The stakes of this finding are enormous. Frameworks such as the Hierarchical Taxonomy of Psychopathology, or HiTOP, were built on the premise that the Diagnostic and Statistical Manual of Mental Disorders fails to carve nature at its joints, producing a rampant comorbidity problem that can be solved by reorganizing symptoms into broad dimensions. Factor analyses of population data consistently reveal two such dimensions, internalizing, which encompasses conditions marked by intense negative emotion such as depression and anxiety, and externalizing, which captures disorders of behavioral and emotional dyscontrol such as conduct disorder and substance use disorder. Because these dimensions correlate at roughly 0.5 to 0.6, researchers proposed an even broader construct, the p-factor, a single dimension claimed to explain a person’s liability to mental disorder, comorbidity among disorders, persistence over time, and symptom severity.

Watts and colleagues argue that this entire inferential chain rests on a statistical artifact. Their central insight borrows from ecology and computer science, fields that have long recognized that measures of association are dramatically influenced by the base rates and skewness of the variables involved. In psychiatric epidemiology, the overwhelming majority of people endorse few or no symptoms. Around 70 percent of the population receives no mental disorder diagnosis in a given year, and between 17 and 40 percent never receive one across their entire lives. When such a flood of asymptomatic cases enters a covariance-based analysis, the shared absence of symptoms inflates the apparent overlap between dimensions, making them look tightly linked when, among genuinely symptomatic people, they may be barely related at all.

To test this idea rigorously, the team turned to the Adolescent Brain Cognitive Development Study, one of the largest longitudinal studies of youth ever conducted, tracking 11,868 nine- and ten-year-olds across 21 US sites. Using caregiver reports on the Child Behavior Checklist, a 113-item assessment of psychiatric symptoms, they measured internalizing, externalizing, attention problems, and thought problems across four annual waves. Crucially, the data were far from normal: at wave one, 81 percent of children showed nonclinical levels of both internalizing and externalizing, and only 5.3 percent exhibited at-risk or clinical levels of both simultaneously. The distributions were highly skewed and kurtotic, violating the assumptions that underpin the standard linear models used throughout psychopathology research.

Instead of assuming linearity, the researchers deployed Bayesian change point models, statistical tools that allow the relationship between two variables to shift at different points along their joint distribution. These models, also known as broken-stick or threshold models, are routinely used in ecology to detect behavioral transitions in animals and environmental thresholds, but had rarely been applied to psychopathology. The results were striking. When externalizing was regressed onto internalizing, the change point occurred at an internalizing score corresponding to roughly two or three symptoms. Below that threshold, a one-point increase in internalizing was associated with a 32 percent increase in externalizing. Above it, the same increase predicted only a 7 percent rise, a slope 4.6 times weaker.

The pattern reversed direction exactly as the nomorbidity hypothesis predicted: associations were strongest among the largely asymptomatic and weakest among those with meaningful symptom burden. The findings replicated when the regression direction was flipped, with a pre-change point slope indicating a 43 percent increase in internalizing per point of externalizing compared with just 6 percent afterward. They also held across all four waves of data, with change points consistently located between internalizing scores of three and five, and the pattern extended to attention problems and thought problems, where pre-change point slopes were uniformly several times steeper than post-change point slopes. At moderate to clinically severe levels, the dimensions were so differentiated that their associations approached negligibility.

Perhaps the most consequential implication concerns the p-factor. If the general factor of psychopathology is largely a product of nomorbid cases, then it may be irrelevant to the very population it is meant to serve, people experiencing clinically significant distress. The authors note a provocative parallel with intelligence research, where Spearman’s law of diminishing returns shows that the general factor of intelligence, g, exerts more explanatory power at lower ability levels, with cognitive abilities becoming more differentiated as intelligence increases. The p-factor may behave analogously, functioning as a dimension of mental health rather than mental illness, and its apparent universality may dissolve once attention shifts to symptomatic individuals.

The study also exposed how badly conventional methods distort the picture. Ordinary least squares regression, the workhorse of psychopathology research, produced substantial prediction errors in these data, overestimating the association between dimensions at nonclinical levels and, critically, at clinically severe levels. Simulation studies conducted by the team showed that when two genuinely different slopes exist in a population, the single-slope linear model yields a biased average with no psychological meaning, systematically overstating comorbidity. Factor analysis makes even stronger assumptions, positing a latent normal distribution and linear relationships that the data violated outright, with factor scores proving even more skewed and kurtotic than the raw sum scores.

The authors anticipated and addressed several objections. Skeptics might invoke Berkson’s bias, arguing that conditioning on severity artificially attenuates associations, but that critique holds only if a single linear relationship exists in the population, which the data contradict. Nor did change point models manufacture nonlinearity artificially: simulations showed the models recover linear estimates when no nonlinearity exists, and sensitivity analyses using caregivers’ own Adult Self-Report data and generalized additive models produced the same results. A recent meta-analysis further corroborates the pattern, finding that the externalizing-internalizing correlation is dramatically stronger in nonenriched samples (0.48) than in psychopathology-enriched samples (0.20), and stronger for current than lifetime diagnoses, precisely the conditions that maximize the proportion of asymptomatic cases.

As the framers of the next DSM grapple with incorporating dimensionality and severity into psychiatric nosology, the message of this research is unambiguous: correlation does not imply comorbidity, just as it does not imply causation. Dimensional structures estimated from community samples may serve population screening well, but they should not be read as evidence of shared etiology or treatment-relevant co-occurrence among patients. The researchers call for the field to embrace nonlinearity and population heterogeneity, report the distributional properties of data, and prioritize symptomatology over statistical correlation. Failing to do so, they warn, risks reifying artifactual transdiagnostic dimensions and abandoning the search for disorder-specific mechanisms, ultimately impeding the discovery of the causes and treatments that patients with genuine mental illness need most.

Subject of Research: Statistical drivers of covariation among higher-order psychopathology dimensions in youth

Article Title: Covariation among higher-order psychopathology dimensions is driven by nomorbidity and not comorbidity

Article References: Watts, A. L., Rosen, A. F. G., Greene, A. L., King, K. M., Meyer, F. A. C., Trull, T. J., Sher, K. J., & Joyner, K. J. (2026). Covariation among higher-order psychopathology dimensions is driven by nomorbidity and not comorbidity. Nature Mental Health. https://doi.org/10.1038/s44220-026-00727-0

Image Credits: AI Generated

DOI: 10.1038/s44220-026-00727-0

Keywords: psychiatry, comorbidity, nomorbidity, p-factor, internalizing, externalizing, HiTOP, psychopathology, Bayesian change point models, ABCD Study, DSM, statistics