Disparities in Cardiac Care of Women: Current Data and Possible Solutions

Healthcare cost and race: analysis of young women with stroke

BACKGROUND

Over the last decade, the prevalence of young stroke has increased 40% particularly among vulnerable populations. These strokes are often more severe with worse outcomes. However, few studies have examined the impact on annual healthcare costs.

METHODS

Data from the 2008 to 2018 Medical Expenditure Panel Survey (MEPS) was used to identify a sample of female stroke survivors aged 18 and 60. MEPS includes demographics, health status, healthcare use, and expenditures for all participants providing the largest nationally representative data source of healthcare costs in the US. First, differences in racial and ethnic healthcare expenditure among young women with stroke were evaluated controlling for insurance type and demographic characteristics. Second, the relationship between healthcare expenditure and 1) time post stroke, 2) comorbidities, 3) healthcare utilization, and 4) post-stroke functional status was assessed. Finally, differential healthcare quality was tested as a potential mitigating differential.

RESULTS

Young Black women with stroke spend roughly 20% more on healthcare than White women after controlling for insurance, time post-stroke, healthcare utilization, and demographic differences. Costs remain 17% higher after controlling for comorbidities. Differences in expenditure are larger if survivors have diabetes, high blood pressure, or high cholesterol (78%, 24%, and 28%, respectively). Higher expenditure could not be explained by higher healthcare utilization, but lower quality of healthcare may explain part of the differential.

CONCLUSION

Young Black women with stroke have 20% greater healthcare expenditure than other groups. Cost differentials cannot be explained by differentials in comorbidities, utilization, time post stroke, or functionality. Additional research is needed to explain these differences.

Sex-specific associations between lifetime diagnosis of bipolar disorder and cardiovascular disease: A cross-sectional analysis of 257,673 participants from the UK Biobank

BACKGROUND

Sex is seldom considered as a potential moderator of the impact of bipolar disorder (BD) on cardiovascular disease (CVD) risk. We aimed to characterize the sex-specific association of CVD and BD using data from the UK Biobank.

METHODS

In a cross-sectional analysis, we compared the odds ratio between women and men with BD for seven CVD diagnoses (coronary artery disease, myocardial infarction, angina, atrial fibrillation, heart failure, stroke, and essential hypertension) and four cardiovascular biomarkers (arterial stiffness index, low-density lipoprotein, C-reactive protein, and HbA1c) in 293 participants with BD and 257,380 psychiatrically healthy controls in the UK Biobank.

RESULTS

After adjusting for age, we found a two- to three-fold stronger association among women than among men between BD and rates of coronary artery disease, heart failure, and essential hypertension, with a significant sex-by-diagnosis interactions. The association remained significant after controlling for self-reported race, education, income, and smoking status. After controlling for potential confounders, there was no significant association between sex and any cardiovascular biomarkers.

LIMITATIONS

These analyses could not disentangle effects of BD from its treatment.

CONCLUSIONS

Our results underscore the importance of incorporating sex and mental illness in risk estimation tools for CVD, and improving screening for, and timely treatment of, CVD in those with BD. Future research is needed to better understand the contributors and mechanisms of sex differences related to CVD risk in BD.

Construction of a femininity score in the UK Biobank and its association with angina diagnosis prior to myocardial infarction

Gender captures social components beyond biological sex and can add valuable insight to health studies in populations. However, assessment of gender typically relies on questionnaires which may not be available. The aim of this study is to construct a gender metric using available variables in the UK Biobank and to apply it to the study of angina diagnosis. Proxy variables for femininity characteristics were identified in the UK Biobank and regressed on sex to construct a composite femininity score (FS) validated using tenfold cross-validation. The FS was assessed as a predictor of angina diagnosis before incident myocardial infarction (MI) events. The FS was derived for 315,937 UK Biobank participants. In 3059 individuals with no history of MI at study entry who had an incident MI event, the FS was a significant predictor of angina diagnosis prior to MI (OR 1.24, 95% CI 1.10-1.39, P < 0.001) with a significant sex-by-FS interaction effect (P = 0.003). The FS was positively associated with angina diagnosis prior to MI in men (OR 1.37, 95% CI 1.19-1.57, P < 0.001), but not in women. We have provided a new tool to conduct gender-sensitive analyses in observational studies, and applied it to study of angina diagnosis prior to MI.

Frontiers of Upstream Stroke Prevention and Reduced Stroke Inequity Through Predicting, Preventing, and Managing Hypertension and Atrial Fibrillation: A Call to Action From the Value in Healthcare Initiative's Predict & Prevent Learning Collaborative

Stroke is one of the leading causes of morbidity and mortality in the United States. While age-adjusted stroke mortality was falling, it has leveled off in recent years due in part to advances in medical technology, health care options, and population health interventions. In addition to adverse trends in stroke-related morbidity and mortality across the broader population, there are sociodemographic inequities in stroke risk. These challenges can be addressed by focusing on predicting and preventing modifiable upstream risk factors associated with stroke, but there is a need to develop a practical framework that health care organizations can use to accomplish this task across diverse settings. Accordingly, this article describes the efforts and vision of the multi-stakeholder Predict & Prevent Learning Collaborative of the Value in Healthcare Initiative, a collaboration of the American Heart Association and the Robert J. Margolis, MD, Center for Health Policy at Duke University. This article presents a framework of a potential upstream stroke prevention program with evidence-based implementation strategies for predicting, preventing, and managing stroke risk factors. It is meant to complement existing primary stroke prevention guidelines by identifying frontier strategies that can address gaps in knowledge or implementation. After considering a variety of upstream medical or behavioral risk factors, the group identified 2 risk factors with substantial direct links to stroke for focusing the framework: hypertension and atrial fibrillation. This article also highlights barriers to implementing program components into clinical practice and presents implementation strategies to overcome those barriers. A particular focus was identifying those strategies that could be implemented across many settings, especially lower-resource practices and community-based enterprises representing broad social, economic, and geographic diversity. The practical framework is designed to provide clinicians and health systems with effective upstream stroke prevention strategies that encourage scalability while allowing customization for their local context.

A Guide to Applying the Sex-Gender Perspective to Nutritional Genomics

Precision nutrition aims to make dietary recommendations of a more personalized nature possible, to optimize the prevention or delay of a disease and to improve health. Therefore, the characteristics (including sex) of an individual have to be taken into account as well as a series of omics markers. The results of nutritional genomics studies are crucial to generate the evidence needed so that precision nutrition can be applied. Although sex is one of the fundamental variables for making recommendations, at present, the nutritional genomics studies undertaken have not analyzed, systematically and with a gender perspective, the heterogeneity/homogeneity in gene-diet interactions on the different phenotypes studied, thus there is little information available on this issue and needs to be improved. Here we argue for the need to incorporate the gender perspective in nutritional genomics studies, present the general context, analyze the differences between sex and gender, as well as the limitations to measuring them and to detecting specific sex-gene or sex-phenotype associations, both at the specific gene level or in genome-wide-association studies. We analyzed the main sex-specific gene-diet interactions published to date and their main limitations and present guidelines with recommendations to be followed when undertaking new nutritional genomics studies incorporating the gender perspective.