Teleconsultations during the COVID-19 pandemic: major themes covered by a hotline in Rio de Janeiro state, Brazil

Teleconsultas durante a pandemia de COVID-19: temas abordados por um call center no estado do Rio de Janeiro, Brasil

Abstract

Background:  Health-related hotlines have dramatically expanded during the COVID-19 pandemic. However, the Brazilian scientific literature has few descriptions of this service.

Objective:  We analyzed the main themes addressed by the hotline ‘160-Central de Atendimento Coronavírus’ (Coronavirus Service Hotline) in Rio de Janeiro state, Brazil, from March to July 2020.

Methods:  Exploring the free-text field of the ‘160-hotline’ service records, we examined a list of 379,324 words registered during 171,224 call records to the ‘160-hotline’. Using an inductive approach to thematic content analysis, we reviewed the word list to identify thematic categories. Word clouds displayed the word frequency for each thematic category.

Results:  We identified five major themes: individual, interpersonal, health promotion and prevention, pharmaceutical treatment, and health care. Through these themes, we identified words associated with COVID-19 symptoms, non-evidence-based COVID-19 treatments (e.g., medicines part of the so-called “COVID tool kit”), and COVID-19 symptoms that, in March–July 2020, were considered unrelated to the disease, but later recognized as important signs of current or past SARS-CoV-2 infection.

Conclusions:  In accordance with previous studies, our findings show that hotlines can be a useful tool for detecting warning signs of symptoms and conditions even before they are recognized as a public health concern.

Keywords:
hotlines; telenursing; COVID-19; coronavirus; Brazil

Resumo

Introdução:  Durante a pandemia de COVID-19, houve expansão de call centers relacionados à saúde, sendo notável a escassez de descrições desses serviços na literatura científica brasileira.

Objetivo:  Analisar os temas abordados pelo call center ‘160-Central de Atendimento Coronavírus’ no estado do Rio de Janeiro, Brasil, entre março e julho de 2020.

Método:  Analisamos o campo de texto livre dos registros de atendimento da ‘linha direta 160’. Examinamos uma lista de 379.324 palavras registradas em 171.224 ligações para o ‘160’. Utilizando uma abordagem indutiva de análise temática de conteúdo, revisamos a lista de palavras para identificar as categorias temáticas. Nuvens de palavras exibiram a frequência das palavras em cada categoria temática.

Resultados:  Cinco categorias temáticas foram identificadas: individual, interpessoal, promoção/prevenção da saúde, tratamento farmacêutico e atenção à saúde. Destacamos palavras associadas a sintomas de COVID-19, tratamentos sem evidência científica para COVID-19 (“kit COVID”) e sintomas de COVID-19 que, entre março e julho de 2020, não eram relacionados à doença, mas foram posteriormente reconhecidos como indicativos de infecção presente ou passada pelo SARS-CoV-2.

Conclusão:  Durante novas pandemias, os call centers podem ser úteis para detectar precocemente sinais de alerta sobre novos sintomas da doença, contribuindo para a vigilância epidemiológica.

Palavras-chave:
linhas diretas; telenfermagem; COVID-19; coronavírus; Brasil

INTRODUCTION

The first case of coronavirus disease 2019 (COVID-19) confirmed in Brazil was reported on February 25, 2020¹. Two weeks later, the World Health Organization (WHO) declared the COVID-19 outbreak a pandemic². On March 13, 2020, Secretaria de Estado de Saúde do Rio de Janeiro (SES-RJ) announced it had identified SARS-CoV-2 community spread in its territory³. Since then, Rio de Janeiro has been one of the Brazilian states with the highest number of confirmed cases and deaths⁴.

In response to the COVID-19 pandemic health crisis, SES-RJ implemented, among several other initiatives, the ‘160-Central de Atendimento Coronavírus’ (Coronavirus Service Hotline) or the ‘160-hotline’ from March 23 to July 20, 2020. The ‘160-hotline’ was a nurse-run hotline with the following goals:

• provide tele-orientation about non-pharmacological measures to prevent new COVID-19 cases;

• conduct web screening to analyze and classify self-reported flu-like symptoms and underlying health conditions that could indicate risk of developing severe COVID-19 disease. The classification of symptoms could be mild, moderate, or severe;

• offer tele-orientation for each classification of symptoms;

• implement individual telemonitoring for at least 14 days or until the symptoms were no longer detected by the individual reporting them;

• refer individuals to healthcare services in case general or respiratory symptoms worsened.

The ‘160-hotline’ was available to receive free calls from the population of all 92 municipalities in the state of Rio de Janeiro (RJS), on a 24/7 basis. During implementation, the ‘160-hotline’ number was disclosed to the public on SES-RJ’s webpage and social media, and on local television channels. Web screening used a “tick-box” method and covered questions on sociodemographic characteristics, flu-like syndrome symptoms, and underlying conditions (potential comorbidities) associated at that time with more severe COVID-19 outcomes. There was also a free-text field called “Observation”, on which tele-nurses could record information in an unstructured free-text format. The free-text field was intended to provide tele-nurses with flexibility to take notes on the call beyond the “tick box” questions.

Health-related hotlines have supported health surveillance efforts in low-, middle-, and high-income countries even before the COVID-19 pandemic⁵,⁶. During the COVID-19 crisis, these hotlines have drastically increased worldwide⁷,⁸.

Health surveillance managers can be better prepared to handle future epidemics and pandemics, such as the COVID-19 pandemic, if they understand the main topics addressed by a hotline during a public health crisis and how it can identify early warning signs. This study aimed to analyze the main themes addressed by the ‘160-hotline’ in the state of Rio de Janeiro from March to July 2020 based on conversations between callers and tele-nurses registered in free-text fields.

METHODS

Study design and data source

We analyzed unstructured free-text comments in the ‘160-hotline’ call records database acquired from SES-RJ. Free-text comments are records made by tele-nurses during a teleconsultation. Free-text data was anonymized, safeguarding personal data, in full agreement with the Brazilian Data Protection Law⁹. The ‘160-hotline’ database comprised 226,467 call records. Calls from cities located outside Rio de Janeiro state accounted for 55,243 records (24%) and were excluded. Thus, we considered free texts from 171,224 call records in our study.

Data processing and cleaning

We listed the words found in the free-text field of the 171,224 call records using the R command ‘strsplit’ to split text strings¹⁰, resulting in 2,615,433 words. Two researchers (LT and PP) read the word list exhaustively, manually replaced acronyms, and corrected misspellings to align the texts with standard Brazilian Portuguese grammar and orthography. We excluded words without semantic relevance to the study (n=2,236,109; 85%), including numbers (n=98,045), symbols not embedded in valid sentences, and articles, pronouns, prepositions, and connectors that were not essential to sentence understanding. The final word list had 379,324 observations and constituted the linguistic corpus analyzed¹¹. For clarity, throughout the article, we refer to these units as “words” instead of minimal linguistically meaningful semantic blocks.

Conceptual framework of the ‘160-hotline’

Using an inductive approach to thematic content analysis, we reviewed the final list of words to identify the emerging thematic categories¹². Based on the emerging themes, we adapted the socio-ecological theoretical framework¹³, which has been applied to many other subjects, including the COVID-19 pandemic¹⁴,¹⁵, to conceptualize the main themes addressed by the ‘160-hotline’. Five major overarching conceptual levels emerged, as follows:

• Individual level. It includes words corresponding to causes of mortality, morbidity, symptoms, signs, or clinical findings described in the 11th edition of the WHO International Statistical Classification of Diseases and Related Health Problems¹⁶. The individual-level stratum also includes words associated with emotions expressed in the context of the COVID-19 pandemic¹⁷;

• Interpersonal level. It includes words related to interpersonal relationships, such as mentions of family, friends, neighbors, and others whose relationships may have been impacted by the COVID-19 pandemic in a variety of ways, such as staying far from elderly parents due to concern for their health, conflicts between couples under strain that may or may not evolve towards separation or divorce, and worries about the kids who remain far from schools and have been tutored online, not always in the best way, from both a technical and pedagogical perspective¹⁸. This is particularly sensitive in a country with substantial digital exclusion and low literacy levels for decades or even centuries;

• Health promotion and prevention level. It comprises words corresponding to health promotion recommendations during the pandemic, such as food safety and healthy nutrition (which foods to prioritize vs. which ones to avoid or keep as an insurmountable minimum due to a variety of causes (costs, lack of time to procure and to cook healthier meals, etc.)¹⁹; as well as non-pharmacological and preventive measures to interrupt the transmission chain of SARS-CoV-2, for example, to avoid crowded places, to keep spaces properly ventilated, to wear masks, to wash hands regularly and thoroughly, and to maintain social distancing²⁰.

• Pharmacological treatment level. It corresponds to words associated with medications used to treat individuals with chronic or acute health conditions, as listed and regulated by the Brazilian Health Regulatory Agency (ANVISA). Since most medications usually provide symptomatic relief but do not have a major influence on severe SARS-CoV-2 complications, they tend to combine evidence-based and empirical therapeutic regimens.

• Health care level. Words at the healthcare level included those related to the operations of health facilities at the primary, secondary, and tertiary care levels, providers (e.g., physicians, nurses), as well as specific procedures (e.g., molecular assays; rapid diagnostic antibody test; imaging) within the Brazilian Unified Health System (SUS) and eventually carried out in the private system (usually reimbursed by private insurance plans, but sometimes out of pocket).

Figure 1 displays the socio-ecological theoretical framework applied to the ‘160-hotline’.

Figure 1
The socio-ecological conceptual framework of the ‘160-hotline’

Data analysis and strategies towards the visualization of findings

We applied a ‘word cloud’ analysis that graphically displayed word frequency in each level/thematic category. ‘Word clouds’ provide a visually friendly alternative to the classic word count used initially in linguistics. Word size in the word cloud roughly corresponds to its relative frequency. The larger the word in the graphic representation, the higher the number of occurrences in the corpus under analysis²¹. Analysis was performed using R 4.0.3/ RSTUDIO 2022.02.3.

Ethics

The Institutional Ethics Review Board of Escola Politécnica de Saúde Joaquim Venâncio/Fiocruz/RJ approved this study [CAAE: 37001620.6.0000.5241].

RESULTS

The analysis of 379,324 words present in the free-text field of the ‘160-hotline’ database revealed that the two main themes addressed by the ‘160-hotline’ were health promotion and prevention, with 136,388 words, and the individual level, with 134,144 words, corresponding to 74% of the corpus analyzed. The three most common words at the health promotion and preventive level were ‘orientation’ (n=29,499), ‘isolation’ (n=20,169), and ‘health’ (n=10,390) (Figure 2).

Figure 2
Word cloud analysis of the main themes addressed by the ‘160-hotline’, Rio de Janeiro state, Brazil, from March to July 2020

Other frequent words at this level were ‘air’, ‘hydration’, ‘hygiene’, and ‘mask’. The three most frequent words at the individual level were ‘COVID-19’ (n=17,110), ‘pain’ (n=16,185), and ‘fever’ (n=13,391) (Figure 2). Other common words at this level were ‘cough’, ‘fatigue’, and ‘dysgeusia’ (i.e., persistent, bad taste, irrespective of a specific food). The words ‘azithromycin’, ‘dipyrone’ (or their corresponding brand names in the Brazilian market), and ‘vitamin(s)’ were highlighted at the treatment level. The words ‘physician’, ‘emergency’, and ‘hospitalized’ were highlighted at the health care level. The words ‘mother’, ‘son’, and ‘husband’ were highlighted at the interpersonal level (Figure 2).

DISCUSSION

We observed that the most frequent words at the health promotion and preventive level are associated with the characteristics and goals of the ‘160-hotline’: providing tele-orientation to mitigate the health crisis and implementing non-pharmacological measures to prevent new COVID-19 cases. As a result, it is not a surprise that words like ‘orientation’, ‘isolation’, ‘hygiene’, ‘masks’, and ‘air’ appeared often. The key words at the individual level, like ‘COVID-19’, ‘pain’, ‘cough’, ‘fever’, and ‘fatigue’, are consistent with the scientific literature on the main symptoms of COVID-19 at the beginning of the pandemic, as reported by patients (i.e., their “emic” expressions, that may or may not correspond to terms used by professionals)²²,²³. The Brazilian Ministry of Health also considered symptoms like ‘fever’, ‘cough’, ‘sore throat’, or ‘difficulty breathing’ to classify an individual as a suspected case of flu-like syndrome/COVID-19 in the context of community SARS-CoV-2 transmission in April 2020²⁴. These findings are also aligned with those observed in COVID-19 hotlines in high-income countries⁷,²⁵.

It called our attention that ‘dysgeusia’ appeared as a self-reported symptom in the ‘160-hotline’. At that time, taste disorders were residual symptoms not clearly associated with SARS-CoV-2 infection and not considered essential for suspected COVID-19 screening²⁶. Later, ‘dysgeusia’ and other taste disorders were clearly correlated with COVID-19 cases²⁶,²⁷. As of November 2020, the WHO included them in the list of the most common COVID-19 symptoms²⁸. This result reinforces the narrative in other studies that health-related hotlines can be a useful tool for prospecting and understanding emerging themes and topics when properly linked to health surveillance systems²⁹. In 43% of municipalities in Bahia, Brazil, calls to the telehealth service reporting COVID-19 symptoms preceded the notification of a COVID-19 case by an average of 30 days. Boaventura et al. note that data obtained from calls to this telehealth service helped predict regions with new COVID-19 cases³⁰. This is because hotlines have been associated with early identification of signs and symptoms at the population level, even before cases reached healthcare services, providing healthcare personnel and managers with the necessary warnings⁶,²⁹.

‘Azithromycin’ was the most dominant word in the treatment level cluster. Other highlighted words, such as ‘amoxicillin’ and ‘antibiotics’, may suggest that when people called the ‘160-hotline’, they were referring to medicines prescribed by a provider for flu-like syndrome/suspected COVID-19 case. ‘Azithromycin’ was commonly prescribed as an off-label treatment for COVID-19 disease in Brazil³¹,³² and other parts of the world³³,³⁴, despite not being recommended, except in the context of secondary bacterial infection. Furthermore, in Brazil, azithromycin was included in the so-called “COVID-19 early treatment” or “COVID tool kit”³²,³⁵, a medication package including ivermectin and chloroquine - also observed in Figure 2 at the same level. The “COVID tool kit” idea was disseminated by the Federal Government, politicians, physicians, and other healthcare providers throughout the Brazilian public healthcare system, promising treatment of mild cases and prevention of severe COVID-19 cases despite the lack of scientific evidence³²,³⁵.

The words ‘physician’, ‘emergency’, and ‘hospitalized’, frequently mentioned in the healthcare context, may reflect individuals concerned about needing to visit an emergency room and then being hospitalized (at the time, a proxy for aggressive interventions such as intubation and artificial ventilation). Other plausible interpretations include people who said they went to an emergency room because they were not feeling well, or tele-nurses referring individuals with breathing difficulties to an emergency room/hospital near their residence or designated address. The latter was a standardized procedure of the ‘160-hotline’. The words ‘mother’, ‘son’, and ‘husband’ at the interpersonal level show worries within the nuclear family that the COVID-19 pandemic has impacted and exacerbated in many ways¹⁸.

Limitations

This analysis used administrative data originally designed for non-research purposes. A valid link (i.e., a unique ID) between the individual and the multiple calls they could make to the ‘160-hotline’ is absent. Therefore, it is not possible to make a 1:1 correlation between the excerpt under analysis and the individual. On the other hand, the corpus linguistics under analysis can represent N statements from the same person at different times. Thus, it is not possible to present the overall demographic characteristics of people who sought help via the ‘160-hotline’, which could be duplicated and inconsistent. From a linguistic point of view, the analysis is consistent and valid, but it cannot be adjusted for the sociodemographic characteristics collected without the necessary standardization and a valid linkage via a common key. This does not affect content analysis but compromises any attempt to generalize it. Attempting to do so would be regarded as “statistical control illusion”, as noted by Christenfeld et al.³⁶. This attempt would jeopardize research integrity. On the other hand, linguistic analysis has a long and successful history, beginning with the original insights of Russian linguists, followed by the famous Prague circle and the French and British schools of linguistics. McElvenny³⁷ summarized part of this history. Very insightful and useful applications of linguistics to public health are available elsewhere³⁸.

Because our analytical procedures fragment the text, our analysis has focused on words or very simple linguistic blocks. When splitting sentences, word analysis might lose contextual information vital to discern their overall meaning. This would require more sophisticated analyses. Notwithstanding, such analyses, like the ones fostered by the comprehensive use of the Lancaster University corpora toolbox, are applied to consolidated corpora. But as their originators clarified, such advanced analyses should be primarily used for comprehensive data/consolidated corpora (“Works with your own data or existing corpora”; verbatim), as opposed to exploratory analyses or corpora in the making, as is the case of our own datasets. We collected our corpus through a helpline in a moment of acute distress and an emerging pandemic. This unexpected challenge evoked haphazard responses from Brazilian authorities, including no concrete response and denial. For example, ‘running nose’ is a vague but common symptom of any flu-like syndrome. Taken as a separate word, ‘running’ can have different meanings, such as physical exercise, running a business, operation, or analysis. Nevertheless, we believe the inductive approach to thematic content and adaptation of the socio-ecological theoretical framework were appropriate for the exploratory analyses of an important tool implemented during a pandemic.

CONCLUSIONS

Our exploratory analysis revealed the major themes addressed by the ‘160-hotline’ implemented in a major state of a middle-income country severely affected by the COVID-19 pandemic. As with any front-line report, our findings show that callers addressed their symptoms, fears, and misinformation, probably asking for inadequate information not based on sound evidence. This also represents, at a local level, the wave of denialism that afflicted the country at that time. Additionally, the ‘160-hotline’ captured symptoms associated with COVID-19 (taste disorders) that were not clearly described in the literature and, four months later, were recognized by the WHO, such as dysgeusia. Our research shows that hotlines can detect early warning signs of symptoms and conditions even before they are recognized as a public health concern.

DATA AVAILABILITY STATEMENT

The data sets generated and/or analyzed during the study are available upon request to the corresponding author.

Funding:

Inova Fiocruz/Fundação Oswaldo Cruz.

REFERENCES

• ^1. Souza WM, Buss LF, Candido DDS, Carrera JP, Li S, Zarebski AE, et al. Epidemiological and clinical characteristics of the COVID-19 epidemic in Brazil. Nat Hum Behav. 2020;4(8):856-65. https://doi.org/10.1038/s41562-020-0928-4
  » https://doi.org/10.1038/s41562-020-0928-4
• ^2. World Health Organization (WHO). WHO Timeline - COVID-19 [Internet]. WHO; 2020 [cited 2022 Dec]. Available at: https://www.who.int/news/item/27-04-2020-who-timeline---covid-19
  » https://www.who.int/news/item/27-04-2020-who-timeline---covid-19
• ^3. Estado do Rio de Janeiro. Nota técnica – SVS/SES-RJ n. 08/2020. Doença pelo Coronavírus (COVID-19), informações atualizadas da nota técnica - SVS/SES-RJ no 01/2020 (quinta atualização) [Internet]. Rio de Janeiro: Secretaria de Estado de Saúde, Subsecretaria de Vigilância em Saúde; 2020 [cited 2022 Dec]. Available at: https://www.saude.rj.gov.br/comum/code/MostrarArquivo.php?C=Mjk3Njc%2C
  » https://www.saude.rj.gov.br/comum/code/MostrarArquivo.php?C=Mjk3Njc%2C
• ^4. Brasil. Ministério da Saúde. Painel Coronavírus [Internet]. Brazil: Ministério da Saúde [cited 2026 Sep 24]. Available at: https://covid.saude.gov.br/
  » https://covid.saude.gov.br/
• ^5. Aluma ADL, Koulmini S, Kalilou S, Igweonu O, Kouassi AF, Traore MA, eta l. Use of call centers in polio eradication efforts in island settlement in Chad. J Immunol Sci. 2021;Spec Issue(2):1113. https://doi.org/10.29245/2578-3009/2021/S2.1113
  » https://doi.org/10.29245/2578-3009/2021/S2.1113
• ^6. González PA, García SP, Delgado RC. Potential role of Emergency Medical System call centres in epidemiological surveillance of seasonal influenza. Cent Eur J Public Health. 2019;27(1):64-7. https://doi.org/10.21101/cejph.a4768
  » https://doi.org/10.21101/cejph.a4768
• ^7. Glatman-Freedman A, Bromberg M, Ram A, Lutski M, Bassal R, Michailevich O, et al. A COVID-19 call center for healthcare providers: dealing with rapidly evolving health policy guidelines. Isr J Health Policy Res. 2020;9(1):73. https://doi.org/10.1186/s13584-020-00433-x
  » https://doi.org/10.1186/s13584-020-00433-x
• ^8. Alkhalifah JM, Seddiq W, Alshehri BF, Alhaluli AH, Alessa MM, Alsulais NM. The role of the COVID-19 pandemic in expediting digital health-care transformation: Saudi Arabia’s experience. Inform Med Unlocked. 2022;33:101097. https://doi.org/10.1016/j.imu.2022.101097
  » https://doi.org/10.1016/j.imu.2022.101097
• ^9. Brasil. Lei n. 13.709, de 14 de agosto de 2018. Lei Geral de Proteção de Dados Pessoais [Internet]. Brazil: Presidência da República, Secretaria-Geral, Subchefia para Assuntos Jurídicos [cited 2022 Dec]. Available at: https://www.planalto.gov.br/ccivil_03/_ato2015-2018/2018/lei/l13709.htm
  » https://www.planalto.gov.br/ccivil_03/_ato2015-2018/2018/lei/l13709.htm
• ^10. Jockers ML. Text analysis with R for students of literature. Cham: Springer International; 2014. https://doi.org/10.1007/978-3-319-03164-4
  » https://doi.org/10.1007/978-3-319-03164-4
• ^11. Stefanowitsch A. Corpus linguistics: A guide to the methodology [Internet]. 2020 [cited 2022 Dec]. Available at: https://zenodo.org/record/3735822
  » https://zenodo.org/record/3735822
• ^12. Elo S, Kyngäs H. The qualitative content analysis process. J Adv Nurs. 2008;62(1):107-15. https://doi.org/10.1111/j.1365-2648.2007.04569.x
  » https://doi.org/10.1111/j.1365-2648.2007.04569.x
• ^13. McLeroy KR, Bibeau D, Steckler A, Glanz K. An ecological perspective on health promotion programs. Health Educ Q. 1988;15(4):351-77. https://doi.org/10.1177/109019818801500401
  » https://doi.org/10.1177/109019818801500401
• ^14. Cowan E, Khan MR, Shastry S, Edelman EJ. Conceptualizing the effects of the COVID-19 pandemic on people with opioid use disorder: an application of the social ecological model. Addict Sci Clin Pract. 2021;16(1):4. https://doi.org/10.1186/s13722-020-00210-w
  » https://doi.org/10.1186/s13722-020-00210-w
• ^15. Jang SH. Social-ecological factors related to preventive behaviors during the COVID-19 pandemic in South Korea. PLoS One. 2022;17(3):e0266264. https://doi.org/10.1371/journal.pone.0266264
  » https://doi.org/10.1371/journal.pone.0266264
• ^16. World Health Organization. ICD-11 [Internet]. World Health Organization [cited 2023 Jan]. Available at: https://icd.who.int/en
  » https://icd.who.int/en
• ^17. Lwin MO, Lu J, Sheldenkar A, Schulz PJ, Shin W, Gupta R, et al. Global sentiments surrounding the COVID-19 pandemic on Twitter: analysis of Twitter trends. JMIR Public Health Surveill. 2020;6(2):e19447. https://doi.org/10.2196/19447
  » https://doi.org/10.2196/19447
• ^18. Cassinat JR, Whiteman SD, Serang S, Dotterer AM, Mustillo SA, Maggs JL, et al. Changes in family chaos and family relationships during the COVID-19 pandemic: Evidence from a longitudinal study. Dev Psychol. 2021;57(10):1597-610. https://doi.org/10.1037/dev0001217
  » https://doi.org/10.1037/dev0001217
• ^19. Brasil. Recomendações de alimentação e COVID-19 [Internet]. Brazil: Ministério da Saúde [cited 2023 Jan]. Available at: https://irp-cdn.multiscreensite.com/63a687e5/files/uploaded/Recomendacoes_de_Alimentacao_COVID-19.pdf
  » https://irp-cdn.multiscreensite.com/63a687e5/files/uploaded/Recomendacoes_de_Alimentacao_COVID-19.pdf
• ^20. Sun KS, Lau TSM, Yeoh EK, Chung VCH, Leung YS, Yam CHK, et al. Effectiveness of different types and levels of social distancing measures: a scoping review of global evidence from earlier stage of COVID-19 pandemic. BMJ Open. 2022;12(4):e053938. https://doi.org/10.1136/bmjopen-2021-053938
  » https://doi.org/10.1136/bmjopen-2021-053938
• ^21. Khanbhai M, Warren L, Symons J, Flott K, Harrison-White S, Manton D, et al. Using natural language processing to understand, facilitate and maintain continuity in patient experience across transitions of care. Int J Med Inf. 2022;157:104642. https://doi.org/10.1016/j.ijmedinf.2021.104642
  » https://doi.org/10.1016/j.ijmedinf.2021.104642
• ^22. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395(10223):497-506. https://doi.org/10.1016/s0140-6736(20)30183-5
  » https://doi.org/10.1016/s0140-6736(20)30183-5
• ^23. Guan WJ, Ni ZY, Hu Y, Liang WH, Ou CQ, He JX, et al. Clinical characteristics of Coronavirus disease 2019 in China. N Engl J Med. 2020;382(18):1708-20. https://doi.org/10.1056/NEJMoa2002032
  » https://doi.org/10.1056/NEJMoa2002032
• ^24. Brasil. Protocolo de manejo clínico do coronavírus (Covid-19) na atenção primária à saúde. Versão 9 [Internet]. Brazil: Ministério da Saúde, Secretaria de Atenção Primária à Saúde; 2020 [cited 2023 Jan]. Available at: https://portaldeboaspraticas.iff.fiocruz.br/wp-content/uploads/2020/05/20200504_ProtocoloManejo_ver09.pdf
  » https://portaldeboaspraticas.iff.fiocruz.br/wp-content/uploads/2020/05/20200504_ProtocoloManejo_ver09.pdf
• ^25. Cher BAY, Wilson EA, Pinsky AM, Townshend RF, Wolski AV, Broderick M, et al. Utility of a telephone triage hotline in response to the COVID-19 pandemic: longitudinal observational study. J Med Internet Res. 2021;23(11):e28105. https://doi.org/10.2196/28105
  » https://doi.org/10.2196/28105
• ^26. Carrillo-Larco RM, Altez-Fernandez C. Anosmia and dysgeusia in COVID-19: A systematic review. Wellcome Open Res. 2020;5:94. https://doi.org/10.12688/wellcomeopenres.15917.1
  » https://doi.org/10.12688/wellcomeopenres.15917.1
• ^27. Saniasiaya J, Islam MA, Abdullah B. Prevalence of olfactory dysfunction in coronavirus disease 2019 (COVID-19): a meta-analysis of 27,492 patients. The Laryngoscope. 2021;131(4):865-78. https://doi.org/10.1002/lary.29286
  » https://doi.org/10.1002/lary.29286
• ^28. World Health Organization. Coronavirus [Internet]. World Health Organization [cited 2023 Jan]. Available at: https://www.who.int/health-topics/coronavirus
  » https://www.who.int/health-topics/coronavirus
• ^29. Oladeji O, Oladeji B, Ismail AS, Abdi A. Application of call centre as COVID 19 alert and surveillance system in pastoralist communities of Somali Region of Ethiopia. J Adv Med Med Res. 2020;32(13):23-8. https://doi.org/10.9734/jammr/2020/v32i1330549
  » https://doi.org/10.9734/jammr/2020/v32i1330549
• ^30. Boaventura VS, Grave M, Cerqueira-Silva T, Carreiro R, Pinheiro A, Coutinho A, et al. Syndromic surveillance using structured telehealth data: case study of the first wave of COVID-19 in Brazil. JMIR Public Health Surveill. 2023;9:e40036. https://doi.org/10.2196/40036
  » https://doi.org/10.2196/40036
• ^31. Lalwani P, Salgado BB, Pereira Filho IV, Silva DSS, Morais TBDN, Jordão MF, et al. B. SARS-CoV-2 seroprevalence and associated factors in Manaus, Brazil: baseline results from the DETECTCoV-19 cohort study. Int J Infect Dis. 2021;110:141-50. https://doi.org/10.1016/j.ijid.2021.07.017
  » https://doi.org/10.1016/j.ijid.2021.07.017
• ^32. Furlan L, Caramelli B. The regrettable story of the “Covid Kit” and the “Early Treatment of Covid-19” in Brazil. Lancet Reg Health. 2021;4:100089. https://doi.org/10.1016/j.lana.2021.100089
  » https://doi.org/10.1016/j.lana.2021.100089
• ^33. Ghazy RM, Almaghraby A, Shaaban R, Kamal A, Beshir H, Moursi A, et al. A systematic review and meta-analysis on chloroquine and hydroxychloroquine as monotherapy or combined with azithromycin in COVID-19 treatment. Sci Rep. 2020;10(1):22139. https://doi.org/10.1038/s41598-020-77748-x
  » https://doi.org/10.1038/s41598-020-77748-x
• ^34. PRINCIPLE Trial Collaborative Group. Azithromycin for community treatment of suspected COVID-19 in people at increased risk of an adverse clinical course in the UK (PRINCIPLE): a randomized, controlled, open-label, adaptive platform trial. Lancet. 2021;397(10279):1063-74. https://doi.org/10.1016/s0140-6736(21)00461-x
  » https://doi.org/10.1016/s0140-6736(21)00461-x
• ^35. Malta M, Vettore MV, Silva CMFP, Silva AB, Strathdee SA. Political neglect of COVID-19 and the public health consequences in Brazil: The high costs of science denial. EClinicalMedicine. 2021;35:100878. https://doi.org/10.1016/j.eclinm.2021.100878
  » https://doi.org/10.1016/j.eclinm.2021.100878
• ^36. Christenfeld NJ, Sloan RP, Carroll D, Greenland S. Risk factors, confounding, and the illusion of statistical control. Psychosom Med. 2004;66(6):868-75. https://doi.org/10.1097/01.psy.0000140008.70959.41
  » https://doi.org/10.1097/01.psy.0000140008.70959.41
• ^37. McElvenny J. A history of modern linguistics: from the beginnings to World War II. Edinburgh: Edinburgh University Press; 2024.
• ^38. Showstack R, Santos MG, Feuerherm E, Jacobson H, Martínez G. Language as a social determinant of health: an applied linguistics perspective on health equity [Internet]. American Association for Applied Linguistics [cited 2024 Apr]. Available at: https://www.aaal.org/news/language-as-a-social-determinant-of-health-an-applied-linguistics-perspective-on-health-equity##
  » https://www.aaal.org/news/language-as-a-social-determinant-of-health-an-applied-linguistics-perspective-on-health-equity##

Correspondence

Correspondence: Lidiane Toledo. E-mail: [e-mail]

Conflict of interests:

nothing to declare.

Editor-in-Chief:

Guilherme Werneck https://orcid.org/0000-0003-1169-1436

Publication Dates

• Publication in this collection
  09 Nov 2026
• Date of issue
  2026

History

• Received
  10 Dec 2023
• Accepted
  12 June 2024