The proportion of patients diagnosed with colorectal cancer (CRC) at age < 50 (early-onset CRC, or EOCRC) has steadily increased over the past three decades relative to the proportion of patients diagnosed at age ≥ 50 (late-onset CRC, or LOCRC), despite the reduction in CRC incidence overall. An important gap in the literature is whether EOCRC shares the same community-level risk factors as LOCRC. Thus, we sought to (1) identify disparities in the incidence rates of EOCRC and LOCRC using geospatial analysis and (2) compare the importance of community-level risk factors (racial/ethnic, health status, behavioral, clinical care, physical environmental, and socioeconomic status risk factors) in the prediction of EOCRC and LOCRC incidence rates using a random forest machine learning approach. The incidence data came from the Surveillance, Epidemiology, and End Results program (years 2000–2019). The geospatial analysis revealed large geographic variations in EOCRC and LOCRC incidence rates. For example, some regions had relatively low LOCRC and high EOCRC rates (e.g., Georgia and eastern Texas) while others had relatively high LOCRC and low EOCRC rates (e.g., Iowa and New Jersey). The random forest analysis revealed that the importance of community-level risk factors most predictive of EOCRC versus LOCRC incidence rates differed meaningfully. For example, diabetes prevalence was the most important risk factor in predicting EOCRC incidence rate, but it was a less important risk factor of LOCRC incidence rate; physical inactivity was the most important risk factor in predicting LOCRC incidence rate, but it was the fourth most important predictor for EOCRC incidence rate. Thus, our community-level analysis demonstrates the geographic variation in EOCRC burden and the distinctive set of risk factors most predictive of EOCRC.

The paper demonstrates how a system dynamics approach can support strategic planning of health care services and can in particular help to balance cost-effectiveness considerations with budget impact considerations when assessing a comprehensive package of stroke care interventions in Singapore. A population-level system dynamics model is used to investigate 12 intervention scenarios based on six stroke interventions (a public information campaign, thrombolysis, endovascular therapy, acute stroke unit (ASU), out-of-hospital rehabilitation, and secondary prevention). Primary outcomes included cumulative discounted costs and quality-adjusted life years (QALYs) gained, as well as cumulative net monetary benefit by 2030. All intervention scenarios result in an increase in net monetary benefit by 2030; much of these gains were realized through improved post-acute care. Findings highlight the importance of coordination of care, and affirms the economic value of current stroke interventions.