Distinct cytokine profiles in late pregnancy in Ugandan people with HIV.

During pregnancy, multiple immune regulatory mechanisms establish an immune-tolerant environment for the allogeneic fetus, including cellular signals called cytokines that modify immune responses. However, the impact of maternal HIV infection on these responses is incompletely characterized. We analyzed paired maternal and umbilical cord plasma collected during labor from 147 people with HIV taking antiretroviral therapy and 142 HIV-uninfected comparators. Though cytokine concentrations were overall similar between groups, using Partial Least Squares Discriminant Analysis we identified distinct cytokine profiles in each group, driven by higher IL-5 and lower IL-8 and MIP-1α levels in pregnant people with HIV and higher RANTES and E-selectin in HIV-unexposed umbilical cord plasma (P-value < 0.01). Furthermore, maternal RANTES, SDF-α, gro α -KC, IL-6, and IP-10 levels differed significantly by HIV serostatus (P < 0.01). Although global maternal and umbilical cord cytokine profiles differed significantly (P < 0.01), umbilical cord plasma profiles were similar by maternal HIV serostatus. We demonstrate that HIV infection is associated with a distinct maternal plasma cytokine profile which is not transferred across the placenta, indicating a placental role in coordinating local inflammatory response. Furthermore, maternal cytokine profiles in people with HIV suggest an incomplete shift from Th2 to Th1 immune phenotype at the end of pregnancy.

Noticing and Responding to Calorie Labels on Restaurant Menus: Patterns in Sexual-Minority Men.

INTRODUCTION: A calorie-labeling policy for restaurant menus was implemented in 2018. Whether and how sexual-minority men use this information has not been evaluated. METHODS: The Men's Body Project, a 2020 cross-sectional survey study of 504 cisgender sexual-minority men (mean age=35.8±10.4 years, 71.0% White, 5.6% Asian, 14.3% Black, 9.1% another/multiple race identities) assessed respondents' awareness of calorie labels on restaurant menus and subsequent responses. Additional questions were asked about weight-change goals, body image, disordered eating behaviors, and muscle-enhancing supplement use. Analyses in 2022-2023 used multivariate logistic regression to assess the associations between noticing calories and weight- and muscularity-oriented behaviors and, among those who noticed calorie labels, whether participants reported using this information to order more or fewer calories. RESULTS: Approximately half of the participants reported noticing calorie labels. Those who did were more likely to report engaging in disordered eating behaviors (OR=2.03). Among participants who noticed menu labels, ordering fewer calories was the most frequent response, whereas 25% reported not changing the caloric content of their order. Many participants (21%) reported ordering both more and fewer calories, and this heterogeneous ordering pattern was associated with both disordered eating (OR=4.70) and muscle-enhancing behaviors (OR=9.42) compared with that among participants who did not report behaviors. Reporting weight-control efforts was associated with ordering fewer calories than participants not doing anything to change their weight (OR=2.53). CONCLUSIONS: Most participants noticed calorie labels on menus, and many reported subsequently ordering fewer calories. Disordered eating and muscle-enhancing behaviors were associated with behavior changes in response to calorie information.

Examining the Impact of Selected Sociodemographic Factors and Cancer-Related Fatalistic Beliefs on Patient Engagement via Health Information Technology Among Older Adults: Cross-Sectional Analysis.

BACKGROUND: Despite the role of health information technology (HIT) in patient engagement processes and government incentives for HIT development, research regarding HIT is lacking among older adults with a high burden of chronic diseases such as cancer. This study examines the role of selected sociodemographic factors and cancer-related fatalistic beliefs on patient engagement expressed through HIT use for patient engagement in adults aged ≥65 years. We controlled for cancer diagnosis to account for its potential influence on patient engagement. OBJECTIVE: This study has 2 aims: to investigate the role of sociodemographic factors such as race, education, poverty index, and psychosocial factors of cancer fatalistic beliefs in accessing and using HIT in older adults and to examine the association between access and use of HIT in the self-management domain of patient activation that serves as a precursor to patient engagement. METHODS: This is a secondary data analysis of a subset of the Health Information National Trend Survey (Health Information National Trend Survey 4, cycle 3). The subset included individuals aged ≥65 years with and without a cancer diagnosis. The relationships between access to and use of HIT to several sociodemographic variables and psychosocial factors of fatalistic beliefs were analyzed. Logistic and linear regression models were fit to study these associations. RESULTS: This study included 180 individuals aged ≥65 years with a cancer diagnosis and 398 without a diagnosis. This analysis indicated that having less than a college education level (P=<.001), being an individual from an ethnic and minority group (P=<.001), and living in poverty (P=.001) were significantly associated with decreased access to HIT. Reduced HIT use was associated with less than a college education (P=.001) and poverty(P=.02). This analysis also indicated that fatalistic beliefs about cancer were significantly associated with lower HIT use (P=.03). Specifically, a 1-point increase in the cancer fatalistic belief score was associated with a 36% decrease in HIT use. We found that controlling for cancer diagnosis did not affect the outcomes for sociodemographic variables or fatalistic beliefs about cancer. However, patients with access to HIT had a self-management domain of patient activation (SMD) score of 0.21 points higher (P=.003) compared with patients who did not have access. SMD score was higher by 0.28 points (P=.002) for individuals who used HIT and 0.14 points higher (P=.04) who had a prior diagnosis of cancer. CONCLUSIONS: Sociodemographic factors (education, race, poverty, and cancer fatalistic beliefs) impact HIT access and use in older adults, regardless of prior cancer diagnosis. Among older adults, HIT users report higher self-management, which is essential for patient activation and engagement.

Traditional Machine Learning Methods versus Deep Learning for Meningioma Classification, Grading, Outcome Prediction, and Segmentation: A Systematic Review and Meta-Analysis.

BACKGROUND: Meningiomas are common intracranial tumors. Machine learning (ML) algorithms are emerging to improve accuracy in 4 primary domains: classification, grading, outcome prediction, and segmentation. Such algorithms include both traditional approaches that rely on hand-crafted features and deep learning (DL) techniques that utilize automatic feature extraction. The aim of this study was to evaluate the performance of published traditional ML versus DL algorithms in classification, grading, outcome prediction, and segmentation of meningiomas. METHODS: A systematic review and meta-analysis were conducted. Major databases were searched through September 2021 for publications evaluating traditional ML versus DL models on meningioma management. Performance measures including pooled sensitivity, specificity, F1-score, area under the receiver-operating characteristic curve, positive and negative likelihood ratios (LR+, LR-) along with their respective 95% confidence intervals (95% CIs) were derived using random-effects models. RESULTS: Five hundred thirty-four records were screened, and 43 articles were included, regarding classification (3 articles), grading (29), outcome prediction (7), and segmentation (6) of meningiomas. Of the 29 studies that reported on grading, 10 could be meta-analyzed with 2 DL models (sensitivity 0.89, 95% CI: 0.74-0.96; specificity 0.91, 95% CI: 0.45-0.99; LR+ 10.1, 95% CI: 1.33-137; LR- 0.12, 95% CI: 0.04-0.59) and 8 traditional ML (sensitivity 0.74, 95% CI: 0.62-0.83; specificity 0.93, 95% CI: 0.79-0.98; LR+ 10.5, 95% CI: 2.91-39.5; and LR- 0.28, 95% CI: 0.17-0.49). The insufficient performance metrics reported precluded further statistical analysis of other performance metrics. CONCLUSIONS: ML on meningiomas is mostly carried out with traditional methods. For meningioma grading, traditional ML methods generally had a higher LR+, while DL models a lower LR-.

Examining the Relationship Between Rurality, Risk Perception, and COVID-19 Vaccine Willingness Among Rural Latinos in Arizona and California.

This study aimed to investigate the relationship between rurality and risk perception of getting or transmitting COVID-19 and willingness to get the COVID-19 vaccine in a sample of Latinos across Arizona and California's Central Valley (n = 419). The results revealed that rural Latinos are more concerned about getting and transmitting COVID-19, but less willing to get vaccinated. Our findings suggest that perceptions of risk alone do not play a sole role in influencing risk management behavior among rural Latinos. While rural Latinos may have heightened perception of the risks associated with COVID-19, vaccine hesitancy persists due to a variety of structural and cultural factors. These factors included limited access to healthcare facilities, language barriers, concerns about vaccine safety and effectiveness, and cultural factors such as strong family and community ties. The study highlights the need for culturally-tailored education and outreach efforts that address the specific needs and concerns of this community to increase vaccination rates and reduce the disproportionate burden of COVID-19 among Latino communities living in rural areas.

Structural racism through Sundown towns and its relationship to COVID-19 local risk and racial and ethnic diversity.

"Sundown towns" across the US prevented racial and ethnic minorities from living and working within their borders as they forced minorities to leave these towns after sunset. The objective of this study was to explore the relationship between sundown town status, COVID-19 local risk index and racial and ethnic diversity. A multi-level hierarchical model was used to examine the effect of historical segregation through sundown towns status on present day COVID-19 local risk index and city-level diversity. Over 2,400 Sundown towns were cataloged across the United States, with the greatest density in the Midwest. Sundown towns, which historically excluded racial and ethnic minorities, had significantly less city-level diversity and lower COVID-19 local risk index compared to non-Sundown towns. Findings show that Sundown towns perpetuate residual segregation which continues to impact current inequities in COVID-19 risk among racial and ethnic minorities at the neighborhood level. We recommend that public health officials for pandemic preparedness should devote greater resources to these historically segregated racial and ethnic minority areas because of the historic structural racism that has placed these places at higher risk.

Machine Learning for Acute Kidney Injury Prediction in the Intensive Care Unit.

Machine learning is the field of artificial intelligence in which computers are trained to make predictions or to identify patterns in data through complex mathematical algorithms. It has great potential in critical care to predict outcomes, such as acute kidney injury, and can be used for prognosis and to suggest management strategies. Machine learning can also be used as a research tool to advance our clinical and biochemical understanding of acute kidney injury. In this review, we introduce basic concepts in machine learning and review recent research in each of these domains.

Quantitative analysis of the selective pressure impression technique using CAD-CAM technology: A pilot clinical study.

STATEMENT OF PROBLEM: Quantitative 3D clinical analysis of the selective pressure impression technique directly measuring tissue displacement during impression making for complete maxillary dentures is lacking. PURPOSE: The purpose of this clinical study was to digitally compare impressions made of the edentulous maxillary ridge by using the selective pressure impression technique with different amounts of relief incorporated into custom tray designs. MATERIAL AND METHODS: Nine participants receiving maxillary complete dentures were enrolled in the study. An initial custom tray was fabricated in urethane dimethacrylate by using the alternative border molding technique without relief and scanned to create a standard tessellation language (STL) file from which 3 groups of custom trays were designed and 3D printed with 0.0-mm (no relief), 1.0-mm, and 3.0-mm relief over the anterior ridge and median palatal suture. Definitive impressions using each of the 4 custom trays were made with a consistent volume of light-body polyvinyl siloxane impression material. The definitive impressions were scanned, and the STL files were superimposed to investigate the topographical differences among the groups, each with respect to the no relief, 3D-printed custom tray definitive impression. Mean volumetric differences for all 3 groups were measured in areas where relief was used and statistically analyzed with the Friedman test (α=.05). RESULTS: No significant difference was found among any of the 3 groups of superimposed impressions in areas of no relief, 1.0-mm, and 3.0-mm relief (P=.558). The mean difference ±standard deviation for each comparison in regions of the anterior ridge and median palatal suture were 0.07 ±0.06 mm for no relief, -0.03 ±0.07 mm for the 1.0-mm tray relief, and -0.04 ±0.09 mm for the 3.0-mm tray relief. The negative values in mean difference indicated less compression of underlying tissues compared with the reference border molded urethane dimethacrylate custom tray impression. CONCLUSIONS: Although results showed less compression when compared with that of the control group, custom tray relief of 1.0 mm and 3.0 mm over the anterior residual alveolar ridge and median palatal suture did not significantly impact the resulting impression topography when compared with no relief custom trays.

Best practices in the real-world data life cycle.

With increasing digitization of healthcare, real-world data (RWD) are available in greater quantity and scope than ever before. Since the 2016 United States 21st Century Cures Act, innovations in the RWD life cycle have taken tremendous strides forward, largely driven by demand for regulatory-grade real-world evidence from the biopharmaceutical sector. However, use cases for RWD continue to grow in number, moving beyond drug development, to population health and direct clinical applications pertinent to payors, providers, and health systems. Effective RWD utilization requires disparate data sources to be turned into high-quality datasets. To harness the potential of RWD for emerging use cases, providers and organizations must accelerate life cycle improvements that support this process. We build on examples obtained from the academic literature and author experience of data curation practices across a diverse range of sectors to describe a standardized RWD life cycle containing key steps in production of useful data for analysis and insights. We delineate best practices that will add value to current data pipelines. Seven themes are highlighted that ensure sustainability and scalability for RWD life cycles: data standards adherence, tailored quality assurance, data entry incentivization, deploying natural language processing, data platform solutions, RWD governance, and ensuring equity and representation in data.

A distributed approach to the regulation of clinical AI.

Regulation is necessary to ensure the safety, efficacy and equitable impact of clinical artificial intelligence (AI). The number of applications of clinical AI is increasing, which, amplified by the need for adaptations to account for the heterogeneity of local health systems and inevitable data drift, creates a fundamental challenge for regulators. Our opinion is that, at scale, the incumbent model of centralized regulation of clinical AI will not ensure the safety, efficacy, and equity of implemented systems. We propose a hybrid model of regulation, where centralized regulation would only be required for applications of clinical AI where the inference is entirely automated without clinician review, have a high potential to negatively impact the health of patients and for algorithms that are to be applied at national scale by design. This amalgam of centralized and decentralized regulation we refer to as a distributed approach to the regulation of clinical AI and highlight the benefits as well as the pre-requisites and challenges involved.

Edge Overlap in Weighted and Directed Social Networks.

With the increasing availability of behavioral data from diverse digital sources, such as social media sites and cell phones, it is now possible to obtain detailed information about the structure, strength, and directionality of social interactions in varied settings. While most metrics of network structure have traditionally been defined for unweighted and undirected networks only, the richness of current network data calls for extending these metrics to weighted and directed networks. One fundamental metric in social networks is edge overlap, the proportion of friends shared by two connected individuals. Here we extend definitions of edge overlap to weighted and directed networks, and present closed-form expressions for the mean and variance of each version for the Erdos-Rényi random graph and its weighted and directed counterparts. We apply these results to social network data collected in rural villages in southern Karnataka, India. We use our analytical results to quantify the extent to which the average overlap of the empirical social network deviates from that of corresponding random graphs and compare the values of overlap across networks. Our novel definitions allow the calculation of edge overlap for more complex networks and our derivations provide a statistically rigorous way for comparing edge overlap across networks.

Performance of intensive care unit severity scoring systems across different ethnicities in the USA: a retrospective observational study.

BACKGROUND: Despite wide use of severity scoring systems for case-mix determination and benchmarking in the intensive care unit (ICU), the possibility of scoring bias across ethnicities has not been examined. Guidelines on the use of illness severity scores to inform triage decisions for allocation of scarce resources, such as mechanical ventilation, during the current COVID-19 pandemic warrant examination for possible bias in these models. We investigated the performance of the severity scoring systems Acute Physiology and Chronic Health Evaluation IVa (APACHE IVa), Oxford Acute Severity of Illness Score (OASIS), and Sequential Organ Failure Assessment (SOFA) across four ethnicities in two large ICU databases to identify possible ethnicity-based bias. METHODS: Data from the electronic ICU Collaborative Research Database (eICU-CRD) and the Medical Information Mart for Intensive Care III (MIMIC-III) database, built from patient episodes in the USA from 2014-15 and 2001-12, respectively, were analysed for score performance in Asian, Black, Hispanic, and White people after appropriate exclusions. Hospital mortality was the outcome of interest. Discrimination and calibration were determined for all three scoring systems in all four groups, using area under receiver operating characteristic (AUROC) curve for different ethnicities to assess discrimination, and standardised mortality ratio (SMR) or proxy measures to assess calibration. FINDINGS: We analysed 166 751 participants (122 919 eICU-CRD and 43 832 MIMIC-III). Although measurements of discrimination were significantly different among the groups (AUROC ranging from 0·86 to 0·89 [p=0·016] with APACHE IVa and from 0·75 to 0·77 [p=0·85] with OASIS), they did not display any discernible systematic patterns of bias. However, measurements of calibration indicated persistent, and in some cases statistically significant, patterns of difference between Hispanic people (SMR 0·73 with APACHE IVa and 0·64 with OASIS) and Black people (0·67 and 0·68) versus Asian people (0·77 and 0·95) and White people (0·76 and 0·81). Although calibrations were imperfect for all groups, the scores consistently showed a pattern of overpredicting mortality for Black people and Hispanic people. Similar results were seen using SOFA scores across the two databases. INTERPRETATION: The systematic differences in calibration across ethnicities suggest that illness severity scores reflect statistical bias in their predictions of mortality. FUNDING: There was no specific funding for this study.

A pragmatic methodology for the evaluation of digital care management in the context of multimorbidity.

Multimorbidity is a defining challenge for health systems and requires coordination of care delivery and care management. Care management is a clinical service designed to remotely engage patients between visits and after discharge in order to support self-management of chronic and emergent conditions, encourage increased use of scheduled care and address the use of unscheduled care. Care management can be provided using digital technology - digital care management. A robust methodology to assess digital care management, or any traditional or digital primary care intervention aimed at longitudinal management of multimorbidity, does not exist outside of randomized controlled trials (RCTs). RCTs are not always generalizable and are also not feasible for most healthcare organizations. We describe here a novel and pragmatic methodology for the evaluation of digital care management that is generalizable to any longitudinal intervention for multimorbidity irrespective of its mode of delivery. This methodology implements propensity matching with bootstrapping to address some of the major challenges in evaluation including identification of robust outcome measures, selection of an appropriate control population, small sample sizes with class imbalances, and limitations of RCTs. We apply this methodology to the evaluation of digital care management at a U.S. payor and demonstrate a 9% reduction in ER utilization, a 17% reduction in inpatient admissions, and a 29% increase in the utilization of preventive medicine services. From these utilization outcomes, we drive forward an estimated cost saving that is specific to a single payor's payment structure for the study time period of $641 per-member-per-month at 3 months. We compare these results to those derived from existing observational approaches, 1:1 and 1:n propensity matching, and discuss the circumstances in which our methodology has advantages over existing techniques. Whilst our methodology focuses on cost and utilization and is applied in the U.S. context, it is applicable to other outcomes such as Patient Reported Outcome Measures (PROMS) or clinical biometrics and can be used in other health system contexts where the challenge of multimorbidity is prevalent.

Performance of intensive care unit severity scoring systems across different ethnicities.

BACKGROUND: Despite wide utilisation of severity scoring systems for case-mix determination and benchmarking in the intensive care unit, the possibility of scoring bias across ethnicities has not been examined. Recent guidelines on the use of illness severity scores to inform triage decisions for allocation of scarce resources such as mechanical ventilation during the current COVID-19 pandemic warrant examination for possible bias in these models. We investigated the performance of three severity scoring systems (APACHE IVa, OASIS, SOFA) across ethnic groups in two large ICU databases in order to identify possible ethnicity-based bias. METHOD: Data from the eICU Collaborative Research Database and the Medical Information Mart for Intensive Care were analysed for score performance in Asians, African Americans, Hispanics and Whites after appropriate exclusions. Discrimination and calibration were determined for all three scoring systems in all four groups. FINDINGS: While measurements of discrimination -area under the receiver operating characteristic curve (AUROC) -were significantly different among the groups, they did not display any discernible systematic patterns of bias. In contrast, measurements of calibration -standardised mortality ratio (SMR) -indicated persistent, and in some cases significant, patterns of difference between Hispanics and African Americans versus Asians and Whites. The differences between African Americans and Whites were consistently statistically significant. While calibrations were imperfect for all groups, the scores consistently demonstrated a pattern of over-predicting mortality for African Americans and Hispanics. INTERPRETATION: The systematic differences in calibration across ethnic groups suggest that illness severity scores reflect bias in their predictions of mortality. FUNDING: LAC is funded by the National Institute of Health through NIBIB R01 EB017205. There was no specific funding for this study.

Unhealthy weight control behaviors among a sample of U.S. journalists.

OBJECTIVE: Research on body image and weight control behaviors among journalists is limited. To fill this knowledge gap, we conducted a cross-sectional study to assess the experiences of unhealthy weight control behaviors (UWCBs), binge eating, and appearance-related pressures among a sample of journalists in the United States (U.S.). METHOD: We administered an online survey to journalists assessing their roles in the news industry, engagement in UWCBs (e.g., vomiting, laxative use, dieting, fasting) and binge eating. Odds ratios of the outcomes were estimated using a series of multivariate logistic regression models. RESULTS: Over 68%, 19%, and 30% of participants reported they went on a diet, fasted for weight control, and binge ate, respectively. Our results suggest on-air journalists demonstrated higher odds of dieting compared to their counterparts who do not work in front of the camera. Furthermore, some journalists reported being subjected to appearance-related pressures in the industry. DISCUSSION: Our results provide a glimpse of weight control behaviors, binge eating, and appearance-related pressures among workers in the U.S. journalism industry. Given the concerning prevalence of purging and binge eating in our sample, future studies should assess whether journalists represent an occupation group that is at high risk of developing eating disorders.

"Yes, but will it work for my patients?" Driving clinically relevant research with benchmark datasets.

Benchmark datasets have a powerful normative influence: by determining how the real world is represented in data, they define which problems will first be solved by algorithms built using the datasets and, by extension, who these algorithms will work for. It is desirable for these datasets to serve four functions: (1) enabling the creation of clinically relevant algorithms; (2) facilitating like-for-like comparison of algorithmic performance; (3) ensuring reproducibility of algorithms; (4) asserting a normative influence on the clinical domains and diversity of patients that will potentially benefit from technological advances. Without benchmark datasets that satisfy these functions, it is impossible to address two perennial concerns of clinicians experienced in computational research: "the data scientists just go where the data is rather than where the needs are," and, "yes, but will this work for my patients?" If algorithms are to be developed and applied for the care of patients, then it is prudent for the research community to create benchmark datasets proactively, across specialties. As yet, best practice in this area has not been defined. Broadly speaking, efforts will include design of the dataset; compliance and contracting issues relating to the sharing of sensitive data; enabling access and reuse; and planning for translation of algorithms to the clinical environment. If a deliberate and systematic approach is not followed, not only will the considerable benefits of clinical algorithms fail to be realized, but the potential harms may be regressively incurred across existing gradients of social inequity.

A Framework for Predicting Impactability of Digital Care Management Using Machine Learning Methods.

Digital care management programs can reduce health care costs and improve quality of care. However, it is unclear how to target patients who are most likely to benefit from these programs ex ante, a shortcoming of current "risk score"-based approaches across many interventions. This study explores a framework to define impactability by using machine learning (ML) models to identify those patients most likely to benefit from a digital health intervention for care management. Anonymized insurance claims data were used from a commercially insured population across several US states and combined with inferred sociodemographic data. The approach involves creating 2 models and the comparative analysis of the methodologies and performances therein. The authors first train a cost prediction model to calculate the differences in predicted (without intervention) versus actual (with onboarding onto digital health platform) health care expenditures for patients (N = 5600). This enables classification impactability if differences in predicted versus actual costs meet a predetermined threshold. Several random forest and logistic regression machine learning models were then trained to accurately categorize new patients as impactable versus not impactable. These parameters are modified through grid search to define the parameters that deliver optimal performance, reaching an overall sensitivity of 0.77 and specificity of 0.65 among all models. This approach shows that impactability for a digital health intervention can be successfully defined using ML methods, thus enabling efficient allocation of resources. This framework is generalizable to analyzing impactability of any intervention and can contribute to realizing closed-loop feedback systems for continuous improvement in health care.