The Importance of Evaluating Health Disparities Research.

Health disparity populations are socially disadvantaged, and the multiple levels of discrimination they often experience mean that their characteristics and attributes differ from those of the mainstream. Programs and policies targeted at reducing health disparities or improving minority health must consider these differences. Despite the importance of evaluating health disparities research to produce high-quality data that can guide decision-making, it is not yet a customary practice. Although health disparities evaluations incorporate the same scientific methods as all evaluations, they have unique components such as population characteristics, sociocultural context, and the lack of health disparity common indicators and metrics that must be considered in every phase of the research. This article describes evaluation strategies grouped into 3 components: formative (needs assessments and process), design and methodology (multilevel designs used in real-world settings), and summative (outcomes, impacts, and cost). Each section will describe the standards for each component, discuss the unique health disparity aspects, and provide strategies from the National Institute on Minority Health and Health Disparities Metrics and Measures Visioning Workshop (April 2016) to advance the evaluation of health disparities research.

Valproic acid reprograms the metabolic aberration of cisplatin treatment via ALDH modulation in triple-negative breast cancer cells.

We recently demonstrated that the histone deacetylase inhibitor valproic acid (VPA) reprograms the cisplatin-induced metabolome of triple-negative breast cancer (TNBC) cells, including a shift in hexose levels. Accordingly, here, we tested the hypothesis that VPA alters glucose metabolism in correlation with cisplatin sensitivity. Two TNBC cell lines, MDA-MB-231 (a cisplatin-resistant line) and MDA-MB-436 (a cisplatin-sensitive line), were analyzed. The glycolysis and oxidative metabolism were measured using the Glycolysis Stress Test kit. The expression of aldehyde dehydrogenases (ALDHs), enzymes linked to drug resistance, was investigated by Western blot and real-time PCR analyses. We additionally studied the influence of ALDH inhibition by disulfiram on the viability of MDA-MB-231 cells and on a TNBC patient-derived organoid system. Cisplatin treatment reduced the extracellular acidification rate in MDA-MB-436 cells but not MDA-MB-231 cells, whereas VPA addition increased the extracellular acidification rate in both cell lines. VPA further reduced the oxygen consumption rate of cisplatin-treated MDA-MB-436 cells, which correlated with cell cycle alterations. However, in MDA-MB-231 cells, the cell cycle distribution did not change between cisplatin/VPA-cisplatin treatments. In both cell lines, VPA increased the expression of ALDH isoform and ALDH1A1 expression. However, only in MDA-MB-231 cells, VPA synergized with cisplatin to augment this effect. Disulfiram sensitized the cells to the cytotoxic effects of the VPA-cisplatin combination. Furthermore, the disulfiram-VPA-chemotherapy combination was most effective in TNBC organoids. Our results show that ALDH overexpression may act as one mechanism of cellular resistance to VPA in TNBC and that its inhibition may enhance the therapeutic efficacy of VPA-chemotherapeutic drug combinations.

Guiding Principles to Address the Impact of Algorithm Bias on Racial and Ethnic Disparities in Health and Health Care.

Importance: Health care algorithms are used for diagnosis, treatment, prognosis, risk stratification, and allocation of resources. Bias in the development and use of algorithms can lead to worse outcomes for racial and ethnic minoritized groups and other historically marginalized populations such as individuals with lower income. Objective: To provide a conceptual framework and guiding principles for mitigating and preventing bias in health care algorithms to promote health and health care equity. Evidence Review: The Agency for Healthcare Research and Quality and the National Institute for Minority Health and Health Disparities convened a diverse panel of experts to review evidence, hear from stakeholders, and receive community feedback. Findings: The panel developed a conceptual framework to apply guiding principles across an algorithm's life cycle, centering health and health care equity for patients and communities as the goal, within the wider context of structural racism and discrimination. Multiple stakeholders can mitigate and prevent bias at each phase of the algorithm life cycle, including problem formulation (phase 1); data selection, assessment, and management (phase 2); algorithm development, training, and validation (phase 3); deployment and integration of algorithms in intended settings (phase 4); and algorithm monitoring, maintenance, updating, or deimplementation (phase 5). Five principles should guide these efforts: (1) promote health and health care equity during all phases of the health care algorithm life cycle; (2) ensure health care algorithms and their use are transparent and explainable; (3) authentically engage patients and communities during all phases of the health care algorithm life cycle and earn trustworthiness; (4) explicitly identify health care algorithmic fairness issues and trade-offs; and (5) establish accountability for equity and fairness in outcomes from health care algorithms. Conclusions and Relevance: Multiple stakeholders must partner to create systems, processes, regulations, incentives, standards, and policies to mitigate and prevent algorithmic bias. Reforms should implement guiding principles that support promotion of health and health care equity in all phases of the algorithm life cycle as well as transparency and explainability, authentic community engagement and ethical partnerships, explicit identification of fairness issues and trade-offs, and accountability for equity and fairness.

Social Determinants of Health Factors for Gene-Environment COVID-19 Research: Challenges and Opportunities.

The characteristics of a person's health status are often guided by how they live, grow, learn, their genetics, as well as their access to health care. Yet, all too often, studies examining the relationship between social determinants of health (behavioral, sociocultural, and physical environmental factors), the role of demographics, and health outcomes poorly represent these relationships, leading to misinterpretations, limited study reproducibility, and datasets with limited representativeness and secondary research use capacity. This is a profound hurdle in what questions can or cannot be rigorously studied about COVID-19. In practice, gene-environment interactions studies have paved the way for including these factors into research. Similarly, our understanding of social determinants of health continues to expand with diverse data collection modalities as health systems, patients, and community health engagement aim to fill the knowledge gaps toward promoting health and wellness. Here, a conceptual framework is proposed, adapted from the population health framework, socioecological model, and causal modeling in gene-environment interaction studies to integrate the core constructs from each domain with practical considerations needed for multidisciplinary science.

Toward informatics-enabled preparedness for natural hazards to minimize health impacts of climate change.

Natural hazards (NHs) associated with climate change have been increasing in frequency and intensity. These acute events impact humans both directly and through their effects on social and environmental determinants of health. Rather than relying on a fully reactive incident response disposition, it is crucial to ramp up preparedness initiatives for worsening case scenarios. In this perspective, we review the landscape of NH effects for human health and explore the potential of health informatics to address associated challenges, specifically from a preparedness angle. We outline important components in a health informatics agenda for hazard preparedness involving hazard-disease associations, social determinants of health, and hazard forecasting models, and call for novel methods to integrate them toward projecting healthcare needs in the wake of a hazard. We describe potential gaps and barriers in implementing these components and propose some high-level ideas to address them.