The Effect of Income on U.S. Prescription Fill Patterns

But an in-depth look of more than 50 million US prescription claims highlights the stark health disparities between wealthy and poor areas, painting a picture of two different Americas: One where higher-income communities fill more prescriptions for lifestyle conditions and mental health disorders, and another where lower-income communities fill more prescriptions for environmental infectious diseases, and nutritional deficiencies.

This analysis is based on a representative sample of 12 months of prescription claims from 39 of the largest Metropolitan Statistical Areas (MSAs) in the US. We looked for conditions that were being disproportionately filled in either rich or poor areas—what might be considered “rich-neighborhood problems” or “poor-neighborhood problems.”

For a full breakdown by condition and complete methodology, read our white-paper here.

Rich neighborhood problems


We defined lifestyle medications as prescription drugs typically used to treat acne, erectile dysfunction, excessive sweating, eyelash growth, facial wrinkles, hair loss, hyperkeratosis, rosacea, and skin discoloration.

Not surprisingly, higher-income communities have higher fills for medications that treat conditions like erectile dysfunction, facial wrinkles, and skin discoloration.

In 2018, there were over 200 fills per 1,000 people for lifestyle medications in the highest-income census tract. Presumably, higher income people can better afford these treatments.

Mental health

We defined mental health medications as prescription drugs typically used to treat ADHD, alcohol addiction, anxiety, bipolar disorder, depression, eating disorders, fatigue, obsessive-compulsive disorder, and panic disorder.

Fills for drugs used to treat mental health conditions are more popular in higher-income areas, with over 1,500 fills per 1,000 people filled for a mental health prescription in 2018.

This trend seems counterintuitive. Studies have long suggested that lower-income communities have higher rates of mental health conditions like depression and anxiety which is opposite from the above fill trends.

What is accounting for this discrepancy? It’s likely a difference in access to treatment. While low-income communities have higher rates of mental health disorders, it’s possible that they have less time and fewer resources for diagnosis and treatment.

Poor neighborhood problems

Environmental infectious conditions

Here, we defined medications related to environmental infectious conditions as those typically used to treat amoebiasis, athlete’s foot, cytomegalovirus, hives, lice, parasitic infections, ringworm, scabies, thrush, and yeast infection.

Treatments for conditions like hives, lice and ringworm—conditions that are caused by environmental factors—are generally filled at the highest rates in low-income communities, but filled at the lowest rates in the poorest and richest census tracts.

This makes some sense. Low-income communities contend with more environmental challenges, like a lack of adequate housing and higher levels of pollutants, which place them at a higher risk of environmentally-caused infections.

So why might the lowest-income census tract fill the fewest prescriptions for infection treatments? Unfortunately, this could again be caused by access to care: Those who need these treatments most often cannot afford them.

Vitamin and nutritional disorders

Here, we defined medications for vitamin/nutritional disorders as prescription drugs typically used to treat anemia, calcium deficiency, iron deficiency, nutritional deficiencies, vitamin B12 deficiency, and vitamin D deficiency.

Fills for drugs treating vitamin and nutritional disorders follow a similar pattern as the infection treatments graph above.

Again, this is not surprising. Low-income communities tend to have a high density of fast-food options and lower access to fresh foods, two factors that can lead to unhealthy eating habits and likely, higher rates of nutritional deficiencies.

For a full breakdown by condition and complete methodology, read our white-paper here.

Co-contributors: Jeroen van Meijgaard, PhD and Clement B. Feyt, MPH

For any questions regarding this report, please email Tori Marsh, MPH at [email protected]

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