Revista da Sociedade Brasileira de Medicina Tropical Journal of the Brazilian Society of Tropical Medicine Vol.:53:(e20200528): 2020 https://doi.org/10.1590/0037-8682-0528-2020 Short Communication Imputation method to reduce undetected severe acute respiratory infection cases during the coronavirus disease outbreak in Brazil Silvano Barbosa de Oliveira[1], Fabiana Ganem[2], Wildo Navegantes de Araújo[1], Jordi Casabona[2],[3],[4], Mauro Niskier Sanchez[1] and Julio Croda[5],[6],[7] [1]. Universidade de Brasília, Brasília, DF, Brasil. [2]. Universidad Autónoma de Barcelona, Spain. [3]. Centre d'Estudis Epidemiològics sobre les ITS i SIDA de Catalunya (CEEISCAT), Institut Català d’Oncologia, Campus de Can Ruti, Catalunya, Spain. [4]. Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain. [5]. Universidade Federal do Mato Grosso do Sul, Faculdade de Medicina, Campo Grande, MS, Brasil. [6]. Yale University School of Public Health, Department of Epidemiology of Microbial Diseases, New Haven, United States of America. [7]. Fundação Oswaldo Cruz, Campo Grande, MS, Brasil. Abstract Introduction: The coronavirus disease (COVD-19) outbreak has overburdened the surveillance of severe acute respiratory infections (SARIs), including the laboratory network. This study was aimed at correcting the absence of laboratory results of reported SARI deaths. Methods: The imputation method was applied for SARI deaths without laboratory information using clinico-epidemiological characteristics. Results: Of 84,449 SARI deaths, 51% were confirmed with COVID-19 while 3% with other viral respiratory diseases. After the imputation method, 95% of deaths were reclassified as COVID-19 while 5% as other viral respiratory diseases. Conclusions: The imputation method was a useful and robust solution (sensitivity and positive predictive value of 98%) for missing values through clinical & epidemiological characteristics. Keywords: COVID-19. SARI. Laboratory test. Signs and symptoms. Imputation method. The coronavirus disease (COVID-19) pandemic had caused Brazil has a surveillance system working at three levels (federal, more than 10 million cases and 500,000 deaths worldwide by state, and municipality) of government installed in public and June 20201. The severe acute respiratory syndrome coronavirus 2 private health units for severe acute respiratory illness (SARI), and (SARS-CoV-2) virus has been spreading fast globally, causing many notification of SARI has been mandatory since 2009. The reported severe cases and deaths. This virus has a higher basic reproduction cases included patients hospitalized because of SARI at any health number (R0) and case fatality rate (CFR) compared to influenza service and mild respiratory cases reported by sentinel networks (R0: 2.5-3.3 and CFR: 0.4%-2.9% versus R0: 1.2-2.3 e CFR: using an online database (Influenza Epidemiological Surveillance 0.15%-0.25%, respectively)2-5. In Brazil, the first confirmed case Information System in Brazil - SIVEP-GRIPE). The discovery was reported on February 25 in Sao Paulo City, and recently at of SARS-CoV-2 in China and suspected cases in Brazil were least one case has been reported in all Brazilian states and almost reported using the REDCap platform, remaining until the country all municipalities (96%)6. reached 1,000 confirmed cases; subsequently, a new system was developed (e-SUS) and used to report mild respiratory cases, and the SARI remained reported on SIVEP. Because of the continuity and consistency, SIVEP has been maintained as an official system Corresponding author: Dr. Julio Croda. to report and monitor the severe cases of COVID-19, including the e-mail: juliocroda@gmail.com deaths from COVID-19 independent of hospitalization.  https://orcid.org/0000-0002-6665-6825 Received 6 August 2020 Although SIVEP is an online platform, inconsistencies in Accepted 17 August 2020 monitoring and case closure opportunities persist. In addition, the www.scielo.br/rsbmt I www.rsbmt.org.br 1/6 Oliveira SB et al. - Undetected COVID-19: imputation method to correct Ministry of Health has reported a high percentage of deaths from validation. Furthermore, the Kappa test was performed to measure SARI without a diagnosis, called “non-specified SARI,” or alerted the concordance between the imputed and observed values. The health authorities to a possible activity of other respiratory viruses significance level was considered as 5% for all analyses. All data in the Brazilian population. Therefore, this study was aimed at were processed using R software, and the data imputation method investigating the clinico-epidemiological characteristics of deaths was performed using the R package Hmisc. from SARI reported in the Influenza Epidemiological Surveillance In Brazil, from January 1 to July 28, 2020, 84,449 deaths Information System in Brazil (SIVEP-Gripe) to correct the absence from SARI were reported. Furthermore, 45,321 (54%) cases were of robust laboratory results for COVID-19. confirmed using RT-PCR for some respiratory viruses, of which We used deaths from SARI reported in the Influenza 42,981 (95%) were confirmed as COVID-19. These proportions Epidemiological Surveillance Information System in Brazil of confirmed COVID-19 cases were different across Brazilian (SIVEP-Gripe) during the COVID-19 outbreak from January 1 states, with the lowest in the Mato Grosso do Sul (19%) state and to June 28, 2020. The death registers were selected using the case the highest in Acre (91%) (Table 1). evolution variable. Considering the overall deaths reported in Brazil, the number All reported cases were classified as follows: (i) COVID-19, of cases undetected for respiratory viruses, indeterminate in RT- with laboratory confirmation through the reverse-transcriptase PCR, not tested, in processing, and without information were polymerase chain reaction (RT-PCR) for SARS-CoV-2; (ii) 21,770 (26%), 553 (1%), 2,829 (3%), 6,404 (8%), and 7,571 (9%), undetected, with laboratory confirmation through RT-PCR for respectively; all of these cases were considered as “missing value,” other viruses; and (iii) missing value, with no confirmation through totaling 39,128 (46%) registers. Important variations were also RT-PCR and an indeterminate result in the processing test. This observed across Brazilian states, highlighting the following five was considered our response variable to the regression model and states with the highest proportions of missing value: Minas Gerais subsequently imputed. (76%), Mato Grosso do Sul (75%), Rio Grande do Sul (73%), Paraná (73%), and Santa Catarina (69%) (Table 1). Before completing the data imputation method, we performed the logistic regression analysis to identify the variables related In the univariate logistic regression model, the age group was to the response. First, we applied the univariate model using the associated with COVID-19 and positively correlated with the odds following predictors: signs and symptoms (fever, cough, throat pain, ratio. The signs and symptoms that showed significant associations dyspnea, respiratory distress, O2 saturation < 95%, diarrhea, and were respiratory distress, fever, cough, throat pain, and dyspnea, vomiting), comorbidities (chronic cardiovascular disease, chronic all indicating inverse odds to be detected for COVID-19. Only four hematological disease, chronic liver disease, asthma, diabetes underlying health conditions presented with significant associations mellitus, chronic neurological disease, other chronic pneumopathy, with COVID-19: chronic cardiovascular disease, diabetes mellitus, immunodeficiency/immunodepression, chronic kidney disease, and chronic kidney disease, and obesity. Individuals that needed obesity), hospitalization (yes/no), intensive care unit stay (yes/no), intensive care were more likely to be detected with COVID-19. ventilation support (invasive, non-invasive, and none), chest X-ray, In the multiple logistic regression, only five variables remained in sex, and age group (<10 years, 10 to 39 years, 40 to 59 years, 60 to the final model: age group, with age of 40 years or above having 69 years, and 70 years or more). The multiple logistic regression approximately eight times more odds to be detected with COVID-19 model was obtained from variables with a p-value less than 10% in compared to age below 10 years; 33% chance for individuals with the univariate regression model, and stepwise method was applied respiratory distress; 10% to 20% more chance for individuals with using the Akaike information criterion, Bayesian information chronic cardiovascular disease and diabetes mellitus, respectively; criterion, and deviance. Subsequently, cases classified as “missing and increasing chance in individuals who require ventilation support value” were subjected to a data imputation method using as (32%: invasive; 38%: non-invasive) (Table 2). predictors the variables selected in the multiple logistic regression. Using the variables defined by the multiple logistic regression, We applied the multiple imputation method to obtain complete the imputation method was applied for all data classified as “missing information for the “missing value” cases for the classification value.” Of the total registers classified as “missing value,” the data of SARI deaths. Imputation was performed using the additive imputation method could classify 37,980 cases (97%). Furthermore, regression method, which comprised procedures of a flexible 1,994 (2%) cases were detected with other viral respiratory diseases additive model (nonparametric regression method) fitted on (undetected for COVID-19), and 35,986 (43%) cases were confirmed samples taken with replacements from original data and missing with COVID-19. Therefore, of the total deaths from SARI that occurred values (dependent variable) and predicted using non-missing values in Brazil from January 1 to July 28, 2020, 95% were reclassified as (independent variable obtained by multiple logistic regression)7-9. COVID-19 while 5% as some other viral respiratory disease (not COVID-19). Hence, all Brazilian states and federal district have at We selected a random sample of SARI deaths that had resulted least 90% of deaths from SARI classified as COVID-19. Only the from COVID-19 and other viral respiratory diseases to validate Maranhão (15%), Mato Grosso (14%), and Mato Grosso do Sul (11%) the data imputation method. It generated randomly missing states presented with more than 10% of SARI deaths classified as other values for 30% of cases, and we applied the imputation method. viral respiratory diseases by the imputation data method (Table 3). Subsequently, the imputed values were compared with the observed values. The sensitivity, specificity, positive predictive value, To validate the data imputation method, simulation showed and negative predictive value were calculated to quantify this high sensitivity (99%) and positive predictive value (99%) and 2/6 Rev Soc Bras Med Trop | on line | Vol.:53:(e20200528): 2020 TABLE 1: Death from severe acute respiratory illness classified by laboratory results reported by Brazilian states (Brazil, January to July, 2020) Total COVID-19 Other viral respiratory diseases Missing* UF nº nº % nº % nº % Brazil 84449 42981 51 2340 3 39128 46 AC 203 184 91 4 2 15 7 AL 1234 618 50 55 4 561 45 AM 4144 1829 44 80 2 2235 54 AP 191 94 49 0 0 97 51 BA 2824 1472 52 176 6 1176 42 CE 7579 3683 49 302 4 3594 47 DF 902 486 54 17 2 399 44 ES 1321 880 67 70 5 371 28 GO 940 401 43 34 4 505 54 MA 2530 944 37 308 12 1278 51 MG 3133 688 22 75 2 2370 76 MS 345 66 19 19 6 260 75 MT 311 99 32 29 9 183 59 PA 5825 3124 54 199 3 2502 43 PB 1470 557 38 93 6 820 56 PE 5243 3834 73 65 1 1344 26 PI 665 346 52 33 5 286 43 PR 2169 530 24 59 3 1580 73 RJ 13019 7514 58 225 2 5280 41 RN 1005 509 51 36 4 460 46 RO 247 135 55 13 5 99 40 RR 337 134 40 3 1 200 59 RS 2009 530 26 6 0 1473 73 SC 811 243 30 12 1 556 69 SE 302 168 56 18 6 116 38 SP 25411 13754 54 406 2 11251 44 TO 265 151 57 3 1 111 42 Source: SIVEP-GRIPE accessed in 06/20/2020. Notes: *included undetected results, indeterminate, not tested, in processing, ignored, and missing. TABLE 2: Demographic information and logistic regression for death from severe acute respiratory illness confirmed to be coronavirus disease in the reverse- transcriptase polymerase chain reaction test (Brazil, January to July, 2020). Total COVID-19 Univariate Multivariate Variables nº nº % OR (95% CI) OR (95% CI) Total 45321 42981 94.8 - - Sex Male 26378 24993 94.7 1.00 - Female 18929 17977 95.0 1.05 (0.96-1.14) - Missing 14 11 78.6 - - Continue... 3/6 Oliveira SB et al. - Undetected COVID-19: imputation method to correct TABLE 2: Continuation. Age group <10 years 224 161 71.9 1.00 1.00 10 to 39 years 2318 2141 92.4 4.73 (3.41-6.58) 5.72 (3.08-10.39) 40 to 59 years 9708 9208 94.8 7.21 (5.31-9.78) 7.71 (4.32-13.34) 60 to 69 years 10341 9826 95.0 7.47 (5.51-10.12) 8.51 (4.76-14.77) >70 years 22730 21645 95.2 7.81 (5.80-10.51) 8.14 (4.60-13.94) Signs and symptoms Respiratory distress 28634 26942 94.1 0.69 (0.61-0.78) 0.77 (0.65-0.92) Fever 29889 28251 94.5 0.82 (0.73-0.92) - Cough 31431 29765 94.7 0.87 (0.77-0.99) - Throat pain 7362 6908 93.8 0.80 (0.72-0.90) - Dyspnea 34513 32618 94.5 0.86 (0.75-0.98) - O2 saturation <95% 29416 27821 94.6 0.95 (0.85-1.07) - Diarrhea 4688 4453 95.0 1.03 (0.89-1.19) - Vomit 2770 2606 94.1 0.86 (0.72-1.01) - Comorbidities Chronic cardiovascular disease 17682 16926 95.7 1.25 (1.10-1.41) 1.10 (0.95-1.28) Diabetes mellitus 14037 13397 95.4 1.15 (1.02-1.30) 1.20 (1.04-1.40) Chronic hematological disease 521 496 95.2 1.04 (0.69-1.56) - Chronic liver disease 590 567 96.1 1.29 (0.84-1.97) - Asthma 935 880 94.1 0.83 (0.62-1.10) - Chronic neurological disease 2698 2572 95.3 1.05 (0.86-1.27) - Other chronic pneumopathy 2384 2267 95.1 1.02 (0.84-1.25) - Immunodeficiency/immunodepression 1744 1662 95.3 1.05 (0.83-1.33) - Chronic kidney disease 2926 2811 96.1 1.30 (1.06-1.59) - Obesity 1700 1639 96.4 1.45 (1.11-1.89) - Hospitalization No 2168 2065 95.2 1.00 - Yes 40429 38466 95.1 0.98 (0.80-1.20) - Missing 4892 4515 92.3 - - Intensive care unit stay No 14289 13525 94.7 1.00 - Yes 20426 19464 95.3 1.14 (1.04-1.26) - Missing 24895 23517 94.5 - - Ventilation support No 5131 4839 94.3 1.00 1.00 Yes, invasive 16944 16061 94.8 1.10 (0.96-1.26) 1.32 (1.08-1.61) Yes, non-invasive 12065 11516 95.4 1.27 (1.09-1.47) 1.38 (1.11-1.70) Source: SIVEP-GRIPE accessed in 06/20/2020. 4/6 Rev Soc Bras Med Trop | on line | Vol.:53:(e20200528): 2020 TABLE 3: Imputed classification of death from severe acute respiratory illness by Brazilian states (Brazil, January to July, 2020). Total COVID-19 Other viral respiratory UF nº nº % nº % Brazil 84449 80022 95 4427 5 AC 203 198 98 5 2 AL 1234 1134 92 100 8 AM 4144 3956 95 188 5 AP 191 187 98 4 2 BA 2824 2579 91 245 9 CE 7579 7082 93 497 7 DF 902 869 96 33 4 ES 1321 1233 93 88 7 GO 940 878 93 62 7 MA 2530 2147 85 383 15 MG 3133 2944 94 189 6 MS 345 307 89 38 11 MT 311 267 86 44 14 PA 5825 5494 94 331 6 PB 1470 1334 91 136 9 PE 5243 5103 97 140 3 PI 665 614 92 51 8 PR 2169 2022 93 147 7 RJ 13019 12512 96 507 4 RN 1005 948 94 57 6 RO 247 231 94 16 6 RR 337 326 97 11 3 RS 2009 1924 96 85 4 SC 811 770 95 41 5 SE 302 281 93 21 7 SP 25411 24409 96 1002 4 TO 265 260 98 5 2 Source: SIVEP-GRIPE accessed in 06/20/2020. substantial values of specificity (71%) and negative predictive value results; however, we do not know which respiratory viruses were (73%). Moreover, the Kappa test showed substantial concordance tested, and more than 20% of deaths were not tested. between the imputation method and the observed SARI reported The simulation of the data imputation method from the real (K= 0.71; p-value < 0.001) (Supplementary Table 1). values proved a useful and robust solution to resolve the problem of The absence of information in the test causing undetected cases the missing values or undetected results without identifying which of viral respiratory diseases is a bias in the information system and respiratory viruses were tested using the clinical & epidemiological understanding the spreading of COVID-19 or other viral respiratory variables. This method presented a high sensitivity and positive diseases in Brazil because only information about detected tests predictive value and substantial values of specificity and negative is reported in SIVEP-Gripe. Almost half of SARI deaths have an predictive value, such as a moderate concordance with the real unknown cause; 26% of SARI deaths had undetected RT-PCR value using the simulation. Another way to validate this method is 5/6 Oliveira SB et al. - Undetected COVID-19: imputation method to correct selecting some imputed cases and trying to investigate the medical CONFLICT OF INTERESTS records to identify more examinations (X-ray, tomography, etc.) that Authors declare no competing interests. The SIVEP-GRIPE help confirm the cases and perform retesting for these cases using csv dataset is available at: https://opendatasus.saude.gov.br/dataset/ a different methodology suitable for laboratory collection. These bd-srag-2020. estimations should be confirmed with empirical data as the quality of the information systems improve. The main limitation of this method is the associated data REFERENCES structure, i.e., if the quality of information is not reasonably good, 1. 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