Determinants of COVID-19 Mortality and Temporal Trends in the Health Regions of the State of São Paulo, Brazil Article Swipe
Tatiana Pestana Barbosa
,
Thaís Zamboni Berra
,
Antônio Carlos Vieira Ramos
,
Yan Mathias Alves
,
Reginaldo Bazon Vaz Tavares
,
Fernando Spanó Junqueira de Paiva
,
Jonas Bodini Alonso
,
Titilade Kehinde Ayandeyi Teibo
,
Juliana Soares Tenório de Araújo
,
Ariela Fehr Tártaro
,
Ricardo Alexandre Arcêncio
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/ijerph22050772
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/ijerph22050772
Background: This study investigated the determinants of COVID-19 mortality and its temporal trends within São Paulo state’s Departamentos Regionais de Saúde (DRS) (health regions) to inform the development of targeted public health interventions. Methods: Utilizing an ecological study design, we analyzed confirmed COVID-19 cases and deaths (February 2020–December 2021) obtained from the COVID Panel, incorporating relevant social and health indicators. The Generalized Additive Model for Location, Scale, and Shape (GAMLSS) was used to identify key determinants, and temporal trends in mortality and vaccination rates were analyzed across each DRS. Results: The average mortality rate was 15.1 deaths per 100,000 inhabitants (median 7.00). Higher chronic disease mortality was associated with an increase in COVID-19 mortality. Moreover, an increase in the percentage of adults led to a decrease in deaths from COVID-19. Conclusions: COVID-19 mortality in São Paulo is shaped by a complex interplay of 12 behavioral, economic, demographic, and environmental factors. Region-specific public health policies should consider these factors, along with geographic, socioeconomic, and budgetary contexts, to effectively address health disparities across the state’s DRS.
Related Topics
Concepts
Coronavirus disease 2019 (COVID-19)
2019-20 coronavirus outbreak
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
Geography
Betacoronavirus
State (computer science)
Demography
Medicine
Environmental health
Virology
Sociology
Outbreak
Computer science
Disease
Infectious disease (medical specialty)
Pathology
Algorithm
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/ijerph22050772
- https://www.mdpi.com/1660-4601/22/5/772/pdf?version=1747193715
- OA Status
- gold
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410346486
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4410346486Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/ijerph22050772Digital Object Identifier
- Title
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Determinants of COVID-19 Mortality and Temporal Trends in the Health Regions of the State of São Paulo, BrazilWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-05-13Full publication date if available
- Authors
-
Tatiana Pestana Barbosa, Thaís Zamboni Berra, Antônio Carlos Vieira Ramos, Yan Mathias Alves, Reginaldo Bazon Vaz Tavares, Fernando Spanó Junqueira de Paiva, Jonas Bodini Alonso, Titilade Kehinde Ayandeyi Teibo, Juliana Soares Tenório de Araújo, Ariela Fehr Tártaro, Ricardo Alexandre ArcêncioList of authors in order
- Landing page
-
https://doi.org/10.3390/ijerph22050772Publisher landing page
- PDF URL
-
https://www.mdpi.com/1660-4601/22/5/772/pdf?version=1747193715Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/1660-4601/22/5/772/pdf?version=1747193715Direct OA link when available
- Concepts
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Coronavirus disease 2019 (COVID-19), 2019-20 coronavirus outbreak, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Geography, Betacoronavirus, State (computer science), Demography, Medicine, Environmental health, Virology, Sociology, Outbreak, Computer science, Disease, Infectious disease (medical specialty), Pathology, AlgorithmTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
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25Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.chronic | 103 |
| abstract_inverted_index.complex | 140 |
| abstract_inverted_index.design, | 38 |
| abstract_inverted_index.disease | 104 |
| abstract_inverted_index.(GAMLSS) | 69 |
| abstract_inverted_index.Additive | 62 |
| abstract_inverted_index.COVID-19 | 7, 42, 112, 131 |
| abstract_inverted_index.Methods: | 33 |
| abstract_inverted_index.Results: | 89 |
| abstract_inverted_index.analyzed | 40, 85 |
| abstract_inverted_index.consider | 155 |
| abstract_inverted_index.decrease | 125 |
| abstract_inverted_index.factors, | 157 |
| abstract_inverted_index.factors. | 149 |
| abstract_inverted_index.identify | 73 |
| abstract_inverted_index.increase | 110, 116 |
| abstract_inverted_index.obtained | 49 |
| abstract_inverted_index.policies | 153 |
| abstract_inverted_index.regions) | 23 |
| abstract_inverted_index.relevant | 55 |
| abstract_inverted_index.targeted | 29 |
| abstract_inverted_index.temporal | 11, 77 |
| abstract_inverted_index.(February | 46 |
| abstract_inverted_index.COVID-19. | 129 |
| abstract_inverted_index.Location, | 65 |
| abstract_inverted_index.Moreover, | 114 |
| abstract_inverted_index.Regionais | 18 |
| abstract_inverted_index.Utilizing | 34 |
| abstract_inverted_index.budgetary | 163 |
| abstract_inverted_index.confirmed | 41 |
| abstract_inverted_index.contexts, | 164 |
| abstract_inverted_index.economic, | 145 |
| abstract_inverted_index.interplay | 141 |
| abstract_inverted_index.mortality | 8, 80, 92, 105, 132 |
| abstract_inverted_index.state’s | 16, 172 |
| abstract_inverted_index.associated | 107 |
| abstract_inverted_index.ecological | 36 |
| abstract_inverted_index.mortality. | 113 |
| abstract_inverted_index.percentage | 119 |
| abstract_inverted_index.Background: | 0 |
| abstract_inverted_index.Generalized | 61 |
| abstract_inverted_index.behavioral, | 144 |
| abstract_inverted_index.development | 27 |
| abstract_inverted_index.disparities | 169 |
| abstract_inverted_index.effectively | 166 |
| abstract_inverted_index.geographic, | 160 |
| abstract_inverted_index.indicators. | 59 |
| abstract_inverted_index.inhabitants | 99 |
| abstract_inverted_index.vaccination | 82 |
| abstract_inverted_index.Conclusions: | 130 |
| abstract_inverted_index.demographic, | 146 |
| abstract_inverted_index.determinants | 5 |
| abstract_inverted_index.investigated | 3 |
| abstract_inverted_index.Departamentos | 17 |
| abstract_inverted_index.determinants, | 75 |
| abstract_inverted_index.environmental | 148 |
| abstract_inverted_index.incorporating | 54 |
| abstract_inverted_index.interventions. | 32 |
| abstract_inverted_index.socioeconomic, | 161 |
| abstract_inverted_index.2020–December | 47 |
| abstract_inverted_index.Region-specific | 150 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 11 |
| citation_normalized_percentile.value | 0.25423116 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |