Reassessing the Nitrate-Crime Hypothesis: A Reproducible Ecological Reanalysis of 48 Contiguous U.S. States

Background: A 2023 preprint highlighted descriptive differences in several crime-related indicators across U.S. states, grouping them based on the percentage of their area with modeled groundwater nitrate levels exceeding 5 mg/L. Building on those initial findings, the current study presents a retrospective, exploratory reanalysis of this 48-state dataset. Methods: Our primary exposure variable was the historical modeled nitrate-area percentage (derived from the USGS GWAVA-DW model), while the primary outcome was the 2014 violent crime rate per 100,000 inhabitants, based on FBI Uniform Crime Reporting data. To evaluate Spearman’s rank correlation, we employed a two-sided Monte Carlo permutation test (100,000 permutations) alongside a paired-state bootstrap percentile interval (10,000 resamples). Eight secondary FBI crime outcomes were also assessed, applying the Holm adjustment for multiple comparisons. As complementary exploratory analyses, we examined a historical categorical grouping (≥10% versus <10% nitrate area) as well as alternative exposure cutoffs. Furthermore, singleton Moran spectral randomization (MSR) sensitivity tests were conducted in both Python and the reference R package adespatial (using 100,000 surrogate pairs across eight specifications). The differences between Python and R in p-values, null means, and null standard deviations all fell within three combined Monte Carlo standard errors. Results: The primary association proved to be negligible (ρ = 0.051; nominal permutation p = 0.728; 95% paired-state bootstrap interval: −0.249 to 0.351). Following the Holm adjustment, none of the secondary outcomes remained statistically significant. Comparing the groups based on the ≥10% threshold revealed a mean difference of 59.40 violent crimes per 100,000 inhabitants (95% Welch CI: −29.05 to 147.86; p = 0.169). Additionally, spatially constrained MSR tests run in the reference R implementation yielded non-significant results. Conclusions: Based on this dataset, there is no statistical evidence supporting a monotonic state-level association between the historical area-based nitrate indicator and 2014 violent crime rates. However, this finding does not definitively rule out the possibility of an underlying association. The results do not support individual-level, causal, or mechanistic interpretations. Furthermore, it should be noted that the primary permutation and bootstrap summaries rely on exchangeability assumptions and should not be treated as spatially robust regression estimates.