Evidence map›Paper›PMID 40910058›Full record

ReviewFrontiers in public health2025

Strategies for population-level identification of post-acute sequelae of COVID-19 through health administrative data.

Cristina Mazzali, Pietro Magnoni, Alberto Zucchi, Giovanni Maifredi, Luca Cavalieri d'Oro, Maria Letizia Gambino, Anna Clara Fanetti, Pietro Giovanni Perotti, Marco Villa, Maria Grazia Valsecchi and 3 more

Abstract readReview
In one paragraph

Review in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Cristina MazzaliEpidemiology Unit, Agency for Health Protection Milan, Milan, Italy.
Pietro MagnoniEpidemiology Unit, Agency for Health Protection Milan, Milan, Italy.
Alberto ZucchiEpidemiology Unit, Agency for Health Protection Bergamo, Bergamo, Italy.
Giovanni MaifrediEpidemiology Unit, Agency for Health Protection Brescia, Brescia, Italy.
Luca Cavalieri d'OroEpidemiology Unit, Agency for Health Protection Brianza, Monza, Italy.
Maria Letizia GambinoEpidemiology Unit, Agency for Health Protection Insubria, Varese, Italy.
Anna Clara FanettiEpidemiology Unit, Agency for Health Protection Montagna, Sondrio, Italy.
Pietro Giovanni PerottiEpidemiology Unit, Agency for Health Protection Pavia, Pavia, Italy.
Marco VillaEpidemiology Unit, Agency for Health Protection Val Padana, Cremona, Italy.
Maria Grazia ValsecchiSchool of Medicine and Surgery and Bicocca Bioinformatics Biostatistics and Bioimaging Centre (B4), University of Milano-Bicocca, Milan, Italy.
Daria ViganiDepartment of Economics and Finance, Catholic University of the Sacred Heart, Milan, Italy.
Claudio LuciforaDepartment of Economics and Finance, Catholic University of the Sacred Heart, Milan, Italy.
Antonio Giampiero RussoEpidemiology Unit, Agency for Health Protection Milan, Milan, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Post-acute sequelae of COVID-19 (PASC) encompass several clinical outcomes, from new-onset symptoms to both acute and chronic diagnoses, including pulmonary and extrapulmonary manifestations. Health administrative data (HAD) from health information systems allow population-level analyses of such outcomes. Our primary aim was to identify clinical conditions potentially attributable to SARS-CoV-2 infection, and the types of HAD and "diagnostic criteria" used for their detection. Methods: We performed a literature review to identify HAD-based cohort studies assessing the association between SARS-CoV-2 infection and medium-/long-term outcomes in the general population. From each included study, we extracted data on design, algorithms used for outcome identification (sources, coding systems, codes, time criteria/thresholds), and whether significant associations with SARS-CoV-2 infection were reported. Results: We identified six studies investigating acute and chronic conditions grouped by clinical domain (cardiovascular, respiratory, neurologic, mental health, endocrine/metabolic, pediatric, miscellaneous). Two studies also addressed the onset of specific symptoms. Cardio/cerebrovascular conditions were most studied, with significant associations reported for deep vein thrombosis, heart failure, atrial fibrillation, and coronary artery disease. Conditions in other domains were less investigated, with inconsistent findings. Only three studies were designed as test-positive vs. test-negative comparisons. Discussion: Heterogeneity in data sources, study design, and outcome definitions hinder the comparability of studies and explain the inconsistencies in findings about associations with SARS-CoV-2 infection. Rigorously designed studies on large populations with wide availability of data from health information systems are needed for population-level analyses on PASC, and especially on its impact on chronic diseases and their future burden on healthcare systems.

Indexed as

COVID-19HumansPost-Acute COVID-19 SyndromeSARS-CoV-2case-detection algorithmCOVID-19health administrative datalong COVIDPASCroutinely collected data

Identifiers

PMID40910058
PMCPMC12405178

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.