We can use the detailed information about cases in Florida to explain why the hospitalization rate has dropped from 25% in March to 8% in June so far (we use data for H1 June to avoid undercounting hospitalizations).
There are fundamentally two effects in play
A) Composition effect: The age distribution of cases in Florida is getting younger and younger, and this is impacting the hospitalization ratios, as the younger cohort is much less likely to need hospitalization.
The chart below shows the those <34 years accounted for less than 25% of recorded cases in March, but near 50% currently.
As illustrated in the chart, the change in the age distribution accounts for about half (47% to be specific) of the drop in the hospitalization rate in June.
For comparison, the composition effect accounts for only 30% in May (using hospitalization ratios from April, we can explain 30% of the drop in the hospitalization ratio with the changing age distribution of recorded cases).
B) Within-group effects account for the part of the decline that cannot be attributed to composition
As the chart below shows, hospitalization rates have dropped notably for all age groups, with the hospitalizations rate cut in half for the oldest cohort, for example.
Some possible explanations for this widespread decline include:
- Testing – in March, many local authorities were still in ‘catch up’ mode, where testing was largely happening at hospitals. In that context, the hospitalization rate will certainly be inflated and registered cases will be overwhelmingly skewed towards the most severe patients.
- Behavior – earlier infections were results of completely unmodified (relaxed) behavior. People could have been in extremely close contact with sick people for extended periods of time (getting high virus loads). This means that exposure levels could have been far more elevated than a newly infected person today. Moreover, the most vulnerable (ie the oldest) are likely to be the most careful now, creating a different ‘selection bias’.
Appendix: Detailed data on hospitalization ratios for all US states
The charts below show a metric of the ‘hospitalization ratios’ for the states that report cumulative hospitalizations. We focus on the change in this metric (to capture daily hospitalizations) and we look two-week trend in this metric, relative to a two-week trend in new cases, 10-days lagged (the average lag from a test positive to hospitalizations).
The charts below shows an alternative metric of the ‘hospitalization ratio’ based on active hospitalizations relative to active cases. This metric will be influenced by how long patients stay in hospital (as opposed to just how many enter the hospital). The trend is clearly down in almost all states, with key exceptions: Texas, South Dakota, North Dakota. Among the larger states, Texas is the clear outlier.