Data Sheet 1_Strategies for population-level identification of post-acute sequelae of COVID-19 through health administrative data.pdf
Introduction<p>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...
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2025
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| _version_ | 1852017460543225856 |
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| author | Cristina Mazzali (22090328) |
| author2 | Pietro Magnoni (22090331) Alberto Zucchi (75853) Giovanni Maifredi (18787936) Luca Cavalieri d’Oro (12237519) Maria Letizia Gambino (22090334) Anna Clara Fanetti (16030418) Pietro Giovanni Perotti (22090337) Marco Villa (4534591) Maria Grazia Valsecchi (8422263) Daria Vigani (22090343) Claudio Lucifora (14676057) Antonio Giampiero Russo (12966419) |
| author2_role | author author author author author author author author author author author author |
| author_facet | Cristina Mazzali (22090328) Pietro Magnoni (22090331) Alberto Zucchi (75853) Giovanni Maifredi (18787936) Luca Cavalieri d’Oro (12237519) Maria Letizia Gambino (22090334) Anna Clara Fanetti (16030418) Pietro Giovanni Perotti (22090337) Marco Villa (4534591) Maria Grazia Valsecchi (8422263) Daria Vigani (22090343) Claudio Lucifora (14676057) Antonio Giampiero Russo (12966419) |
| author_role | author |
| dc.creator.none.fl_str_mv | Cristina Mazzali (22090328) Pietro Magnoni (22090331) Alberto Zucchi (75853) Giovanni Maifredi (18787936) Luca Cavalieri d’Oro (12237519) Maria Letizia Gambino (22090334) Anna Clara Fanetti (16030418) Pietro Giovanni Perotti (22090337) Marco Villa (4534591) Maria Grazia Valsecchi (8422263) Daria Vigani (22090343) Claudio Lucifora (14676057) Antonio Giampiero Russo (12966419) |
| dc.date.none.fl_str_mv | 2025-08-20T05:32:13Z |
| dc.identifier.none.fl_str_mv | 10.3389/fpubh.2025.1637112.s001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Data_Sheet_1_Strategies_for_population-level_identification_of_post-acute_sequelae_of_COVID-19_through_health_administrative_data_pdf/29947487 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Public Health and Health Services not elsewhere classified COVID-19 PASC long COVID health administrative data routinely collected data case-detection algorithm |
| dc.title.none.fl_str_mv | Data Sheet 1_Strategies for population-level identification of post-acute sequelae of COVID-19 through health administrative data.pdf |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | Introduction<p>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.</p>Methods<p>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.</p>Results<p>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.</p>Discussion<p>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.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_72cffbdbfbe0d50de425468b7fb102fe |
| identifier_str_mv | 10.3389/fpubh.2025.1637112.s001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29947487 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Data Sheet 1_Strategies for population-level identification of post-acute sequelae of COVID-19 through health administrative data.pdfCristina Mazzali (22090328)Pietro Magnoni (22090331)Alberto Zucchi (75853)Giovanni Maifredi (18787936)Luca Cavalieri d’Oro (12237519)Maria Letizia Gambino (22090334)Anna Clara Fanetti (16030418)Pietro Giovanni Perotti (22090337)Marco Villa (4534591)Maria Grazia Valsecchi (8422263)Daria Vigani (22090343)Claudio Lucifora (14676057)Antonio Giampiero Russo (12966419)Public Health and Health Services not elsewhere classifiedCOVID-19PASClong COVIDhealth administrative dataroutinely collected datacase-detection algorithmIntroduction<p>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.</p>Methods<p>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.</p>Results<p>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.</p>Discussion<p>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.</p>2025-08-20T05:32:13ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.3389/fpubh.2025.1637112.s001https://figshare.com/articles/dataset/Data_Sheet_1_Strategies_for_population-level_identification_of_post-acute_sequelae_of_COVID-19_through_health_administrative_data_pdf/29947487CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/299474872025-08-20T05:32:13Z |
| spellingShingle | Data Sheet 1_Strategies for population-level identification of post-acute sequelae of COVID-19 through health administrative data.pdf Cristina Mazzali (22090328) Public Health and Health Services not elsewhere classified COVID-19 PASC long COVID health administrative data routinely collected data case-detection algorithm |
| status_str | publishedVersion |
| title | Data Sheet 1_Strategies for population-level identification of post-acute sequelae of COVID-19 through health administrative data.pdf |
| title_full | Data Sheet 1_Strategies for population-level identification of post-acute sequelae of COVID-19 through health administrative data.pdf |
| title_fullStr | Data Sheet 1_Strategies for population-level identification of post-acute sequelae of COVID-19 through health administrative data.pdf |
| title_full_unstemmed | Data Sheet 1_Strategies for population-level identification of post-acute sequelae of COVID-19 through health administrative data.pdf |
| title_short | Data Sheet 1_Strategies for population-level identification of post-acute sequelae of COVID-19 through health administrative data.pdf |
| title_sort | Data Sheet 1_Strategies for population-level identification of post-acute sequelae of COVID-19 through health administrative data.pdf |
| topic | Public Health and Health Services not elsewhere classified COVID-19 PASC long COVID health administrative data routinely collected data case-detection algorithm |