United States

Background
Coronary artery disease (CAD) is the leading cause of death in the United States. In Hawaii, three of every ten deaths are due to CAD. Access to cardiac catheterization centers (CCC) is crucial for reducing morbidity and mortality, but most CCCs are concentrated in the capital, Honolulu. No prior studies have assessed the distribution of CCCs in relation to the demand and capacity for catheterization services in Hawaii using Geographic Information Systems (GIS). This study analyzed both current demand vs. capacity for CCCs, as well as projected optimal CCC locations based on time and distance considerations on the island of Oahu. 

Methods
GIS is a powerful tool with which location-allocation problems may be addressed. Data from the Hawaii Department of Health was implemented to estimate the number of patients in need of CCCs. Patients were then randomly distributed across residential areas in the location-allocation model. Using network analysis in GIS, residents’ travel time from their homes to the nearest CCCs was approximated. Travel time and demand vs. capacity maps were created to enable assessment of the accessibility of CCCs in each region and to highlight areas with unmet demand.

Results
Time-to-care analysis showed significant time burdens for patients in certain areas of Oahu. Under optimal traffic conditions, it took &gt;60 minutes for patients in West Oahu and the North Shore, and &gt;40 minutes for patients in Northeast Oahu to reach the nearest CCCs. Analysis of the demand vs. capacity of CCCs revealed the CCC in Central Oahu, which serves many of the patients with significant time burdens, exceeded its annual capacity by more than 985 patients. Meanwhile, all CCCs in South Oahu had significantly higher capacity than demand. To best decrease time burdens and mitigate capacity concerns for the greatest number of patients, an additional CCC should be established in West Oahu.

Conclusion
GIS analysis showed that the current CCCs on Oahu are not positioned to optimize patient outcomes. Patients in areas with significant time-to-CCC burdens likely face worse outcomes when they need emergent catheterization. This disparity could be remedied with the addition of CCCs closer to these regions, particularly in West Oahu. Looking forward, this model may be applied to other healthcare resources, such as emergency departments, to analyze current demand and geographical distributions and to influence future resource allocation. GIS has the power to improve healthcare efficiency, thus enhancing clinical outcomes.

AMA Research Challenge 2023

GIS Analysis of Cardiac Catheterization Centers on the Island of Oahu


Background
Coronary artery disease (CAD) is the leading cause of death in the United States. In Hawaii, three of every ten deaths are due to CAD. Access to cardiac catheterization centers (CCC) is crucial for reducing morbidity and mortality, but most CCCs are concentrated in the capital, Honolulu. No prior studies have assessed the distribution of CCCs in relation to the demand and capacity for catheterization services in Hawaii using Geographic Information Systems (GIS). This study analyzed both current demand vs. capacity for CCCs, as well as projected optimal CCC locations based on time and distance considerations on the island of Oahu. 

Methods
GIS is a powerful tool with which location-allocation problems may be addressed. Data from the Hawaii Department of Health was implemented to estimate the number of patients in need of CCCs. Patients were then randomly distributed across residential areas in the location-allocation model. Using network analysis in GIS, residents’ travel time from their homes to the nearest CCCs was approximated. Travel time and demand vs. capacity maps were created to enable assessment of the accessibility of CCCs in each region and to highlight areas with unmet demand.

Results
Time-to-care analysis showed significant time burdens for patients in certain areas of Oahu. Under optimal traffic conditions, it took >60 minutes for patients in West Oahu and the North Shore, and >40 minutes for patients in Northeast Oahu to reach the nearest CCCs. Analysis of the demand vs. capacity of CCCs revealed the CCC in Central Oahu, which serves many of the patients with significant time burdens, exceeded its annual capacity by more than 985 patients. Meanwhile, all CCCs in South Oahu had significantly higher capacity than demand. To best decrease time burdens and mitigate capacity concerns for the greatest number of patients, an additional CCC should be established in West Oahu.

Conclusion
GIS analysis showed that the current CCCs on Oahu are not positioned to optimize patient outcomes. Patients in areas with significant time-to-CCC burdens likely face worse outcomes when they need emergent catheterization. This disparity could be remedied with the addition of CCCs closer to these regions, particularly in West Oahu. Looking forward, this model may be applied to other healthcare resources, such as emergency departments, to analyze current demand and geographical distributions and to influence future resource allocation. GIS has the power to improve healthcare efficiency, thus enhancing clinical outcomes.

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Purpose for Review 
This perspective piece has two goals: first, to describe issues related to artificial intelligence-based applications for cancer control as they may impact health inequities or disparities; and second, to report on a review of systematic
reviews and meta-analyses of artificial intelligence-based tools for cancer control to ascertain the extent to which discussions of justice, equity, diversity, inclusion, or health disparities manifest in syntheses of the field’s best evidence.
Recent Findings 
We found that, while a significant proportion of existing syntheses of research on AI-based tools in cancer control use formal bias assessment tools, the fairness or equitability of models is not yet systematically analyzable across studies. Issues related to real-world use of AI-based tools for cancer control, such as workflow considerations, measures of usability and acceptance, or tool architecture, are more visible in the literature, but still addressed only in a minority of reviews.
Summary
Artificial intelligence is poised to bring significant benefits to a wide range of applications in cancer control, but more thorough and standardized evaluations and reporting of model fairness are required to build the evidence base for AI-based
tool design for cancer and to ensure that these emerging technologies promote equitable healthcare.

Artificial Intelligence and Cancer Control: Toward Prioritizing Justice,
Equity, Diversity, and Inclusion (JEDI) in Emerging Decision Support
Technologies

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**Abstract:**



Analyzing the Quality of Reporting of AI Studies for Hip Fracture Detection

Background
The biopsychosocial model suggests that health outcomes are determined by the interplay of biological, psychological, and social factors. Within this framework, health follows a social gradient, in which socioeconomic status has a direct impact on health outcomes. In the United States, each group on the social ladder has better health than the group below it. These patterns of inequity are manifested through differential access to resources, including housing, education, and healthcare. 

In order to reduce these health disparities, the Upstate Medical Legal Partnership (MLP) was developed as an integrated, upstream approach to improve health outcomes for marginalized populations throughout Upstate South Carolina. More specifically, through a collaboration between Furman University, Prisma Health Upstate, and South Carolina Legal Services, the Upstate MLP coordinates and streamlines access to non-medical assistance when a medical problem is caused or exacerbated by a social or legal problem. The objective of this study was to qualitatively explore the impact of the MLP on health and quality of life for pediatric clients and their families. 

Methods
This study utilized the WHO social determinants of health (SDoH) framework to develop an interview guide. The interview guide assessed the effect of the MLP on health status, health access, and other SDoH domains. The MLP team interviewed 18 caregivers of pediatric patients who were referred to the MLP for assistance. Data analysis was conducted using the qualitative coding software NVivo using a grounded theory approach to understand the impact of MLP intervention on the health and well-being of pediatric clients and their families. 

Results
Prior to receiving MLP assistance, the caretakers reported three main areas of stress: difficulty balancing work and childcare, challenges adhering to treatment programs, and inhibited overall family functioning. However, by securing additional financial resources (SSI, Medicaid, TANF), facilitating custody proceedings, and providing social support for these families, the MLP was able to reduce the amount of stress perceived by these caregivers. Several downstream effects of this reduced caregiver stress were identified. Caregivers reported improved treatment compliance for themselves and their children, improved ability to balance work and childcare, and improved overall family functioning. 

Conclusion
Medical Legal Partnerships have the potential to improve health outcomes for caregivers and their children by securing economic resources, finalizing custody arrangements, and providing social support. The study demonstrated that MLP intervention allowed caregivers to have increased treatment compliance and improved family functioning, which has positive health implications for the pediatric clients and their caregivers.


Downstream Impacts of the Upstate Medical Legal Partnership: How Embedding Lawyers in Hospitals Can Be Used to Tackle the Social Determinants of Health

Background
8.3 percent of Americans do not have health insurance. Even for the 91.7 percent of patients with insurance, many surgical procedures are not covered, or are only partially covered by insurance. Previous studies have found that privately insured patients undergo elective hand surgeries in the 4th fiscal quarter at a significantly greater rate than publicly insured patients. This suggests that surgical decision making and timing may be subject to similar forces to those that govern consumer spending. 

It has been shown that rates of cosmetic surgery are positively correlated with US GDP, while rates of reconstructive surgery are not. Thus, surgeries may be differentially affected by personal finance.

Here we investigate the association between economics and surgical procedure utilization while attempting to understand how surgical procedures are differentially affected by the United States economy.

Methods
Healthcare Cost and Utilization Project net data were reviewed from January 1, 2001, through December 31, 2014, to collect hospital inpatient data for nine groups of surgical procedures: angioplasty, appendectomy, coronary artery bypass graft (CABG), cesarean delivery, cholecystectomy, fracture repair, joint arthroplasty, hysterectomy, and oophorectomy. Procedures that did not require prior authorization by Medicare were deemed non-elective, while procedures that did were deemed elective.

Statistical analysis was performed using IBM SPSS Statistics v29. Year-over-year average percent change in procedure utilization was calculated and plotted over time. Bivariate correlations were made for individual procedures with US GDP and Real Disposable Personal Income data.

Results
During the period studied 51,172,734 procedures were performed. For non-elective procedures, the largest year-over-year decrease of 7.82% occurred in 2010. For elective procedures, the largest year-over-year decrease of 4.10% occurred in 2011.

Cesarean delivery (r=0.584, 0.603), hip arthroplasty (r=0.968, 0.955), and knee arthroplasty (r=0.975, 0.981) were significantly positively correlated with US GDP and Real Disposable Personal Income, respectively. Angioplasty (r=-0.781, -0.778), appendectomy (r=-0.957, -0.958), CABG (r=-0.954, -0.956), cholecystectomy (r=-0.854, -0.789), hysterectomy (r=-0.948, -0.932), and oophorectomy (r=-0.972, -0.966) were significantly negatively correlated with US GDP and Real Disposable Personal Income, respectively. Surgical fracture repair was significantly correlated with neither US GDP nor Real Disposable Personal Income.

Conclusion
Qualitatively there was no difference in the year-over-year average percent change in surgical procedure utilization for elective versus non-elective surgeries. Three distinct groups of procedures were identified based on their negative, positive, or lack of correlation with the United States economy. Taken together, our findings suggest that the strength of the United States economy has a nuanced, non-negligible, impact on surgical procedure utilization.


Impact of United States Economic Strength on Surgical Procedure Utilization

Background : Worldwide studies show an increasing trend of sleep disorders in recent times. We aimed to assess the association of epidemiology and socio-demographic disparities of organic sleep disorders (OSDs) using the National Inpatient Sample Database in the US population.

Methods : A retrospective study of the NIS (2016-2018) using the ICD-10-CM codes was performed. Hospitalizations with the diagnosis of OSDs (REM sleep behavior disorder, parasomnia, circadian rhythm, insomnia, hypersomnia, and narcolepsy) were identified. Weighted analysis was performed using an unpaired t-test & chi-square test. Mix effect multivariate regression analysis was performed to evaluate epidemiology & demographic disparities associated with OSDs.

Results : Out of 87,761,798 hospitalizations, 2,128,329 (2.43%) had organic sleep disorders. Amongst OSDs, prevalence of insomnia-2.32%, REM sleep behavior disorder-0.1%, parasomnia-0.1%, circadian rhythm-0.1%, hypersomnia-0.04%, and narcolepsy-0.06%. Population with OSDs was older (60 vs 58 years) and OSDs were higher in the age group 50-75 years (2.8% vs >75 years: 2.38% vs 18-50 years: 1.98%), male (2.53% vs female: 2.35%), White race (2.74% vs Native American: 2.06% vs Black: 1.83% vs Hispanic: 1.69%) and median household income of 76-100th percentile (2.61% vs 0-25th: 2.26%). Prevalence of hypertension (61.48% vs 55.58%), obesity (17.00% vs 15.77%), drug abuse (13.80% vs 6.70%), alcohol dependence (10.50% vs 5.81%), smoking (22.57% vs 16.10%) and AIDS (10.79% vs 6.03%) was higher amongst OSDs than no OSDs (p<.0001). In regression analysis, we found patients with age 50-75 years (aOR 1.08, 95%CI 1.06-1.10), female (1.05, 1.04-1.05), median income 76-100 percentile (1.10, 1.09-1.11), hypertension (1.18, 1.17-1.19), dyslipidemia (1.27, 1.26-1.28), obesity (1.09, 1.08-1.1) and AIDS (1.88, 1.86-1.9) had significantly higher odds of OSDs.

Conclusion : We identified a higher prevalence of OSD in the male, white, and high socioeconomic status population with insomnia being the most common type. Early identification and risk mitigation are essential to reduce the burden of ODSs. Our study is confounded by the absence of severity, outcomes of disease, causality, and long term follow up of patients

Epidemiology And Socio-demographic Disparities Associated With Organic Sleep Disorders Among USA Hospitalizations - A National Estimate

Background: While the impact of social and environmental exposure on cardiovascular risks is often reported individually, the combined effect is poorly understood. 
Objectives: We aim to study the association between a combination of social and environmental determinants of health and the incremental impact of environmental factors on cardiovascular disease (CVD) and risk factors prevalence.
Methods: Utilizing the 2022 Environmental Justice Index (EJI), socio-environmental EJI ranks were divided into quartiles, Q1-Q4 (Q1: the least, Q4: the most vulnerable census tracts). Age-adjusted rate ratios (RR) of health measures reported in the Prevention Population-Level Analysis and Community Estimates (PLACES) data were compared between quartiles using multivariable Poisson regression. A second regression model was used to assess the independent effect of environmental burden and social vulnerability. 
Results: The prevalence of CVD and other adverse health measures increased with EJI and EBM quartiles. Q4-EJI was associated with a higher rate of coronary artery disease (CAD) (RR: 1.684, 95%CI: .660-1.708, p<0.001) and stroke (RR: 2.112, 95%CI:2.078-2.147, p<0.001) compared with Q1-EJI. Similarly, the risk of CAD (RR:1.143, 95% CI:1.127–1.159, p<0.001) and stroke (RR:1.118, 95% CI:1.102-1.135, p<0.001) was significantly higher in Q4 than in Q1-EBM. Similar results were observed for chronic kidney disease, hypertension, diabetes, obesity, high cholesterol, lack of health insurance, sleep < 7 hours, no leisure time physical activity, and impaired mental and physical health > 14 days.
Conclusion: The prevalence of CVD and its risk factors is higher in areas with increased social and environmental adversities, and environmental exposure plays an important role independent of social factors.

The Impact of Environmental Injustice on Cardiovascular Diseases and Risk Factors in the United States

Background
Socioeconomic status (SES) is an essential predictor for healthcare outcomes, including dermatological health. With marginalized Black and Hispanic communities disproportionately overrepresenting the population in poverty in the 2022 Census, their ability to afford quality care and attain access to dermatologists for routine skin cancer screenings remain unresolved health equity issues. With the lack of research on dermatologic malignancies and contributing disparities affecting minority populations, it is essential to understand the grave impacts of SES on this population. This literature review explores the financial restraints of underserved communities and the effects of not being able to afford skin cancer screening.

Methods
An exhaustive literature review was conducted, utilizing Still OneSearch, DynaMed, Google Scholar, PubMed, MEDLINE, and National Institutes of Health online databases. The search strategy included the keywords “socioeconomic status”, “poverty”, “skin cancer screening”, and “underserved or marginalized” to discover related articles. Filters for peer-reviewed sources with publication dates between 2018 - 2023 were applied, further narrowing the results. Once studies of interest were selected, pertinent components were referenced following AMA format.

Results 
Individuals with low SES are less likely to attain specialized dermatological care due to financial strain, lack of insurance coverage, and decreased accessibility. Other contributing factors include the absence of affordable transportation and job inflexibility to attend appointments. Due to these economic challenges preventing early skin cancer detection, patients are frequently diagnosed with thicker melanoma lesions. Patients with limited income, having Medicaid or Medicare insurance rather than private insurance, are more often diagnosed with advanced malignant lesions with inferior prognoses. In a cross-sectional review of the National Health Interview Survey (NHIS), 20.34% (95% Cl 18.97–21.71%) of skin cancer patients reported worrying about medical costs, and 51.41% of uninsured patients expressed additional concerns regarding housing. In a 2018 study, 42.4% of patients with malignant skin lesions had depleted their life savings within the first two years of their diagnoses. Besides economic burden, patients with financial stress had lower odds of following up with their specialist or participating in clinical trials. Patients making annually less than USD 50,000 were 32% less likely than those with higher incomes to enroll in clinical trials. 

Conclusion
Patients from socioeconomically disadvantaged backgrounds habitually suffer disproportionate dermatological outcomes compared to their affluent counterparts. Recognizing the impacts of socioeconomic status on skin health and screening will have countless effects on healthcare advocacy and potentially influence policy change, making dermatologic care more accessible and equitable.

Beyond the Margins: Examining the Impact of Socioeconomic Status on Skin Cancer Screening in Marginalized Communities

Background
While prehospital drug-assisted airway management utilizing both a sedative agent and neuromuscular blocking agent (NMBA) has been well studied, sedative-assisted intubation (SAI) without concurrent NMBA use has not been studied at the national level. Further, we have been unable to identify any prehospital SAI studies focusing on ketamine. This study’s purpose is to analyze SAI practices by emergency medical services (EMS) across the United States and understand ketamine’s role.

Methods
Using the National Emergency Medical Services Information System (NEMSIS) 2021 dataset, we included adult patients who received either ketamine, a benzodiazepine (midazolam, lorazepam, or diazepam), etomidate, or a benzodiazepine-etomidate combination to facilitate SAI. We excluded patients who experienced cardiac arrest prior to any intubation attempts. Primary outcome measures were first-pass intubation success, overall intubation success, and the incidence of post-intubation complications. Using multivariate logistic regression to account for significant baseline measures, we calculated the adjusted odds ratio (OR) of intubation success for each induction agent. 

Results
Of the 4,128 eligible patients, 1,170 underwent SAI using ketamine. The overall intubation success rate using ketamine was 75.47% (884/1,171) and not significantly different compared to the other three sedative agents, with lower success rates compared to etomidate (1,177/1,517, 77.59%, P=.21) and a benzodiazepine-etomidate combination (361/453, 79.69%, P=.08), and greater success compared to benzodiazepines (709/988, 71.76%, P=.06). Compared to the other sedative agents, ketamine had significantly greater incidences of hypoxemia (oxygen saturation less than 90%) (n=416, P<.001), hypotension (systolic blood pressure less than 90 mmHg) (n=337, P<.001), hypocarbia (n=592, P<.001), and cardiac arrest (n=61, P<.001). In multivariate logistic regression using benzodiazepines as the intercept, ketamine was not associated with greater first-pass success (OR 1.03; 95% confidence interval [CI] 0.85-1.24) or overall intubation success (OR 1.17; 95% CI 0.95-1.43).

Conclusion
Compared to other induction agents, ketamine in SAI was not associated with greater intubation success and was associated with a greater incidence of complications. Further investigation is needed to delineate SAI practices in paramedic systems with and without NMBAs, as well as to examine how EMS choice of induction agent may vary amongst different patient subgroups.

The use of ketamine for prehospital sedative-assisted intubation

Background
Rural EMS agencies have long struggled to financially maintain operations because of low call volumes and higher operating costs, and closures have only increased as higher portions of the rural population are uninsured or covered under Medicare and Medicaid. Similarly, community health workers (CHWs) do not have enough volume in rural areas to be financially sustainable. Community paramedicine (CP) models, which combine elements of EMS and CHWs, have demonstrated remarkable success in improving care and reducing costs. However, these models have had limited uptake in rural areas due to reimbursement and regulatory challenges and the models’ emphasis on paramedics rather than inexpensive emergency medical technicians-basic (EMT-Bs) or advanced emergency medical technicians (AEMTs).

Methods
We conducted a narrative literature review to assess the feasibility of dual-certifying EMT-Bs and AEMTs as CHWs to improve the financial sustainability of rural EMS agencies and improve healthcare access. We used Pubmed to search for both original articles and reviews on rural CP, and Google was used to identify gray literature relating to the regulatory, financial, and governance structures of CP and CHWs. Both scholarly and gray literature were only included if they included studies in the USA and were within the last 10 years. 

Results
The scope of practice and training of CP programs varied widely, but most programs involved care coordination, health assessments, health education and promotion, and prevention of ED visits and rehospitalizations. EMT-Bs and AEMTs are in 29% and 10% of CP programs, respectively, even though the majority of rural EMS personnel are non-paramedics. Five states provide Medicaid reimbursement specifically for CP services, and fourteen states provide EMS reimbursement for treatment without transport, but not specifically CP services. Medicare only pays for some CP services under the experimental ET3 model. Conversely, at least 29 state Medicaid agencies reimburse for CHW services, and 21 states have CHW certification programs with another 14 states currently developing them. Medicare also pays for CHW services as of 2021. 

Conclusion
Training rural EMT-Bs/AEMTs to become certified CHWs may be a viable method of increasing EMS agencies’ income by increasing their reimbursable patient encounters in states that reimburse CHWs. This would enable rural EMS providers to avoid the regulatory and reimbursement challenges of other CP programs while increasing the financial sustainability of their agency. While the CHW and EMT-B scopes of practice will limit the extent of care they can provide more than paramedic-based programs, they could significantly expand rural access to both EMS and CHW services with appropriate physician oversight.

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GIS Analysis of Cardiac Catheterization Centers on the Island of Oahu

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