Vision Loss in Children from Immigrant and Nonimmigrant Households: Evidence from the National Survey of Children's Health 2018-2020

The aim of this study was to determine whether immigrant generation is associated with caregiver-reported vision loss in children adjusting for sociodemographic characteristics. Nationally representative data from the National Survey of Children's Health (2018-2020) was used. The primary exposure was immigrant generation defined as: first (child and all reported parents were born outside the United States); second (child was born in the United States and at least one parent was born outside the United States); third or higher (all parents in the household were born in the United States). The main outcome was caregiver-reported vision loss in child. Adjusted odds ratios (aOR) and 95% confidence intervals were computed based on immigration generation. The study sample included 84,860 US children aged 3-17 years. First generation children had higher adjusted odds of caregiver-reported vision loss (aOR 2.30; 95% CI 1.21, 4.35) than third or higher generation children after adjusting for demographic characteristics and social determinants of health. For Hispanic families, first generation (aOR 2.99; 95% CI 1.34, 6.66), and second-generation children (aOR 1.70; 95% CI 1.06, 2.74) had a higher adjusted odds of vision loss compared with third or higher generation children. Even when adjusting for sociodemographic characteristics, first generation children had greater odds of vision loss, especially in Hispanic households, than third generation children. Immigration generation should be treated as an independent risk factor for vision loss for children and is a social determinant of eye health.

A Secondary Data Analysis of Technology Access as a Determinant of Health and Impediment in Social Needs Screening and Referral Processes

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.

Association Between Pharmacy Proximity With Cardiovascular Medication Use and Risk Factor Control in the United States

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.

Access to community pharmacies based on drive time and by rurality across the contiguous United States

BACKGROUND

Considering that mail-order pharmacy use remains low in the United States, geographic accessibility of community pharmacies (pharmacy access) can have an outsized impact on a community's access to services and care, especially among rural residents. However, previous measurements of pharmacy access rely on methods that do not capture all aspects of geographic access.

OBJECTIVES

This study aimed to measure pharmacy access across the contiguous United States and by rural, suburban, and urban areas using drive-time analysis and an improved methodological approach.

METHODS

The 2-step floating catchment area method was used to measure pharmacy access by considering the supply capacity of pharmacies, population demand for pharmacies, and the interaction between them within a reasonable travel time range. This method is a methodologically improved approach compared with previous methods for measuring geographic access. Network analysis was used to measure drive time from the population-weighted centroids of census tracts to the geocoded location of community pharmacies. Census tract-level pharmacy access was measured using a 10- and 20-minute drive time. Census tracts were also categorized based on population per square mile as rural (< 1000), suburban (1000-3000), and urban (> 3000).

RESULTS

Across the contiguous United States, 79.9% and 91.1% of census tracts had access to at least 1 pharmacy per 10,000 people within a 10- and 20-minute drive time, respectively. Rural census tracts had the lowest share of access to at least 1 pharmacy per 10,000 people compared with suburban and urban tracts and for both drive times.

CONCLUSION

Community pharmacies are highly accessible health care access points, specifically in urban and suburban areas. Pharmacies should be considered to expand access to services with limited geographic accessibility such as treatment programs for opioid use disorders, primary care, and healthy foods.

Associations between food insecurity and other social risk factors among U.S. adults

BACKGROUND

Food insecurity (FI) often co-exists with other social risk factors, which makes addressing it particularly challenging. The degree of association between FI and other social risk factors across different levels of income and before and during the COVID-19 pandemic is currently unknown, impeding the ability to design effective interventions for addressing these co-existing social risk factors.

OBJECTIVE

To determine the association between FI and other social risk factors overall and across different levels of income-poverty ratios and before (2019) and during (2020-2021) the pandemic.

DESIGN

We used nationally representative data from the 2019-2021 National Health Interview Survey for our cross-sectional analysis. Social risk factors available in NHIS included difficulties paying for medical bills, difficulties paying for medications, receiving income assistance, receiving rental assistance, and "not working last week".

SUBJECTS

93,047 adults (≥18 years old).

KEY RESULTS

Individuals with other social risk factors (except receiving income assistance) were more likely to report FI, even after adjusting for income and education inequalities. While poverty leads to a higher prevalence of FI, associations between FI and other social risk factors were stronger among people with higher incomes, which may be related to their ineligibility for social safety net programs. Associations were similar before and during the pandemic, perhaps due to the extensive provision of social safety net programs during the pandemic.

CONCLUSIONS

Future research should explore how access to a variety of social safety net programs may impact the association between social risk factors. With the expiration of most pandemic-related social supports, further research and monitoring are also needed to examine FI in the context of increasing food and housing costs. Our findings may also have implications for the expansion of income-based program eligibility criteria and screening for social risk factors across all patients and not only low-income people.

Food Insecurity Disparities Among Immigrants in the U.S

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.

Disparities in food insecurity between sexual minority and heterosexual adults - a higher burden on bisexual individuals

Background

Sexual 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.

Methods

We 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.

Results

Overall, 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.

Conclusion

Our 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.

Indications of food insecurity in the content of telephone calls to a community referral system

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.

Associated factors with patient-reported unmet food needs among emergency department adult patients - A social need perspective

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.

Applying a Health Access Framework to Understand and Address Food Insecurity

The prevalence of food insecurity (FI) in United States households has fluctuated between 10% and 15% for the past two decades, well above the Healthy People 2030 goal. FI is associated with increased use of healthcare services and the prevalence of multiple health conditions. Our current efforts to address FI may be limited by measures that lack granularity, timeliness, and consideration of larger food access barriers (e.g., availability of food providers and lack of knowledge regarding where to obtain food). If the Healthy People 2030 goal of reducing FI to 6% is to be met, we need better and faster methods for monitoring and tracking FI in order to produce timely interventions. In this paper, we review key contributors of FI from an access barrier perspective, investigate the limitations of current FI measures, and explore how data from one nonprofit organization may enhance our understanding of FI and facilitate access to resources at the local level. We also propose a conceptual framework illustrating how nonprofit organizations may play an important role in understanding and addressing FI and its intertwined social needs, such as housing and healthcare problems.

Incorporation of Information-Seeking Behavior Into Food Insecurity Research

INTRODUCTION

Limited or uncertain availability of nutritionally adequate and safe foods affects the health of individuals. Because of its association with chronic health conditions, addressing food insecurity may improve health outcomes and decrease health-related costs. This study explores whether and how information seeking as captured by calls made to United Way 2-1-1 can be used to identify food-insecure areas and information deserts-communities with low proportions of residents accessing government food resources but with high rates of 2-1-1 calls for emergency food resources.

METHODS

Details regarding calls made to United Way of Salt Lake 2-1-1 for emergency food resources between 2014 and 2018 (n=63,221) were analyzed in 2019. Using GIS methods, areas with the highest number of calls for emergency food resources (hot spots) were identified; multinomial logistic regression was used to identify community-level sociodemographic predictors of food insecurity.

RESULTS

Areas with a smaller proportion of the population aged <18 years, more female householders, and more African Americans are associated with higher odds of being food-insecure.

CONCLUSIONS

Patterns of information seeking about emergency food resources suggest that, despite statewide access to federal means-tested food programs, significant food needs remain. This novel approach in food insecurity research can help public health officials and health systems address an important social determinant of health by identifying areas vulnerable to food insecurity. In addition, this work may be useful in benchmarking food needs, information seeking, and replicating analyses where similar data are available.

A vulnerability assessment for the HCV infections associated with injection drug use

After the 2014-2015 HIV outbreak in Scott County, Indiana, United States Centers for Disease Control and Prevention (CDC) conducted a nationwide analysis to identify vulnerable counties to an outbreak of Hepatitis C Virus (HCV)/Human Immunodeficiency Virus (HIV) and prevent such an outbreak in the future. We developed a jurisdiction-level vulnerability assessment for HCV infections associated with injection drug use (IDU) in Utah. We used three years of data (2015-2017) from 15 data sources to construct a regression model to identify significant indicators of IDU. A ZIP Code, county, or individual-level measure of IDU does not exist, therefore, CDC has suggested using HCV cases as a proxy for IDU. We used the Social Vulnerability Index to highlight vulnerable areas to HCV outbreaks and applied Geographical Information System (GIS) to identify hot spots of HCV infections (i.e. current/ongoing HCV transmissions). Rates of skin infection, buprenorphine prescription, administered naloxone, teen birth, and per capita income were associated with HCV infections. The opioid epidemic is dynamic and over time, it impacts different communities through its sequelae such as HCV outbreaks. We need to conduct this vulnerability assessment frequently, using updated data, to better target our resources. Moreover, we should consider evaluating whether the improvement of HCV screening has an impact on controlling HCV outbreaks. The analysis informs Utah's agencies and healthcare officials to target resources and interventions to prevent IDU-related HCV outbreaks. Our results inform policymakers at the national level on possible indicators of HCV outbreaks as well.

Evidence of Modeling Impact in Development of Policies for Controlling the Opioid Epidemic and Improving Public Health: A Scoping Review

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.

Risk Factors of Lyme Disease: An Intersection of Environmental Ecology and Systems Science

Lyme disease (LD) cases have been on the rise throughout the United States, costing the healthcare system up to $1.3 billion per year, and making LD one of the greatest threats to public health. Factors influencing the number of LD cases range from environmental to system-level variables, but little is known about the influence of vegetation (canopy, understory, and ground cover) and human behavioral risk on LD cases and exposure to infected ticks. We determined the influence of various risk factors on the risk of exposure to infected ticks on 22 different walkways using multinomial logistic regression. The model classifies the walkways into high-risk and low-risk categories with 90% accuracy, in which the understory, human risk, and number of rodents are significant indicators. These factors should be managed to control the risk of transmission of LD to humans.

Spatial dynamics of access to primary care for the medicaid population

Primary care (PC) has always been underestimated and underinvested by the United States health system. Our goal was to investigate the effect of Medicaid expansion and the Affordable Care Act (ACA) provisions on PC access in Broome County, NY, a county that includes both rural and urban areas, and can serve as a benchmark for other regions. We developed a spatial system dynamics model to capture different stages of PC access for the Medicaid population by using the health belief model constructs and simulate the effect of several hypothetical interventions on PC utilisation. The government data portals used as data sources for calibrating our model include the New York State Department of Health, the Medicaid Delivery System Reform Incentive Payment (DSRIP) dashboards, and the US census. In our unique approach, we integrated the simulation results within Geographical Information System (GIS) maps, to assess the influence of geospatial factors on PC access. Our results identify hot spot demographic areas that have poor access to PC service facilities due to transportation constraints and a shortage in PC providers. Our decision support tool informs policymakers about programmes with the strongest impact on improving access to care, considering spatial and temporal characteristics of a region.

Model-based risk assessment and public health analysis to prevent Lyme disease

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.

The Ebola Crisis and the Corresponding Public Behavior: A System Dynamics Approach

BACKGROUND

The interaction of several sociocultural and environmental factors during an epidemic crisis leads to behavioral responses that consequently make the crisis control a complex problem.

METHODS

The system dynamics approach has been adopted to study the relationships between spread of disease, public attention, situational awareness, and community's response to the Ebola epidemic.

RESULTS

In developing different simulation models to capture the trend of death and incidence data from the World Health Organization for the Ebola outbreak, the final model has the best fit to the historical trends. Results demonstrate that the increase of quarantining rate over time due to increase in situational awareness and performing safe burials had a significant impact on the control of epidemic. However, public attention did not play a significant role.

CONCLUSION

The best fit to historical data are achieved when behavioral factors specific to West Africa like studying the Situational Awareness and Public Attention are included in the model. However, by ignoring the sociocultural factors, the model is not able to represent the reality; therefore, in the case of any epidemics, it is necessary that all the parties and community members find the most significant behavioral factors that can curb the epidemic.