Dataset Information

On the Progression of COVID-19 in Portugal: A Comparative Analysis of Active Cases Using Non-linear Regression.

ABSTRACT: Portugal is often portrayed as a relatively successful case in the control of COVID-19's March 2020 outbreak in Europe due to timely confinement measures, commonly referred to as the "lockdown". As in other European Union member states, by late April, Portugal was preparing the phased loosening of such measures scheduled for the beginning of May. Despite a modest reduction in infection rates by that time, there was insufficient data to reliably forecast imminent scenarios. Using the South Korea data as scaffold, which became a paradigmatic case of recovery following a high number of infected people, we fitted the Portuguese data to biphasic models using non-linear regression and compared the two countries. The models, which yielded a good fit, showed that recovery would be slow, with over 50% active cases months after the lockdown. These findings acted at the time as a warning, showing that a high number of infected individuals, together with an unknown number of asymptomatic carriers, could increase the risk of a slow recovery, if not of new outbreaks. A month later, the models showed more favorable outcomes. However, shortly after, as the effects of leaving the lockdown became evident, the number of infections began rising again, leaving Portugal in a situation of inward and outward travel restrictions and baffling even the most conservative forecasts for the clearing of the pandemic.

SUBMITTER: Milhinhos A

PROVIDER: S-EPMC7516009 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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On the Progression of COVID-19 in Portugal: A Comparative Analysis of Active Cases Using Non-linear Regression.

Milhinhos Ana A Costa Pedro M PM

Frontiers in public health 20200911

Portugal is often portrayed as a relatively successful case in the control of COVID-19's March 2020 outbreak in Europe due to timely confinement measures, commonly referred to as the "lockdown". As in other European Union member states, by late April, Portugal was preparing the phased loosening of such measures scheduled for the beginning of May. Despite a modest reduction in infection rates by that time, there was insufficient data to reliably forecast imminent scenarios. Using the South Korea ...[more]

PMID: 33014981

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Project description: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.

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Project description:ObjectivesThe SARS-CoV-2 virus has spread worldwide, leading governments to implement mitigation measures. Understanding the reluctance to adhere to non-pharmacological interventions might help promote adherence to these measures. This study aimed to identify factors associated with non-adherence to the first lockdown in Portugal.Study designCross-sectional study.MethodsThis study used data from a Portuguese community-based survey entitled 'COVID-19 Barometer: Social Opinion'. Data were collected on risk perception, health status and social experiences using a snowball sampling technique. The event of interest corresponded to participants who reported not staying home during the lockdown period, serving as a proxy for non-adherence to lockdown. Logistic regression was used to identify factors associated with non-adherence to the first lockdown.ResultsResponses from 133,601 individual questionnaires that were completed during the first week of the first lockdown in 2020 were analysed. A minority of participants (5.6%) reported non-adherence to lockdown (i.e. leaving home for reasons other than essential situations). Working in the workplace was the factor with the strongest association of non-adherence to the lockdown. Several other factors were also associated with non-adherence to the first lockdown; namely, being a man, being a student, having a low level of education, having a low income, living alone or with a high-infection-risk professional (e.g. doctor, nurse, pharmaceutical, health technician, firefighter, police officer, military, essential services worker), perceiving the risk of getting COVID-19 to be high, not having social support in case of infection, feeling agitated, sad or anxious every day, and considering the preventive measures to be unimportant or inadequate.ConclusionsNon-adherence to lockdown was associated with socio-economic, trust and perception factors. Future research should investigate the mechanisms underlying these associations to help identify the population groups who are most at risk of non-adherence.

Dataset Information

On the Progression of COVID-19 in Portugal: A Comparative Analysis of Active Cases Using Non-linear Regression.

Publications

On the Progression of COVID-19 in Portugal: A Comparative Analysis of Active Cases Using Non-linear Regression.

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