Space-time analysis of the first year of COVID-19 pandemic in the city of Rio de Janeiro, Brazil

ABSTRACT:

Objective:

To describe the space-time evolution of cases and deaths due to COVID-19 in the Rio de Janeiro municipality, Brazil, during the first year of the pandemic.

Methods:

An ecological study was carried out. The units of analysis were the neighborhoods of the city of Rio de Janeiro. Incidence and mortality rates, excess risk, Global Moran's Index (Moran's I), local indicator for spatial association, standardized incidence ratio, and standardized mortality ratio were estimated for neighborhoods in the municipality of Rio de Janeiro.

Results:

Over the first year of the pandemic, registries in the city of Rio de Janeiro included 204,888 cases and 19,017 deaths due to COVID-19. During the first three months of the pandemic, higher incidence rates were verified in the municipality compared with the state of Rio de Janeiro and Brazil, in addition to higher mortality rates compared with the state of Rio de Janeiro and Brazil from May 2020 to February 2021. Bonsucesso was the neighborhood with the highest incidence and mortality rates, and throughout the neighborhoods and months, there is no synchrony between the worst moments of the COVID-19 pandemic.

Conclusion:

The authors emphasize the need for implementing more rigid control and prevention measures, increasing case detection, and accelerating the COVID-19 immunization campaign.

Keywords:
COVID-19; SARS-CoV-2 infection; Spatial analysis; Epidemiology; Public health

RESUMO:

Objetivo:

Descrever a evolução espaço temporal de detecção de casos e mortalidade por COVID-19 no município do Rio de Janeiro durante o primeiro ano da pandemia.

Métodos:

Foi realizado um estudo ecológico cujas unidades de análise foram os bairros do município do Rio de Janeiro. Calcularam-se as taxas de incidência e mortalidade, excesso de risco, índice de Moran global (I de Moran), indicador local de associação espacial, razão de incidência padronizada e razão de mortalidade padronizada para bairros do município do Rio de Janeiro.

Resultados:

Foram notificados 204.888 casos e 19.017 óbitos por COVID-19. O município apresentou durante os três primeiros meses de pandemia taxas de incidência superiores às do estado do Rio de Janeiro e do Brasil e taxas de mortalidade superiores às do estado do Rio de Janeiro e Brasil de maio de 2020 até fevereiro de 2021. Bonsucesso foi o bairro com maiores taxas de incidência e mortalidade, e ao longo dos bairros e dos meses não há sincronia entre os piores momentos da pandemia de COVID-19.

Conclusão:

Ressaltamos a necessidade de implantação de medidas mais rígidas para controle e prevenção da COVID-19, aumento na detecção de casos e aceleração da campanha de imunização.

Palavras-chave:
COVID-19; SARS-CoV-2; Análise espacial; Epidemiologia; Saúde coletiva

INTRODUCTION

The 2019 novel coronavirus disease, known as COVID-19, caused by the SARS-CoV-2 virus, was first notified to the World Health Organization (WHO) on December 31, 2019, by China¹. With the rapid spread of the virus, on January 30, 2020 the WHO declared a Public Health Emergency of International Concern (PHEIC)². On March 11, the spread of the disease was classified as a pandemic³. Until February 28, 2021, over 113 million cases and 2.5 million deaths were registered worldwide⁴.

In Brazil, COVID-19 was declared a Public Health Emergency of National Concern (PHENC) on February 3, 2020, even before the confirmation of the first case, which occurred on February 26, 2020 in the state of São Paulo⁵,⁶. One year later, on February 28, 2021, over 10.5 million cases and 254 thousand deaths were registered in the country. The Southeast region has 36% of cases and 46% of deaths in Brazil, and the state of Rio de Janeiro is the fifth with the highest number of cases (583,044) and the second in deaths (33,080) in the country⁶.

The first case of COVID-19 in the municipality of Rio de Janeiro was registered on March 6, 2020, 11 days after the first case in Brazil. As the second most populous city in the country, until April 9, 2021, it was the third city with the highest number of cases (235,005) and the second in number of deaths (21,436)⁷,⁸.

The city of Rio de Janeiro has an economic and social dynamic of high connectivity with other urban centers, in addition to having one of the highest concentrations of slums in the country, which favors the spread of the disease in the territory⁹. In addition, the percentage of social distancing of the state of Rio de Janeiro has decreased in the last months, and the challenges faced by the Brazilian Unified Health System (SUS), such as the historical inequality in access to health care and scarcity of resources, favor the rapid spread of COVID-19¹⁰,¹¹.

The objective of the present study was to describe the space-time evolution of cases and deaths due to COVID-19 in the city of Rio de Janeiro, Brazil, during the first year of the pandemic.

METHODS

This is an ecological study whose units of analysis were the neighborhoods of the municipality of Rio de Janeiro. The city has 163 neighborhoods, divided into 10 planning areas. These areas have important differences regarding social indicators and characteristics of occupation of the geographic space (Supplementary Material #1). In 2020, the municipality had an estimated population of 6,752,339 inhabitants¹²,¹³. Data on cases and deaths from COVID-19 per neighborhood were collected from the Rio COVID-19 Panel of the Municipal Department of Health of Rio de Janeiro (Secretaria Municipal de Saúde do Rio de Janeiro – SMS-RJ) for the period from March 6, 2020 to March 6, 2021 (one year)⁷. Data on deaths from COVID-19 for the state of Rio de Janeiro and Brazil were collected, respectively, from the COVID-19 Panel of the Rio de Janeiro State Department of Health and the Coronavirus Panel of the Brazilian Ministry of Health for the period from March 6, 2020 to March 6, 2021, comprising a period of one year⁶,¹⁴.

The incidence and mortality rates (per 100 thousand inhabitants) due to COVID-19 in the municipality of Rio de Janeiro, in the state of Rio de Janeiro, and in Brazil were calculated based on population estimates provided by the Brazilian Institute of Geography and Statistics (IBGE)¹⁵. In addition, the 2020 population for each neighborhood in the city of Rio de Janeiro was estimated based on populations from the 2000 and 2010 IBGE Censuses by interpolation¹⁵.

The standardized incidence ratio (SIR) and the standardized mortality ratio (SMR) according to neighborhood in the municipality of Rio de Janeiro, per month, were estimated using as standard the COVID-19 incidence and mortality coefficients of the municipality in the period from March 2020 to February 2021. These measures estimate the occurrence of cases and deaths due to COVID-19, in the neighborhoods of the city of Rio de Janeiro, in relation to what would be expected if the same rates observed in the city were applied to the neighborhoods. Such methods have been previously used to investigate the current COVID-19 pandemic, as well as the occurrence of other diseases, such as tuberculosis, in Brazil and in other countries⁸,¹⁶,¹⁷. 95% confidence intervals (CI) were considered based on the method proposed by Vandenbroucke (1982)¹⁸.

Incidence and mortality rates per neighborhoods in the city were also used to calculate the Global Moran's Index (Moran's I), which analyzes global spatial autocorrelation, and the local indicator of spatial association (LISA), to locally identify clusters in the municipality and their statistical significance. Moran's global and local indices aim to identify patterns of spatial distribution of the presented indicators. The high-high, low-low, high-low, and low-high quadrants were presented using a scatterplot and a cluster map¹⁹.

Analyses were performed using the R 3.6.1 software, and the thematic maps were created using QGIS 3.14 and GeoDa 1.14.0 software. The scripts of the analyses performed in the R software are presented in the Supplementary Material. The study was carried out with aggregated secondary data and, therefore, there was no need for approval by a Research Ethics Committee.

RESULTS

During the first year of the pandemic, 204,888 confirmed cases and 19,017 confirmed deaths due to COVID-19 were registered in the municipality of Rio de Janeiro. The incidence rate was lower on July 2020 (180.41 cases/100 thousand inhab.) and February 2021 (142.86 cases/100 thousand inhab.) and higher on November (313.82 cases/100 thousand inhab.) and December 2020 (392.90 cases/100 thousand inhab.). In turn, the mortality rate was lower on March (0.36 cases/100 thousand inhab.) and April 2020 (10.58 cases/100 thousand inhab.) and higher on May (47.02 deaths/100 thousand inhab.) and June 2020 (40.72 deaths/100 thousand inhab.) (Figure 1). In the state of Rio de Janeiro, the incidence rate was lower on March (18.17 cases/100 thousand inhab.) and April 2020 (164.54 cases/100 thousand inhab.) and higher on December 2020 (562.56 cases/100 thousand inhab.) and January 2021 (425.68 cases/100 thousand inhab.). The mortality rate was lower on March 2020 (0.70 deaths/100 thousand inhab.) and February 2021 (7.98 deaths/100 thousand inhab.) and higher on May (41.62 deaths/100 thousand inhab.) and December 2020 (24.48 deaths/100 thousand inhab.) (Figure 1). Brazil had the lowest incidence rates on March (2.70 cases/100 thousand inhab.) and April 2020 (37.62 cases/100 thousand inhab.) and the highest rates on January (721.94 cases/100 thousand inhab.) and February 2021 (635.89 cases/100 thousand inhabitants). Conversely, the mortality rate was lower on March (0.09 deaths/100 thousand inhab.) and April 2020 (2.69 deaths/100 thousand inhab.) and higher on January (13.96 deaths/100 thousand inhab.) and February 2021 (14.37 deaths/100 thousand inhab.) (Figure 1). The incidence rate in the city of Rio de Janeiro was considered low when compared with those in the state of Rio de Janeiro and Brazil, but a high mortality rate was verified in the city during the first year of the pandemic.

Figure 1
Incidence and mortality rates (per 100 thousand inhabitants) of COVID-19 in the city of Rio de Janeiro, in the state of Rio de Janeiro, and in Brazil between March 2020 and February 2021.

The incidence rate of COVID-19 found in the city of Rio de Janeiro, for the entire period, was 3,063 per 100 thousand inhabitants. The highest incidence rate in the municipality was verified in Bonsucesso neighborhood (13,071.1/100 thousand inhab.), followed São Cristóvão (9,968.7/100 thousand inhab.), Gávea (9,779.1/100 thousand inhab.), Jacaré (7,921.1/100 thousand inhab.), Centro (7,742.9/100 thousand inhab.), São Conrado (7,552.1/100 thousand inhab.), and Humaitá (7,466.6/100 thousand inhab.) neighborhoods (Figure 2A).

Figure 2
Spatial distribution of the incidence rate (A), the mortality rate (B), excess risk of the incidence rate (C), and excess risk of the mortality rate (D) of COVID-19. City of Rio de Janeiro, March 2020 to February 2021.

The mortality rate due to COVID-19 verified in the city of Rio de Janeiro was 279 per 100 thousand inhabitants. The highest mortality rate in the city was also verified in Bonsucesso neighborhood (960.1/100 thousand inhab.), followed by São Cristóvão (844.3/100 thousand inhab.), Camorim (665.8/100 thousand inhab.), Campo dos Afonsos (658.4/100 thousand inhab.), Vista Alegre (618.2/100 thousand inhab.), Jacaré (609.3/100 thousand inhab.), and Vila da Penha (556.3/100 thousand inhab.) neighborhoods (Figure 2B). It is worth emphasizing that the spatial distribution of the mortality rate differs from the spatial analysis of the incidence rate, as they are concentrated in different areas of the city.

With the excess risk, it was possible to verify that Bonsucesso, São Cristóvão, Gávea, Jacaré, Centro, São Conrado, Humaitá, Joá, Jardim Botânico, Leblon, Ipanema, Praça da Bandeira, Camorim, Flamengo, Rocha, Campo dos Afonsos, Vila Isabel, and Anil neighborhoods have an incidence rate between two and four times higher than that of the municipality (Figure 2C). In turn, Bonsucesso, São Cristóvão, Camorim, Campos dos Afonsos, Vista Alegre, and Jacaré neighborhoods have a mortality rate between two and four times higher than that of the municipality (Figure 2D). The spatial analysis of the excess risk of incidence rate and the excess risk of mortality rate demonstrated the neighborhoods with the most outliers in relation to the general rates of the municipality.

Moran Index of incidence and mortality rates showed statistical significance (p=0.001 and p=0.02, respectively) for global spatial autocorrelation of COVID-19 in the city of Rio de Janeiro (Figures 3A and 3B, respectively). Moran Index of incidence and mortality rates are different from each other (and significant), which reinforces the idea that there are pockets of incidence and mortality clustered in different places in the city of Rio de Janeiro.

Figure 3
Global and local spatial autocorrelation index for incidence (A) and mortality (B) rates of COVID-19. City of Rio de Janeiro, March 2020 to February 2021.

By adopting the LISA method for incidence and mortality rates, autocorrelation at the local level and spatial clusters were observed. Jardim Botânico, Barra da Tijuca, Botafogo, Lagoa, Engenho Novo, Leblon, Vidigal, Riachuelo, Maracanã, Humaitá, Copacabana, Joá, Praça da Bandeira, Gávea, and Ipanema neighborhoods were classified as high-high for incidence rates (Figure 3A). In turn, Vila Valqueire, Praça da Bandeira, Riachuelo, and Maracanã neighborhoods were classified as high-high for mortality rates (Figure 3B). The high-high classification indicates where the situation is more severely spatially-dependent and where incidence and mortality rates are likely to increase in the coming months.

The frequency measures (SIR and SMR) found for the neighborhoods mostly exceeded the rates observed in the city of Rio de Janeiro during the first year of the COVID-19 pandemic.

When comparing the situation of the neighborhoods in the city of Rio de Janeiro with that of the city itself, according to the SIR, it is observed that, even for neighborhoods belonging to the same municipal planning areas (PA), there is not always synchrony between the worst moments of the COVID-19 pandemic (Supplementary Material #2). In PA 1.0, apparently there is no pattern of synchronous SIR concentration or peaks. In PA 2.1, it is observed that, for most neighborhoods, the highest SIR values occurred between June and October 2020. Conversely, in PA 2.2, the most worrisome SIR values were mostly verified between November 2020 and February 2021. In PA 3.1, apparently there is a concentration of high SIR values between April and November 2020. In PA 3.2, heterogeneity in the SIR values is evident, and it is not possible to highlight peaks or concentrations of high values of this measure. In PA 3.3, a concentration of high SIR values was observed, comprising the period from April to November 2020. PA 4.0 presented high SIR values at two different times, between June and August 2020, and in the period between November 2020 and February 2021. In PA 5.1, the worst SIR values are concentrated between March and June 2020. In PA 5.2, the worst SIR values were observed between March and May 2020 and from October 2020 to February 2021. Conversely, in PA 5.3, the worst moment of the pandemic comprised the months of March and April 2020 and also from December 2020 to February 2021.

When comparing the mortality rates of the neighborhoods with those of the city of Rio de Janeiro according to SMR, the same pattern is verified, and even neighborhoods belonging to the same PA have different disease burdens and worse asynchronous moments (Supplementary Material #3). In PA 1.0, the highest SMR values were verified between April and June 2020, similar to those observed in PA 2.1. Conversely, in PA 2.2, the most worrisome SMR values were mostly verified between November 2020 and February 2021. In PA 3.1, the concentration of SMR values apparently follows the same pattern presented in the SIR. In PA 3.2, heterogeneity in the SMR values is also evident, and it is not possible to highlight peaks or concentrations of high values. In PA 3.3, a concentration of high SMR values was observed, comprising the period from April to November 2020. In PA 4.0, 5.1, 5.2, and 5.3, no trends or patterns were observed.

DISCUSSION

The current COVID-19 pandemic presents itself, so far, as the greatest health challenge of this century. In a municipality such as Rio de Janeiro, marked by its great intra-urban inequality — especially in the social, economic, and demographic dimensions —, the challenges have even been greater.

The city of Rio de Janeiro receives a large flow of people from other countries. It is a tourist city and one of the main mobility hubs in Latin America²⁰. In this sense, it was expected that, at the beginning of the pandemic, the city would present high incidence rates of COVID-19, when compared with those in the state of Rio de Janeiro and Brazil²¹. The high mortality rate, together with the low rate of reported cases in the city of Rio de Janeiro, when compared with the state of Rio de Janeiro and Brazil, suggests that the underreporting of cases in the city can have substantial values²². Similar patterns of high incidence in more densely populated cities and mobility hubs were observed in the state of São Paulo²³.

The city of Rio de Janeiro follows a pattern of excess mortality similar to that of other Brazilian cities that are also deemed hubs as for the movement of people, whether for tourism or commercial routes. The study conducted by Orellana et al.²⁴ describes, at the beginning of the pandemic, that the four main cities with this excess were Manaus, with 112% (95%CI 103–121), followed by Fortaleza, with 72% (95%CI 67 −78); Rio de Janeiro, with 42% (95%CI 40–45); and São Paulo, with 34% (95%CI 32–36).

The difference in the spatial distribution of incidence and mortality rates in the neighborhoods can be explained by the socioeconomic heterogeneity and intra-urban inequality present in the city of Rio de Janeiro. The achieved results corroborate the spatial analyses carried out by Cavalcante et al.²⁵ and Santos et al.²⁶, also concerning incidence and mortality rates due to COVID-19 in the city of Rio de Janeiro. According to these studies, the highest incidence rates were concentrated in the richest neighborhoods, whereas the highest mortality rates were concentrated and growing among the poorest neighborhoods. Similar patterns were also reported on both geographic and socioeconomic issues in the distribution of other diseases strongly related to social vulnerability such as tuberculosis²⁷ and dengue²⁸.

There is, in fact, a marked spatial heterogeneity in the distribution of excess deaths from COVID-19 in Brazil, with a similar relation to that found for the city of Rio de Janeiro. In the country, the most affected regions were the Southeast, Northeast, and North. In addition, in capitals, the excess deaths are proportionally greater, although there is a tendency for the pandemic to become more internalized in the most recent scenario. It is speculated that factors related to socioeconomic status, urban segregation, occupation, and poor remuneration, as well as limited access to health services and ethnicity/color, may be determinants of the excess deaths in state capitals²⁹. Furthermore, it is worth recognizing that COVID-19 is not necessarily the direct cause of excess mortality. Deaths caused by the overload or collapse of health services, the interruption of treatment for chronic diseases, or the resistance of patients to seek health care due to the fear of being infected by the new coronavirus make the situation in these places even more delicate³⁰.

In addition to the spatial analyses, the variation in SIR and SMR values between neighborhoods indicates that the spread and burden of COVID-19 during the pandemic does not uniformly occur along the CAs. It is worth mentioning that geographic and socioeconomic inequality presents itself as a differential for a greater or lesser chance of surviving or dying from infection by the same virus. This evidence has been demonstrated by several other studies, which highlight a notable difference in access to health services, notably in the waiting time to meet healthcare demands and referral to more complex care³¹–³⁷. Another equally relevant aspect is that social vulnerability also presents itself in the possibility of social distancing. Individuals who live in extreme poverty reside in slums, with a high rate of household density; they have more precarious jobs and inadequate income, which forces them to look for alternatives to complement their household income³³,³⁶,³⁸.

Another aspect worth emphasizing is health management in the municipality. This division, which encompasses primary health care (PHC), has been praised for several years and has been used as a model for health management in other municipalities, especially in the fight against infectious diseases such as dengue, tuberculosis, and leprosy³⁹. However, as of 2017, PHC has been dismantled in the city of Rio de Janeiro, with major cuts in funding and a consequent reduction in coverage by the Family Health Strategy, which decreased from 62% in 2017 to 55% in 2019³⁹.

The authors of the present study emphasize that mitigating the pandemic, considering the context of inequality discussed in this study, requires the implementation of strict measures of social distancing, as well as non-pharmacological measures at the individual level, such as the correct use of masks. These measures have had a positive impact on reducing the spread of the virus⁴⁰,⁴¹. Moreover, the implementation of these strategies requires broadening inspection to avoid noncompliance with the rules, a phenomenon considerably observed and reported in the municipality¹⁰,⁴²,⁴³. Definitely, such control measures should be primarily accompanied by the creation of social protection measures for the vulnerable population such as the regularity of emergency aid and concessions to small producers and/or small businesses⁴⁴. Finally, the authors emphasize the need for accelerating the immunization campaign for proper control of the COVID-19 pandemic, by increasing the purchase of vaccines together with the organization of the vaccination flow, allowing everyone to quickly and safely receive the vaccine and avoiding overcrowding and poor distribution of vaccines⁴⁴–⁴⁷.

The study has limitations. Underreporting should be considered, as the number of cases and deaths is directly dependent on the implemented testing policy and control measures. Brazil, as a whole, does not have a uniform and well-defined testing policy or control plan, which results in the country having one of the lowest testing rates in the world⁴,⁸. Even with the possibility of using the clinical-epidemiological criterion, which allows notification of COVID-19 without the laboratory test, it is evident that an individual with COVID-19 is only notified when seeking healthcare services; therefore, people with mild or asymptomatic cases who did not sought for medical care were not considered in this study. Furthermore, the population considered to calculate the rates was an estimate calculated based on the 2000 and 2010 Censuses, as the the Demographic Census, initially planned for 2020, has not been conducted yet. All in all, incidence and mortality rates, despite being the best alternatives to make comparisons between different locations, may not accurately reflect information concerning neighborhoods with small populations. Therefore, the authors recommend for systematic studies to be carried out to monitor the evolution of the pandemic in the city of Rio de Janeiro, seeking to collaborate on the formulation of health policies that protect people with the same characteristics in possible new public health emergencies.

• Financial support: Fundação Oswaldo Cruz, Programa INOVA – Edital Geração do Conhecimento para COVID-19. #VPPCB-005-FIO-20-2-83

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Publication Dates

• Publication in this collection
  29 Oct 2021
• Date of issue
  2021

History

• Received
  14 May 2021
• Reviewed
  28 June 2021
• Accepted
  21 July 2021
• Preprint posted on
  29 July 2021