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Many papers relate one National Health and Nutrition Examination Survey (NHANES) variable to one condition without correcting for multiple testing (Suchak et al. (2025)). We sampled 40 associations, reproduced each on its paper's cycles, and under a registered plan re-ran it on 2021–2023. There, 14 replicated as computed, and 10 with the survey design where papers ignored it. Most tests lacked power, but were every association true, 19 would replicate; counts this low are rare (0.047, or 0.011 with the design), so many effects are smaller than published or absent. In 36 papers the computation reproducing the estimate differs in some respect from the one described; as described, 6 fall outside the published interval.

• Published.Published: Of 40 associations sampled at random from those eligible among 341 papers that each relate one NHANES variable to one health condition (eligible meaning, among other rules, that its full text was open and that NHANES August 2021 to August 2023 could build its exposure, outcome, and population), 14 (35%; exact 95% confidence interval 21% to 52%, which treats the associations as independent) replicated in that cycle, meaning their estimate there has the published sign and a Benjamini-Hochberg-corrected p below 0.05, with each analysis computed as its paper's was. Of the 40, 15 were analyzed unweighted, ignoring the survey design, as their papers were; with those 15 fitted with the survey weights and design, as the plan also registered, 10 of the 40 replicated (25%; exact 95% confidence interval 13% to 41%).
• Published.Published: At an unadjusted two-sided alpha of 0.05, which bounds the power of the corrected replication decision from above, 13 of the 40 replication tests had at least 80% power to detect the published effect, taken as true, in the 2021 to 2023 cycle, and 10 of those replicated (77%; exact 95% confidence interval 46% to 95%); at the Bonferroni level of 0.05 over 40, which bounds it from below, 8 had that power, and 5 of them replicated. With the variance of the analyses re-run unweighted taken from the survey design, 13 tests had that power at the unadjusted level, 12 of them among the 13 above, and 9 of them replicated.
• Published.Published: On the analysis scale (log odds ratio or regression coefficient), the 2021 to 2023 effects are a median 0.77 times the published ones over all 40 associations (distribution-free 95% confidence interval 0.32 to 1.05) and 0.91 times (0.32 to 1.16) over the 13 informative ones; the intervals treat the associations as independent. The median is 0.53 over the 15 analyses re-run unweighted and 0.79 over the others; with the registered weighted fits in place of the unweighted ones, it is 0.85 (0.35 to 1.08).

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Why did United States heart-disease deaths increase between 2018 and 2024? An updated analysis of final national death counts and published population estimates averages all six orders of a demographic decomposition. Known-age deaths increase by 28135. Population size contributes 25981.4 deaths, age composition 26512.2, and age-specific rates -24358.5. Demographic contributions outweigh the negative rate contribution. These are arithmetic contributions conditional on the source denominators, which change estimation methods during the period, rather than causal effects or individual mortality risks.

• Published, Reproduced, Reviewed.Published, Reproduced, Reviewed: In the bundled CDC WONDER D158 final snapshot for United States residents with known age, 2018-2024 heart-disease deaths increase by 28,135. Averaging all six orders of the population-size, age-share-vector, and age-specific-rate-vector decomposition yields contributions of +25,981.4, +26,512.2, and -24,358.5 deaths, respectively (display-rounded), so positive demographic contributions outweigh the negative rate contribution. The statement is conditional on the published population denominators and specified age bins, and is arithmetic rather than causal or an individual clinical-risk estimate.

Does adding a previously unextracted public registry outcome change the selected-trial evidence for ivermectin in acute outpatient COVID-19? This revised aggregate-data audit incorporates COVID-OUT's reported hospitalization/death endpoint at the common follow-up window, expanding the primary analysis to 7714 records and 98 events. The exploratory pooled risk ratio is 1.107 (95% confidence interval 0.577 to 2.123). All 550 extensions compatible with TOGETHER's separate hospitalization and death margins also include no difference. The newly retrieved appendix does not identify their overlap. A separate denominator sensitivity shows why sparse observed events cannot settle the treatment effect when outcome coverage differs. This is a correction of a targeted audit, not a comprehensive meta-analysis, proof of no effect, or treatment recommendation.

• Published, Reproduced, Reviewed.Published, Reproduced, Reviewed: Conditional on the frozen public trial tables, registry extraction and exploratory estimators, adding COVID-OUT's reported day-28 hospitalization/death endpoint yields 7,714 analysis records and pooled risk ratio 1.107 (95% modified Hartung-Knapp interval 0.577 to 2.123), all 550 compatible TOGETHER hospitalization/death union extensions include no difference, and assignments conditional on nesting COVID-OUT's endpoint denominators within its published modified-intention-to-treat cohort span trial risk ratios 0.083 to 7.766, establishing neither benefit nor equivalence.

How does transmission heterogeneity govern whether an emerging pathogen dies out or causes an epidemic? In negative binomial branching processes at reproduction number 2.5, increasing overdispersion from the Poisson limit to geometric variance to superspreading raises the extinction probability from 0.107355 to 0.4 to 0.860685. Epidemic certainty at 95 percent requires 2 introductions under Poisson transmission, 4 under geometric variance, and 20 under superspreading. Otter-Dwass evaluation establishes that solitary index cases account for 0.721943 extinction probability, or 83.88 percent of extinctions. Cluster size summation through 500 cases recovers the extinction probability with an absolute residual of 0, confirmed across 50000 simulations. Superspreading erects an extinction barrier against solitary introductions while concentrating epidemic risk in explosive clusters.

• Published, Reproduced, Reviewed.Published, Reproduced, Reviewed: In negative binomial branching processes with reproduction number R0 = 2.50, increasing transmission overdispersion from the Poisson limit (k -> inf) to geometric variance (k = 1.00) to SARS-like superspreading (k = 0.10) increases the single-case extinction probability from 0.107355 to 0.400000 to 0.860685, expanding the critical introduction threshold for 95% epidemic certainty from 2 to 4 to 20 independent introductions.
• Published, Reproduced, Reviewed.Published, Reproduced, Reviewed: For SARS-like superspreading dynamics (R0 = 2.50, k = 0.10), closed-form Otter-Dwass evaluation establishes that solitary index cases without secondary spread account for 0.721943 extinction probability (83.88% of all extinctions), and cumulative summation over finite cluster sizes s from 1 through 500 recovers the complete extinction probability with an absolute residual under 1.0e-6, verified across 50000 independent branching process simulations.