Inequalities In Health Care Research Articles

Gender Inequality in Out-of-pocket Expenditure on Healthcare Among Persons with Disabilities in India

Background People with disabilities (PWDs) often face higher risk of financial burden towards healthcare. Higher out-of-pocket expenditure (OOPE) is a barrier towards accessing medical services, resulting in healthcare inequities between men and women. There is a lack of studies on gender disparities in healthcare burden among PWD in India. The present study examines gender inequality towards healthcare expenditure among PWDs in India. Methods Unit level data from the Persons with Disabilities in India Survey under the National Sample Survey, 76th round that covered 118,152 households involving 576,569 individuals of whom 106,894 had at least one disability was used. We performed secondary analysis using descriptive statistics, bivariate estimates, Blinder–Oaxaca decomposition and multilevel log-linear regression to meet the objective of the study. Results OOPE as a percent of monthly per capita expenditure was higher among males across all states of India except in Puducherry, Dadra and Nagar Haveli, Lakshadweep and Mizoram. This varied from 0.6% in Jammu and Kashmir to 20% in Nagaland. Multilevel analysis confirms that OOPE was significantly lesser among females ( β = –0.070, 95% confidence interval = – 0.129 to 0.01) compared to males after controlling demographic and disability-related predictors. PWDs reported higher healthcare burden as a percent of total household expenditure in India, and relatively less amount was spent on female healthcare compared to male healthcare. Conclusion A substantial gender inequality for healthcare expenditure is found among PWDs in India. This makes for an urgent call to health researchers and policymakers towards gender-sensitive research, policy formulation and program implementation for PWDs in India.

Abstract P450: Assessing Admission Risk in Under-Resourced Neighborhoods: An Analysis by Race

Introduction: Cardiovascular disease (CVD) disproportionately affects socioeconomically disadvantaged communities and people of racial minority backgrounds. The Area Deprivation Index (ADI) is a validated measure of census-level characteristics based on income, education, employment, housing, and more. Although ADI is often linked to adversity, it is less well known if ADI is associated with racial disparities in CVD admission among cardiac patients living in disadvantaged communities. Hypothesis: We hypothesized that among a cohort of cardiac patients, risk for CVD admissions would be associated with ADI and that risk would vary by race. Methods: We identified ADI among n=27694 patients receiving care in a large health care system. We compared individual’s demographic variables using chi square for categorical variables and t-test for continuous variables. Using, ANOVA we determined differences in ADI by race. We further used ADI to identify neighborhoods with disproportionate rates CVD admissions using linear regression. Results: Figure Panel A shows that the ADI of American Indians, Blacks, and Asians was higher compared with that of Whites. Completion of an ANOVA test provided a p-value <0.05, showing that the average ADI is not equal between race groups. Panel B shows a linear association between higher ADI and increased risk of CVD event. Yet, some block groups were outliers with lower than predicted CVD events, labeled as low utilizers. Conclusion and Implications: Racial minority groups tended to have a higher ADI, yet some groups at high risk for CVD had disproportionately lower CVD events. These findings along with our team’s future work will inform interventions to address neighborhood level social determinants of cardiac health. Interventions that account for the unique social and healthcare contexts and inequities specific to at-risk communities will in turn help address the health challenges unique to these groups.

The Association of Race, Ethnicity, and Insurance Status with the Visual Acuity of Retinoblastoma Survivors in the IRIS® Registry

ABSTRACT Purpose To identify sociodemographic factors associated with the visual outcomes of retinoblastoma survivors. Methods Retrospective cohort study using a US-based clinical data registry. All individuals < 18 years of age with a history of retinoblastoma in the Intelligent Research in Sight (IRIS®) Registry (1/1/2013–12/31/2020). The primary outcome was visual acuity below the threshold for legal blindness (20/200 or worse) in at least one eye. Multivariable logistic regression was used to evaluate the association between visual outcomes and age, sex, laterality, race, ethnicity, type of insurance, and geographic location. Results This analysis included 1545 children with a history of retinoblastoma. The median length of follow-up was 4.1 years (IQR, 2.2–5.9 years) and the median age at most recent clinical visit was 12 years (IQR, 8–16 years). Retinoblastoma was unilateral in 54% of cases. Poor vision in at least one eye was identified in 78% of all children and poor vision in both eyes in 17% of those with bilateral disease. Poor visual outcomes were associated with unilateral diagnosis (OR, 1.55; 95% CI,1.13–2.12; p = .007), Black race (OR, 2.03; 95% CI, 1.19–3.47; p = .010), Hispanic ethnicity (OR, 1.65; 95% CI, 1.16–2.37; p = .006), and non-private insurance (OR, 1.47; 95% CI, 1.02–2.10; p = .037). Conclusions Poor visual outcomes appear to be more common among Black, Hispanic, and publicly insured children with a history of retinoblastoma, raising concerns regarding healthcare inequities. Primary care physicians should ensure that young children receive red reflex testing during routine visits and consider retinoblastoma in the differential diagnosis of abnormal eye exams.

Gender Inequality: A Multi-Perspective Analysis

Gender equality is a fundamental human right that has long been denied, and despite progress in some areas, significant challenges still remain in achieving true gender equality. The research motivation of this paper mainly stems from the pursuit of the universal value of social justice and human rights. The aim of gender equality studies is to understand and address the problems caused by gender inequality and gender discrimination in society. In this paper, the author analyzes gender inequality from three perspectives, namely gender inequality in the workplace, education, and health care. It can be summarized that gender inequality at the workplace mainly refers to the situation that women are put at a disadvantage in their workplace, gender inequality in education mainly refers to the unfair treatment of girls in school, and gender inequality in health care mainly refers to the large differences in maternal mortality between low- and high-income countries.

Health equity assessment of machine learning performance (HEAL): a framework and dermatology AI model case study

Artificial intelligence (AI) has repeatedly been shown to encode historical inequities in healthcare. We aimed to develop a framework to quantitatively assess the performance equity of health AI technologies and to illustrate its utility via a case study. Here, we propose a methodology to assess whether health AI technologies prioritise performance for patient populations experiencing worse outcomes, that is complementary to existing fairness metrics. We developed the Health Equity Assessment of machine Learning performance (HEAL) framework designed to quantitatively assess the performance equity of health AI technologies via a four-step interdisciplinary process to understand and quantify domain-specific criteria, and the resulting HEAL metric. As an illustrative case study (analysis conducted between October 2022 and January 2023), we applied the HEAL framework to a dermatology AI model. A set of 5420 teledermatology cases (store-and-forward cases from patients of 20 years or older, submitted from primary care providers in the USA and skin cancer clinics in Australia), enriched for diversity in age, sex and race/ethnicity, was used to retrospectively evaluate the AI model's HEAL metric, defined as the likelihood that the AI model performs better for subpopulations with worse average health outcomes as compared to others. The likelihood that AI performance was anticorrelated to pre-existing health outcomes was estimated using bootstrap methods as the probability that the negated Spearman's rank correlation coefficient (i.e., "R") was greater than zero. Positive values of R suggest that subpopulations with poorer health outcomes have better AI model performance. Thus, the HEAL metric, defined as p (R>0), measures how likely the AI technology is to prioritise performance for subpopulations with worse average health outcomes as compared to others (presented as a percentage below). Health outcomes were quantified as disability-adjusted life years (DALYs) when grouping by sex and age, and years of life lost (YLLs) when grouping by race/ethnicity. AI performance was measured as top-3 agreement with the reference diagnosis from a panel of 3 dermatologists per case. Across all dermatologic conditions, the HEAL metric was 80.5% for prioritizing AI performance of racial/ethnic subpopulations based on YLLs, and 92.1% and 0.0% respectively for prioritizing AI performance of sex and age subpopulations based on DALYs. Certain dermatologic conditions were significantly associated with greater AI model performance compared to a reference category of less common conditions. For skin cancer conditions, the HEAL metric was 73.8% for prioritizing AI performance of age subpopulations based on DALYs. Analysis using the proposed HEAL framework showed that the dermatology AI model prioritised performance for race/ethnicity, sex (all conditions) and age (cancer conditions) subpopulations with respect to pre-existing health disparities. More work is needed to investigate ways of promoting equitable AI performance across age for non-cancer conditions and to better understand how AI models can contribute towards improving equity in health outcomes. Google LLC.

Using simulation modelling to transform hospital planning and management to address health inequalities

Health inequalities are a perennial concern for policymakers and in service delivery to ensure fair and equitable access and outcomes. As health inequalities are socially influenced by employment, income, and education, this impacts healthcare services among socio-economically disadvantaged groups, making it a pertinent area for investigation in seeking to promote equitable access. Researchers widely acknowledge that health equity is a multi-faceted problem requiring approaches to understand the complexity and interconnections in hospital planning as a precursor to healthcare delivery. Operations research offers the potential to develop analytical models and frameworks to aid in complex decision-making that has both a strategic and operational function in problem-solving. This paper develops a simulation-based modelling framework (SimulEQUITY) to model the complexities in addressing health inequalities at a hospital level. The model encompasses an entire hospital operation (including inpatient, outpatient, and emergency department services) using the discrete-event simulation method to simulate the behaviour and performance of real-world systems, processes, or organisations. The paper makes a sustained contribution to knowledge by challenging the existing population-level planning approaches in healthcare that often overlook individual patient needs, especially within disadvantaged groups. By holistically modelling an entire hospital, socio-economic variations in patients' pathways are developed by incorporating individual patient attributes and variables. This innovative framework facilitates the exploration of diverse scenarios, from processes to resources and environmental factors, enabling key decision-makers to evaluate what intervention strategies to adopt as well as the likely scenarios for future patterns of healthcare inequality. The paper outlines the decision-support toolkit developed and the practical application of the SimulEQUITY model through to implementation within a hospital in the UK. This moves hospital management and strategic planning to a more dynamic position where a software-based approach, incorporating complexity, is implicit in the modelling rather than simplification and generalisation arising from the use of population-based models.

Evaluating the impacts of supply-demand dynamics and distance decay effects on public transit project assessment: A study of healthcare accessibility and inequalities

Previous studies evaluating the impacts of transport interventions on accessibility to healthcare have largely overlooked competition among patients for limited resources and the tendency to use healthcare located closer to them (i.e., distance decay effects). This study aims to demonstrate how overlooking supply-demand dynamics and distance decay effects can distort the evaluation of a new public transit service's impacts on healthcare accessibility and inequalities. Specifically, using a new bus rapid transit (BRT) service in Columbus, Ohio, USA, as an example, we compare three types of measures: 1) cumulative-opportunity, 2) multimodal two-step floating catchment area (2SFCA), and 3) multimodal generalized 2SFCA (G2SFCA) accessibility metrics. To understand the similarities in results obtained from different accessibility measures, we use the Pearson correlation coefficients of standardized accessibility change scores. Further, we leverage an inequality index, the Palma ratios, to examine how inequality assessments can be sensitive to the choice of accessibility measure. The correlation analysis reveals notable differences among the three measures. This indicates that neglecting supply-demand dynamics and distance decay effects can lead to differences in results, potentially distorting transit project evaluations and misleading stakeholders. Moreover, although overall conclusions about inequalities are largely consistent, we observed nuanced and statistically significant differences in Palma ratios when derived from cumulative-opportunity metrics compared to the multimodal 2SFCA and multimodal generalized 2SFCA measures. Our findings underscore the importance of considering supply-demand dynamics and distance decay effects for a more realistic and accurate assessment of new transit service's impacts on healthcare accessibility and inequalities.

The Toxic Stress of Racism and Its Relationship to Frailty.

Significant morbidity and mortality from COVID-19-related illnesses have been observed among people of color within the United States. While theories involving healthcare inequity and political division have emerged to explain this observation, the role of chronic stress and inflammation is also being explored. Toxic stress is experienced disproportionately by race, ethnicity, and socioeconomic status and increases frailty and vulnerability to diseases such as COVID-19. C-reactive protein (CRP) is a biomarker associated with the inflammatory response that is typically elevated due to exposure to acute or chronic traumatic stress, as well as COVID-19. This study explored the relationship between CRP and Hispanic/non-Hispanic ethnicity among adults hospitalized with COVID-19 via a secondary analysis of retrospective electronic health record (EHR) data collected from a community healthcare system in Southern California. A total of 1,744 cases representing hospitalized adults with COVID-19 were reviewed. Data were extracted from the EHR to reflect demographics, medical diagnoses, medications, CRP, and comorbidity burden. Frequencies, percentages, and measures of central tendency were assessed to understand the distribution of data. Associations were conducted using Pearson's r and the chi-square test of independence. Differences between groups were examined via independent samples t-tests. The sample was 52% Hispanic, 56% male, and the mean age was 62 years (SD = 16.1). The mean age of Hispanic cases was younger than non-Hispanic cases (p < .001, η = 0.289). Serum CRP was significantly higher in the Hispanic cases, with a high degree of association (p < .001, η = 0.472). In addition, higher CRP levels were significantly associated with the need for mechanical ventilation (p < .001, φc = 0.216). No significant relationships were found between CRP and age, body mass index (BMI), or comorbidity burden. Findings challenge the assumption that the disproportionate morbidity and mortality suffered by the Hispanic population due to COVID-19 was due to age, BMI, or comorbidities such as metabolic syndrome or heart disease. CRP in the Hispanic population should be further investigated to understand its relationship to chronic stress, frailty, and risk for COVID-19 in this population.

Perinatal healthcare for women at risk of children’s social care involvement: a qualitative survey of professionals in England

BackgroundWomen with complex health needs are more at risk of having children’s social care involvement with their newborns than other mothers. Around the time of pregnancy, there are opportunities for...

Effects of the COVID-19 Pandemic on the Medical Use of Elderly Patients with Hypertension: A Nationwide Cohort Study in Korea.

The coronavirus disease 2019 (COVID-19) pandemic has disrupted healthcare services, including chronic disease management, for vulnerable groups, such as older individuals with hypertension. This study aimed to evaluate hypertension management in South Korea's elderly population during the pandemic using treatment consistency indices such as the continuity of care (COC), modified, modified continuity index (MMCI), and most frequent provider continuity (MFPC). This study used the Korea Disease Control and Prevention Agency-COVID-19-National Health Insurance Service cohort (K-COV-N cohort) from the National Health Insurance Service between 2017 and 2021. The research included a total of 4,097,299 hypertensive patients aged 65 years or older. We defined 2018 and 2019 as the baseline period before the COVID-19 pandemic and 2020 and 2021 as the COVID-19 period and calculated the indices of medical continuity (number of visits, COC, MMCI, and MFPC) on a yearly basis. The number of visits decreased during the COVID-19 period compared to the baseline period (59.64±52.75 vs. 50.49±50.33, P<0.001). However, COC, MMCI, and MFPC were not decreased in the baseline period compared to the COVID-19 period (0.71±0.21 vs. 0.71±0.22, P<0.001; 0.97±0.05 vs. 0.96±0.05, P<0.001; 0.8±0.17 vs. 0.8±0.17, P<0.001, respectively). COVID-19 had no significant impact on the continuity of care but affected the frequency of outpatient visits for older patients with hypertension. However, this study highlights the importance of addressing healthcare inequalities, especially in older patients with hypertension, during pandemics and advocates for policy changes to ensure continued care for vulnerable populations.

Validated geographic search filters for bibliographic databases: a scoping review protocol.

The purpose of this scoping review is to identify validated geographic search filters and report their methodology and performance measures. Data on specific geographic areas can be required for evidence syntheses topics, such as the investigation of regional inequalities in health care or to answer context-specific epidemiological questions. Search filters are useful tools for reviewers aiming to identify publications with common characteristics in bibliographic databases. Geographic search filters limit the literature search results to a specific geographic feature (eg, a country or region). We will include reports on validated geographic search filters that aim to identify research evidence about a defined geographic area (eg, a country/region or a group of countries/regions). This review will be conducted in accordance with JBI methodology for scoping reviews. The literature search will be conducted in PubMed and Embase. The InterTASC Information Specialists' Sub-Group Search Filter resource and Google Scholar will also be searched. Reports published in any language, from database inception to the present, will be considered for inclusion. Two researchers will independently screen the title, abstract, and full text of the search results. A third reviewer will be consulted in the event of any disagreements. The data extraction will include study characteristics, basic characteristics of the geographical search filter (eg, country/region), and the methods used to develop and validate the search filter. The extracted data will be summarized narratively and presented in a table. Open Science Framework https://osf.io/5czhs.

Conjoint Analysis of Telemedicine Preferences for Hypertension Management Among Adult Patients.

Background: Telemedicine has been differentially utilized by different demographic groups during COVID-19, exacerbating inequities in health care. We conducted conjoint and latent class analyses to understand factors that shape patient preferences for hypertension management telemedicine appointments. Methods: We surveyed 320 adults, oversampling participants from households that earned <$50K per year (77.2%) and speak a language other than English at home (68.8%). We asked them to choose among 2 hypothetical appointments through 12 conjoint tasks measuring 6 attributes. Individual utilities for attributes were constructed using logit estimation, and latent classes were identified and compared by demographic and clinical characteristics. Results: Respondents preferred in-person visits (0.353, standard error [SE] = 0.039) and video appointments conducted through a secure patient portal (0.002, SE = 0.040). Respondents also preferred seeing a clinician with whom they have an established relationship (0.168, SE = 0.021). We found four latent classes: "in-person" (26.5% of participants) who strongly weighted in-person appointments, "cost conscious" (8.1%) who prioritized the lowest copay ($0 to $10), "expedited" (19.7%) who prioritized getting the earliest appointment possible (same/next day or at least within the next week), and "comprehensive" (45.6%) who had preferences for in-person care and telemedicine appointments through a secure portal, low copayments, and the ability to see a familiar clinician. Conclusions: Appointment preferences for hypertension management can be segmented into four groups that prioritize (1) in-person care, (2) low copayments, (3) expedited care, and (4) balanced preferences for in-person and telemedicine appointments. Evidence is needed to clarify whether aligning appointment offerings with patients' preferences can improve care quality, equity, and efficiency.

Clinical Considerations in Working with Children and Adolescents of Color: Past, Present, and Future

Background: Systemic racism and health inequality for Children and Adolescents of Color (CAoC) and their families need to be acknowledged and addressed in the provision of mental health treatment by child and adolescent psychiatrists. The lack of parity for behavioral health drives the lack of integration of “mental” health with overall health and the lack of funding, policy, planning, and practices to support the social and emotional health of children and their families. Additionally, the unequal treatment faced by Black, Indigenous, and People of Color (BIPOC) children and their families further impacts their overall health and mental health outcomes. Implicit biases, conscious and unconscious, influence clinical judgments, lead to errors in diagnostic and treatment decisions and impair child and adolescent psychiatrists’ ability to fully partner with families to treat, advocate for, and improve the clinical and life trajectories of this diverse group of young people and families that child and adolescent psychiatrists are called to serve. Methods: Using a case vignette, this paper discusses historical examples showing how child and adolescent psychiatrists’ implicit bias may manifest when working with CAoC. Medical training and clinical practice have paid scant attention to the broader impacts of systemic racism and inequities in healthcare until recent years. Discussion: This article provides clinical recommendations for clinicians to navigate these factors through trauma-centered and patient-centered care. Viewing through the lens of intergenerational racial trauma and acknowledging one’s own bias, clinicians can better help and support CAoC as they strive toward a brighter future.

What does it mean for a clinical AI to be just: conflicts between local fairness and being fit-for-purpose?

There have been repeated calls to ensure that clinical artificial intelligence (AI) is not discriminatory, that is, it provides its intended benefit to all members of society irrespective of the...

National prevalence of vision impairment and blindness and associated risk factors in adults aged 40 years and older with known or undiagnosed diabetes: results from the SMART-India cross-sectional study

National estimates of the prevalence of vision impairment and blindness in people with diabetes are required to inform resource allocation. People with diabetes are more susceptible to conditions such as diabetic retinopathy that can impair vision; however, these are often missed in national studies. This study aims to determine the prevalence and risk factors of vision impairment and blindness in people with diabetes in India. Data from the SMART-India study, a cross-sectional survey with national coverage of 42 147 Indian adults aged 40 years and older, collected using a complex sampling design, were used to obtain nationally representative estimates for the prevalence of vision impairment and blindness in people with diabetes in India. Vulnerable adults (primarily those who did not have capacity to provide consent); pregnant and breastfeeding women; anyone deemed too ill to be screened; those who did not provide consent; and people with type 1 diabetes, gestational diabetes, or secondary diabetes were excluded from the study. Vision impairment was defined as presenting visual acuity of 0·4 logMAR or higher and blindness as presenting a visual acuity of 1·0 logMAR or higher in the better-seeing eye. Demographic, anthropometric, and laboratory data along with geographic distribution were analysed in all participants with available data. Non-mydriatic retinal images were used to grade diabetic retinopathy, and risk factors were also assessed. A total of 7910 people with diabetes were included in the analysis, of whom 5689 had known diabetes and 2221 were undiagnosed. 4387 (55·5%) of 7909 participants with available sex data were female and 3522 (44·5%) participants were male. The estimated national prevalence of vision impairment was 21·1% (95% CI 15·7-27·7) and blindness 2·4% (1·7-3·4). A higher prevalence of any vision impairment (29·2% vs 19·6%; p=0·016) and blindness (6·7% vs 1·6%; p<0·0001) was observed in those with ungradable images. In known diabetes, diabetic retinopathy (adjusted odds ratio [aOR] 3·06 [95% CI 1·25-7·51]), vision-threatening diabetic retinopathy (aOR 7·21 [3·52-14·75]), and diabetic macular oedema (aOR 5·41 [2·20-13·33]) were associated with blindness in adjusted analysis. Common sociodemographic risk factors for vision impairment and blindness include older age, lower educational attainment, and unemployment. Based on the estimated 101 million people with diabetes in 2021 and the interpretation of the data from this study, approximately 21 million people with diabetes have vision impairment in India, of whom 2·4 million are blind. Higher prevalence is observed in those from lower socio-economic strata and policy makers should focus on these groups to reduce inequalities in health care. Global Challenge Research Fund of United Kingdom Research and Innovation through the Medical Research Council.

Harnessing the Power of Mobile Phone Technology: Screening and Identifying Autism Spectrum Disorder With Smartphone Apps.

Integrating smartphone applications into screening and identifying autism spectrum disorder (ASD) represents a promising and innovative frontier within healthcare. This forward-looking paper examines the current landscape of ASD screening apps, shedding light on their potential advantages and addressing and navigating significant challenges. One of the most compelling aspects of these apps lies in their potential to democratize access to ASD screening, effectively breaking down geographical barriers. By using the widespread availability of smartphones, these apps make it possible for individuals, caregivers, and healthcare providers to engage in early ASD screening from virtually anywhere. This accessibility is especially crucial in underserved areas or regions with limited access to specialized healthcare services. Moreover, these apps offer a degree of objectivity that traditional screening methods may need help to match. By relying on data-driven algorithms and machine learning, they can provide a more impartial assessment of a child's behavior, minimizing the potential for subjective bias. This objectivity, combined with the ability to monitor and assess a child's development over time, empowers caregivers with valuable insights into their child's progress. However, as with any technological advancement in healthcare, integrating smartphone apps for ASD screening is not without its share of ethical and privacy considerations. Ensuring informed consent is obtained, especially when collecting data from children, is complex and critical. Striking the right balance between collecting necessary data and protecting an individual's privacy requires careful thought and transparent communication. Additionally, the "digital divide" represents a challenge that needs to be acknowledged and addressed. Not all individuals and families have equal access to smartphones or the technological literacy required to use these apps effectively. This disparity in access must be considered when developing and implementing app-based screening solutions to prevent exacerbating existing healthcare inequalities. Nevertheless, the future of ASD screening apps holds significant promise. Advancements in technology, including integrating advanced sensors, wearables, augmented reality, and machine learning, can further enhance the accuracy and depth of screening. Interdisciplinary collaboration between researchers, developers, clinicians, and educators is crucial to ensure that these apps are effective, culturally sensitive, and user-friendly. Furthermore, integrating these apps into broader healthcare systems, including electronic health records and telehealth platforms, can streamline the screening process and enable a more seamless transition from screening to diagnosis and intervention.

The intersection of pediatric anesthesiology and social determinants of health.

There is increasing evidence of racial and ethnic disparities in pediatric perioperative care, which indicates a need to identify factors driving disparities. Social determinants of health (SDOH) play a fundamental role in pediatric health and are recognized as key underlying mechanisms of healthcare inequities. This article summarizes recent research exploring the influence of SDOH on pediatric perioperative outcomes. Despite the scarcity of research exploring SDOH and pediatric perioperative outcomes, recent work demonstrates an association between SDOH and multiple outcomes across the perioperative care continuum. Measures of social disadvantage were associated with preoperative symptom severity, longer hospital stays, and higher rates of postoperative complications and mortality. In some studies, these adverse effects of social disadvantage persisted even when controlling for medical comorbidities and clinical severity. The existing literature offers compelling evidence of the impact of SDOH on perioperative outcomes in children and reveals a critical area in pediatric anesthesia that necessitates further exploration and action. To improve outcomes and address care inequities, future efforts should prioritize the integration of SDOH assessment into pediatric perioperative research and practice.

Health Disparities During The covid-19 Pandemic in the U.S. Territories

The people of the United States territories have faced healthcare inequalities for years due to ongoing, complex issues with their public health infrastructure. During the 2009 influenza pandemic, the U.S. territories faced several dilemmas such as insufficient funding, a lack of laboratory equipment, personal protective equipment, and surveillance to predict the spread of the disease, due to the ongoing health disparities and complications with their public health infrastructure (Dopson, 2016). These patterns have also been seen during the COVID-19 pandemic in addition to new dilemmas such as damaged physical public health infrastructure. The objective of this research was to compare and contrast the effects of COVID-19 in the U.S. territories to the U.S. while examining the social and cultural conditions of these territories. A descriptive analysis of data on covid mortality rates and vaccination rates has been completed showing that disparities did occur throughout the pandemic. These dependent variables were explored for all of the U.S. territories and thoroughly compared to one another to make a descriptive assessment of the vaccination and mortality outcomes. Relevant social and cultural conditions were examined as well (i.e. effects on infrastructure from natural disasters, and policies in place during the pandemic). Conclusions about these outcomes within the territories have allowed us to recognize distinct patterns from the latest data available. There are differences in how these disparities occur in the U.S. territories and how COVID-19 has affected them. Through this study, there have been new themes seen throughout the pandemic such as a sense of community and support, geographic isolation, and politics that may have contributed to the vaccination rates and mortality rates seen in these territories. The implications of this study push for future research to be completed as casual analyses were not completed and should be looked at moving forward.

Neuroimaging data repositories and AI-driven healthcare-Global aspirations vs. ethical considerations in machine learning models of neurological disease.

Neuroimaging data repositories are data-rich resources comprising brain imaging with clinical and biomarker data. The potential for such repositories to transform healthcare is tremendous, especially in their capacity to support machine learning (ML) and artificial intelligence (AI) tools. Current discussions about the generalizability of such tools in healthcare provoke concerns of risk of bias-ML models underperform in women and ethnic and racial minorities. The use of ML may exacerbate existing healthcare disparities or cause post-deployment harms. Do neuroimaging data repositories and their capacity to support ML/AI-driven clinical discoveries, have both the potential to accelerate innovative medicine and harden the gaps of social inequities in neuroscience-related healthcare? In this paper, we examined the ethical concerns of ML-driven modeling of global community neuroscience needs arising from the use of data amassed within neuroimaging data repositories. We explored this in two parts; firstly, in a theoretical experiment, we argued for a South East Asian-based repository to redress global imbalances. Within this context, we then considered the ethical framework toward the inclusion vs. exclusion of the migrant worker population, a group subject to healthcare inequities. Secondly, we created a model simulating the impact of global variations in the presentation of anosmia risks in COVID-19 toward altering brain structural findings; we then performed a mini AI ethics experiment. In this experiment, we interrogated an actual pilot dataset (n = 17; 8 non-anosmic (47%) vs. 9 anosmic (53%) using an ML clustering model. To create the COVID-19 simulation model, we bootstrapped to resample and amplify the dataset. This resulted in three hypothetical datasets: (i) matched (n = 68; 47% anosmic), (ii) predominant non-anosmic (n = 66; 73% disproportionate), and (iii) predominant anosmic (n = 66; 76% disproportionate). We found that the differing proportions of the same cohorts represented in each hypothetical dataset altered not only the relative importance of key features distinguishing between them but even the presence or absence of such features. The main objective of our mini experiment was to understand if ML/AI methodologies could be utilized toward modelling disproportionate datasets, in a manner we term "AI ethics." Further work is required to expand the approach proposed here into a reproducible strategy.

Assessment of rehabilitation effectiveness in patients with COPD as part of the project "PulmoRehab – Access to healthcare services through a personalized care system for patients with COPD, including remote monitoring and tele-rehabilitation based on Artificial Intelligence methods"

Introduction. In the project “PulmoRehab – Access to Healthcare Services through a Personalized Patient Care System for COPD including Remote Monitoring and Tele-rehabilitation Based on Artificial Intelligence Methods,” SP ZOZ Specialized Hospital Ministry of Internal Affairs and Administration in Głuchołazy and 10 partners conducted research on tele-rehabilitation for people suffering from Chronic Obstructive Pulmonary Disease (COPD). The tele-rehabilitation program, based on personalized approach and technology, allowed patients to use therapy at home or a convenient location. The project aimed to reduce social inequalities in healthcare by providing remote access to health services for COPD patients. Materials and Methods. The study involved 80 patients aged 50 to 76 years, hospitalized at Ministry of Internal Affairs and Administration’s Specialist Hospital of St. John Paul II, meeting specific criteria. Qualification for the tele-rehabilitation program took into account the assessment of exercise tolerance, dyspnea, fatigue, lung ventilatory function, and saturation. The program included exercises adapted to the individual needs of the patients. Results. Analysis of the results conducted using XLSTAT software 2021.2.2 showed statistically significant differences between spirometric values before and after rehabilitation. Similarly, significant improvement was observed in the results of the 6MWT test and blood saturation. The tele-rehabilitation program proved effective in improving respiratory health parameters in COPD patients. Conclusions. 1. The tele-rehabilitation program improves lung ventilatory function in people with COPD. It reduces the feeling of dyspnea, decreases fatigue, improves SpO2, and increases exercise tolerance, demonstrating the beneficial effects of the rehabilitation applied. 2. The obtained results encourage further research on a representative group with the use of randomization.