Pith. sign in

REVIEW 4 major objections 7 minor 69 references

A Hierarchical Multilevel Inference Framework for Structural Cardiovascular Risk Modeling: County-Scale Analysis of Cardiovascular Mortality in Ohio and Pennsylvania (1999-2020)

T0 review · 4 major / 7 minor · reviewed 2026-07-09 · glm-5.2

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Three-model framework reveals Rust Belt heart deaths diverge by race, sex, and pollution

desk verdict Legitimate empirical application with a real state-differential PM2.5 finding, but the year-coefficient contradiction between Normal and Poisson models is unresolved and the supplementary equations appear missing. read the letter →

arxiv 2607.06916 v1 pith:FSEXOERQ submitted 2026-07-08 stat.AP

Christopher Blaszczak-Boxe , Jose Perez-Chavez , Sium Gebremeriam , King-Fai Li , Yuk L. Yung , Mohammad Russel This is my paper
classification stat.AP
keywords mortalitycardiovascularframeworkhierarchicalmodelspennsylvaniastructuralcounty-level
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that cardiovascular mortality cannot be understood through a single statistical lens. By fitting three multilevel models simultaneously—Normal regression on age-adjusted rates, Poisson regression on raw death counts, and Poisson regression with a log-population offset—the authors separate demographic effects (race, sex, age) from county-level structural variation and environmental exposure signals. Applied to 22 years of county-level data in Ohio and Pennsylvania across seven CVD subtypes, the framework shows that age-adjusted rates and absolute counts tell different stories: age-adjusted mortality declined, but absolute deaths for several subtypes stagnated or reversed after 2010. Black populations faced 44–240% higher mortality and males roughly 100 excess deaths per 100,000 compared to females. PM2.5 was a statistically significant predictor of ischemic and hypertensive mortality in Pennsylvania but weak or null in Ohio, suggesting that industrial legacy and exposure geography shape which risk factors are detectable. The population-offset Poisson models reduced unexplained county-level variance by 50–75% while preserving residual structural disparities, demonstrating that much—but not all—of the spatial heterogeneity in raw counts is population scale rather than genuine structural inequity.

What carries the argument

The machinery is a two-level hierarchical regression: individuals (or stratified count records) nested within counties, with fixed effects for year, race, sex, PM2.5, and O3, and county-level random intercepts capturing unobserved spatial heterogeneity. The same model structure is fit three ways—Normal on age-adjusted rates, Poisson on raw counts, and Poisson with log(population) as a fixed-coefficient offset—so that each distributional assumption exposes a different facet of the data. The random-intercept variance (Omega_u) serves as a diagnostic: its contraction when the population offset is applied quantifies how much of the raw-count heterogeneity is population scale versus genuine结构性ine

What would settle it

If a within-county exposure analysis (e.g., using census-tract or ZIP-code-level pollution data linked to individual mortality records) showed that the PM2.5 coefficients vanish or reverse sign after controlling for within-county residential segregation, the paper's claim that PM2.5 is a robust predictor of ischemic and hypertensive mortality in Pennsylvania would be undermined.

Watch

Extended reading notes

Core claim

The central claim is that triangulating three distributional assumptions on the same nested data reveals complementary dimensions of cardiovascular risk that no single model captures: Normal models isolate relative disparities in age-adjusted rates, Poisson models capture absolute burden, and population-offset Poisson models normalize for county size while preserving residual structural variance. The cross-model comparison shows that demographic effects (race and sex) dwarf pollutant effects by an order of magnitude, but PM2.5 retains small, coherent, statistically significant associations for specific subtypes—particularly ischemic and hypertensive mortality in Pennsylvania.

Load-bearing premise

The paper assigns county-level annual average PM2.5 and O3 concentrations to all mortality records within that county, then interprets the resulting coefficients as exposure-response estimates. If within-county pollution gradients correlate with race or socioeconomic status—for instance, if Black residents disproportionately live near emission sources—the county-level coefficient will blend true exposure effects with residential segregation, biasing the estimate. The paper's

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 7 minor

Summary. This manuscript applies multilevel (hierarchical) regression models to county-level cardiovascular disease (CVD) mortality data in Ohio and Pennsylvania (1999–2020), using MLwiN. Three model specifications are fit for seven CVD subtypes: Normal (age-adjusted rates), Poisson (raw counts), and Poisson with a log(population) offset. Fixed effects include year, sex, race, PM2.5, and O3; county-level random intercepts capture spatial heterogeneity. The authors find persistent racial and sex disparities, modest PM2.5 associations (stronger in Pennsylvania), and substantial county-level variance that is reduced but not eliminated by population offsets. The paper positions itself as a methodological complement to the Global Burden of Disease (GBD) framework, offering subnational resolution.

Significance. The study's ambition to provide subtype-specific, county-level multilevel models for two Rust Belt states over two decades is reasonable, and the dual Normal/Poisson/offset design is a defensible strategy for triangulating rate-based and count-based evidence. The documentation of persistent racial and geographic disparities is consistent with the existing literature. However, the manuscript's significance is substantially undermined by a critical internal inconsistency in the temporal results and by the absence of the promised supplementary equations, which prevents verification of the model specifications. The central claim that the three model types provide 'complementary perspectives' is not adequately supported when two of the three yield directly contradictory temporal trends without a coherent explanation.

major comments (4)
  1. Section 3.7, point 1 (Temporal Trends): The Normal (age-adjusted) models produce positive year coefficients (PA: +7.09, OH: +5.20), while the Poisson (raw count) models produce negative year coefficients (PA: −0.017, OH: −0.009). The authors attribute the Normal model's positive trend to 'a likely artefact of ageing and evolving diagnostic classification, which can inflate adjusted rates even as absolute deaths decline.' This explanation is incoherent: the outcome in the Normal model is already age-adjusted, so population aging cannot explain a positive temporal trend in age-adjusted rates. Furthermore, the paper's own Figure 3 shows age-adjusted rates declining over this period, which is consistent with the Poisson model but contradicts the Normal model. This is a load-bearing inconsistency: the central claim that the models offer 'complementary perspectives' is unsupported if two of三种三
  2. Section 3.7, point 1 and Section 3.6 (Pennsylvania Results): The claim that the positive year coefficient in the Normal model reflects 'evolving diagnostic classification' is asserted without evidence. No sensitivity analysis is conducted to test whether ICD coding changes (e.g., ICD-10 implementation in 1999) or other coding artifacts drive the result. If the Normal model is contaminated by coding artifacts, this should be demonstrated or the model should be re-specified. As it stands, the reader cannot determine whether the Normal model results reflect a real phenomenon or a specification error, and the paper does not investigate this.
  3. Supplementary Material (pages 49–67): The manuscript repeatedly states that 'complete model equations are provided in the Supplementary Material' (Abstract; Section 2.3; Section 3.5). However, the supplementary pages in the submitted manuscript contain largely empty pages with figure captions (e.g., 'Figure 1S (a-u)') but no actual equations. Without the equation-level outputs, the model specifications cannot be verified, and the claim of reproducibility is not met. The complete equation-level outputs referenced in Sections 3.6 and S3 must be included for the manuscript to be evaluated properly.
  4. Ecological inference limitation: The models assign county-level annual average PM2.5 and O3 concentrations to all individuals within a county, but the mortality data are aggregated counts stratified by race/sex/age, not linked individual records. If within-county exposure gradients correlate with race or socioeconomic status (e.g., Black residents disproportionately live near pollution sources within a county), the county-level coefficient will be biased. This is the standard ecological inference problem. The paper does not discuss this limitation or attempt any sensitivity analysis for ecological bias. Given that PM2.5 associations are a key finding (Section 3.7, point 4), this limitation should be explicitly acknowledged and its potential impact discussed.
minor comments (7)
  1. Section 2.1 describes the framework as 'machine learning–enhanced,' but no machine learning methods are actually applied. MLwiN uses MCMC and IGLS, not machine learning. This characterization should be removed or clarified.
  2. Section 2.2 mentions 'Outlier detection and winsorization of extreme mortality values' but does not specify what thresholds were used or how many observations were affected. The imputation method for missing air quality data is described as 'county-level temporal interpolation' without detail. Reproducibility requires sensitivity to these choices.
  3. The Normal model equation (Section 2.3) lists 'AgeGroup' as a fixed effect, but the outcome is age-adjusted mortality. Including age group as a predictor of age-adjusted rates is unusual; the authors should clarify what this coefficient means in this context.
  4. Figure 4 and Figure 5 are described in detail in the text, but the actual figures appear to be missing or not rendered in the submitted manuscript. The captions are present but the figures themselves cannot be verified. Ensure all figures are properly included.
  5. The reference list includes incomplete citations (e.g., Ref 28: 'GBD Collaborative Network. (2023)... forthcoming'; Ref 35: 'Global Burden of Disease Study 2023: Results' with a future date). These should be updated or marked as preprints.
  6. Section 3.4 claims the study's architecture 'lays groundwork for synthetic counterfactuals, predictive hotspot mapping, and planetary health analogues, broadening the applicability of this approach to climate-sensitive disease risk modeling and exobiological systems thinking.' This is speculative overreach. The manuscript does not demonstrate any of these capabilities. This language should be toned down or removed.
  7. The declaration states ChatGPT (v5.1) was used to 'help provide additional qualitative and quantitative interpretations of MLwiN-MLA equations produced.' The authors should clarify which interpretations were AI-assisted and how they were verified.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity detected: models are fit to external data and coefficients are estimated, not assumed or defined in terms of their outputs.

full rationale

The paper applies standard multilevel regression models (Normal, Poisson, Poisson-with-offset) to externally sourced data (CDC WONDER mortality records, EPA AQS pollution data). The model equations (Section 2.3) specify mortality or death counts as functions of year, race, sex, PM2.5, O3, and county-level random intercepts. The coefficients (e.g., β_year, β_race, β_PM2.5) are estimated from the data via MLwiN, not defined in terms of the outcomes. No prediction or claimed result reduces by construction to a fitted input or a self-definitional relationship. The comparison with the Global Burden of Disease (GBD) framework is framed as methodological complementarity (Section 3.4), not as deriving the present results from GBD outputs. Self-citation is minimal and does not create dependency loops: the paper cites standard epidemiological and statistical literature (e.g., Raudenbush & Bryk 2002; Diez Roux 2001; Pope et al. 2015) for methodological justification, but these are external references whose results are not assumed as inputs. The variance reduction observed when adding log(population) offsets (Section 3.5, Panel C) is a standard statistical consequence of normalizing count data by exposure, not a circular artifact. The contradictory temporal trends between Normal and Poisson models (positive vs. negative year coefficients) raise correctness and interpretation concerns, but this is a specification or model-fitting issue, not circularity—the coefficients are still estimated from data, not defined in terms of each other. The derivation chain is self-contained against external benchmarks.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new mathematical entities, particles, forces, or postulated objects. All model components (random intercepts, fixed effects, offsets) are standard multilevel regression constructs. The free parameters are conventional regression coefficients and variance components estimated from data. The axioms are domain assumptions about ecological exposure assignment and spatial independence that are standard but unverified in this analysis.

free parameters (3)
  • County-level random intercept variance (Ω_u) = Varies by model; e.g., 1345.2 (OH Normal), 0.545 (OH offset), 1397.1 (PA Normal), 0.570 (PA offset)
    Estimated from data as variance of county random effects; standard in multilevel models but a fitted quantity.
  • Fixed-effect coefficients (β_0 through β_6) = Varies by subtype and model; e.g., Black race +1.754 (OH offset), PM2.5 +0.010 (PA offset)
    Standard regression coefficients estimated from data.
  • Individual-level residual variance (e_0ij) = Not explicitly reported
    Part of the Normal model specification; estimated but not reported in summary tables.
assumptions (4)
  • domain assumption County-level annual average PM2.5 and O3 concentrations validly represent individual-level chronic exposure for all residents within a county.
    Section 2.2 assigns county-level AQS measurements to all individuals; no within-county exposure gradients are modeled.
  • domain assumption Observations within counties are independent conditional on random intercepts (no residual spatial autocorrelation).
    The multilevel model specifies random intercepts at county level but does not include a spatial correlation structure; adjacent counties may share unmodeled exposures.
  • domain assumption CDC WONDER mortality data accurately classify CVD subtypes consistently across 1999–2020.
    The paper notes 'evolving diagnostic classification' (Section 3.7) as a possible explanation for contradictory year coefficients but does not verify ICD coding consistency.
  • standard math Age-adjustment to the 2000 U.S. standard population removes confounding by age structure across counties and time.
    Standard demographic assumption; the Normal models use age-adjusted rates as outcomes.

how reviews work

0 comments
Cite this review

Pith. "Pith review of A Hierarchical Multilevel Inference Framework for Structural Cardiovascular Risk Modeling: County-Scale Analysis of Cardiovascular Mortality in Ohio and Pennsylvania (1999-2020)." pith.science (2026). https://pith.science/paper/FSEXOERQ

@misc{pith2026260706916,
  author       = {Pith},
  title        = {Pith review of: A Hierarchical Multilevel Inference Framework for Structural Cardiovascular Risk Modeling: County-Scale Analysis of Cardiovascular Mortality in Ohio and Pennsylvania (1999-2020)},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FSEXOERQ}},
  note         = {Machine review of arXiv:2607.06916}
}
read the original abstract

Cardiovascular mortality is shaped by interacting demographic, environmental, and structural processes operating across multiple spatial scales. Conventional epidemiologic analyses often rely on aggregate summaries or single-model formulations that obscure hierarchical variation and contextual heterogeneity. We present a reproducible multilevel statistical inference framework integrating Normal (age-adjusted), Poisson (count-based), and population-offset Poisson models to quantify cardiovascular mortality across nested geographic units while separating demographic effects from structural variation. The framework was applied to county-level mortality data from Ohio and Pennsylvania (1999-2020) using MLwiN hierarchical models for seven cardiovascular disease (CVD) subtypes. Fixed effects included year, sex, race, PM2.5, and O3, while county-level random intercepts captured spatial heterogeneity. Complete model equations are provided in the Supplementary Material. The framework reveals complementary perspectives on cardiovascular risk unavailable from a single model. Age-adjusted mortality declined more rapidly in Pennsylvania than Ohio, whereas Poisson models identified post-2010 stagnation or reversal for several CVD subtypes. Black populations experienced elevated mortality risks, males exhibited higher mortality than females, and PM2.5 showed stronger associations with ischemic and hypertensive mortality in Pennsylvania. Population-offset models reduced unexplained variance while preserving county-level structural disparities. Beyond cardiovascular epidemiology, this work introduces a generalizable hierarchical statistical framework for structurally nested health systems. The methodology provides a scalable foundation for disease surveillance, environmental health assessment, health equity research, reproducible statistical analysis, and AI-assisted scientific inference.

Figures

Figures reproduced from arXiv: 2607.06916 by the authors.

Figure 1
Figure 1. Global and United States trends in cardiovascular disease (CVD) [PITH_FULL_IMAGE:figures/full_fig_p014_1.png] view at source ↗
Figure 2
Figure 2. State-level trends in cardiovascular disease (CVD) mortality by race in Ohio and Pennsylvania, 1968–2020. (a) Ohio. Long-term heart-disease death rates and Black–White mortality ratios from 1968 to 2015 (Van Dyke et al., 2018) alongside CDC WONDER data (1999–2020) showing age-adjusted total CVD mortality rates per 100 000 population by race. Declines in age-standardized mortality have plateaued since 2010, with pers… view at source ↗
Figure 3
Figure 3. Raw (absolute), age-adjusted, and age-stratified cardiovascular disease (CVD) mortality by race, gender, and age in Pennsylvania and Ohio, 1999–2020. (a–b) Total heart disease mortality by race for Pennsylvania (a) and Ohio (b), showing both absolute (left panels) and age-adjusted (right panels) rates per 100 000 population with 95% confidence intervals and Poisson uncertainty bands. Black or African American popula… view at source ↗

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

69 extracted references · 69 canonical work pages

  1. [1]

    Circulation , author =

    Benjamin EJ, Virani SS, Callaway CW, et al. Heart Disease and Stroke Statistics— 2019 Update: A Report From the American Heart Association . Circulation. 2019;139(10):e56–e528. doi:10.1161/CIR.0000000000000659

    work page doi:10.1161/cir.0000000000000659 2019

  2. [2]

    Recent Trends in Cardiovascular Mortality in the United States and Public Health Goals

    Sidney S, Quesenberry CP, Jaffe MG, et al. Recent Trends in Cardiovascular Mortality in the United States and Public Health Goals . JAMA Cardiology. 2016;1(5):594–599. doi:10.1001/jamacardio.2016.1326

    work page doi:10.1001/jamacardio.2016.1326 2016

  3. [3]

    Fine-Particulate Air Pollution and Life Expectancy in the United States

    Pope CA III , Ezzati M, Dockery DW. Fine-Particulate Air Pollution and Life Expectancy in the United States . N Engl J Med. 2009;360(4):376 –386. doi:10.1056/NEJMsa0805646

    work page doi:10.1056/nejmsa0805646 2009

  4. [4]

    Particulate Matter Air Pollution and Cardiovascular Disease: An Update to the Scientific Statement From the American Heart Association

    Brook RD, Rajagopalan S, Pope CA III, et al. Particulate Matter Air Pollution and Cardiovascular Disease: An Update to the Scientific Statement From the American Heart Association . Circulation. 2010;121(21):2331 –2378. doi:10.1161/CIR.0b013e3181dbece1

    work page doi:10.1161/cir.0b013e3181dbece1 2010

  5. [5]

    Vital Signs: Disparities in Age - Specific Mortality Among Adults Aged 25 –64 Years — United States, 1999–2017

    Ritchey MD , Loustalot F, Bowman BA, et al. Vital Signs: Disparities in Age - Specific Mortality Among Adults Aged 25 –64 Years — United States, 1999–2017. MMWR Morb Mortal Wkly Rep. 2020;69(8):243 –248. doi:10.15585/mmwr.mm6908e1

    work page doi:10.15585/mmwr.mm6908e1 1999

  6. [6]

    Reversing the Decline in Cardiovascular Mortality: What Are the Next Steps? JAMA Cardiology

    Sidney S , Quesenberry CP, Jaffe MG, et al. Reversing the Decline in Cardiovascular Mortality: What Are the Next Steps? JAMA Cardiology. 2022;7(1):105–106. doi:10.1001/jamacardio.2021.4803

    work page doi:10.1001/jamacardio.2021.4803 2022

  7. [7]

    COVID-19 and Cardiovascular Disease Mortality in the United States

    Khubchandani J , Price JH, Wiblishauser M. COVID-19 and Cardiovascular Disease Mortality in the United States . J Am Coll Cardiol . 2022;79(1):56 –58. doi:10.1016/j.jacc.2021.10.041

    work page doi:10.1016/j.jacc.2021.10.041 2022

  8. [8]

    The Gulf Oil Spill

    Goldstein BD , Osofsky HJ, Lichtveld MY. The Gulf Oil Spill . N Engl J Med. 2011;364(14):1334–1348. doi:10.1056/NEJMra1007197

    work page doi:10.1056/nejmra1007197 2011

Show all 69 references
  1. [9]

    Integrated Science Assessment (ISA) for Particulate Matter (Final Report, 2019)

    EPA (United States Environmental Protection Agency) . Integrated Science Assessment (ISA) for Particulate Matter (Final Report, 2019) . EPA/600/R - 19/188. 43

    2019

  2. [10]

    Structural Racism and Health Inequities in the USA: Evidence and Interventions

    Bailey ZD , Krieger N, Agénor M, Graves J, Linos N, Bassett MT. Structural Racism and Health Inequities in the USA: Evidence and Interventions . Lancet. 2017;389(10077):1453–1463. doi:10.1016/S0140-6736(17)30569-X

    2017 doi

  3. [11]

    Person and Place: The Compounding Effects of Race/Ethnicity and Rurality on Health

    Probst JC , Moore CG, Glover SH, Samuels ME. Person and Place: The Compounding Effects of Race/Ethnicity and Rurality on Health . Am J Public Health. 2004;94(10):1695–1703. doi:10.2105/AJPH.94.10.1695

    2004 doi

  4. [12]

    Ambient Air Pollution and Incidence of Cardiovascular Events: Results From the Women's Health Initiative Observational Study

    Turner MC, et al. Ambient Air Pollution and Incidence of Cardiovascular Events: Results From the Women's Health Initiative Observational Study. Circulation. 2011;123(16):1630–8

    2011

  5. [13]

    Air pollution and health

    Brunekreef B , Holgate ST. Air pollution and health. Lancet. 2002;360(9341):1233–42

    2002

  6. [14]

    https:/ /wonder.cdc.gov/

    Centers for Disease Control and Prevention (CDC) WONDER Database. https:/ /wonder.cdc.gov/

  7. [15]

    Environmental Protection Agency (EPA) Air Quality System (AQS)

    U.S. Environmental Protection Agency (EPA) Air Quality System (AQS). https:/ /www.epa.gov/aqs

  8. [16]

    Integrated Science Assessment for Particulate Matter

    EPA (2018). Integrated Science Assessment for Particulate Matter. U.S. Environmental Protection Agency

    2018

  9. [17]

    Braveman P, Egerter S, Williams DR. (2011). The social determinants of health: coming of age. Annual Review of Public Health, 32, 381–398

    2011

  10. [18]

    Diez Roux AV. (2001). Investigating neighborhood and area effects on health. American Journal of Public Health, 91(11), 1783–1789

    2001

  11. [19]

    Krieger N. (2014). Discrimination and health inequities. International Journal of Health Services, 44(4), 643–710

    2014

  12. [20]

    Marmot M, Bell R. (2012). Fair society, healthy lives. Public Health, 126(Suppl 1), S4–S10

    2012

  13. [21]

    Multilevel Modeling for Public Health and Health Services Research: Health in Context, Springer Open, 2020, https:/ /doi.org/10.1007/978-3-030-34801-4

    Leyland, AH and Groenewegen, PP. Multilevel Modeling for Public Health and Health Services Research: Health in Context, Springer Open, 2020, https:/ /doi.org/10.1007/978-3-030-34801-4. 44

    2020 doi

  14. [22]

    A User’s Guide to MLwiN, v3.07

    Rasbash J, Steele F, Browne WJ, Goldstein H. A User’s Guide to MLwiN, v3.07. Bristol: Centre for Multilevel Modelling, University of Bristol; 2023. Available from: CMM website

    2023

  15. [23]

    Centers for Disease Control and Prevention (CDC). (2022). Heart Disease Facts. https:/ /www.cdc.gov/heartdisease/facts.htm

    2022

  16. [24]

    World Health Organization (WHO). (2024). WHO Mortality Database . https:/ /www.who.int/data/data-collection-tools/who-mortality-database

    2024

  17. [25]

    Ritchie, H., Roser, M., & Ortiz -Ospina, E. (2024). Cardiovascular disease. Our World in Data. https:/ /ourworldindata.org/cardiovascular-disease

    2024

  18. [26]

    CDC WONDER. (2022). Underlying Cause of Death 1999 –2020. https:/ /wonder.cdc.gov/

    2022

  19. [27]

    Case, A., & Deaton, A. (2015). Rising morbidity and mortality in midlife among White non-Hispanic Americans in the 21st century. Proceedings of the National Academy of Sciences , 112(49), 15078 –15083. https:/ /doi.org/10.1073/pnas.1518393112

    2015 doi

  20. [28]

    GBD Collaborative Network. (2023). Global Burden of Disease Study 2023. The Lancet, forthcoming

    2023

  21. [29]

    National Institutes of Health (NIH). (2023). Minority Health and Health Disparities: Definitions. https:/ /www.nimhd.nih.gov/about/overview/

    2023

  22. [30]

    American Public Health Association (APHA). (2021). Racism is a Public Health Crisis. https:/ /www.apha.org/topics-and-issues/health-equity/racism- and-health

    2021

  23. [31]

    D., Krieger, N., Agénor, M., Graves, J., Linos, N., & Bassett, M

    Bailey, Z. D., Krieger, N., Agénor, M., Graves, J., Linos, N., & Bassett, M. T. (2017). Structural racism and health inequities in the USA: evidence and interventions. The Lancet , 389(10077), 1453 –1463. https:/ /doi.org/10.1016/S0140- 6736(17)30569-X

    2017 doi

  24. [32]

    R., Lawrence, J

    Williams, D. R., Lawrence, J. A., & Davis, B. A. (2019). Racism and Health: Evidence and Needed Research. Annual Review of Public Health , 40, 105 –125. https:/ /doi.org/10.1146/annurev-publhealth-040218-043750

    2019 doi

  25. [33]

    E., Greer, S., Odom, E., et al

    Van Dyke, M. E., Greer, S., Odom, E., et al. (2018). Heart disease death rates among Blacks and Whites aged ≥35 years —United States, 1968 –2015. MMWR 45 Morbidity and Mortality Weekly Report , 67(5), 1 –8. https:/ /doi.org/10.15585/mmwr.ss6705a1

    2018 doi

  26. [34]

    A., Mensah, G

    Roth, G. A., Mensah, G. A., Johnson, C. O., et al. (2020). Global burden of cardiovascular diseases and risk factors, 1990 –2019. Journal of the American College of Cardiology, 76(25), 2982–3021

    2020

  27. [35]

    Global Burden of Disease Collaborative Network. (2023). Global Burden of Disease Study 2023: Results . Institute for Health Metrics and Evaluation. https:/ /www.healthdata.org

    2023

  28. [36]

    Institute f or Health Metrics and Evaluation (IHME). (2024). About the GBD Project. https:/ /www.healthdata.org/gbd

    2024

  29. [37]

    S., Abbafati, C., et al

    Vos T., Lim, S. S., Abbafati, C., et al. (2020). Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: A systematic analysis. The Lancet, 396(10258), 1204–1222. https:/ /doi.org/10.1016/S0140-6736(20)30925- 9

    2020 doi

  30. [38]

    W., & Bryk, A

    Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage Publications

    2002

  31. [39]

    Diez-Roux, A. V. (2000). Multilevel analysis in public health research. Annual Review of Public Health , 21(1), 171 –192. https:/ /doi.org/10.1146/annurev.publhealth.21.1.171

    2000 doi

  32. [40]

    Lawson, A. B. (2018). Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology. Chapman and Hall/CRC

    2018

  33. [41]

    C., Edwards, S

    Gray, S. C., Edwards, S. E., & Miranda, M. L. (2013). Race, socioeconomic status, and air pollution exposure in North Carolina. Environmental Research , 126, 152–158

    2013

  34. [42]

    P., Millet, D

    Clark, L. P., Millet, D. B., & Marshall, J. D. (2017). Changes in transportation - related air pollution exposures by race –ethnicity and socioeconomic status: Outdoor nitrogen dioxide in the United States in 2000 and 2010. Environmental Health Perspectives, 125(9), 097012

    2017

  35. [43]

    L., Peng, R

    Bell, M. L., Peng, R. D., & Dominici, F. (2007). The exposure–response curve for ozone and risk of mortality and the adequacy of current ozone regulations. Environmental Health Perspectives, 114(4), 532–536. 46

    2007

  36. [44]

    A., Turner, M

    Pope, C. A., Turner, M. C., Burnett, R. T., et al. (2015). Relationships between fine particulate air pollution, cardiometabolic disorders, and cardiovascular mortality. Circulation Research, 116(1), 108–115

    2015

  37. [45]

    M., Adler, N., Moffet, H

    Kanaya, A. M., Adler, N., Moffet, H. H., et al. (2014). Heterogeneity of diabetes outcomes among Asians and Pacific Islanders in the US. Diabetes Care, 34(5), 930–937

    2014

  38. [46]

    E., Navas-Acien, A., & Kaufman, J

    Cosselman, K. E., Navas-Acien, A., & Kaufman, J. D. (2015). Environmental factors in cardiovascular disease. Nature Reviews Cardiology, 12(11), 627–642

    2015

  39. [47]

    H., et al

    Shin, H. H., et al. (2019). Association between long-term exposure to PM2.5 and blood pressure: A systematic review and meta -analysis. Environmental Research, 170, 422–431

    2019

  40. [48]

    R., & Jackson, P

    Williams, D. R., & Jackson, P. B. (2005). Social sources of racial disparities in health. Health Affairs, 24(2), 325–334

    2005

  41. [49]

    E., et al

    Moran, A. E., et al. (2014). The global burden of ischemic heart disease in 1990 and 2010: The Global Burden of Disease Study 2010. Circulation, 129(14), 1493– 1501

    2014

  42. [50]

    R., et al

    Cummings, J. R., et al. (2017). Geographic variation in health insurance coverage. Health Services Research, 52(S1), 2010–2031

    2017

  43. [51]

    Kind, A. J. H., & Buckingham, W. R. (2018). Making neighborhood-disadvantage metrics accessible — The Neighborhood Atlas. New England Journal of Medicine, 378(26), 2456–2458

    2018

  44. [52]

    Kunzli, N., et al. (2005). Ambient air pollution and atherosclerosis in Los Angeles. Environmental Health Perspectives, 113(2), 201–206

    2005

  45. [53]

    E., et al

    Bild, D. E., et al. (2002). Multi-Ethnic Study of Atherosclerosis: Objectives and design. American Journal of Epidemiology, 156(9), 871–881

    2002

  46. [54]

    A., et al

    Taylor, H. A., et al. (2005). Toward resolution of cardiovascular health disparities in African Americans: Design and methods of the Jackson Heart Study. Ethnicity & Disease, 15(4 Suppl 6), S6-4–S6-17

    2005

  47. [55]

    Hoek, G., et al. (2013). Long-term air pollution exposure and cardio-respiratory mortality: A review. Environmental Health, 12(1), 43. 47

    2013

  48. [56]

    Murray, C. J. L., et al. (2020). Global burden of 87 risk factors in 204 countries and territories, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019. The Lancet, 396(10258), 1223–1249

    2020

  49. [57]

    Centers for Disease Control and Prevention (CDC). (2023). National Vital Statistics Reports: Mortality data. https:/ /www.cdc.gov/nchs/products/nvsr.htm 48 Supplementary Material – contains all MLwiN-MLA Equations 49 50 51 52 53 54 55 56 57 Figure 1S (a-u). Normal (age-adjuste...

    2023

  50. [58]

    Male individuals (+96.947, SE = 1.220) and Black individuals (+56.389, SE = 2.665) show significantly higher rates, underscoring entrenched structural disparities

    Total Cardiovascular Disease 71 Normal Model (Age-Adjusted Rates): The coefficient for Year (−7.090, SE = 0.309) confirms a sustained decline in age-adjusted CVD mortality from 1999 to 2020, reflecting public health improvements (e.g., treatment advances, risk reduction campai...

    1999

  51. [59]

    Notably, ozone is negatively associated (−0.137, SE = 0.054), suggesting an inverse relationship potentially influenced by rural-urban distribution of ozone exposure

    Heart Failure Normal Model: The positive coefficients for Male (+5.542, SE = 0.334) and Black (+4.396, SE = 0.965) individuals reinforce demographic vulnerability. Notably, ozone is negatively associated (−0.137, SE = 0.054), suggesting an inverse relationship potentially infl...

  52. [60]

    The Year term is consistently negative, supporting long-term declines in mortality

    Ischemic Heart Disease (IHD) Across all models, Male sex and Black race remain significant contributors. The Year term is consistently negative, supporting long-term declines in mortality. PM2.5 and ozone terms vary slightly by model, with subtle negative trends in the Normal ...

  53. [61]

    Interestingly, Asian Americans show sharply reduced rates (−159.862, SE = 4.244), supporting known protective profiles in some subethnic groups

    Acute Myocardial Infarction (AMI) Normal Model: The largest Year slope decline (−3.791, SE = 0.205) among all subtypes demonstrates exceptional progress in AMI management and prevention. Interestingly, Asian Americans show sharply reduced rates (−159.862, SE = 4.244), supporti...

  54. [62]

    Atherosclerosis This subtype, while rarer, shows unique environmental signatures. PM2.5 and ozone show stronger associations here than in other subtypes—particularly in the offset model—suggesting that chronic pollutant exposure may disproportionately influence long-term arter...

  55. [63]

    This likely reflects hypertensive disease’s stronger behavioral and genetic etiology compared to acute pollutant sensitivity

    Hypertensive Heart Disease Normal Model: Here, sex and race disparities persist, but environmental effects are subdued. This likely reflects hypertensive disease’s stronger behavioral and genetic etiology compared to acute pollutant sensitivity. Poisson/Offset Models: Offset m...

  56. [64]

    PM2.5 retains modest significance; however, the interpretability of this grouping warrants caution and suggests need for disaggregation in future studies

    Other Heart Diseases This catch-all category shows the most varied patterns across models, perhaps due to its heterogeneous composition. PM2.5 retains modest significance; however, the interpretability of this grouping warrants caution and suggests need for disaggregation in f...

    1999

  57. [65]

    Temporal Trends (Year) 74 In both states, year showed negative associations in Poisson models, indicating declining CVD death rates over time. However, Pennsylvania exhibited a steeper annual decline, particularly for Total CVD: • PA (Poisson, raw): −0.017 (SE = 0.001) • OH (P...

    2019

  58. [66]

    Race-Based Mortality Disparities Black or African American individuals consistently exhibited the highest positive coefficients across both states and all models. However, effect sizes were generally larger in Ohio, indicating a more severe racial disparity: • Total CVD (Norma...

  59. [67]

    Sex-Based Mortality Effects Across every disease subtype and model, male sex was a strong and statistically significant predictor of increased mortality. However, Pennsylvania generally exhibited larger gender effects, especially in Normal models: • Total CVD (Normal): o PA: +...

  60. [68]

    These findings parallel recent evidence linking PM₂.₅ to ischemic and heart failure mortality via systemic inflammation and oxidative stress pathways

    Pollution Exposure Effects (PM₂.₅ and Ozone) Pollutant effects were more statistically robust in Pennsylvania, particularly under Poisson log-offset models: • PM₂.₅ (Total CVD, Poisson offset): o PA: +0.010 (SE = 0.001) o OH: +0.009 (SE = 0.001) • Ozone (Total CVD, Poisson off...

  61. [69]

    Variance Components and County Coverage 76 • In general, Pennsylvania models exhibited higher between-county variance (Ωᵤ), especially in Normal models: o PA Total CVD (Normal): Ωᵤ = 1397.136 (SE = 249.860) o OH Total CVD (Normal): Ωᵤ = 1345.208 (SE = 78.126) • The Poisson log...

Pith tools

Reviewed July 9, 2026 · model on record in the stance chip above.