Exacerbate Health Disparities Research Articles

Clinicians’ perspectives on race-specific guidelines for hypertensive treatment

Despite the general consensus that there is no biological basis to race, racial categorization is still used by clinicians to guide diagnosis and treatment plans for certain diseases. In medicine, race is commonly used as a rough proxy for unmeasured social, environmental, and genetic factors. The American College of Cardiology's Eighth Joint National Committee's (JNC 8) guidelines for the treatment of hypertension provide race-specific medication recommendations for Black versus non-Black patients, without strong evidence for race-specific physiological differences in drug response. Clinicians practicing family or geriatric medicine (n = 21) were shown a video of a mock hypertensive patient with genetic ancestry test results that could be viewed as discordant with their phenotype and self-identified race. After viewing the videos, we conducted in-depth interviews to examine how clinicians value and prioritize different cues about race -- namely genetic ancestry data, phenotypic appearance, and self-identified racial classifications – when making treatment decisions in the context of race-specific guidelines, particularly in situations when patients claim mixed-race or complex racial identities. Results indicate that clinicians inconsistently follow the race-specific guidelines for patients whose genetic ancestry test results do not match neatly with their self-identified race or phenotypic features. However, many clinicians also emphasized the importance of clinical experience, side effects, and other factors in their decision making. Clinicians' definitions of race, categorization of the patient's race, and prioritization of racial cues greatly varied. The existence of the race-specific guidelines clearly influences treatment decisions, even as clinicians' express uncertainty about how to incorporate consideration of a patient's genetic ancestry. In light of widespread debate about removal of race from medical diagnostics, researchers should revisit the clinical justification for maintaining these race-specific guidelines. Based on our findings and prior studies indicating a lack of convincing evidence for biological differences by race in medication response, we suggest removing race from the JNC 8 guidelines to avoid risk of perpetuating or exacerbating health disparities in hypertension.

Understanding and Mitigating Bias in Imaging Artificial Intelligence.

Artificial intelligence (AI) algorithms are prone to bias at multiple stages of model development, with potential for exacerbating health disparities. However, bias in imaging AI is a complex topic that encompasses multiple coexisting definitions. Bias may refer to unequal preference to a person or group owing to preexisting attitudes or beliefs, either intentional or unintentional. However, cognitive bias refers to systematic deviation from objective judgment due to reliance on heuristics, and statistical bias refers to differences between true and expected values, commonly manifesting as systematic error in model prediction (ie, a model with output unrepresentative of real-world conditions). Clinical decisions informed by biased models may lead to patient harm due to action on inaccurate AI results or exacerbate health inequities due to differing performance among patient populations. However, while inequitable bias can harm patients in this context, a mindful approach leveraging equitable bias can address underrepresentation of minority groups or rare diseases. Radiologists should also be aware of bias after AI deployment such as automation bias, or a tendency to agree with automated decisions despite contrary evidence. Understanding common sources of imaging AI bias and the consequences of using biased models can guide preventive measures to mitigate its impact. Accordingly, the authors focus on sources of bias at stages along the imaging machine learning life cycle, attempting to simplify potentially intimidating technical terminology for general radiologists using AI tools in practice or collaborating with data scientists and engineers for AI tool development. The authors review definitions of bias in AI, describe common sources of bias, and present recommendations to guide quality control measures to mitigate the impact of bias in imaging AI. Understanding the terms featured in this article will enable a proactive approach to identifying and mitigating bias in imaging AI. Published under a CC BY 4.0 license. Test Your Knowledge questions for this article are available in the supplemental material. See the invited commentary by Rouzrokh and Erickson in this issue.

Exploring Stigma Experience and Coping Strategies Among Women Living with HIV/AIDS in China: A Phenomenological Study.

As of the end of 2022, over 20 million women worldwide, aged 15 and above, are living with HIV. Stigma remains a formidable barrier for women living with HIV/AIDS, hindering their access to healthcare and exacerbating health disparities. Indeed, some women living with HIV/AIDS can successfully confront and overcome stigma. There remains a paucity of qualitative research exploring the stigma coping strategies of women living with HIV/AIDS in China. This study was aimed to gain the deeper understanding of stigma experienced by women living with HIV/AIDS and coping strategies. We recruited diverse participants using snowball sampling and purposive sampling. Semi-structured personal in-depth interviews were conducted with 30 women living with HIV/AIDS from December 2022 to June 2023. The samples were from four HIV/AIDS designated hospitals. The data were analyzed using the Colaizzi seven-step model. The experiences of stigma among women living with HIV/AIDS included family role (wife/mother/grandmother) collapse and disgusted by family, resignation in being shunned by others, helplessness due to social exclusion, grief at being devaluated, and resentment for experiencing injustice. The coping strategies used to deal with stigma included concealing their conditions, avoiding socialization, and attempting to retaliate against society. Healthcare professionals are recommended to offer women living with HIV/AIDS effective emotional support and guidance to cope with stigma. The study highlights the stigma they face, providing valuable evidence for policymakers. Recommendations emphasize the importance of developing services addressing both physical and psychological needs of women living with HIV/AIDS.

“Neutrality Is Affirming”: How Do Sexual and Gender Minority Adults Find and Define Affirming Healthcare?

Sexual and gender minority (SGM) individuals have unique healthcare needs yet frequently encounter negative healthcare experiences. These negative encounters with the healthcare system may lead to SGM individuals delaying or avoiding healthcare, which may exacerbate health disparities. We sought to qualitatively examine the healthcare experiences of SGM adults to better understand how SGM adults define SGM-affirming healthcare experiences. We also aimed to add to the literature by asking SGM participants about their future aging and healthcare concerns such as family involvement in medical decisions and end-of-life care. Nineteen semistructured individual interviews were conducted with SGM adults to gather information about the process of finding an SGM-affirming provider, coming out in a medical setting, the components of affirming care, the results of affirming care, the components of nonaffirming care, the consequences of nonaffirming experiences, and aging concerns regarding healthcare. A thematic analysis approach was used to analyze interview data. Results showed unique insights into the techniques used to find SGM-affirming providers and what SGM adults hope for in their healthcare encounters. This qualitative study builds on a body of literature that has documented significant disparities in healthcare access and quality for SGM individuals. The results of this study highlight important aspects of SGM-affirming care, outline understudied healthcare concerns for SGM people, and may guide healthcare providers in improving the quality-of-care experiences for their SGM patients.

Perspective: Nutrition Health Disparities Framework: A Model to Advance Health Equity

Disparities in nutrition, such as poor diet quality and inadequate nutrient intake, arise from multiple factors and are related to adverse health outcomes such as obesity, diabetes, cardiovascular disease, and some cancers. The aim of the current perspective is to present a nutrition-centric socioecological framework that delineates determinants and factors that contribute to diet and nutrition-related disparities among disadvantaged populations. The Nutrition Health Disparities Framework (NHDF) describes the domains (biological, behavioral, physical/built environment, sociocultural environment, and healthcare system) that influence nutrition-related health disparities through the lens of each level of influence (that is, individual, interpersonal, community, and societal). On the basis of the scientific literature, the authors engaged in consensus decision making in selecting nutrition-related determinants of health within each domain and socioecological level when creating the NHDF. The framework identifies how neighborhood food availability and access (individual/built environment) intersect with cultural norms and practices (interpersonal/sociocultural environment) to influence dietary behaviors, exposures, and risk of diet-related diseases. In addition, the NHDF shows how factors such as genetic predisposition (individual/biology), family dietary practices (interpersonal/behavioral), and food marketing policies (societal) may impact the consumption of unhealthy foods and beverages and increase chronic disease risk. Family and peer norms (interpersonal/behavior) related to breastfeeding and early childhood nutrition interact with resource-poor environments such as lack of access to preventive healthcare settings (societal/healthcare system) and low usage of federal nutrition programs (societal/behavioral), which may increase risk of poor nutrition during childhood and food insecurity. The NHDF describes the synergistic interrelationships among factors at different levels of the socioecological model that influence nutrition-related outcomes and exacerbate health disparities. The framework is a useful resource for nutrition researchers, practitioners, food industry leaders, and policymakers interested in improving diet-related health outcomes and promoting health equity in diverse populations.

Harmonizing Artificial Intelligence Governance; A Model for Regulating a High-risk Categories and Applications in Clinical Pathology: The Evidence and some Concerns

The Canadian healthcare system, grappling with issues like systemic and intelligently established structural anti-black racism, including indigenous nations; even within Pathology and Laboratory Medicine Communities: and deteriorating outcomes, sees potential in AI to address challenges, though concerns exist regarding exacerbating discriminatory practices. In clinical pathology, AI demonstrates superior diagnostic accuracy compared to pathologists in a study, emphasizing its potential to improve healthcare. However, AI governance is crucial to navigating ethical, legal, and societal concerns. The Royal College of Physicians of Canada acknowledges the transformative impact of AI in healthcare but stresses the need for responsible AI tools co-developed by diverse teams. Despite positive attitudes towards AI in healthcare, concerns about patient safety, privacy, and autonomy highlight the necessity for comprehensive education, engagement, and collaboration. Legal concerns, including liability and regulation, pose challenges, emphasizing the need for a robust regulatory framework. AI application in healthcare is categorized as high-risk, demanding stringent regulation to ensure safety, efficacy, and fairness. A parallel is drawn to drug regulation processes, suggesting a similar approach for AI. The lack of transparency in AI-based decision-making raises ethical questions, necessitating measures to address biases and ensure patient privacy. Social accountability is crucial to prevent AI from exacerbating health disparities and harming marginalized communities. In conclusion, while AI offers potential benefits in clinical pathology, a cautious approach with comprehensive governance measures is essential to mitigate risks and ensure ethical AI integration into healthcare.

Meeting the Health and Social Needs of America's Unhoused and Housing-Unstable Populations: A Position Paper From the American College of Physicians.

Access to safe and stable housing has both a direct and indirect effect on health. Experiencing homelessness and housing instability can induce stress and trauma, worsening behavioral health and substance use. The absence of safe and stable living conditions can make it challenging to rest, recuperate, and recover from health ailments and can pose barriers to treatment adherence. Homelessness and housing instability is associated with high rates of numerous diseases and chronic conditions. Its cyclical relationship with other social drivers of health can exacerbate health disparities. As a result, unhoused persons experience unique health challenges and require a health care system and professionals designed to meet their distinct needs. Physicians and other health professionals have a role in educating themselves about the needs of unhoused patients as well as making themselves aware of community and government resources available to these populations. Policymakers must support health professionals in these efforts by supporting the data infrastructure needed to facilitate these referrals to resources, supporting research into best practices for caring for these populations, and investing in community-based organization capacity. Policy action is needed to address the underlying drivers of homelessness, including a dearth of affordable housing, while also addressing the short-term need for safe shelter now. In this position paper, the American College of Physicians (ACP) recognizes the need to address universal access to housing to fulfill one's right to health. ACP offers several recommendations to prevent homelessness and promote the necessary health care and social needs of unhoused populations.

Bacterial vaginosis testing gaps for transmasculine patients may exacerbate health disparities.

Bacterial vaginosis (BV) is associated with non-optimal changes in the vaginal microbiome and increased susceptibility to STIs and HIV in cisgender women. Much less is known about the sexual health of transmasculine people and susceptibility to BV, STIs, and HIV. This study's objective was to assess BV testing and outcomes of transmasculine and cisgender women patient populations at a large, LGBTQ + federally qualified health center. Retrospective electronic health record data were extracted for eligible patients having at least one primary care visit between January 1, 2021, and December 31, 2021. Transmasculine patients were limited to those with a testosterone prescription in 2021. We conducted log binomial regression analysis to determine the probability of receiving a BV test based on gender identity, adjusting for sociodemographic characteristics. During 2021, 4,903 cisgender women patients and 1,867 transmasculine patients had at least one primary care visit. Compared to cisgender women, transmasculine patients were disproportionately young, White, queer, privately insured, living outside Chicago, and had a lower rate of BV testing (1.9% v. 17.3%, p < 0.001). Controlling for sociodemographics, transmasculine patients were less likely to receive a BV test [Prevalence Ratio = 0.19 (95% CI 0.13-0.27)]. The low rate of BV testing among transmasculine patients may contribute to disparities in reproductive health outcomes. Prospective community- and provider-engaged research is needed to better understand the multifactorial determinants for sexual healthcare and gender-affirming care for transmasculine patients. In particular, the impact of exogenous testosterone on the vaginal microbiome should also be determined.

SNVstory: inferring genetic ancestry from genome sequencing data

BackgroundGenetic ancestry, inferred from genomic data, is a quantifiable biological parameter. While much of the human genome is identical across populations, it is estimated that as much as 0.4% of the genome can differ due to ancestry. This variation is primarily characterized by single nucleotide variants (SNVs), which are often unique to specific genetic populations. Knowledge of a patient's genetic ancestry can inform clinical decisions, from genetic testing and health screenings to medication dosages, based on ancestral disease predispositions. Nevertheless, the current reliance on self-reported ancestry can introduce subjectivity and exacerbate health disparities. While genomic sequencing data enables objective determination of a patient's genetic ancestry, existing approaches are limited to ancestry inference at the continental level.ResultsTo address this challenge, and create an objective, measurable metric of genetic ancestry we present SNVstory, a method built upon three independent machine learning models for accurately inferring the sub-continental ancestry of individuals. We also introduce a novel method for simulating individual samples from aggregate allele frequencies from known populations. SNVstory includes a feature-importance scheme, unique among open-source ancestral tools, which allows the user to track the ancestral signal broadcast by a given gene or locus. We successfully evaluated SNVstory using a clinical exome sequencing dataset, comparing self-reported ethnicity and race to our inferred genetic ancestry, and demonstrate the capability of the algorithm to estimate ancestry from 36 different populations with high accuracy.ConclusionsSNVstory represents a significant advance in methods to assign genetic ancestry, opening the door to ancestry-informed care. SNVstory, an open-source model, is packaged as a Docker container for enhanced reliability and interoperability. It can be accessed from https://github.com/nch-igm/snvstory.

Clinical Impacts of Implementing the 2021 Race-Free Chronic Kidney Disease Epidemiology Collaboration Estimated Glomerular Filtration Rate.

Estimated glomerular filtration rate (eGFR) has become incorporated into multiple clinical management situations. Historically, equations included a Black race coefficient, which lacked biological plausibility and created potential to exacerbate health disparities. A new equation created in 2021 changed the weighting of age, sex, and creatinine by modeling against a diverse cohort and removing the Black race coefficient. A variety of clinical outcomes including kidney disease risk stratification, medication dosing, patient eligibility for clinical trials, and kidney donation are impacted by implementation of the new equation. Nearly 2 years after its initial publication, many studies have reported on observed analytical performance of the 2021 eGFR determined as diagnostic concordance and percentage of estimates within 30% of measured GFR. Additionally, the potential clinical impacts following adoption of the new eGFR among different patient populations has also been reported. Here we review these studies with a focus on assessing the data associated with the transition from 2009 to 2021 Chronic Kidney Disease Epidemiology Collaboration equations. The reported interindividual variation in eGFR performance is significantly larger than any potential benefit derived from race coefficients. Both the 2021 eGFR and the 2009 eGFR analytical performance fall short of the validation cohort performance in most cohorts. However, the 2021 analytical is similar or better than the 2009 eGFR in most cohorts. Implementing the 2021 eGFR will remove a systematic overestimation of kidney function among Black patients.

ARTIFICIAL INTELLIGENCE IN HEALTHCARE: A REVIEW OF ETHICAL DILEMMAS AND PRACTICAL APPLICATIONS

The fusion of Artificial Intelligence (AI) and healthcare heralds a new era of innovation and transformation, yet it is not without its ethical quandaries. This comprehensive review traverses the intricate landscape where AI meets healthcare, delving into the ethical dilemmas that arise alongside practical applications. The ethical considerations span a spectrum, encompassing issues of patient privacy, transparency, accountability, and the inadvertent perpetuation of biases within AI algorithms. Privacy concerns emerge as a central ethical dilemma as healthcare providers leverage AI to process vast amounts of patient data. Striking a delicate balance between harnessing the power of AI for diagnostic and predictive purposes and safeguarding sensitive medical information is a critical challenge. Moreover, the review scrutinizes the ethical implications of AI algorithms and their potential to perpetuate biases, inadvertently exacerbating health disparities. A nuanced examination of bias mitigation strategies becomes imperative to ensure that AI technologies contribute to equitable healthcare outcomes. In tandem with ethical considerations, the review illuminates the practical applications reshaping the healthcare landscape. AI-driven diagnostics, predictive modeling, and personalized treatment plans emerge as transformative tools, enhancing clinical decision-making and patient outcomes. The efficient allocation of resources, streamlined workflows, and the acceleration of drug discovery processes showcase the tangible benefits of AI integration. This review aspires to guide healthcare practitioners, policymakers, and technologists in navigating the ethical crossroads of AI in healthcare. By fostering an awareness of ethical pitfalls and emphasizing responsible AI development, stakeholders can collaboratively shape a future where AI augments healthcare delivery, upholds ethical standards, and ultimately improves the quality of patient care.&#x0D; Keywords: AI, Healthcare, Ethics, Review, AI Application.

Climate and environmental changes exacerbate health disparities in pregnant people and their offspring. How can we protect women and their babies?

The effects of climate and environmental changes (CEC) are being felt globally and will worsen over the next decade unless significant changes are made on a global level. Climate change is having serious consequences for health, particularly for vulnerable women and their offspring and less resilient individuals in communities with socioeconomic inequalities. To protect human health from CEC effects, efforts need to be directed toward building resilience strategies. Building political and economic power, as well as directly addressing CEC-related challenges, are critical components of climate resilience. Effective communication and tailored methods to engage women in preventive strategies are also necessary to ameliorate the deleterious effects of CEC on women's health. Furthermore, women from marginalized communities face more CEC-associated challenges. Therefore, effective policies and programs targeting these at-risk populations-are crucial to improve the overall state of global health. In closing, it is time to increase awareness of the effects of CECs on women's health and their transgenerational effects in order to ensure that all people, regardless of race, ethnicity, education and income are protected from the detrimental effects of CECs.

A greening theory of change: How neighborhood greening impacts adolescent health disparities.

Neighborhoods are one of the key determinants of health disparities among young people in the United States. While neighborhood deprivation can exacerbate health disparities, amenities such as quality parks and greenspace can support adolescent health. Existing conceptual frameworks of greening-health largely focus on greenspace exposures, rather than greening interventions. In this paper, we develop and propose a Greening Theory of Change that explains how greening initiatives might affect adolescent health in deprived neighborhoods. The theory situates greening activities and possible mechanisms of change in the context of their ability to modify distal social determinants of health factors, stemming from macrostructural and historical processes that lead to resource inequalities, affecting both the social and built environment in which adolescents live and develop. The framework illustrates both short- and long-term health, economic, and security effects of greening. We also describe how the theory informed the development of Project VITAL (Vacant lot Improvement to Transform Adolescent Lives) in Baltimore, MD, which aims to (1) build a citywide sharable database on vacant lot restoration activities, (2) evaluate the impact of greening initiatives on adolescent health outcomes, (3) conduct cost-effectiveness analyses, and (4) develop best practices for greening programs for improved adolescent health.

Prior authorization for diabetes medications: Clinical outcomes and health disparities

BackgroundPrior authorization (PA) is a utilization management strategy used by health plans to ensure affordable, cost-effective care; however, PA may lead to therapy delay/abandonment and exacerbate health disparities. The purpose of this observational study was to assess the clinical outcomes and any health disparities associated with PA for diabetes mellitus (DM) medications. Materials and methodsThis was a cohort study of US adult patients from health plans with integrated and non-integrated system providers who were prescribed a DM medication that required a PA. Patients were categorized into three groups: received the requested DM medication (PA Med) or a new, alternative DM medication (DM Med), or did not receive the requested or new DM medication (No Med). The primary outcome was change in hemoglobin A1c (HbA1c). Adjusted and unadjusted analyses were performed. Patient characteristics associated with the No Med group were identified, also, with multivariable logistic regression modeling. Results6305 patients were included: 2434, 1323, and 2548 in the PA Med, DM Med, and No Med groups, respectively. Patients in the PA Med (−0.9 %) and DM Med (−1.0 %) groups had statistically significantly greater reductions in HbA1c compared to the No Med group (−0.4 %) in both unadjusted and adjusted analyses (all p < 0.05). Patients who were Hispanic/Latino, had a non-integrated system prescriber, and had a higher burden of chronic disease were statistically significantly associated with the No Med group. ConclusionsReceiving a new DM medication following PA was associated with better clinical outcomes but health disparities were present in the PA process.

RACIAL DISPARITIES IN OCCUPATIONAL EXPOSURES AND LATER-LIFE HEALTH

Abstract Racial and ethnic health disparities remain a persistent and urgent social problem. Researchers are beginning to explore the role of work experiences, both good and bad, in attenuating or exacerbating health disparities. The recent availability of the entire life course experience of work in the Health and Retirement Study (HRS) Life History Mail Survey (LHMS) has spurred innovative research on the role of occupational exposures on health disparities in later life. Linking these data to the information available in the Occupation Information Network (O*NET) provides a rich characterization of lifetime occupational exposures. Sheftel et al. use work histories from the LHMS to study the role of racial segregation in occupations on later life dementia. They show that complex work with data during working ages may be protective against dementia at older ages, potentially contributing to the differentials in dementia prevalence by race/ethnicity. Park et al. similarly use data on lifetime work histories, to investigate the role of accumulated years of physically demanding work (PDW) in young and middle adulthood on the number of functional limitations at age 60. They show that cumulative PDW is strongly associated with functional limitations at age 60 and accounts for a part of the racial/ethnic gap in functional limitations at age 60. Both papers will discuss policy implications of findings. Andrasfay will provide critical commentary and discussion.

LGBTQ-RELATED DISCRIMINATION REDUCES SLEEP QUALITY IN OLDER ADULTS: EVIDENCE FROM VUSNAPS AND UCNETS

Abstract Sleep is a critical protective factor for health outcomes with low-quality sleep linked to increased risk of diabetes, cardiovascular disease, and cognitive decline. Theoretical work proposes that minority stress may negatively affect sleep quality for LGBTQ and other minoritized populations. We examine differences in sleep quality across sexual orientation and gender identity and test the relationship between minority stress and sleep quality for LGBTQ adults. Data come from 1) UC Berkeley Social Networks Study (UCNets), a panel study of 50–70-year-olds with an oversample of older LGBTQ adults and 2) the Vanderbilt University Social Networks, Aging, and Policy Study (VUSNAPS), a panel study of older LGBTQ adults aged 50-76. LGBTQ identity includes participants who are transgender and/or gay, lesbian, bisexual, or something else. We estimate Poisson models predicting sleep quality as a function of LGBTQ identity and exposure to LGBTQ-related discrimination. Sleep quality is measured using three items available in both studies: 1) the number of nights per week trouble falling asleep, 2) number of nights per week trouble staying asleep, and 3) number of mornings per week waking well rested. Experiences of LGBTQ-related discrimination in the last year were measured using the Daily Heterosexist Experiences Questionnaire. We find that LGBTQ identity is significantly associated with poorer sleep quality compared to non-LGBTQ peers. Among LGBTQ adults, sleep quality declines as the number and intensity of recent experiences of LGBTQ-related discrimination increases. Poorer sleep quality may be an underlying mechanism exacerbating health disparities across sexual orientation and gender identity.

Enhancing the Retention of Healthcare Workers in Rural Areas: A Systematic Review

Introduction: Rural areas globally face significant challenges in retaining healthcare workers, impacting the delivery of essential health services and exacerbating health disparities. This systematic review aimed to evaluate the effectiveness of various strategies implemented over the last 15 years to enhance healthcare worker retention in rural settings, with the goal of identifying evidence-based interventions that can inform policy and practice to improve rural healthcare delivery. Methods: The review focused exclusively on interventional studies and clinical trials published from 2007 to 2022. A comprehensive search of multiple databases, including PubMed, MEDLINE, Embase, CINAHL, and the Cochrane Library, was conducted using specific search terms related to healthcare worker retention in rural areas. Inclusion criteria targeted studies evaluating quantifiable outcomes of retention strategies, with exclusion criteria set to omit non-interventional studies, research focused on urban settings, and non-English publications. The study selection, data extraction, and quality assessment processes were rigorously conducted to ensure the reliability of the findings. Results: Twelve studies met the inclusion criteria, revealing a broad range of interventions from financial incentives and educational programs to supportive workplace interventions and community engagement strategies. Financial incentives demonstrated a notable positive impact on retention, with risk ratios ranging from 1.2 to 1.75, indicating a 20% to 75% increase in retention rates. Supportive workplace interventions also showed effectiveness, with a risk ratio of up to 1.4. However, the results for educational and community engagement strategies were more variable, suggesting the need for tailored approaches. Conclusions: The review highlights the effectiveness of targeted interventions in enhancing the retention of healthcare workers in rural areas, particularly financial incentives and supportive workplace interventions. These findings underscore the importance of implementing multifaceted, evidence-based strategies tailored to the unique challenges of rural healthcare settings to improve workforce stability and healthcare delivery.

Sexual violence against sexual minority women in STEM: Compound backlash

BackgroundRecent research has found that gender parity (i.e., the ratio of women to men) in Science, Technology, Engineering, and Math (STEM) disciplines is associated with sexual violence (SV) victimization for women. This finding may reflect a type of backlash wherein SV is a means of punishing women who are perceived to be violating their gender roles and threatening the male hegemony. Sexual minorities, who are likewise disproportionately victims of SV, report experiencing heterosexist hostility and harassment in STEM disciplines. There is reason to suspect that the combination of these marginalized identity positions (e.g., a sexual minority woman in gender-balanced STEM) may amplify perceived gender role violations and exacerbate the risk of SV victimization. MethodsData were collected from undergraduate women at five institutions of higher education in the United States. Sampling was stratified by STEM vs. non-STEM majors and male-dominated vs. gender-balanced majors. Sexual violence was measured via the revised Sexual Experiences Survey. We tested the interaction of sexual minority status and gender parity in STEM on SV via fixed effects OLS regression. ResultsSexual minority women in gender-balanced STEM were most frequently victims of SV. Women in male-dominated STEM majors were at no greater risk of SV victimization, regardless of sexual minority status, than their peers in non-STEM majors. ImplicationsThese findings suggest the possibility of a compound form of backlash, wherein women are exponentially victimized because their sexual identity and their membership in these STEM fields are seen as dual challenges to the male hegemony. If true, this could exacerbate health disparities rather than promote health equity for these sexual minority women. Findings point to the ongoing need to challenge hegemonic gender norms, focus prevention programs on populations most at risk, and ensure they are provided the appropriate resources for support.

Smoking, Stress, and Health Risk Behaviors During the Early Phase of COVID-19 Lockdowns Among Residents of Flint, Michigan

Abstract: Food insecurity and smoking commonly co-occur in underserved populations, exacerbating health disparities. This study assesses smoking and its associations with mental health and health risk behaviors among residents of Flint, Michigan in the early phase of COVID lockdowns. The survey captured smoking status, mental health indicators, and health risk behaviors among 106 who had financial needs and limited mobility and received community nutritional services. Comparisons by demographics showed that those who lived alone, used marijuana, and had frequent alcohol drinking tended to smoke more than their counterparts. Results from logistic regression showed that those with high levels of stress, marijuana use, and alcohol consumption were more likely to smoke. Living alone was associated with smoking and health risk behaviors. This research suggests the need for smoking prevention and community health practices addressing stress, health risk behaviors, and circumstances that might drive the association with living alone such as loneliness.

Digital Determinants of Health: Health data poverty amplifies existing health disparities-A scoping review.

Artificial intelligence (AI) and machine learning (ML) have an immense potential to transform healthcare as already demonstrated in various medical specialties. This scoping review focuses on the factors that influence health data poverty, by conducting a literature review, analysis, and appraisal of results. Health data poverty is often an unseen factor which leads to perpetuating or exacerbating health disparities. Improvements or failures in addressing health data poverty will directly impact the effectiveness of AI/ML systems. The potential causes are complex and may enter anywhere along the development process. The initial results highlighted studies with common themes of health disparities (72%), AL/ML bias (28%) and biases in input data (18%). To properly evaluate disparities that exist we recommend a strengthened effort to generate unbiased equitable data, improved understanding of the limitations of AI/ML tools, and rigorous regulation with continuous monitoring of the clinical outcomes of deployed tools.