Change in country-level COVID-19 case fatality rate is explicable by improved testing; no apparent role of medical care or disease-specic knowledge

Observed case fatality rate (CFR) of COVID-19 has decreased since the beginning of the pandemic. Reasons for this decline include improved knowledge of COVID19 pathogenesis, leading to improved medical care of conrmed cases. However, ascertainment also plays a role: as more low-risk individuals are tested and more mild cases identied, observed CFR will decline. Previously I showed that geography-level CFR was cross-sectionally negatively associated with test density; here I test for similar trends within geography over six months, and check plausibility of various posited causes. Although CFR varied between geographies, its association with testing did not: in 162 geographies, CFR dropped by an average of 18% (median 21; IQR 5–30) for each doubling of test density. Change in CFR within a given geography was not associated either with that geography’s medical spending or with whether the bulk of cases occurred early or late in the pandemic. This shows that medical interventions, including those specic to COVID-19, have only a minimal effect on total CFR. Two major conclusions follow. First, interventions to reduce CFR should be evaluated by comparing groups that received the intervention to those who did not: decline in CFR after an intervention is not evidence of effectiveness. Second, improving clinical care of conrmed COVID-19 cases has only a minimal effect on death rates. To minimize the total death toll of COVID-19, policymakers should prioritize reducing infections.


Introduction
Observed case fatality rate (CFR) of COVID-19 has dropped since the beginning of the pandemic, from 10% to 3% in the US 1 and similarly elsewhere. 2 Cited reasons for this decline include improved understanding of disease pathogenesis leading to better treatment, as well as the abandonment of ineffective therapies. 2 However, ascertainment bias almost certainly also plays a role: early in the pandemic, tests were reserved for individuals at high risk of complications and many low-risk cases were missed. Seroprevalence data from early in the pandemic, 3 when CFR in the US based on testing approached 10%, 1 suggested that when all cases were included true CFR was usually below 1% and often below 0.50%. Indeed, since asymptomatic or less-symptomatic cases often outnumber those with severe symptoms, 4 it is likely that observed CFR is in ated by a factor of two or more.
It has already been shown 5 that CFR of COVID-19 is lower in geographic regions where test density is higher. This association is similar on all continents, even though baseline CFR is higher on some continents than others. However, these cross-sectional data are confounded by geography-level factors such as population age structure, presence of comorbidities, and access to critical care. Thus while it is likely that ascertainment bias plays a role in the observed drop in CFR as test density increases, it is not known how large this effect is.
The current paper investigates the role of test density in driving changes in observed geography-level CFR over a six-month period. As alternative explanations for observed changes in CFR I consider per capita medical spending as an indicator of critical-care access; and timing of the epidemic as an indicator of each geography's ability to bene t from rapidly-evolving knowledge. This technique eliminates variation in CFR driven by geography-level factors, and thus allows an unbiased estimate of the role of ascertainment in driving observed decreases in COVID-19 lethality.

Study Design and Data Collection
I estimate the slope of the association between change in test density, and change in observed CFR, in resolved cases of COVID-19 between July 3, 2020 and January 5, 2021. I then investigate the role of two factors (timing of the epidemic and medical spending) in driving this change.
COVID-19 data are from every geography for which they were available from a publicly-available real-time counter 1 and in which at least one death had occurred by the earlier timepoint. Timing of the epidemic was expressed as the percent of total cumulative cases at the later timepoint that occurred after the earlier timepoint. Medical spending (USD per capita, 2015) was downloaded from the World Health Organization. 6 Data Analysis For each geography I calculate the association between CFR (ratio between total deaths and total resolved cases) and test density per million residents, between the two timepoints.
CFR, test density, total COVID-19 cases, and medical spending were all approximately log-normally distributed and thus were log-transformed for normality in all analyses. Initial checks found four geographies had extreme values for CFR change (Monaco, Central African Republic, and Venezuela at the low end; Uzbekistan at the high end). These were excluded in sensitivity analyses.
Timing of the epidemic (percent of total cases that occurred after July 3) was skewed and thus it was categorized into early, middle, and late based on 10 th and 50 th percentiles.
Models were computed using log-log linear regression weighted by log average number of cases (mean of case count at the two timepoints.) Outputs were then reverse-transformed and expressed as percent change in CFR per doubling of test density.
Geographies with high medical spending, such as much of Europe, tended to have higher CFR at both timepoints than did geographies with low medical spending, such as much of Africa ( Figure 1). However, changes in CFR with test density were similar across the range of medical spending. The slopes of the regression lines for different geographies were nearly parallel on the log-log scale ( Figure 1) and there was no visible association between medical spending and rate of change of CFR with test density (Figure  2.) This null association was supported by statistical models, which found that rate of change in CFR was not associated with either medical spending or timing of the epidemic (both p >0.10), either before or after exclusion of outliers.

Conclusions
Case fatality rate of COVID-19 declined with increasing test density, suggesting ascertainment bias. While CFR itself was consistently higher in some geographies than others, its rate of decline did not vary with factors hypothesized to affect it. This suggests that decline in CFR was almost entirely explicable by improved testing, with no detectable effect of either access to medical care or existence of COVID-19speci c knowledge.
The 18% decrease of CFR with each doubling of test density within the same geography over time was almost the same as that previously observed between geographies at a single timepoint, 5 further supporting the role of test bias in driving it.
These ndings have two major implications. First, research into CFR and its drivers should explicitly consider ascertainment bias. Evaluation of interventions should compare groups that received it to those who did not, rather than interpreting a decline in CFR after an intervention as evidence of effectiveness. Second, improvements in clinical care appear to have only a minor effect on case fatality rate. To minimize the death toll of COVID-19, the priority should be to reduce infection.

Declarations
Author Contributions Figure 1 Case fatality rate of COVID-19 declines with test density, regardless of access to medical care Lines for each geography represent change between July 3, 2020; and January 5, 2021. Color coding for medical spending per capita. Geographies are labeled if, and only if, CFR increased over the study period.
Page 7/7 Figure 2 Lower case fatality rate of COVID-19 with more testing, regardless of medical spending Color coding for continent. Bubble size for number of cases.