ArticleLancet regional health. Americas2026
Incidence rates of twelve chronic diseases/conditions in US adults: findings from a population-based study.
Article in Lancet regional health. Americas, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Background: There are no contemporary studies simultaneously examining the incidence of multiple chronic diseases/conditions in US adults. We estimated one-year incidence rates for 12 chronic diseases/conditions with high public burden. Methods: Data were from the nationally representative National Health Interview Survey 2019-2020 Longitudinal Cohort (N = 10,415). We assessed incidence rates for anxiety, arthritis, asthma, cancer, chronic obstructive pulmonary disease (COPD), chronic pain, coronary heart disease (CHD), depression, diabetes, high cholesterol, hypertension, and obesity. We calculated overall and sex-specific age-standardized incidence rates per 1000 person-years (PY) and examined associations with baseline age, sex, race/ethnicity, education, insurance, and smoking status. Findings: In the sample, 51.7% ([95% CI: 50.3-53.1]; N = 5624, representing 129.7 million adults) were female, 20.5% ([95% CI: 20-22.2]; N = 3405, representing 21.1 million adults) were aged 65+ years, 63.2% ([95% CI: 60.9-65.4]; N = 7495, representing 158.5 million adults) were Non-Hispanic White, 16.5% ([95% CI: 14.7-18.4]; N = 1153, representing 41.5 million adults) were Hispanic/Latino, 28.8% ([95% CI: 27.4-30.2]; N = 4228, representing 72.3 million adults) graduated college, and 72.4 ([95% CI: 70.8-73.9]; N = 7287, representing 167.0 million adults) had private health insurance. One-year incidence rates were lowest for diabetes, COPD and CHD: 13.8/1000 PY (95% CI: 10.8-16.8), 14.4/1000 PY (95% CI: 11.8-17.0), and 14.7/1000 PY (95% CI: 12.7-17.0), respectively. The highest rates were observed for high cholesterol and chronic pain, 85.7/1000 PY (95% CI: 79.4-91.9) and 85.3/1000 PY (95% CI: 78.7-92.0), respectively, while all other rates were between these extremes. Females had higher rates of anxiety (69.6 [95% CI: 60.5-75.7] vs 36.4 [95% CI: 29.8-43.1]) and depression (63.7 [95% CI: 55.1-72.3] vs 34.5 [95% CI: 28.4-40.7]), while males had higher rates of hypertension (77.3 [95% CI: 72.3-82.3] vs 56.5 [95% CI: 48.7-64.3]). Incidence-related risk factors differed across diseases/conditions, with age the most consistent predictor. Interpretation: This comprehensive assessment documented striking variation in chronic disease/condition incidence. The findings provide essential evidence for prioritizing and coordinating prevention initiatives across the chronic disease/condition spectrum. Funding: US National Institutes of Health.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.