Health equity, defined as everyone having the opportunity to be as healthy as possible, has long been a primary goal of public health. Still, across the world, people don't have the same opportunities to see doctors, take part in research, or access medications and genetic testing. This disparity of care can affect rates of disease and disability – and most importantly, hinder quality and length of life.
Genomic medicine holds great promise for improving medical care by giving us a better understanding of disease and the knowledge to prevent, diagnose, and treat conditions. Health equity in genomic medicine is viewed as “the global applicability of genomic knowledge, fair and even access to genomic services such as testing and counselling, and unbiased implementation of genomic medicine.” At the moment, disparities in genomic medicine are the result of the underrepresentation of diverse groups within research and the neglect of diseases that may be particularly prevalent in marginalised groups. As of 2019, over 80% of genome datasets, according to some estimates, originate from people of European ancestry, which means that many human ancestries are not well-represented.
Enhancing diversity in genomic research can significantly advance medical science. By ensuring broader representation in studies, we can gain deeper insights into diseases that disproportionately impact marginalised groups. This inclusive approach not only leads to more effective treatments for these conditions but also contributes to the overall improvement of healthcare for all individuals.
But against a backdrop of these definitions, what stands out most is that health disparities are preventable. They tend to be experienced by socially disadvantaged populations defined by factors such as race or ethnicity, gender, education or income, disability, geographic location (e.g., rural or urban), or sexual orientation. And they’re directly related to the historical and current unequal distribution of social, political, economic, and environmental resources.
In many cases, racial and ethnic minority groups, women, people living in rural communities, people who are uninsured or underinsured, and those with lower education and income are most affected by health disparities. For example, among breast cancer survivors, non-Hispanic Black women in the US are less likely to have BRCA testing than non-Hispanic White women. Similarly, physicians who primarily serve communities of colour have been shown to be less likely to refer a patient to a genetic counsellor or for genetic testing. This lack of testing can affect not only treatment for current conditions, but also their preventative care and future health.
By examining the role of genetics in different populations, we can better understand diseases and their unique impacts. This understanding is crucial for developing tailored healthcare strategies that address the specific needs of diverse groups. Here, we highlight the critical influence of genetics on health disparities, supported by real-world scenarios:
Comprehensive disease understanding across populations: Including diverse groups in genetic studies enables the identification of disease-genetics correlations specific to certain populations. For example, the APOL1 gene variant, more common in individuals of African descent, is closely associated with kidney disease. Recognising such links can lead to tailored and more effective healthcare strategies.
Clarifying variants of uncertain significance: In populations that are underrepresented in genetic research, certain genetic variants remain poorly understood and are labelled as Variants of Uncertain Significance (VUSs). This often leads to confusion and anxiety, particularly in communities with limited genomic literacy. Expanding genetic testing to diverse groups can demystify these VUSs and clarify their implications for health.
Equitable newborn screening through sequencing: Implementing sequencing in newborn screening offers a path to generate comprehensive genomic data across different populations. It's important to ensure that these efforts are equitable in their diagnostic outcomes, highlighting the necessity for inclusive participation from diverse ancestries and health backgrounds.
Inclusive carrier screening panels: The current underrepresentation in genomic databases compromises the precision of genetic test results, notably in carrier screening panels. These panels often miss critical genes or alleles relevant to non-European groups, thereby reducing their effectiveness. Addressing this gap in research can lead to more accurate predictions and enhanced support for currently underrepresented communities.
To effectively address health disparities, it's essential to understand the intricate relationship between genetics, social determinants, and environmental influences. Integrating genetic research within broader societal frameworks and ensuring diverse participation in studies are key to advancing towards health equity.
The full potential of precision medicine can only be realised through inclusive and diverse data collection, along with the integration of advanced tools like machine learning for public health surveillance.
Diversity and inclusivity in data collection: For genomic data to be truly effective in healthcare, it's essential that the data collected is diverse and inclusive. Expanding data collection to include a more comprehensive range of ethnicities and backgrounds is crucial for developing treatments and interventions that are effective for a broader population.
Next-generation tools and machine learning: The use of advanced technologies like machine learning in analysing genomic data is a significant leap forward. These tools can process and analyse large datasets much more efficiently than traditional methods, leading to quicker and more accurate identification of genetic markers associated with various diseases.
Public health surveillance and genetic disorders: Tools like the FIND-FH algorithm, which is used in health systems to identify familial hypercholesterolemia, demonstrate the potential of integrating genomic data into public health surveillance. By identifying genetic disorders early, interventions can be more targeted and effective, potentially reducing the overall burden of these diseases.
Integration into population-based cancer registries: Incorporating genomic data into cancer registries is a significant step in personalising cancer care. By understanding the molecular markers unique to different cancer subtypes, healthcare providers can tailor treatments to the individual, potentially increasing the efficacy of these treatments. Furthermore, this integration aids in the surveillance of hereditary cancers, allowing for earlier detection and intervention.
The merging of genomic data with healthcare, bolstered by cutting-edge technologies and an emphasis on diversity and inclusivity, is set to transform our approach to healthcare, offering more tailored and fair solutions in disease management and treatment.
Here, we’ve highlighted the importance of integrating genomic data into healthcare, with a focus on diversity and inclusivity. By ensuring a more representative collection of genomic data and employing advanced analytical tools, we can make significant strides in understanding and addressing health disparities. The efforts to include diverse populations in genomic research and to incorporate these insights into public health practices are crucial steps towards more personalised and equitable healthcare. This approach not only benefits individual patient care but also enhances our overall understanding of diseases and their impact on different communities, ultimately leading to better health outcomes for all.
To learn more about how diversity, equity, and inclusion in genomic research can drive better healthcare for all, download our whitepaper: