Evidence map›Paper›PMID 35461149›Full record

Observational studyInternational journal of medical informatics2022

Improving morbidity information in Portugal: Evidence from data linkage of COVID-19 cases surveillance and mortality systems.

Rodrigo Feteira-Santos, Catarina Camarinha, Miguel de Araújo Nobre, Cecília Elias, Leonor Bacelar-Nicolau, Andreia Silva Costa, Cristina Furtado, Paulo Jorge Nogueira

Abstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in International journal of medical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
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

8 authors.

Rodrigo Feteira-SantosEPI Task-Force FMUL, Faculdade de Medicina, Universidade de Lisboa. Avenida Professor Egas Moniz, 1649-028 Lisboa, Portugal; Instituto de Medicina Preventiva e Saúde Pública, Faculdade de Medicina, Universidade de Lisboa. Avenida Professor Egas Moniz, 1649-028 Lisboa, Portugal; Área Disciplinar Autónoma de Bioestatística (Laboratório de Biomatemática), Faculdade de Medicina, Universidade de Lisboa. Avenida Professor Egas Moniz, 1649-028 Lisboa, Portugal; Instituto de Saúde Ambiental, Faculdade de Medicina, Universidade de Lisboa. Avenida Professor Egas Moniz, 1649-028 Lisboa, Portugal. Electronic address: rodrigosantos@medicina.ulisboa.pt.
Catarina CamarinhaEPI Task-Force FMUL, Faculdade de Medicina, Universidade de Lisboa. Avenida Professor Egas Moniz, 1649-028 Lisboa, Portugal; Instituto de Medicina Preventiva e Saúde Pública, Faculdade de Medicina, Universidade de Lisboa. Avenida Professor Egas Moniz, 1649-028 Lisboa, Portugal; Unidade de Epidemiologia, Instituto de Medicina Preventiva e Saúde Pública, Faculdade de Medicina, Universidade de Lisboa. Avenida Professor Egas Moniz, 1649-028 Lisboa, Portugal.
Miguel de Araújo NobreEPI Task-Force FMUL, Faculdade de Medicina, Universidade de Lisboa. Avenida Professor Egas Moniz, 1649-028 Lisboa, Portugal; Instituto de Medicina Preventiva e Saúde Pública, Faculdade de Medicina, Universidade de Lisboa. Avenida Professor Egas Moniz, 1649-028 Lisboa, Portugal; Clínica Universitária de Estomatologia, Faculdade de Medicina, Universidade de Lisboa. Avenida Professor Egas Moniz, 1649-028 Lisboa, Portugal.
Cecília EliasEPI Task-Force FMUL, Faculdade de Medicina, Universidade de Lisboa. Avenida Professor Egas Moniz, 1649-028 Lisboa, Portugal; Unidade de Saúde Pública Francisco George, ACES Lisboa Norte, Administração Regional de Saúde de Lisboa e Vale do Tejo. Largo Professor Arnaldo Sampaio, 1500-559 Lisboa, Portugal.
Leonor Bacelar-NicolauEPI Task-Force FMUL, Faculdade de Medicina, Universidade de Lisboa. Avenida Professor Egas Moniz, 1649-028 Lisboa, Portugal; Instituto de Medicina Preventiva e Saúde Pública, Faculdade de Medicina, Universidade de Lisboa. Avenida Professor Egas Moniz, 1649-028 Lisboa, Portugal; Área Disciplinar Autónoma de Bioestatística (Laboratório de Biomatemática), Faculdade de Medicina, Universidade de Lisboa. Avenida Professor Egas Moniz, 1649-028 Lisboa, Portugal; Instituto de Saúde Ambiental, Faculdade de Medicina, Universidade de Lisboa. Avenida Professor Egas Moniz, 1649-028 Lisboa, Portugal.
Andreia Silva CostaEPI Task-Force FMUL, Faculdade de Medicina, Universidade de Lisboa. Avenida Professor Egas Moniz, 1649-028 Lisboa, Portugal; Instituto de Saúde Ambiental, Faculdade de Medicina, Universidade de Lisboa. Avenida Professor Egas Moniz, 1649-028 Lisboa, Portugal; CIDNUR - Centro de Investigação, Inovação e Desenvolvimento em Enfermagem de Lisboa, Escola Superior de Enfermagem de Lisboa. Avenida Professor Egas Moniz, 1600-190 Lisboa, Portugal; CRC-W-Católica Research Centre for Psychological, Family and Social Wellbeing, Universidade Católica Portuguesa. Palma de Cima, 1649-023 Lisboa, Portugal.
Cristina FurtadoEPI Task-Force FMUL, Faculdade de Medicina, Universidade de Lisboa. Avenida Professor Egas Moniz, 1649-028 Lisboa, Portugal; Instituto de Medicina Preventiva e Saúde Pública, Faculdade de Medicina, Universidade de Lisboa. Avenida Professor Egas Moniz, 1649-028 Lisboa, Portugal; Instituto de Saúde Ambiental, Faculdade de Medicina, Universidade de Lisboa. Avenida Professor Egas Moniz, 1649-028 Lisboa, Portugal; Unidade de Epidemiologia, Instituto de Medicina Preventiva e Saúde Pública, Faculdade de Medicina, Universidade de Lisboa. Avenida Professor Egas Moniz, 1649-028 Lisboa, Portugal; Instituto Nacional de Saúde Doutor Ricardo Jorge. Av. Padre Cruz, 1600-560 Lisboa, Portugal.
Paulo Jorge NogueiraEPI Task-Force FMUL, Faculdade de Medicina, Universidade de Lisboa. Avenida Professor Egas Moniz, 1649-028 Lisboa, Portugal; Instituto de Medicina Preventiva e Saúde Pública, Faculdade de Medicina, Universidade de Lisboa. Avenida Professor Egas Moniz, 1649-028 Lisboa, Portugal; Área Disciplinar Autónoma de Bioestatística (Laboratório de Biomatemática), Faculdade de Medicina, Universidade de Lisboa. Avenida Professor Egas Moniz, 1649-028 Lisboa, Portugal; Instituto de Saúde Ambiental, Faculdade de Medicina, Universidade de Lisboa. Avenida Professor Egas Moniz, 1649-028 Lisboa, Portugal.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCOVID-19 rapidly spread around the world, putting health systems under unprecedented pressure and continuous adaptations. Well-established health information systems (HIS) are crucial in providing data to allow evidence-based policymaking and public health interventions in the pandemic response. This study aimed to compare morbidity information between two databases for COVID-19 management in Portugal and identify potential complementarities.

methodsThis is an observational study using records from both COVID-19 cases surveillance (National Epidemiological Surveillance System; SINAVE) and related deaths (National e-Death Certificates Information System; SICO) systems, which were matched on sex, age, municipality of residence and date of death. After the linkage, morbidity reported in SINAVE and identified in SICO, through the application of Charlson and Elixhauser comorbidity indexes algorithms, were compared to evaluate agreement level.

resultsOverall, 2285 matched cases were analyzed, including 53.9% males with a median age of 84 years. According to the method of data reporting assessment, the presence of any morbidity ranged between 26.3% and 62.5%. The reporting of ten morbidities could be compared between the information reported in SINAVE and SICO databases. The proportion of simultaneous reporting in both databases ranged between 5.7% for diabetes and 0.0% for human immunodeficiency virus infection or coagulopathy. Minimal or no agreement was found when assessing the similarity of the morbidity reporting in both databases, with neoplasms showing the highest level of agreement (0.352, 95% IC: 0.277-0.428; p < 0.001).

conclusionDifferent information about reported morbidity could be found in two HIS used to monitor COVID-19 cases and related deaths, as data are independently collected. These results show that the interoperability of SICO and SINAVE databases would potentially improve available HIS and improve available information to decision-making and address COVID-19 pandemic management.

Indexed as

COVID-19Aged, 80 and overFemaleHumansInformation Storage and RetrievalMaleMorbidityPandemicsPortugalCOVID-19Health information systemMorbiditySystems interoperability

Identifiers

PMID35461149
PMCPMC9012514

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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.