Prevalence of Food Insecurity and Its Associated Factors Among Ambulatory Hand Patients in a Large Metropolitan Area - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites. Search Log in Dashboard Publications Account settings Log out Search… Search NCBI Primary site navigation Search Logged in as: Dashboard Publications Account settings Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide PERMALINK Copy As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice J Hand Surg Glob Online . 2026 Apr 7;8(3):101011. doi: 10.1016/j.jhsg.2026.101011 Search in PMC Search in PubMed View in NLM Catalog Add to search Prevalence of Food Insecurity and Its Associated Factors Among Ambulatory Hand Patients in a Large Metropolitan Area Srivathsan Ramesh Srivathsan Ramesh , MD ∗ Department of Orthopedics, University of Texas Health Science Center at Houston, Houston, TX Find articles by Srivathsan Ramesh ∗ , Elizabeth Meyer Elizabeth Meyer , BS ∗ Department of Orthopedics, University of Texas Health Science Center at Houston, Houston, TX Find articles by Elizabeth Meyer ∗ , Jack J Strubel Jack J Strubel , BA ∗ Department of Orthopedics, University of Texas Health Science Center at Houston, Houston, TX Find articles by Jack J Strubel ∗ , Blaire Peterson VanderWeele Blaire Peterson VanderWeele , BS † Department of Orthopedics, University of Texas Health Science Center at San Antonio, San Antonio, TX Find articles by Blaire Peterson VanderWeele † , Benjamin Fitch Benjamin Fitch , BS † Department of Orthopedics, University of Texas Health Science Center at San Antonio, San Antonio, TX Find articles by Benjamin Fitch †, ∗ , Christina Brady Christina Brady , MD † Department of Orthopedics, University of Texas Health Science Center at San Antonio, San Antonio, TX Find articles by Christina Brady † , James Saucedo James Saucedo , MD ∗ Department of Orthopedics, University of Texas Health Science Center at Houston, Houston, TX Find articles by James Saucedo ∗ Author information Article notes Copyright and License information ∗ Department of Orthopedics, University of Texas Health Science Center at Houston, Houston, TX † Department of Orthopedics, University of Texas Health Science Center at San Antonio, San Antonio, TX ∗ Corresponding author: Benjamin Fitch, 8300 Floyd Curl Drive San Antonio TX, 78229. [email protected] Received 2026 Mar 6; Accepted 2026 Mar 7; Collection date 2026 May. © 2026 The Authors This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). PMC Copyright notice PMCID: PMC13090993 PMID: 42004415 Abstract Purpose Food insecurity (FI) is recognized as an important social determinant of health (SDH) that may disproportionately affect orthopedic patients. However, there are limited publications evaluating FI within the orthopedic hand population. The objective of this study is to evaluate the incidence of FI among hand clinic patients to identify key SDHs that may inform targeted interventions and improve clinical care for patients with hand pathologies. Methods This prospective cross-sectional study was conducted using patient surveys at outpatient orthopedic hand clinics at an academic institution. Adults ≥18 years with hand or distal upper-extremity pathology were included; excluded were pediatric patients and patients with injuries proximal to the elbow, concomitant nonupper extremity injuries, malignancy, or chronic neuropathy/pain. Surveys included Household Food Security Survey-6 for FI, the Hospital Anxiety and Depression Scale for anxiety and depression, Disabilities of the Arm, Shoulder, and Hand ( Quick DASH) for upper-extremity function, and the Risk Analysis Index for frailty. Data collected included demographics, employment, housing status, insurance, zip code, injury characteristics, and secondary outcomes such as infection, reoperation, amputation, and nonunion. Statistical analysis was performed with P < .05 as significant. Results Of the screened patients, 21% were FI. Those with low or very low food security had higher average scores on the Hospital Anxiety and Depression Scale, Quick DASH score, and Risk Analysis Index compared to those with marginal or high food security. These findings indicate that lower food security status is associated with worse mental health, frailty, and a higher level of disability. Conclusions This study shows an association between patients experiencing FI and factors known to affect outcomes such as frailty, depression, and anxiety. This is revealed further by a statistically significant difference in Quick DASH scores between food-insecure and food-secure patients. FI is an untapped SDH thar may prove modifiable for an at-risk population. Type of study/level of evidence Symptom Prevalence Study IV. Key words: Food insecurity (FI), Frailty, Hand surgery, Social determinants of health (SDH) Social determinants of health (SDH) are complex and often overlooked contributors to patient comorbidity and outcomes. SDH are strongly linked to nonaccidental and traumatic injuries, with Braveman and Gottlieb 1 estimating that almost half of preventable deaths in the United States can be attributed to SDH. Hey et al 2 similarly found that the incidence of traumatic injury was significantly greater in patients with greater SDH burden. Food insecurity (FI), defined by the United States Department of Agriculture (USDA) as “a household-level economic and social condition of limited or uncertain access to adequate food,” is a SDH that affects more than 15 million US households as of 2017. 3 FI has garnered interest in the orthopedic literature recently, as several studies have documented a disproportionately high prevalence of FI among orthopedic patients. 4 , 5 Vo et al 6 found that 37% of orthopedic trauma patients in a large community hospital experienced FI during the management of their injuries. Additional work by Leary et al 7 reported high rates of FI in its community cohort treated for orthopedic injury, with predictors of FI including lower household income and living alone. Social deprivation similarly influences upper-extremity injury patterns, with disadvantaged patients experiencing higher injury rates, more severe presentations, and reduced access to follow-up care. 8 These findings exemplify the broader pattern that disadvantaged patients facing musculoskeletal injury have both higher injury risk and greater barriers to recovery. In addition to physical vulnerability, FI also carries psychological consequences. In their systematic review, Arenas et al 9 determined that there is a strong association between FI and both anxiety and depression. These psychological factors also play a crucial role in the care and healing of orthopedic fractures, as they are associated with increased pain, decreased physical function, and lower quality of life, ultimately impacting recovery and return to activity. 10 Given these established connections, there remains limited literature examining FI within ambulatory orthopedic subspecialties, such as hand surgery, where socioeconomic stressors, injury patterns, and disability burden may differ from trauma-specific populations. With rising costs of health care and food alike, it is important to characterize patient needs and highlight blind spots that may affect care. This study examines the prevalence of FI and associated medical comorbidities and SDH in multiple orthopedic hand clinics across a large metropolitan center, identifying (1) the burden of food insecurity in ambulatory hand patients; (2) its relationship to frailty, mental health, and upper-extremity disability; and (3) the demographic and socioeconomic predictors that place certain patients at heightened risk. Materials and Methods Patients from an academic health care system were screened for FI (using the Household Food Security Survey [HFSS]), frailty (using the Risk Analysis Index [RAI]), anxiety (using the Hospital Anxiety and Depression Scale-Anxiety [HADS-A]), depression (using the Hospital Anxiety and Depression Scale-Depression [HADS-D]), and upper-extremity disability (using Disabilities of the Arm, Shoulder, and Hand [ Quick DASH]) during the time period of February 17, 2025, to June 4, 2025. The HFSS is a tool used to screen and quantify risk for FI. Scores are interpreted as 0 (high), 1 (marginal), 2–4 (low), and 5–6 (very low) food security. The RAI is a validated frailty assessment that takes into account age, comorbidities, functional status, weight loss, and patient-reported health. Scores range from 0 to 81, with higher scores indicating higher frailty. The general range of scores includes the following: ≤20 indicates robust, 21–29 indicates normal or “prefrail”, 30–39 indicates frail, and ≥41 indicates very frail. The HADS is a self-report measure assessing anxiety (HADS-A) and depression (HADS-D) in medically ill or general adult populations. There are 12 items (7 for each subscale). Each subscale score ranges from 0 to 21 with the following defined score ranges: 0–7, normal; 8–10, mild symptoms; 11–14, moderate symptoms; 15–21, severe symptoms. The Quick DASH survey is a shorter version of the DASH, evaluating upper-extremity disability and symptoms. It consists of 11 items, each scored 1–5. Final scores are converted to a 0–100 scale, with 0 signifying no disability and 100 signifying the most severe disability. The Quick DASH does not have universal categorical cutoffs, but higher scores consistently indicate greater functional impairment. Demographic information about the patients was also collected, including age, sex assigned at birth, race, educational attainment, marital status, employment status, smoking history, and housing situation. Patients were excluded if they were under 18 years old; otherwise, all patients visiting the clinic were offered a survey. Statistical tests comparing proportions of categorical variables were performed using the chi-squared test, and tests of the Pearson correlation coefficient were performed using the Student t distribution. A multivariable logistic regression model was established, with statistical testing using the maximum likelihood approach. For coefficients of specific parameters, the Wald chi-squared statistic was used. Because of the large number of available variables relative to the number of subjects, a stepwise selection approach was used in which the best available variable was selected into the model one at a time, until there were none left with a P value less than .1 for the Wald chi-squared statistic. Results from this study were objectively compared to results from a pilot conducted by this study's investigators at the Veterans Affairs health care system located in south Texas approximately 6 months prior. The earlier veteran study did not collect as many demographic variables and did not measure upper-extremity disability. Results Data were collected for 184 patients. Some patients did not have a full set of screening measures. One patient with high food security (HFSS = 0) did not complete screening questionnaires for any of the other measures, so that patient was excluded from the study. Among the remaining 183 patients, 32 did not have an RAI frailty score (most often because either age or sex or both was missing). Those patients remained in the study for analysis involving HADS and Quick DASH. A total of 151 patients had complete screening data. Table 1 provides demographic information and screening scores for the 183 patients with HFSS, HADS, and Quick DASH results and frailty results for the 151 patients who had complete screening data. Table 2 summarizes the relationship between FI and each of the other characteristics on a categorical basis as well as on a numerical (continuous value) basis. Table 3 summarizes the relationship between frailty and the anxiety and depression screening scores. Multivariable analysis was conducted for the 151 patients with a complete set of screening data for the concepts included, and the results are presented in Tables 4and5. Table 1. Summary of Demographic and Screening Scores Within the Patient Cohort Characteristic Result No. of patients 183 Mean age, y (95% confidence limit) 52.8 (50.0–55.6) [Min age, Max age] [15, 83] Sex (assigned at birth) (% F) 58.4% Race (%) White non-Hispanic 54.1% Black non-Hispanic 17.5% Hispanic 19.7% Asian 4.9% Other 3.8% Marital status (%) Married or living as married 56.3% Not married 43.7% Education level (%) Less than high school degree 6.0% High school degree 27.3% Some college/vocational degree/Assoc. degree 20.2% Bachelor’s degree or more 46.4% Employment (%) Full time 47.0% Part time/other arrangement 21.3% Unemployed/disabled 14.2% Retired 15.8% Unknown 1.6% Housing situation (%) Owns home/has mortgage 63.4% Rents home 22.4% Lives with family members 13.1% Other 1.1% Smoking history (%) Current 8.2% Former 25.7% Never 63.9% Unknown 2.2% Consistent transportation to medical appointments (% Yes) 96.7% FI screening No. screened 183 No.: HFSS ≥ 1 (%) 43 (23.5%) No.: Marginal security (%) 18 (9.8%) No.: low food security (%) 16 (8.7%) No.: very low food security (%) 9 (4.9%) Frailty screening No. screened 151 No.: frail [RAI ≥ 30] (%) 13 (8.6%) No.: very frail [RAI ≥ 41] (%) 3 (2.0%) Anxiety screening (HADS-A) No. screened 183 No.: HADS-A ≥ 8 (%) 55 (30.1%) Moderate: HADS-A = 11–15 (%) 17 (9.3%) Severe: HADS-A ≥ 16 (%) 3 (1.6%) Depression screening (HADS-D) No. screened 183 No. HADS-D ≥ 8 (%) 31 (16.9%) Moderate: HADS-D = 11–15 (%) 9 (4.9%) Severe: HADS-D ≥ 16 (%) 0 (0%) Disability screening ( Quick DASH) No. screened 182 No. Quick DASH ≥ 50 53 (29.1%) No. Quick DASH ≥ 75 16 (8.8%) Open in a new tab Table 2. Relationship between FI and Other Screened Concepts Category HFSS = 0 HFSS ≥ 1 P Value Frail (RAI ≥30) .0035 Y 6 (5%) 7 (21%) N 112 (95%) 26 (79%) Anxious (HADS-A ≥ 8) .0006 Y 33 (24%) 22 (51%) N 107 (76%) 21 (49%) Depressed (HADS-D ≥ 8) <.0001 Y 13 ( 9%) 18 (42%) N 127 (91%) 25 (58%) Disability ( Quick DASH ≥ 50) <.0001 Y 29 (21%) 24 (56%) N 111 (79%) 19 (44%) Pearson correlation coefficient HFSS Score P Value Assessment Frailty score (RAI) 0.076 .35 No correlation Anxiety score (HADS-A) 0.265 .0003 Weak Depression score (HADS-D) 0.401 <.0001 Weak Disability score ( Quick DASH) 0.398 <.0001 Weak Open in a new tab Table 3. Relationship Between Frailty and Anxiety/Depression/Disability Category RAI < 30 RAI ≥ 30 P Value Anxious (HADS-A ≥ 8) .65 Y 40 (29%) 3 (23%) N 98 (71%) 10 (77%) Depressed (HADS-D ≥ 8) .51 Y 22 (16%) 3 (23%) N 116 (84%) 10 (77%) Disability ( Quick DASH ≥ 50) .047 Y 38 (28%) 7 (54%) N 100 (72%) 6 (46%) Pearson correlation coefficient RAI Score P Value Assessment Anxiety score (HADS-A) -0.177 .017 Very Weak Depression score (HADS-D) 0.134 .070 No Correlation Disability score ( Quick DASH) 0.215 .0035 Weak Open in a new tab The mean age of the 151 patients with all screening measures was 53.1 years (SD = 17.8), with a median of 56 years and an overall range from 15 to 82 years old. The percentage of women participants was 59%. Results of the other demographic variables are similar for the 151 patients with all screening measures compared to the 183 patients with a missing frailty score, as shown in Table 1 . Patients in this study were slightly more likely to be women, White non-Hispanic, married (or living as married), have a Bachelor’s degree, working full time, own a home, and have a history of never smoking. As shown in Table 2 , 21% of the patients screened showed concern for food insecurity in concordance with the protocol. Food insecurity had a statistically significant association with each of the concepts screened, with patients having FI also being more likely to be frail (21% vs 5%), anxious (51% vs 24%), depressed (42% vs 9%), and upper-extremity disabled (56% vs 21%). When exploring correlation of the numerical scores with the numerical version of the HFSS, linear correlations of FI with anxiety, depression, and disability were statistically >0, but were weak (HFSS vs HADS(A), r = 0.265; HFSS vs HADS(D), r = 0.401; HFSS vs Quick DASH, r = 0.398). The correlation of FI relative to frailty was statistically no different than 0 (HFSS vs RAI, r = 0.076). As shown in Table 3 , frailty was weakly associated with anxiety and upper-extremity disability but not depression. Unsurprisingly, patients with an elevated score on the HADS-A were more likely to have an elevated score on the HADS-D. Table 4 presents estimated odds ratios for the likelihood of a patient having FI, adjusting for all other variables shown in the table. The table shows that, all other factors being equal, patients who are not married have approximately three times the odds of FI as those who are married. Likewise, patients who have an education level of high school or less, rent their home rather than own it, have an upper-extremity disability, or are depressed have 3–5 times the odds of FI compared those with the opposite characteristic. Patients identified as frail via the RAI have 10 times the odds of FI, and younger persons are slightly more likely to report FI than older patients (a 3% difference for every increase in 1 year of age). When the HADS-A for anxiety is used instead of HADS-D for depression, similar results are obtained because anxiety and depression are so well correlated. Table 4. Estimated odds ratios for FI (Categorical): Multivariable Analysis. Parameter Odds Ratio (95% CI) P Value Age 0.97 (0.94–1.00) .042 Race, non-White versus White 2.64 (0.94–7.38) .065 Marital status, Not Married versus Married 3.31 (1.05–10.4) .041 Education, HS or less versus More than HS 2.99 (1.10–8.06) .032 Rents home versus owns home 3.25 (1.05–10.1) .010 Disabled per Quick DASH, Y versus N 4.91 (1.78–13.5) .002 Depressed per HADS-D, Y vs N 4.50 (1.38–14.6) .013 Frailty per RAI, Y versus N 10.1 (1.98–52.0) .006 Open in a new tab CI, confidence interval. Table 5 shows that when using the numerical versions of the scales, age and frailty do not have a statistically significant association, but each increase in the Quick DASH score of 1 point increases the odds of FI by approximately 4% and each increase of 1 point on the HADS-D increases the odds of FI by approximately 25%. Table 5. Estimated odds ratios for FI (Numerical): Multivariable Analysis. Parameter Odds Ratio (95% CI) P Value Age Not significant .40 Race, non-White versus White 2.61 (0.95–7.15) .062 Marital status, not married versus married 3.97 (1.27–12.4) .018 Education, HS or less versus more than HS 3.13 (1.14–8.62) .027 Rents home versus owns home 3.18 (1.05–9.65) .014 Quick DASH score 1.04 (1.01–1.06) .004 HADS(D) score 1.25 (1.07–1.45) .004 RAI score Not significant .61 Open in a new tab CI, confidence interval. Discussion This study found that, among patients seeking care at outpatient orthopedic hand clinics within this population, nearly one-fourth of them were food insecure. Further, patients with FI were more likely to be frail, anxious, and depressed. The presence of FI was independently associated with multiple SDHs, including marital status, education level, housing stability, and upper-extremity disability. In our cohort, 21% of patients experiencing FI were frail compared to only 5% of food-secure patients. Considering that undernutrition is heavily associated with frailty, it is no surprise that food insecurity would also have this association. 11 , 12 This trend aligns with findings from a large population-based study in India, showing that older adults facing FI had nearly 2.7 times the odds of being frail compared to their food-secure counterparts. 13 Specifically, patients with FI had higher rates of exhaustion, unintentional weight loss, and weak grip strength. In addition, FI has consistently been linked to impaired mobility, decreased muscle mass, and poorer nutritional quality, all of which accelerate physiologic decline in older adults. Studies in US senior populations have similarly demonstrated that limited access to nutrient-dense foods contributes to chronic inflammation and sarcopenia, both key drivers of frailty. 14 , 15 These consistent patterns across diverse settings highlight the considerable role that food access plays in physical health outcomes among older adults. Over half of food-insecure patients had positive anxiety screens, and nearly half had positive depression screens. These rates were significantly higher than in their food-secure counterparts. These findings reinforce the idea that FI is not merely an economic issue but is also entwined with broader psychosocial stressors. Although it is well-established that FI can exacerbate symptoms of depression and anxiety, the reverse is also likely true. In addition, patients with poor mental health may struggle to navigate systems of support or maintain employment, thereby increasing their risk for FI. 16 , 17 The bidirectional nature of these relationships emphasizes the need for a multidisciplinary approach to care, particularly in trauma settings where recovery often depends not only on surgical or rehabilitative treatment, but also on addressing the social and emotional context of the patient. Additionally, the orthopedic literature has demonstrated that untreated mental health conditions have been associated with slower recovery, greater perceived pain, and lower adherence to postoperative instructions, suggesting that FI may indirectly compound surgical or rehabilitative challenges through its psychological impact. 18 Multivariable regression modeling confirmed that FI is independently associated with a range of demographic and clinical factors. Notably, patients who were unmarried, had lower educational attainment, and rented their homes had significantly increased odds of experiencing FI. This aligns with the literature. Results from the 2020–2021 National Health Interview Survey showed that in older adult populations, lower education is a predictor of FI, and marital separation or being unmarried is linked to a higher risk of inadequate food access, presumably via reduced social and economic support. 19 Similarly, in rural Texas, older adults who were single, widowed, or divorced had higher odds of reporting FI, mediated through diminished social capital. 20 These factors also influence a patient’s ability to attend follow-up visits, adhere to therapy regimens, and coordinate transportation or caregiving needs, elements that are essential for optimal upper-extremity recovery. 21 This study also found a strong association between FI and upper-extremity disability, as measured by Quick DASH. Patients with greater upper-extremity disability reported higher levels of FI, and each unit increase in Quick DASH score independently raised the odds of FI. Upper-extremity dysfunction may limit an individual’s ability to prepare meals, shop for groceries, or carry households, representing a direct effect on functionality related to food access. These findings furthermore suggest a cyclical interaction: patients experiencing disability may have reduced earning capacity, exacerbating FI, which in turn can negatively affect healing, function, and overall wellbeing. Our results are broadly consistent with those from the earlier Veterans Affairs pilot, although some notable differences emerged. Although both studies found strong associations between FI and frailty, anxiety, and depression, the strength of these relationships was more pronounced in the current (non-veteran) population. For example, frailty was more strongly predictive of FI in our non-veteran sample (odds ratio [OR] = 10) compared to the Veterans Affairs cohort (OR = 2.21). The veteran population was older, more predominantly men, and had higher overall rates of FI (29 vs 23.5%). This finding suggests that although FI is pervasive in both groups, Veterans show a higher overall prevalence. This disparity has been linked to a higher prevalence of military service-related disability, mental health disorders, and limited incomes among the veteran population, all of which disproportionately increase vulnerability to FI. 22 , 23 There are several limitations to consider. Although our study includes a diverse patient population from multiple orthopedic hand clinics, it remains a single health system study, which may limit generalizability. Further, some screening measures had incomplete data, particularly the RAI frailty score, which may have introduced bias into our multivariable models. Although associations were statistically significant, many were modest in magnitude, and causality cannot be established because of the cross-sectional nature of the study. Longitudinal studies are needed to assess whether addressing FI through integration of screening and subsequently intervention through education or resource referral pathways can directly improve recovery outcomes, functional status, or mental health in this population. Discussion of FI and the social and physical sequelae of poor nutrition has been ongoing in medical literature, but recently it has garnered the attention of the orthopedic community. Despite its relevance, to our knowledge, studies assessing the relationship among nutrition, wound healing, and bone healing in orthopedic populations remain limited. Our findings in this study show that FI is an incredibly prevalent social factor in orthopedic patients that is worthy of further evaluation and discussion. Looking forward, further research is needed to assess the impact of FI on orthopedic injury patterns, treatment outcomes, and recovery trajectories. Furthermore, the development of useful methods for both hand surgeons and other physicians to address and treat FI in the clinical setting are paramount. With rising costs of food and ever-present threats to government benefits and public assistance programs, it is vital for the medical community to further elucidate the impact of FI and implement effective interventions to better serve our patients in need. Conflicts of Interest Dr Saucedo, MD has stocks owned in Edge Surgical and Clover. No benefits in any form have been received or will be received by the other authors related directly to this article. Acknowledgments We would like to give a special thanks to Dr. Hannah Dineen, Dr. Matthew Koepplinger, Dr. Candice Teunis-Washko, and Dr. Kyle Woerner for their assistance and allowing us to sample their clinic patients. 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