---
title: "Wealth-related inequality in excessive red meat consumption among hypertensive adults in Iran"
id: "plos-one-23-assessment-and-decomposition-of-excessive-red-meat-consumption-among"
canonical_url: "https://medichelpline.com/clinical-feed/plos-one-23-assessment-and-decomposition-of-excessive-red-meat-consumption-among"
content_type: "clinical_feed_article"
specialty: "Cardiology"
source_name: "PLOS ONE (Medicine)"
source_url: "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0359650"
published_at: "2026-10-05T14:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Wealth-related inequality in excessive red meat consumption among hypertensive adults in Iran
## Provenance & Clinical Metadata
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- **Specialty:** [Cardiology](https://medichelpline.com/clinical-feed/cardiology.md)
- **Primary Source:** PLOS ONE (Medicine)
- **Source URL:** [Original Journal Publication](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0359650)
- **Published At:** 2026-10-05T14:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- This nationally representative analysis used 2021 Iranian STEPS data to assess wealth-related inequality in **red meat consumption** among 9,024 adults with hypertension. - Excessive consumption was defined as consuming red meat four or more times per week; 16.5% of the hypertensive sample met this threshold. - A positive and statistically significant concentration index (CI = 0.179, p < 0.001) indicated that excessive red meat consumption was concentrated among wealthier individuals (pro-rich inequality). - The study constructed a household **wealth index** using principal component analysis of assets and housing characteristics and ranked participants into quintiles. - Wagstaff decomposition analysis was used to partition the observed inequality into contributions from measured determinants using average marginal effects from logistic regression. - Higher educational attainment (≥12 years) made the largest single contribution to the observed inequality (20.6% of the explained component). - Other contributors included unpaid work (6.8%), unemployment (4.4%), and age 40–59 years (4.2%). - The included covariates together explained 29.4% of the observed wealth-related inequality; the remaining portion was unexplained by the model. - The authors conclude that socioeconomic differences—especially **education**—help drive unequal patterns of frequent red meat consumption among people with hypertension and recommend targeted nutrition interventions and policies that account for socioeconomic context. - Ethical approvals, weighting for survey design, and complete-case analysis approaches were reported; data access requires application to the NIHR as described in the source.
## Clinical Analysis & Structured Key Points
Assessment and decomposition of excessive red meat consumption among hypertensive adults: A Wagstaff decomposition analysis | PLOS One Browse Subject Areas ? Click through the PLOS taxonomy to find articles in your field. For more information about PLOS Subject Areas, click here . Article Authors Metrics Comments Media Coverage Reader Comments Figures Figures Abstract Introduction Red meat consumption is an important dietary factor in hypertension management, yet its distribution may vary across socioeconomic groups. This study examines wealth-related inequality in red meat consumption among hypertensive adults in Iran. Objective To quantify wealth-related inequality in red meat consumption among hypertensive adults in Iran and explore factors associated with this inequality. Methods Utilizing data from the 2021 Iranian STEPS survey, a nationally representative sample of 9,024 hypertensive adults was analyzed. Excessive consumption was defined as consuming red meat four or more times per week. Wealth-related inequality in consumption was then assessed using a wealth index derived from principal component analysis (PCA) of household assets and living conditions. Wagstaff decomposition analysis was employed to disaggregate the concentration index (CI) and explore the contribution of each factor to the observed inequality. Results The hypertensive adults included in the study had a mean age (95% CI) of 56.1 years (55.8, 56.5). The proportion of excessive red meat consumption was 16.5%. A significant wealth-related inequality was observed in excessive red meat consumption (CI = 0.179, p < 0.001), indicating that excessive consumption was concentrated among wealthier individuals. Wagstaff decomposition analysis showed that higher educational attainment (≥12 years) was the largest contributor to the observed inequality (20.6%), followed by unpaid work (6.8%), unemployment (4.4%), and age 40–59 years (4.2%). Overall, the included determinants explained 29.4% of the observed inequality. Conclusion This study identified significant wealth-related inequality in excessive red meat consumption among hypertensive adults in Iran, with higher consumption concentrated among wealthier groups. Educational attainment was the largest contributor to the observed inequality. Policies should consider socioeconomic differences in dietary behaviors and promote targeted nutrition interventions to reduce cardiovascular risk and improve dietary equity. Citation: Karimi S, Kaveh S, Emamgholipour sefiddashti S, Akbarpour S, Dorri S, Azizpour Y (2026) Assessment and decomposition of excessive red meat consumption among hypertensive adults: A Wagstaff decomposition analysis. PLoS One 21(10): e0359650. https://doi.org/10.1371/journal.pone.0359650 Editor: James Mockridge, PLOS: Public Library of Science, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND Received: November 12, 2025; Accepted: September 16, 2026; Published: October 5, 2026 Copyright: © 2026 Karimi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: The data used in this study were collected through the STEPS survey conducted by the Endocrinology and Metabolism Research Institute (EMRI), Tehran University of Medical Sciences, under the supervision of the National Institute for Health Research (NIHR), Iran. The NIHR, operating under the Ministry of Health and Medical Education (MOHME), provided the primary ethical approval and is the authorized body responsible for data governance and access control. Researchers interested in accessing these data for legitimate academic purposes must submit a formal request either to the corresponding author or directly to the NIHR via its official email ( nihr@tums.ac.ir ). All requests should include a research proposal explaining the intended use of the data. The NIHR committee reviews applications in accordance with national regulations and ethical guidelines. Approved requests may require signing a Data Use Agreement to ensure confidentiality and proper data protection. Funding: The study was financially supported by the Endocrinology & Metabolism Research Institute at Tehran University of Medical Sciences (Contract No: 75033). The funder had no role in the study design, data collection, data analysis, data interpretation, manuscript preparation, or the decision to publish. Competing interests: The authors declare no competing interests. Abbreviations: CC, Concentration Curve; CI, Concentration Index; CIs, Confidence Intervals; DBP, Diastolic blood pressure; NCDs, Non-communicable diseases; PCA, Principal component analysis; SBP, Systolic blood pressure; STEPS, STEPwise approach to NCD risk factor surveillance; WHO, World Health Organization;; CVD, Cardiovascular disease;; LMICs,, Low- and middle-income countries;; VIF, Variance inflation factor;; COVID-19,, Coronavirus disease 2019. Introduction Hypertension is one of the leading global health challenges, affecting about one in three adults aged 30–79 years and contributing substantially to cardiovascular disease (CVD), stroke, and premature mortality [ 1 , 2 ]. Its prevalence remains relatively consistent across countries, ranging only slightly, from 32% in high-income settings to 34% in low-income settings [ 2 ]. Emerging evidence further indicates that excessive meat consumption, particularly of red and processed varieties, is a significant risk factor for hypertension [ 3 , 4 ]. Despite this burden, a considerable proportion of individuals with hypertension remain undiagnosed, and among those diagnosed, many do not receive treatment or fail to achieve adequate blood pressure control [ 1 , 2 ]. Dietary modification is a cornerstone of hypertension management [ 5 ]. Simulation studies suggest that replacing half of meat and dairy with plant-based foods could prevent approximately 37000 deaths [ 6 ]. Cohort analyses further indicate that even relatively low levels of red and processed meat consumption are positively associated with both all-cause and cardiovascular mortality when compared with zero intake [ 7 ]. Despite such evidence, a substantial proportion of adults with hypertension still do not meet dietary recommendations, reflecting excessiveor unhealthy patterns of red meat consumption that may worsen disease progression and elevate the risk of cardiovascular complications [ 8 ]. Importantly, dietary behaviors are not uniformly distributed across populations. Socioeconomic and demographic factors—including income, education, employment status, and place of residence—play a critical role in shaping access to and preferences for different food types [ 9 ]. As a result, inequalities in red meat consumption patterns may be associated with wider disparities in hypertension outcomes, particularly in resource-constrained settings [ 10 ]. However, there is a notable scarcity of equity-focused research in low- and middle-income countries (LMICs), including Iran. Most existing studies emphasize overall consumption levels or health associations, without specifically examining the socioeconomic determinants of excessive red meat intake among high-risk subgroups such as hypertensive adults. Understanding these inequities is essential for guiding evidence-based public health strategies that can improve dietary quality and promote equity in health outcomes. To address this challenge, analytical methods capable of disentangling the sources of inequality are required. Wagstaff’s decomposition analysis, widely applied in health economics to quantify wealth-related inequalities, helps identify the relative contribution of key determinants [ 11 ]. This study is among the first to apply Wagstaff decomposition to red meat consumption patterns specifically in a hypertensive population, moving beyond descriptive estimates to quantify the determinants of wealth-related inequality. This study seeks to quantify the wealth-related inequality in excessive red meat consumption among hypertensive adults in Iran and decompose its contributing factors using Wagstaff analysis. Methods Data source and study population The present analysis was based on data from the STEPwise Approach to non-communicable disease (NCD)Risk Factor Surveillance (STEPS) survey in 2021 in Iran, a nationwide cross-sectional study designed to monitor major NCD risk factors. Participant recruitment for this study started in early 2020, was temporarily halted due to COVID-19, and resumed in early 2021 and continued until completion. A representative sample of adults aged 18 years and above was selected using a systematic cluster random sampling approach. Sampling was carried out across all 31 provinces, with proportional allocation based on provincial population size. In total, 3,176 clusters were defined, each including nine individuals from both urban and rural settings, yielding a final sample of 27,874 adults [ 12 ]. Individuals were eligible if they were Iranian residents at the time of the survey and met the minimum age requirement. Participants with conditions that prevented meaningful participation, those unable to provide biological samples, or pregnant women were excluded. Prior to enrollment, informed consent was obtained, and participants were assured of confidentiality and data protection [ 12 ]. In the present study, a subpopulation of individuals with hypertension was selected. Hypertension was defined as systolic blood pressure (SBP) of 140 mmHg or higher, diastolic blood pressure (DBP) of 90 mmHg or higher, or through self-reported, physician diagnosis or current use of antihypertensive medications. Of 9,572 hypertensive individuals, 548 were excluded due to incomplete information on key variables, including meat consumption (n = 10) wealth index (n = 459), education level (n = 58), comorbidity (n = 21). Observations with missing values were excluded using complete-case analysis. The final analytic sample consisted of 9,024 participants ( Fig 1 ). Download: PNG larger image TIFF original image Fig 1. Flowchart of the study population. https://doi.org/10.1371/journal.pone.0359650.g001 Definition of variables Outcome variable. The outcome variable was excessive red meat consumption. In the STEPS survey, participants were asked: “How often do you usually consume red meat such as beef, veal, lamb, goat, mutton, or camel?” Response options included: (1) less than once a month or never, (2) one to three times per month, (3) one to three times per week, (4) four to six times per week, (5) one to two times per day, and (6) three or more times per day. Participants reporting red meat consumption four or more times per week were classified as excessive consumers. This threshold was selected to identify frequent red meat consumption, consistent with evidence suggesting that higher levels of red meat intake are associated with adverse health outcomes [ 13 – 15 ]. Covariates. Age was calculated based on the date of birth recorded in the questionnaire and categorized into three groups: 18–39 years, 40–59 years, and 60 years or older. Sex was self-reported and classified as male or female. Marital status was categorized into two groups: married and single, with the single category including never-married, divorced, and widowed participants. Area of residence was determined based on the address recorded in the questionnaire and classified into two categories: urban and rural. Education level was categorized into four groups: no formal education, 1–6 years of schooling, 7–11 years, and 12 years or more. Job status was classified as employed, unemployed, unpaid work, or retired. Comorbidity was defined as the presence of at least one additional chronic condition among individuals with hypertension. Four chronic conditions were considered: diabetes, hypercholesterolemia, CVD, and cancer. Diabetes, hypercholesterolemia, and CVD were defined as self-reported conditions if participants indicated that a physician or health worker had ever diagnosed them with the respective condition, or if they were currently taking medication for it. Cancer was defined as a self-reported condition if participants reported that, within the past 12 years, a physician or health worker had told them that they had cancer. Each condition was coded as a binary variable (yes/no). A comorbidity indicator was then created by summing these variables. Participants were categorized into two groups: no comorbidity (no additional chronic condition) and any comorbidity (presence of at least one of the specified chronic conditions in addition to hypertension). Wealth index. The wealth index was constructed through principal component analysis (PCA) of household assets and housing characteristics, including car ownership, housing quality indicators (such as floor and wall materials), access to basic utilities, and selected durable goods. The derived wealth score was subsequently categorized into five quintiles, from the poorest to the wealthiest. Statistical analysis Inequality in red meat consumption among individuals with hypertension was estimated using survey sample weights to ensure national representativeness. Weighting procedures were implemented after data cleaning and included adjustments for overall and stage-specific non-response (with age-targeted corrections considering the impact of the COVID-19 pandemic), followed by post-stratification by province, residential area, sex, and age [ 12 ]. All weights were subsequently combined and applied in the final analyses to reduce potential bias and improve the generalizability of results. Descriptive statistics were presented using percentages and 95% confidence intervals (CIs). We first calculated inequality in excessive red meat consumption using the concentration curve (CC) and the concentration index (CI), which are two widely recognized and commonly used methods for assessing wealth-related inequalities across socioeconomic groups [ 11 , 16 ]. The CC offers a visual representation of inequality by plotting the cumulative proportion of the outcome variable (y-axis) against the cumulative proportion of the population ranked from poorest to richest (x-axis). A 45-degree line represents perfect equality. If the curve lies above this line, the outcome is concentrated among poorer individuals (pro-poor inequality), while a curve below the line indicates concentration among wealthier groups (pro-rich inequality) [ 17 ]. The further the curve is from the equality line, the greater the inequality. The CI is twice the area between the concentration curve and the line of equality and provides a concise numerical summary of the CC [ 18 ]. It is calculated as twice the covariance between the outcome variable and the fractional rank of individuals in the socioeconomic distribution, divided by the mean of the outcome. It can be expressed as ( equation 1 ) [ 19 ]: (1) Here, y i is the outcome for individual i, μ is its mean or the proportion of the health variable, and R i represents the fractional rank in the wealth distribution [ 19 ]. A positive CI means the outcome is more common among the richer population, a negative CI indicates concentration among the poorer, and a value of zero represents perfect equality. For binary outcomes, the theoretical limits of the CI are not fixed at –1 and +1 but instead depend on the mean of the variable [ 20 ]. To make the results comparable and interpretable, we used the Wagstaff normalization, which can be expressed as ( equation 2 ) [ 20 ]: (2) Where μ represents the mean of the outcome, and CI refers to the standard concentration index. This adjustment ensures that the index remains within the –1 to +1 range, providing a clear and consistent framework for interpreting pro-poor and pro-rich inequalities. Second, we applied the Wagstaff decomposition approach [ 11 ] to decompose the CI and determine how much each explanatory variable contributes to the overall inequality [ 17 , 21 ]. This technique is based on regression analysis of the association between the outcome variable and its determinants for any linear additive regression model of health outcome, such as ( equation 3 ): (3) The CI for y can be written as follows [ 11 , 17 ] ( equation 4 ): (4) where µ k represents the mean of y, xˉ k is the mean of the x k , CI k is the concentration index of x k , and GCε denotes the generalized concentration index of ε. Equation (4) indicates that the overall CI can be expressed as a weighted sum of the concentration indices of the k regressors, with the weight for each x k given by the elasticity of y with respect to x k (η k = β k ). The residual component represents the proportion of inequality unexplained by the included variables. Because the outcome variable (excessive red meat consumption) was binary, a logistic regression model was fitted. Average marginal effects (dy/dx) were estimated and used instead of the log-odds coefficients in the decomposition analysis to facilitate interpretation [ 17 ]. This linear approximation is widely accepted and provides a clear picture of how each factor contributes to inequality [ 22 ]. Equation (5) shows the linear approximation used for the non-linear estimates: (5) Where is the marginal effect (dy/dx) of each x. Finally, the percentage contribution of each determinant was derived using the adjusted method proposed by Yiengprugsawan et al. [ 23 ], which accounts for the total explained component aligned with the direction of the overall CI, offering a more accurate interpretation of the contributions. Multicollinearity among explanatory variables was assessed using variance inflation factors (VIF), and no evidence of problematic collinearity was observed (maximum VIF = 2.23). All statistical analyses were conducted using Stata version 17 (StataCorp, College Station, TX, USA), with the conindex command used to calculate the CI. Ethics statement Ethical approval for this study was obtained from the Research Ethics Committee of the Endocrinology & Metabolism Research Institute at Tehran University of Medical Sciences (Approval No. IR.TUMS.EMRI.REC.1403.111) on 25 November 2024. The data used in this study were derived from the original study, in which all participants were aged 18 years or older, provided written informed consent, and all procedures were conducted in accordance with the principles of the Declaration of Helsinki. Results Table 1 provides an overview of the baseline characteristics of the study population. The study comprised 9,024 adults aged 18 years and older, with a mean age of 56.1 [95% CI: 55.8, 56.5] years. The proportion of excessive red meat consumption was 16.5%. The distribution of red meat consumption among participants reveals demographic variations. Download: PNG larger image TIFF original image Table 1. Distribution of the baseline characteristics by red meat consumption (Non-Excessive vs. Excessive). https://doi.org/10.1371/journal.pone.0359650.t001 The distribution of age differed significantly between the non-excessive and excessive red meat consumption groups (p-value = 0.02). Compared with the non-excessive consumption group, the excessive consumption group included a higher proportion of participants aged 40–59 years (47.18% vs. 42.8%) and lower proportions of participants aged 18–39 years (12.46% vs. 13.30%) and ≥60 years (40.36% vs. 43.89%). In contrast, the distribution of sex was similar between the two groups (p-value = 0.20), although the excessive consumption group had a slightly lower proportion of females (53.87% vs. 55.86%) and a slightly higher proportion of males (46.13% vs. 44.14%). Likewise, no significant difference was observed in the distribution of area of residence (p-value = 0.12); however, the excessive consumption group had a marginally higher proportion of urban residents (76.9
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