/j.jgo.2014.10.003 Kwon SC, Han BH, Kranick JA et al (2018) Racial and Ethnic Difference in Falls Among Older Adults: Results from the California Health Interview Survey. J Racial Ethn Health Disparities 5(2):271–278. 10.1007/s40615-017-0367-8 Yang ZC, Lin H, Jiang GH et al (2023) Frailty Is a Risk Factor for Falls in the Older Adults: A Systematic Review and Meta-Analysis. J Nutr Health Aging 27(6):487–495. 10.1007/s12603-023-1935-8 Homann B, Plaschg A, Grundner M et al (2013) The impact of neurological disorders on the risk for falls in the community dwelling elderly: a case-controlled study. BMJ Open 3(11):e003367. 10.1136/bmjopen-2013-003367 Namoos A, Thomson N, Olson C, Sheppard V, Aboutanos M (2025) Physical and Psychological Burdens Among Breast Cancer Survivors: Evaluating Post-Treatment Gait Impairment, Falls, and Depression Using Real-World Data. Eur J Cancer Care (Engl) 2025(1):5558563. 10.1155/ecc/5558563 Namoos A, Thomson N, Bradley S, Rudderman A, Aboutanos M (2024) Memory Loss and Missteps: Investigating Fall Risks in Alzheimer’s and Dementia Patients. Adv Geriatr Med Res 6(3):e240005. 10.20900/agmr20240005 Xue L, Boudreau RM, Donohue JM et al (2021) Persistent polypharmacy and fall injury risk: the Health, Aging and Body Composition Study. BMC Geriatr 21(1):710. 10.1186/s12877-021-02695-9 Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":69815,"visible":true,"origin":"","legend":"\u003cp\u003ePropensity score distributions before and after matching\u003cbr\u003e\nCopy/right of the graph to TriNetX Platform\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8124537/v1/a3f42d31c553cd6685336cad.png"},{"id":96969044,"identity":"19893f63-cdad-47e8-b56a-861da834deee","added_by":"auto","created_at":"2025-11-28 07:08:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":154262,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan Meier Survival Curve by Dementia Status\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8124537/v1/1b548d7c1f4d568d59df8e71.png"},{"id":97248962,"identity":"35c0cc05-22c3-47b2-970e-c23686fa5494","added_by":"auto","created_at":"2025-12-02 13:08:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1493853,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8124537/v1/0f237115-1d7c-4970-9477-789ee38e8103.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dementia as an Independent Predictor of Falls in Older Breast Cancer Survivors: Evidence From a Real World Multicenter Electronic Health Record Network ","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eRecent research in cancer neuroscience is reshaping our understanding of how tumors and the brain communicate. Cancer is not confined to unchecked cell growth; it interacts with neural circuits and the central nervous system in ways that influence tumor progression, repair, inflammation, and even systemic vulnerability. In parallel, the burdens of cancer treatment often extend beyond the tumor management; treatments affect brain health, cognition, neurological resilience, and physical function\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. In survivors of breast cancer, this \u0026ldquo;neural legacy\u0026rdquo; can manifest as subtle to overt cognitive dysfunction, neuropathy, and neurologic comorbidities.\u003c/p\u003e\u003cp\u003eMany breast cancer survivors report persistent cognitive changes such as slower information processing, difficulties with attention or multitasking, and memory lapses\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. This phenomenon, often termed cancer-related cognitive impairment (CRCI), has been documented across many studies, even years after treatment ends\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. In survivors, factors such as surgical stress, chemotherapy, radiation, endocrine therapy, brain aging, and systemic inflammation may act in concert to weaken neural reserve and plasticity\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Moreover, these cognitive changes do not occur in isolation: they often overlap with other treatment sequelae such as peripheral neuropathy, fatigue, sarcopenia, and functional decline\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn older adults, cognitive vulnerability is a known contributor to fall risk\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Impaired attention, slowed reaction time, reduced capacity to adapt to environmental challenges, and poorer executive control all compromise physical balance and increase the likelihood of missteps or instability\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Among patients with dementia or mild cognitive impairment, fall rates are significantly higher compared to peers with normal cognitive function\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Yet, little is known about how this risk plays out in the context of cancer survivorship, where multiple intersecting vulnerabilities (e.g., older age, prior treatments, comorbid disease, polypharmacy) coexist.\u003c/p\u003e\u003cp\u003eBreast cancer survivors represent a particularly important population in this regard. Because breast cancer is common and survival rates are high, a growing number of women live long after diagnosis and therapy\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. As this survivor population ages, neurological and functional health become central to quality of life and independence. However, in this group, the interplay between preexisting or new-onset dementia and fall risk remains largely unexplored. Anecdotally, clinicians observe that older survivors with cognitive impairment may fall more, but published evidence is scarce\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOur study seeks to fill this gap by quantifying the comparative risk of falls among older breast cancer survivors with versus without dementia, using a large real-world electronic health record network. We also aim to disentangle the independent effect of dementia after adjusting for demographic, frailty, neurologic and psychiatric comorbidities, and medication burden. We hypothesize that dementia will remain a robust independent predictor of fall risk, even in this medically complex population. By doing so, we hope to inform fall-prevention strategies tailored for cognitively impaired cancer survivors and guide risk stratification in survivorship care.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eData Source\u003c/span\u003e. This retrospective cohort study utilized data from the TriNetX Research Network, a federated platform aggregating de-identified electronic health records from more than 100 health care organizations. TriNetX enables the construction of complex cohort queries based on diagnosis, procedure, demographic, and temporal parameters while maintaining full compliance with HIPAA and GDPR standards. All data are de-identified prior to analysis; therefore, institutional review board approval was not required.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eStudy Population\u003c/span\u003e. Cohorts were identified using ICD-10-CM code C50 (malignant neoplasm of breast) and AJCC stage information from the TriNetX Research Network\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Eligible participants were women aged 65 years or older with stage I\u0026ndash;III breast cancer and complete follow-up data. Stage 0 cases were excluded because in situ disease rarely leads to treatment-related cognitive or physical decline, while stage IV cases were excluded due to advanced illness and high baseline fall risk. Limiting the sample to stages I\u0026ndash;III ensured a comparable survivorship population for evaluating dementia-related fall risk. After applying these criteria, 1,684 dementia cases and 55,837 non-dementia comparators were retained for baseline analyses. Minor differences between the initial and analytic counts reflect automated TriNetX data-quality filters, which exclude records with missing variables or inconsistent temporal relationships between diagnoses, such as dementia documented before the breast cancer diagnosis. At the time of analysis, data were contributed by 109 of 111 participating health care organizations across the network.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTime Window and Index Event.\u003c/span\u003e A five-year look-back period (1,825 days) was applied to ensure accurate temporal alignment between dementia and breast cancer diagnoses. The index date was defined as the first recorded breast cancer diagnosis meeting all inclusion criteria. The observation window extended from 1,825 days before to 1 day prior to the index event, ensuring that dementia or comparator diagnoses occurred within a clinically relevant period while excluding cases in which dementia pre-dated the cancer diagnosis.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eDementia Cohort Definition.\u003c/span\u003e The dementia cohort included women aged 65 years and older with a documented diagnosis of unspecified dementia \u003cb\u003e(\u003c/b\u003eICD-10-CM F03\u003cb\u003e)\u003c/b\u003e recorded on or after the index breast cancer diagnosis. This specification allowed same-day documentation but excluded any dementia diagnosis occurring before cancer diagnosis to capture cognitive decline concurrent with or following breast cancer. The comparison cohort comprised patients who met all other eligibility criteria but had no record of dementia \u003cb\u003e(\u003c/b\u003eF03\u003cb\u003e)\u003c/b\u003e during the observation period. This temporal structure minimized the risk of reverse causality by ensuring that dementia represented post-diagnostic or concurrent impairment rather than preexisting cognitive decline.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eOutcome Definition\u003c/span\u003e. The primary outcome was the occurrence of a fall following the breast cancer diagnosis. Fall-related events were defined using ICD-10-CM codes W19 (unspecified fall), W19.XXXA (unspecified fall, initial encounter), Z91.81 (history of falling), and R29.6 (repeated falls). To ensure that only incident falls were captured, patients with documented fall diagnoses prior to the index breast cancer diagnosis were excluded. The index date was defined as the earliest recorded date of breast cancer diagnosis. Patients were followed from one day after the index date until the last available encounter, death, or censoring.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCovariates.\u003c/span\u003e Clinical covariates were selected based on prior literature and clinical relevance. These included demographic factors (age, race, and ethnicity), frailty and musculoskeletal conditions (osteoporosis, sarcopenia, cachexia, muscle wasting), neurological disorders (polyneuropathy, Parkinson\u0026rsquo;s disease, sequelae of cerebral infarction, unsteadiness, and other gait abnormalities), psychiatric conditions (major depressive disorder and depressive episodes), comorbidities (heart failure, malnutrition, pressure ulcer), and medication burden, represented by long-term drug therapy (ICD-10-CM Z79) as a proxy for polypharmacy ( Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCovariates and ICD-10-CM Codes Used in the Study\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eICD-10-CM Code(s)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDefinition / Description\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003eFrailty and Musculoskeletal Conditions\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOsteoporosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM81.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAge-related osteoporosis without current pathological fracture\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSarcopenia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM62.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAge-related loss of skeletal muscle mass and strength\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCachexia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eR64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWasting syndrome characterized by weight loss and muscle atrophy\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMuscle wasting / Atrophy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM62.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMuscle wasting and atrophy, not elsewhere classified\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e\u003cb\u003eNeurological Disorders\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePolyneuropathy, unspecified\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG62.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePeripheral nerve disorder of unspecified cause\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDrug-induced polyneuropathy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG62.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNeuropathy secondary to medication or toxin exposure\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eParkinson\u0026rsquo;s disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIdiopathic Parkinson\u0026rsquo;s disease\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSequelae of cerebral infarction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eI69.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eResidual effects following stroke or cerebral infarction\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnsteadiness on feet\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eR26.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDifficulty maintaining balance during ambulation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOther abnormalities of gait and mobility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eR26.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGait disturbance not classified elsewhere\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003ePsychiatric Conditions\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMajor depressive disorder, recurrent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRecurrent episodes of major depression\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDepressive episode\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSingle episode of depression\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHeart failure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eI50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHeart failure of any type (systolic, diastolic, or combined)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMalnutrition, unspecified\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eE46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUnspecified protein-calorie malnutrition\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSevere protein-calorie malnutrition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eE43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSevere form of protein-calorie deficiency\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePressure ulcer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eL89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePressure-induced skin ulcer\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMedication Burden / Polypharmacy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLong-term (current) drug therapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eZ79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLong-term use of medications as a proxy for polypharmacy\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eStatistical Analysis\u003c/span\u003e. Descriptive statistics summarized baseline characteristics for dementia and non-dementia cohorts. Continuous variables were compared using Student\u0026rsquo;s t-tests, and categorical variables were compared using chi-square tests. Standardized mean differences were calculated to evaluate balance between groups, with a value below 0.1 indicating adequate balance. Propensity score matching (1:1 nearest neighbor) was performed to reduce confounding, and covariate balance was reassessed post-matching. Kaplan-Meier curves were generated to compare fall-free survival between cohorts, and differences were evaluated using the log-rank test. Cox proportional hazards regression models were fitted to estimate the independent association between dementia and risk of falling, adjusting for age, race, comorbidities, frailty, neurologic, and psychiatric conditions, as well as polypharmacy. Hazard ratios (HRs) with 95% confidence intervals (CIs) were reported, and statistical significance was defined as a two-sided p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All analyses were conducted within the TriNetX analytics environment using its built-in statistical functions.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eBaseline Demographics.\u003c/span\u003e Breast cancer survivors with dementia (n\u0026thinsp;=\u0026thinsp;1,683) differed substantially from those without dementia (n\u0026thinsp;=\u0026thinsp;48,926) in demographic composition and fall-related health profiles (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). All participants were female and aged 65 years or older. The dementia cohort was significantly older, with a mean current age of 84.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.44 years compared with 76.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.54 years among survivors without dementia (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The mean age at index cancer diagnosis was also higher in the dementia group (79.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.40 years) than in the non-dementia group (67.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.84 years; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Racial and ethnic distributions varied significantly. Survivors with dementia were more frequently White (66%) or Black/African American (25%) than those without dementia (59% and 11%, respectively; both p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). In contrast, the non-dementia cohort included a greater proportion of patients with Unknown race (18% vs 2%) and other race (6% vs 2%). Rates of Asian, Hispanic/Latino, Native Hawaiian/Pacific Islander, and American Indian/Alaska Native race categories were low and did not differ significantly. Similarly, more dementia patients were identified as Not Hispanic or Latino (70% vs 50%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), whereas Unknown ethnicity was more common in the non-dementia group (46% vs 27%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). These findings indicated significant baseline heterogeneity between the two cohorts prior to adjustment. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline Demographic Characteristics of Breast Cancer Survivors With and Without Dementia\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDemographics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTerm / Code\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDementia Cohort (n\u0026thinsp;=\u0026thinsp;1,683)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo Dementia Cohort (n\u0026thinsp;=\u0026thinsp;48,926)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStd. Diff.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCurrent Age, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e84.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e76.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.1432\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge at Index, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e67.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.5523\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eEthnicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot Hispanic or Latino\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70% (n\u0026thinsp;=\u0026thinsp;1,174)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50% (n\u0026thinsp;=\u0026thinsp;24,562)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.4073\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnknown Ethnicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27% (n\u0026thinsp;=\u0026thinsp;446)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46% (n\u0026thinsp;=\u0026thinsp;22,487)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.4134\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHispanic or Latino\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3% (n\u0026thinsp;=\u0026thinsp;63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4% (n\u0026thinsp;=\u0026thinsp;1,877)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.8449\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0049\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003eRace\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66% (n\u0026thinsp;=\u0026thinsp;1,104)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e59% (n\u0026thinsp;=\u0026thinsp;28,657)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.1452\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBlack or African American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25% (n\u0026thinsp;=\u0026thinsp;414)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11% (n\u0026thinsp;=\u0026thinsp;5,618)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.3462\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAsian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5% (n\u0026thinsp;=\u0026thinsp;86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4% (n\u0026thinsp;=\u0026thinsp;2,083)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0896\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0404\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnknown Race\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2% (n\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18% (n\u0026thinsp;=\u0026thinsp;9,038)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.5607\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOther Race\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2% (n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6% (n\u0026thinsp;=\u0026thinsp;3,140)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.2472\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNative Hawaiian / Pacific Islander\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1% (n\u0026thinsp;=\u0026thinsp;13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1% (n\u0026thinsp;=\u0026thinsp;344)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.7383\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0081\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAmerican Indian / Alaska Native\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1% (n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;1% (n\u0026thinsp;=\u0026thinsp;46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0855\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eBaseline Fall-Related Diagnoses\u003c/span\u003e. Before the index breast cancer diagnosis, fall-related conditions were more frequently documented among survivors with dementia than among those without dementia. Unspecified falls were recorded in 26% (n\u0026thinsp;=\u0026thinsp;434) of the dementia cohort and in 2% (n\u0026thinsp;=\u0026thinsp;1,038) of the non-dementia cohort (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; standardized difference 0.73). Initial encounters for unspecified falls occurred in 25% (n\u0026thinsp;=\u0026thinsp;415) of survivors with dementia and in 2% (n\u0026thinsp;=\u0026thinsp;980) of those without dementia (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; standardized difference 0.71). The history of falling was present in 15% (n\u0026thinsp;=\u0026thinsp;259) of the dementia cohort and in 1% (n\u0026thinsp;=\u0026thinsp;403) of the non-dementia cohort (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; standardized difference 0.55). Repeated falls were recorded in 11% (n\u0026thinsp;=\u0026thinsp;180) of survivors with dementia compared with less than 1% (n\u0026thinsp;=\u0026thinsp;191) of those without dementia (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; standardized difference 0.46). Fall-related injuries coded as injury of unspecified body region (T14) were reported in 8% (n\u0026thinsp;=\u0026thinsp;141) of the dementia cohort and in 1% (n\u0026thinsp;=\u0026thinsp;576) of the non-dementia cohort (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; standardized difference 0.34) ( Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline Fall-Related Diagnoses in Breast Cancer Patients With and Without Dementia\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTerm / Code\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDementia Cohort 1 (n\u0026thinsp;=\u0026thinsp;1,683)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo Dementia Cohort 2 (n\u0026thinsp;=\u0026thinsp;48,926)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStd. Diff.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eW19 \u0026ndash; Unspecified fall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26% (n\u0026thinsp;=\u0026thinsp;434)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2% (n\u0026thinsp;=\u0026thinsp;1,038)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.7266\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eW19.XXXA \u0026ndash; Unspecified fall, initial encounter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25% (n\u0026thinsp;=\u0026thinsp;415)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2% (n\u0026thinsp;=\u0026thinsp;980)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.7069\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZ91.81 \u0026ndash; History of falling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15% (n\u0026thinsp;=\u0026thinsp;259)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1% (n\u0026thinsp;=\u0026thinsp;403)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.5537\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR29.6 \u0026ndash; Repeated falls\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11% (n\u0026thinsp;=\u0026thinsp;180)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0% (n\u0026thinsp;=\u0026thinsp;191)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.4622\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT14 \u0026ndash; Injury of unspecified body region\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8% (n\u0026thinsp;=\u0026thinsp;141)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1% (n\u0026thinsp;=\u0026thinsp;576)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.3425\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003ePropensity Score Matching\u003c/h3\u003e\n\u003cp\u003eAfter 1:1 propensity score matching, 1,602 patients were retained in each cohort. Matching achieved excellent balance across all demographic covariates, with standardized mean differences below 0.06 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Mean current age was virtually identical between dementia and non-dementia patients (84.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.50 vs 84.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.56 years, p\u0026thinsp;=\u0026thinsp;0.98), as was age at index diagnosis (79.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.39 vs 79.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.52 years, p\u0026thinsp;=\u0026thinsp;0.99). Racial and ethnic distributions were similarly well balanced after matching. The proportion of White (65.6% vs 65.4%) and Black/African American (24.3% vs 25.5%) patients was nearly identical, and no residual significant differences remained across Asian, Hispanic/Latino, or other racial categories (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Ethnic proportions also aligned closely (Not Hispanic or Latino 69.7% vs 72.0%; p\u0026thinsp;=\u0026thinsp;0.15) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline Characteristics Before and After Propensity Score Matching\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBefore Matching: Dementia Cohort 1(n\u0026thinsp;=\u0026thinsp;1,683)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBefore Matching: No Dementia Cohort 2 (n\u0026thinsp;=\u0026thinsp;48,926)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStd. Diff.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAfter Matching: Dementia Cohort 1 (n\u0026thinsp;=\u0026thinsp;1,602)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAfter Matching: No Dementia Cohort 2(n\u0026thinsp;=\u0026thinsp;1,602)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eStd. Diff.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eP-Value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCurrent Age (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e84.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e76.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.1432\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e84.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e84.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.984\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.0007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge at Index (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e67.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.5523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e79.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e79.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.986\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.0006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot Hispanic or Latino\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,174 (69.76%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24,562 (50.20%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.4073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1,116 (69.66%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1,153 (71.97%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.0508\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnknown Ethnicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e446 (26.50%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22,447 (45.96%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.4134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e426 (26.59%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e393 (24.53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.0472\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHispanic or Latino\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63 (3.74%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,877 (3.84%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.8449\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e60 (3.75%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e56 (3.50%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.705\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.0134\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003e\u003cb\u003eRace\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,104 (65.60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28,657 (58.57%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.1452\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1,051 (65.61%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1,048 (65.42%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.911\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.0039\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBlack or African American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e414 (24.60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5,618 (11.48%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.3462\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e390 (24.35%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e408 (25.47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.462\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.0260\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAsian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e86 (5.11%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,083 (4.26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0896\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0404\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e83 (5.18%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e85 (5.31%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.874\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.0056\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnknown Race\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35 (2.08%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9,038 (18.47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.5607\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e35 (2.19%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e27 (1.69%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.304\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.0363\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOther Race\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27 (1.60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3,140 (6.42%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.2472\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e27 (1.69%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e23 (1.44%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.568\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.0201\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNative Hawaiian / Pacific Islander\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (0.77%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e344 (0.70%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.7383\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e13 (0.81%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10 (0.62%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.530\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.0222\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAmerican Indian / Alaska Native\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (0.59%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46 (0.09%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0855\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10 (0.62%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10 (0.62%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eBaseline Fall-Related Diagnoses.\u003c/span\u003e Prior to propensity score matching, fall-related conditions were markedly more prevalent among breast cancer survivors with dementia compared to those without. Unspecified falls (W19) were documented in 25.8% of the dementia cohort versus 2.1% of the non-dementia cohort (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Similarly, initial encounters for unspecified falls (W19.XXXA) occurred in 24.7% versus 2.0% of patients, respectively (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). A documented history of falling (Z91.81) was present in 15.4% of dementia patients compared with 0.8% of those without dementia (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Repeated falls (R29.6) were identified in 10.7% versus 0.4% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and injuries of unspecified body region (T14) were more frequent among dementia patients (8.4% vs 1.2%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e\u003cp\u003eAfter 1:1 propensity score matching, these conditions were well balanced across groups. The matched dementia and non-dementia cohorts each included 1,602 patients, with nearly identical frequencies of unspecified falls (22.9% vs 23.1%; p\u0026thinsp;=\u0026thinsp;0.87), initial encounters for unspecified falls (21.8% vs 22.2%; p\u0026thinsp;=\u0026thinsp;0.77), history of falling (12.6% vs 11.2%; p\u0026thinsp;=\u0026thinsp;0.25), repeated falls (8.4% vs 7.0%; p\u0026thinsp;=\u0026thinsp;0.14), and injury of unspecified body region (7.4% vs 7.9%; p\u0026thinsp;=\u0026thinsp;0.60). All standardized mean differences were below 0.06, confirming adequate balance in fall-related diagnoses following matching ( Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline Fall-Related Diagnoses Before and After Propensity Score Matching\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003cp\u003eFall-Related Diagnoses\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBefore Matching: Dementia Cohort 1 (n\u0026thinsp;=\u0026thinsp;1,683)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBefore Matching: No Dementia Cohort 2 (n\u0026thinsp;=\u0026thinsp;48,926)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStd. Diff.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAfter Matching: Dementia Cohort 1 (n\u0026thinsp;=\u0026thinsp;1,602)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAfter Matching: No Dementia Cohort 2 (n\u0026thinsp;=\u0026thinsp;1,602)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eP-Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eStd. Diff.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnspecified Fall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e434 (25.79%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,038 (2.12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.7266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e366 (22.85%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e370 (23.10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.8666\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.0059\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnspecified Fall, Initial Encounter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e415 (24.66%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e980 (2.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.7069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e349 (21.79%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e356 (22.22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.7653\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.0105\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistory of Falling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e259 (15.39%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e403 (0.82%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.5537\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e201 (12.55%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e180 (11.24%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.2517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.0405\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRepeated Falls\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e180 (10.70%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e191 (0.39%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.4622\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e134 (8.37%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e112 (6.99%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.1443\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.0516\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInjury of Unspecified Body Region\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e141 (8.38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e576 (1.18%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.3425\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e119 (7.43%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e127 (7.93%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.5955\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.0188\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eCopy/right of the graph to TriNetX Platform\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eFall Outcomes and Risk Estimates\u003c/h2\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMeasures of Association.\u003c/span\u003e After excluding patients with falls prior to the index period (cancer diagnosis), breast cancer survivors with dementia exhibited a significantly higher risk of subsequent falls compared with those without dementia. Among the dementia cohort, 17.8% (202 of 1,134) experienced a fall during follow-up compared with 6.5% (3,096 of 47,639) in the non-dementia cohort. The absolute risk difference was 11.3% (95% CI 9.1\u0026ndash;13.6, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), while the relative risk was 2.74 (95% CI 2.41\u0026ndash;3.12). Similarly, the odds of falling were more than three times higher in the dementia group compared to the non-dementia group (odds ratio 3.12, 95% CI 2.67\u0026ndash;3.65) (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMeasures of Association (Excluding Patients with Outcome Prior to the Time Window)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCohort\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePatients in Cohort\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePatients with Outcome\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRisk (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBC 1\u0026ndash;3 With Dementia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e17.81%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBC 1\u0026ndash;3 Without Dementia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47,639\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3,096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e6.50%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeasure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ez\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRisk Difference\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.31%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(9.08%, 13.55%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.996\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRisk Ratio\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(2.41, 3.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOdds Ratio\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(2.67, 3.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e549 patients in Cohort 1 and 1,287 patients in Cohort 2 were excluded for having outcome prior to time window.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eKaplan\u0026ndash;Meier Survival Analysis\u003c/span\u003e. Survival analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) further highlighted the heightened fall risk in patients with dementia. The median survival time to first fall in the dementia cohort was 4,161 days, with only 47.6% of patients remaining fall-free at the end of the follow-up window, compared with 78.9% in the non-dementia cohort. The log-rank test revealed a highly significant difference between survival curves (χ\u0026sup2; = 876.3, df\u0026thinsp;=\u0026thinsp;1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The hazard of falling was more than six times higher among breast cancer survivors with dementia (HR\u0026thinsp;=\u0026thinsp;6.57, 95% CI 5.69\u0026ndash;7.59, p\u0026thinsp;=\u0026thinsp;0.0008).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNumber of Fall Instances\u003c/span\u003e. When the frequency of fall episodes was evaluated, dementia patients averaged slightly more fall events than those without dementia (2.44\u0026thinsp;\u0026plusmn;\u0026thinsp;8.20 vs 2.04\u0026thinsp;\u0026plusmn;\u0026thinsp;5.05). However, this difference was not statistically significant (t\u0026thinsp;=\u0026thinsp;1.05, df\u0026thinsp;=\u0026thinsp;3,296, p\u0026thinsp;=\u0026thinsp;0.29). Median fall count was one in both cohorts, suggesting that while dementia substantially increases the likelihood and timing of falls, the total number of fall episodes per patient among those who do fall does not differ meaningfully by dementia status.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCox Proportional Hazards Analysis\u003c/h3\u003e\n\u003cp\u003eA multivariable Cox proportional hazards model was used to examine predictors of fall-related outcomes among older breast cancer survivors. Dementia status was independently associated with a significantly higher hazard of falls (HR\u0026thinsp;=\u0026thinsp;1.43, 95% CI: 1.25\u0026ndash;1.63, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) after adjustment for demographic, clinical, and functional covariates (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAge and Demographics.\u003c/span\u003e Each one-year increase in age at index was associated with a 5.4% increase in fall risk (HR\u0026thinsp;=\u0026thinsp;1.05, 95% CI: 1.05\u0026ndash;1.06, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Race and ethnicity were significant predictors: Black or African American patients had a 30% higher hazard of falls compared with White patients (HR\u0026thinsp;=\u0026thinsp;1.31, 95% CI: 1.20\u0026ndash;1.42, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while Hispanic or Latino patients had the highest relative risk (HR\u0026thinsp;=\u0026thinsp;1.77, 95% CI: 1.55\u0026ndash;2.03, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eFrailty and Musculoskeletal Conditions\u003c/span\u003e. Among frailty-related variables, osteoporosis (HR\u0026thinsp;=\u0026thinsp;1.48, 95% CI: 1.34\u0026ndash;1.62, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and cachexia (HR\u0026thinsp;=\u0026thinsp;2.44, 95% CI: 1.45\u0026ndash;4.08, p\u0026thinsp;=\u0026thinsp;0.001) were strong independent predictors of falls. Sarcopenia and muscle wasting were not statistically significant (p\u0026thinsp;=\u0026thinsp;0.44 and p\u0026thinsp;=\u0026thinsp;0.71, respectively).\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNeurological Disorders\u003c/span\u003e. Polyneuropathy was associated with a 57% higher hazard of falls (HR\u0026thinsp;=\u0026thinsp;1.58, 95% CI: 1.34\u0026ndash;1.85, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while Parkinson\u0026rsquo;s disease (HR\u0026thinsp;=\u0026thinsp;1.50, 95% CI: 1.12\u0026ndash;2.01, p\u0026thinsp;=\u0026thinsp;0.007) and sequelae of cerebral infarction (HR\u0026thinsp;=\u0026thinsp;1.53, 95% CI: 1.15\u0026ndash;2.03, p\u0026thinsp;=\u0026thinsp;0.003) were also significant. Unsteadiness on feet (HR\u0026thinsp;=\u0026thinsp;1.35, 95% CI: 1.08\u0026ndash;1.71, p\u0026thinsp;=\u0026thinsp;0.010) and other gait abnormalities (HR\u0026thinsp;=\u0026thinsp;1.42, 95% CI: 1.22\u0026ndash;1.66, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) further contributed to elevated fall risk.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ePsychiatric Conditions\u003c/span\u003e. Both depressive episodes (HR\u0026thinsp;=\u0026thinsp;1.78, 95% CI: 1.60\u0026ndash;1.98, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and recurrent major depressive disorder (HR\u0026thinsp;=\u0026thinsp;1.50, 95% CI: 1.26\u0026ndash;1.79, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were independently associated with increased fall risk. Delirium and mild cognitive impairment were not significant predictors (p\u0026thinsp;=\u0026thinsp;0.51 and p\u0026thinsp;=\u0026thinsp;0.28, respectively).\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eComorbidities.\u003c/span\u003e Heart failure was a significant predictor of falls (HR\u0026thinsp;=\u0026thinsp;1.36, 95% CI: 1.20\u0026ndash;1.53, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas malnutrition, pressure ulcers, and visual loss did not reach statistical significance (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.12).\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMedication Burden\u003c/span\u003e. Long-term drug therapy, used as a proxy for polypharmacy (ICD-10-CM Z79), was among the strongest predictors, associated with more than a twofold increased hazard of falls (HR\u0026thinsp;=\u0026thinsp;2.62, 95% CI: 2.41\u0026ndash;2.84, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariable Cox Proportional Hazards Model for Predictors of Fall-Related Outcomes Among Older Breast Cancer Survivors\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e\u003cp\u003eCox Model Results\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCode\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCovariate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHazard Ratio\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStandard Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ez\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eP \u0026gt;|z|\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e95% Confidence Interval\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCohort 1 or Cohort 2 Membership\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.430\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.358\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5.278\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e(1.252, 1.634)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAge at Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e27.837\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e(1.051, 1.058)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2054-5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBlack or African American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.306\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e6.109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e(1.199, 1.423)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEthnicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2135-2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHispanic or Latino\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.773\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.573\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e8.364\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e(1.551, 2.028)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eFrailty and Musculoskeletal Conditions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eM81.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOsteoporosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.389\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e7.975\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e(1.341, 1.624)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eM62.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSarcopenia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.467\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.383\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.768\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.443\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e(0.551, 3.905)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eR64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCachexia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.435\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.890\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.263\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.383\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e(1.454, 4.077)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eM62.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMuscle wasting\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.922\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.372\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.710\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e(0.600, 1.417)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eNeurological Disorders\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eG62.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePolyneuropathy, unspecified\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.575\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.454\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5.546\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e(1.341, 1.849)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eG62.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDrug-induced polyneuropathy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.654\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.424\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.262\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-1.619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e(0.391, 1.093)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eG20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eParkinson's disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.498\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.404\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.698\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e(1.117, 2.008)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI69.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSequelae of cerebral infarction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.423\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.928\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e(1.150, 2.027)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eR26.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUnsteadiness on feet\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.354\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.303\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.586\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e(1.076, 1.705)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eR26.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOther abnormalities of gait and mobility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.418\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.418\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e(1.215, 1.656)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003ePsychiatric Conditions\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDepressive episode\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.779\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.576\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e10.749\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e(1.602, 1.977)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMajor depressive disorder, recurrent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.501\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.406\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.521\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e(1.259, 1.790)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDelirium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.887\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.662\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.508\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e(0.622, 1.265)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eG31.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMild cognitive impairment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.277\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e(0.907, 1.407)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHeart failure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.355\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.304\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.877\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e(1.199, 1.531)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eE46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUnspecified protein-calorie malnutrition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.277\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.782\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e(0.757, 1.448)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eE43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUnspecified severe protein-calorie malnutrition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.869\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.634\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.526\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e(0.564, 1.340)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eL89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePressure ulcer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.722\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.326\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-1.531\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e(0.476, 1.096)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eH54.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUnspecified visual loss\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.782\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.434\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e(0.833, 1.531)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMedication Burden / Polypharmacy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eZ79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLong term (current) drug therapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.615\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.961\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e22.607\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e(2.406, 2.842)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis large real-world cohort study provides new insights into the relationship between dementia and fall risk among older breast cancer survivors. We observed that survivors with dementia were more than twice as likely to experience a fall compared with survivors without dementia, and this increased risk persisted after controlling for a comprehensive set of demographics, clinical and treatment-related factors. The absolute risk difference of 11.3 percentage points translates into a substantial burden of fall-related events in this population. These findings align with broader geriatric literature showing that cognitive impairment markedly increases the likelihood of falls: older adults with dementia fall two to three times more often than cognitively healthy peers, and between 60\u0026ndash;80% of people with dementia fall annually\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Our results extend these observations by showing that dementia remains an independent predictor of falls even after accounting for frailty, neurological disorders, psychiatric conditions and polypharmacy, suggesting a distinctive vulnerability among cognitively impaired cancer survivors.\u003c/p\u003e\u003cp\u003eSeveral covariates emerged as important contributors to fall risk. Increasing age at cancer diagnosis was associated with a higher hazard of falling, consistent with evidence that falls affect more than thirty percent of adults aged sixty-five years and older\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Race and ethnicity were also significant predictors: Black and Hispanic survivors faced greater hazards compared with White survivors\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Frailty-related conditions such as osteoporosis and cachexia, neurologic disorders including polyneuropathy, Parkinson disease and sequelae of cerebral infarction, and psychiatric disorders such as depressive episodes all conferred elevated fall risk\u003csup\u003e\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Notably, long-term use of medications a proxy for polypharmacy was one of the strongest predictors, more than doubling the hazard of falling\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. These findings highlight the multifactorial nature of falls, which are influenced by interactions between neuromuscular function, cognition, comorbidity burden, medication effects and sociodemographic factors. They also underscore the importance of comprehensive geriatric assessment in oncology practice, with attention to medication management and mental health.\u003c/p\u003e\u003cp\u003eOur findings have several implications for clinical care and survivorship planning. First, they emphasize the need for proactive fall-prevention strategies tailored to older breast cancer survivors with dementia. Interventions such as balance and strength training, home safety assessments, medication review and cognitive support should be integrated into survivorship care plans. Second, the strong association between polypharmacy and falls supports efforts to deprescribe non-essential medications and to coordinate care across oncology, primary care and geriatrics. Third, the elevated risk among Black and Hispanic survivors signals potential disparities in access to supportive services or differences in comorbidity profiles; culturally informed interventions and equitable access to fall-prevention programs are needed. Finally, because older adults with dementia fall frequently, clinicians should consider routine cognitive screening for breast cancer survivors and engage caregivers in fall-prevention education. Future research should examine the efficacy of multifaceted interventions in this population, explore mechanisms linking cognitive impairment and falls, and investigate how cancer treatments interact with dementia-related neuropathology to influence mobility and balance.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eStrength\u003c/span\u003e. The study\u0026rsquo;s design affords several notable strengths that enhance confidence in its findings. Leveraging the TriNetX platform allowed us to assemble one of the largest real-world cohorts of older breast cancer survivors with and without dementia to date. Because TriNetX aggregates records from over 100 participating health systems, the resulting sample encompasses diverse patient populations and practice settings, which improves the generalizability of the results. Moreover, the use of rigorous statistical methods including one-to-one propensity score matching and adjustments for a comprehensive array of demographic, clinical and medication variables which helped minimize confounding and ensured that the observed associations were not driven by imbalances between cohorts. Our precise outcome definitions, which incorporated multiple International Classification of Diseases codes (ICD- code) for fall events, captured a wide spectrum of falls from single incidents to recurrent episodes and fall-related injuries. This approach aligns with broader evidence that cognitive impairment markedly increases fall risk: older adults with dementia fall two to three times more frequently than cognitively healthy peers, and between sixty and eighty percent of individuals with dementia fall each year. These methodological strengths yield robust, broadly applicable insights into fall risk among this medically complex population. The Kaplan Meier survival analysis provided an unadjusted age-based estimate of fall-free survival, illustrating the cumulative incidence of falls over time before accounting for other covariates in multivariable models.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eLimitation\u003c/span\u003e. Several limitations should be considered when interpreting the results. The retrospective nature of electronic health record analysis precludes causal inference and relies on accurate coding. Falls that did not result in medical attention were not captured, potentially underestimating the true incidence of fall events. Although we adjusted for many potential confounders, residual confounding may persist because data on certain factors such as the severity and type of dementia, detailed cancer treatments, functional status or living environment were unavailable. The \u0026ldquo;unspecified dementia\u0026rdquo; category used here includes heterogeneous etiologies, which prevented us from examining how different dementia subtypes might influence fall risk. Additionally, while TriNetX provides a large and diverse sample, its composition may not fully represent populations outside North America, and coding practices may vary across contributing institutions, introducing some misclassification bias. These factors highlight the need for future prospective studies with more granular data to confirm and extend the current findings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCONFLICT OF INTEREST\u003c/h2\u003e\u003cp\u003eThe authors declare that there are no conflicts of interest regarding the publication of this paper. None of the authors have any competing financial or non-financial interests in relation to the work described. This includes no direct relationships such as employment, consultancies, stock ownership, honoraria, paid expert testimony, patent applications/registrations, and grants or other funding that could influence the work reported in this manuscript.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFUNDING\u003c/h2\u003e\u003cp\u003eThis work was supported by the Wright Center under the Clinical and Translational Science Award (CTSA) Grant number UM1TR004360.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e\u0026bull; Asmaa Namoos conceptualized the study, designed the analysis framework, conducted the TriNetX data extraction and statistical analyses, interpreted the findings, and drafted the manuscript.\u0026bull; Oxana Palesh contributed to study conceptualization, data interpretation, and critical manuscript revision for intellectual content.\u0026bull; Faika Zanjani provided guidance on gerontology and aging-related interpretation, contributed to methodological refinement, and reviewed the final manuscript for accuracy and coherence.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe extend our gratitude to the informatics team at Virginia Commonwealth University\u0026rsquo;s (VCU) C. Kenneth and Dianne Wright Center for Clinical and Translational Research, especially Tamas Gal and his team members, Evan French and Patrick Shi, for their invaluable support in data extraction through TriNetX platform.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available upon reasonable request. Interested researchers can obtain access to the data by submitting a formal request to the corresponding author at [email protected]. The data is not publicly available due to privacy or ethical restrictions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMiller K (2022) Cancer treatment and survivorship statistics, 2022 - Miller \u0026ndash;\u0026thinsp;2022 - CA: A Cancer Journal for Clinicians - Wiley Online Library. 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Eur J Cancer Care (Engl) 2025(1):5558563. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1155/ecc/5558563\u003c/span\u003e\u003cspan address=\"10.1155/ecc/5558563\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNamoos A, Thomson N, Bradley S, Rudderman A, Aboutanos M (2024) Memory Loss and Missteps: Investigating Fall Risks in Alzheimer\u0026rsquo;s and Dementia Patients. Adv Geriatr Med Res 6(3):e240005. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.20900/agmr20240005\u003c/span\u003e\u003cspan address=\"10.20900/agmr20240005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXue L, Boudreau RM, Donohue JM et al (2021) Persistent polypharmacy and fall injury risk: the Health, Aging and Body Composition Study. BMC Geriatr 21(1):710. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12877-021-02695-9\u003c/span\u003e\u003cspan address=\"10.1186/s12877-021-02695-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Breast cancer, dementia, falls, survivorship, geriatric oncology, electronic health records, TriNetX, risk prediction, neurological comorbidity, polypharmacy","lastPublishedDoi":"10.21203/rs.3.rs-8124537/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8124537/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction\u003c/h2\u003e\u003cp\u003eFalls are a major source of morbidity in older adults and pose particular concern in cancer survivors who may experience treatment related neurological and functional decline. Dementia is a known risk factor for falls, yet its contribution to fall risk among breast cancer survivors has not been well defined.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis retrospective cohort study used de identified electronic health records from the TriNetX Research Network, which includes more than 100 health care organizations. Women aged 65 years or older with stage 1 to stage 3 breast cancer were eligible. Dementia was identified using ICD 10 code F03 recorded on or after the first qualifying cancer diagnosis. Propensity score matching used a 1 to 1 nearest neighbor approach. The primary outcome was incident fall events identified by ICD 10 codes for unspecified falls, initial fall encounters, history of falling, and repeated falls. Multivariable Cox proportional hazards models estimated independent predictors of falls. Follow up began 1 day after diagnosis and continued through the last recorded encounter.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eA total of 49 576 breast cancer survivors met inclusion criteria, of whom 1 683 (3.4%) had dementia. Before matching, fall related diagnoses were significantly more common in patients with dementia, including unspecified falls (26% vs 2%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and history of falling (15% vs 1%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). After 1 to 1 matching, 1 602 survivors remained in each cohort with standardized mean differences\u0026thinsp;\u0026lt;\u0026thinsp;0.06 across all variables. During follow up, 17.8% of survivors with dementia experienced a fall compared with 6.5% without dementia. This corresponded to an absolute risk difference of 11.3% (95% CI 9.1% to 13.6%), a risk ratio of 2.74 (95% CI 2.41 to 3.12), and an odds ratio of 3.12 (95% CI 2.67 to 3.65). The Kaplan Meier analysis showed significantly lower fall free survival in the dementia cohort (log rank p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The adjusted Cox model showed that dementia remained an independent predictor of falls (hazard ratio 1.43, 95% CI 1.25 to 1.63). Additional strong predictors included long term drug therapy (hazard ratio 2.62, 95% CI 2.41 to 2.84), osteoporosis (hazard ratio 1.48, 95% CI 1.34 to 1.62), polyneuropathy (hazard ratio 1.58, 95% CI 1.34 to 1.85), and depressive episode (hazard ratio 1.78, 95% CI 1.60 to 1.98).\u003c/p\u003e\u003ch2\u003eConclusions and Relevance\u003c/h2\u003e\u003cp\u003eDementia was associated with a substantially elevated fall risk among older breast cancer survivors, even after extensive adjustment for comorbidity, neurological conditions, psychiatric disorders, and medication burden. Recognition of this risk may help clinicians identify a subgroup of survivors who require closer monitoring and more precise evaluation during routine care.\u003c/p\u003e","manuscriptTitle":"Dementia as an Independent Predictor of Falls in Older Breast Cancer Survivors: Evidence From a Real World Multicenter Electronic Health Record Network","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-28 07:08:05","doi":"10.21203/rs.3.rs-8124537/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b5b10d61-67a9-4baf-8e29-bdc586fbb177","owner":[],"postedDate":"November 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-29T11:09:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-28 07:08:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8124537","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8124537","identity":"rs-8124537","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}