Socicitation Rate in ICU during the COVID-19 Pandemic: Benford's Law in the Cariri Health Region

Authors

DOI:

https://doi.org/10.14295/idonline.v20i81.4396

Keywords:

Benford's Law, COVID-19, ICU, Health regulation, Cancellations, Family refusal, Collective panic, Upcoding, Hospital financing, Health audit , Ceará, Cariri, Pandemic

Abstract

Introduction: The COVID-19 pandemic placed unprecedented pressure on health systems worldwide, generating an objective health crisis alongside documented collective panic, media-amplified nocebo effects, and financial incentives potentially favoring diagnostic upcoding. On February 16, 2021, Brazil's Ministry of Health changed the ICU financing model from a fixed payment per enabled bed — regardless of occupancy — to payment per effectively occupied bed (R$1,600/bed-day). Objective: To analyze 2,602 ICU transfer requests (February–September 2021) in the Cariri health region, Ceará, Brazil, under two perspectives: Benford's Law application to detect registration inconsistencies; and analysis of 921 cancelled requests (35.4%) as markers of collective panic, family refusal, and demand distortion. Methods: Chi-square (χ²) and Mean Absolute Deviation (MAD) tests were applied to first digits of daily requests (n=203 observation-days), waiting times by outcome (n=2,602), and multilevel comparative analysis with COVID-19 epidemiological data. Results: 1,681 requests were fulfilled (64.6%) and 921 cancelled (35.4%). Cancelled requests showed significantly older patients (68.9 vs. 59.0 years; p<0.001); 48.8% of patients aged 80+ had requests cancelled. The cancellation peak was May 2021 (46.2%) — coinciding with the peak of social panic, not the epidemiological peak. Benford's Law on daily requests showed severe non-conformity (MAD=0.0399; χ²=85.76; p<0.0001); paradoxically, actual waiting times showed excellent conformity (MAD=0.0060). Conclusion: Findings suggest overlapping phenomena: collective panic generating disproportionate demand with high family refusal rates in elderly patients; upcoding favored by the shift to production-based reimbursement; and artificial fragmentation of requests generating the statistical signature identified by Benford's Law. Simultaneously, the pay-per-occupied-bed model — combined with beds fully released through a decentralized regulation system — may constitute one of the solutions to the historically low ICU occupancy rates in the public health system, by aligning financial incentives with actual resource utilization.

Downloads

Download data is not yet available.

Author Biographies

Maria Cléa de Sá Roriz Neves, Hospital Regional do Cariri, Juazeiro do Norte, Ceará, Brasil.

Graduada em Medicina pela Faculdade de Medicina de Juazeiro do Norte (FMJ). Residência Médica em Cirurgia Geral, João Pessoa - PB. Diretora Técnica do Hospital Regional do Cariri (Juazeiro do Norte - CE).

Hermes Melo Teixeira Batista, Universidade Estadual do Ceará (UECE). Quixeramobim, Ceará. Brasil.

Mestrado e Doutorado em Ciências da Saúde pela Faculdade de Medicina do ABC, São Paulo. Servidor público da Secretaria de Saúde do Estado do Ceará, Estando como Coordenador Médico do Complexo Regulador do Cariri. Médico anestesiologista do Hospital Regional do Cariri e Médico anestesiologista do HUJB-EBSERH. Possui Título de Especialista em Anestesiologia pela Sociedade Brasileira de Anestesiologia desde 2004. Membro afiliado da European Society of Anesthesiology. Superintendência Regional de Saúde do Cariri / SRSUL – Juazeiro do Norte/CE. . UECE/FUNECE – Quixeramobim/CE. 

Rondinelle Alves do Carmo, Universidade Estadual do Ceará (UECE). Quixeramobim, Ceará. Brasil.

Graduação em Farmácia pela Universidade Federal do Ceará (UFC). Pós Graduação em Hematologia Clínica pela UFC e Mestrado em Gestão de Tecnologia e Inovação em Saúde pelo Hospital Sírio Libanês. Especialista em Gestão em Saúde pela Escola Nacional de Saúde Pública e Gestão de Assistência Farmacêutica pela Universidade Federal de Santa Catarina. UECE/FUNECE – Quixeramobim/CE;

Tereza Cristina Mota de Souza Alves, Universidade Estadual do Ceará (UECE). Quixeramobim, Ceará. Brasil.

Cirurgiã-dentista. Mestra em Gestão em Saúde - Universidade Estadual do Ceará (UECE). Especialista em Saúde Pública (UECE). Especialista em Gestão de Redes de Atenção à Saúde - Fundação Oswaldo Cruz (FIOCRUZ). Especialista em Gestão de Políticas de Saúde informadas por evidências - Instituto Sírio Libanês. Atualmente, cursando Especialização em Excelência Operacional em Saúde - Hospital Israelita Albert Einstein. É superintendente da Região de Saúde do Cariri. UECE/FUNECE – Quixeramobim/CE.

References

1. CNN Brasil. Ministério da Saúde muda forma de repasse de recursos para UTI de Covid-19. São Paulo: CNN Brasil; 16 fev 2021. Disponível em: https://www.cnnbrasil.com.br

2. Secretaria da Saúde do Estado do Ceará (SESA). Boletim Epidemiológico COVID-19 nº 33. Fortaleza: SESA; outubro de 2021.

3. Kennedy AP, Yam SC. On the authenticity of COVID-19 case figures. PLoS One. 2020;15(12):e0243123. DOI: https://doi.org/10.1371/journal.pone.0243123

4. Balashov VS, Yan Y, Zhu X. Using the Newcomb-Benford law to study the association between a country's COVID-19 reporting accuracy and its development. Sci Rep. 2021;11(1):22914. DOI: https://doi.org/10.1038/s41598-021-02367-z

5. Durtschi C, Hillison W, Pacini C. The effective use of Benford's law to assist in detecting fraud in accounting data. J Forensic Account. 2004;5(1):17-34.

6. Newcomb S. Note on the frequency of use of the different digits in natural numbers. Am J Math. 1881;4(1):39-40. DOI: https://doi.org/10.2307/2369148

7. Benford F. The law of anomalous numbers. Proc Am Philos Soc. 1938;78(4):551-72.

8. Idrovo AJ, Manrique-Hernández EF, Fernández-Niño JA. Quality of epidemiological data collected during public health emergencies: the case of H1N1 influenza. Epidemiol Infect. 2011;139(10):1584-91. DOI: https://doi.org/10.1017/S095026881100015X

9. Nigrini MJ. Benford's Law: Applications for forensic accounting, auditing, and fraud detection. Hoboken: Wiley; 2012. DOI: https://doi.org/10.1002/9781119203094

10. Bagus P, Peña-Ramos JA, Sánchez-Bayón A. COVID-19 and the political economy of mass hysteria. Int J Environ Res Public Health. 2021;18(4):1376. DOI: https://doi.org/10.3390/ijerph18041376

11. Amanzio M, Howick J, Bartoli M, Cipriani GE, Kong J. How do nocebo phenomena provide a theoretical framework for the COVID-19 pandemic? Front Psychol. 2020;11:589884. DOI: https://doi.org/10.3389/fpsyg.2020.589884

12. Lima CKT, Carvalho PMM, Lima IAAS, et al. The emotional impact of coronavirus 2019-nCoV (new coronavirus disease). Psychiatry Res. 2020;287:112915. DOI: https://doi.org/10.1016/j.psychres.2020.112915

13. Vindegaard N, Benros ME. COVID-19 pandemic and mental health consequences: systematic review of the current evidence. Brain Behav Immun. 2020;89:531-42. DOI: https://doi.org/10.1016/j.bbi.2020.05.048

14. Hsiao WC. Fraud, waste and abuse in healthcare claims. Testimony before CA DMHC; 2022.

15. Carlin CS, Dowd B, Zhu JM. Upcoding linked to up to two-thirds of growth in highest-intensity hospital discharges in 5 states, 2011–19. Health Aff. 2024;43(6). doi:10.1377/hlthaff.2024.00596. DOI: https://doi.org/10.1377/hlthaff.2024.00596

16. Wynia MK, Cummins DS, VanGeest JB, Wilson IB. Physician manipulation of reimbursement rules for patients. JAMA. 2000;283(14):1858-65. DOI: https://doi.org/10.1001/jama.283.14.1858

17. Conselho Nacional de Secretários de Saúde (CONASS). Painel Conass COVID-19. Brasília: CONASS; 2021.

18. World Health Organization. WHO COVID-19 Dashboard. Geneva: WHO; 2021.

19. Brasil. Ministério da Saúde. Portaria GM/MS nº 1.341, de 23 de junho de 2021.

20. Joffe AR, Elliott A. Long COVID as a functional somatic symptom disorder caused by abnormally precise prior expectations during Bayesian perceptual processing. SAGE Open Med. 2023;11:20503121231194400. DOI: https://doi.org/10.1177/20503121231194400

21. Moreau VH. Inconsistencies in countries COVID-19 data revealed by Benford's law. Big Data Soc. 2021;8(2):1-13. DOI: https://doi.org/10.3233/MAS-210517

22. Silva L, Figueiredo Filho D. Using Benford's law to assess the quality of COVID-19 register data in Brazil. J Public Health. 2021;43(1):107-110. DOI: https://doi.org/10.1093/pubmed/fdaa193

23. Office of Inspector General, U.S. Dept. of Health and Human Services. Trend toward more expensive hospital stays in Medicare. Washington, DC: OIG/HHS; 2021.

24. Cavalcante JR, Abreu ASS, Pantoja LS, et al. COVID-19 no Brasil: tendências, desafios e perspectivas para a saúde pública. Rev Panam Salud Publica. 2022;46:e10.

25. CONASEMS. CIT: pactuada portaria que muda repasse de recursos para leitos de UTI Covid-19. Brasília: CONASEMS; 26 fev 2021.

26. Kitsios F. Applying Benford's law to COVID-19 data: the case of the European Union. J Public Health. 2022;44(2):e221-e227. DOI: https://doi.org/10.1093/pubmed/fdac005

27. Brasil. Ministério da Saúde. Portaria GM/MS nº 4.226, de 31 de dezembro de 2021.

Published

2026-05-31

How to Cite

Neves, M. C. de S. R., Batista, H. M. T., Carmo, R. A. do, & Alves, T. C. M. de S. (2026). Socicitation Rate in ICU during the COVID-19 Pandemic: Benford’s Law in the Cariri Health Region. ID on Line. Revista De Psicologia, 20(81), 49–68. https://doi.org/10.14295/idonline.v20i81.4396

Issue

Section

Artigos