Evidence map›Paper›PMID 37032325›Full record

ArticleBMC health services research2023

Transformative dimensions of resilience and brittleness during health systems' collapse: a case study in Brazil using the Functional Resonance Analysis Method.

Paulo Victor Rodrigues de Carvalho, Hugo Bellas, Jaqueline Viana, Paula de Castro Nunes, Rodrigo Arcuri, Valéria da Silva Fonseca, Ana Paula Morgado Carneiro, Alessandro Jatobá

Open access · goldAbstract read
In one paragraph

Article in BMC health services research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
11.5field-weighted citation impact, top 1% of its field
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

11 citing papers in PubMed, 32 citations in OpenAlex.

  1. Review
  2. Review
  3. Predictive estimations of health systems resilience using machine learning.BMC medical informatics and decision making · 2025
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. 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 at 3 institutions in 1 country.

Paulo Victor Rodrigues de CarvalhoInstituto de Engenharia Nuclear (IEN), Rio de Janeiro, Brazil.
Hugo BellasCentro de Estudos, Estratégicos Antônio Ivo de Carvalho (CEE), Fundação Oswaldo Cruz, Rio de Janeiro, Brazil.
Jaqueline VianaCentro de Estudos, Estratégicos Antônio Ivo de Carvalho (CEE), Fundação Oswaldo Cruz, Rio de Janeiro, Brazil.
Paula de Castro NunesCentro de Estudos, Estratégicos Antônio Ivo de Carvalho (CEE), Fundação Oswaldo Cruz, Rio de Janeiro, Brazil.
Rodrigo ArcuriPrograma de Pós-Graduação Em Engenharia de Produção (TPP), Universidade Federal Fluminense (UFF), Niterói, Brazil.
Valéria da Silva FonsecaCentro de Estudos, Estratégicos Antônio Ivo de Carvalho (CEE), Fundação Oswaldo Cruz, Rio de Janeiro, Brazil.
Ana Paula Morgado CarneiroEscola Nacional de Saúde Pública Sergio Arouca (ENSP), Fundação Oswaldo Cruz, Rio de Janeiro, Brazil.
Alessandro JatobáCentro de Estudos, Estratégicos Antônio Ivo de Carvalho (CEE), Fundação Oswaldo Cruz, Rio de Janeiro, Brazil. alessandro.jatoba@fiocruz.br.
Fundação Oswaldo Cruz · BRNuclear Engineering Institute · BRUniversidade Federal Fluminense · BR

Funding

CORE GRANT FOR VISION RESEARCHP30EY001186 · NEI · MEDICAL RESEARCH INSTITUTE OF SAN FRAN · PI KELLER, EDWARD L · 1985 to 1985
–
Conselho Nacional de Desenvolvimento Científico e Tecnológico 304770/2020Conselho Nacional de Desenvolvimento Científico e Tecnológico 307029/2021-2Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro 260003/001186/2020Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro E-26/201.252/2022Fundação Oswaldo Cruz 310815559697153
6 · The paper itself

Abstract

backgroundAs health systems struggle to tackle the spread of Covid-19, resilience becomes an especially relevant attribute and research topic. More than strength or preparedness, to perform resiliently to emerging shocks, health systems must develop specific abilities that aim to increase their potential to adapt to extraordinary situations while maintaining their regular functioning. Brazil has been one of the most affected countries during the pandemic. In January 2021, the Amazonas state's health system collapsed, especially in the city of Manaus, where acute Covid-19 patients died due to scarcity of medical supplies for respiratory therapy.

methodsThis paper explores the case of the health system's collapse in Manaus to uncover the elements that prevented the system from performing resiliently to the pandemic, by carrying out a grounded-based systems analysis of the performance of health authorities in Brazil using the Functional Resonance Analysis Method. The major source of information for this study was the reports from the congressional investigation carried out to unveil the Brazilian response to the pandemic.

resultsPoor cohesion between the different levels of government disrupted essential functions for managing the pandemic. Moreover, the political agenda interfered in the abilities of the system to monitor, respond, anticipate, and learn, essential aspects of resilient performance.

conclusionsThrough a systems analysis approach, this study describes the implicit strategy of "living with Covid-19", and an in-depth view of the measures that hampered the resilience of the Brazilian health system to the spread of Covid-19.

Indexed as

COVID-19BrazilDelivery of Health CareGovernment ProgramsHumansPandemicsHealth systems resilienceInfectious diseasesManagementNational health servicePublic health emergencies

Identifiers

PMID37032325
PMCPMC10084590
OpenAlexW4362736621

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.