Artigos

Epidemiological surveillance is crucial for planning and informing decisions related to preparedness and response to health emergencies1. Recent episodes of emerging and re-emerging zoonotic outbreaks highlight the importance of developing efficient early warning systems (EWS)2. However, the reality of frequently ill-equipped and overburdened health services hinders the development of such EWS and the refinement of epidemiological surveillance capabilities3,4.
Investing in automated processes for disease surveillance represents an achievable solution for establishing an EWS, particularly in low-resource settings. The growing availability of electronic health records (EHR) and administrative health databases provide valuable epidemiological information5,6. In this context, the Rio de Janeiro Health Department has recently established the Epidemiological Intelligence Center (CIE), which incorporates technological resources aligned to epidemic intelligence to efficiently use data for enhancing epidemiological surveillance7.
The epidemiological surveillance system in Brazil was established in the early 1970s and is regarded as a comprehensive and well-organized system in which data is compiled and secured in a stable and structured database8. It relies on the active notification of a pre-specified list of diseases and conditions, which imposes timeliness and cost-efficiency limitations. Brazil's Primary Health Care (PHC) system provides comprehensive health assistance with great granularity as part of the Unified Health System (SUS), the largest public and universal health system in the world. For funding and planning purposes, all PHC encounters are regularly registered in a database centrally managed by the Ministry of Health (MoH). Integrating the PHC database into epidemiological surveillance activities ensures timeliness to the system, as no duplication of registry is required. This cost-efficient approach might allow the development of an EWS even in low resources-settings4,9.

In this paper, we explore the use of the PHC database in anticipating the detection of acute respiratory infection (ARI) outbreaks compared to the traditional epidemiological surveillance in the city of Rio de Janeiro. This system was developed as part of the ÆSOP initiative, a previously described EWS10.
Methods
We compared the timeliness of detection of ARI outbreaks when using the PHC database to the traditional epidemiological surveillance system (SIVEP-Gripe) in the city of Rio de Janeiro.
Data sources
We extracted data for PHC encounters from the National Information System on PHC (SISAB). The SISAB database harbors data on all publicly funded PHC encounters, coded by either the International Classification of Diseases (ICD-10) or the International Classification of Primary Care (ICPC-2). We used weekly counts of PHC encounters due to influenza-like illness (ILI) in the city of Rio de Janeiro. We included a list of 50 diagnostic codes to identify encounters possibly related to ILI (The database description and the scripts are available at https://github.com/cidacslab/AESOP-Data-Documentation/tree/main/DataPipeline).
The traditional epidemiological surveillance system for acute respiratory infections in Brazil is based on the mandatory notification of all hospitalized or deceased cases of Severe Acute Respiratory Syndrome (SARS) to the System on Notifiable Diseases (SIVEP-Gripe). We extracted weekly counts of all SARS cases in Rio de Janeiro from the openly available, non-identified SIVEP-Gripe database provided by the Ministry of Health (MoH). A SARS case is defined as any patient presenting with at least two of the following influenza-like illness symptoms: fever; cold shiver; sore throat; headache; cough; runny nose; loss of smell; loss of taste, plus any of the following signs of case severity: dyspnea or shortness of breath; chest pressure or persistent chest pain; oxygen saturation below 95% in ambient air; cyanosed lips or face. Additionally, all death cases due to SARS are reported, regardless of hospitalization.
Analysis
To evaluate whether modeling/monitoring the PHC database anticipates the detection of ARI outbreaks by the traditional epidemiological surveillance system, we compared the proportion of PHC encounters due to ILI and the total number of SARS notifications, per week in two distinct periods with well-documented ARI outbreaks: the first period ranges from October 6, 2019 to May 2, 2020 (epidemiological weeks 41-2019 to 18-2020), which encompasses the first wave of Covid-19 cases in Brazil,[11] and the second period ranges from October 3, 2021 to April 30, 2022 (epidemiological weeks 40-2021 to 17-2022), which encompasses a massive H3N2 outbreak followed by the first Omicron-variant Covid-19 wave in Rio de Janeiro12.
Additionally, we tested whether an EWS model applied to the PHC time series would trigger timely warnings in relation to the first pandemic wave in 2020. We used data from January 1, 2017, to December 31, 2019, to establish a baseline of ILI-related PHC encounters by fitting a general linear model with negative binomial distribution, with a term for year to control for annual trends, and harmonic terms to control for seasonality. Additionally, we added an offset term with the total count of PHC encounters at the week to account for changes in the health-seeking behavior4. We used the baseline to predict the expected number of ILI-related PHC encounters from January 1 to May 2, 2020. The predicted counts with the corresponding 95% confidence interval defined the threshold for the EWS triggering a warning. The sustained high fluctuations of PHC encounters due to the Covid-19 pandemic from 2020 to 2022, along with the misleadingly low numbers due to lockdowns, hindered us from using a similar methodology for evaluating the PHC-based EWS capabilities for triggering timely and accurate warnings in the second study period.
All analyses were conducted using R software version 4.3.1 and the packages surveillance13.
Research ethics
The study is based on secondary, aggregated, non-identified data, and was approved by the Ethical Review Board of the Oswaldo Cruz Foundation – Instituto Gonçalo Moniz, CAAE 61444122.0.0000.0040.
Results
We identified 659,230 ILI-related PHC encounters from October 6, 2019, to May 2, 2020, and 702,886 from October 3, 2021, to April 30, 2022, which corresponds to a median of 17,804 (Interquartile Range (IQR): 12,910-27,250) and 13,908 (IQR: 9,637 -22,672) encounters per week in the first and second study periods, respectively. The proportion of ILI-related PHC encounters ranged from 6.5% to 25.0% per week in the first period, and from 4.6% to 47.5%, in the second period. There were 7,581 SARS notifications in the first period, and 93% of these were registered between the 13 and 17 epidemiological weeks (March 22 to April 25). In the second period, from October 2021 to January 2022, there were 13,660 SARS notifications (median: 351 IQR: 278-516 per week).
Figure 1 presents the weekly time series of the proportion of ILI-related PHC encounters and SARS notifications in Rio de Janeiro across both study periods. In the first period (Figure 1A), representing the initial wave of the Covid-19 pandemic, both curves follow a similar pattern until early March 2020, when the proportion of ILI-related PHC encounters departs from the baseline, occurring approximately two weeks earlier than the corresponding rise in SARS notifications.
Figure 1B exhibits two episodes of upward departures from the baseline level: a first peak in mid-November 2021, followed by a second peak in early January 2022. In both episodes, the rise in the proportion of ILI-related PHC encounters anticipates the increase in SARS notifications by one week.
Figure 2 shows the EWS based on PHC encounters. The model identified warnings of an ILI-related outbreak since the last week of February, which anticipates the identification of a rising slope in the SARS notification time series.
Discussion
Our findings demonstrate that leveraging PHC administrative data provides timely and valuable insights for epidemiological surveillance. Systematic monitoring of ILI-related PHC encounters ARI outbreaks anticipates in one to two weeks the currently in place epidemiological surveillance system. Additionally, we show that using a PHC-based EWS provided timely warnings anticipating the rise of the first Covid-19 wave in Rio de Janeiro. The unusually high and low case numbers during the Covid-19 pandemic precluded us from evaluating the EWS capabilities for the period from October 3, 2021, to April 30, 2022 (epidemiological weeks 40-2021 to 17-2022).
Similar to previous studies, we found that using data-based syndromic surveillance offers useful information for early outbreak detection4,9,14. A comprehensive review including 68 scientific publications found that 42 of those concluded that data-based EWS successfully functioned independently as surveillance systems, while 16 reported that EWS contributed to the existing surveillance systems14. A report on the establishment of a syndromic surveillance system in Liberia during the Covid-19 pandemic concluded that the existing infrastructure of health data collection can be leveraged to monitor a variety of diseases with pandemic potential4.
When discussing the establishment of innovative EWS, it is important to address the needs of low and middle-income countries (LMIC). In this context, relying on an automated, administrative data-based EWS, such as that used in this study10, offers a cost-efficient approach to overcome infrastructure limitations4,9. Moreover, incorporating PHC data into EWS promotes significant advantages for surveillance, such as detecting unusual symptom patterns before a surge in severe cases. This enables the timely deployment of response measures, potentially averting health system overloads14. Also, the PHC system in Brazil offers great granularity, reaching underserved populations, even where more advanced healthcare facilities are lacking4.
Our study presents potential limitations. The EWS presented here relies on the continuous availability of data, therefore the existence of areas or periods with substantial data gaps or fluctuations in data quality over time could impact the system’s performance4,15. Additionally, health-seeking behavior and data collection practices may suffer significant changes due to unforeseen external factors, such as the lockdowns enacted during Covid-19, or natural disasters and holiday periods, all of which will affect the stability of the baseline, thus hindering an accurate estimation of the EWS.
Conclusion
Primary health care data-based early warning system anticipates outbreak detection when compared to traditional epidemiological surveillance. These findings support the benefits of leveraging administrative health data to enhance surveillance in low-resource settings through a cost-efficient approach. This, in turns, allows a more rapid response and control measures, ultimately enhancing the resilience of health systems and enhancing preparedness for future threats.
References
1. World Health Organization. COVID?19 Strategic Preparedness and Response Plan. Operational Planning Guidelines to Support Country Preparedness and Response. 22 May 2020. Available from https://www.who.int/publications/i/item/draft-operational-planning-guidance-for-un-country-teams [Accessed on October 31 2024]
2. Baker RE, Mahmud AS, Miller IF, Rajeev M, Rasambainarivo F, Rice BL, Takahashi S, Tatem AJ, Wagner CE, Wang LF, Wesolowski A, Metcalf CJE. Infectious disease in an era of global change. Nat Rev Microbiol 2022; 20:193–205.
3. Richards CL, Iademarco MF, Atkinson D, Pinner RW, Yoon P, Mac Kenzie WR, Lee B, Qualters JR, Frieden TR. Advances in Public Health Surveillance and Information Dissemination at the Centers for Disease Control and Prevention. Public Health Rep 2017; 132(4):403-410.
4. Isabel R Fulcher, Emma Jean Boley, Anuraag Gopaluni, Prince F Varney, Dale A Barnhart, Nichole Kulikowski, Jean-Claude Mugunga, Megan Murray, Michael R Law, Bethany Hedt-Gauthier, the Cross-site COVID-19 Syndromic Surveillance Working Group. Syndromic surveillance using monthly aggregate health systems information data: methods with application to COVID-19 in Liberia. Int Journal of Epidemiol 2021; 50(4):1091–1102.
5. Lewis AE, Weiskopf N, Abrams ZB, Foraker R, Lai AM, Payne PRO, Gupta A. Electronic health record data quality assessment and tools: a systematic review. J Am Med Inform Assoc 2023; 30:1730–40.
6. European Centre for Disease Prevention and Control. Data quality monitoring and surveillance system evaluation – A handbook of methods and applications, European Centre for Disease Prevention and Control. 2014. Available from https://data.europa.eu/doi/10.2900/35329 [Assessed on October 31 2024]
7. Cruz DMO, Ferreira CD, Carvalho LF, Saraceni V, Durovni B, Cruz OG, Garcia MHO, Aguilar GMO. Inteligência epidemiológica, investimento em tecnologias da informação e as novas perspectivas para o uso de dados na vigilância em saúde. Cad. Saúde Pública 2024; 40(8):e00160523.
8. Teixeira MG, Penna GO, Risi JB, Penna ML, Alvim MF, Moraes JC, Luna E. Seleção das doenças de notificação compulsória: critérios e recomendações para as três esferas de governo. Inf. Epidemiol. Sus 1998. 7(1):7-28.
9. Bagaria J, Jansen T, Marques DF, Hooiveld M, McMenamin J, de Lusignan S, Vilcu AM, Meijer A, Rodrigues AP, Brytting M, Mazagatos C, Cogdale J, van der Werf S, Dijkstra F, Guiomar R, Enkirch T, Valenciano M; I-MOVE-COVID-19 study team. Rapidly adapting primary care sentinel surveillance across seven countries in Europe for COVID-19 in the first half of 2020: strengths, challenges, and lessons learned. Eurosurveillance 2022; 27(26):2100864.
10. Ramos PIP, Marcilio I, Bento AI, Penna GO, Oliveira JF, Khouri R, Andrade RFS, Carreiro RP, Oliveira VA, Galvão LAC, Landau L, Barreto ML, van der Horst K, Barral-Neto M, AESOP Collaborating Teams. Combining Digital and Molecular Approaches Using Health and Alternate Data Sources in a Next-Generation Surveillance System for Anticipating Outbreaks of Pandemic Potential. JMIR Public Health Surveill 2024; 10:e47673.
11. de Souza WM, Buss LF, Candido DDS, Carrera JP, Li S, Zarebski AE, Pereira RHM, Prete CA Jr, de Souza-Santos AA, Parag KV, Belotti MCTD, Vincenti-Gonzalez MF, Messina J, da Silva Sales FC, Andrade PDS, Nascimento VH, Ghilardi F, Abade L, Gutierrez B, Kraemer MUG, Braga CKV, Aguiar RS, Alexander N, Mayaud P, Brady OJ, Marcilio I, Gouveia N, Li G, Tami A, de Oliveira SB, Porto VBG, Ganem F, de Almeida WAF, Fantinato FFST, Macário EM, de Oliveira WK, Nogueira ML, Pybus OG, Wu CH, Croda J, Sabino EC, Faria NR. Epidemiological and clinical characteristics of the COVID-19 epidemic in Brazil. Nat Hum Behav 2020; 4(8):856-865.
12. Nott R, Fuller TL, Brasil P, Nielsen-Saines K. Out-of-Season Influenza during a COVID-19 Void in the State of Rio de Janeiro, Brazil: Temperature Matters. Vaccines 2022; 10(5):821.
13. Meyer S, Held L, Höhle M. Spatio-Temporal Analysis of Epidemic Phenomena Using the R Package surveillance. Journal of Statistical Software 2017; 77(11):1–55.
14. Meckawy R, Stuckler D, Mehta A, Al-Ahdal T, Doebbeling BN. Effectiveness of early warning systems in the detection of infectious diseases outbreaks: a systematic review. BMC Public Health 2022; 22(1):2216.
15. Florentino P, Bertoldo-Junior J, Barbosa GCG, Cerqueira-Silva T, Oliveira VA, Garcia MHO, Penna GO, Boaventura V, Ramos PIP, Barral-Neto M, Marcilio I. Impact of Primary Health Care data quality on their use for infectious disease surveillance. JMIR Preprints 30/09/2024:67050. Doi 10.2196/preprints.67050.