How have reporting methods and cause-of-death coding affected US COVID death counts?

Checked on December 20, 2025
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Executive summary

Reporting methods and cause-of-death coding materially shaped the U.S. COVID-19 death totals: death certificates using ICD-10 code U07.1 captured deaths when clinicians listed COVID-19 as an underlying or contributing cause [1] [2], but variable state timeliness, incomplete testing early on, differing certification practices, and alternative surveillance definitions produced both undercounts and counting inconsistencies that are visible when compared with excess‑death analyses [3] [4] [5].

1. How death certificates and ICD coding set the baseline number

The National Vital Statistics System counts COVID-19 deaths when COVID-19 is recorded on the death certificate and coded to ICD‑10 U07.1, a rule that includes presumed as well as laboratory‑confirmed cases and thereby established the official baseline for national tallies [1] [2], while CDC guidance and training urged clinicians to list COVID‑19 when it contributed to death to improve surveillance [6].

2. Reporting lags and jurisdictional variation blurred the day‑to‑day and state‑by‑state picture

Provisional NVSS data are affected by delays between date of death, certificate completion, and coding; only about 63% of U.S. deaths are reported within 10 days and some jurisdictions take weeks to months, which means daily or weekly totals understate fatalities until backfills occur and complicate direct comparisons across states [1] [3] [2].

3. Excess‑death methods reveal likely undercounting and indirect effects

Analysts used all‑cause excess mortality to capture deaths missed by direct COVID coding — because COVID may not be mentioned on a certificate, testing was limited, or deaths were indirectly pandemic‑related — and found substantial “excess” deaths beyond those coded to COVID, suggesting under‑ascertainment especially early in the pandemic [4] [5] [7].

4. Certification quality, coding practices, and non‑uniform approaches introduced systematic differences

Studies and CDC technical notes show that differences in how certifiers selected underlying versus contributing causes, the manual nature of coding, and pressures when death volumes surged produced variable specificity; some jurisdictions used keyword searches or clinical review to capture COVID mentions earlier than ICD coding, while others relied strictly on final codes, generating systematic variation in counts and subgroup comparisons [8] [9] [2].

5. Alternative surveillance definitions and changing clinical context altered attribution over time

Because real‑time needs led some agencies to use proxies — for example, “death within X days of a positive test” — those metrics worked early on but lost accuracy as testing, treatments, and admission screening changed, meaning different surveillance tools produced divergent estimates of COVID‑related mortality at different phases [10]; moreover, provisional reports exclude late submissions and may shift as final NVSS coding is completed [11] [3].

6. Consequences for equity analyses and public‑health decision making

State differences in certification, misclassification of race/ethnicity on death certificates, and under‑reporting in some areas affect subgroup mortality estimates and can mask disparities — researchers warn that these coding and reporting artifacts complicate comparisons across populations and may undercount COVID burdens in marginalized communities [12] [5].

7. What the combined evidence supports — and its limits

Taken together, NVSS rules and CDC coding guidance created a transparent, standardized foundation (U07.1) that nonetheless produced conservative counts in some places and times because of reporting lags, limited testing, inconsistent certifier practice, and alternative operational definitions; excess‑death analyses and local auditing (e.g., keyword or clinician review) provide convergent evidence of additional pandemic mortality not always captured by death‑certificate tabulations, but final quantification depends on methods and assumptions and some aspects (like exact misclassification rates) remain incompletely measured in available data [4] [5] [8] [9].

Want to dive deeper?
How do excess‑death calculation methods differ and which are most robust for pandemic attribution?
What changes to death‑certification training and coding have been recommended after COVID‑19 to improve future pandemic mortality counts?
How have state‑level reporting lags and practices affected estimates of racial and regional disparities in COVID‑19 mortality?