Infection fatality rate of COVID-19 in community-dwelling elderly populations
Infection fatality rate of COVID-19 in community-dwelling elderly populations is a dataset published in European Journal of Epidemiology (2022). On theSindex it has a DataRank of 1.6, placing it in the top 37.2% of the data-sharing corpus. It has been cited 56 times, with 35 citing works in its 1-hop citation network. Its calibrated FAIR score is 50/100.
Abstract
This mixed design synthesis aimed to estimate the infection fatality rate (IFR) of Coronavirus Disease 2019 (COVID-19) in community-dwelling elderly populations and other age groups from seroprevalence studies. Protocol: https://osf.io/47cgb . Eligible were seroprevalence studies done in 2020 and identified by any of four existing systematic reviews; with ≥ 500 participants aged ≥ 70 years; presenting seroprevalence in elderly people; aimed to generate samples reflecting the general population; and whose location had available data on cumulative COVID-19 deaths in elderly (primary cutoff ≥ 70 years; ≥ 65 or ≥ 60 also eligible). We extracted the most fully adjusted (if unavailable, unadjusted) seroprevalence estimates; age- and residence-stratified cumulative COVID-19 deaths (until 1 week after the seroprevalence sampling midpoint) from official reports; and population statistics, to calculate IFRs adjusted for test performance. Sample size-weighted IFRs were estimated for countries with multiple estimates. Thirteen seroprevalence surveys representing 11 high-income countries were included in the main analysis. Median IFR in community-dwelling elderly and elderly overall was 2.9% (range 1.8-9.7%) and 4.5% (range 2.5-16.7%) without accounting for seroreversion (2.2% and 4.0%, respectively, accounting for 5% monthly seroreversion). Multiple sensitivity analyses yielded similar results. IFR was higher with larger proportions of people > 85 years. The IFR of COVID-19 in community-dwelling elderly is lower than previously reported.
›Data sources & pipeline
FAIR Checklist
Context only (not used in score)- Has DOI
- Open Access
- Dataset classification
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →
DataRank Breakdown
Base Score Contribution
0.606
From this paper's citation signal
Citation Network Contribution
1.0
From 28 citing papers with measurable signal
Top 5 citers driving the network score
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
- Dexamethasone in Hospitalized Patients with Covid-19New England Journal of Medicine202110,026 citationsDataRank 1.4
- Factors associated with COVID-19-related death using OpenSAFELYNature20206,697 citationsDataRank 1.3
- Infection fatality rate of COVID-19 inferred from seroprevalence dataBulletin of the World Health Organization2020595 citationsDataRank 0.959
- Population-level COVID-19 mortality risk for non-elderly individuals overall and for non-elderly individuals without underlying diseases in pandemic epicentersEnvironmental Research2020320 citationsDataRank 0.866
- SARS-CoV-2 reinfections: Overview of efficacy and duration of natural and hybrid immunityEnvironmental Research2022299 citationsDataRank 0.856
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 37% comes from its base citations and 63% from the citation network (28 citing papers contributed measurable signal).
- Base score B(p)
- log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
- Network N(p)
- Σ over citers of log1p(Cq) ÷ max(outdegreeq, 1). Being cited by a highly-cited paper with few references counts most.
- Damping factor d = 0.85
- DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
- Self-citations excluded
- Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
Click a node to highlight its connections. Use scroll to zoom. Drag to pan.