Food insecurity and social determinants of health among immigrants and natives in Portugal

Food Security ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 579-589 ◽  
Author(s):  
Violeta Alarcão ◽  
Sofia Guiomar ◽  
Andreia Oliveira ◽  
Milton Severo ◽  
Daniela Correia ◽  
...  
Author(s):  
Macarius M. Donneyong ◽  
Teng-Jen Chang ◽  
John W. Jackson ◽  
Michael A. Langston ◽  
Paul D. Juarez ◽  
...  

Background: Non-adherence to antihypertensive medication treatment (AHM) is a complex health behavior with determinants that extend beyond the individual patient. The structural and social determinants of health (SDH) that predispose populations to ill health and unhealthy behaviors could be potential barriers to long-term adherence to AHM. However, the role of SDH in AHM non-adherence has been understudied. Therefore, we aimed to define and identify the SDH factors associated with non-adherence to AHM and to quantify the variation in county-level non-adherence to AHM explained by these factors. Methods: Two cross-sectional datasets, the Centers for Disease Control and Prevention (CDC) Atlas of Heart Disease and Stroke (2014–2016 cycle) and the 2016 County Health Rankings (CHR), were linked to create an analytic dataset. Contextual SDH variables were extracted from the CDC-CHR linked dataset. County-level prevalence of AHM non-adherence, based on Medicare fee-for-service beneficiaries’ claims data, was extracted from the CDC Atlas dataset. The CDC measured AHM non-adherence as the proportion of days covered (PDC) with AHM during a 365 day period for Medicare Part D beneficiaries and aggregated these measures at the county level. We applied confirmatory factor analysis (CFA) to identify the constructs of social determinants of AHM non-adherence. AHM non-adherence variation and its social determinants were measured with structural equation models. Results: Among 3000 counties in the U.S., the weighted mean prevalence of AHM non-adherence (PDC < 80%) in 2015 was 25.0%, with a standard deviation (SD) of 18.8%. AHM non-adherence was directly associated with poverty/food insecurity (β = 0.31, P-value < 0.001) and weak social supports (β = 0.27, P-value < 0.001), but inversely with healthy built environment (β = −0.10, P-value = 0.02). These three constructs explained one-third (R2 = 30.0%) of the variation in county-level AHM non-adherence. Conclusion: AHM non-adherence varies by geographical location, one-third of which is explained by contextual SDH factors including poverty/food insecurity, weak social supports and healthy built environments.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ashley R. Banks ◽  
Bethany A. Bell ◽  
David Ngendahimana ◽  
Milen Embaye ◽  
Darcy A. Freedman ◽  
...  

Abstract Background Food insecurity and other social determinants of health are increasingly being measured at routine health care visits. Understanding the needs and behaviors of individuals or families who screen positive for food insecurity may inform the types of resources they need. The goal of this research was to identify modifiable characteristics related to endorsement of two food insecurity screener questions to better understand the resources necessary to improve outcomes. Methods Analysis was conducted focusing on cross-sectional survey data collected in 2015–2016 from participants (N = 442) living in urban neighborhoods in Ohio with limited access to grocery stores. Food insecurity was assessed by the endorsement of at least one of two items. These were used to categorize participants into two groups: food insecure(N = 252) or food secure (N = 190). Using logistic regression, we estimated the association between several variables and the food insecure classification. Results Those that used their own car when shopping for food had lower odds of reporting food insecurity, as did those with affirmative attitudes related to the convenience of shopping for and ease of eating healthy foods. As shopping frequency increased, the odds of food insecurity increased. Food insecurity also increased with experience of a significant life event within the past 12 months. There was an 81% increase in the odds of reporting food insecurity among participants who received Supplemental Nutrition Assistance Program benefits compared to those not receiving Supplemental Nutrition Assistance Program benefits. Conclusions Along with referrals to SNAP, clinicians can further address screening-identified food insecurity through provision of transportation supports and linkages to other social services while collaborating on community initiatives to promote convenient and easy access to healthy foods. The needs and behaviors associated with screens indicating food insecurity also have implications for impacting other SDH, and thus, health outcomes.


2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Mukoso N. Ozieh ◽  
Emma Garacci ◽  
Rebekah J. Walker ◽  
Anna Palatnik ◽  
Leonard E. Egede

Abstract Background A growing body of evidence supports the potential role of social determinants of health on health outcomes. However, few studies have examined the cumulative effect of social determinants of health on health outcomes in adults with chronic kidney disease (CKD) with or without diabetes. This study examined the cumulative impact of social determinants of health on mortality in U.S. adults with CKD and diabetes. Methods We analyzed data from National Health and Nutrition Examination Surveys (2005–2014) for 1376 adults age 20 and older (representing 7,579,967 U.S. adults) with CKD and diabetes. The primary outcome was all-cause mortality. CKD was based on estimated glomerular filtration rate and albuminuria. Diabetes was based on self-report or Hemoglobin A1c of ≥6.5%. Social determinants of health measures included family income to poverty ratio level, depression based on PHQ-9 score and food insecurity based on Food Security Survey Module. A dichotomous social determinant measure (absence vs presence of ≥1 adverse social determinants) and a cumulative social determinant score ranging from 0 to 3 was constructed based on all three measures. Cox proportional models were used to estimate the association between social determinants of health factors and mortality while controlling for covariates. Results Cumulative and dichotomous social determinants of health score were significantly associated with mortality after adjusting for demographics, lifestyle variables, glycemic control and comorbidities (HR = 1.41, 95%CI 1.18–1.68 and HR = 1.41, 95%CI 1.08–1.84, respectively). When investigating social determinants of health variables separately, after adjusting for covariates, depression (HR = 1.52, 95%CI 1.10–1.83) was significantly and independently associated with mortality, however, poverty and food insecurity were not statistically significant. Conclusions Specific social determinants of health factors such as depression increase mortality in adults with chronic kidney disease and diabetes. Our findings suggest that interventions are needed to address adverse determinants of health in this population.


Author(s):  
Charlie M. Wray ◽  
Janet Tang ◽  
Lenny López ◽  
Katherine Hoggatt ◽  
Salomeh Keyhani

Abstract Importance While the association between Social Determinants of Health (SDOH) and health outcomes is well known, few studies have explored the impact of SDOH on hospitalization. Objective Examine the independent association and cumulative effect of six SDOH domains on hospitalization. Design Using cross-sectional data from the 2016–2018 National Health Interview Surveys (NHIS), we used multivariable logistical regression models controlling for sociodemographics and comorbid conditions to assess the association of each SDOH and SDOH burden (i.e., cumulative number of SDOH) with hospitalization. Setting National survey of community-dwelling individuals in the US Participants Adults ≥18 years who responded to the NHIS survey Exposure Six SDOH domains (economic instability, lack of community, educational deficits, food insecurity, social isolation, and inadequate access to medical care) Measures Hospitalization within 1 year Results Among all 55,186 respondents, most were ≤50 years old (54.2%), female (51.7%, 95% CI 51.1–52.3), non-Hispanic (83.9%, 95% CI 82.4–84.5), identified as White (77.9%, 95% CI 76.8–79.1), and had health insurance (90%, 95% CI 88.9–91.9). Hospitalized individuals (n=5506; 8.7%) were more likely to be ≥50 years old (61.2%), female (60.7%, 95% CI 58.9–62.4), non-Hispanic (87%, 95% CI 86.2–88.4), and identify as White (78.5%, 95% CI 76.7–80.3), compared to those who were not hospitalized. Hospitalized individuals described poorer overall health, reporting higher incidence of having ≥5 comorbid conditions (38.9%, 95% CI 37.1–40.1) compared to those who did not report a hospitalization (15.9%, 95% CI 15.4–16.5). Hospitalized respondents reported higher rates of economic instability (33%), lack of community (14%), educational deficits (67%), food insecurity (14%), social isolation (34%), and less access to health care (6%) compared to non-hospitalized individuals. In adjusted analysis, food insecurity (OR: 1.36, 95% CI 1.22–1.52), social isolation (OR: 1.17, 95% CI 1.08–1.26), and lower educational attainment (OR: 1.12, 95% CI 1.02–1.25) were associated with hospitalization, while a higher SDOH burden was associated with increased odds of hospitalization (3–4 SDOH [OR: 1.25, 95% CI 1.06–1.49] and ≥5 SDOH [OR: 1.72, 95% CI 1.40–2.06]) compared to those who reported no SDOH. Conclusions Among community-dwelling US adults, three SDOH domains: food insecurity, social isolation, and low educational attainment increase an individual’s risk of hospitalization. Additionally, risk of hospitalization increases as SDOH burden increases.


2021 ◽  
Vol 28 (Supplement_1) ◽  
Author(s):  
T Parekh

Abstract Funding Acknowledgements Type of funding sources: None. Background Stroke is the third leading cause of death in the United States, with evident differences in health outcomes by race and socioeconomic factors. We aim to focus on social determinants of health by race/ethnicity and education level that greatly influences the health-related quality of life in stroke survivors. Method Using the 2017 Behavior Risk Factor Surveillance System (BRFSS) survey data, the direct age-adjusted prevalence was standardized to the 2000 projected US population. Multivariable weighted logistic regression models were post-estimated to calculate marginal effects of age, gender, education, and race on social determinants of health (housing insecurity, food insecurity, healthcare access hardship) at mean values of other predictors for stroke survivors. Models were adjusted for demographics, socioeconomic position, and stroke risk factors. Marginal effects (ME) reported as predicted probabilities. Result Among stroke survivors, nearly 27% reported housing insecurity and healthcare access hardship, and 48% reported food insecurity. The prevalence of housing insecurity was significantly higher among female (31.69%) than male (21.98%) survivors, and of race, highest among Non-Hispanic-Black (37.49%), lower among Non-Hispanic-Whites (23.83%), and lowest among Hispanics (17.20%) stroke survivors. In contrast, food insecurity was highest among Hispanics (63.71%). Healthcare access hardship was similar across the group with a comparatively lower prevalence in Non-Hispanic-White stroke survivors (25.32%). The predicted probability of housing insecurity was significantly higher among young adults compared to older adults aged 65 or above [ME 26.8 (95CI: 14.5-39.1 vs. ME 1.4 (95CI: 0.9-2.0)]. Of race, Black, NH stroke survivors showed a higher probability of housing insecurity [ME 12.4 (95CI: 6.3-18.3)], while the probability of food insecurity [ME 39.3 (95CI: 11.1-67.6)] and healthcare access was higher among other Non-Hispanic groups. The probability of any insecurities was similar among male and female stroke survivors. Stroke survivors with less than high school education showed a significantly higher probability of housing and food insecurity, in addition to healthcare access. Conclusion Social inequalities along with racial disparities in stroke survivors necessitate tailored intervention to reduce the burden of stroke. It is crucial to address socioeconomic factors such as housing, food, and healthcare access that promote the development of stroke risk factors. Abstract Figure.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
John M Morrison ◽  
Sarah M. Marsicek ◽  
Akshata M Hopkins ◽  
Robert A Dudas ◽  
Kimberly R Collins

Abstract Background Social determinants of health (SDoH) play an important role in pediatric health outcomes. Trainees receive little to no training on how to identify, discuss and counsel families in a clinical setting. The aim of this study was to determine if a simulation-based SDoH training activity would improve pediatric resident comfort with these skills. Methods We performed a prospective study of a curricular intervention involving simulation cases utilizing standardized patients focused on four social determinants (food insecurity, housing insecurity, barriers to accessing care, and adverse childhood experiences [ACEs]). Residents reported confidence levels with discussing each SDoH and satisfaction with the activity in a retrospective pre-post survey with five-point Likert style questions. Select residents were surveyed again 9–12 months after participation. Results 85% (33/39) of residents expressed satisfaction with the simulation activity. More residents expressed comfort discussing each SDoH after the activity (Δ% 38–47%; all p < .05), with the greatest effect noted in post-graduate-year-1 (PGY-1) participants. Improvements in comfort were sustained longitudinally during the academic year. More PGY-1 participants reported engaging in ≥ 2 conversations in a clinical setting related to food insecurity (43% vs. 5%; p = .04) and ACEs (71% vs. 20%; p = .02). Discussion Simulation led to an increased resident comfort with discussing SDoH in a clinical setting. The greatest benefit from such a curriculum is likely realized early in training. Future efforts should investigate if exposure to the simulations and increased comfort level with each topic correlate with increased likelihood to engage in these conversations in the clinical setting.


2018 ◽  
Vol In Press (In Press) ◽  
Author(s):  
Nasibeh Sharifi ◽  
Mahrokh Dolatian ◽  
Zohreh Mahmoodi ◽  
Fatemeh Mohammadi Nasrabadi ◽  
Yadollah Mehrabi

2020 ◽  
Author(s):  
Macarius M. Donneyong ◽  
Teng-Jen Chang ◽  
John W. Jackson ◽  
Michael A. Langston ◽  
Paul D. Juarez ◽  
...  

AbstractBackgroundNon-adherence to antihypertensive medication treatment (AHM) is a complex health behavior with determinants that extend beyond the individual patient. The structural and social determinants of health (SDH) that predispose populations to ill health and unhealthy behaviors could be potential barriers to long-term adherence to AHM. However, the role of SDH in AHM non-adherence have been understudied.Therefore, we aimed to define and identify the SDH factors associated with non-adherence to AHM and to quantify the variation in county-level non-adherence to AHM explained by these factors.MethodsTwo cross-sectional datasets, the Centers for Disease Control and Prevention (CDC) Atlas of Heart Disease and Stroke (2014-2016 cycle) and the 2016 County Health Rankings (CHR), were linked to create an analytic dataset. Contextual SDH variables were extracted from both the CDC-CHR linked dataset. County-level prevalence of AHM non-adherence, based on Medicare fee-for-service beneficiaries’ claims data, was extracted from the CDC Atlas dataset. The CDC measured AHM non-adherence as the Proportion of Days Covered (PDC) with AHM during a 365-day period for Medicare Part D beneficiaries and aggregated these measures at the county-level. We applied Confirmatory Factor Analysis (CFA) to identify the constructs of social determinants of AHM non-adherence. AHM non-adherence variation and its social determinants were measured with structural equation models.ResultsAmong 3,000 counties in the US, the weighted mean prevalence of AHM non-adherence (PDC<80%) in 2015 was 25.0%, Standard Deviation (SD), 18.8%. AHM non-adherence was directly associated with poverty/food insecurity (β=0.31, P-value<0.001) and weak social supports (β=0.27, P-value<0.001), but inversely with healthy built environment (β= −0.10, P-value=0.02). These three constructs explained a third (R2=30.0%) of the variation in county-level AHM non-adherence.ConclusionAHM non-adherence varies by geographical location, a third of which is explained by contextual SDH factors including poverty/food insecurity, weak social supports and healthy built environments.


2021 ◽  
Vol 8 ◽  
pp. 2333794X2110609
Author(s):  
Pyone David ◽  
Nadia K. Qureshi ◽  
Lina Ha ◽  
Vera Goldberg ◽  
Erin McCune ◽  
...  

This study demonstrates the challenges of establishing social determinants of health (SDH) screening at well child visits (WCVs) during the COVID-19 pandemic. We conducted a 6-month pre-intervention retrospective chart review (2/2020-8/2020) and 6-month post-intervention prospective chart review (8/2020-2/2021) of an SDH screening and referral protocol at a single suburban academic pediatric clinic. WCVs were screened for food, financial, and transportation needs. With the new protocol, 46% of eligible WCVs (n = 1253/2729) had documented screening results. Self-report of screened visits found 34.6% with financial strain, 32% with worry about food insecurity, 25.1% with food insecurity, 5.3% with medical transportation difficulties, and 6% with daily living transportation difficulties. There was an increase in resources offered during the post-intervention period (OR = 11.5 [7.1-18.6], P < .001). There was also an increase in resident physician self-reported knowledge in providing referrals ( P = .04).


Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 972-972
Author(s):  
Sharjeel Syed ◽  
Kristen Wroblewski ◽  
Radhika Peddinti ◽  
Gabrielle Lapping-Carr ◽  
Wendy S Darlington

Abstract Sickle cell disease (SCD) is a chronic, debilitating condition that negatively impacts patient quality of life (QOL). In addition to causing frequent crises that lead patients to seek medical attention, it can also exacerbate socioeconomic inequities patients with SCD often already face. A specific subset of patients, adolescents and young adults (AYA), defined by the NCI as individuals ages 15-39, are particularly at risk, which can lead to worse outcomes and increased healthcare utilization (HCU). However, analyses on social determinants of health, QOL, HCU, and clinical disease outcomes (CDO) in SCD are limited, particularly among the AYA population. Our group seeks to investigate the impact of social determinants of health on patients with SCD. We have previously reported on food insecurity (FI), QOL, and HCU in children with SCD. This project aims to specifically study the interplay of these metrics further, while also incorporating CDO. Furthermore, we seek to understand these relationships in AYA patients and how they may be uniquely related in this population. We hypothesize that FI is associated with decreased QOL, increased HCU, and worse CDO. We also hypothesize that the magnitude of this association is greater in AYA patients. We designed an observational study where patients with SCD ages 0- 24 years were recruited during routine SCD visits from June 2015- June 2019. We designed a baseline survey to measure FI and QOL using validated instruments, including the USDA Food Security Short Form and the PedsQL TM Sickle Cell Disease module. All patients were also consented to participate in our clinical registry, allowing for abstraction of HCU and CDO. Surveys were scored and transformed via established methods: USDA FS (range 0-6; &gt;1 indicating some level of FI), and PedsQL TM (range 0-100; ≤60 indicating low QOL). Chart review captured number of ER visits, admissions, annual rate of vaso-occlusive crises (VOC) and acute chest syndrome (ACS). Other CDOs were also captured and these included presence of neurocognitive/psychiatric conditions (i.e., silent stroke, DSM diagnosis), ischemic events like avascular necrosis, and surgeries like cholecystectomies. Linear regressions, Chi squared analyses, Wilcoxon rank-sum tests, and Fisher's exact tests were performed to check for differences within and amongst these variables based on AYA status. Of surveyed patients (n=115), 56% were female, 39% were AYA, and 75% had SS disease. Some level of food insecurity (FS score &gt; 1) was present in 34% of our population (compared to 10.5% of households nationally per the USDA) with no difference observed between AYA and non-AYA patients (Coleman 7). Average QOL score was 74, but this differed significantly between AYA and non-AYA patients. Specifically, total QOL scores for AYA patient were 10 points lower (p=0.003) and AYA patients were three times as likely to have QOL scores &lt; 60 (p=0.005). Additionally, as previously reported, all patients in the cohort with FI had lower QOL (p=0.008). FI was also tested against HCU. While no difference was observed in number of ER visits, median admissions were twice as high for those with FI (p=0.07). This relationship was not affected by AYA status, but AYA patients did have 1.5 times as many ER visits and admissions combined (p=0.03). Food insecurity was associated with certain CDO measures such as VOC rates, which were four times higher in FI patients (p=0.03), and ACS rates (p=0.03). VOC/ACS rates did not differ between AYA and non-AYA patients and AYA status did not affect the relationship between FI and these CDO. However, AYA patients did demonstrate higher rates of neurocognitive/psychiatric conditions (p=0.009), cholecystectomies (p=0.03), and avascular necrosis (p=0.003). This study indicates that FI among our SCD patients was highly prevalent, associated with worse QOL, and increased HCU. We also show that FI is associated with worse CDO, which is an impetus for future intervention. Although AYA status did not significantly affect the magnitude of these relationships, it was associated with worsened QOL, increased HCU, and worse CDO. We plan to further study these trends with additional variables associated with sickle cell CDO, the role of preventative care in these populations, and how other social determinants of health impact the care and outcomes of our patients. Coleman, et al. Household Food Security in the United States in 2019, USDA, Economic Research Service. Disclosures No relevant conflicts of interest to declare.


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