Hidden in Plain Sight - Reconsidering the Use of Race Correction in Clinical Algorithms

The relationship between dietary inflammatory index (DII) and early renal injury in population with/without hypertension: analysis of the National health and nutrition examination survey 2001-2002

BACKGROUND

Inflammation plays a crucial role in occurrence of kidney injury, and specific dietary patterns can influence systemic inflammation levels. However, the relationship between dietary inflammatory potential and early-stage kidney damage remains unclear.

METHOD

2,108 participants was recruited from 2001-2002 National Health and Nutrition Examination Survey (NHANES). Dietary Inflammatory Index (DII) is utilized to assess dietary inflammatory potential, calculated through a 24-h dietary recall questionnaire. Early renal injury was evaluated using urinary albumin to creatinine (UACR), cystatin C (CysC), β-2 microglobulin (β2M), and estimated glomerular filtration rate (eGFR) based on serum creatinine (eGFRs), cystatin C (eGFRc), and both Scr and CysC (eGFRs&c). Participant characteristics were analyzed, and association between DII, hypertension, and early renal injury markers was explored using multiple linear and logistic regression models.

RESULTS

The average age of participants was 53.9 years. DII exhibited a positive correlation with UACR (β = -0.048[0.017,0.078]), β2M (β = 0.019[0.010,0.027]), CysC (β = 0.012 [0.004,0.021]). Conversely, a negative correlation was observed between DII and eGFRc (β = -1.126[-1.554, -0.699]), eGFRs&c (β=-1.101[-1.653, -0.549]). A significant association was observed between hypertension and abnormality of early kidney damage markers. Subgroup analysis reveals that the positive correlation between DII and the occurrence of abnormal markers of early kidney damage is only observed in individuals with hypertension. Furthermore, an interaction between DII and hypertension was detected in eGFRs&c (OR:1.250[1.042, 1.499], p for interaction = 0.03).

CONCLUSION

Higher levels of DII may be associated with occurrence of early kidney damage. For individuals with hypertension, avoiding excessive consumption of pro-inflammatory foods may reduce the risk of renal injury.

Association of genetic ancestry with pre-eclampsia in multi-ethnic cohorts of pregnant women

OBJECTIVES

Maternal self-reported ethnicity is recognised as a risk factor for pre-eclampsia in clinical screening tools and models. This study investigated whether ethnicity is acting as a proxy for genetic variants in this context.

STUDY DESIGN

A total of 436 women from multi-ethnic backgrounds recruited to two UK observational pregnancy hypertension cohort studies were genotyped. Genetically-computed individual ancestry estimates were calculated for each individual through comparison to the multi-ethnic 1000 Genomes reference panel genotypes. Regression models for pre-eclampsia using clinical risk factors including self-reported ethnicity with and without ancestry estimates were built and compared using Likelihood Ratio Tests (LRT).

MAIN OUTCOME MEASURES

Pre-eclampsia (early- and late-onset).

RESULTS

In these multi-ethnic cohorts (mean age 34.9 years; 41.3 % White, 34.2 % Black, 13.1 % Asian ethnic backgrounds; 82.6 % chronic hypertension), discrepancies between self-reported ethnicity and genetically-computed individual ancestry estimates were present in all ethnic groups, particularly minority groups. Genetically-computed pan-African ancestry percentage was associated with early-onset (< 34 weeks) pre-eclampsia in adjusted models (aOR 100 % vs 0 % African ancestry: 3.81, 95 % CI 1.04-14.14, p-value 0.044) independently of self-reported ethnicity and established clinical risk factors. Addition of genetically-computed African ancestry to a clinical risk factor model including self-reported ethnicity, improved model fit (Likelihood ratio test p-value 0.023).

CONCLUSIONS

Self-reported maternal ethnicity is an imperfect proxy for genetically-computed individual ancestry estimates, particularly in ethnic minority groups. Genetically-computed African ancestry percentage was associated with early-onset pre-eclampsia independently of self-reported maternal ethnicity. Well-powered studies in multi-ethnic cohorts are required to delineate the genetic contribution to pre-eclampsia.

A New Era of Data-Driven Cancer Research and Care: Opportunities and Challenges

People diagnosed with cancer and their formal and informal caregivers are increasingly faced with a deluge of complex information, thanks to rapid advancements in the type and volume of diagnostic, prognostic, and treatment data. This commentary discusses the opportunities and challenges that the society faces as we integrate large volumes of data into regular cancer care.

Race as a Risk Marker, Not a Risk Factor: Revising Race-Based Algorithms to Protect Racially Oppressed Patients

Emerging consensus in the medical and public health spheres encourages removing race and ethnicity from algorithms used in clinical decision-making. Although clinical algorithms remain appealing given their promise to lighten the cognitive load of medical practice and save time for providers, they risk exacerbating existing health disparities. Race is a strong risk marker of health outcomes, yet it is not a risk factor. The use of race as a factor in medical algorithms suggests that the effect of race is intrinsic to the patient or that its effects can be distinct or separated from other social and environmental variables. By contrast, incisive public health analysis coupled with a race-conscious perspective recognizes that race serves as a marker of countless other dynamic variables and that structural racism, rather than race, compromises the health of racially oppressed individuals. This perspective offers a historical and theoretical context for the current debates regarding the use of race in clinical algorithms, clinical and epidemiologic perspectives on "risk," and future directions for research and policy interventions that combat color-evasive racism and follow the principles of race-conscious medicine.

Pulmonary Function after Nonmyeloablative Hematopoietic Cell Transplant for Sickle Cell Disease

Rationale: Sickle cell disease (SCD) is a monogenetic condition with recurring vasoocclusive events causing lifelong pulmonary morbidity and mortality. There is increasing access to curative therapies, such as hematopoietic cell transplant (HCT), for people living with SCD. However, more information on pulmonary function in adults with SCD after HCT is needed to best guide decisions for HCT and post-HCT care. Objectives: To test the hypothesis that forced expiratory volume in 1 second (FEV1) and other pulmonary function testing (PFT) parameters remain stable 3 years after HCT. Methods: People living with SCD undergoing nonmyeloablative HCT in a prospective cohort at the NIH Clinical Center from 2004 to 2019 were evaluated for enrollment. Global Lung Function Initiative reference equations and descriptive statistics were calculated before HCT and annually for 3 years. Six-minute-walk distance (6MWD) testing was performed. Generalized estimating equations were employed to evaluate interindividual changes in PFT parameters and 6MWD. Results: Of 97 patients with SCD undergoing HCT, 41 (42%) were female with median (25th, 75th percentile) age 31.8 (24.8, 38.0) years. Each year of measurement included the following numbers of subjects available for analysis with PFTs: baseline (n = 97), Year 1 (n = 91), Year 2 (n = 72), and Year 3 (n = 55); and the following numbers of subjects available for analysis with 6MWD: baseline (n = 79), Year 1 (n = 73), Year 2 (n = 57), and Year 3 (n = 41). Pre-HCT FEV1 was median (25th, 75th percentile) 68.3% (61.3%, 80.3%) and 69.2% (60.8%, 77.7%) 3 years after HCT, and pre-HCT diffusing capacity of the lung for carbon monoxide (DlCO) was 60.5% (53.0%, 66.3%) and 64.6% (55.1%, 73.4%) 3 years after HCT. Generalized estimating equations estimated that DlCO percent predicted increased significantly by 3.7% (95% confidence interval, 1.0%, 6.3%), and the 6MWD significantly increased by 25.9 (6.6, 45.2) meters 3 years after HCT, whereas there was no significant change in percent predicted FEV1 or FVC compared with before HCT. Conclusions: Overall, PFT results remained stable and there was an improvement in DlCO and 6MWD in this predominantly adult cohort undergoing nonmyeloablative HCT for SCD. Allogeneic HCT for SCD may cease the cycle of vasoocclusive pulmonary injury and prevent continued damage. Multicenter studies are needed to evaluate the long-term lung health effects of HCT for SCD in adults and children.

Racial and Ethnic Disparities in Occult Hypoxemia Prevalence and Clinical Outcomes Among Hospitalized Patients: A Systematic Review and Meta-analysis

BACKGROUND

There is growing concern that pulse oximeters are routinely less accurate in hospitalized patients with darker skin pigmentation, in turn increasing risk of undetected (occult) hypoxemia and adverse clinical outcomes. The aim of this systematic review and meta-analysis was to synthesize evidence on racial and ethnic disparities in occult hypoxemia prevalence and clinical impacts of undetected hypoxemia.

METHODS

Ovid MEDLINE, Embase, and CINAHL databases were searched for relevant articles published through January 2024. Eligible studies must have been conducted among adults in inpatient or outpatient settings and report occult hypoxemia prevalence stratified by patient race or ethnicity, or clinical outcomes stratified by patient race or ethnicity and occult hypoxemia status. Screening for inclusion was conducted independently by two investigators. Data extraction and risk of bias assessment were conducted by one investigator then checked by a second. Outcome data were synthesized using random-effects meta-analyses.

RESULTS

Fifteen primary studies met eligibility criteria and reported occult hypoxemia prevalence in 732,505 paired oximetry measurements from 207,464 hospitalized patients. Compared with White patients, occult hypoxemia is likely more common among Black patients (pooled prevalence ratio = 1.67, 95% CI 1.47 to 1.90) and among patients identifying as Asian, Latinx, Indigenous, multiracial, or other race or ethnicity (pooled prevalence ratio = 1.39, 95% CI 1.19 to 1.64). Findings from studies reporting clinical outcomes suggest that Black patients with undetected hypoxemia may experience poorer treatment delivery outcomes than White patients with undetected hypoxemia. No evidence was found from outpatient settings.

DISCUSSION

This review and included primary studies rely on self-identified race or ethnicity, which may obscure variability in occult hypoxemia risk. Findings underscore that clinicians should be aware of the risk of occult hypoxemia in hospitalized patients with darker skin pigmentation. Moreover, oximetry data from included studies suggests that the accuracy of pulse oximeters could vary substantially from patient to patient and even within individual patients.

TRIAL REGISTRATION

PROSPERO ( CRD42023402152 ).

The clinical, mechanistic, and social impacts of air pollution on atopic dermatitis

Atopic dermatitis (AD) is a complex disease characterized by dry, pruritic skin and significant atopic and psychological sequelae. Although AD has always been viewed as multifactorial, early research was dominated by overlapping genetic determinist views of either innate barrier defects leading to inflammation or innate inflammation eroding skin barrier function. Since 1970, however, the incidence of AD in the United States has increased at a pace that far exceeds genetic drift, thus suggesting a modern, environmental etiology. Another implicated factor is Staphylococcus aureus; however, a highly contagious microorganism is unlikely to be the primary etiology of a noncommunicable disease. Recently, the roles of the skin and gut microbiomes have received greater attention as potentially targetable drivers of AD. Here too, however, dysbiosis on a population scale would require induction by an environmental factor. In this review, we describe the evidence supporting the environmental hypothesis of AD etiology and detail the molecular mechanisms of each of the AD-relevant toxins. We also outline how a pollution-focused paradigm demands earnest engagement with environmental injustice if the field is to meaningfully address racial and geographic disparities. Identifying specific toxins and their mechanisms can also inform in-home and national mitigation strategies.

A Clinician's Guide to Understanding Bias in Critical Clinical Prediction Models

This narrative review focuses on the role of clinical prediction models in supporting informed decision-making in critical care, emphasizing their 2 forms: traditional scores and artificial intelligence (AI)-based models. Acknowledging the potential for both types to embed biases, the authors underscore the importance of critical appraisal to increase our trust in models. The authors outline recommendations and critical care examples to manage risk of bias in AI models. The authors advocate for enhanced interdisciplinary training for clinicians, who are encouraged to explore various resources (books, journals, news Web sites, and social media) and events (Datathons) to deepen their understanding of risk of bias.

Estimated GFR in the Korean and US Asian Populations Using the 2021 Creatinine-Based GFR Estimating Equation Without Race

Rationale & Objective

In 2021, the new Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) updated the creatinine-based estimated glomerular filtration rate (eGFR) equation and removed the coefficient for race. The development and validation of this equation involved binarizing race into African American and non-African American, involving few Asian participants. This study aimed to examine the difference between the 2021 equation and the previous 2009 equation on CKD prevalence estimates in 2 Asian populations.

Study Design

Observational study using 2 national surveys.

Setting & Participants

Participants from the 2019 Korea National Health and Nutrition Survey and participants self-reported as Asian from the 2011-2020 US National Health and Nutrition Survey.

Exposure

eGFR using 2009 and 2021 CKD-EPI creatinine equation.

Outcomes

Prevalence of CKD (eGFR <60 mL/min/1.73 m2 or urine albumin-creatinine ratio ≥30 mg/g).

Analytical Approach

Sampling-weighted prevalence estimated using the 2009 and 2021 equations as well as the percentage of individuals with CKD G3+ using the 2009 equation being reclassified as not having CKD G3+ using the 2021 equation.

Results

The prevalence of CKD estimated using the 2021 equation was 9.75% (95% confidence intervals [CI], 8.80-10.80%) in Koreans and 11.60% (95% CI, 10.23-13.13%) in US Asians. The prevalence of CKD estimated using the 2021 equation was slightly lower than that using the 2009 equation in both Korean and US Asian populations by 0.63% (95% CI, 0.44-0.90%) and 0.84% (95% CI, 0.52-1.34%), respectively. Furthermore, 32.8% and 30.2% of Koreans and US Asians with CKD G3-5, respectively, estimated using the 2009 equation were reclassified as not having CKD G3-5 when the eGFR was calculated using the 2021 equation.

Limitations

Measured GFR was not available.

Conclusions

Use of the 2021 CKD-EPI creatinine equation leads to a small decrease in CKD prevalence in both Korean and US Asian populations, and of similar magnitude, resulting in significant reclassification among those originally classified as having CKD G3+.

Cultivating Diversity, Equity, and Inclusion in Pulmonary and Critical Care Training: A Path Toward Health Care Excellence

Pulmonary and Critical Care Medicine (PCCM) fellowship training faces increasing competition but lacks diversity, hindering health care excellence. Despite a growing interest in the field, programs lack diverse representation. Addressing this issue is crucial to combat health disparities and bias, benefiting trainees, practitioners, and patients. Sustainable solutions are vital for achieving diversity, equity, and inclusion in PCCM. Strategies for achieving equity among training programs include adopting inclusive recruitment practices, recognizing differential attainment, addressing bias, fostering an equitable academic climate, and implementing multifaceted strategic processes to enhance diversity in mentorship including recognition and compensation for diversity and equity work.

Decolonization and antiracism: intersecting pathways to global health equity

In this paper, as Black scholars, we address ways that interventions designed to promote equity in health can create pathways for coupling decolonization with antiracism by drawing on the intersection of the health of Africans and African Americans. To frame this intersection, we offer the Public Health Critical Race Praxis (PHCRP) and the PEN-3 Cultural Model as antiracism and decolonization tools that can jointly advance research on colonization and racism globally. We argue that racism is a global reality; PHCRP, an antiracism framework, and PEN-3, a decolonizing framework, can guide interventions to promote equity for Africans and African Americans.

Addressing AI Algorithmic Bias in Health Care

This Viewpoint discusses the bias that exists in artificial intelligence (AI) algorithms used in health care despite recent federal rules to prohibit discriminatory outcomes from AI and recommends ways in which health care facilities, AI developers, and regulators could share responsibilities and actions to address bias.

Projected Changes in Statin and Antihypertensive Therapy Eligibility With the AHA PREVENT Cardiovascular Risk Equations

Importance

Since 2013, the American College of Cardiology (ACC) and American Heart Association (AHA) have recommended the pooled cohort equations (PCEs) for estimating the 10-year risk of atherosclerotic cardiovascular disease (ASCVD). An AHA scientific advisory group recently developed the Predicting Risk of cardiovascular disease EVENTs (PREVENT) equations, which incorporated kidney measures, removed race as an input, and improved calibration in contemporary populations. PREVENT is known to produce ASCVD risk predictions that are lower than those produced by the PCEs, but the potential clinical implications have not been quantified.

Objective

To estimate the number of US adults who would experience changes in risk categorization, treatment eligibility, or clinical outcomes when applying PREVENT equations to existing ACC and AHA guidelines.

Design, Setting, and Participants

Nationally representative cross-sectional sample of 7765 US adults aged 30 to 79 years who participated in the National Health and Nutrition Examination Surveys of 2011 to March 2020, which had response rates ranging from 47% to 70%.

Main Outcomes and Measures

Differences in predicted 10-year ASCVD risk, ACC and AHA risk categorization, eligibility for statin or antihypertensive therapy, and projected occurrences of myocardial infarction or stroke.

Results

In a nationally representative sample of 7765 US adults aged 30 to 79 years (median age, 53 years; 51.3% women), it was estimated that using PREVENT equations would reclassify approximately half of US adults to lower ACC and AHA risk categories (53.0% [95% CI, 51.2%-54.8%]) and very few US adults to higher risk categories (0.41% [95% CI, 0.25%-0.62%]). The number of US adults receiving or recommended for preventive treatment would decrease by an estimated 14.3 million (95% CI, 12.6 million-15.9 million) for statin therapy and 2.62 million (95% CI, 2.02 million-3.21 million) for antihypertensive therapy. The study estimated that, over 10 years, these decreases in treatment eligibility could result in 107 000 additional occurrences of myocardial infarction or stroke. Eligibility changes would affect twice as many men as women and a greater proportion of Black adults than White adults.

Conclusion and Relevance

By assigning lower ASCVD risk predictions, application of the PREVENT equations to existing treatment thresholds could reduce eligibility for statin and antihypertensive therapy among 15.8 million US adults.

Tailoring Risk Prediction Models to Local Populations

Importance

Risk estimation is an integral part of cardiovascular care. Local recalibration of guideline-recommended models could address the limitations of existing tools.

Objective

To provide a machine learning (ML) approach to augment the performance of the American Heart Association's Predicting Risk of Cardiovascular Disease Events (AHA-PREVENT) equations when applied to a local population while preserving clinical interpretability.

Design, Setting, and Participants

This cohort study used a New England-based electronic health record cohort of patients without prior atherosclerotic cardiovascular disease (ASCVD) who had the data necessary to calculate the AHA-PREVENT 10-year risk of developing ASCVD in the event period (2007-2016). Patients with prior ASCVD events, death prior to 2007, or age 79 years or older in 2007 were subsequently excluded. The final study population of 95 326 patients was split into 3 nonoverlapping subsets for training, testing, and validation. The AHA-PREVENT model was adapted to this local population using the open-source ML model (MLM) Extreme Gradient Boosting model (XGBoost) with minimal predictor variables, including age, sex, and AHA-PREVENT. The MLM was monotonically constrained to preserve known associations between risk factors and ASCVD risk. Along with sex, race and ethnicity data from the electronic health record were collected to validate the performance of ASCVD risk prediction in subgroups. Data were analyzed from August 2021 to February 2024.

Main Outcomes and Measures

Consistent with the AHA-PREVENT model, ASCVD events were defined as the first occurrence of either nonfatal myocardial infarction, coronary artery disease, ischemic stroke, or cardiovascular death. Cardiovascular death was coded via government registries. Discrimination, calibration, and risk reclassification were assessed using the Harrell C index, a modified Hosmer-Lemeshow goodness-of-fit test and calibration curves, and reclassification tables, respectively.

Results

In the test set of 38 137 patients (mean [SD] age, 64.8 [6.9] years, 22 708 [59.5]% women and 15 429 [40.5%] men; 935 [2.5%] Asian, 2153 [5.6%] Black, 1414 [3.7%] Hispanic, 31 400 [82.3%] White, and 2235 [5.9%] other, including American Indian, multiple races, unspecified, and unrecorded, consolidated owing to small numbers), MLM-PREVENT had improved calibration (modified Hosmer-Lemeshow P > .05) compared to the AHA-PREVENT model across risk categories in the overall cohort (χ23 = 2.2; P = .53 vs χ23 > 16.3; P < .001) and sex subgroups (men: χ23 = 2.1; P = .55 vs χ23 > 16.3; P < .001; women: χ23 = 6.5; P = .09 vs. χ23 > 16.3; P < .001), while also surpassing a traditional recalibration approach. MLM-PREVENT maintained or improved AHA-PREVENT's calibration in Asian, Black, and White individuals. Both MLM-PREVENT and AHA-PREVENT performed equally well in discriminating risk (approximate ΔC index, ±0.01). Using a clinically significant 7.5% risk threshold, MLM-PREVENT reclassified a total of 11.5% of patients. We visualize the recalibration through MLM-PREVENT ASCVD risk charts that highlight preserved risk associations of the original AHA-PREVENT model.

Conclusions and Relevance

The interpretable ML approach presented in this article enhanced the accuracy of the AHA-PREVENT model when applied to a local population while still preserving the risk associations found by the original model. This method has the potential to recalibrate other established risk tools and is implementable in electronic health record systems for improved cardiovascular risk assessment.

Simulated Misuse of Large Language Models and Clinical Credit Systems

Large language models (LLMs) have been proposed to support many healthcare tasks, including disease diagnostics and treatment personalization. While AI may be applied to assist or enhance the delivery of healthcare, there is also a risk of misuse. LLMs could be used to allocate resources via unfair, unjust, or inaccurate criteria. For example, a social credit system uses big data to assess "trustworthiness" in society, penalizing those who score poorly based on evaluation metrics defined only by a power structure (e.g., a corporate entity or governing body). Such a system may be amplified by powerful LLMs which can evaluate individuals based on multimodal data - financial transactions, internet activity, and other behavioral inputs. Healthcare data is perhaps the most sensitive information which can be collected and could potentially be used to violate civil liberty or other rights via a "clinical credit system", which may include limiting access to care. The results of this study show that LLMs may be biased in favor of collective or systemic benefit over protecting individual rights, potentially enabling this type of future misuse. Moreover, experiments in this report simulate how clinical datasets might be exploited with current LLMs, demonstrating the urgency of addressing these ethical dangers. Finally, strategies are proposed to mitigate the risk of developing large AI models for healthcare.

Factors associated with venous thromboembolism pharmacoprophylaxis initiation in hospitalized medical patients: the Medical Inpatients Thrombosis and Hemostasis study

BACKGROUND

Although guidelines recommend risk assessment for hospital-acquired venous thromboembolism (HA-VTE) to inform prophylaxis decisions, studies demonstrate inappropriate utilization of pharmacoprophylaxis in hospitalized medical patients. Predictors of pharmacoprophylaxis initiation in medical inpatients remain largely unknown.

OBJECTIVES

To determine factors associated with HA-VTE pharmacoprophylaxis initiation in adults hospitalized on medical services.

METHODS

We performed a cohort study using electronic health record data from adult patients hospitalized on medical services at 4 academic medical centers between 2016 and 2019. Main measures were candidate predictors of HA-VTE pharmacoprophylaxis initiation, including known HA-VTE risk factors, predicted HA-VTE risk, and bleeding diagnoses present on admission.

RESULTS

Among 111 550 admissions not on intermediate or full-dose anticoagulation, 48 520 (43.5%) received HA-VTE pharmacoprophylaxis on the day of or the day after admission. After adjustment for age, sex, race/ethnicity, and study site, the strongest clinical predictors of HA-VTE pharmacoprophylaxis initiation were malnutrition and chronic obstructive pulmonary disease. Thrombocytopenia and history of gastrointestinal bleeding were associated with decreased odds of HA-VTE pharmacoprophylaxis initiation. Patients in the highest 2 tertiles of predicted HA-VTE risk were less likely to receive HA-VTE pharmacoprophylaxis than patients in the lowest (first) tertile (OR, 0.84; 95% CI, 0.81-0.86 for the second tertile; OR, 0.95; 95% CI, 0.92-0.98 for the third tertile).

CONCLUSION

Among patients not already receiving anticoagulants, HA-VTE pharmacoprophylaxis initiation during the first 2 hospital days was lower in patients with a higher predicted HA-VTE risk and those with risk factors for bleeding. Reasons for not initiating pharmacoprophylaxis in those with a higher predicted HA-VTE risk could not be assessed.

Early Newborn Metabolic Patterning and Sudden Infant Death Syndrome

Importance

Sudden infant death syndrome (SIDS) is a major cause of infant death in the US. Previous research suggests that inborn errors of metabolism may contribute to SIDS, yet the relationship between SIDS and biomarkers of metabolism remains unclear.

Objective

To evaluate and model the association between routinely measured newborn metabolic markers and SIDS in combination with established risk factors for SIDS.

Design, Setting, and Participants

This was a case-control study nested within a retrospective cohort using data from the California Office of Statewide Health Planning and Development and the California Department of Public Health. The study population included infants born in California between 2005 and 2011 with full metabolic data collected as part of routine newborn screening (NBS). SIDS cases were matched to controls at a ratio of 1:4 by gestational age and birth weight z score. Matched data were split into training (2/3) and testing (1/3) subsets. Data were analyzed from January 2005 to December 2011.

Exposures

Metabolites measured by NBS and established risk factors for SIDS.

Main Outcomes and Measures

The primary outcome was SIDS. Logistic regression was used to evaluate the association between metabolic markers combined with known risk factors and SIDS.

Results

Of 2 276 578 eligible infants, 354 SIDS (0.016%) cases (mean [SD] gestational age, 38.3 [2.3] weeks; 220 male [62.1%]) and 1416 controls (mean [SD] gestational age, 38.3 [2.3] weeks; 723 male [51.1%]) were identified. In multivariable analysis, 14 NBS metabolites were significantly associated with SIDS in a univariate analysis: 17-hydroxyprogesterone, alanine, methionine, proline, tyrosine, valine, free carnitine, acetyl-L-carnitine, malonyl carnitine, glutarylcarnitine, lauroyl-L-carnitine, dodecenoylcarnitine, 3-hydroxytetradecanoylcarnitine, and linoleoylcarnitine. The area under the receiver operating characteristic curve for a 14-marker SIDS model, which included 8 metabolites, was 0.75 (95% CI, 0.72-0.79) in the training set and was 0.70 (95% CI, 0.65-0.76) in the test set. Of 32 infants in the test set with model-predicted probability greater than 0.5, a total of 20 (62.5%) had SIDS. These infants had 14.4 times the odds (95% CI, 6.0-34.5) of having SIDS compared with those with a model-predicted probability less than 0.1.

Conclusions and Relevance

Results from this case-control study showed an association between aberrant metabolic analytes at birth and SIDS. These findings suggest that we may be able to identify infants at increased risk for SIDS soon after birth, which could inform further mechanistic research and clinical efforts focused on monitoring and prevention.

Drivers of variation in postpartum opioid prescribing across hospitals participating in a statewide maternity care quality collaborative

BACKGROUND

We describe variation in postpartum opioid prescribing across a statewide quality collaborative and assess the proportion due to practitioner and hospital characteristics.

METHODS

We assessed postpartum prescribing data from nulliparous, term, singleton, vertex births between January 2020 and June 2021 included in the clinical registry of a statewide obstetric quality collaborative funded by Blue Cross Blue Shield of Michigan. Data were summarized using descriptive statistics. Mixed effect logistic regression and linear models adjusted for patient characteristics and assessed practitioner- and hospital-level predictors of receiving a postpartum opioid prescription and prescription size. Relative contributions of practitioner and hospital characteristics were assessed using the intraclass correlation coefficient.

RESULTS

Of 40,589 patients birthing at 68 hospitals, 3.0% (872/29,412) received an opioid prescription after vaginal birth and 87.8% (9812/11,177) received one after cesarean birth, with high variation across hospitals. In adjusted models, the strongest patient-level predictors of receiving a prescription were cesarean birth (aOR 899.1, 95% CI 752.8-1066.7) and third-/fourth-degree perineal laceration (aOR 25.7, 95% CI 17.4-37.9). Receiving care from a certified nurse-midwife (aOR 0.63, 95% CI 0.48-0.82) or family medicine physician (aOR 0.60, 95%CI 0.39-0.91) was associated with lower prescribing rates. Hospital-level predictors included receiving care at hospitals with <500 annual births (aOR 4.07, 95% CI 1.61-15.0). A positive safety culture was associated with lower prescribing rates (aOR 0.37, 95% CI 0.15-0.88). Much of the variation in postpartum prescribing was attributable to practitioners and hospitals (prescription receipt: practitioners 25.1%, hospitals 12.1%; prescription size: practitioners 5.4%, hospitals: 52.2%).

DISCUSSION

Variation in postpartum opioid prescribing after birth is high and driven largely by practitioner- and hospital-level factors. Opioid stewardship efforts targeted at both the practitioner and hospital level may be effective for reducing opioid prescribing harms.

Disparities in Osteoporosis Prevention and Care: Understanding Gender, Racial, and Ethnic Dynamics

PURPOSE

Osteoporosis, the most prevalent metabolic bone disease, significantly impacts global public health by increasing fracture risks, particularly among post-menopausal women and the elderly. Osteoporosis is characterized by decreased bone mineral density (BMD) and deterioration of bone tissue, which leads to enhanced fragility. The disease is predominantly diagnosed using dual X-ray absorptiometry (DXA) and is significantly influenced by demographic factors such as age and hormonal changes. This chapter delves into the condition's complex nature, emphasizing the pervasive gender and racial disparities in its screening, diagnosis, and treatment.

RECENT FINDINGS

Recent findings highlight a substantial gap in the management of osteoporosis, with many individuals remaining under-screened and under-treated. Factors contributing to this include the asymptomatic early stages of the disease, lack of awareness, economic barriers, and inconsistent screening practices, especially in under-resourced areas. These challenges are compounded by disparities that affect different genders and races unevenly, influencing both the prevalence of the disease and the likelihood of receiving adequate healthcare services. The summary of this chapter underscores the urgent need for targeted strategies to overcome these barriers and improve health equity in osteoporosis care. Proposed strategies include enhancing public and healthcare provider awareness of osteoporosis, broadening access to diagnostic screenings, and integrating personalized treatment approaches. These efforts aim to align with global health objectives to mitigate the impacts of osteoporosis and ensure equitable health outcomes across all demographic groups.

Recommendations to promote fairness and inclusion in biomedical AI research and clinical use

OBJECTIVE

Understanding and quantifying biases when designing and implementing actionable approaches to increase fairness and inclusion is critical for artificial intelligence (AI) in biomedical applications.

METHODS

In this Special Communication, we discuss how bias is introduced at different stages of the development and use of AI applications in biomedical sciences and health care. We describe various AI applications and their implications for fairness and inclusion in sections on 1) Bias in Data Source Landscapes, 2) Algorithmic Fairness, 3) Uncertainty in AI Predictions, 4) Explainable AI for Fairness and Equity, and 5) Sociological/Ethnographic Issues in Data and Results Representation.

RESULTS

We provide recommendations to address biases when developing and using AI in clinical applications.

CONCLUSION

These recommendations can be applied to informatics research and practice to foster more equitable and inclusive health care systems and research discoveries.

Bibliometric analysis of artificial intelligence in healthcare research: Trends and future directions

Objective

The presence of artificial intelligence (AI) in healthcare is a powerful and game-changing force that is completely transforming the industry as a whole. Using sophisticated algorithms and data analytics, AI has unparalleled prospects for improving patient care, streamlining operational efficiency, and fostering innovation across the healthcare ecosystem. This study conducts a comprehensive bibliometric analysis of research on AI in healthcare, utilising the SCOPUS database as the primary data source.

Methods

Preliminary findings from 2013 identified 153 publications on AI and healthcare. Between 2019 and 2023, the number of publications increased exponentially, indicating significant growth and development in the field. The analysis employs various bibliometric indicators to assess research production performance, science mapping techniques, and thematic mapping analysis.

Results

The study reveals insights into research hotspots, thematic focus, and emerging trends in AI and healthcare research. Based on an extensive examination of the Scopus database provides a brief overview and suggests potential avenues for further investigation.

Conclusion

This article provides valuable contributions to understanding the current landscape of AI in healthcare, offering insights for future research directions and informing strategic decision making in the field.

What is the most appropriate age for the first cardiac screening of athletes?

For sporting organisations that conduct screening of athletes, there are very few consistent guidelines on the age at which to start. Our review found the total rate of sudden cardiac arrest or death is very low between the ages of 8-11 years (less than 1/100,000/year), increasing to 1-2/100,000/year in both elite athletes and community athletes aged 12-15 years and then steadily increases with age. The conditions associated with sudden cardiac death in paediatric athletes and young adult athletes are very similar with some evidence that death from coronary artery abnormalities occurs more frequently in athletes 10-14 years old. The decision when to begin a screening program involves a complex interplay between requirements and usual practices in a country, the rules of different leagues and programs, the age of entry into an elite program, the underlying risk of the population and the resources available. Given the incidence of sudden cardiac arrest or death in young people, we recommend beginning cardiac screening no earlier than 12 years (not later than 16 years). The risk increases with age, therefore, starting a program at any point after age 12 has added value. Importantly, anyone with concerning symptoms (e.g. collapse on exercise) or family history of an inherited cardiac condition should see a physician irrespective of age. Finally, no screening program can capture all abnormalities, and it is essential for organisations to implement a cardiac emergency plan including training on recognition and response to sudden cardiac arrest and prompt access to resuscitation, including defibrillators.

Race-specific FRAX models are evidence-based and support equitable care: a response to the ASBMR Task Force report on Clinical Algorithms for Fracture Risk

Task Force on 'Clinical Algorithms for Fracture Risk' commissioned by the American Society for Bone and Mineral Research (ASBMR) Professional Practice Committee has recommended that FRAX® models in the US do not include adjustment for race and ethnicity. This position paper finds that an agnostic model would unfairly discriminate against the Black, Asian and Hispanic communities and recommends the retention of ethnic and race-specific FRAX models for the US, preferably with updated data on fracture and death hazards. In contrast, the use of intervention thresholds based on a fixed bone mineral density unfairly discriminates against the Black, Asian and Hispanic communities in the US. This position of the Working Group on Epidemiology and Quality of Life of the International Osteoporosis Foundation (IOF) is endorsed both by the IOF and the European Society for Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases (ESCEO).

Use of kidney trajectory charts as an adjunct to chronic kidney disease guidelines- a qualitative study of general practitioners

OBJECTIVES

Chronic kidney disease (CKD) affects up to 11% of the population. General practice is at the forefront of the identification of patients with declining kidney function, and appropriate monitoring and management of patients with CKD. An individualized and patient-centred approach is currently recommended in guidelines, but would be enhanced by more detailed guidance on how this should be applied to different age groups, such as use of a kidney trajectory chart. We explored the opinion of general practitioners (GPs) about the potential utility of kidney trajectory charts.

METHODS

Qualitative study interviewing 27 Australian GPs about their management of chronic kidney disease. GPs were presented with charts that plotted percentiles of kidney function (eGFR) with age and discussed how they would use the charts manage to patients with declining kidney function. GPs' opinion was sought as to how useful these charts might be in clinical practice.

RESULTS

Most GPs were positive about the use of kidney trajectory charts to assist them with recognition and management of declining kidney function in general practice: e.g, comments included a "valuable tool", "a bit of an eye opener"," will help me explain to the patients", "I'll stick it on my wall.". GPs responded that the charts could help monitor patients, trigger early recognition of a younger patient at risk, and assist with older patients to determine when treatment may not be warranted. GPs also thought that charts could also be useful to motivate patients and help them monitor their own condition.

CONCLUSIONS

Use of percentile charts in conjunction with the current CKD guidelines help support a patient-centred model of care. Kidney trajectory charts can help patients to understand their risk of further kidney damage or decline. Research on the use of these charts in clinical practice should be undertaken to further develop their use.

Mapping the regulatory landscape for artificial intelligence in health within the European Union

Regulatory frameworks for artificial intelligence (AI) are needed to mitigate risks while ensuring the ethical, secure, and effective implementation of AI technology in healthcare and population health. In this article, we present a synthesis of 141 binding policies applicable to AI in healthcare and population health in the EU and 10 European countries. The EU AI Act sets the overall regulatory framework for AI, while other legislations set social, health, and human rights standards, address the safety of technologies and the implementation of innovation, and ensure the protection and safe use of data. Regulation specifically pertaining to AI is still nascent and scarce, though a combination of data, technology, innovation, and health and human rights policy has already formed a baseline regulatory framework for AI in health. Future work should explore specific regulatory challenges, especially with respect to AI medical devices, data protection, and data enablement.

Rationale and Design of the Australasian Registry of Screening ECGs in National Athletes Project

BACKGROUND

Cardiac screening of elite athletes is widely recommended by Australasian sporting federations, but data are not structured to be shared. Data are lacking from underrepresented groups to inform ECG interpretation guidelines. The ARENA (Australasian Registry of Screening ECGs in National Athletes) project is a retrospective and prospective, multicenter, longitudinal, observational registry of athlete cardiac screening results and outcomes. The aim is to create a repository to improve our understanding of the diagnoses and outcomes of screening.

METHODS

Participating sports that conduct cardiac screening of athletes will contribute data. This includes an initial collection (retrospective data, waiver of consent) and future prospective data (opt-out consent). Data include sex, age, sport/event, screening date, ECG findings, cardiac test results, follow-up details, sport participation status, cardiac diagnoses, and major cardiovascular outcomes defined as sudden cardiac arrest/death, cardiac syncope or implanted cardioverter defibrillator shock, cardiac hospitalization, and arrhythmias requiring intervention. Comparisons will be made between diagnoses, outcomes, and ECG features and analyzed by sport and sex. The ARENA project was developed in collaboration with sporting bodies, team physicians, and players association representatives and endorsed by the Australasian College of Sport & Exercise Physicians and Sports Medicine Australia.

CONCLUSIONS

The ARENA project will provide a long-term international data repository to improve our understanding of ECG interpretation, cardiac screening and diagnoses, and the prevalence of cardiovascular outcomes in screened athletes. A unique aim is to address evidence gaps in underrepresented athlete groups, specifically female athletes and Indigenous populations. Results will inform screening policies and guidelines.

Race adjustments in clinical algorithms can help correct for racial disparities in data quality

Despite ethical and historical arguments for removing race from clinical algorithms, the consequences of removal remain unclear. Here, we highlight a largely undiscussed consideration in this debate: varying data quality of input features across race groups. For example, family history of cancer is an essential predictor in cancer risk prediction algorithms but is less reliably documented for Black participants and may therefore be less predictive of cancer outcomes. Using data from the Southern Community Cohort Study, we assessed whether race adjustments could allow risk prediction models to capture varying data quality by race, focusing on colorectal cancer risk prediction. We analyzed 77,836 adults with no history of colorectal cancer at baseline. The predictive value of self-reported family history was greater for White participants than for Black participants. We compared two cancer risk prediction algorithms-a race-blind algorithm which included standard colorectal cancer risk factors but not race, and a race-adjusted algorithm which additionally included race. Relative to the race-blind algorithm, the race-adjusted algorithm improved predictive performance, as measured by goodness of fit in a likelihood ratio test (P-value: <0.001) and area under the receiving operating characteristic curve among Black participants (P-value: 0.006). Because the race-blind algorithm underpredicted risk for Black participants, the race-adjusted algorithm increased the fraction of Black participants among the predicted high-risk group, potentially increasing access to screening. More broadly, this study shows that race adjustments may be beneficial when the data quality of key predictors in clinical algorithms differs by race group.

An antiracism framework for educating nursing professionals

BACKGROUND

A conceptual, methodological, and theoretical framework is needed in Nursing Education to center racism, in the curriculum, as a root cause of health inequity.

PURPOSE

To provide Nursing and health professions' educators with a comprehensive unifying framework to fundamentally conceptualize and deliver a curriculum which positions racism's impact as a root cause of health inequities.

METHODS

Critical race theory is the underpinning for a historical analysis of racism and a critique of scientific racism, whiteness, and white supremacy ideologies that perpetuate harmful and lethal outcomes for racialized individuals and communities.

RESULTS

This framework conceptualizes learning, unlearning, relearning, and reflective practice as the fundamental process needed to transformative nursing education and advance health equity.

DISCUSSION

Methodological application is given for 1) unlearning harmful white supremacy ideology 2) learning that racism as it is embedded in every sector of American life and racial inequities are inherent in the health care system 3) relearning the importance of counternarratives and building structural competency and 4) engaging in reflective practice to challenge deficit paradigms assigned to racialized people and their communities.

CONCLUSION

The Antiracism Framework provides foundational principles, guiding steps, and rationale for curricula that acknowledges the critical role of racism as a barrier to achieving health equity.

Atherosclerotic Cardiovascular Disease Risk Estimates Using the Predicting Risk of Cardiovascular Disease Events Equations

Importance

In 2023, the American Heart Association (AHA) developed the Predicting Risk of Cardiovascular Disease Events (PREVENT) equations to estimate 10-year risk of atherosclerotic cardiovascular disease (ASCVD), as an update to the 2013 pooled cohort equations (PCEs). The PREVENT equations were derived from contemporary cohorts and removed race and added variables for kidney function and statin use.

Objective

To compare national estimates of 10-year ASCVD risk using the PCEs and PREVENT equations and how these equations affect recommendations for primary prevention statin therapy.

Design, Setting, and Participants

This cross-sectional study included adults aged 40 to 75 years who participated in the National Health and Nutrition Examination Survey from 2017 to March 2020. Adults were defined as eligible for primary prevention statin use based on the 2019 AHA/American College of Cardiology guideline on the primary prevention of cardiovascular disease. Data were weighted to be nationally representative and were analyzed from December 27, 2023, to January 31, 2024.

Main Outcomes and Measures

The 10-year ASCVD risk and eligibility for primary prevention statin therapy based on PREVENT and PCE calculations.

Results

In the weighted sample of 3785 US adults (mean [SD] age, 55.7 [9.7] years; 52.5% women) without known ASCVD, 20.7% reported current statin use. The mean estimated 10-year ASCVD risk was 8.0% (95% CI, 7.6%-8.4%) using the PCEs and 4.3% (95% CI, 4.1%-4.5%) using the PREVENT equations. Across all age, sex, and racial subgroups, compared with the PCEs, the mean estimated 10-year ASCVD risk was lower using the PREVENT equations, with the largest difference for Black adults (10.9% [95% CI, 10.1%-11.7%] vs 5.1% [95% CI 4.7%-5.4%]) and individuals aged 70 to 75 years (22.8% [95% CI, 21.6%-24.1%] vs 10.2% [95% CI, 9.6%-10.8%]). The use of the PREVENT equations instead of the PCEs could reduce the number of adults meeting criteria for primary prevention statin therapy from 45.4 million (95% CI, 40.3 million-50.4 million) to 28.3 million (95% CI, 25.2 million-31.4 million). In other words, 17.3 million (95% CI, 14.8 million-19.7 million) adults recommended statins based on the PCEs would no longer be recommended statins based on PREVENT equations, including 4.1 million (95% CI, 2.8 million-5.5 million) adults currently taking statins. Based on the PREVENT equations, 44.1% (95% CI, 38.6%-49.5%) of adults eligible for primary prevention statin therapy reported currently taking statins, equating to 15.8 million (95% CI, 13.4 million-18.2 million) individuals eligible for primary prevention statins who reported not taking statins.

Conclusions and Relevance

This cross-sectional study found that use of the PREVENT equations was associated with fewer US adults being eligible for primary prevention statin therapy; however, the majority of adults eligible for receiving such therapy based on PREVENT equations did not report statin use.

How structural racism, neighborhood deprivation, and maternal characteristics contribute to inequities in birth outcomes

Decades of disparities in health between infants born to Black and White mothers have persisted in recent years, despite policy initiatives to improve maternal and reproductive health for Black mothers. Although scholars have increasingly recognized the critical role that structural racism plays in driving health outcomes for Black people, measurement of this relationship remains challenging. This study examines trends in preterm birth and low birth weight between 2007 and 2018 separately for births to Black and White mothers. Using a multivariate regression model, we evaluated potential factors, including an index of racialized disadvantage as well as community- and individual-level factors that serve as proxy measures for structural racism, that may contribute to White-Black differences in infant health. Finally, we assessed whether unequal effects of these factors may explain differences in birth outcomes. We found that differences in the effects of these factors appear to explain about half of the underlying disparity in infant health.

Considering Adverse Effects of Common Antihypertensive Medications in the ED

PURPOSE OF REVIEW

To evaluate the adverse effects of common antihypertensive agents utilized or encountered in the Emergency Department.

RECENT FINDINGS

All categories of antihypertensive agents may manifest adverse effects, inclusive of adverse drug reactions (ADRs), drug-to-drug interactions, or accidental overdose. Adverse effects, and specifically ADRs, may be stratified into the organ systems affected, might require specific time-sensitive interventions, could pose particular risks to vulnerable populations, and may result in significant morbidity, and potential mortality. Adverse effects of common antihypertensive agents may be encountered in the ED, necessitating that ED systems of care are poised to prevent, recognize, and intervene when adverse effects arise.

A Multigenerational Model of Environmental Risk for Black, Indigenous, and People of Color (BIPOC) Children and Families

BACKGROUND

In recent years, public discourse has increasingly brought institutional and structural racism to the foreground of discussion on the well-being of BIPOC (Black, Indigenous, and People of Color) communities. Environmental toxicity in combination with the social triggers of institutional and structural racism are among the factors that shape the short- and long-term health of BIPOC Americans across multiple lifespans.

OBJECTIVES

We outline a 2 + Generation Model for examining the mechanisms through which institutional and structural racism promotes the intergenerational transmission of environmental health risk and family and interpersonal relationships across the life course and across multiple generations. We present the model's theoretical underpinnings and rationale, discuss model limitations and needed sources of data, and implications for research, policy, and intervention.

DISCUSSION

Parents and children are not only biologically linked in terms of transmission of environmental toxicities, but they are also linked socially and intergenerationally. The 2 + Generation Model foregrounds family and interpersonal relationships occurring within developmental contexts that are influenced by environmental toxicity as well as institutional and structural racism. In sum, the 2 + Generation Model highlights the need for an equity-first interdisciplinary approach to environmental health and redirects the burden of risk reduction away from the individual and onto the institutions and structures that perpetuate the racial disparities in exposure. Doing so requires institutional investment in expanded, multigenerational, and multimethod datasets. https://doi.org/10.1289/EHP13110.

A Call to Action: Supporting Black Maternal and Infant Health Using the Collective Impact Model

This commentary advocates for a comprehensive approach to addressing the Black maternal and infant health crisis, utilizing the collective impact model with health equity at its center. Black women in the United States face alarmingly high rates of maternal morbidity and mortality compared to white women. Black women are twice as likely to have premature and low birthweight babies than white women, exposing both the expectant woman and child to various health risks. This crisis stems from systemic racism, implicit bias in healthcare, and a lack of targeted health communications for pregnant Black women. The urgency of this situation requires a bold and unified response through collaboration and coordination among healthcare providers, local and grassroots community-based organizations (CBOs), and digital health communicators. A comprehensive Black maternal and infant health campaign embedded within the collective impact model and led by a dedicated backbone organization would facilitate the coordination and involvement of diverse stakeholders. Central to these efforts should be the acknowledgment that systemic racism perpetuates health inequities. Consequently, any initiatives to improve health outcomes should prioritize health equity by valuing and incorporating Black women's perspectives. This involves crafting a responsive strategy and placing Black women at the forefront of content creation, program strategy, and evaluation. Through a collaborative effort involving healthcare partners, CBOs, and health communicators, we can have an impact far more significant than any single initiative. Immediate action is needed to dismantle systemic barriers and ensure every Black woman and infant receives the care and support they deserve. Black maternal health disparities in the United States have been widely acknowledged and studied. It is well-established that Black women face significantly higher rates of maternal morbidity and mortality compared to their white counterparts, indicative of a severe healthcare crisis. This opinion piece contributes to the discourse by proposing a comprehensive solution grounded in the collective impact model, which emphasizes collaboration and coordination across various stakeholders. This approach represents a shift from past siloed efforts, aiming to tackle the urgent issue of Black maternal and infant health with a multidisciplinary approach centered on health equity.

Thyroid Cancer Incidence and Tumor Size in New Mexico American Indians, Hispanics, and Non-Hispanic Whites, 1992 to 2019

BACKGROUND

The incidence of thyroid cancer in the United States has risen dramatically since the 1970s, driven by an increase in the diagnosis of small tumors. There is a paucity of published New Mexico (NM) specific data regarding thyroid cancer. We hypothesized that due to New Mexico's unique geographic and cultural makeup, the incidence of thyroid cancer and tumor size at diagnosis in this state would differ from that demonstrated on a national level.

METHODS

The New Mexico Tumor Registry (NMTR) was queried to include all NM residents diagnosed with thyroid cancer between 1992 and 2019. For 2010 to 2019, age-adjusted incidence rates were calculated via direct method using the 2000 United States population as the adjustment standard. Differences in incidence rate and tumor size by race/ethnicity and residence (metropolitan vs non-metropolitan) were assessed with rate ratios between groups. For 1992 to 2019, temporal trends in age-adjusted incidence rates for major race/ethnic groups in NM [Non-Hispanic White (NHW), Hispanic, and American Indian (AI)] were assessed by joinpoint regression using National Cancer Institute software.

RESULTS

Our study included 3,161 patients for the time period 2010 to 2019, including NHW (1518), Hispanic (1425), and AI (218) cases. The overall incidence rates for NM AIs were lower than those for Hispanics and NHWs because of a decreased incidence of very small tumors (<1.1 cm). The incidence rates for large tumors (>5.1 cm) was equivalent among groups. In the early 2000s, Hispanics also had lower rates of small tumors when compared to NHWs but this trend disappeared over time.

CONCLUSION

AIs in New Mexico have been left out of the nationwide increase in incidental diagnosis of small thyroid tumors. This same pattern was noted for Hispanics in the early 2000s but changed over time to mirror incidence rates for NHWs. These data are illustrative of the health care disparities that exist among New Mexico's population and how these disparities have changed over time.

Identifying low acuity Emergency Department visits with a machine learning approach: The low acuity visit algorithms (LAVA)

OBJECTIVE

To improve the performance of International Classification of Disease (ICD) code rule-based algorithms for identifying low acuity Emergency Department (ED) visits by using machine learning methods and additional covariates.

DATA SOURCES

We used secondary data on ED visits from the National Hospital Ambulatory Medical Survey (NHAMCS), from 2016 to 2020.

STUDY DESIGN

We established baseline performance metrics with seven published algorithms consisting of International Classification of Disease, Tenth Revision codes used to identify low acuity ED visits. We then trained logistic regression, random forest, and gradient boosting (XGBoost) models to predict low acuity ED visits. Each model was trained on five different covariate sets of demographic and clinical data. Model performance was compared using a separate validation dataset. The primary performance metric was the probability that a visit identified by an algorithm as low acuity did not experience significant testing, treatment, or disposition (positive predictive value, PPV). Subgroup analyses assessed model performance across age, sex, and race/ethnicity.

DATA COLLECTION

We used 2016-2019 NHAMCS data as the training set and 2020 NHAMCS data for validation.

PRINCIPAL FINDINGS

The training and validation data consisted of 53,074 and 9542 observations, respectively. Among seven rule-based algorithms, the highest-performing had a PPV of 0.35 (95% CI [0.33, 0.36]). All model-based algorithms outperformed existing algorithms, with the least effective-random forest using only age and sex-improving PPV by 26% (up to 0.44; 95% CI [0.40, 0.48]). Logistic regression and XGBoost trained on all variables improved PPV by 83% (to 0.64; 95% CI [0.62, 0.66]). Multivariable models also demonstrated higher PPV across all three demographic subgroups.

CONCLUSIONS

Machine learning models substantially outperform existing algorithms based on ICD codes in predicting low acuity ED visits. Variations in model performance across demographic groups highlight the need for further research to ensure their applicability and fairness across diverse populations.

Evaluating enrollment and representation in COVID-19 and HIV vaccine clinical trials

Background

Vaccine clinical trials should strive to recruit a racially, socioeconomically, and ethnically diverse range of participants to ensure appropriate representation that matches population characteristics. Yet, full inclusion in research is often limited.

Methods

A single-center retrospective study was conducted of adults enrolled at Brigham and Women's Hospital (Boston, MA) between July 2020 and December 2021. Demographic characteristics, including age, race, ethnicity, ZIP code, and sex assigned at birth, were analyzed from both HIV and COVID-19 vaccine trials during the study period, acknowledging the limitations to representation under these parameters. We compared the educational attainment of vaccine trial participants to residents of the Massachusetts metropolitan area, geocoded participants' addresses to their census block group, and linked them to reported median household income levels from publicly available data for 2020. Frequency and quartile analyses were carried out, and spatial analyses were performed using ArcGIS Online web-based mapping software (Esri).

Results

A total of 1030 participants from four COVID-19 vaccine trials (n = 916 participants) and six HIV vaccine trials (n = 114 participants) were included in the analysis. The median age was 49 years (IQR 33-63) and 28 years (IQR 24-34) for the COVID-19 and HIV vaccine trials, respectively. Participants identifying as White were the majority group represented for both the COVID-19 (n = 598, 65.3%) and HIV vaccine trials (n = 83, 72.8%). Fewer than 25% of participants identified as Hispanic or Latin. Based on ZIP code of residence, the median household income for COVID-19 vaccine clinical trial participants (n = 846) was 102,088 USD (IQR = 81,442-126,094). For HIV vaccine clinical trial participants (n = 109), the median household income was 101,266 USD (IQR 75,052-108,832).

Conclusion

We described the characteristics of participants enrolled for HIV and COVID-19 vaccine trials at a single center and found similitude in geographical distribution, median incomes, and proportion of underrepresented individuals between the two types of vaccine candidate trials. Further outreach efforts are needed to ensure the inclusion of individuals from lower educational and socioeconomic brackets. In addition, continued and sustained efforts are necessary to ensure inclusion of individuals from diverse racial and ethnic backgrounds.

Racial Disparities in Sports Cardiology: A Review

Importance

Racial disparities in cardiovascular health, including sudden cardiac death (SCD), exist among both the general and athlete populations. Among competitive athletes, disparities in health outcomes potentially influenced by social determinants of health (SDOH) and structural racism remain inadequately understood. This narrative review centers on race in sports cardiology, addressing racial disparities in SCD risk, false-positive cardiac screening rates among athletes, and the prevalence of left ventricular hypertrophy, and encourages a reexamination of race-based practices in sports cardiology, such as the interpretation of screening 12-lead electrocardiogram findings.

Observations

Drawing from an array of sources, including epidemiological data and broader medical literature, this narrative review discusses racial disparities in sports cardiology and calls for a paradigm shift in approach that encompasses 3 key principles: race-conscious awareness, clinical inclusivity, and research-driven refinement of clinical practice. These proposed principles call for a shift away from race-based assumptions towards individualized, health-focused care in sports cardiology. This shift would include fostering awareness of sociopolitical constructs, diversifying the medical team workforce, and conducting diverse, evidence-based research to better understand disparities and address inequities in sports cardiology care.

Conclusions and Relevance

In sports cardiology, inadequate consideration of the impact of structural racism and SDOH on racial disparities in health outcomes among athletes has resulted in potential biases in current normative standards and in the clinical approach to the cardiovascular care of athletes. An evidence-based approach to successfully address disparities requires pivoting from outdated race-based practices to a race-conscious framework to better understand and improve health care outcomes for diverse athletic populations.

The mediating role of systemic inflammation and moderating role of racialization in disparities in incident dementia

BACKGROUND

Exposure to systemic racism is linked to increased dementia burden. To assess systemic inflammation as a potential pathway linking exposure to racism and dementia disparities, we investigated the mediating role of C-reactive protein (CRP), a systemic inflammation marker, and the moderating role of the racialization process in incident dementia.

METHODS

In the US Health and Retirement Study (n = 6,908), serum CRP was measured at baseline (2006, 2008 waves). Incident dementia was classified by cognitive tests over a six-year follow-up. Self-reported racialized categories were a proxy for exposure to the racialization process. We decomposed racialized disparities in dementia incidence (non-Hispanic Black and/or Hispanic vs. non-Hispanic white) into 1) the mediated effect of CRP, 2) the moderated portion attributable to the interaction between racialized group membership and CRP, and 3) the controlled direct effect (other pathways through which racism operates).

RESULTS

The 6-year cumulative incidence of dementia is 12%. Among minoritized participants (i.e., non-Hispanic Black and/or Hispanic), high CRP levels ( ≥ 75th percentile or 4.73μg/mL) are associated with 1.26 (95%CI: 0.98, 1.62) times greater risk of incident dementia than low CRP ( < 4.73μg/mL). Decomposition analysis comparing minoritized versus non-Hispanic white participants shows that the mediating effect of CRP accounts for 3% (95% CI: 0%, 6%) of the racial disparity, while the interaction effect between minoritized group status and high CRP accounts for 14% (95% CI: 1%, 27%) of the disparity. Findings are robust to potential violations of causal mediation assumptions.

CONCLUSIONS

Minoritized group membership modifies the relationship between systemic inflammation and incident dementia.

Use of Maternal Race and Weight Provides Equitable Performance in Serum Screening for Open Neural Tube Defects

BACKGROUND

Maternal serum alpha-fetoprotein (AFP) levels are used in screening for open neural tube defects (ONTD). Historical reports show that AFP levels and maternal weights are higher in self-reported Black than White individuals, but recent reports question the need to account for these variables in screening. Our study compares screening performance with and without accounting for race.

METHODS

Retrospective analysis was performed on deidentified prenatal screening records including maternal weight and self-reported race of White or Black. Gestational age-specific medians and weight-adjusted multiples of the median levels were calculated separately for each group and using a race-agnostic analysis. Outcome measures included the proportion of screen-positive results.

RESULTS

Records for analysis (n = 13 316) had an ultrasound confirmed gestational age between 15 and 21 completed weeks, singleton pregnancy, and self-reported race. Race was Black for 26.3%. AFP levels for pregnancies in Black individuals were higher than in White individuals: 6% to 11% depending on gestational age. Race-specific gestational age and maternal weight analyses resulted in similar screen-positive rates for self-reported White and Black individuals at 0.74% vs 1.00%, respectively (P = 0.14). However, use of race-agnostic analyses resulted in a screen-positive rate that was 2.4 times higher in Black than White individuals (P < 0.001).

CONCLUSION

These data show that the historical method of accounting for maternal race and weight in prenatal screening for ONTD provides equitable performance. Using a race-agnostic methodology results in an increased screen-positive rate and a disproportionate rate of required follow-up care for individuals who self-identify as Black.