This dashboard focuses on county-level variation in four COVID outcome measures: mortality, hospitalization rate, hospitalization duration, and median out-of-pocket cost. Taken separately, each map shows one part of the burden profile. Taken together, they suggest that the counties with the worst outcomes are not simply the counties where hospitalization is most common. The more informative pattern is how frequently patients are hospitalized, how long they remain hospitalized, and whether that care is taking place under higher patient financial burden.
Hospitalization rate alone does not cleanly explain mortality. Several counties have relatively similar hospitalization rates but noticeably different death rates, which means admission frequency by itself is too coarse to identify where the system appears most strained. For example, Barnstable, Plymouth, Suffolk, and Hampshire all sit in a fairly narrow hospitalization-rate range, yet their mortality outcomes differ substantially. That gap matters because it shifts attention away from the simple question of how many patients enter the hospital and toward the more revealing question of what happens to them once they are there.
The clearest pattern in these county summaries is the relationship between hospitalization duration and mortality. Counties with longer average hospital stays tend to have lower mortality, while counties with shorter average stays tend to have worse outcomes. One plausible interpretation is that longer stays reflect patients remaining in care long enough to receive more complete treatment, whereas shorter stays may indicate greater discharge pressure, reduced capacity, or a system operating under more stress. This does not prove a causal mechanism on its own, but it gives a much cleaner descriptive account of the variation than hospitalization rate by itself.
Financial burden sharpens that interpretation. The counties that combine shorter stays with higher median out-of-pocket cost tend to look more vulnerable overall. Barnstable is the clearest example: it has high mortality, a comparatively short average hospitalization duration, a high hospitalization rate, and the highest patient cost burden in the set shown here. That combination makes it the county that most strongly fits a story of overlapping medical and financial strain. Plymouth also leans in this direction, though less dramatically.
By contrast, counties such as Suffolk and Hampshire suggest a different pattern. Their hospitalization rates are not especially low, but their longer average stays coincide with lower mortality. Those counties therefore weaken any interpretation based only on how often patients are admitted, and they strengthen the argument that the duration and quality of care matter more than admission volume alone. In other words, high hospitalization is not automatically the same thing as the highest burden if patients are remaining in care longer and outcomes are better.
Franklin should be treated as an explicit outlier. It has a high hospitalization rate and high cost burden, yet very low mortality. Because its county sample is much smaller than the large urban counties, it is important not to force it into the broader pattern too aggressively. The correct use of Franklin in the story is not to ignore it, but to mark it as a case that reminds us these are descriptive patterns rather than deterministic rules.
The main conclusion from this dashboard is that the most useful county-level story is not a simple ranking of where hospitalization is highest. Instead, the stronger interpretation is that worse COVID outcomes appear where hospitalization remains common, average stays are shorter, and patient financial burden is heavier. Mortality is therefore better understood here as part of a broader burden profile rather than as the direct result of hospitalization rate alone.
Work on this project was split evenly. We individually explored the synthetic data for ideation and data clean-up purposes, however, we worked together to complete the final iteration of the dashboard. Individually, Micaela cleaned the datasets to create one summarize dataset outlining covid outcomes and individual factors like coverage gap, focusing on COVID. Maaso individually cleaned the dataset to explore flu and general immunization rates as a potential avenue. When we came together with these explorations, we decided on focus points including our 4 dashboard sections and collectively remodeled our analyses to focus on COVID but make more detaied decisions on how to calculate coverage gap and what narrative we wanted to tell.