Purpose
Neck pain is a leading musculoskeletal cause of disability in the United States. This study aimed to assess temporal, geographic, and demographic disparities in its burden across US states from 1990 to 2021.
Methods
Global Burden of Disease 2021 data for all 50 US states and the District of Columbia were analyzed for 1990–2021. Counts and age-standardized rates per 100,000 population for prevalence, incidence, and years lived with disability (YLD) were compared by region, state, sex, age group, and sociodemographic index (SDI) level.
Results
In 2021, neck pain accounted for 9,430,000 (95% uncertainty interval [UI], 7,400,000–11,590,000) prevalent cases, 1,900,000 (95% UI, 1,490,000–2,320,000) incident cases, and 920,000 (95% UI, 620,000–1,300,000) YLD, increases of 41.17%, 37.44%, and 38.72%, respectively, from 1990. Although age-standardized incidence increased globally by 1.18% (0.07%–2.39%) over 1990–2021, the United States had small declines in age-standardized incidence (−0.11%; 95% UI, −0.18% to −0.06%), prevalence (−0.20%; 95% UI, −0.32% to −0.10%), and YLD (−1.12%; 95% UI, −2.02% to −0.22%). Neck pain rates were highest in the District of Columbia, Georgia, and Delaware and lowest in North Carolina, Alaska, and Wyoming. The burden peaked at ages 40–49 years and was higher in females. Age-standardized YLD rates for neck pain were significantly positively correlated with the SDI (Spearman’s ρ=0.601, P<0.001).
Conclusion
The geographic and demographic disparities in neck pain burden across US states indicate a need for targeted interventions.

Purpose
Accurate prediction of blood glucose variability is crucial for effective diabetes management, as both hypoglycemia and hyperglycemia are associated with increased morbidity and mortality. However, conventional predictive models rely primarily on patient-specific biometric data, often neglecting the influence of patient–provider interactions, which can significantly impact outcomes. This study introduces Cyclic Dual Latent Discovery (CDLD), a deep learning framework that explicitly models patient–provider interactions to improve prediction of blood glucose levels. By leveraging a real-world intensive care unit (ICU) dataset, the model captures latent attributes of both patients and providers, thus improving forecasting accuracy.
Methods
ICU patient records were obtained from the MIMIC-IV v3.0 critical care database, including approximately 5,014 instances of patient–provider interaction. The CDLD model uses a cyclic training mechanism that alternately updates patient and provider latent representations to optimize predictive performance. During preprocessing, all numeric features were normalized, and extreme glucose values were capped at 500 mg/dL to mitigate the effect of outliers.
Results
CDLD outperformed conventional models, achieving a root mean square error of 0.0852 on the validation set and 0.0899 on the test set, which indicates improved generalization. The model effectively captured latent patient–provider interaction patterns, yielding more accurate glucose variability predictions than baseline approaches.
Conclusion
Integrating patient–provider interaction modeling into predictive frameworks can increase blood glucose prediction accuracy. The CDLD model offers a novel approach to diabetes management, potentially paving the way for artificial intelligence-driven personalized treatment strategies.

Citations

• Artificial Intelligence Algorithms for Insulin Management and Hypoglycemia Prevention in Hospitalized Patients—A Scoping Review
  Eileen R. Faulds, Melanie Natasha Rayan, Matthew Mlachak, Kathleen M. Dungan, Ted Allen, Emily Patterson
  Diabetology.2026; 7(1): 19.     CrossRef