Decoding Nonadherence to Hypertensive Medication in New York City

A Population Segmentation Approach

Yan Li, Foram Jasani, Dejun Su, Donglan Zhang, Lizheng Shi, Stella S. Yi, José A. Pagán

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
JournalJournal of Primary Care and Community Health
Volume10
DOIs
StatePublished - Feb 1 2019

Fingerprint

Antihypertensive Agents
Population
Hypertension
Insurance Coverage
Health
Poverty
Health Insurance
Health Surveys
Health Personnel
Economics

Keywords

  • community health
  • disease management
  • health promotion
  • managed care
  • medications
  • prevention
  • primary care

ASJC Scopus subject areas

  • Community and Home Care
  • Public Health, Environmental and Occupational Health

Cite this

Decoding Nonadherence to Hypertensive Medication in New York City : A Population Segmentation Approach. / Li, Yan; Jasani, Foram; Su, Dejun; Zhang, Donglan; Shi, Lizheng; Yi, Stella S.; Pagán, José A.

In: Journal of Primary Care and Community Health, Vol. 10, 01.02.2019.

Research output: Contribution to journalArticle

Li, Yan ; Jasani, Foram ; Su, Dejun ; Zhang, Donglan ; Shi, Lizheng ; Yi, Stella S. ; Pagán, José A. / Decoding Nonadherence to Hypertensive Medication in New York City : A Population Segmentation Approach. In: Journal of Primary Care and Community Health. 2019 ; Vol. 10.
@article{4ed4a86c7846498a93b0d52e4053ebd0,
title = "Decoding Nonadherence to Hypertensive Medication in New York City: A Population Segmentation Approach",
abstract = "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.",
keywords = "community health, disease management, health promotion, managed care, medications, prevention, primary care",
author = "Yan Li and Foram Jasani and Dejun Su and Donglan Zhang and Lizheng Shi and Yi, {Stella S.} and Pag{\'a}n, {Jos{\'e} A.}",
year = "2019",
month = "2",
day = "1",
doi = "10.1177/2150132719829311",
language = "English (US)",
volume = "10",
journal = "Journal of primary care & community health",
issn = "2150-1319",
publisher = "Sage Periodicals Press",

}

TY - JOUR

T1 - Decoding Nonadherence to Hypertensive Medication in New York City

T2 - A Population Segmentation Approach

AU - Li, Yan

AU - Jasani, Foram

AU - Su, Dejun

AU - Zhang, Donglan

AU - Shi, Lizheng

AU - Yi, Stella S.

AU - Pagán, José A.

PY - 2019/2/1

Y1 - 2019/2/1

N2 - 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.

AB - 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.

KW - community health

KW - disease management

KW - health promotion

KW - managed care

KW - medications

KW - prevention

KW - primary care

UR - http://www.scopus.com/inward/record.url?scp=85061568794&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85061568794&partnerID=8YFLogxK

U2 - 10.1177/2150132719829311

DO - 10.1177/2150132719829311

M3 - Article

VL - 10

JO - Journal of primary care & community health

JF - Journal of primary care & community health

SN - 2150-1319

ER -