Evidence map›Paper›PMID 41325726›Full record

ArticleHealth & place2026

Adherence to the combined Mediterranean-dietary approaches to stop hypertension diet is shaped by neighborhood socio-economics and food environments.

Jessie L Heneghan, Kavya Velmurugan, Colleen Weatherwax, Sarah M Bartsch, Corby K Martin, Tiffany M Powell-Wiley, Nevin Cohen, Megan A McCrory, Abigail Horn, Kevin L Chin and 7 more

Abstract read
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In one paragraph

Article in Health & place, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Jessie L HeneghanCenter for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York, NY, USA; Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York, NY, USA; Artificial Intelligence, Modeling, and Informatics for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York, NY, USA.
Kavya VelmuruganCenter for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York, NY, USA; Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York, NY, USA; Artificial Intelligence, Modeling, and Informatics for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York, NY, USA.
Colleen WeatherwaxCenter for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York, NY, USA; Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York, NY, USA; Artificial Intelligence, Modeling, and Informatics for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York, NY, USA.
Sarah M BartschCenter for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York, NY, USA; Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York, NY, USA; Artificial Intelligence, Modeling, and Informatics for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York, NY, USA.
Corby K MartinPennington Biomedical Research Center, Baton Rouge, LA, USA.
Tiffany M Powell-WileySocial Determinants of Obesity and Cardiovascular Risk Laboratory, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Cardiovascular Branch, Bethesda, MD, USA; Intramural Research Program, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA.
Nevin CohenCUNY Urban Food Policy Institute, City University of New York (CUNY) Graduate School of Public Health and Health Policy, New York, NY, USA.
Megan A McCroryDepartment of Health Sciences, Boston University, Boston, MA, USA.
Abigail HornInformation Sciences Institute and Department of Industrial and Systems Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA.
Kevin L ChinCenter for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York, NY, USA; Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York, NY, USA; Artificial Intelligence, Modeling, and Informatics for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York, NY, USA.
Tej D ShahCenter for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York, NY, USA; Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York, NY, USA; Artificial Intelligence, Modeling, and Informatics for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York, NY, USA.
Katherine T FraserCUNY Urban Food Policy Institute, City University of New York (CUNY) Graduate School of Public Health and Health Policy, New York, NY, USA.
Samuele A PetruccelliCenter for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York, NY, USA; Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York, NY, USA; Artificial Intelligence, Modeling, and Informatics for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York, NY, USA.
Alexis M DibbsCenter for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York, NY, USA; Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York, NY, USA; Artificial Intelligence, Modeling, and Informatics for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York, NY, USA.
Sheryl A ScannellCenter for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York, NY, USA; Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York, NY, USA; Artificial Intelligence, Modeling, and Informatics for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York, NY, USA.
Kayla de la HayeInstitute for Food System Equity, Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA.
Bruce Y LeeCenter for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York, NY, USA; Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York, NY, USA; Artificial Intelligence, Modeling, and Informatics for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York, NY, USA. Electronic address: bruceleemdmba@gmail.com.

Funding

Project 4: Virtual Public Health Precision Nutrition LaboratoryU54TR004279 · NCATS · GRADUATE SCHOOL OF PUBLIC HEALTH AND HEALTH POLICY · 2023 to 2025
$4.1M
AHRQ HHS R01 HS028165NCATS NIH HHS U54 TR004279NIGMS NIH HHS R01 GM127512
6 · The paper itself

Abstract

introductionOver the years, different diets have been recommended often with little consideration to how feasible they may be to follow given a person's circumstances and surroundings (e.g., the food environment). Therefore, we sought to determine how difficult it would be for people to maintain the Mediterranean and Dietary Approaches to Stop Hypertension (MED-DASH) versus default to the Typical American diet (TAD) in three different income-level neighborhoods in Los Angeles, California.

methodsWe developed geospatially explicit ABMs of three neighborhoods in Los Angeles that had varied socioeconomics and food environments with varying income levels - one lower-income (Boyle Heights), one middle-income (Inglewood), and one higher-income (Santa Monica). We tested how well the virtual residents (represented by computational agents) could adhere to the MED-DASH compared to defaulting to TAD. To summarize outcomes of average dietary adherence levels among agents within each neighborhood, we used means and 95 % confidence intervals (CIs) for each diet scenario.

resultsAdherence to the MED-DASH diet was on average only 57.43 % (95 % CI: 55.67 %-59.19 %) in the lower-income neighborhood, 62.39 % (95 % CI: 60.59-64.19 %) in the middle-income neighborhood, and 68.02 % (95 % CI: 66.21-69.82 %) in the higher-income neighborhood. Decreasing by 50 % the average price of foods that comprise the MED-DASH diet increased adherence by 17.24 %, 10.36 %, and 1.88 % in the neighborhoods, respectively.

conclusionsDietary recommendations, especially precision nutrition approaches, should take into consideration the surrounding food environment and ways to make suggested diets more feasible.

Indexed as

Dietary Approaches To Stop HypertensionDiet, MediterraneanHypertensionNeighborhood CharacteristicsPatient ComplianceResidence CharacteristicsFemaleHumansLos AngelesMaleMiddle AgedSocioeconomic FactorsAgent-based modelDiet adherenceFood environmentsMED-DASH dietPrecision nutrition

Identifiers

PMID41325726
PMCPMC13352695

What Socratic holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.