Socicitation Rate in ICU during the COVID-19 Pandemic: Benford's Law in the Cariri Health Region
DOI:
https://doi.org/10.14295/idonline.v20i81.4396Keywords:
Benford's Law, COVID-19, ICU, Health regulation, Cancellations, Family refusal, Collective panic, Upcoding, Hospital financing, Health audit , Ceará, Cariri, PandemicAbstract
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.
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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.
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