Science, data, technology helped combat the pandemic. They can help long after
Emerging technologies like machine learning and predictive analytics can help public health officials quickly and accurately identify vulnerable, underserved se...
The COVID-19 pandemic has shown how emerging technologies like machine learning and predictive analytics can help public health officials quickly and accurately identify vulnerable, underserved segments of the population and efficiently get them the care and guidance they need.
From access to testing to vaccine distribution to medical care, these tools have played a key role in combating the pandemic in at-risk and often overlooked communities across the nation.
As officials look for lessons from the pandemic response that can apply for decades to come, it is important not to forget the power of these tools in the public health sphere, but instead harness them to tackle all the pervasive inequities of health care laid bare during the pandemic.
The Biden Administration’s recent budgetary request for $6.5 billion to launch a new government agency for breakthrough health research is a step in the right direction. This proposed health agency, which would be called ARPA-H, would focus on research on cancer, diabetes and Alzheimer’s, and could be a key place to leverage scientific, data-driven systems to effectively reach populations at heightened risk for the ailments and diseases impacting underserved communities.
Diabetes, for example, is one disease that disproportionately impacts the Black community in the U.S., with the Department of Health and Human Services finding that African American adults are 60 percent more likely than white adults to be diagnosed with diabetes and twice as likely to die from it. Given these disparities, it is welcome news that the Centers for Disease Control and Prevention is expanding the U.S. Diabetes Surveillance System to now overlay diabetes data with 15 social vulnerability variables and acknowledging that Social Determinants of Health (SDOH) play a significant role in influencing a person’s ability to successfully prevent Type 2 diabetes or manage diabetes. SDOH looks at the conditions in the environments where people live that directly impact their health, such as air quality, access to healthy foods and physical activity opportunities, and availability of public transportation.
Another area where federal agencies are using data to tackle a public health issue is the work done by the National Cancer Institute to address the disparities in care many women face when treating cervical cancer. While effective prevention—not just early detection—and treatment have existed for decades, cervical cancer still takes the lives of approximately 4,000 women in the United States each year. By using clinical data and SDOH factors, the NCI was able to pinpoint certain populations at greater risk for cervical cancer — Black women in the South, white women in Appalachia, Native American women on the Plains — and recommend specific actions public health officials can take to expand access to health care and combat cancer.
These two efforts, however, should be just the starting point, as there is so much opportunity to improve prevention and care if we hope to close the health equity gap and prevent diseases that continue to devastate underserved communities.
Chronic Kidney Disease (CKD) is one of those diseases. While there have been many studies done showing how SDOH directly impacts a person’s chances of getting CKD, doctors and public health officials have not widely used predictive analytics tools to help with diagnostic assistance and guide treatment decisions. By using data from several sources, such as insurance claims, clinical data, live feeds from health exchanges, and demographic information for SDOH, we could better be able to locate those vulnerable to the disease and mitigate its risk. These same methods can be applied to patients suffering from everything from cardiovascular disease to strokes — both of which also inordinately impact Black and Hispanic communities.
Then there is the looming mental health crisis facing America. While even before COVID-19, Black and Brown Americans were 20% more likely to experience a mental health issue, the pandemic has drastically increased the emotional toll experienced by many. More than 4 in 10 adults reported symptoms of anxiety or depressive disorders last December, according to a survey conducted by the National Center for Health Statistics — nearly four times as many at that point in 2019. Leveraging data can help public health officials steer those in need toward affordable care.
COVID-19 has shown us the power of these tools in combating the public health crisis. We must continue to expand the use of predictive analytics and other scientific, data-driven technologies in the health care sector to guarantee that the post-pandemic world is a healthier and more equitable place.
Gary Velasquez is the co-founder and CEO of Cogitativo, a data science company.
Science, data, technology helped combat the pandemic. They can help long after
Emerging technologies like machine learning and predictive analytics can help public health officials quickly and accurately identify vulnerable, underserved se...
The COVID-19 pandemic has shown how emerging technologies like machine learning and predictive analytics can help public health officials quickly and accurately identify vulnerable, underserved segments of the population and efficiently get them the care and guidance they need.
From access to testing to vaccine distribution to medical care, these tools have played a key role in combating the pandemic in at-risk and often overlooked communities across the nation.
As officials look for lessons from the pandemic response that can apply for decades to come, it is important not to forget the power of these tools in the public health sphere, but instead harness them to tackle all the pervasive inequities of health care laid bare during the pandemic.
The Biden Administration’s recent budgetary request for $6.5 billion to launch a new government agency for breakthrough health research is a step in the right direction. This proposed health agency, which would be called ARPA-H, would focus on research on cancer, diabetes and Alzheimer’s, and could be a key place to leverage scientific, data-driven systems to effectively reach populations at heightened risk for the ailments and diseases impacting underserved communities.
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Diabetes, for example, is one disease that disproportionately impacts the Black community in the U.S., with the Department of Health and Human Services finding that African American adults are 60 percent more likely than white adults to be diagnosed with diabetes and twice as likely to die from it. Given these disparities, it is welcome news that the Centers for Disease Control and Prevention is expanding the U.S. Diabetes Surveillance System to now overlay diabetes data with 15 social vulnerability variables and acknowledging that Social Determinants of Health (SDOH) play a significant role in influencing a person’s ability to successfully prevent Type 2 diabetes or manage diabetes. SDOH looks at the conditions in the environments where people live that directly impact their health, such as air quality, access to healthy foods and physical activity opportunities, and availability of public transportation.
Another area where federal agencies are using data to tackle a public health issue is the work done by the National Cancer Institute to address the disparities in care many women face when treating cervical cancer. While effective prevention—not just early detection—and treatment have existed for decades, cervical cancer still takes the lives of approximately 4,000 women in the United States each year. By using clinical data and SDOH factors, the NCI was able to pinpoint certain populations at greater risk for cervical cancer — Black women in the South, white women in Appalachia, Native American women on the Plains — and recommend specific actions public health officials can take to expand access to health care and combat cancer.
These two efforts, however, should be just the starting point, as there is so much opportunity to improve prevention and care if we hope to close the health equity gap and prevent diseases that continue to devastate underserved communities.
Chronic Kidney Disease (CKD) is one of those diseases. While there have been many studies done showing how SDOH directly impacts a person’s chances of getting CKD, doctors and public health officials have not widely used predictive analytics tools to help with diagnostic assistance and guide treatment decisions. By using data from several sources, such as insurance claims, clinical data, live feeds from health exchanges, and demographic information for SDOH, we could better be able to locate those vulnerable to the disease and mitigate its risk. These same methods can be applied to patients suffering from everything from cardiovascular disease to strokes — both of which also inordinately impact Black and Hispanic communities.
Then there is the looming mental health crisis facing America. While even before COVID-19, Black and Brown Americans were 20% more likely to experience a mental health issue, the pandemic has drastically increased the emotional toll experienced by many. More than 4 in 10 adults reported symptoms of anxiety or depressive disorders last December, according to a survey conducted by the National Center for Health Statistics — nearly four times as many at that point in 2019. Leveraging data can help public health officials steer those in need toward affordable care.
COVID-19 has shown us the power of these tools in combating the public health crisis. We must continue to expand the use of predictive analytics and other scientific, data-driven technologies in the health care sector to guarantee that the post-pandemic world is a healthier and more equitable place.
Gary Velasquez is the co-founder and CEO of Cogitativo, a data science company.
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