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Characterization and identification of variations in types of primary care visits before and during the COVID-19 Pandemic in Catalonia: big data analysis study

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dc.contributor.author López Seguí, Francesc, 1991-
dc.contributor.author Hernandez Guillamet, Guillem
dc.contributor.author Pifarré Arolas, Hèctor
dc.contributor.author Marin-Gomez, Francesc X.
dc.contributor.author Ruiz-Comellas, Anna
dc.contributor.author Ramírez-Morros, Anna Maria
dc.contributor.author Adroher i Mas, Cristina
dc.contributor.author Vidal Alaball, Josep
dc.date.accessioned 2023-11-28T07:07:21Z
dc.date.available 2023-11-28T07:07:21Z
dc.date.issued 2021
dc.identifier.citation Lopez F, Hernandez G, Pifarré H, Marin-Gomez FX, Ruiz A, Ramirez AM, et al. Characterization and identification of variations in types of primary care visits before and during the COVID-19 Pandemic in Catalonia: big data analysis study. J Med Internet Res. 2021 Sep;23(9):e29622. DOI: 10.2196/29622
dc.identifier.issn 1438-8871
dc.identifier.uri http://hdl.handle.net/10230/58389
dc.description.abstract Background: The COVID-19 pandemic has turned the care model of health systems around the world upside down, causing the abrupt cancellation of face-to-face visits and redirection of the model toward telemedicine. Digital transformation boosts information systems—the more robust they are, the easier it is to monitor the health care system in a highly complex state and allow for more agile and reliable analysis. Objective: The purpose of this study was to analyze diagnoses from primary care visits and distinguish between those that had higher and lower variations, relative to the 2019 and 2020 periods (roughly before and during COVID-19), to identify clinical profiles that may have been most impaired from the least-used diagnostic codes for visits during the pandemic. Methods: We used a database from the Primary Care Services Information Technologies Information System of Catalonia. We analyzed the register of visits (n=2,824,185) and their International Classification of Diseases (ICD-10) diagnostic codes (n=3,921,974; mean 1.38 per visit), as approximations of the reasons for consultations, at 3 different grouping levels. The data were represented by a term frequency matrix and analyzed recursively in different partitions aggregated according to date. Results: The increase in non–face-to-face visits (+267%) did not counterbalance the decrease in face-to-face visits (–47%), with an overall reduction in the total number of visits of 1.36%, despite the notable increase in nursing visits (10.54%). The largest increases in 2020 were visits with diagnoses related to COVID-19 (ICD-10 codes Z20-Z29: 2.540%), along with codes related to economic and housing problems (ICD-10 codes Z55-Z65: 44.40%). Visits with most of the other diagnostic codes decreased in 2020 relative to those in 2019. The largest reductions were chronic pathologies such as arterial hypertension (ICD-10 codes I10-I16: –32.73%) or diabetes (ICD-10 codes E08-E13: –21.13%), but also obesity (E65-E68: –48.58%) and bodily injuries (ICD-10 code T14: –33.70%). Visits with mental health–related diagnostic codes decreased, but the decrease was less than the average decrease. There was a decrease in consultations—for children, adolescents, and adults—for respiratory infections (ICD-10 codes J00-J06: –40.96%). The results show large year-on-year variations (in absolute terms, an average of 12%), which is representative of the strong shock to the health system. Conclusions: The disruption in the primary care model in Catalonia has led to an explosive increase in the number of non–face-to-face visits. There has been a reduction in the number of visits for diagnoses related to chronic pathologies, respiratory infections, obesity, and bodily injuries. Instead, visits for diagnoses related to socioeconomic and housing problems have increased, which emphasizes the importance of social determinants of health in the context of this pandemic. Big data analytics with routine care data yield findings that are consistent with those derived from intuition in everyday clinical practice and can help inform decision making by health planners in order to use the next few years to focus on the least-treated diseases during the COVID-19 pandemic.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher JMIR Publications
dc.relation.ispartof Journal of Medical Internet Research. 2021 Sep;23(9):e29622
dc.rights © Francesc Lopez Segui, Guillem Hernandez Guillamet, Héctor Pifarré Arolas, Francesc X Marin-Gomez, Anna Ruiz Comellas, Anna Maria Ramirez Morros, Cristina Adroher Mas, Josep Vidal-Alaball. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 14.09.2021. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.title Characterization and identification of variations in types of primary care visits before and during the COVID-19 Pandemic in Catalonia: big data analysis study
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.2196/29622
dc.subject.keyword COVID-19
dc.subject.keyword Primary care
dc.subject.keyword Diagnose variations
dc.subject.keyword Big data
dc.subject.keyword ICD10
dc.subject.keyword Health system
dc.subject.keyword Big data
dc.subject.keyword Primary care
dc.subject.keyword Healthcare system
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


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