Phthalate exposure from plastics and cardiovascular disease: global estimates of attributable mortality and years life lost

Sara Hyman, Jonathan Acevedo, Chiara Giannarelli, Leonardo Trasande
eBioMedicine
New York University Grossman School of Medicine, New York, NY, USA

Table of Contents

Overall Summary

Study Background and Main Findings

This study aimed to quantify the global burden of cardiovascular disease (CVD) mortality attributable to exposure to di-2-ethylhexylphthalate (DEHP), a common chemical additive used to soften plastics. Motivated by emerging evidence linking DEHP to cardiovascular risks and the need for global data to inform policy discussions like the Global Plastics Treaty negotiations, the researchers sought to estimate the number of deaths and years of life lost (YLL) associated with DEHP exposure worldwide.

The researchers employed a quantitative disease burden modeling approach. They integrated several large datasets: cardiovascular mortality rates for individuals aged 55-64 in 2018 from the Institute for Health Metrics and Evaluation (IHME), corresponding population estimates from the World Bank, and regional DEHP exposure levels primarily based on 2008 biomonitoring data (from surveys like NHANES in the US, CHMS in Canada, COPHES/DEMCOPHES in Europe) or estimated from a meta-analysis for other regions. A hazard ratio (HR), representing the increased risk of CVD mortality per unit increase in DEHP exposure, was derived and extrapolated from a previous US-based longitudinal study. Using the Population Attributable Fraction (PAF) methodology, they calculated the proportion of CVD deaths likely attributable to DEHP exposure in 200 countries and territories, subsequently estimating the total excess deaths and YLL. They further estimated the portion specifically linked to plastics (assuming 98% of DEHP exposure originates from plastic sources).

The study estimated that in 2018, approximately 356,238 global deaths among 55-64 year olds were attributable to DEHP exposure, accounting for 13.5% of all cardiovascular deaths in this age group. Over 349,000 of these deaths were attributed specifically to DEHP from plastics. This translated to an estimated 10.47 million YLL globally. The research highlighted significant geographic disparities: while the Middle East and South Asia had the highest percentage of CVD deaths attributed to DEHP (average 16.8%), these regions along with East Asia and the Pacific accounted for the largest absolute number of attributable deaths (nearly 73% of the global total), partly due to large populations in the target age range. Regions like Europe and the USA had lower attributable percentages (8.4% and 10.4%, respectively). The study also noted variations in burden inequality within regions.

The authors concluded that DEHP exposure, largely from plastics, represents a significant and unequally distributed contributor to global cardiovascular mortality. They emphasized that these findings underscore an urgent need for global and local regulatory interventions to reduce DEHP exposure, particularly targeting high-burden regions and supporting developing economies facing challenges with plastic production and waste management. The study provides quantitative evidence intended to inform ongoing international policy efforts aimed at mitigating the health impacts of plastic pollution.

Research Impact and Future Directions

This study provides a concerning, quantitative estimate of the global cardiovascular disease (CVD) burden attributable to exposure to the plastic additive DEHP, suggesting it caused over 356,000 deaths among 55-64 year olds in 2018, representing 13.5% of CVD mortality in this group. The research effectively highlights significant geographic disparities, with regions like the Middle East, South Asia, and East Asia/Pacific bearing a disproportionately high burden, linking these findings directly to the urgent need for policy interventions, such as those being discussed for the Global Plastics Treaty.

The study's primary strength lies in its global scope and its attempt to synthesize complex data from various sources (mortality, population, exposure estimates) into a single burden model. However, its conclusions must be interpreted cautiously due to significant methodological limitations inherent in its design. As a modeling study relying on extrapolations and aggregated data, it cannot definitively establish causality between DEHP exposure and CVD mortality. Key limitations include the reliance on regional exposure estimates (often based on meta-analysis and imputation, especially for data-sparse regions) from 2008 to predict 2018 mortality, and the crucial extrapolation of a hazard ratio derived from a single US-based cohort study to diverse global populations. This extrapolation assumes the risk relationship observed in one specific population applies universally, which may not hold true given variations in genetics, lifestyle, co-exposures, and healthcare access.

Furthermore, the study focuses only on four DEHP metabolites, excluding numerous other potentially harmful chemicals associated with plastics (e.g., other phthalates, bisphenols) and the physical effects of micro/nanoplastics. This narrow focus likely leads to an underestimation of the total cardiovascular burden attributable to plastics overall. The use of aggregate data also prevents adjustment for individual-level confounding factors (like socioeconomic status, diet, or smoking) that could influence both exposure levels and CVD risk. Therefore, while the study provides valuable, policy-relevant evidence suggesting a substantial health impact from DEHP and highlighting critical geographic inequalities, the precise magnitude of the attributable burden remains uncertain. It strongly indicates a significant problem requiring urgent regulatory attention and further region-specific research, but the specific numbers should be viewed as plausible estimates within a range of uncertainty, rather than definitive counts.

Critical Analysis and Recommendations

Effective Study Encapsulation (written-content)
Concise and Structured Overview: The summary effectively encapsulates the study's core components (background, methods, findings, interpretation) in a structured way. This provides readers with a rapid understanding of the research scope and key takeaways.
Section: Summary
Prominent Quantitative Results (written-content)
Highlights Key Quantitative Findings: The summary prominently features the main numerical results (estimated deaths, YLL, attributable percentage). This immediately conveys the magnitude and significance of the study's findings regarding DEHP's impact.
Section: Summary
Clear Study Motivation (written-content)
Clear Rationale and Context: The summary clearly links emerging evidence on DEHP's cardiovascular risks to the practical need for global data to inform international policy (Global Plastics Treaty). This establishes the study's relevance and importance from the outset.
Section: Summary
Action-Oriented Interpretation (written-content)
Actionable Interpretation: The summary directly translates the findings into implications, emphasizing regional disparities and the need for regulatory action. This highlights the study's potential impact on public health policy.
Section: Summary
Effective Contextualization (written-content)
Strong Contextual Framing of CVD Burden: The introduction effectively sets the stage by describing CVD as a major, persistent global health challenge, acknowledging past successes but highlighting recent concerning trends. This establishes the importance of identifying contributing factors like DEHP.
Section: Introduction
Clear Focus on DEHP Risk (written-content)
Clear Introduction of Plastics as Emerging Risk: The text clearly identifies exposure to plastic chemicals, specifically DEHP, as a novel and under-recognized environmental risk factor for CVD. This effectively focuses the reader on the paper's central theme.
Section: Introduction
Biological Plausibility Established (written-content)
Summarizes Mechanistic Links (DEHP & CVD): The introduction concisely outlines potential biological pathways (e.g., PPAR modulation, oxidative stress) linking DEHP to adverse cardiovascular outcomes. This provides biological plausibility for the association being investigated.
Section: Introduction
Strong Justification and Policy Link (written-content)
Strong Rationale and Policy Relevance: The introduction effectively justifies the study by highlighting the lack of global DEHP burden estimates and explicitly linking this gap to policy needs (Global Plastics Treaty). This underscores the study's timeliness and potential impact.
Section: Introduction
Suggestion: Enhance Mechanism-Outcome Connection (written-content)
Clarify Mechanism-Outcome Links: While mechanisms (PPAR activation, oxidative stress) and outcomes (atherosclerosis, mortality) are mentioned, the text could more explicitly connect how these mechanisms contribute to the specific outcomes. This would strengthen the narrative flow and enhance understanding of the biological pathway for non-experts.
Section: Introduction
Transparent Data Sourcing (written-content)
Clear Identification of Data Sources: The methods section clearly lists the sources for key data inputs (population, mortality, exposure levels). This enhances transparency and allows for assessment of data quality and potential limitations.
Section: Methods
Clear Hazard Ratio Derivation (written-content)
Detailed Hazard Ratio Calculation: The derivation and application of the hazard ratio (HR) for DEHP-associated CVD mortality, including reliance on a prior study and the specific formula used, are well-described. This clarifies a critical component of the burden estimation.
Section: Methods
Transparent Burden Calculation (written-content)
Clear Calculation Procedures for Burden Estimation: The use of standard epidemiological formulas (Population Attributable Fraction) for calculating attributable deaths and YLL is clearly presented. This enhances the traceability and reproducibility of the burden estimates.
Section: Methods
Robustness Assessment via Sensitivity Analyses (written-content)
Comprehensive Sensitivity Analyses: The description of planned sensitivity analyses (e.g., alternative YLL data, exposure models, plastic fractions) demonstrates methodological rigor. This shows an effort to assess the robustness of the findings to key assumptions.
Section: Methods
Suggestion: Justify Quantile Grouping Choice (written-content)
Provide Rationale for Quantile Groupings: The methods describe applying specific exposure percentiles (10th, 25th, etc.) to corresponding population segments but don't explicitly state the rationale for these specific groupings. Briefly explaining this choice would enhance methodological clarity.
Section: Methods
Clear Headline Results (written-content)
Clear Presentation of Headline Findings: The results section clearly states the main quantitative estimates (total global deaths, attributable percentage) upfront. This immediately communicates the study's primary conclusions on the scale of the issue.
Section: Results
Emphasis on Geographic Inequality (written-content)
Highlights Geographic Disparities: The results effectively emphasize the significant inequalities in DEHP exposure and attributable mortality burden across different world regions. This highlights a key finding regarding the uneven distribution of risk.
Section: Results
Nuanced Intra-Regional Analysis (written-content)
Analysis of Intra-Regional Disparities: The study examines how the burden varies within regions based on exposure quantiles (Fig 2), revealing differing levels of inequality. This adds nuance beyond simple regional averages.
Section: Results
Effective Data Presentation (Table 1) (graphical-figure)
Data Consolidation and Structure (Table 1): Table 1 effectively consolidates complex regional and quantile-specific data on mortality and YLL into a structured format. This facilitates comparison and supports the main quantitative findings reported in the text.
Section: Results
Effective Visualization of Disparities (Fig 2) (graphical-figure)
Regional Comparison and Trend Visualization (Fig 2): Figure 2 effectively uses line graphs to illustrate trends and allow direct comparison of attributable mortality percentages across exposure quantiles for different world regions. This visually supports the findings on inter- and intra-regional disparities.
Section: Results
Suggestion: Improve Narrative Link (Exposure to Outcome) (written-content)
Enhance Narrative Flow Between Exposure and Mortality Findings: The results present regional exposure variations and mortality disparities but could more explicitly connect these textually. Strengthening the narrative link showing how exposure patterns translate to mortality outcomes would improve coherence.
Section: Results
Effective Synthesis of Findings (written-content)
Clear Summary and Contextualization of Findings: The discussion effectively reiterates the main findings and immediately places them in the context of regional disparities and policy relevance. This reinforces the study's key messages.
Section: Discussion
Economic Impact Assessment (written-content)
Inclusion of Economic Cost Analysis: Presenting potential societal costs using different valuation methods (SCYLL, VSL) adds a significant dimension to the impact assessment. This translates the health burden into economic terms, strengthening the case for intervention.
Section: Discussion
Real-World Contextualization (written-content)
Integration with Industrial and Regulatory Context: The discussion connects the findings to real-world factors like plastic production trends, waste management, and existing regulations. This contextualizes the results and highlights areas for policy focus.
Section: Discussion
Actionable Policy Recommendations (written-content)
Strong Policy Relevance and Recommendations: The authors derive clear, actionable policy recommendations (e.g., targeted regulations, international collaboration) from their findings. This emphasizes the study's practical utility for decision-makers.
Section: Discussion
Thorough Discussion of Limitations (written-content)
Comprehensive Acknowledgement of Limitations: The discussion thoroughly addresses multiple study limitations (e.g., reliance on regional estimates, data heterogeneity, single HR source, limited metabolites). This demonstrates scientific rigor and encourages cautious interpretation.
Section: Discussion
Suggestion: Quantify/Qualify Impact of Limitations (written-content)
Explicitly Link Limitations to Potential Impact on Estimates: The discussion notes limitations but could more explicitly state how each major limitation (e.g., excluding other chemicals, extrapolating US HR) likely biases the estimates (e.g., probable underestimation of total plastic burden, uncertainty in regional accuracy). This would strengthen the critical interpretation of the results' magnitude and certainty.
Section: Discussion
Suggestion: Elaborate on Economic Estimate Differences (written-content)
Briefly Discuss Discrepancy in Economic Cost Estimates: The discussion presents vastly different economic costs from SCYLL vs. VSL methods without fully explaining the reason for the difference or its policy implications. Briefly clarifying the conceptual basis (cost-of-illness vs. willingness-to-pay) and the significance of the billion-vs-trillion dollar range would add depth.
Section: Discussion

Section Analysis

Summary

Key Aspects

Strengths

Suggestions for Improvement

Introduction

Key Aspects

Strengths

Suggestions for Improvement

Methods

Key Aspects

Strengths

Suggestions for Improvement

Results

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

Table 1: Aggregate cardiovascular mortality burden due to DEHP, main estimates.
Figure/Table Image (Page 7)
Table 1: Aggregate cardiovascular mortality burden due to DEHP, main estimates.
First Reference in Text
identified (Table 1), of which over 349,000 were plastic-attributable deaths.
Description
  • Overview of Content: This table presents estimated numbers of deaths and Years of Life Lost (YLL) - a measure of premature mortality - linked to cardiovascular disease (CVD) that are specifically attributed to exposure to a chemical called Di(2-ethylhexyl) phthalate (DEHP). DEHP is commonly used to make plastics more flexible. The estimates are for the year 2018, based on DEHP exposure levels from 2008.
  • Data Stratification by Region and Exposure Quantile: The data is broken down into major world regions (like Africa, Asia-EPA for East Asia/Pacific, Asia-MESA for Middle East/South Asia, etc.) and also by different levels of DEHP exposure within the population, divided into segments called quantiles (specifically, the 10th, 25th, 50th, 75th, and 95th percentiles). Quantiles represent ranges of exposure, allowing comparison between lower and higher exposed groups.
  • Global Burden Estimates: Globally, the table estimates a total of 356,238 deaths and 10,473,621 YLL were attributable to DEHP exposure in 2018 among individuals aged 55-64.
  • Regional Disparities: The table highlights significant differences between regions. For instance, the Middle East and South Asia (Asia-MESA) region shows the highest average percentage of CVD deaths attributed to DEHP (16.807%), whereas Europe shows a lower average percentage (8.374%).
  • Specific Metrics Presented: For each region and exposure quantile, the table shows the estimated number of deaths, the corresponding YLL, and the percentage of total CVD deaths that these attributable deaths represent.
Scientific Validity
  • Data Sources and Integration: The estimates are derived using established methodologies, combining baseline mortality data from a reputable source (IHME), population data (World Bank), and DEHP exposure data from regional biomonitoring or prior meta-analyses (Acevedo et al., 2025). This multi-source approach is standard for global burden studies.
  • Methodology for Attributable Burden Calculation: The calculation relies on applying a hazard ratio for DEHP exposure and CVD mortality derived from a specific US cohort study (Trasande et al., 2022) to global populations. While using a single study's HR introduces uncertainty, it is based on available longitudinal data. The use of population attributable fractions is an appropriate epidemiological technique for this type of estimation.
  • Temporal Lag Consideration: The use of a 10-year lag between exposure measurement (2008) and mortality outcome (2018) is methodologically appropriate to account for the latency period often associated with chronic disease development following environmental exposures.
  • Handling of Missing Data: The footnote acknowledges specific data imputations (e.g., MEHP for Australia, MECPP for Canada), which is important for transparency regarding data limitations and the methods used to address them.
  • Model Assumptions and Potential Conservatism: The table presents 'main estimates' likely based on linear exposure models (as suggested by sensitivity analyses mentioned elsewhere in the paper). The validity hinges on the assumptions of these models and the generalizability of the hazard ratio. As noted in the discussion/sensitivity analysis, these estimates might be conservative.
Communication
  • Data Consolidation and Structure: The table effectively consolidates complex data on DEHP-attributable cardiovascular mortality and YLL across multiple regions and exposure quantiles into a structured format. Column and row labels are generally clear, facilitating comparison.
  • Summary Statistics: Including total deaths, total YLL, and average attributable percentages for each region and globally provides a concise summary of the main findings.
  • Granularity by Exposure Quantile: The breakdown by exposure quantiles (10th, 25th, 50th, 75th, 95th) allows for a nuanced view of how the attributable burden varies with exposure levels within different populations.
  • Transparency via Footnote: The detailed footnote explaining data sources (IHME, biomonitoring surveys, Acevedo et al. 2024 meta-analysis), calculation methods (hazard ratio application, quantile weighting), and specific imputations (e.g., MEHP for Australia) is essential for transparency and allows readers to understand the basis of the estimates.
  • Alignment with Text: The presentation aligns well with and directly supports the key quantitative findings reported in the Results text, such as the total global deaths and regional disparities.
Fig. 1: Aggregate DEHP-attributable mortality world maps among 200 countries...
Full Caption

Fig. 1: Aggregate DEHP-attributable mortality world maps among 200 countries and eight world regions.

Figure/Table Image (Page 8)
Fig. 1: Aggregate DEHP-attributable mortality world maps among 200 countries and eight world regions.
First Reference in Text
from DEHP in 2018 occurred in the continent of Asia (Fig. 1a).
Description
  • Content Overview: This map (labeled 'a') shows how the total estimated number of cardiovascular disease (CVD) deaths worldwide attributed to Di(2-ethylhexyl) phthalate (DEHP) exposure are distributed across different continents or large regions. It essentially shows the 'global share' of the burden held by each area.
  • Visualization Method: The map uses different shades of purple to represent the proportion, or fraction, of the total global DEHP-attributable CVD deaths found in each region. Darker shades indicate a larger share.
  • Key Findings Displayed: The legend indicates the scale, ranging from 0.1 (meaning 10% of the global total) to 0.4 (meaning 40% of the global total). The continent of Asia is shown in the darkest shade, indicating it accounts for the largest proportion of these deaths globally, aligning with the reference text stating the majority occurred in Asia.
Scientific Validity
  • Representation of Model Output: The map accurately reflects the calculated spatial distribution of the absolute number of DEHP-attributable deaths derived from the underlying burden model. Its validity is contingent upon the accuracy and assumptions of that model (e.g., hazard ratios, exposure estimates, population data).
  • Focus on Absolute Impact: This visualization appropriately highlights the impact of population size combined with attributable risk, identifying regions with the largest absolute public health impact in terms of numbers of deaths.
  • Data Aggregation Level: The aggregation into broad regions (continents/subcontinents) is a necessary simplification for global visualization but masks potentially significant intra-regional variations.
Communication
  • Geographic Visualization of Absolute Burden: The choropleth map effectively visualizes the geographical distribution of the absolute burden of DEHP-attributable CVD deaths, allowing for rapid identification of continents contributing the largest share to the global total.
  • Clarity of Legend and Gradient: The color gradient and legend (0.1 to 0.4) are clear and intuitively represent the proportion of the total global deaths occurring in each region.
  • Focus on Global Share: This panel clearly communicates where the highest number of attributable deaths are concentrated globally, complementing the relative risk information in Panel B.
Fig. 2: Percent change in attributable cardiovascular mortality due to DEHP...
Full Caption

Fig. 2: Percent change in attributable cardiovascular mortality due to DEHP exposure by quantile in across eight world regions.

Figure/Table Image (Page 9)
Fig. 2: Percent change in attributable cardiovascular mortality due to DEHP exposure by quantile in across eight world regions.
First Reference in Text
These patterns over percentiles are visualised in Fig. 2.
Description
  • Purpose of the Graph: This graph shows how the estimated percentage of cardiovascular deaths linked to DEHP exposure changes as the level of exposure increases within different populations around the world.
  • X-axis Explanation (Exposure Quantile): The horizontal axis (x-axis) represents the 'Quantile of exposure in the population'. A quantile divides the population into groups based on their exposure level; moving from left to right means looking at groups with progressively higher exposure to DEHP (e.g., 25th percentile means higher exposure than 25% of the population, 75th percentile means higher exposure than 75%).
  • Y-axis Explanation (Attributable Mortality): The vertical axis (y-axis) shows the 'Percentage attributable CV mortality'. This is the estimated percentage of cardiovascular deaths within a specific exposure group that can be attributed to DEHP exposure.
  • Regional Lines: Each colored line represents a different major world region (e.g., Africa, Europe, USA, Asia-MESA). The graph plots how the attributable mortality percentage changes across exposure quantiles for each region.
  • Observed Trends and Patterns: The lines generally slope upwards, indicating that for most regions, higher levels of DEHP exposure are associated with a higher percentage of attributable cardiovascular deaths. However, the starting points (attributable percentage at lower quantiles) and the steepness of the lines vary significantly between regions.
  • Smoothing Technique (LOESS): The caption mentions that the curves are smoothed using LOESS regression, a statistical technique used to create a smooth line through data points to visualize trends more clearly.
Scientific Validity
  • Representation of Model Output: The graph visually represents the calculated population attributable fractions derived from applying region-specific exposure quantile data and hazard ratios. Its validity is directly tied to the robustness of these underlying calculations and the assumptions of the burden model.
  • Use of Exposure Quantiles: The use of exposure quantiles (derived from estimated or measured population distributions) is an appropriate way to stratify the population and examine dose-response-like patterns in attributable burden.
  • Smoothing Method Appropriateness: The application of LOESS smoothing is a valid technique for visualizing trends in potentially noisy data derived from quantile estimates, but the degree of smoothing could influence the perceived shape of the relationship.
  • Visualization of Heterogeneity: The graph accurately depicts the heterogeneity in attributable burden across regions and exposure levels, supporting the study's conclusions about geographic disparities and the differential impact of exposure depending on the region.
Communication
  • Trend Visualization: The use of a line graph effectively illustrates the trend of increasing attributable mortality with increasing exposure quantiles for most regions.
  • Regional Comparison: Plotting multiple regions on the same axes facilitates direct comparison of both the baseline attributable risk (y-intercept approximation) and the steepness of the exposure-response relationship across different geographical areas.
  • Legend Clarity: The legend clearly identifies the color corresponding to each world region.
  • Use of Smoothing: The LOESS smoothing helps to visualize the general trend for each region, reducing noise from specific quantile estimates, although it might obscure finer details.
  • Support for Textual Findings: The visualization directly supports the textual description of disparities, showing how some regions (e.g., USA, Africa) have a large difference between low and high quantiles, while others (e.g., Asia-MESA) have high attributable mortality even at lower exposure quantiles.
  • Color Choice and Accessibility: While distinct, some colors in the plot might be difficult to differentiate for individuals with color vision deficiency. Consider using a combination of colors and line styles (e.g., dashed, dotted) for better accessibility.

Discussion

Key Aspects

Strengths

Suggestions for Improvement

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