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Introduction: Although health systems increasingly integrate social needs screening and referrals into routine care, the effectiveness of these interventions and for whom they work remains unclear. Methods: Patients (N=4,608) seen in the emergency department were screened for social needs (e.g., transportation, housing, food) and offered an opportunity to receive outreach from community service specialists. Results: Among 453 patients with 1 or more social needs who requested assistance, outreach specialists connected with 95 (21.0%). Patients preferred to be contacted through their telephone (n=21, 60.2%), email (n=126, 28.0%), someone else's telephone (n=30, 6.7%), or first by telephone followed by email (n=23, 5.1%). Preferred contact method varied by patient age; endorsement of unmet transportation, housing, and utility needs; receipt of service outreach; and differences in emergency department utilization from the 6 months before the index visit to the 6 months after. Conclusions: Because limited access to a stable telephone or internet connection may prevent patients from connecting with resource referrals, social needs interventions may not benefit the most underserved populations who are at the highest risk of negative health outcomes. Future research should investigate whether communication preferences are an important indicator of needs and how to adapt social needs screening and referral processes so that they are more accessible to populations who may experience more frequent disruptions in methods utilized for digital communication.

Background Poor neighborhood‐level access to health care, including community pharmacies, contributes to cardiovascular disparities in the United States. The authors quantified the association between pharmacy proximity, antihypertensive and statin use, and blood pressure (BP) and low‐density lipoprotein cholesterol (LDL‐C) among a large, diverse US cohort. Methods and Results A cross‐sectional analysis of Black and White participants in the REGARDS (Reasons for Geographic and Racial Differences in Stroke) study during 2013 to 2016 was conducted. The authors designated pharmacy proximity by census tract using road network analysis with population‐weighted centroids within a 10‐minute drive time, with 5‐ and 20‐minute sensitivity analyses. Pill bottle review measured medication use, and BP and LDL‐C were assessed using standard methods. Poisson regression was used to quantify the association between pharmacy proximity with medication use and BP control, and linear regression for LDL‐C. Among 16 150 REGARDS participants between 2013 and 2016, 8319 (51.5%) and 8569 (53.1%) had an indication for antihypertensive and statin medication, respectively, and pharmacy proximity data. The authors did not find a consistent association between living in a census tract with higher pharmacy proximity and antihypertensive medication use, BP control, or statin medication use and LDL‐C levels, regardless of whether the area was rural, suburban, or urban. Results were similar among the 5‐ and 20‐minute drive‐time analyses. Conclusions Living in a low pharmacy proximity census tract may be associated with antihypertensive and statin medication use, or with BP control and LDL‐C levels. Although, in this US cohort, outcomes were similar for adults living in high or low pharmacy proximity census tracts.

Introduction: Food insecurity negatively impacts public health and costs the U.S. healthcare system $53 billion annually. Immigrants are at higher risk of food insecurity. We sought to (1) characterize the prevalence of food insecurity among immigrants (i.e., noncitizens and naturalized citizens) and U.S.-born citizens and (2) determine whether Supplemental Nutrition Assistance Program utilization and income–poverty ratio levels impact the relationship between immigration status and food insecurity. Methods: Multivariable logistic regression models were used to determine the odds of food insecurity (dependent variables) using nationally representative data from the 2019–2020 National Health Interview Survey. Independent variables included immigration status, Supplemental Nutrition Assistance Program utilization, income–poverty ratio, and other important demographics. AORs with their 95% CIs are reported. Analysis was conducted in 2022. Results: After controlling for independent variables, noncitizens had 1.28 (95% CI=1.02, 1.61) times higher odds of food insecurity than U.S.-born citizens. There was no food insecurity disparity between naturalized citizens and U.S.-born citizens. However, the association between immigration status and food insecurity varied significantly at different levels of Supplemental Nutrition Assistance Program utilization and income–poverty ratio. There were no food insecurity disparities between immigrants and U.S.-born citizens when they utilized the Supplemental Nutrition Assistance Program and when they had an income below 200% federal poverty level. Noncitizens who did not utilize the Supplemental Nutrition Assistance Program or those with an income above 200% federal poverty level were more likely to report food insecurity than their U.S.-born counterparts (AOR=1.32, 95% CI=1.01, 1.73 and AOR=1.88, 95% CI=1.24, 2.86, respectively). Moreover, naturalized citizens with an income above 200% federal poverty level were also more likely to report food insecurity than their U.S.-born counterparts (AOR=1.61, 95% CI=1.21, 2.14). Conclusions: Supplemental Nutrition Assistance Program utilization may likely eliminate food insecurity disparities among immigrants and U.S.-born citizens. However, among non−Supplemental Nutrition Assistance Program utilizers, significant food insecurity disparities remained between noncitizens and U.S.-born citizens after adjusting for independent variables. In addition, among individuals with incomes above 200% federal poverty level, significant food insecurity disparities were observed between immigrants and U.S.-born citizens. More research is needed to further understand the role that fear of deportation, ineligibility or lack of awareness about eligibility for the Supplemental Nutrition Assistance Program, and other factors such as structural racism play in food insecurity disparities between immigrants and U.S.-born citizens.

BackgroundSexual minorities—individuals who identify as gay/lesbian, bisexual, or other non-heterosexual individuals—experience higher rates of food insecurity (FI) compared to heterosexual individuals. During the COVID-19 pandemic, discrimination and structural racism, which are known risk factors for food insecurity, were perpetuated against sexual and racial/ethnic minorities. However, to our knowledge, a nationally representative analysis of the impact of the pandemic on food insecurity by sexual minority status and based on race/ethnicity is missing. We aimed to determine the degree of association between FI and sexual minority adults overall, before (2019) and during (2020–2021) the pandemic, and stratified by race/ethnicity.MethodsWe used nationally representative data from the 2019–2021 National Health Interview Survey (NHIS). We specified multivariable logistic regression models to determine the association between FI and identifying as a sexual minority adult (≥18 years old), including gay/lesbian, bisexual, and other non-heterosexual individuals.ResultsOverall, we only observed FI disparities between bisexuals and heterosexuals (aOR 1.61 [95% CI 1.31–1.99]). Stratified by year, this association was significant only during the pandemic. Stratified by race/ethnicity, non-Hispanic white and non-Hispanic black individuals identifying as bisexual also experienced a significantly higher FI rate than their heterosexual counterparts.ConclusionOur results may be a manifestation of the disproportionate impact of discrimination on bisexual individuals’ FI experiences. With the growing number of legislative bills targeting the rights of sexual minorities, we expect to see a higher burden of FI among bisexuals, particularly, bisexual people of color. Future intersectional research regarding FI among bisexual and racial/ethnic minority individuals would further elucidate how membership in multiple minority groups may contribute to a higher risk of FI.

Background Food insecurity is a social determinant of health that impacts more than 10% of U.S. households every year. Many unexpected events make food-insecure people and those with unmet food needs seek information and help from both formal (e.g., community organizations) and informal (e.g., family/friends) resources. Food-related information seeking through telephone calls to a community referral system—211 network—has been used as a proxy for food insecurity but the context of these calls has not been characterized and the validity of this proxy measure is unknown. Objective To investigate the content of food-related telephone calls to 211 and explore the indications of food insecurity during these calls. Methods We conducted a secondary qualitative analysis on the transcripts of food-related calls to Utah’s 211. From February to March 2022, 25 calls were sampled based on the location of callers to ensure the representation of rural residents. 13 calls from metropolitan and 12 calls from nonmetropolitan ZIP Codes were included. Using a purposive sampling approach, we also made sure that the sample varied with regard to race and ethnicity. Calls were transcribed and de-identified by our community partner—Utah’s 211 and were analyzed using a thematic analysis approach by our research team. Results Three themes emerged from the qualitative analysis including referral to 211, reasons for food-related calls, and reasons for unmet food needs. Results highlight the complex social environment around 211 food-related callers, lack of knowledge about available food resources, and indications of food insecurity in calls. Conclusion Information seeking for food-related resources through 211 is a problem-solving source for people living in a complex social environment. Indications of food insecurity through these calls validate the use of these calls as a proxy measure for food insecurity. Interventions should be designed to increase awareness about the available resources and address the co-existing social needs with food insecurity.

Food insecurity is a complex problem affected by a number of factors from individual to societal. While individual-level demographic information and population-level social determinants of health (SDoH) are commonly used to identify patients at risk of food insecurity and to direct resources, a more comprehensive understanding of food insecurity requires integrating multi-level data. Our goal is to identify factors associated with food insecurity using patient, health system, and population level data. Between January 2019 and April 2020, we screened adult patients visiting an academic health sciences emergency department in Utah using a 10-item social needs screener. Patients’ demographic data were linked to their screener responses. ZIP Code-level food-related SDoH such as accessibility to food providers, measured by geographic information systems methods, were assigned to patients. We then applied multilevel logistic regression modeling to identify factors associated with unmet food needs at two different levels—individual and ZIP Code. Unmet food needs were identified by asking patients if they felt there was not enough money for food in the last month, which grossly represents food insecurity. On a sample of 2,290 patients, 21.61% reported unmet food needs. Patient-reported housing, medical care, and utility needs along with Supplemental Nutrition Assistance Program participation and primary care provider utilization were highly associated with unmet food needs. Our efforts to identify the population at risk of food insecurity should be centered around patient-reported social needs. Our results suggest that addressing food insecurity in health care settings should include assessing social needs in primary care.

Background: Opioid addiction and overdose rates are reaching unprecedented levels in the U.S., with around 47,736 overdose deaths in 2017. Many stakeholders affect the opioid epidemic, including government entities, healthcare providers and policymakers, and opioid users. Simulation and conceptual modeling can help us understand the dynamics of the opioid epidemic by simplifying the real world and informing policymakers about different health interventions that could reduce the deaths caused by opioid overdose in the United States every year. Objectives: To conduct a scoping review of simulation and conceptual models that propose policies capable of controlling the opioid epidemic. We demonstrate the strengths and limitations of these models and provide a framework for further improvement of future decision support tools. Methods: Using the methodology of a scoping review, we identified articles published after 2000 from eight electronic databases to map the literature that uses simulation and conceptual modeling in developing public health policies to address the opioid epidemic. Results: We reviewed 472 papers of which 14 were appropriate for inclusion. Each used either system dynamics simulation modeling, mathematical modeling, conceptual modeling, or agent-based modeling. All included studies tested and proposed strategies to improve health outcomes related to the opioid epidemic. Factors considered in the models included physicians prescribing opioids, trafficking, users recruiting new users, and doctor shopping; no model investigated the impact of age and spatial factors on the dynamics of the epidemic. Key findings from these studies were (1) prevention of opioid initiation is better than treatment of opioid addiction, (2) the analysis of an intervention’s impact should include both benefits and harms, and (3) interventions with short-term benefits might have a counterproductive impact on the epidemic in long run. Conclusions: While most studies examined the role of prescription opioids and trafficking on this epidemic, the transition of patients from prescription opioid use to nonprescription use including heroin and synthetic opioids such as fentanyl impacts the system significantly and results in an epidemic with quite different characteristics than what it had a decade ago. We recommend including the impact of age and geographic location on the opioid epidemic using modeling methods.

The number of Lyme disease (LD) cases in the northeastern United States has been dramatically increasing with over 300 000 new cases each year. This is due to numerous factors interacting over time including low public awareness of LD, risk behaviours and clothing choices, ecological and climatic factors, an increase in rodents within ecologically fragmented peri-urban built environments and an increase in tick density and infectivity in such environments. We have used a system dynamics (SD) approach to develop a simulation tool to evaluate the significance of risk factors in replicating historical trends of LD cases, and to investigate the influence of different interventions, such as increasing awareness, controlling clothing risk and reducing mouse populations, in reducing LD risk. The model accurately replicates historical trends of LD cases. Among several interventions tested using the simulation model, increasing public awareness most significantly reduces the number of LD cases. This model provides recommendations for LD prevention, including further educational programmes to raise awareness and control behavioural risk. This model has the potential to be used by the public health community to assess the risk of exposure to LD.