Decoding Nonadherence to Hypertensive Medication in New York City: A Population Segmentation Approach.

OBJECTIVE

Nearly one-third of adults in New York City (NYC) have high blood pressure and many social, economic, and behavioral factors may influence nonadherence to antihypertensive medication. The objective of this study is to identify profiles of adults who are not taking antihypertensive medications despite being advised to do so.

METHODS

We used a machine learning-based population segmentation approach to identify population profiles related to nonadherence to antihypertensive medication. We used data from the 2016 NYC Community Health Survey to identify and segment adults into subgroups according to their level of nonadherence to antihypertensive medications.

RESULTS

We found that more than 10% of adults in NYC were not taking antihypertensive medications despite being advised to do so by their health care providers. We identified age, neighborhood poverty, diabetes, household income, health insurance coverage, and race/ethnicity as important characteristics that can be used to predict nonadherence behaviors as well as used to segment adults with hypertension into 10 subgroups.

CONCLUSIONS

Identifying segments of adults who do not adhere to hypertensive medications has practical implications as this knowledge can be used to develop targeted interventions to address this population health management challenge and reduce health disparities.