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REVIEW 3 major objections 4 minor 35 references

A fast approach for analyzing spatio-temporal patterns in ischemic heart disease mortality across US counties (1999-2021)

T0 review · 3 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Using 23 years of county-level data for every contiguous US county, this paper claims the long decline in ischemic heart disease mortality slowed after 2009, flattened after 2014, and turned into a slight rise after 2019.

desk verdict Solid, reproducible bigDM application; honest about limits, but the headline change-point claim rests on single imputation that could create the pattern. read the letter →

arxiv 2411.14849 v1 pith:I7AEM4FO submitted 2024-11-22 stat.AP

A. Urdangarin , T. Goicoa , P. Congdon , MD. Ugarte This is my paper · ORCID
classification stat.AP
keywords ischemicheartdiseasemortalityspatio-temporalmappingdivide-and-conquerBayesianmodelscounty-leveltrendsurban-ruraldisparitiessuppressedcountimputationchangepointsUnitedStatescounties
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

Using 23 years of county-level death counts for the contiguous United States, the paper asks whether the long national decline in ischemic heart disease mortality has continued evenly across places and populations. It answers no: the decline decelerated after 2009, flattened after 2014, and turned into a slight increase after 2019, with a 2020 peak that may reflect COVID-19 misclassification. The authors build this picture from Bayesian spatio-temporal models made fast by splitting the country into state-based partitions, and they impute the roughly 12.5% of county-year counts suppressed by confidentiality rules before smoothing. A reader should care because national averages hide the county and rural-urban disparities that determine where prevention efforts are needed, and because the post-2014 stall lines up with the stagnation of US life expectancy.

What carries the argument

The central mechanism is a hierarchical Bayesian spatio-temporal disease mapping model. Death counts follow $Y_{it} \sim \text{Poisson}(e_{it} r_{it})$, and the log relative risk is decomposed as $\log r_{it} = \alpha_0 + \xi_i + \gamma_t + \delta_{it}$, with $\xi_i$ a spatially structured county effect, $\gamma_t$ a first-order random walk over years, and $\delta_{it}$ a Type II space-time interaction (structured in time, unstructured in space). To handle 3105 counties over 23 years, the paper replaces one global fit with a 'divide and conquer' scheme: partition the US into state-based subregions, add first-order neighboring counties across borders, fit the model separately in each partition, and allow different smoothing parameters per partition. Before fitting, suppressed counts (values below 10) are imputed with rounded, truncated predictions from a per-year Bayesian spatial model, so the smoothing machinery operates on a complete data grid.

What would settle it

Fit the same model to the complete, un-suppressed death records for a subset of counties; if the post-2014 flattening or the post-2019 uptick disappears, the reported trajectory is an artifact of imputation.

Watch

Extended reading notes

Core claim

The paper's central claim is that the risk of dying from ischemic heart disease in US counties followed a four-phase national trajectory over 1999-2021: a clear decline to about 2009, a slowing decline from 2009 to 2014, a flat trend from 2014 to 2019, and an increase after 2019 with a peak in 2020. Beneath that national curve, no single trend describes the country. The West remains below the national risk, the Northeast above it; rural counties carry higher risk than urban counties in the West, Midwest, and South; and counties such as Valley (Montana) and Menominee (Michigan) show rising trends even while the national risk falls. The authors identify 2009 and 2019 as candidate change points while cautioning that the two post-2019 years cannot separate a persistent increase from a transient pandemic effect.

Load-bearing premise

The load-bearing premise is that the roughly 12.5% of county-year death counts suppressed because they are below 10 can be replaced by spatially predicted, rounded, and truncated values without systematically distorting the smoothed trends; the paper itself concedes the results may be sensitive to this imputation.

Editorial extensions

If this is right

  • If the trajectory is right, the decades-long US decline in heart disease mortality has levelled off, so public health planning should target a stalled epidemic rather than assume continuation of historical improvements.
  • The 2009 and 2019 change points give surveillance systems concrete moments to monitor, especially whether the post-2019 increase persists outside the pandemic years.
  • Rural counties in the West, Midwest, and South emerge as the highest-risk areas, reinforcing that national campaigns need county-level and rural-specific strategies.
  • The partitioned fitting strategy makes it practical to screen spatio-temporal disease patterns for all US counties in minutes on a desktop, not just for heart disease but for any condition with similar data.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A consequence the authors leave implicit is that a real post-2014 flattening strengthens the case that cardiovascular mortality is the main brake on US life-expectancy gains, since ischemic heart disease is the leading cause of death.
  • The imputation step could be audited by fitting the same pipeline to complete, un-suppressed death records for a subset of states; if the flattening or the post-2019 uptick disappears, the finding would be an artifact of filling in small counts.
  • Extending the series through 2023 and later would settle whether the post-2019 increase is persistent or a one-off COVID-19 spike, something the paper's data ending in 2021 cannot resolve.
  • The same partitioned workflow transfers to other suppressed-count chronic diseases such as stroke or heart failure, where county-level screening could reuse the imputation and smoothing machinery.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 4 minor

Summary. This manuscript analyzes county-level ischaemic heart disease mortality in the 48 contiguous US states over 1999-2021 using CDC WONDER data, in which counts below 10 are suppressed. Missing counts are imputed by fitting a year-specific BYM2 spatial model and replacing each censored count with the rounded posterior median predicted count truncated at 9. The imputed data are then modeled with a partition-based 'divide and conquer' spatio-temporal model (bigDM) with ICAR spatial prior, RW1 temporal prior, and Type II interaction. The main finding is that the US decline in IHD mortality slowed after 2014 and there was a slight increase after 2019, with regional and urban/rural disparities. The paper also reports model selection favoring the partitioned ICAR/Type II model over alternatives and provides reproducible code and data.

Significance. If correct, the paper offers a computationally practical pipeline for Bayesian spatio-temporal disease mapping on a large domain with censored counts, and its descriptive finding of a flattening IHD mortality trend is consistent with prior literature (Mehta et al.; Vaughan et al.). The full reproducibility (code and data on GitHub) and the comparison with global models are strengths. The main risk is that the central temporal claim is identified only after a single imputation of 12.5% of the observations, and the paper's own discussion acknowledges that the results 'may be sensitive to this method' and that the post-2019 rise cannot be separated from COVID-19 with only two years of data. Given the time-varying missingness, this is a load-bearing limitation rather than a presentation issue.

major comments (3)
  1. [Section 2.2 and Eq. (2)] The imputation treats synthetic counts as observed, and no imputation uncertainty is propagated into the posterior credible intervals in Figures 5-7 or into the DIC/WAIC values in Table 2. Because suppression is deterministic (count < 10) and the fraction of suppressed cells increases over time as IHD mortality falls, the imputation error is time-varying and can distort the very slopes used to identify the post-2014 deceleration and post-2019 uptick. The rounded posterior medians truncated at 9 are not draws from the posterior predictive distribution, so the headline change-point claim could be an artifact of the imputation scheme. A censoring-appropriate reanalysis (e.g., multiple imputation, a censored likelihood as in Valeriano et al. 2021, or a sensitivity analysis excluding counties with any imputed counts) is needed before the central claim can be accepted.
  2. [Section 3, Figure 7] The paper's sanity check for imputation effects, based on comparing model-based rural trends with observed-only SMRs, only checks aggregate levels; it does not target the post-2014 slope or the post-2019 increase. At the aggregation level used, a time-varying bias in imputed counts can cancel or be masked, so this check does not address whether the change-point pattern is an artifact. The check should be repeated for the slope estimates, for example by comparing the model's year-to-year trend with an observed-only SMR trend restricted to counties without any suppression.
  3. [Abstract, Section 3, Discussion] The claimed change-point dates are stated inconsistently. The abstract says 'slowdown in the decrease of IHD mortality in the US after 2014'; the text near Figure 6 says 'a slowdown in the decrease is observed after 2009 and a flattening in the trend is observed after 2014'; and the Discussion says 'our analysis suggests 2009 and 2019 as potential change points.' Because these dates are the central result, the paper needs to state a single, precisely defined claim and either justify the date or formally test for a change point rather than relying on 'mere visual inspection.'
minor comments (4)
  1. [Section 3, Figure 4] The text 'from 1991 to 2021' should read 'from 1999 to 2021'.
  2. [Section 2.2] The phrase 'As suggested by the reviewers' is an artifact of the review process and should be removed or rephrased.
  3. [Abstract] The phrase 'differences exists' should be 'differences exist'.
  4. [Section 3] The expression 'the starting of the COVID-19 pandemic' should be 'the start of the COVID-19 pandemic', and the sentence 'From this year onwards, the average risks in rural areas is the highest' needs grammatical correction.

Circularity Check

0 steps flagged · score 1.0 of 10

No circular derivation; the central trends are posterior summaries of a standard spatio-temporal model, not fitted inputs relabeled as predictions.

full rationale

The paper's central claim (deceleration after 2014 and increase after 2019) is a descriptive summary of the posterior temporal trend from model (2), a standard Poisson ICAR/BYM2 + RW1 + Type II interaction model. No parameter of that model is defined in terms of the target trend, and the trend is not a fitted quantity that is then relabeled as a prediction; the change points are identified by visual inspection of the fitted curves, and previous literature is cited for corroboration, not as the source of the estimate. The imputation step (Section 2.2) does replace suppressed counts with spatial-model predictions, and the paper explicitly warns that the results may be sensitive to this method; however, the imputation model contains no temporal trend component, so the post-2014 flattening and post-2019 uptick are not imposed by construction. Any bias from truncation at 9 is a robustness or identifiability issue, not circularity. Self-citations to the bigDM package and the divide-and-conquer papers provide computational machinery and are not used to justify the epidemiological conclusion, so they are not load-bearing. Overall, no step in the derivation reduces to its own input.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

No new theoretical entities are introduced. The free parameters are procedural choices (imputation truncation, neighbor order) rather than fitted constants. The main assumptions are standard disease mapping priors plus the validity of spatial imputation for suppressed counts.

free parameters (2)
  • truncation threshold for imputed counts = 9
    Predictions above 9 are rounded back to 9 because suppressed counts are always 9 or below; this choice affects the imputed dataset and hence the fitted trends (Section 2.2).
  • neighbor order k for border buffers = 1
    First-order neighbors are added to each state partition to mitigate border effects; this choice affects smoothing but the paper notes robustness to partition choice (Section 2.5).
assumptions (4)
  • standard math Poisson likelihood for county-year death counts
    Used throughout Section 2.4; standard for disease mapping.
  • domain assumption ICAR/RW1/Type II interaction priors adequately capture residual spatio-temporal structure
    Model selection chooses this combination (Table 2), but any prior is an assumption about how unexplained risk varies.
  • domain assumption Missing counts can be imputed using spatial borrowing of strength from neighboring counties
    Section 2.2; if suppression is correlated with unobserved risk factors in rural areas, imputed values may be biased.
  • domain assumption Expected counts computed with a single national rate make risks comparable across counties and years
    Section 2.3; ignores age/sex standardization and assumes the national rate is an appropriate baseline for every county.

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Cite this review

Pith. "Pith review of A fast approach for analyzing spatio-temporal patterns in ischemic heart disease mortality across US counties (1999-2021)." pith.science (2026). https://pith.science/paper/I7AEM4FO

@misc{pith2026241114849,
  author       = {Pith},
  title        = {Pith review of: A fast approach for analyzing spatio-temporal patterns in ischemic heart disease mortality across US counties (1999-2021)},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/I7AEM4FO}},
  note         = {Machine review of arXiv:2411.14849}
}
read the original abstract

Ischaemic heart disease (IHD) remains the primary cause of mortality in the US. This study focuses on using spatio-temporal disease mapping models to explore the temporal trends of IHD at the county level from 1999 to 2021. To manage the computational burden arising from the high-dimensional data, we employ scalable Bayesian models using a "divide and conquer" strategy. This approach allows for fast model fitting and serves as an efficient procedure for screening spatio-temporal patterns. Additionally, we analyze trends in four regional subdivisions, West, Midwest, South and Northeast, and in urban and rural areas. The dataset on IHD contains missing data, and we propose a procedure to impute the omitted information. The results show a slowdown in the decrease of IHD mortality in the US after 2014 with a slight increase noted after 2019. However, differences exists among the counties, the four big geographical regions, and rural and urban areas.

Figures

Figures reproduced from arXiv: 2411.14849 by the authors.

Figure 1
Figure 1. (top) illustrates the geographic division and demographic classification of the US in 2021. Large and medium metros constitute 1.3% and 29.9% of the counties respectively whereas rural metros account for 68.8% of the counties. The majority of urban counties are located along the coastal borders of the US [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. SMRs in space (SMRi) over the period 1999-2021 (top). The thickest violet lines are used to limit the four main geographic regions of US, West, Midwest, South and Northeast. The temporal trend of the SMRs (SMRt) is shown at the bottom. 7 [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Boxplots of county spatio-temporal SMRs grouped by year. 2.4 Spatio-temporal models Spatio-temporal disease mapping models are widely used to describe the geographical pattern of a disease and its evolution in time. In this study, we will apply spatio-temporal models to understand the temporal evolution of IHD mortality risks at a county level. Various proposals are available for the spatial, temporal, and spatio-te… view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Posterior median estimates of the relative risks, rˆit = exp(α0 + ξi + γt + δit), for the years 1999, 2004, 2009, 2014, 2019 and 2021 derived from the partitioned spatio-temporal model with ICAR prior and Type II interaction. The relative risks of counties with missing…
Figure 5
Figure 5. Figure 5: Temporal evolution of IHD mortality risks (rˆit = exp(α0 + ξi + γt + δit)) and their 95% credible intervals, for most populated counties (first and second columns) as well as the less populated county without suppressed counts (third column) within the West (top row), …
Figure 6
Figure 6. Figure 6: The mean of IHD mortality risks of the counties in West (orange), Midwest (pink), South (yellow) and Northeast (green) regions during the period 1999-2021. Temporal evolution of the mean IHD mortality risks of all the counties in the US is represented in grey color. fr…
Figure 7
Figure 7. Figure 7: Mean of IHD mortality risks in Rural (green), Medium (light blue) and Large (dark blue) metros classifying by the geographic region (West, Midwest, South and Northeast) in the period 1999-2021. In grey color the temporal trend of IHD risks in each geographic region wit…

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