This study investigates the impact of the COVID-19 pandemic on Emergency Department (ED) visits resulting in hospital admissions across Massachusetts, stratified by diagnosis. We hypothesized that the pandemic altered both seasonal patterns and overall hospitalization levels, with varying effects across disease categories. Weekly ED admission counts from January 2019 through January 2025 were obtained from the Massachusetts Department of Public Health. These data were stratified into nine ICD-10-based categories: cardiovascular diseases, digestive diseases, infectious diseases, infectious respiratory diseases, injuries, pneumonia, renal diseases, other respiratory diseases, and other diseases. This allowed us to examine cause-specific disruptions in ED utilization over time. To estimate deviations from expected admission patterns, we adapted statistical methods used in public health to assess excess mortality during emergencies. Expected admission levels were modeled under non-pandemic conditions to identify deficits during the pandemic. Three temporal periods were analyzed: pre-pandemic, pandemic, and post-pandemic. We used the excessmort R package to fit Poisson and mixed-effects models to the weekly counts for each category. These models captured smooth trends and seasonality while accounting for overdispersion and correlation. Generalized linear models (Poisson and quasi-Poisson) were used to estimate fixed temporal effects, while generalized linear mixed models included random effects to account for within-category correlation over time. Findings revealed heterogeneous disruptions across categories. The most severe and sustained deficits were observed for pneumonia, respiratory diseases, and the “other” category. In contrast, cardiovascular and injury-related admissions declined sharply early in the pandemic but showed partial recovery. These results highlight the uneven impact of the pandemic on acute care utilization, shaped by disease-specific urgency, public health responses, and healthcare system strain, consistent with prior research on indirect effects and healthcare prioritization during public health emergencies.