This study analyzes data from 2,858,576 adults using the Nationwide Admission Database (HCUP) from 2019 to 2024, focusing on the incidence and readmission rates of diverticulitis within 30 and 60 days. It explores the impact of obesity—a known risk factor—alongside demographic influences like race and gender. Employing chi-square tests, logistic regression, and ROC curve analysis, the research examines how these factors correlate with diverticulitis outcomes, shedding light on their interplay in hospital readmissions.

Data were collected from the HCUP database, reflecting hospital admissions spanning 2019 to 2024 for 2,858,576 adults. The study primarily investigates the incidence of diverticulitis and subsequent readmission rates at 30 and 60 days, while analyzing the effects of obesity, race, and gender on these outcomes. Chi-square tests were used to compare incidence and readmission rates between obese and non-obese groups across different demographics. Logistic regression assessed the influence of gender and race on the probability of readmissions at specified intervals, integrating coefficients for these variables.

Between 2019 and 2024, HCUP’s Nationwide Admission Database was used to analyze 2.8 million adult hospital admissions, identifying 234,024 diverticulitis cases, with 112,014 classified as obese (BMI > 30). Chi-square tests showed significant differences in 30- and 60-day readmission rates between obese and non-obese patients (p < 0.001). Diverticulitis incidence also varied by obesity (p = 0.025). Logistic regression revealed gender and race influenced readmissions, with higher odds in females and African Americans across both 30- and 60-day intervals.

The study confirms a strong correlation between obesity and increased readmission rates for diverticulitis, highlighting obesity's significant impact on the condition's recurrence. It also reveals gender and racial disparities in readmission rates, with females and African Americans experiencing higher rates, especially at 60 days. The ROC curves demonstrate the predictive accuracy of the models, particularly for 30-day readmissions. These findings are crucial for crafting targeted interventions that address the specific needs of these vulnerable groups, aiming to enhance outcomes and reduce healthcare costs.