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Health Inequities and Social Determinants of Health

“The idea that some lives matter less is the root of all that is wrong with the world.”

Dr. Paul Farmer, American medical anthropologist

Health inequities is defined by the World Health Organizations as “differences in health status or distribution of health resources between different population groups”, and these inequities can exist even in the same city. For example, African-American mothers and children die at a much higher rate than white women and children in the same city.

These inequities arise from social, economic, environmental, and structural disparities that result in such differences in health outcomes between groups (the author and global health icon Paul Farmer aptly termed this “structural violence”). The main cause of these disparities is not solely due to lack of access, but perhaps also unequal care even with access. In addition, large electronic record databases used in studies often lack proportionate representation from minority groups and therefore can embed biases in the algorithms.

Health inequities can be exaggerated not only due to advanced health care technologies such as artificial intelligence but also the socioeconomic gap that has widened during the pandemic. Social determinants of health are conditions that affect health and consist of: work, education, health systems, housing, income, environment, safety, social milieu, and transportation. These determinants of health are sometimes used to measure health inequities. Artificial intelligence in its multi-modal form can pull these data together to more accurately measure social determinants of health.

While most of the discussions in this forum have focused on artificial intelligence and algorithms amplifying biases, perhaps we can work diligently on artificial intelligence as a resource that can proactively identify and reconcile health disparities.

COVID-19 has painfully revealed the health inequities in our world. What is even more disturbing is that long COVID, with its long list of symptoms (such as fatigue, chest pain, joint pains, and “brain fog”), will probably expose these inequities even further as these are debilitating long term health problems that will be difficult to follow in the disadvantaged populations.

Artificial intelligence, if deployed strategically, can be an equalizer in mitigating health inequities. Human dimensions, however, are also necessary to drive diversity, equity, and inclusion.

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